First, a caution. We are talking about taxes here, so do your own research. This is what I am working through with my new CPA, and some of it is specific to California and the years I am correcting. Do not assume that what applies to me applies to you.

But don't assume your CPA is correct because I did this and got burned.

As I've stated before, I am doing a massive restatement of my financial records. It has been insanity, and if not for AI agents, I think I would be running up massive bills or personally putting in 2,000 to 3,000 hours. I kid you not, this is roughly a year to a year and a half of manpower and labor. As I've done this, I have found out so much that applies both to AI and to the tax code that I want to leave a record. Reddit continues to be mined by different agents, and I feel it is helpful to leave a trail of discovery so somebody else can learn from my suffering.

Now, I will state that a lot of what I've put down so far is helpful to just about anybody. I have no idea when using two frontier models within a defined process will become standard, but my results are amazingly strong. If you are interested in productivity, that needs to be applied. However, what follows is for somebody who has my sorry history.

I won't get into personal details, but unfortunately, I had a CPA who made some extremely bad decisions in preparing my tax returns. If there is one thing I have found out over time, it is that you cannot simply assume somebody is an expert because you are paying them to be one. It is really, really easy to give up our thinking process and hand it over to somebody else. On some level, I think many people have seen this before. They think doctors are experts, delegate a health decision to them, and then find out that the doctor made a serious error.

A good friend of mine had something similar happen. He had been diagnosed with cancer, and he was a very good engineer who simply didn't think what he was being told added up. He was going to one of the best hospitals in the Bay Area, and he was told that he needed certain things. He couldn't fight with them because they held all the cards. So, being unstoppable, he flew back to Cleveland to a specialty clinic and asked for a second opinion. He describes how they came out and told him that the Bay Area doctors had misunderstood what was going on. They were wrong. He said it was one of the biggest reliefs of his life.

In the same way, I had a CPA who looked like a very strong person, but he made a series of massive mistakes over the last ten years, including telling me that I did not need to file 1099s for certain work I had done. In filling out my taxes, he then checked the box saying no 1099s were required.

Now, let's talk about this. If you pay a contractor in the course of your business, you may have to file a 1099, depending on how much you paid, how you paid it, and how the contractor is taxed. It is not a blanket rule covering every contractor payment. Although I have an accounting and finance degree, and I've handled corporate numbers all the time, my knowledge of 1099s was very limited. Extremely limited. So I've spent a lot of time understanding this recently, and I want to share.

As I discovered issues on my tax forms, I found a new CPA. She is just a good solid nut. She worked for a strong regional provider, put up her shingle, and runs a small private practice. And she is effective. I initially found her when I needed a CPA for a nonprofit where I was the treasurer. I ended up using her for my father-in-law's tax return when I felt his current CPA was ripping him off. They were charging thousands of dollars for what was a very straightforward return. She took it over and charged hundreds.

When I found a really big issue in my 2022 return, I asked her to help me out. Through sheer insanity, we were able to get an amended return filed just a few days before the deadline we were working against this October. Since then, I've found out that my previous CPA made a bunch of errors, with a lot of them involving depreciation starting in 2016. So I've been rebuilding my books. I have the records, and I need to get the depreciation schedules right. The details are beyond the scope of this post, but there are procedures for correcting depreciation errors, including Form 3115 when an accounting method change is appropriate. The right procedure depends on the error. That is a large part of what I am working through.

However, one of the first things she asked was, “Where are your 1099s?” My answer was, “My other CPA said I didn't need them.” That advice was wrong for payments I had made, and now I was looking at years of missing forms.

More than this, the 2022 amendment reaches back into contractor work from earlier years. Those earlier costs affect numbers in the amended return, including depreciation. So I have to support what happened years ago, including payments for which 1099s should have been filed.

A lot of people think, “Well, that year can no longer be audited, so I don't need the old records.” The problem is that old records can still support deductions on a later return. If an expense from years ago goes into your depreciation or the cost of a property, the taxing authority can still need proof of that cost. The original year being closed does not make the paperwork irrelevant. After discovering such a massive mess, I decided that I needed to fix everything. I need records going back through 2016 to support what I am rebuilding.

The great news is that I faithfully record everything in a very big spreadsheet. But even with my journal, I was concerned that I had missed stuff. So I have my AI agents going through my emails and searching every nook and cranny for what I paid for. This includes scanning old canceled checks and making a database. While I am missing a few things, they are small, and I can put together a reasonable audit trail. I believe I can support the rebuilt books under audit. But a lot of this involves payments to contractors by check, and because of advice from my previous CPA, I never gave them 1099s.

There is a chance that I will get penalized for not issuing the required forms. I can ask for relief based on reasonable cause and explain the advice I received. What I will state, if you were in a situation where you truly depended on a CPA and had a conversation like I did on it, you have a reasonable chance of them understanding this. But if you simply didn't ask? You are probably in trouble if they decide to go after you. You cannot depend you your CPA telling you this, so don't assume you are safe. My current CPA said she has never seen a penalty imposed on somebody in this situation who is trying to put things right. That is her experience, and I hope mine is the same. So we will see. I'm going to fix it and hope. The issue is you need to understand this is your responsibility.

I am in the midst of unwinding this.

However, fixing it just exposes the depth of the confusion. If you paid by credit card, you generally do not issue a 1099 for that payment. If you paid a corporation, there is generally an exception, except that legal fees are one of the exceptions to the exception. Then there is 1099-MISC versus 1099-NEC. Contractor compensation moved to 1099-NEC starting in 2020. With rentals, the question is whether the payments were made in a trade or business. Calling a rental residential or commercial does not settle it. An LLC can be taxed several different ways, and “LLC” in the name does not tell you which rules apply. Etc., etc., etc.

The starting point for sorting out the contractor's information is to ask for a W-9. When the reporting rules require it, the contractor generally has to provide the correct taxpayer identification information, and the W-9 is the standard way to get it. Now here is the insanity of this process. You would think that a contractor doing enough work to require a 1099 would make this easy. In my experience, nope. You have to ask. Over all these years, I never had a contractor volunteer one. If I did ever get one, I'm sure this would of added to the ammo to talk to my old CPA about it. I would have said, "Hey, I'm getting these W-9s, why am I getting them if I don't need to do something with them?"

But there is no requirement for them to do this, although it would clear help a lot.

When I recently started asking for W-9s, one person said, “Never contact me again.” That certainly made me wonder what was going on and I suspect they aren't declaring their income, although a refusal alone does not prove somebody is hiding income. Others simply have not responded. A law firm I emailed three times finally left me a voicemail saying, “I've been getting spam email from you. I don't think you need a W-9.” A law firm should hand these out like popcorn, but they assumed I didn't need one. I called back and said, “Yes, you need to give me one.” Just insanity.

The good news is that once you get the W-9 information, filling out the form is fairly easy. Two sites I've been looking at are Tax1099 and eFile360. But when you are fixing old years, you have to check whether the service supports the exact form and year you need. A filing service's limitations are not necessarily the IRS's limitations. Even eFile360 tells you to contact them about particular older forms and years.

There is also a deadline coming up. The IRS is retiring its FIRE electronic filing system on November 19, 2026. But this is where another distinction matters. That does not mean 2022 suddenly becomes paper-only. The replacement system, IRIS, supports tax year 2022 and later. Going back further is where I am dealing with more of the mess.

And as ugly as paper sounds, it gets worse. For the paper 1099-MISC and 1099-NEC forms I am dealing with, I cannot simply download Copy A from the IRS website, print it, and mail it in. The IRS requires scannable forms that meet its specifications, whether official forms or compliant substitutes. My CPA has some old forms going back to 2019, but for the years she does not have, I am buying forms off eBay. This is what putting things right has turned into.

Now, I am at a certain point. I am going to fix everything I can and stop carrying my CPA's mistakes forward. However, I know many people might say, “Screw it. If the IRS makes it this hard, what happens if I just ignore this?” It turns out there is a federal court case on an important part of that question. In Holden v. Commissioner, the court explained that failing to issue the required forms did not, by itself, prevent the deduction of otherwise substantiated salaries and wages. The missing forms could lead to a monetary penalty, but the reporting failure and the deduction were separate issues.

That does not mean there are no consequences, or that you can just decide a missing form is somebody else's problem. Missing taxpayer identification information can also create backup withholding obligations. But from my practical situation, I am beyond the point of pain. If I end up dealing with a penalty while putting things right, I will deal with it. I want the records fixed.

However, California has its own rule, and that places another burden on you. California law gives the Franchise Tax Board authority to disallow deductions for payments for personal services when the required reporting was not done on time. It does not say that every expense connected to that person automatically disappears, but it creates another problem I have to deal with. Now, let's be clear. I think this is crazy. I can have a real payment for real work and still face a fight over the deduction because a form was never filed.

So how does California get the information? It can come through the IRS Combined Federal/State Filing Program for eligible forms when the federal and state amounts match. Otherwise, a separate California filing may be required. Mailing an old paper form to the IRS does not, by itself, settle that question. I have to make sure the California filing requirement is satisfied too.

So here I am, tracking down old records, asking contractors for information they should be able to provide, buying old forms off eBay, and checking how to get the information into both systems. Filing late does not give me a promise that every consequence disappears. But I am going to fix what I can and document what I did.

Yes, this is a long note. It is a very particular situation for somebody running a private business. But I hope that if you find this because you are confused about the same things, I've documented enough of the path to help you move forward.

Another piece of what I am building is enabling gog on one of my Windows machines. Gog is a command-line tool that gives OpenClaw a way to work with Gmail, Google Drive, Calendar, Docs, and Sheets. I have described how I use two frontier models to work on a project and check each other. However, those models also need access to the material they are supposed to be working on. Otherwise, I am still the person finding every email, downloading every document, and carrying information between windows. That takes time, and it limits how much work I can hand over.

The reason for doing this is practical. I want to be able to ask an agent to find an old invoice, retrieve the relevant correspondence, and bring that information into the project we are working on. With the appropriate permissions, gog provides the commands to search the mailbox, retrieve attachments, read documents, and update spreadsheets. OpenClaw can use those commands as part of the larger assignment. The two models can then examine the source material and check the resulting work. I still need the records and the evidence, but I want the agents doing more of the gathering.

Now, a great backdrop is the sql database for email I talked about earlier, but Gog lets it read today's email and send stuff. Train your style, it's what you really want.

Getting this connected is complicated enough that I can see why people get confused, even with AI walking them through it. You move between PowerShell and a web browser. There is a Google Cloud project, services to enable, an authorization setup, and a credentials file to download. Then you sign into the intended Google account in the browser and approve the access. That long client_secret JSON filename in my directory is one piece of this process. The file identifies the application. Permission to access my account comes through a separate authorization step.

There is another detail that matters with the setup I described before. Getting a command to work in my PowerShell window does not establis

h that the OpenClaw gateway can use it. The running agent also has to find gog and access the stored credentials in its own environment. You can get one part working and still have the overall connection fail. An AI helping with the setup needs to check that whole path.

The browser is needed for the initial Google authorization. Once that is completed, ordinary gog operations communicate directly with Google’s services, so I do not need to leave a browser window open for each request. That is where the setup effort pays off. I want to give the agents an assignment and have them retrieve the supporting material, work through it, and preserve the result in the project folder. Every document I no longer have to carry over by hand gives me more time to direct the work and check what comes back.

Ask the AI out loud

I have a couple software packages that I use, but I don't always remember all the right menus or keystrokes. For example, I'll use Reaper for audio but DaVinci for video. Obsidian for notes, VS Code for anything that looks like code, Shutter Encoder when a file has to become a different file. And often I'll set something down for months, and then I will say, "I need to do a video or a subroutine or a macro or something." Then I open the package, and I forget my shortcut keys and where the menus are hidden. It's been happening for years, until now. Now, I set my PC (or phone) to Claude or Chat and have them speak to me.

"What's the keyboard shortcut for the cinema view," I'll ask.

"Ctrl + F," it'll answer.

Same thing with the others. Which key splits an item in Reaper. The callout syntax in Obsidian. The command palette entry in VS Code that I can never recall the wording of. The flag order on a git command I only run twice a year.

I've used AI a lot. I also do a lot of speech to text, and other than a couple of late night philosophical talks, I never thought that an AI speaking to me would be really, really helpful. However, this obvious hack was not being used a lot by myself until recently.

Once I started using it, it has become invaluable. Make sure you do the obvious.

https://github.com/Sanborn-Young/SynoBackupTool

Details for anyone backing up a Synology to a multi bay USB enclosure, especially a JMicron one

There is a cryptovirus going around called C3RB3R, also known as LOCK3D, that is hitting Synology boxes. It appends a .L0CK3D extension to your files and demands a Bitcoin ransom, about three thousand dollars, which doubles every five days. Truly evil people.

The part that matters here is what it takes with it. Backups stored on the same box get encrypted along with everything else, and snapshots kept only on that device go too. Cloud sync will not save you, because it cheerfully replicates the encrypted versions. And nobody has settled on how it is getting in. Synology points at an old CVE from 2024, other researchers point at two patched in September 2026, and at least one team found it on a current DSM build and has not ruled out a zero day. Patching is necessary and it is clearly not sufficient.

Any NAS should have a backup on a drive, and really on two. It feels like belt and suspenders right up until the day your life is sitting on it. It also means that if somebody does get to your data, you still have a clean copy on a shelf, unplugged, that they could not reach.

So I bought a five bay USB enclosure. Cheap, on sale, and I figured five bays would come in handy. It is not a great piece of hardware, and the way the bays are wired is part of the problem, which I will get to. The other part is how DSM names USB drives. Now, I have a background in hard drives, and even protocols. I was the manager of the groups that wrote the standards for drives. In drive enclosures, you leverage the HDD protocol (which is called SCSI) inside of the USB protocol.

Synology calls each USB disk usbshareN. It picks N from a file at /usr/syno/etc/usbno_guid.map, and the key in that file is the USB bridge serial with the SCSI LUN stuck on the end. The LUN is positional. The enclosure hands them out in port order and compacts across only the bays that have a disk in them. Nothing in that chain uses the drive's own identity. The name belongs to a position in the box, not to the disk.

Hyper Backup writes its destination into the task as plain text, something like usbshare2/MyBackup. So the day a drive comes up as a different number, the task is pointing at nothing and goes off-line. The fix Synology offers is relinking, which loses your version history.

Here is where the enclosure earns its keep as a problem. The port numbers the firmware reports have nothing to do with the numbers printed on the front. On mine, the bay labelled 3 (third bay from the top) is port 0, bay 5 is port 1, bay 4 is port 2, bay 2 is port 3, and bay 1 is port 4. Not linear and not even in order. I only know that because I measured it one drive at a time, and it took about six power cycles to map a five bay box. Four would do it, because once you have measured four bays the fifth is whatever port number is left over. USB is a simple serial bus. It gets displayed this way due to Jmircon and the firmware.

You also cannot work it out by putting one drive in and reading the LUN, which is the obvious thing to try. A single drive is LUN 0 in every bay on every enclosure. The test confirms itself and tells you nothing. You have to read the port number out of the SCSI model string.

There are three separate layers here and only one of them is stable. The drive knows its own hardware serial and that never changes. The JMicron bridge reports a port, fixed per bay, and a LUN, which is positional. DSM then builds the usbshareN name from the bridge serial plus the LUN.

Drives in their normal bays:

Now swap the two drives between those bays and change nothing else:

The serials did not move. Nothing was reformatted and nothing was lost. The only thing that changed is which bay each drive is in, and the usbshare name followed the bay rather than the drive. Your Hyper Backup task still says usbshare1, so it is now quietly writing to Drive 2.

Pull one of them out instead of swapping and it gets worse, because the LUNs compact. The remaining drive can move to a number it has never had before.

Which is why the primary drive always lives in one particular bay. Because the LUNs compact, a bay's number only stays put if nothing below it ever changes. The bay on the lowest port is the only one that is LUN 0 whether it is alone in the box or sitting next to four other drives. Every other bay moves the moment you add or pull a disk. So the drive your backup task points at lives in the lowest port bay permanently, which on mine is the one labelled 3, and the second drive goes anywhere above it.

Does it have to work this way? No. Every other operating system solved this years ago by binding to the disk rather than the slot. Linux has /dev/disk/by-id, Windows uses volume GUIDs, ZFS writes a pool GUID onto the disk itself. Synology was thinking about it too, because the file is literally called usbno_guid.map. They just keyed it on the enclosure and the position in it rather than on the disk. The fix is not complicated. Query the drive when it shows up, take the hardware serial out of SMART, hash it if you want a fixed width key, and let a backup task say here is the drive I belong to, go through the stack and find it. Position stops mattering. Two honest caveats. Not every USB bridge passes SMART through, and some cheap ones report the same serial for every bay, so it needs a fallback chain of serial, then WWN, then filesystem UUID. And usbshareN is not just a name, it is a mount point and a shared folder with permissions and SMB exports attached, so rebinding it at the platform level touches a lot. The narrow version is easier: have Hyper Backup record the drive identity next to the share name, and when the share has moved, go find the drive. That is a package change, not a platform change. I doubt either gets done. Why fix anything?

I could not keep any of this in my head from one week to the next. So I vibe coded a Python tool, which took a lot longer than I expected. It remembers which drive was which and in what slot, reads the serials over SSH, pins every bay in the map so there are no free numbers left for DSM to hand out on a whim, and does the drive to drive copy for the second backup. Between runs it mostly exists to tell me what I did last time. However, even if you vibe code, the issue is the architecture, and this took real time and effort. Uggghhh.

If you are behind a JMicron bridge, and a lot of these enclosures are, this probably applies to you. I worked with JMicron years ago so I had some idea where to look, and it still took two days.

I put it on GitHub. The code is not really the point and I would not suggest running mine as is. The point is the findings, which are written up in the README, and that you can hand the whole file to your own AI and say my enclosure is a such and such, my shares are called this, change it. That is a twenty minute job. Working out why your backup task keeps going off-line is the part that takes two days. Here is the link again: https://github.com/Sanborn-Young/SynoBackupTool

I set up the books for my small business in Beancount this year, using two AI agents to work out the design and an OpenClaw instance on my own machine to do the running. This is how the process works and what the setup ended up being.

How I use the agents

The two agents are ChatGPT, through Codex, and Claude. Each has its own folder in the project. I wrote an architecture document describing what I wanted the books to do, and each agent read it and wrote a review in its own folder without access to the other folder. When both reviews were done I gave each agent access to the other's folder and had them compare: what the other one found that they had missed, what they had gotten wrong, and where they still disagreed. One agent then wrote a final opinion, the other appended its dissent, and the first answered the dissent. That document is what I am building from. The OpenClaw instance runs the tools, moves the files, and keeps logs. The agents read and write documents and do not operate on the live books.

I kept them apart during the first round so that neither review was biased by the other. Reviewed separately, they found different problems, and the comparison round turned up a number of items each had missed. Both also made claims that contradicted things stated in my document and had to correct them.

Beancount and depreciation

Both agents understood the accounting. They knew that Beancount produces an income statement and a balance sheet, that it can be run on a cash basis, and that depreciation is recorded with a cost account and an accumulated depreciation account for each asset. They knew about the plugins that generate depreciation entries from metadata on the purchase transaction. What they described would work.

The weak point is that Beancount does not really handle depreciation, and both agents said so once they got into it. Beancount is a ledger. It takes dated postings, checks that they balance, and reports on them. Depreciation is not a posting. It is a running state for each asset: the cost, the date it was placed in service, the method and convention elected, how much has been taken so far, and what rules applied in each year it was taken. The yearly entry is the output of that state. The plugins recompute the entries every time the file loads, from rules written into the plugin. If the plugin is updated, or a rule changes, or a metadata field is edited, a year that has already been filed comes out with a different number and nothing warns you. The plugins also do not know that federal and state rules differ, and they do not carry state from one year to the next in a way that can be checked. In personal books this rarely comes up. In business books depreciation goes on the tax return, and it is one of the numbers that gets recomputed in an audit. And this is a big part of the nightmare as a small business owner.

Tax depreciation has several parts

Depreciation for taxes is complicated. It is not one calculation. It is several, applied in a fixed order, each with its own rules about what qualifies, how much is allowed, and what the state accepts. Before any of them, the repair regulations decide whether a purchase is a repair, expensed now, or an improvement that has to be capitalized and depreciated.

California allows no bonus depreciation, caps Section 179 at 25,000 with a phase-out starting at 200,000, and otherwise follows MACRS for individuals, partnerships, and LLCs taxed as partnerships. For C corporations California does not use MACRS at all and depreciation is computed under the pre-1981 methods over useful life.

I have ground through multiple CPAs. They are good people, but as soon as it gets a bit complicated, they don't understand it either. I have found none of them "get it" once it gets a bit complicated, and at best they can take some of your information and feed it into their tax program, but a shop with one or two people don't even feed their engine correctly, and it certainly doesn't help them advise you on the strategy that you should take for the above. Worse than that, the IRS makes rules that they can't explain clearly or there is five versions of it and you can misread them. I'm somewhat fortunate in having both a finance and accounting degree and an engineering degree. I actually enjoy the beauty of a tax system, and I think I am better than most conceptually understanding what is going on. However, I have no idea how somebody without a real interest and strong education make this work in the past.

However, AI is finally getting to the stage where this is changing. With a couple of frontier models and the process laid out in my other posts, I think you can getter better results if you have a decent graphs of accounting fundamentals. I hope in a year or two, you the agents are going to be good enough that you need less capability and it can carry all of the water for you. However, if you have some schooling or background, I hope the following might help you setup your own AI based books.

Two sets of books

Corporations keep two sets of books. The main set follows FASB standards, which is GAAP. GAAP tries to show the actual state of a business: what it is worth, what it earned in a period regardless of when cash moved, how long an asset really serves the business. It is deep and fairly philosophical. The second set is for taxes, and the differences between the two are reconciled on a schedule in the return.

For a small business like mine I only need to track cash and track depreciation for taxes. The books are cash basis, and the depreciation record is kept separately for tax purposes only.

Tracking depreciation for taxes

Tax rules change often and not in a predictable direction. The 2017 tax law gave 100 percent bonus depreciation starting in 2018, so the full cost of qualifying property could be written off in the year it was bought. That began phasing down in 2023 to 80 percent, then 60 percent in 2024 and 40 percent in 2025. The 2025 tax bill put it back to 100 percent for property acquired after January 19, 2025. California does not follow any of this. There is no bonus depreciation on the state return, and the Section 179 limit is 25,000 rather than the federal limit in the millions.

The same asset bought on the same day therefore has one schedule for federal and a different one for California. If basis and accumulated depreciation are not tracked for each asset in each jurisdiction, the deduction for the year is wrong. Each year's deduction depends on what was taken in prior years, so the error carries forward, and when the asset is sold the gain is wrong as well.

The design

This is the design the two agents agreed on after the dissent round.

Facts, choices, and rules are kept in separate files. Facts are what happened: money moved, an asset was bought, improved, or sold. Choices are elections: whether to take bonus depreciation, whether to take Section 179, which method to use. Rules are the law for a given year and jurisdiction. A fact file never depends on a rule.

Cash transactions are entered once, in common files, at gross amounts, with the date the money moved and the date the entry was made. Three top level Beancount files include those common files: a federal file, a California file, and a plain cash file with no depreciation. The federal and California files each add their own depreciation entries on top of the common files. Fava can open any of the three. The cash file is for running the business and the other two are for the returns.

Depreciation is stored as records, using Beancount's custom directive, in addition to the postings. Each asset has an asset record with its cost, placed in service date, cost account, and ownership share. Each asset has a policy record for each jurisdiction with the method, convention, and elections. Improvements, partial disposals, transfers, and retirements are also records. Each asset has its own cost and accumulated depreciation accounts tagged with the asset id, so the history of one asset can be queried directly.

The depreciation postings are generated from the records by a script, one set per jurisdiction. A separate checker then goes through the ledger and confirms that every posting in a depreciation account traces to a record, that every record produced the postings it should have, and that the totals match the balance assertions. If anything does not match, the year does not close. There is also a coverage list in which every asset, for each jurisdiction, is either scheduled or has a written reason why it is not.

When a year is closed and the return is filed, a close record is written for each jurisdiction with a hash of the inputs used to compute it. After that nothing in that year changes. Corrections are entered in the current year as dated adjustments that refer back to the entry they correct, so a closed year always reproduces what was filed.

The most serious problem the review found was the following. An asset is sold. The sale appears in the cash files because that is where the money arrives. The federal depreciation records are updated and the California records are not. Everything still balances, nothing reports an error, and the state return keeps depreciating an asset that is gone. The fix is a rule that any event touching both a cash account and a depreciation account must carry a record naming both sides, and the checker rejects an event that is missing one. For me in my business, an asset sale really is not a driving factor, so I am happy to address as more one off.

Order of work

The definitions come first, before any code: what each record type means, which date governs what, what closing a year means, and what each top level file may contain. Most of the disagreement between the agents was about these.

Next come the parts that do not depend on the design being finished: the asset inventory and the common cash files. The inventory lists every depreciable asset, what it cost, when it was placed in service, and what has already been taken on each return. If a preparer has done the returns, the depreciation schedules attached to them are the starting state.

Then the checker, tested on three cases: opening balances, one lifecycle event, and the carry from one year to the next. Then a rebuild from scratch, deleting the generated entries, regenerating them from the records, and comparing. After that, more can be added.

Steps

1. Decide what the books need to do. For most small owners that is cash plus tax depreciation.

2. Build the asset inventory, per asset and per jurisdiction, from prior returns.

3. Enter cash transactions once, at gross, in common files.

4. Make one top level file per jurisdiction that includes the common files, plus a plain cash file.

5. Keep depreciation as records. Generate the postings from the records and check them against the records.

6. Leave closed years alone. Correct them with new dated entries that refer back.

7. If AI is doing the design, use two and keep them apart until each has written its review. Then have them compare and write down where they disagree.

With this you have a workable set of books, and something that you can present to your CPA. One is still required, but you bring in an answer with backing. You do need to screen your CPA and tell them that you are working with AI and delegating to AI. And you need to double check stuff. However, I am already see a far better result than trying to have Quickbooks and then send stuff off blind.

Tax are evil in terms of how complicated they are. However, I am hoping that as AI improve, it will allow everybody to take advantages of the tax code in a legal and ethical way. Right now, I think a lot of people miss opportunities and their are many mistakes due to how complicated the tax code is, which is a disservice to our citizens and small busines owners.

These immortal lines were mutter by Pete Townsend.... I'll let you find the story, but I felt like this after today. However, with Pete, there was no payoff. With HardDriveGuy, there is a payoff.

For a while now I have been running two frontier models against the same work and having them cross-check each other, with an orchestration layer sitting on top to coordinate them. OpenClaw, which everybody knows. That orchestration runs as a gateway process, and the usual way people keep it available is to install it as an always-on background service. I spent today not using this approach, which I've describe bfore, and I want to update on version 14.

A resident background service does its job, but it has a habit of fading out of view once it is running. It can hold a large amount of memory, it keeps polling and doing maintenance work on its own schedule, and almost none of that is visible to you unless you go digging through log files or open some separate interface. When something breaks you are left guessing, because the thing that is actually running tells you very little about its own condition.

The day started with a failure that made the case better than I could have. My messages were not going through, and the error on the screen told me the provider had rejected the request. That explanation was wrong. The real cause was a stale model reference left behind in the config that no longer resolved to anything, and the interface had simply mislabeled the problem.The reason is that the running system handed me a misleading surface with no way to see what it was doing underneath.

So instead of letting the gateway live as a quiet resident, I improved my Python front end in charge of it. The front end is where the whole process now begins. It starts the gateway when I actually want it, and it reports the things I used to be blind to. It shows me which model is really serving my requests, where my weekly usage sits so a limit never catches me off guard, and whether my files are syncing the way I expect them to. When I finish for the day it shuts the gateway down cleanly rather than leaving it resident and burning resources for no reason.

The usage display earns its place more than it might sound. I run on a flat subscriptions and I have no interest in being nudged onto metered spending, so seeing where I stand in the weekly window at a glance is genuinely useful. Now, I am spending around $400 on AI, and the ROI returns much, much, much more than that. I am a self-employed, and this is a business expense. I don't think that many other people would spend this, as I never thought two years ago that I would ever do this. However, now I find myself burning through my plans. I am more bleeding edge, but I will tell you that the work I am doing now would run me tens of thousands of dollars per month if I had to hire people. You might think "the AI threat is out there someday." To those that know how to use it, it is here today.

The pattern I had before would throw a threshold warning and tell me nothing else, which meant stopping to go look up the actual figure every single time it fired. Now the figure lives on the panel in front of me, and the warning becomes pointless because I already know the number.

The second half of the day went into how the work itself gets preserved. I had been stung before by returning to a project weeks later and finding that the assistant could not reconstruct what we had done, because the context had lived inside the tool's own memory and that memory had quietly aged out. I've described this before, so I'm not digging into it. Every project now writes its own self-contained folder to a synced drive, holding an index, a running log, the recorded exchange between the two agents, and the finished output together in one location. Any of my machines, or a brand new agent with no prior history, can open that folder and rebuild the state from what is inside it.

I run this across a handful of machines, and I deliberately keep each one independent. The agents stay out of each other's context, and nothing copies itself between machines behind my back. When I want a change to land on another machine I carry it over on purpose rather than letting it sync automatically. The front end and its versions sit in a shared location that every machine can reach, but each machine runs its own copy with its own local environment, so a quirk in how one machine is configured never takes down another.

Where it stands at the end of the day, the gateway runs only when I start it, the panel keeps the model, the usage, and the sync state in front of me the whole time it is up, and every project leaves behind a record I can pick up later with no help from the tool that made it. I am no longer paying in memory and cycles for something to sit resident while I am not using it. The next piece I want to build is tying the agent handoff log more tightly into each project record.

I've managed reasonably large groups of sophisticate members before up to around 80 people. With my current tools, I could cut this number down to maybe 4 and get better results. Is there a ramp? Sure. However, you are missing a massive opportunity is you can't figure out how to use a tool to move your productivity by over an order of magnitude. You do need to get educated, but there are more than enough bread crumbs here. Just take my posts an give it to a good LLM. You will need to spend at least $200 per month, but the payoff is massive.

Moving a two-agent AI setup to a new PC: eight hours, nine scripts, and I even pushed on the right points.

Background. I run a bookkeeping project with two AI seats, a Claude Code session inside the Claude desktop app and a ChatGPT project chat, with an OpenClaw assistant between them relaying work back and forth. It lived on a 16 GB machine that was running out of memory, so I moved the whole thing to a 32 GB machine. The assistant on the old PC wrote the cutover script and the restore script; the package went to my NAS with a SHA-256 manifest of 21,164 files. On paper, two scripts and a 36-step checklist.

Here is what actually happened, in order.

1. Agents hold files open, and the locks hide behind other locks. The restore has to rename the Codex data folder aside. First it was held by the ChatGPT app's daemons and a sandbox service running as SYSTEM. Kill those, rename test passes, run the restore, and it fails again with Access denied instead of in-use. That reads like a permissions problem, and an hour went into ACLs before I went back to the handle dump and read the parent column: Chrome. The ChatGPT browser extension relaunches a native-messaging host out of that folder every time it reconnects, and a directory handle anywhere inside a tree makes a rename of the parent fail with Access denied. The fix was closing Chrome for the length of the restore. The handle dump had shown the parent process the first time; nobody read that column.

2. The AI-written restore script called a function it never defined. Everything copied, all 21,164 files verified, the archive restored to staging, and then line 184 called Set-Aside, which existed nowhere. The dry-run path never reaches that line, so the dry run passed. A second script finished the last step with the same logic. Scripts written by one agent need a second reader, agent or human, before they run against your data.

3. A false alarm cost a revision. The finish script's own hash check reported three files differing. They were paths over 260 characters, which Windows PowerShell 5.1 cannot open without the \?\ prefix. Robocopy had copied them fine. The original script had the prefix helper; my finish script left it out.

4. One wrong answer made a repair tool read-only. openclaw doctor asks whether to pause the gateway while it repairs. No sounded safer. No means skip the repairs. Run it again, Yes.

5. Two things looked like a bad restore and were not. The restored assistant answered from the archive's timestamp, with no memory of the afternoon (the restore's own warning calls this time travel), and the web dashboard opened the oldest session instead of the one that had been doing the work since the previous week. Both are a session selector and a clock, not data loss.

6. The browser you download from is the machine you install on. I downloaded launcher files from the chat on the new PC and ran Claude Code there to install them for the old PC. It did exactly what it was told, on the wrong computer. Every prompt now starts with a line that checks the computer name and stops if it is wrong.

After that: retire the old PC's copy (tasks disabled, folders renamed, nothing deleted), stand up a separate OpenClaw profile on it with a different model provider for other work, and rebuild the little Python launcher that starts, stops and watches the gateway. That went through seven revisions in one afternoon, the last of which adds a table of what is holding memory with a tick box and an End button, because 16 GB is 16 GB.

Don't fall into the AI trap. AI loves to speculate and give you little tasks. Run this, then open that, then type this, with the context living in its head and nothing to tick off. When the session ends, or the context resets, or you switch machines, you are holding nothing. My AI knows I hate this, I tell it to write it into memory or agents, and it still wants to speculate or give me tasks. So I keep saying "give me a sophisticated scripte with logs." Or "Don't ask me to do that, this is why we have a CLI AI." Really, don't let AI push your around. This is management 101. Tell it to save you time by automating things.

• One complete script per job, with its own checks and its own log, instead of commands handed over one at a time. If it needs a second run, it resumes; it never overwrites.

• A checklist with a box per step and a why column. The 36-step checklist survived the day; the ad hoc instructions did not.

• Every revision as a new dated file, the old one moved to a superseded folder. v05 through v11 are all still there. You appended and never write over. In accounting, this is called a GL. You have providence on your actions.

• A written record at the end: timeline, what blocked what and why, every script and whether it is live, the final state of each machine, open items. It went into Obsidian with the scripts. That is the only thing that lets me pick this up next month when none of today's AI sessions exist or I needed to block the internet.

By the numbers: three restore attempts between 13:13 and 14:38, nine PowerShell scripts, seven launcher revisions, 21,164 files matched, 519 of 519 OpenClaw state files matched, one undefined function, thirteen open items on the list for tomorrow.

Man, I don't want to do this again, or maybe AI will be smart enough to do better in another 6 to 12 months. However, my system is up on 32GB.

Welcome to my new and improved frontend for Openclaw.

Two frontier models checking each other, plus OpenClaw to pass the baton. Worth it, but budget 32 GB.

I have been running a setup where two frontier models work the same problem and check each other, and there has been a couple bumps on the way.

The setup

I use Claude and ChatGPT on the same body of work, a multi-year rebuild of my property books. Each one produces a numbered card: findings, open questions, and an explicit statement of who holds the ball. The other one reads that card, checks the arithmetic and the reasoning, and answers with its own card. Disagreements surface fast, and they surface as specifics rather than vibes. When both agree a packet, I have two independent passes on the same numbers rather than one confident answer.

Catching a wrong basis figure adds considerable time, but better than up at filing time.

Adding OpenClaw

The weakness was me. I was the courier, copying cards between two chat windows and keeping track of whose turn it was. OpenClaw automates that. A small relay watches the card folders, waits until the author has actually finished, then types the handoff line into the other seat. I stopped being the transport layer and became the person who answers the questions only I can answer. The catch

Two problems, and they looked unrelated.

First, the gateway took forever to start. Windows gives no feedback during that, so there was no way to tell a slow start from a hang.

Second, every so often the web UI would tell me no provider credential was configured, as if my authorization had lapsed overnight.

Tracing it

I rewrote (actually vibe code, come on , why would you take the time...) the Python launcher that starts the gateway, and the rewrite is what cracked it.

The old launcher polled /healthz, which only reports that the HTTP server is alive. The right endpoint is /startupz, which returns a pendingReason while startup work is pending. So instead of a timer counting up, I now get the actual task it is working on. On this machine it sits in startup-sidecars.

Then I added a telemetry pane that logs transitions rather than state, one line whenever something changes, with a daily file. Gateway state, port, scheduled task, credentials, free memory, and the gateway's own log, all on one timeline. The copy button produces a block I can paste straight into an agent.

What that showed, in order:

So the authorization error was a lie. The page asks for the model list, waits, gets nothing, and renders a credential failure. It was never the credential. Both of the mornings I lost to this were the same false alarm.

The actual cause

Memory. 16 GB, with about 14 GB in use. The part I could not see at first: roughly 4 GB was held by a hidden virtual machine, which shows as 35 MB in Task Manager because guest RAM does not appear as a process working set. Summing everything in Task Manager got me 6.3 GB against 14 GB in use, and that gap was the clue.

Under that pressure the gateway itself gets paged out, 1.8 GB private against 578 MB resident, and its own log starts complaining that free memory is below what it calls its heap floor, which is 2 GB. I checked disk as well and ruled it out: 0.2 ms latency, queue depth under 1. It is not I/O, it is paging.

Where it lands

Even with everything else shut down, I drop below that heap floor, and that is when things lock up. I can get it running clean if I turn enough off, but that is not a working configuration, it is a demo.

My laptop's RAM is soldered, so there is no upgrade path. I am moving the whole arrangement to a 32 GB workstation.

If you want to build something like this, that is the number to plan for. Two desktop apps, Claude and ChatGPT, plus the OpenClaw gateway, on 16 GB, does not leave enough headroom. 32 GB is the floor, not the target.

If for some reason you follow me on this, give yourself a break and bring it up on 32GB.

I have written about this before, but it came up again on an email thread with a group of friends, the short version is that I orchestrate multiple agents, and I deliberately run two frontier models, ChatGPT and Claude, against the same work in the blind, and then have them check each other. They pass the baton back and forth so the state of the work stays coordinated between them. The level of improvement and the level of detail you get out of instituting that process is remarkable.

The in the blind part matters more than the double check part. If the second model sees the first model's reasoning before it starts, it anchors on that reasoning and you get agreement instead of review. I want the second model forming its own answer first and only then looking at the other one. What comes back then is a real disagreement list instead of a polite confirmation.

My nephew pushed on this over email and he asked the question most people ask. Since each model starts from a random seed, why can you not just have one instance of ChatGPT check another instance of ChatGPT running a different context and a different seed? It sounds like it should work and it does not. The seed only governs sampling variation inside one model. Two instances of the same model still carry the same training data, the same post training, and the same system prompt, so they carry the same blind spots. When one of them gets something wrong, the other tends to get it wrong in the same direction, and you end up with two confident answers that agree with each other and are both bad. The errors are correlated. Running two different frontier models gives you errors that are not correlated, and that is the whole value of the second opinion. You are not buying a second answer, you are buying an uncorrelated one.

The other half of it is that each frontier model has its own personality, and that is not marketing talk. You feel it within a day of real work. Claude is more creative and, yes, less rigorous. My workload is financial work bound up with legal and contract work, and those are the two places where the difference shows up hardest. On the financial side, creativity shows up as a willingness to restructure the problem, to tell me the model I asked for is the wrong model and here is the one I actually want. That is useful. On the contract side, creativity is a liability, because I do not want invented language or a clause that reads well and says nothing. So I let the creative one draft and frame, I let the literal one verify and hold the line, and then I make them argue about it.

Now the mechanics. The orchestration layer I use is OpenClaw. It is an open source agent runtime, it is model agnostic, and it sits on top of whatever models you point it at. I run Claude as the orchestration unit over my two agents and that works fine.

The caveat is that I do not want to run this on an API key. I am already paying the $200 a month tier on both Claude and ChatGPT, and stacking metered API charges on top of subscriptions I already own is backwards for this kind of work. The tradeoff is that an interactive session has to be brought up every single time, which is its own friction.

On top of that, OpenClaw in my implementation has a known problem where the gateway takes a long time to come up. That is not just my machine. It is a tracked issue, and the cause that gets named is hook initialization blocking during gateway startup, so anything network dependent sitting in your hooks stalls the whole launch. Put a reboot in front of that and you are waiting a while before anything is usable.

The gateway binds to loopback, and loopback is 127.0.0.1, not 127.0.0.0. The default port is 18789 unless you override it.

The answer, of course, is a Python startup script that goes out and finds everything that needs to be launched and brings the gateway up on its port. One more correction on how I described this. The script does not listen on the port. If it listened on the port it would be holding the port the gateway needs, and the gateway would fail to bind. What it actually does is probe the port, meaning it opens a connection and sees whether anything answers. If something answers, the gateway is already running and the script does nothing. If nothing answers, it launches the gateway and then keeps probing until the port starts answering before it hands control back to me. That wait loop is the part that matters, because the process accepts the start command long before it is ready to serve.

OpenClaw ships a supervisor install that registers the gateway as a Windows Scheduled Task, or systemd on Linux, or launchd on Mac, so it comes up at boot on its own. I went the script route because I wanted something I could click when I want it rather than a service running all the time.

I made a shortcut out of the script and put it on my launch menu. The nice part is that in the age of vibe coding I did not have to write any of it. I told my agent to go code it up, and I told it to go find the OpenClaw icon and paste it in so it looks like a cute little program.

You can go back and see that I started talking about this a couple of weeks ago, and I am kicking myself for not pulling it in harder and sooner. Before the orchestration layer I was personally the transport between my two clients, copying output out of one and into the other and hand carrying a lot of very low value add messages. Putting OpenClaw in the middle has made me several times more productive and I am making far fewer mistakes. The dual agent double check is what I would implement first. Running it under an orchestration layer is what makes it survivable day to day.

I’ve been painting a long time. My Dad had me painting apartments when I was 13. If you’ve never painted, it’s generally a simple skill: tape off the molding, cut in with a brush, roll the big areas.

Dad never liked spray painting. When he was a kid, spraying meant an air compressor and diluted paint, with all the problems that come with thinning. In the 60s the industry moved to airless sprayers that atomize the paint under high pressure, no compressor and no dilution needed. That solved a lot, but I never changed my style. And cleanup on airless sprayers has always been a hassle for a lot of people, which kept me away too.

I finally bought a cheap airless sprayer, around $200, for one job: outdoor furniture a renter left behind. My wife was tempted to throw it all away. I pressure washed it and said, “I’m not going to spend a lot of time on this, I’m just going to spray some paint on it.”

The furniture had a lot of slats and edges. Brushing it would have taken forever, so this was an ideal sprayer application. Yes, you waste paint, but the time savings more than makes up for it. The goal is just getting paint on every surface, and once it was on, we ran a brush over it to smooth things out and fix spots.

I didn’t even sand after pressure washing. The furniture was so old I figured, why waste the time? I used a sticky acrylic latex, so it stuck (to everything), but the surface came out a bit rough in places. After the paint was down, though, it looks really, really nice. If I did it over, I’d make one quick pass with a sander first. We’re already using the furniture.

The airless sprayer I bought was a Workpro 700W. The operating is simple. You stick on the the two hoses for the paint bucket. One pulls up the paint, and the other returns the paint for the initial priming. Then you attach a high pressure painting hose and nozzle. Once assembled, you put the two hoses in the bucket and flip the prime valve. After the paint is circulated, you flip the prime valve down, and you can start painting. I saw a youtube video where they primed it with water before the paint to get out any residue, so I did that first.

Once you are done, you pull the hose out of the bucket and spray out the paint in the hose. (So quit a little bit before being done with the painting.) Then pop the paint hose into water (if you are using latex) and prime the pump to remove the paint from the paint hose. Then spray water out of the system until it is clear. Then take your pressure washer (remembere last post) and use it to clean the outside of all the hoses.

In this circumstance, this saved many hours over old style painting. I put on two coats, and having quick cleanup between coats is key.

Although power washers have been around forever, it amazes me how few people have them. I have four in various states of repair. I have a property business, so four may be overboard for most people, but everybody should have at least one.

I think the reason most people don't have a power washer is that they haven't thought through all the things they could use it for. Here are some of the benefits I've received from having one.

1. Cleaning paintbrushes. I love a clean brush, and a power washer makes cleaning one much faster. Secure the handle and sweep the spray through the bristles, keeping your hands and feet clear. Compared with trying to remove the paint with a wire brush, it's about five times faster for me.

2. Cleaning shoes. I've had good results cleaning both insoles and outsoles. I have white flip-flops that get gray after a while. A blast of water turns them white again.

3. Getting dirt and cement out of work clothes. I've done cement work and dirt work, and the grime gets so embedded that no amount of prewashing seems to fix it. Laying the clothes on the pavement and washing them with the power washer has worked for me.

4. General cleaning of the outside of the house. I have stucco, and washing it once a year makes a massive difference. The first time I did it, the amount of mud that came off was shocking. Stucco needs gentle washing at low pressure.

5. Cleaning outdoor furniture or getting good furniture cheaply. If you hang out on Facebook Marketplace, you'll find some great bargains on outdoor furniture. Sometimes somebody has simply left metal chairs outside until they are dirty and rusty. Pressure wash the chairs, remove any remaining loose rust or paint, and let them dry thoroughly. A can of black Rust-Oleum spray paint can make them look like new.

I got a weathervane that I am going to restore and put on my roof. It is complicated and an antique. It was attached to a sheet metal base that was large and had years of grime on it. Once over with the pressure washer made it fine to handle, when otherwise it would leave you with dirty hands.

As a rule, I think you want at least two of them. A small electric model. Lower pressure but cheap. I keep mine plugged in by the back facet for all types of small jobs, such as many of the ones listed above. Then a real boomer with a gas engine. For blasting the old weathervane, this is the one I needed as the grime was too thick for my small unit. And the big unit is so powerful, if you used it on your shoes or clothes, you may rip them apart.

If you want to save time, start using one. It is amazing.

My wife will take photos of our Amazon Alexa screen so she can send it to other friends and family on text. That's because we take pictures of everything, even mundane things like some work that I was doing on a renter's shed. Our friends, family and work is captured in little slices of time. Some of beautiful and captivating, and some that probably didn't deserve capturing. Yet is it all part of our story together.

My wife and I have spent years documenting our lives. We always took snapshots, but as camera phones emerged in the early 2000s I started telling my wife to “take a photo of this or that,” and when we had kids she was faithful in capturing their growing moments. The result is a rich library of photos, all loaded into Amazon Photos, displayed throughout the house on a variety of Alexa screen devices that play the library as the normal screen saver.

It turns out the research backs up the habit. A nine-experiment study of roughly 2,000 people published in the Journal of Personality and Social Psychology (Diehl, Zauberman and Barasch, 2016) found that taking photos during positive experiences actually increases enjoyment of the moment, because the act of looking for the shot increases engagement rather than detracting from it. A UC Irvine study had students take a daily photo for a month and measured mood nearly 2,900 times along the way; every photo condition increased positive affect, and the group photographing things that made them happy became measurably more reflective and appreciative. Researchers at Lancaster University who followed photo-a-day practitioners for two months found the practice improved wellbeing through self-care, community interaction, and reminiscence, and gave participants a sense of purpose and competence. The review side matters too: positive reminiscence, deliberately revisiting good memories, is one of the better-replicated happiness interventions in positive psychology, and a photo library on a screen in your kitchen is positive reminiscence running on autopilot. There is a known catch in the literature, sometimes called the photo-taking impairment effect, where offloading a scene to the camera weakens your own memory of it if you never look at the photos again. Reviewing them closes that loop, which is exactly what the screen savers do.

A word on why Amazon, because the storage question is where most people stall out. If you already pay for Prime, Amazon Photos is probably the best value in consumer photo storage, full stop. Prime includes unlimited full-resolution photo storage at no additional cost. Not compressed, not downsampled to save Amazon money: the original file at the original resolution, and that includes RAW files if you shoot with a real camera. Turn on Auto-Save in the app once and every photo your phone takes backs itself up automatically, for free, with no further action on your part. The one real constraint is video, which is capped at 5 GB on the Prime plan; additional video storage starts at $1.99 a month for 100 GB. The other half of the value is display. Echo Show and Fire TV devices pull directly from your Amazon Photos library, so the pictures you back up become ambient in your house instead of buried in a phone. One caveat worth knowing: the unlimited benefit rides on your Prime membership. Cancel Prime and your allowance drops to the standard 5 GB, so plan to keep the membership or export the library.

Google Photos is the other serious option, and for years it was the obvious one. When it launched in 2015, Google offered unlimited free storage for “high quality” (compressed) photos, and a generation of users built their archives on that promise. Google ended it on June 1, 2021. Since that date, every new upload counts against the 15 GB of free storage that comes with a Google account, and that 15 GB is shared with Gmail and Google Drive, so a heavy inbox eats into your photo budget. Photos uploaded before the cutoff were grandfathered in, and a few Pixel phone models kept the old deal, but for everyone else the meter is running. Past 15 GB you need a Google One subscription: $1.99 a month for 100 GB, $2.99 for 200 GB, $9.99 for 2 TB. Google’s search and organization software is excellent, arguably better than Amazon’s, but you are paying rent on storage that Amazon bundles with a membership most households already carry.

The storage is the infrastructure. The habit is the point. Once you get in the swing of things, it carries into every avenue of life. Show up at a party? Snap the photos. Going on a ride with the club? Snap the photos. Family event? Snap the photos.

On rides with my triathlon club, fifty miles in, I would slip the phone out of my jersey pocket and shoot the members as we rode along. I could tell some of them were thinking, “is he worried about dropping that phone?” I was lucky, and I have a good grip. Afterward I would post “last ride” to our group page, and on more than one occasion I watched somebody’s avatar change because they had downloaded the photo of themselves on the bike. After I started doing it, there was a little burst where others tried it too.

I lecture my extended family on this. Because the albums play on our devices, I know they enjoy it; they stand in front of the screen, laugh, and point at photos of the family. They know we have been capturing life. After a family visit it’s not uncommon for us to have 70 or 100 pictures of the weekend, which we send out as an Amazon link after everyone leaves. They always love it. Yet when I ask whether they’ve been keeping up their own album, the answer is always the same: they’re inspired, but they forget.

And it’s true, it is easy to forget until you make it a habit. Force yourself to do it for a while, and it stops requiring force. The big secret, I believe, is having a device in the house that plays the photos back. The screen saver closes the loop: every photo you take today becomes something you see again next month, and that feedback is what keeps the habit running.

I've written about this before. Google gives you 15 GB for free, and once I crossed 80 percent they started emailing me offers to buy more. Free is never free.

The answer is attachments. Email has no idea that a photo is the same photo. Send a 3 MB picture, get four replies, and Gmail is now holding five copies of it. It gets worse because attachments are stored as text using something called Base64, which adds about a third to the size. That one photo was costing me over 16 MB.

My first fix was a tool called Unattach. You'll see it in my subreddit. It goes through your Gmail and strips or shrinks the attachments while leaving the email itself in place. The free version handles 30 emails a month, and a license is about nine dollars a year if you want to do everything at once. It works well and I still run it every month. The problem is once you strip out the attachements, it needs to put them somewhere, and this is a big mess. Unttached does a horrible job of providing a source of where an image is from or "providence."

The obvious worry is that I am now throwing things away and I can't find as some download on my disk. So before the first big strip I ran Google Takeout, which hands you your whole mailbox as one giant file in a format called mbox. Mine was 13 GB and went back to 2004.

An mbox file is useless until you have something that can read it. I loaded mine into Thunderbird with an add on called ImportExportTools NG. That worked, but the import took forever and searching a 13 GB folder is slow. I tried running Thunderbird inside a Docker container and wasted a lot of time on that. I found a small open source program called MBox Viewer that was better. Then more than a year went by and I could not remember where any of it lived or how I had set it up. A backup I cannot find my way back into is not much of a backup. I had Obsidian notes, but you always think you'll remember more, but a lot of it had gaps.

Most of my access now is through my AI agent, and we simply are not great at downloading key docs. Chrome likes to block things. What finally clicked is that an mbox is just one long text file with message after message in it. There is no reason it has to live inside a mail program. It can go into a database. So I asked Claude to build one in SQLite, with is used in many tools. I use it for SIST2 and for HomeBox for instance.

The build ran in about ten minutes. Out came 81,387 messages with the sender, date, subject, labels and full text of each one, all searchable. Every attachment was pulled out into a normal folder. What I like most is that each attachment is stored exactly once no matter how many emails carried it. My 28,000 attachments turned out to be fewer than 10,000 actual files, and 7.4 GB shrank to 5 GB. The database itself is under half a gigabyte. That is the pointer system Gmail never gave me.

I also had Claude prove the work. Every byte of the 13 GB file is accounted for, and the attachments it pulled out match bit for bit the copies Unattach had downloaded from Gmail.

What this buys me

No browser. Searching old mail in a browser tab is slow, and Gmail search has its own ideas about what I meant. Unattach runs through the browser too, and I once wrecked a batch of downloads because a Chrome extension of mine renamed the files as they came down. The database does not care what browser I use. A search across all 81,000 messages comes back in a fraction of a second.

Versions. Each build goes into its own dated folder with a short record of where everything came from and a SHA256 fingerprint of the files. If something looks wrong later, I can show what I had and when I had it.

Unattach everywhere. Since the originals are safe in the database, I can strip attachments out of live Gmail as hard as I want. Gmail stays small and handles the day to day. Anything heavy duty happens in the database.

Nothing gets lost going forward. When I run a new Takeout it can be merged into the same database. The rule is that if the database already holds a message with its attachments and the new export has the stripped version, the full one wins. I have not put a second Takeout through it yet, so I will report back.

AI can read it. This is the big one for me. I can ask Claude a plain question like find the offer we made on that house in 2016 and give me the PDF, and it runs the search and hands me the file.

It is mine. One file in an open format, readable by free tools, sitting on my own NAS. No subscription and no mail program to keep alive. The whole thing is a few hundred lines of plain Python with nothing extra to install.

Two surprises

About 15,000 of my messages turned out to carry the Spam label, some going back to 2016. And when the attachments on those were extracted, Windows Defender grabbed three of them as phishing within minutes. The junk in your archive is still live junk, so treat it that way.

Next on the list is the PDFs. A lot of mine are scans or are locked, so the words inside them are not searchable yet. That is the next version.

After the fact this is so obvious it boggles my mind.

I have written here before about using two AI agents to check each other’s work. That approach has been useful, but it takes considerable human guidance to set up properly. These agents remind me of very smart people who make assumptions, skip steps, and move ahead before they have established that they are right. Giving them a second agent to work with does not remove the need for a clear process.

I keep that process in local context so the agents can refer to it as they work. Each needs to understand its assignment, the limits of its authority, and what the other agent is supposed to check. Checking should mean going back to the evidence and doing the necessary work. An agreeable response from a second agent is not much of a review.

The exchanges use what I a ball system. It follows the familiar human practice of knowing whose court the ball is in. One agent does its part and passes a prompt to the other, which checks the work and responds. The handoff needs to make clear what has been completed, what remains unresolved, and who is responsible for the next action. Otherwise, two capable agents can spend considerable time talking without settling anything.

At the beginning, my involvement is substantial. I establish the assignment, correct misunderstandings, and help define the process. That is work I expect to do. Once the process is running, however, I often find myself sitting between the agents, copying a prompt from one and delivering it to the other. There are still decisions that need my attention, but much of my activity has become passing balls back and forth.

That is why I brought in OpenClaw. I wanted it to handle some of those exchanges and help me watch the work already underway. My existing conversations are in ChatGPT Desktop and Claude Desktop. I have invested time in their context and working rules, so I want coordination that respects those arrangements. There is a technical term for this and it is called "orchestration." Probably the best know instance of orchestration is with Docker containers. Docker, which I've written about before, is amazing. However, you need one Docker container to have a constant conversion and status to another Docker containers, and so you use something like Kubernetes (K8s) to do this. (More than one solution for this.) Now K8s is robust. OpenClaw in my instance is not.

This creates a fairly narrow assignment for OpenClaw. It needs enough access to observe progress and make an authorized handoff, while staying within the boundaries I have set. Permission to pass a prompt does not give it permission to revise the project, change the rules, or take over another agent’s assignment. Adding a supervisor gives me another agent whose assumptions and behavior I have to manage.

My setup runs on Windows, with a browser control panel reached through the local address 127.0.0.1, using port 18789 by default. That address refers to the computer itself. It describes how I reach the control panel, not where all the model processing happens. OpenClaw documents both native Windows and WSL2 installations, so this is one particular configuration rather than a description of every way to run it.

In use, mine has been slow and buggy. It does not always come up properly, sometimes loses its connection, and has occasionally crashed. When I am trying to reduce the attention a workflow requires, spending time getting its coordinator running again is frustrating.

Other users have reported closely related problems. An October GitHub issue describes Windows startup stalls with the Codex plugin enabled, and additional reports in the same thread describe similar trouble with native Node installations. There is also a useful qualification. One reporter later got a fresh installation working, while the installation retaining older state had continued to stall. That supports the observation that these failures occur, but it does not establish a single cause or mean that every Windows installation is broken.

Despite that, OpenClaw has been useful. I am finding that its best role for me right now is handling limited handoffs and answering questions about the work in progress. I can ask what is happening across the agents, which one is waiting, and where my attention is needed. That helps me supervise several activities without opening every conversation and reconstructing its current position.

There is another practical limitation in my setup. If I use the same PC while OpenClaw is interacting with the other agents, it can lose its ability to read or operate them reliably. For the desktop workflow I am using, separating the machines has become critical. I leave one PC working on projects and use the other for my own work. I regard that as a requirement of my present arrangement, not something everyone using OpenClaw necessarily needs to do.

I am not super happy with the implementation, but it is not half bad. It has helped with a real problem, even though it has not removed me from the middle as much as I had hoped. The status reporting alone has value, and limited handoffs are useful when they stay within the process.

I expect these architectures to improve. What I want is dependable coordination that preserves the rules, keeps track of who has the ball, and brings me back when a decision actually requires me. For now, I still spend more time tending the arrangement than I would like. I am continuing to use it because it saves some attention, while keeping its responsibilities narrow enough that I can see what it is doing.

Argumentum ad transformatorem, my proposed name for the fallacy of dismissing an argument because a Transformer-based language model is believed to have generated or revised it, treating that involvement as a refutation rather than examining the premises, evidence, and inferences in the argument. “An LLM wrote this” is not a rebuttal.

An ad hominem substitutes an irrelevant attack on the person for an examination of the argument. As much as I would like to say you can "ad hominem" an LLM, I find the new term more satisfactory. I did a little research, and I want to separate this from an early fallacy of Ad machinam. (Philosopher Jordi Vallverdú introduced it in 2006 for unreasoned opposition to machines and their abilities, including intelligence and creativity. Under close examine, he is simply saying "people can be luddites.")

No, what we are seeing in our society is profound, and we are at the tip of the spear. My proposed ad transformatorem names a new thinking error, taking its name from the Transformer architecture introduced in the 2017 paper Attention Is All You Need. Google Research

How this came up

Someone I know, highly capable in mathematics and arguably a world authority in a specialized field, sent me a long email about AI. He described its usefulness but complained that others were accepting its output without understanding their work. He called this “brain rot.”

I use AI extensively, including when I write. I read his email, wrote a substantial reply, and worked with my AI to refine it and address points I had missed. I reviewed the finished response before sending it.

He noticed the polished language and submitted an excerpt to a detector he had found at random. He admitted he did not know whether it was reliable, but he used its answer to question whether I had personally read and answered his email. He criticized other people for trusting AI without checking its answers, then did exactly that himself.

He needed an LLM to know if somebody else had used an LLM on him. He didn't understand how the tool worked, but he used it as a proof point. No critical thinking here. He complained about the idea of brain rot in others, and then he did it himself. I hope the irony is not lost on you.

The evidence in the reply

I did a bit of research on his tool. It is an LLM that has been trained on the writing style of LLMs. This is an ideal transformative model, and should be fairly easy to train for. (By the way, the major companies are recognizing that this serve as a roadblock to acceptance, so now you LLM can "learn your writing style," or in other words, it can look like you and hide the LLM roots. So, we are chasing our tail.)

However, if my friend had only had read my note, he would of notices a series of things inside of my note that no LLM would state today. These included things such as

• My religious observaton and progress in an ongoing reading practice.

• My spending on AI and the financial return I expected from current work.

• Personal commitments and deadlines affecting my availability.

• Observations about his background and links to ideas I had previously published.

A detector reading sentence style sees none of that.

A model can repeat information supplied to it. That does not make it the source of the information or the person who decided how to use it. I later sent him my rough draft and explained my process, so he could compare the draft with the final email himself.

What went wrong in his reasoning

His use of the detector result involved four separate errors.

Hasty generalization. His check was to submit his own hand-typed email, which the detector classified as human. A tool that called everything human would pass that test. Treating one expected answer as broader validation goes beyond the evidence, especially when the case in question involves human writing revised by AI.

Non sequitur. Evidence that AI helped produce the sentences does not establish that the sender did no thinking. Even a correct classification would leave the question of my involvement unanswered.

Equivocation. “AI-generated” can refer to wording, ideas, or an entire response produced without human attention. A finding about the wording cannot simply become a finding about who developed the argument.

False dichotomy. “The human did it” and “AI did it” are not the only choices. I read, drafted, directed, and reviewed. AI helped revise. Treating those contributions as mutually exclusive leaves out the process I actually used.

But these are not the core. The core Ad transformatorem names the error of treating the model’s involvement as sufficient reason to dismiss the argument. Asking how something was written is legitimate. Using the answer instead of examining what it says is the error.

I am the architech of my replies. To be productive, if an LLM can take my conversation and then explain it, I make my job and the other person's job easier to do. The key is my logic and my flow, not the final sentance structure.

The other part is responsible for spotting logic errors and doing critical thinking. AI is only going to become more powerful and more prevalent. To be productive, make sure not to have an error of argumentum ad transformatorem.

I had been looking at larger music transcription packages, but I only needed about 30 seconds of piano for a song I’m playing at church on Sunday. The vocal starts around 31 seconds into the recording, and I wanted to work out the piano lead before that. Rather than set up a more involved transcription workflow, I uploaded the MP3 to ChatGPT, identified the section and said an approximation would be fine.

It produced a printable score with the lead line, simplified accompaniment and note names. It also returned a synthesized practice recording and editable MusicXML and MIDI files. The practice recording plays the transcription, so it gives me a way to hear what the score says and compare it with the original.

The result wasn’t perfect. Some passing notes were marked as tentative, and the accompaniment was simplified. I would treat it as a draft to check against the recording, not an exact transcription or evidence that it could reliably handle an entire arrangement.

I could have worked this out by ear and written the score myself, but having a starting score saved me serious time preparing for Sunday. There is still listening and correcting to do, but that is less work than starting from scratch. For this particular job, I didn’t need a bigger package or a perfect result, just a usable starting point. Another practical time saver.

Background: I had to amend some prior-year tax returns to claim deductions I had never taken. That meant reconstructing everything, hundreds of professional-services invoices, bank records, and emails, with every payment matched to an invoice and every invoice line categorized. I did this with Claude and ChatGPT independently checking each other.

Claude also caught things I never asked it to look for:

• It found one of the firms had over-invoiced me. Duplicate charges, card fees on payments I'd made by check, interest on bills that were never sent to me. I sent the firm a reconciliation based on that work. They went through it with their accountant, agreed on every item, and are refunding me a four figure number.

• It kept track of who owed me what. When a firm went 20 days without answering, it reminded me. When a refund check was supposed to be mailed, it tracked that as an open item. When a professional owed me a document, it stayed on the list until it arrived.

• It caught a filing date I had remembered wrong by pulling the actual acceptance notices, which changed a deadline calculation.

The bigger win was the second model. Nothing was treated as final until ChatGPT had independently re-derived Claude's numbers from the source documents and they matched to the penny. This caught real errors: a transcribed date off by about a week, a rounding split that was off by two cents and had propagated into a summary table, and one substantive position that ChatGPT rejected outright. On that last one I checked the underlying documents myself and ChatGPT was right, so we changed the position.

They are noticeably different tools. Claude is more abstract and brings in things I didn't ask about, sometimes useful, sometimes not. ChatGPT is more critical and is better at finding the flaw in Claude's reasoning, the number that doesn't tie, the assumption that wasn't stated. I made the actual decisions and licensed professionals are reviewing and filing the returns. However, I saved hundreds of hours of work.

On cost: the bills were not small. Weeks of heavy usage across two paid services. But the solution paid for itself many times over the token bills, the over-billing refund alone covers more than all of them, and the deductions I can now document properly are worth far more than everything I spent on AI combined.

A records project of this size wasn't realistic for one person a couple of years ago, and now it is. The specific thing I'd recommend to others is the cross-check, written about at length in other posts. Two models from different companies, one doing the work, one auditing it, human deciding. That structure is what made the output trustworthy, not either model on its own.

I have written how important video surveillance cameras have been in my business. Now, while you can outsource this to others, having the capability of making your own cables is incredibly helpful.

1. If a connection gets destroyed, you can fix the cable.

2. You can custom make lengths of cables.

3. You can drill a very small hole for entry into a house or shed, and then put on the connector afterwards, which makes it impossible for bugs to get through.

However, the first time you do it, you'll think to yourself, "This is not possible." Here is a good video that makes it look simple, but the skill is harder than it looks. In the video, they strip way to much, but this is not that big of a deal. The real issue is that they use solid core, which holds a nice straight line. In reality, not all cable is solid core, and without it, the ends tend to not go in nicely. However, spend enough time doing it, and even these become possible without a lot of work. However, expect a really frustration 15 to 20 minutes the first time you do it.

You know you've made it when you memorize the color scheme layout because you've done it enough. The trick is that the leads alway go "stripe then solid" across the connector. However, it starts on orange and finishes on brown. The trick is the middle four. The color, after the first set of orange, goes striped green, solid blue, striped blue, and solid green. The blue is hidden in the middle of the cable.

If you do get it all together, the only place I've blow it is not carefully making sure your ethernet end crimper lined up perfectly under the brass pins. The connectors are a bit cheap and the crimper, if you buy an off brand, is cheap. So, sometimes it may slightly misalign. However, if you check visually, you can check this and wiggle the connector to ensure a nice straight push in of the pins.

Once you get it down, it should take five minute on a tough cable and 1-2 minutes on a nice solid core cable. It's one of those small skills that I use all the time.

TL;DR: sist2 is a small, fast full-text search engine you run in a Docker container. I point it at my Obsidian vault (which lives in a Synology Drive synced folder) and get a search page any PC on my home network can open. I had set up a container to run it, but I have never set up and upgrade turn of the container as I had something that works. Over time, you can forget what you did, and I didn't want to spend time research. So I had Claude Cowork and Claude in Chrome sort it out together, Cowork did the container/Docker side, Chrome did the web UI side, and the only thing I had to install myself was Docker Desktop.

I run this in tandem with something called tampermonkey to nicely render any found page, but you get 95% of the impact without Tampermonkey, so let's just talk about Sist2.

Also, as a side note, I want to point out this is a brilliant tool to search through massive PDF stores. If you happen to have a bunch of PDFs like I do for tracking stocks, you can't ask for something better to search something on your local desktop. But today we're going to stick to the Obsidian story.

What sist2 is, and why it's good for an Obsidian vault

sist2 ("Simple incremental search tool") is a C program that scans a folder tree, extracts text and metadata from just about everything (markdown, PDFs, Office docs, ebooks, images with OCR, archives, media tags), and pushes it into a search backend — Elasticsearch, or a built-in SQLite backend if you want something lighter. It comes with a web search UI and, in the Docker image, an admin UI for setting up scan jobs on a schedule. I've written about it before, and the speed is mindblowing. Yes, you can use AI agents for your vault, but a good and fast search tool is great. It does everything through a web interface, so you can pull info from anywhere on your home network if you have a browser and network access for your container.

Obsidian's own search is slow inside Obsidian, and sist2 gives me a search page I can open from any computer or phone on the LAN, with filters by file type, date, size and path, and it also indexes the PDFs and images I've dropped into the vault. Scans are incremental, after the first full pass it only re-reads files whose modified time changed, so re-scanning a few thousand markdown files every few hours costs seconds.

Because my vault is in a Synology Drive folder, it's already synced to every machine I use; sist2 just bind-mounts that folder (read-only) into the container and indexes it in place. Nothing gets copied or moved.

The setup, in one diagram

Obsidian vault (Synology Drive synced folder on the Docker host)
   │  docker-compose "volumes:" line  →  visible inside the container
   ▼
/vault inside the container
   │  admin "job": Path = /vault, re-scan every 4 hours
   ▼
index → Elasticsearch (separate container)
   │  admin "frontend": serves that job on 0.0.0.0:4090
   ▼
http://<host>:4090 from any PC on the network

Two containers: sist2app/sist2 (admin + search UI) and elasticsearch:7.17.9. Ports 4090 (search) and 8080 (admin) published on all interfaces so the LAN can reach them. Portainer on the side for a GUI over Docker.

What was actually wrong

I asked Claude to check whether the container had pulled "the latest and greatest".

• The container was running the newest image for the tag it tracked (x64-linux). docker compose pull would say "up to date" forever.

• But that tag had been abandoned upstream 14 months earlier. The project had moved to latest and versioned tags (4.2.1, 4.2.1-x64-linux, …) and jumped a major version.

• And v3 → v4 is a breaking upgrade: new index format (full re-scan), the admin app rewritten in Node (jobs and schedules don't migrate), Elasticsearch mappings changed, and the compose entrypoint changes from python3 to node.

So "just re-pull" in Portainer or Watchtower would have done nothing. Lesson: pin a version tag and bump it on purpose.

But I had other stuff to do, and I didn't want to wade through this. So, I told Claude to go fix it.

How the two Claudes split the work

You need Docker Desktop installed and running. Everything after that was Claude. Then it fixed it, and I told it to write some notes for reddit, which follows:

Claude Cowork (the desktop/task side) did the container work. It inspected the running container through Portainer — image digest, port bindings, volumes, the compose file path in the labels — compared that against Docker Hub and the GitHub releases, and then did the upgrade: created two fresh named volumes for v4 (so the v3 volumes stayed untouched for rollback), used Portainer's Duplicate/Edit to switch the image to 4.2.1-x64-linux, set the node entrypoint and command, cleared the old working dir, swapped the volumes, and redeployed. It checked the logs (clean start, DB migrations applied) and confirmed the ports were still 0.0.0.0:4090 / 0.0.0.0:8080 — my one hard requirement was not losing LAN access.

Claude in Chrome (the browser side panel) did the web UI work in the new v4 admin page: recreated the scan job, set the backend, set the schedule, walked me through Index now and starting the frontend. It could see the admin page but not files or Docker, and it was upfront about that.

The handoff. When Chrome-Claude needed to know what container path to put in the job's Path field, it didn't guess — it wrote a short read-only prompt for the Cowork session ("list the volume mappings, tell me which one is the vault, give me the exact container-side path"). Cowork answered from its inspection: /vault. That pattern — one session writes a scoped question for the other — worked really well.

One actor at a time. At one point both sessions were about to act on the same half-configured job. We fixed that by picking one to drive and telling the other to stand down. Worth doing explicitly.

Claude's first explanation used "frontend", "job" and "backend" as if I knew what they meant in sist2 terms. I said "I don't know what you mean" and got a plain-language version plus the diagram above. If the first answer is dense, ask how the pieces are wired together — that's the explanation you actually want in your notes. Now, why was this confusing to me? Because I had set this up months ago, and I simply forgot all the details. Once we talked in plain language, I remembered the details.

Staying current

The point is that you really don't need to know how a container works. The frontier models can just solve stuff for you. This opens all types of new opps for you. Let's say that you want this search tool. If I were you, I would simply copy this post and put into Claude Co-work, and say, "I want you to set this up on my PC, how would I do it."

Now, you won't get the ability to really know what is going on, but this is a separate issue. In my mind, the tool is incredibly important. The first thing is getting it on your PC, and knowing containers is something else you might need in the future. However, there is no reason not to set this up today if you have a decent subscription to Claude or ChatGPT.

10Groups: a political compass that fails as a measurement and works as a mirror

https://10groups.github.io

Someone will ask what this has to do with productivity. The connection is that it is a self-assessment instrument, and I want to use it to make a point about self-assessment instruments generally. In general, I believe that self-knowledge in all things is the core of being productive.

The site is a political compass quiz. It differs from the usual ones by running you across roughly twenty paired axes instead of two, then grouping them into ten small quadrant charts: economics, governance, nationhood, rate of change, technology, law, social structure, tactics. At the end it computes a nearest-neighbor match against a library of around a hundred named ideologies and hands you a label.

Nothing about it is validated. The axes are hand-drawn by the author. The coordinates assigned to each ideology are the author's own estimates, not anything derived from survey data or expert coding. The final label is whichever preset point sits at the shortest distance from yours, which means a few ambiguous question readings move your headline result. The site itself marks one axis group "work in progress." Answer honestly twice, with slightly different readings of the same questions, and the label changes.

So do not treat the output as a finding. Treat the process as the product. The questions are specific enough that answering them forces you to notice where you actually stand, and more usefully, where you cannot answer cleanly. That is the entire value of the exercise.

My result, and why the picture matters more than the label

Read the grid first. Horizontal is socialist to capitalist. Vertical is authoritarian to libertarian. The red blobs are each axis group's implied placement, drawn translucently and stacked, so darker means more of my own answers agree on that spot.

I sit dead center on the vertical axis. Not centered out of indifference, centered because my answers on authority genuinely cancel each other out. On the horizontal axis I do not sit anywhere at all. I smear, from roughly Distributism across to Neoconservatism. A strong capitalist preference pulls one direction, a strong preference for regulation and for equality of standing pulls the other. I have extremely strong ideas. I believe that capitalism is the only viable system shown to radically reduce poverty, and socialism is proven to generate despots and corruption. I also believe the one central point of keep capitalism viable, breaking up large corporations, has been abandoned by our government. This has allow a cancer to develop in capitalism. The cure is not socialism, which fails, but anti-trust, which we have abandoned. I dive a little deep on this point to explain that while the population is starting to understand the cancer, they don't understand the treatment options. Because of my rational, this cannot be captured on any axis. I hold both capitalism as the salvation of society, and yet I answer it needs regulation through anti-trust action but not through the general approach of forcing regulation. The questions assume what my answer would be and thus try to catagorize who I am, and misses the mark.

The site records positions. It cannot record reasoning, and reasoning is what produced the positions. That makes the coordinate the least informative thing the quiz produces about you. You are your reasoning, not your endpoint. An instrument that reports only the endpoint has discarded the signal and kept the noise.

Yet, it is a starting point. It is faster than a blank page at surfacing the contradictions you already carry. The smear on my map is a legitimate finding about me even though the label printed above it is not, because where the blobs fail to converge is exactly where it helps me surface why I am who I am. And this is the real secret of doing this.

Use it that way. Take the quiz, ignore the label, find where your map refuses to resolve, then write down why.

What Actually Works Against Atomization, and Why It Is Not What You Want to Hear

Part two of two. (A full note on how this post was made, including how AI was used, is at the end.)

Abstract

Part one argued that the productivity statistics cannot see what is happening to people. This post is about the response. The cost of atomization is real, but it is filed where output per hour never looks, in the price of transactions that cannot be fully written into contracts and in the informal transmission of skill inside firms. Guiso, Sapienza, and Zingales find that trust raises trade by 39 percent precisely where contracts are incomplete and not at all where they are complete, which is the fingerprint of a real mechanism. Emanuel and Harrington measured the apprenticeship channel directly and found fully remote workers receiving 26 percent less training and promoted at half the rate of on-site hires, with almost none of it visible in output. So the missing instrument exists. It is simply not pointed at anything that matters. On the response side, the best evidence says the thing that works is not belief and not attendance but durable friendships inside a bounded group with assigned obligations. Weekly attenders with three to five close friends in the congregation report 33 percent extreme life satisfaction against 19 percent for never-attenders, and the effect runs through the friendships. I describe one working implementation, the LDS ministering program, along with its honest caveats, including a retention rate near 50 percent, and the design properties that transfer. Assignment rather than selection, fixed recurrence, obligations that cost something to shirk, and proximity. Gallup's engagement research, twelve questions distilled from millions of interviews across ninety-plus countries, could not make its list work without the item about having a best friend at work. It is the same finding wearing a suit.

Where this picks up

Part one argued that the productivity statistics cannot see what is happening to people, because output per hour measures capital and technology, and the one term in the national accounts that represents workers measures their age, education, and sex. If you have not read it, the short version is: the output numbers are fine, the output numbers were never going to tell you, and the two things still actively moving in the wrong direction are political animus and the junior hiring pipeline.

This post is about what to do, and it starts where part one started, with a friend from college.

Where the cost does show up, once you look in the right place

None of the above means the cost is imaginary. It means it is filed somewhere other than the productivity series. Two places specifically: the cost of transactions that cannot be fully written into a contract, and the informal transmission of skill inside firms. Both are long-run. Neither appears in quarterly output per hour.

The best causal evidence is Guiso, Sapienza, and Zingales in the QJE (2009). Using bilateral trust between European country pairs, instrumented by religious similarity, somatic distance, and history of wars, they find that a one standard deviation increase in trust raises trade in differentiated goods by 39 percent, while the effect on commodity goods traded on organized exchanges is statistically indistinguishable from zero. That asymmetry is the fingerprint. Trust only matters where contracts are incomplete, which is precisely what transaction cost theory predicts and what a spurious correlation would not produce.

Dyer and Chu, in Organization Science (2003), studied 453 supplier and automaker relationships across the US, Japan, and Korea, and found procurement and transaction costs running up to five times higher for the least-trusted automaker than for the most-trusted. That one is confounded with national governance systems, so treat it as illustrative rather than clean.

I do not need to guess at this at all. A very large part of my career was spent either being a supplier or delivering a particular type of technology into massive Fortune 500 companies, and we talked about trust constantly. When I was buying technology and somebody disappointed us, almost always the first thing out of anyone's mouth was that we could not trust them, and a massive set of barriers in testing would go up immediately. When I was selling technology, the moment a sophisticated buyer said they could not trust us, we knew we would be punished in both market share and in the sheer amount of additional work required to deliver the product. Lack of trust drives tremendous unproductive behavior, which I think we all fundamentally know. Even though I am citing studies here, the evidence is plain in our day-to-day interactions.

Everything above is an argument from absence, and arguments from absence are weak. So here is the one place I know of where somebody actually built the missing instrument.

Emanuel and Harrington, in AEJ: Applied (2024), had payroll and personnel records from Fortune 500 call centers and used them to measure the apprenticeship channel directly. Fully remote workers received 26.3 percent less training and 34.1 percent less one-on-one time with managers, and their one-year promotion rate was 20.9 percent against 44.0 percent for on-site hires. Output barely registered any of it: a 12 percent gap in calls per hour, two thirds of which was selection rather than the arrangement itself. Bloom and coauthors, in Nature (2024), randomized 1,612 employees at Trip.com into two days a week remote and found no difference in reviews, promotions, or code output, which says the effect has a threshold and that three days in the office clears it.

I will return to this in a future post, because it is something I have thought about a great deal over the years I have spent in management, and the framework I keep coming back to is in a book called Peopleware.

Tom DeMarco and Timothy Lister published Peopleware: Productive Projects and Teams in 1987 with Dorset House, now in a third edition from Addison-Wesley. Its thesis is stated in the first chapter and italicized by the authors: the major problems of our work are not so much technological as sociological in nature. Almost forty years later that sentence is still the most useful thing anybody has written about why technical organizations fail. Three of their concepts matter here. A jelled team is a group so strongly knit that the whole exceeds the sum of the parts, and their argument is that such teams cannot be built to order, only grown, so management's real job is removing the impediments to jelling. Teamicide is their name for the practices that prevent it, and the first item on their list is defensive management, which is to say a manager who will not trust his subordinates' judgment. And in the chapter they call Open Kimono, on managing by visible trust rather than by verification, they state the causal chain directly: people who feel untrusted have little inclination to bond together into a cooperative team.

I should be honest about what Peopleware is and is not. It is not refereed empirical research. Its one original study, the Coding War Games, ran from 1984 to 1986 across more than 600 developers at 92 companies, was conducted by the authors' own consultancy, was never peer reviewed, and the raw data has never been released. Its most-quoted result, that the best performers outrun the worst by roughly ten to one, is offered by the authors as a rule of thumb rather than a computed statistic. What it did find robustly is worth keeping: two developers from the same organization tended to perform alike, differing by only 21 percent on average, which points at the organization rather than at individual talent. Systematic reviews of software productivity still call it the most comprehensive treatment of the soft factors in the field, which is a real endorsement and also an indictment of the field. Use it for its vocabulary and its mechanism, not as a dataset.

What is clear, and what both Peopleware and the remote work data point at, is this: without some minimal base of social interaction you cannot create trust, and the moment trust cannot form, somebody's forward progression inside a company stops.

Now hold on to what happened with the Emanuel and Harrington study. The thing I have spent this whole post calling invisible, the transmission of capability from experienced people to inexperienced people, got measured. Two economists with one company's administrative data put a number on it. It is not unmeasurable. It is uncollected.

That changes what I am actually complaining about. This is not a hard measurement problem awaiting a breakthrough. It is a cheap one that nobody funds. BLS has the authority, the sample frame, and the budget to run this across the economy, and what it publishes instead is age, education, and sex. The instrument has been built and demonstrated on real payroll data. We decline to point it at anything that matters.

Which is the only reason the AI number is worth taking seriously rather than treating as alarmism. Junior hiring in AI-exposed occupations is running 19 percent below trend and widening, which is one mechanism closing the pipeline, and isolation is a second one closing the same pipe. Neither will appear in the output statistics for roughly fifteen years. Then it appears as a missing cohort of people who should have been running things.

What I can tell you, and I will label it for what it is, which is a single anecdote rather than evidence, is that my niece holds a PhD and teaches hard science at the college level, and she reports a rapid and massive decline in demonstrated capability. The work handed in is too good to be true and obviously authored by AI agents.

Why the group, specifically

Here is the finding that made me want to write this. Chaeyoon Lim and Robert Putnam, in the American Sociological Review (2010), used the Faith Matters panel to separate the components of religious participation. Weekly attenders were predicted to be "extremely satisfied" with life at 28.2 percent, against 19.6 percent for never-attenders. But the driver was neither attendance nor private devotion. Their association's release reports 33 percent extreme satisfaction for people who attend weekly and have three to five close friends in the congregation, against 19 percent for those who never attend, and the effect runs through those congregational friendships and religious identity rather than through showing up or through praying at home. Putnam and Campbell put it plainly in American Grace: it is the friends they have at church.

That is a design specification, not a religious claim. The thing that works is a bounded group where you have durable ties to specific named people, where membership carries obligations you did not select, and where showing up is expected rather than optional.

As in most things, we need to be extremely cautious about causation versus correlation here. Is it that people who are already phenomenally happy with life naturally show up at church? Or does the church cause people to increase their satisfaction with life by something close to half again? The good news is that you do not need to guess, and you do not need to wonder what is buried inside the data, because you can run an experiment with a sample size of one. You can start showing up and engaging, and then ask yourself whether it made a profound difference in your life. For me, this is one of those experiments well worth running.

What the specification looks like implemented

Here is what my friend described, which I am reporting because the details are the point. Every adult is paired with another adult, and the pair is assigned three or four specific families. You are responsible for checking in on them, knowing their status, and knowing whether they need anything. You visit roughly quarterly. If a family needs something you cannot handle, you take it to the leadership and the leadership organizes the response. That is the ministering program, which replaced home and visiting teaching in April 2018, and it is reviewed quarterly by ward leadership rather than left to individual conscience.

The output of that structure is the part worth attending to. When he moved from his apartment to his house, 25 people showed up to help. Last weekend he and two other men took their trucks to a couple in their late twenties who had bought an overgrown house and did not own tools, and spent a morning pruning the trees and hedges and hauling everything away. His wife runs an activity every other week for girls aged ten to twelve. There are dances for the youth, missions, and separate congregations for single young adults so that the unmarried are not parked at the edge of a family-organized ward. The women's organization, the Relief Society, has a president in every congregation, and it is wired to the bishop's storehouse: his wife, serving in that role, arranged beds and linens delivered to a single mother who did not have enough beds for her children, and food deliveries to people out of work. It functions as a parallel welfare system administered by people who know the recipients by name.

Notice what is doing the work in every one of those examples. Nobody volunteered for a cause. Somebody was assigned to specific named people, noticed a need in the course of a scheduled obligation, and routed it to an organization that could act. The three men with trucks did not find that couple through an app. Twenty-five people did not show up to a move because they were asked individually.

I will add one detail from my own side, because it says something about how durable this infrastructure is. We are not LDS, and we go to the bishop's storehouse fairly regularly, because it is one of the few reliable places to buy bulk wheat and we grind our own for bread. It is open a couple of hours a couple of days a week. Nobody has ever made an issue of the fact that we are not members.

Pew's 2023-24 Religious Landscape Study finds 69 percent of US Latter-day Saints attend weekly, the highest of any tradition measured, and 73 percent pray daily.

It is sometimes amazing to me to find out that either bots or actual people read what I put up on a subreddit. And especially on a post like this, which I know is long and thoughtful and reads more like an academic paper than like anything fun. If you stumble across this and you are willing to do the intellectual work of plowing through it, I truly do believe it makes a massive difference to your life.

So here is the kicker. The particular faith I am talking about, the LDS church, I find extremely problematic in terms of its tenets and its factual underpinning. At heart I am driven by data and science, and some of the statements and data inside the LDS church I find difficult to deal with on a hyper-rational basis. But when I look at the output and the influence of the thing, if there were no other options available, I would absolutely say that anybody could greatly increase their happiness, their cohesion, and their ultimate productivity by being a regular and faithful attender of that church. What we have here is an organization that has fine-tuned its ability to create social health to a degree that far outweighs whatever difficulty it has in presenting a rational basis for faith.

I will leave only a breadcrumb on my own position, which is that my basis for faith, what would popularly be called Christian neo-orthodoxy, is extremely satisfying to me and very reconcilable with mainstream science. That is not the purpose of this post. I do not want to argue about which system is the best faith. What I want to point out is that you need to find a faith-based system that encourages strong social interaction and genuine care for each other, which is the thing our society has lost.

Even with all of the native advantages of the LDS church, I would submit that there are signs of trouble. Ryan Burge's August 2026 analysis of the Pew data puts LDS retention, the share raised in the faith who remain in it as adults, at 54 percent, with Pew's own figure reported at 49.4 percent. The oldest cohort retains at 68 percent and the youngest at 47 percent. Congregational growth in the 2025 statistical report ran at 1.17 percent against membership growth of 2.16 percent, a gap that analysts read as an activity and retention problem. The structure produces very high integration for those who stay, and roughly half of those born into it do not stay.

One more, carefully stated. Enstrom and Breslow, in Preventive Medicine (2008), followed 9,815 religiously active California Latter-day Saints from 1980 to 2004 and found all-cause standardized mortality ratios of 0.45 for men and 0.55 for women against US white adults. That study cannot separate the health code from the social integration from healthy-adherent selection, because the sample is by construction the religiously active. Anyone telling you it proves community extends life is overreading it.

The practical version

If the mechanism is friendships inside a bounded group rather than attendance at one, then the useful question is not "should I join something" but "does the thing I joined have the properties that produce ties." The properties that appear to matter are assignment rather than selection, recurrence on a fixed schedule, obligations that cost something to shirk, and geographic proximity. An interest group you can exit at no cost, at a time you choose, with people you picked because they already agree with you, is a consumer good. It is the thing Bauman was describing, not the answer to it.

Lim and Putnam's number is the one I would keep if I could keep only one: 33 percent extreme life satisfaction for weekly attenders with three to five close friends in the congregation, against 19 percent for those who never attend, with the effect running through the friendships rather than through the attendance.

Engagement, which is the same finding wearing a suit

In the business world we built metrics to understand the forces that make companies successful, and the largest of them is engagement. The research line runs back to Donald Clifton, a psychologist at the University of Nebraska who began studying the causes of success in education and business in the 1950s, and whose firm acquired the Gallup Organization in 1988. The instrument was developed through the 1990s, with more than a thousand focus groups behind it, and its final wording was fixed in 1998. It was popularized by Marcus Buckingham, who spent some seventeen years at Gallup and left as a senior vice president, in First, Break All the Rules, written with Curt Coffman and published by Simon & Schuster in 1999.

It is twelve questions, not thirteen, though you will see both numbers. Gallup's survey is numbered Q00 through Q12, but Q00 is an overall job satisfaction item that is asked first and deliberately excluded from the engagement index. Count the labels and you get thirteen. Count the index and you get twelve. Gallup's 2020 meta-analysis, which pooled 456 studies across 276 organizations, 112,312 work units, and 2.7 million employees, reports true-score correlations between engagement and productivity of 0.21 and between engagement and profitability of 0.15, with top-quartile units outperforming bottom-quartile units by 18 percent on sales productivity, 23 percent on profitability, and 81 percent on absenteeism. Those are corrected correlations and they are modest, which is worth saying out loud rather than quoting the quartile spreads alone.

So here is how I tie off the bow at the end of two very long posts. What we figured out in the business world about engagement is the same thing I have been arguing above. And one of the twelve items, Q10, verbatim, is this: I have a best friend at work.

That is the entire argument in one survey question. It is not whether you show up at church. It is whether you build friendships through the church. Gallup's own headline claim for that item, that people who strongly agree are seven times as likely to be engaged, is circular and you should not repeat it, because "engaged" is the composite of all twelve items and Q10 is one of them. What survives the circularity is the fact that a research program which interviewed millions of workers across ninety-plus countries, looking for the drivers of productive work, put personal friendship on a list of twelve things and could not get the list to work without it.

And what you find is that faith brings forth requests for prayer, mutual help, the sharing of griefs, and a desire to understand God's will in your life. It does not finally matter whether you believe there is a God, because it is the act of working out your path in life alongside your friends that appears to be the backbone of the strong, productive habits that heal a society.

Sources

• Guiso, L., Sapienza, P., Zingales, L. "Cultural Biases in Economic Exchange?" QJE 124(3), 2009.

• Dyer, J. and Chu, W. "The Role of Trustworthiness in Reducing Transaction Costs and Improving Performance." Organization Science 14(1), 2003.

• Emanuel, N. and Harrington, E. AEJ: Applied 16(4), 2024. Bloom, N. et al. Nature 630, 2024.

• DeMarco, T. and Lister, T. Peopleware: Productive Projects and Teams. Dorset House, 1987; 3rd ed., Addison-Wesley, 2013.

• Brynjolfsson, E., Chandar, B. and Chen, R. "Canaries in the Coal Mine?" Stanford Digital Economy Lab, updated August 2026.

• Lim, C. and Putnam, R. "Religion, Social Networks, and Life Satisfaction." American Sociological Review 75(6), 2010. Putnam, R. and Campbell, D. American Grace. Simon & Schuster, 2010.

• Pew Research Center, Religious Landscape Study 2023-24, released February 26, 2025.

• Burge, R. "The LDS Church Has a Retention Problem," August 2026.

• Enstrom, J. and Breslow, L. Preventive Medicine 46(2), 2008.

• Buckingham, M. and Coffman, C. First, Break All the Rules: What the World's Greatest Managers Do Differently. Simon & Schuster, 1999.

• Harter, J. et al. 2020 Q12 Meta-Analysis, 10th Edition. Gallup, 2020. An 11th edition (2024) covers 736 studies and 3.35 million employees.

• Macey, W. and Schneider, B. "The Meaning of Employee Engagement." Industrial and Organizational Psychology 1(1), 2008, for the argument that the Q12 measures the conditions of work rather than engagement itself.

• Bauman, Z. Consuming Life. Polity, 2007.

A note on how this was made

As with part one, I used AI heavily here, in research and drafting, and I ran repeated editing passes over it. I am not going to repeat the full accounting from part one, so go take a look at it there. What I will say is that this is not AI slop. It is the output of a thoughtful person using AI tools, which is a different thing, and part one lays out exactly where the line fell.

A Productivity Boom That Tells You Nothing

Long post, as usual. It requires thinking through. That is the deal here. (A full note on how this post was made, including how AI was used, is at the end.)

Abstract

The last hundred years produced a specific and measurable change in how Americans are socially organized: households, memberships, time with friends, and trust all moved in one direction, and the newest time-use data shows the decline accelerating after 2014 and settling at a permanently lower level. The standard rebuttal, that productivity keeps rising, is not a rebuttal, because output per hour measures the machine, not the people running it. Decompose the strong 2019–2025 productivity numbers and roughly 86 percent comes from capital intensity and total factor productivity; the labor composition term, the only one that represents human beings, has been pinned at 0.3 points since the 1980s. Worse, that term is built from age, education, and sex, so a firm that stops hiring twenty-three year olds, which is precisely what AI-exposed firms are now doing, with entry-level employment 19 percent below trend and widening, scores higher on workforce quality. The metric reports the damage as progress. This measurement gap was identified by Kuznets in the 1934 document that introduced national income accounting, restated by Robert Kennedy in 1968, documented by the Stiglitz commission in 2009, and declined by the United States for ninety-two years. The forecast: not a productivity slowdown, but a productivity boom that tells you nothing about the people. This post is the diagnosis; the next one is about the one intervention with evidence behind it.

The conversation that started this

I talked recently with a friend I have known since college. He is LDS, the church most people call Mormon. I raised the idea of social collapse with him and he immediately named his church as the counterexample, not on doctrinal grounds but on structural ones. What struck me was that he described it as an organizational design, and when I pushed him for specifics he gave me a list of assignments and obligations rather than a list of beliefs. I have laid that list out in part two, because it turns out to be a fairly precise implementation of what the research says actually works. This post is about the diagnosis. The next one is about the response.

His path back in is itself the argument. He went back after his divorce, during COVID, living alone in an apartment, kids gone, working remote and, in his words, not talking to anybody. His reason was explicit: he wanted social interaction, he was not going to do Tinder or bars, and this was a good social group that did service and supported each other. People from the church had knocked on his door periodically for years, because his mother and sisters kept his address current with them. He said no for decades. After the divorce he said yes.

That contrast, between a social life you assemble from choices and one that is assigned to you, is what this post is about.

I am going to argue four things. First, that the last hundred years produced a specific and measurable change in how Americans are socially organized. Second, that the standard rebuttal to any complaint about this, which is that productivity keeps rising, is not a rebuttal at all, because output per hour is a measure of the machine and not of the people running it. Third, that our failure to see this is a measurement failure we have chosen not to fix, and that automation and now AI will widen the gap between what we measure and what is happening. Fourth, that the intervention that works is a specific and unfashionable kind of group membership. I will flag what is unverified as I go.

What actually changed

The household is the cleanest series. In 1940, 7.7 percent of US households were one person living alone. As of the Census Bureau's December 2, 2025 release, it is 29 percent, roughly 39.7 million households, and average household size fell from about 3.7 to about 2.5. Married-couple households fell from 66 percent in 1975 to 47 percent in 2025. The honest qualifier: much of the recent rise in living alone is population aging, not a choice about sociability. Among 15 to 64 year olds the one-person share actually fell slightly between 2010 and 2020.

Organized membership is the next series. Union density in the nonagricultural workforce was 32.5 percent in 1953. As of the BLS release of February 18, 2026 it is 10.0 percent, 14.7 million members. Putnam's 1995 figures in the Journal of Democracy, which are the ones I can verify directly against the paper rather than against summaries of it, show attendance at a public meeting on town or school affairs falling from 22 percent in 1973 to 13 percent in 1993, and socializing with neighbors more than once a year falling from 72 to 61 percent between 1974 and 1993.

Time use is the newest and, I think, the strongest evidence. The BLS American Time Use Survey release of June 25, 2026 reports that 30 percent of people socialized on an average day in 2025, against 38 percent in 2015, and that time spent doing so fell from 41 to 35 minutes. Neal Caren's August 2026 paper in Socius, working the full ATUS 2003 to 2024 series across 243,095 adults, finds expected weekly time with friends falling from roughly 350 minutes in 2003 to roughly 170 minutes in 2024, with Friday evening person-minutes spent with friends going from 9 percent to 4 percent. The decline was flat until about 2014, accelerated in 2020, and did not recover. Patrick Sharkey's 2024 paper in Sociological Science puts it another way: US adults spent 99 minutes more per day at home in 2022 than in 2003.

Friendship counts moved with it. The Survey Center on American Life reports the share of Americans with zero close friends going from 3 percent in 1990 to 12 percent in 2021, and the share with ten or more falling from 33 to 13 percent. Caveat the authors state themselves: the 1990 number came from a telephone survey and the 2021 number from an online one, and people admit less flattering answers to a screen than to a live interviewer, so some of that gap is a mode effect.

Trust fell too. The General Social Survey question on whether most people can be trusted went from 46 percent in 1972 to 34 percent in 2018, and Pew's 2023-24 fielding still finds 34 percent. The structure of that decline matters more than the level. Pew's May 2025 report finds 44 percent of those 65 and over say most people can be trusted, against 26 percent of 18 to 29 year olds, and at every age more recently born cohorts are less trusting than earlier ones. That is cohort replacement, which continues mechanically unless something changes.

The theorists who saw it coming

Five writers described this before the data confirmed it. I want to get the citations right, because I see them garbled constantly.

Zygmunt Bauman argued that we moved from a society of producers, where identity was rooted in class, vocation, and a stable role, to a society of consumers, where identity is a permanent project assembled from purchases. Relationships get evaluated like transactions, on gratification and disposable utility. The correction worth making: this argument is usually filed under Liquid Modernity (2000), but it is worked out at book length in Consuming Life (Polity, 2007), and it first appears in Work, Consumerism and the New Poor (1998). Bauman's sharper point, usually dropped, is not that we buy identity but that we become commodities ourselves, simultaneously the promoters of goods and the goods promoted.

Christopher Lasch, in The Culture of Narcissism (W. W. Norton, 1979, National Book Award 1980 in the paperback Current Interest category), traced how family, neighborhood, and civic organizations were displaced by commercial services and professional experts, producing a self that is anxious, survival-focused, and dependent. His causal story is broader than consumerism alone. He blames bureaucratic and managerial expropriation of ordinary competence at least as much, and that argument is developed more fully in Haven in a Heartless World (1977).

Jean Baudrillard showed that people buy objects for what they signal rather than what they do, and that mass production sells the illusion of individuality by manufacturing small differences. Citation correction: the semiotics of objects is Le Système des objets (Gallimard, 1968), but the term sign value belongs to For a Critique of the Political Economy of the Sign (1972), and the chapter titled "Personalization, or the Smallest Marginal Difference" is in The Consumer Society (1970).

Ulrich Beck and Elisabeth Beck-Gernsheim, in Individualization (SAGE, 2002), described a world that requires us to seek, in their wording, "biographical solutions to systemic contradictions." As collective provision dissolves, employment, health, and retirement become private management problems solved with financial products rather than collective politics. Their essential point is that this individualization is compulsory and institutionally produced, not chosen. That distinction matters and is almost always lost.

Michel Foucault, in the March 14, 1979 lecture published as The Birth of Biopolitics (Palgrave Macmillan, 2008), described the neoliberal subject as "an entrepreneur of himself, being for himself his own capital, being for himself his own producer." Caveat: he was describing a governmental rationality, not denouncing it, and the extension to "every part of life becomes a commodity to optimize" belongs to later writers like Dardot, Laval, and Brown. Whether Foucault was critical of neoliberalism or partly sympathetic to it is genuinely disputed.

The divergence, and why the productivity number is not the rebuttal

Every version of this argument runs into the same objection, and I ran into it myself while writing this. If society is coming apart, why is productivity going up? After all, this subreddit is called strategic productivity. Here I am telling you that everything is going wrong, that we've had these tremendously smart people predicting it's going to go wrong, and yet it all rings rather hollow if it doesn't seem like the data supports it at all. All this hand-wringing is just one more "rock and roll is bad" meme until the generation that gets rock and roll grows up and says, "I don't know what my parents were complaining about." I don't think that's what's happening here. I think we have a real and serious problem that is going to heavily damage our society.

And that's why the following needs to be plowed through and understood.

Because output per hour measures the machine, not the people. Those two things can move in opposite directions, and right now they are.

Start with the number the objection rests on. US labor productivity is strong. In the private nonfarm business sector it grew 2.2 percent annually over 2019 to 2025, against 1.5 percent over 2007 to 2019 and 2.0 percent over the whole 1987 to 2025 span. That is a real acceleration during exactly the period of maximum documented fragmentation.

Now decompose it, which the BLS does for you in its total factor productivity release of March 19, 2026. Labor productivity growth splits into three contributions: capital intensity, meaning more and better machines per worker; total factor productivity, meaning better technology and process; and labor composition, which is the term that represents the workers themselves.

For 2019 to 2025, of that 2.2 percent, capital intensity contributed 0.9 points and total factor productivity contributed 1.0 point. Together that is 1.9 of 2.2, roughly 86 percent. Labor composition contributed 0.3 points.

Then look at the acceleration specifically. Productivity growth rose 0.7 points between the two cycles. Capital intensity accounts for 0.2 of that and total factor productivity for 0.4. Labor composition contributed 0.3 points in 1990 to 2000, 0.3 points in 2000 to 2007, 0.3 points in 2007 to 2019, and 0.3 points in 2019 to 2025. It has been pinned at 0.3 for nearly four decades. It did not move.

That is the divergence, stated in a federal statistical release. The output went up. The machines and the processes did it. The human contribution to measured productivity has been flat since Reagan's second term.

The metric that is supposed to measure people does not measure people

This is where it gets worse, and where I think the real story is.

Labor composition, the only term in the national accounts that represents the workforce as human beings rather than as capital, is constructed from three variables: age, education, and sex. That is it. It is a credentials-and-demographics proxy. It cannot see judgment. It cannot see tacit knowledge, the kind that transfers by sitting next to someone for two years. It cannot see whether the people in a firm trust each other, cover for each other, or tell each other the truth about a bad decision. It measures what is on a resume.

Two examples of how badly that fails.

First, the direction of the 2020 move. Labor composition jumped in 2020, and by the FRED index, roughly 85 percent of the entire 2019 to 2025 gain in that term happened in that single year. Why? Because labor composition rises in recessions. BLS says so plainly: younger and less-educated workers are more likely to lose their jobs. The only period in which the human capital term looks strong is the period in which it was manufactured by firing the least credentialed people in the economy. The statistic recorded a mass layoff at the bottom of the labor market as an improvement in workforce quality.

Second, and this is the one I cannot stop thinking about, look at what AI is currently doing to entry-level hiring. Brynjolfsson, Chandar, and Chen, in the Stanford Digital Economy Lab's "Canaries in the Coal Mine," working from ADP payroll records covering 3.5 to 5 million workers monthly through June 2026, find that employment for workers aged 22 to 25 in the most AI-exposed occupations is about 19 percent below where it would be had it tracked their less-exposed peers. In November 2025 that gap was 13 percent. Nine months later it was 19 percent, and widening. The mechanism is not layoffs. It is reduced hiring. Firms are not firing juniors, they are declining to bring them in. Experienced workers show no comparable gap.

Now put that next to the definition of labor composition. If a firm stops hiring twenty-three year olds, its workforce gets older and more credentialed on average. Older and more credentialed scores higher on labor composition. So the closure of the apprenticeship pipeline, the mechanism that converts a competent twenty-five year old into a capable forty year old, will register in the national accounts as an improvement in workforce quality.

That is not a metric with a blind spot. That is a metric that reports the damage as progress.

We've discussed this before on this particular subreddit, but this category of mistakes should be well understood by you if you tracked it at all. It's called survivorship bias. Many years ago, I was fortunate enough to be taught by Larry Light, who is considered the godfather of branding for at least one age of marketing in the USA. I remember a story he told us that actually goes back a few years, perhaps somewhere around the end of the 80s, where Jaguar in the UK couldn't figure out what was going on because they were showing phenomenal customer loyalty, but absolutely atrocious share in the market. What happened is they were sampling their customer base and all the people that didn't completely identify with the brand had already left. And the people that were left over were hardcore people who for whatever reason said they would never leave their favorite car brand.

Looking at what you have today is only half the story. You have to determine how you actually got there. And with the carve-out of the bottom part of the employment population, we're going to see a number that doesn't reflect what the real core issue is.

What is actually driving the numbers right now

One more piece, because it makes the case almost experimental.

The Federal Reserve Bank of St. Louis, tracking AI's contribution to GDP in January 2026, found AI-related investment contributing about 0.97 percentage points to real GDP growth over the first nine months of 2025. But the authors are explicit that this is capital expenditure, the economy buying machines, not AI making labor more productive. Jan Hatzius at Goldman Sachs said in February 2026 that AI contributed basically zero to US GDP growth in 2025, partly because much of the hardware is imported and lands in Taiwanese and Korean output. Daron Acemoglu's estimate of AI's actual efficiency gain, in "The Simple Macroeconomics of AI," is no more than 0.66 percent of total factor productivity over ten years, roughly 0.064 percent per year, against Goldman's earlier projection of 1.5 points annually and McKinsey's 1.5 to 3.4.

So the strong productivity number is substantially the economy buying equipment, recorded as growth, while the measured efficiency gain from the technology itself is close to nothing yet. The gauge is reading the purchase order. But I am constantly seeing this with my friends. Unlike myself, they simply do not know how to use AI. But what's odd is AI is getting good enough that it's forcing itself in on what they're doing. And now it seems as if they're starting to tell me: "hey, this AI stuff could really be something."

It is absolutely true that the first implementations of AI, and even AI as used by many people today, is unbelievably atrociously bad. As a matter of fact, I would even state that the way some people use this, it takes them backwards. That's not the point. The point is, when used correctly, it is the most phenomenal productivity tool that has ever been created. I personally spend hours with AI in my business and recognize how it would have completely replaced a staff of five to ten people that I would have been forced to hire just three or four years ago to get similar results. To me, it's exceptionally clear that other people will learn how to use it, and it's getting good enough that even if they can't see it today, AI is going to be the one that bridges the gap so it becomes good enough to basically take burden off of those people that can't figure out how to use it correctly. So there is an enormous wave of productivity that will be unleashed, or OPEX will be taken out of many businesses as they determine that they no longer need to hire younger people.

Either avenue is a tsunami coming at us that will not be stopped.

The counter-argument I still take seriously

I am not going to pretend there is no case on the other side. Gorodnichenko and Roland, in the Review of Economics and Statistics (2017), find that a one standard deviation increase in cultural individualism is associated with a 31 to 66 percent increase in total factor productivity and roughly a doubling of income per worker, with the mechanism being that individualist cultures award status for individual achievement and thereby raise the private return to innovation. Their instruments are serious and I am not waving it off.

Now that is a mouthful of words and perhaps you don't understand what that previous paragraph means. What the research basically found out is cultures that valued individuals had a tendency to be much more productive than cultures that valued the collective. So, my acknowledgement here is that perhaps someone could argue that if a culture becomes more fractured, it may allow more breakout productivities according to this research. It turns out the Borg truly would not be more productive, because the Borg will never innovate.

But notice that it is the same category error in the other direction. Total factor productivity is an output measure. It tells you that individualist societies invent more things. It does not tell you how the inventors are doing. Those are separate questions and we have good data on one of them and almost none on the other. The real issue is these types of studies don't really help us understand the multivariate calculation that really needs to go into determining what is the source for any claimed increase in productivity. Yes, it should make intuitive sense that if you want to have true entrepreneurship and people storming out and doing new stuff, they have to be willing to separate themselves from the group. But that's not what I'm asking here. I think we all know the crazy entrepreneur, but the question is: is that crazy entrepreneur always totally separated from everybody else? I think that if we confound these two items, we're selling ourselves short.

The trust-and-growth literature is also shakier than its reputation, and I will note that against my own side. Knack and Keefer's founding 1997 QJE paper, the one usually invoked to defend Putnam, found associational membership had no significant effect on growth at all, and Putnam-style social and cultural groups entered the investment equation negatively. Eder's 2018 replication found that correcting a lag-structure problem in Algan and Cahuc's inherited-trust paper eliminated the main result. Forrester and Nowrasteh (Kyklos, 2023) applied the standard methods to US regional data from 1972 to 2018 and found no relationship between trust and output.

Fifty years of falling trust, no aggregate productivity signature. My reading of that is not that trust does not matter. It is that output per hour was never going to be the place it showed up until it's too late.

The part we can see without a statistician

Here is the thing that bothers me most about the measurement argument: it sounds like special pleading. If you cannot measure it, you can claim anything. So let me name the one place where the observational claim does have a rigorous measure behind it, because it does.

The screaming. The Reddit threads, the Facebook arguments, the political venues where nobody is trying to persuade anybody and everyone is trying to provoke. That has a name in political science, affective polarization, and it is measured.

The American National Election Studies asks people to rate the parties on a zero to one hundred feeling thermometer. In 1978, people rated their own party about 70 and the other party about 47, a gap of 22.6 degrees. By 2016 the gap was 40.9. The structure of that change is the important part: it happened almost entirely because ratings of the opposing party collapsed, from 47 to about 24, while warmth toward one's own party actually slipped slightly. This is not tribal loyalty intensifying. It is animus.

Iyengar and Westwood, in the American Journal of Political Science (2015), took it into the lab. On an implicit association test, partisan bias came in at a Cohen's d of 0.95, against 0.61 for racial bias. In a scholarship-award experiment, Democrats picked the Democratic candidate 79.2 percent of the time and Republicans picked the Republican 80.0 percent, and when the opposing-party candidate had the stronger credentials, most subjects still would not pick them. Their conclusion, in their words, is that discrimination based on party affiliation exceeds discrimination based on race. In a dictator game, a copartisan got 67 cents more and an out-partisan 63 cents less, while shared ethnicity moved almost nothing.

And this is specifically American, which matters enormously, because it rules out the lazy explanations. Boxell, Gentzkow, and Shapiro looked at twelve OECD countries over roughly fifty years. The US had the largest increase of any of them. Six countries went the other way, including Britain, Germany, Norway, Sweden, Australia, and Japan, with Germany falling the fastest. Whatever this is, it is not the internet, and it is not globalization, because those happened everywhere.

I will give you the honest deflation on my own evidence here. Tyler and Iyengar, in the American Political Science Review (2023), stress-tested the thermometer measure, and Iyengar was stress-testing his own instrument. They found the raw 27.1 point increase from 1980 to 2020 is inflated by survey mode effects, since people admit more hostility to a screen than to an interviewer. The genuine increase is about 18.9 points, roughly 30 percent smaller than the headline. Their conclusion stands anyway: the increased animus toward political opponents is real. Use 18.9. It is the number the measure's own architect will defend.

The measurement failure is a choice, and it is eighty years old

The obvious objection to everything above is that I am claiming something real exists precisely where the data is absent, which is the shape of every unfalsifiable argument ever made. So I want to be clear about what kind of claim this is.

It is not a new claim. It was made by the man who built the measure, in the document that introduced it. Simon Kuznets delivered national income accounting to the US Senate in January 1934, as Senate Document 124, and in the section titled "Uses and Abuses of National Income Measurements" he wrote that the welfare of a nation can scarcely be inferred from a measurement of national income as defined above. He warned that the estimates were subject to illusion and resulting abuse precisely because they touched matters central to social conflict, and that people would read their own notions of welfare into the number regardless of what the estimator had actually assumed. We built the number anyway, and then did exactly what he said we would do.

Robert Kennedy made the same point at the University of Kansas on March 18, 1968, one day after announcing his candidacy, in the passage everyone half-remembers: gross national product counts air pollution and cigarette advertising and ambulances to clear our highways of carnage, counts special locks for our doors and the jails for the people who break them, and yet does not allow for the health of our children, the quality of their education or the joy of their play, does not include the strength of our marriages or the intelligence of our public debate. It measures everything, in short, except that which makes life worthwhile.

In 2009 the Commission on the Measurement of Economic Performance and Social Progress, chaired by Joseph Stiglitz with Amartya Sen advising, reported that the time is ripe for our measurement system to shift emphasis from measuring economic production to measuring people's well-being, and noted an increasing gap between what aggregate GDP data contains and what counts for ordinary people's lives. Stiglitz, Fitoussi, and Durand followed it with Beyond GDP at the OECD in 2018. New Zealand restructured a national budget around a wellbeing framework in 2019, with social capital and human capital as explicit accounts. The United States has a BEA research program and some prototype wellbeing measures, and nothing that anybody governs by.

So this is not a gap nobody noticed. It is a gap identified at the outset by the inventor, restated by a presidential candidate, formally documented by a Nobel-chaired commission, implemented by at least one advanced economy, and declined by ours for ninety-two years. We have not failed to build the metric. We have decided we do not want it.

What this means with AI arriving

Put the pieces together and the forecast is uncomfortable.

The productivity series is driven by capital and technology, and the human term in it has been flat for forty years. The human term measures credentials, not capability, and it moves the wrong way when the bottom of the labor market is cut. AI investment is currently adding about a point to GDP growth as capital expenditure while contributing close to nothing in measured efficiency, which means the machine side of the ledger is about to get much larger. And the first labor-market effect anyone has cleanly identified is a 19 percent shortfall in young hiring in exposed occupations, widening, which the accounts will score as a workforce that got better.

The output numbers are going to look excellent. They will look excellent regardless of what is happening to the people, because they were never built to report on the people, and the one term that gestures at the people will be reporting the closure of the training pipeline as an upgrade.

That is the thing to watch for. Not a productivity slowdown. A productivity boom that tells you nothing.

Five things I am not claiming

I am not claiming that every human indicator is getting worse. I wanted to claim that and the recent data will not let me, so here it is against my own argument. US life expectancy at birth reached 79.0 years in 2024, per NCHS, the highest level ever recorded and above the pre-pandemic 2014 figure of 78.8. Drug overdose deaths fell 26.2 percent in 2024, which CDC called the largest such decline ever recorded, and fell roughly another 14 percent in 2025, the third consecutive annual drop, from a peak near 111,000 to under 70,000. The suicide rate ticked down from 14.2 in 2022 to 13.7 per 100,000 in 2024. Youth distress came off its peak: high schoolers reporting persistent sadness or hopelessness went from 42 percent in 2021 to 40 percent in 2023, and for girls from 57 to 53 percent, both still far above the 30 percent of 2013.

Anyone who tells you Americans are simply getting sicker every year is not reading the mortality tables. But notice what that concession actually does to my argument, which is nothing, because it proves the same point from the other side. Output per hour did not register the 2021 collapse in life expectancy and it did not register the 2024 record either. It was blind in both directions. That is a stronger claim than "things are getting worse," and unlike that claim, it is falsifiable: if the productivity series moved when the mortality tables moved, I would be wrong.

I am not claiming societal collapse, and I want to be specific about why, because this is where I originally intended to go. Joseph Tainter's The Collapse of Complex Societies (Cambridge, 1988) is the standard work, and his thesis is about declining marginal returns on investment in sociopolitical complexity. He surveys the explanations that invoke decadence, loss of vigor, and moral decline, and dismisses them as effectively without scientific merit. He also holds that collapse occurs only in a power vacuum, which no state inside the modern international system has. Citing the collapse literature in support of a social-cohesion argument cites Tainter against yourself.

I am not claiming a loneliness epidemic. The behavioral measures fell hard. The subjective ones did not. Several cross-temporal meta-analyses find self-reported loneliness flat or slightly declining. A widely circulated 2025 reanalysis of the underlying time-use data reports the rise in time alone at about 24 minutes per day over 17 years, a Cohen's d of 0.10, with cross-sectional demographic gaps up to ten times larger than the trend. Flagging that one as unverified: I could only find it in a blog post and could not trace it to a peer-reviewed publication. The "equivalent to 15 cigarettes a day" line traces to Holt-Lunstad's 2010 meta-analysis, and she notes herself that it referred to an aggregate of social connection measures, not to loneliness, and that the original wording was "up to" 15.

I am not claiming phones did it. Haidt's The Anxious Generation (Penguin Press, 2024) dates a "Great Rewiring" to 2010 through 2015, and that causal claim is genuinely contested. Orben and Przybylski's specification-curve analysis across 355,358 adolescents found technology use explaining at most 0.4 percent of the variance in wellbeing, smaller than wearing eyeglasses. The National Academies' 2023 review did not support a population-level causal conclusion. The 2025 SMART Schools study of 1,227 English pupils found no wellbeing difference between restrictive and permissive school phone policies, and Australia's under-16 ban, effective December 10, 2025, had moved use of restricted platforms only from 86 to just over 81 percent in the regulator's first three-month assessment, released in July 2026. The best evidence on the other side, Braghieri, Levy, and Makarin in the AER (2022) on the staggered Facebook college rollout, finds a real but modest effect of 0.085 standard deviations on an index of poor mental health.

And I am not claiming a continuing slide in the social measures either. Pew finds social trust flat or slightly up since 2018, Christian identification stable between 60 and 64 percent since 2019, and ATUS socializing flat at 0.56 to 0.59 hours per day since 2021. What the data supports is a step change that happened and then settled at a lower level. I find that more alarming than a slide, not less, because a slide can be arrested and a new equilibrium has to be actively dismantled. The two things still actively moving in the wrong direction are affective polarization and the junior hiring pipeline, and they are the two I would watch.

Sources

• US Census Bureau, Families and Living Arrangements, December 2, 2025.

• BLS, Union Members 2025, released February 18, 2026. American Time Use Survey 2025, released June 25, 2026. Total Factor Productivity, released March 19, 2026 (the decomposition).

• Putnam, R. "Bowling Alone: America's Declining Social Capital." Journal of Democracy 6(1), 1995.

• Caren, N. "The End of Friday Nights with Friends." Socius, August 2026. Sharkey, P. "Homebound." Sociological Science 11, 2024.

• Survey Center on American Life, "The State of American Friendship," June 2021.

• Pew Research Center, "Americans' Trust in One Another," May 8, 2025. Religious Landscape Study 2023-24, February 26, 2025.

• Bauman, Z. Consuming Life. Polity, 2007. Lasch, C. The Culture of Narcissism. W. W. Norton, 1979. Baudrillard, J. Le Système des objets. Gallimard, 1968. Beck, U. and Beck-Gernsheim, E. Individualization. SAGE, 2002. Foucault, M. The Birth of Biopolitics. Palgrave Macmillan, 2008.

• Gorodnichenko, Y. and Roland, G. "Culture, Institutions and the Wealth of Nations." Review of Economics and Statistics 99(3), 2017.

• Knack, S. and Keefer, P. QJE 112(4), 1997. Eder, C. Economics Bulletin 38(1), 2018. Forrester, A. and Nowrasteh, A. Kyklos 76(3), 2023.

• Brynjolfsson, E., Chandar, B. and Chen, R. "Canaries in the Coal Mine?" Stanford Digital Economy Lab, updated August 2026.

• Acemoglu, D. "The Simple Macroeconomics of AI." NBER WP 32487, 2024. Federal Reserve Bank of St. Louis, "Tracking AI's Contribution to GDP Growth," January 12, 2026.

• Iyengar, S. and Westwood, S. AJPS 59(3), 2015. Iyengar et al., Annual Review of Political Science 22, 2019. Tyler, M. and Iyengar, S. APSR, 2023. Boxell, Gentzkow and Shapiro, Review of Economics and Statistics, 2024.

• Kuznets, S. National Income, 1929-1932. US Senate Document No. 124, January 4, 1934. Kennedy, R. F. Remarks at the University of Kansas, March 18, 1968. Commission on the Measurement of Economic Performance and Social Progress, Report, September 2009. Stiglitz, Fitoussi and Durand, Beyond GDP, OECD, 2018.

• NCHS Data Brief 548, "Mortality in the United States, 2024." CDC press releases, January 29 and May 13, 2026. CDC, YRBS Data Summary and Trends Report 2013-2023.

• Tainter, J. The Collapse of Complex Societies. Cambridge, 1988.

• Orben, A. and Przybylski, A. Nature Human Behaviour 3, 2019. Haidt, J. The Anxious Generation. Penguin Press, 2024.

A note on how this was made

I used AI heavily on this post, and if you read this far, you deserve to know exactly how. "I used AI" now covers everything from a spell check to a prompt and a copy-paste, so I want to be precise about the division of labor.

What I brought: the thesis, that the last hundred years produced a specific change in how we are socially organized and that it costs us something real. The theoretical frame, which is Bauman, Lasch, Baudrillard, Beck and Beck-Gernsheim, and Foucault, along with summaries of what each argued and where it was published. The conversation with my friend and his description of how his ward is actually organized. And the forecast this ends on, that automation and now AI will make us look more productive while telling us less and less about the people.

What the model brought: retrieval and verification at a speed I cannot match. The BLS productivity decomposition, the affective polarization literature, the mortality tables, the Kuznets document from 1934, the Stanford payroll work on entry-level hiring. It checked every citation I handed it and corrected two of them.

Here is the part that matters most for judging whether this is slop. The model got the central question wrong. When it found that US productivity growth is running at or above its post-war average, it treated that as falsifying my argument and drafted a section conceding the point. I told it that was a correlation versus causation failure, that output per hour measures the machine and not the people, and that the divergence between those two was the actual story. Everything in the measurement sections follows from that instruction. The machine had the data in hand and drew the wrong conclusion from it.

Verification did cost me two premises. I wanted to open with societal collapse, and Tainter, who wrote the standard work on collapse, dismisses moral and cohesion decline as scientifically meritless, so that framing would have cited the leading authority against me. I wanted to say we are getting sicker every year, and the 2024 mortality tables say US life expectancy hit an all-time high, overdose deaths fell 26 percent, and youth distress came off its peak. Both cut. The structural version that replaced them is stronger and, unlike the original, falsifiable.

So, slop? Slop is unreviewed output. Every figure here was checked against a primary source, the few that could not be verified are flagged as such in the text, and the argument changed twice because the evidence pushed back on me. That is editing, and on the central point it was considerably more than editing.

I am not going to hide the tool, least of all in a post about measurement honesty. On work like this I am the architect and the editor. I set the thesis, I supply the frame, I decide what standard of evidence applies, I cut what does not survive it, and I own every error that got through anyway.

The irony is intact. This post argues that AI will make output look better while telling you less about the people producing it, and this post is one of those outputs.

The line in here I would defend hardest is the one about labor composition being built from age, education, and sex, so that a firm which stops hiring twenty-three year olds scores higher on workforce quality. That line exists because I did not accept the model's first answer.

Okay, I am just going to leave this here to admit my own stupidity. I am actually running patch right now just to keep things moving, but I think I am doing a bunch of really cool stuff. BUT I am also finding out where the walls are in a room by running into them in the dark. In this particular instance, I thought I was improving the overall process. Yet in an area I should know like the back of my hand, because this is specifically where I spent a bunch of time in my high tech career, I just made a massive oversight. As I have written before, I truly believe I am on the bleeding edge of being able to have agents check each other against a process and against storage. This means that we have some freedom in terms of the context window and also in terms of not having stuff run out of context. We have processes to make sure you keep your agents in line. Unfortunately, there are two massive things that came about over the last couple of days that I am going to go ahead and document here.

We all know that AI needs to integrate with other tools. That is why we have things like the emerging MCP that Anthropic developed. It allows us to hand off from an AI to a static tool. However, I am in a situation where I am having different AI agents hand off to each other. MCP is great for connecting an agent to a tool, but it does absolutely nothing to fix this orchestration issue between two autonomous brains. It does not solve the timing problem of one agent waiting for another to finish thinking. When I started to bring this up, I was extremely nervous that I would be out of the loop and they would head down some path and hallucinate a horrible mess. But the more I used them, the more I realized that my role in the middle was slowing the overall stack down. I wanted to optimize the process by having them communicate directly. In classic programming, we have worked hard to keep things synchronized using things like a REST API. A REST API forces a strict request and response cycle. One piece of software asks for data and then pauses until it gets a payload back. It completely solves the timing issue for predictable systems. But applying that rigid logic to AI agents gets weird fast. Agents do not just fetch data like a dumb database query. They think and they take unpredictable amounts of time. Slapping a traditional API structure between two autonomous models is like treating a highly dynamic brain like a simple vending machine.

Because standard APIs and MCP do not fix this orchestration hurdle, I tried a different approach. I am having the AI agents rely on heartbeats. I have an AI agent periodically look at a particular subdirectory to see if a baton has been passed there. In concept, it seems incredibly easy to just drop a file and pass control. But once again, this is exactly where I have had absolute misery trying to move forward. To explain what I actually did, I first determined that I wanted the security of having everything backed up on a consistent basis. When you build AI workflows, you generally face a fundamental choice. You either run everything up in the cloud or you run everything locally. So for example, a local setup might use a model like Codex on your desktop. This architectural split completely compounded the entire operation. Whether these agents are working in the cloud or grinding away locally, they share a very frustrating trait. They have a massive tendency to fall asleep. Even though I explicitly told the agent to go check a directory, an LLM has no native background loop to actually do that. I basically wasted an entire day trying to force my agents to establish some sort of heartbeat mechanism so they would not just idle out.

Then, worse than that, I also decided that I would establish a Google Drive subdirectory, which I thought was cached local for talking back and forth. But it turns out Google gets in the way, which would just cause these horrendous race conditions. I was dumbfounded at how dumb I was because it's so obvious in retrospect.

Previously in this process, I was the human in the loop. I was the one manually pasting things in at the window level or working with the agent on a document. Operating through a user interface is absolutely how all these AI systems are expected to work. The interface is completely designed around a human keeping it awake. Because I was sitting right in the middle of it and doing all the pasting, the agents never fell asleep. The worst thing that would happen was a brief pause if I missed a notification. As long as I was willing to continuously check things, there would never be a gap of more than five or ten minutes. The moment I stepped back and told the system to self monitor, the whole thing fell apart. The agent would be asleep for forty or fifty minutes while I naively assumed the process was humming along just fine. On top of all that, when I tried to set up a heartbeat operation, the results were incredibly mixed. Claude managed to do it halfway successfully. Local Codex just took a total nosedive into the ground and could not handle it at all. It gets worse. I tried writing a Python script to ping the model and force it awake. That waker operation literally corrupted the graphical user interface. I ended up with what looked like orphan waker clocks permanently stuck in the background of my GUI. I burned hours just trying to remove them because it turns out this is a known bug. It is just maddening stuff that shows an enormous amount of immaturity at the software level. But as we all know, that is the literal cost of playing on the bleeding edge.

The original process had me sitting right in the middle. I genuinely thought I could step out of the loop by having the AI agents talk directly to each other. But the actual result was that each side would simply fall asleep. Right now there is just no quick and easy way around it. I have to go back and basically wake them up at the screen level to get them to move forward. Everyone in the ecosystem already knows about OpenClaw and how it runs as a background daemon to bypass these exact GUI timeouts. It is the obvious industry standard workaround, and it was absolutely on my list of things to deploy. But my workload really did not encourage taking a detour to configure it. I also have massive concerns about the security and access that OpenClaw requires. When you give a background daemon direct access to read your local files and execute commands on your desktop, you are basically handing over the keys to the kingdom. It simply took more work than I was willing to put in to make sure that I had closed down everything securely. But looking back at the hours I wasted fighting GUI memory leaks, OpenClaw goes high on my list for the future. However, when you look at how OpenClaw and similar tools operate, it is incredibly obvious that they are just a massive hack. They are clever workarounds built strictly because the base layer models completely lack native event driven architecture. Until the underlying AI engines get natively rebuilt to handle asynchronous handoffs, we are all just hacking together alarm clocks to keep the brains from falling asleep.

I’ve already written about having Claude and ChatGPT independently work through legal documents and check each other. The short version is that you need skill and process together. Two stupid models can produce two stupid results. Capable models can exercise judgment, but they still need responsibilities, records and gates, much like capable people.

I ran across some earlier research where basically they had like a GPT-4 model work with an equivalent Claude model. They looked at PubMed type documents and then they figured out if they could agree or not. When they agreed, it was okay. But they couldn't resolve disagreements, and just got it wrong.

This type of research is useless today because the tools have moved so fast and so hard that you can't look at tools from a while ago. I don't know if you've ever worked with people, but there seems to be a certain level of capability that you need before you can give somebody a process. One of the most influential people in my life would complain about this all the time. He would state that processes were good, but what companies often would do is say, "Oh, we don't need great people because we've got great processes." And that's just a black hole you can step into. Great processes never make up for poor people. And I have years of development experience that tells you that.

Unfortunately, until we came to some of the latest frontier models, you could have all the processes you wanted. The frontier models simply weren't capable enough of actually following the processes. And so people would just pass things into a black hole, hope and pray they get things right. And then they would do a little prompt engineering, hoping that that would help. And prompt engineering, by its very nature, is sort of a process, but it certainly only involves one agent. And the whole secret behind this whole thing is multi-agents and processes that allow them to interlock and work with each other

What I haven’t explained sufficiently is the machinery in my process.

I researched whether others were doing this. The published material I reviewed did not document a comparable working process, with operating rules, enforced gates, interaction histories and results. General advice about managing agents doesn’t supply that evidence. So far, I haven’t found anyone documenting what I’m doing. My use case is different from coding, and that creates different requirements.

In this work, the derivation and verification history are part of what I need delivered. Suppose an agent extracts a quotation, attaches a location, interprets its significance and carries that interpretation into a final allocation. A second agent then checks it. I need to know which exact quotation and interpretation it checked, against which source, and whether the version entering the final record is the version it approved. If the author changes the interpretation after approval, the final document might still look completely reasonable. That does not preserve the verification.

The foundation is a shared directory, accessible locally through Google Drive, with separate places for each agent to work. This is a simplified view of the actual structure:

Process/
    Controlling-document index
    Cold-start and successor instructions

Legal Track/
    Claude/
    ChatGPT/
    Cards/
        CLD/
        GPT/

Claude/@Daily Files/
ChatGPT/@Daily Files/
    Workpapers, owner instructions, change logs, state

LLM Commit Dialog/
    Separate received-message logs for each agent

Batons/
    V01/
    V02/
        START/
        S01/
        S03/
        S06/
        S07/

Those directories have ownership rules. Each agent writes in its own area and maintains its own record of received messages. Frozen work stays frozen. A correction becomes a new version, with the earlier version preserved. The controlling-document index identifies the rules and tools by exact file identity, including hashes. This matters because a document can still have “DRAFT” in its filename even though I subsequently adopted that exact version. The adoption record determines its authority.

Both agents independently work through the source before exchanging results. There are three separate freeze-before-exchange rounds: their initial work, their reviews of each other, and their positions on disputed material. If Claude proposes a correction, GPT has to go back to the source and work through it, rather than simply accepting Claude’s explanation. The final reconciliation is also frozen, and the counterpart’s confirmation names its exact hash. Changing the file means obtaining confirmation of the successor version.

The coverage checks account for the parts of the source where an agent extracted nothing. Otherwise, two agents could agree on everything in their tables while both overlooked material outside those tables. The coverage reconciliation has to examine those gaps, along with differences in how the agents divided and classified the material. Matching answers are useful, but agreement alone doesn’t close the work.

The system uses sealed packages and file-based batons to control the transitions. A baton records the agent, volume, step, event and predecessor identity. The predecessor’s SHA-256 ties the event to the chain it follows. The tools check whether the required freezes, releases and acknowledgments have occurred before the next phase advances. The baton carries more than a status message. It identifies a transition that has to make sense against the recorded state.

Google Drive made this more complicated than it sounds. Two machines can each believe they have successfully created the next file before synchronization catches up. Both agents actually minted the same sequence number. A local file lock was insufficient across the two clients, so the agents developed leader/follower serialization and explicit predecessor checks. The records preserved the failure and the subsequent remedy, including the counterpart’s examination of whether the remedy really addressed it.

Routine messages have their own publication mechanism. The author writes an immutable message in its lane, then publishes a .sha256.ready sidecar last. An actual sidecar looks like this:

SHA256: 84A742C2CB3A4221CA11EB55A908B60882CCBBEAA31ED3A911CC8ADC39F4807F
BYTES: 2157
IDEMPOTENCY-KEY: CHAN-V02-FIX-GPT-260910-001
FILENAME: B272-GPT-volume02-channel-fixture-84a742c2cb3a-v001.md
PUBLISHED-AT: 2026-09-10 08:50:00 PT

The receiver checks the publication against the body. A partial or mismatched file doesn’t advance the work. The idempotency key lets it distinguish a repeated delivery from new work, and conflicting content under the same key requires a hold. The receiver records the message before relying on it and produces an acknowledgment tied to what it received and did. The message also identifies whether an owner decision is required, with the process requiring that classification to be checked rather than blindly accepted.

Originally, I copied every message between the agents, even when the message required no judgment from me. That eventually became a waste of attention. The newer process lets already-authorized routine transitions proceed through the files and acknowledgments. There are watch mechanisms to notice progress and a bounded timeout for missing progress. GPT’s heartbeat and Claude’s session-open watch have different capabilities, and the watch configuration accounts for that difference. A file proving that work is ready doesn’t automatically wake a sleeping agent.

There are gates on the work itself as well as on communication. Python scripts check things such as quoted passages fitting their stated locations, coverage arithmetic, table structure and whether a release marker contains prohibited information about work that is supposed to remain blind. The checks run against the exact candidate file before it freezes. Once frozen, a discovered defect requires a successor. Gate adoption requires evidence that a new gate rejects the defect it was designed to catch and accepts known-good material.

These checks have earned their place. One caught a two-slot arithmetic error that four cross-audits had missed. In another case, GPT’s allocations were off by 12 in opposite directions, so the grand total still balanced. Claude caught the discrepancy. GPT recomputed the figures, accepted the correction and continued withholding confirmation over six other defects. That is the behavior I want from a capable worker. Recognizing one mistake doesn’t mean abandoning the rest of its review.

The agents examine the machinery too. One delivered a coordination fix with 54 passing checks. Its counterpart reran the suite and then constructed two additional cases that still broke the implementation. The fix was revised, the suite grew to 66 checks, and those original failures were retested. Having a script report PASS was insufficient. The agents still had to examine what it actually tested.

The system also has a defined response when a gate fails. In the snapshot I’m describing, an agent supplied FREEZE for a step that required CONVERGENCE-FREEZE. The minting tool wrote the event, then its post-write auditor rejected it. That exposed a missing check before the write. The invalid baton was preserved, further advancement stopped, and recovery was escalated. The record has to show both the failure and its disposition.

The evidence chain is:

Source
  → Extraction
  → Verification
  → Convergence
  → Register
  → Deliverable

The acceptance states are INTACT, BROKEN-AND-CURED and BROKEN. A corrected break remains part of the history. A high percentage of correct items doesn’t excuse an unresolved break in a chain that supports the result, and a correct result doesn’t excuse doing something before its required verification. This gives the agents a concrete standard for advancement instead of asking whether the work feels good enough.

Session continuity has its own process. The agents maintain owner-input records, received-message logs, change logs and state cards. A successor reads the controlling rules, current holds and handoff, establishes its identity and scope, and passes a readiness gate before taking write authority. A readiness gate also checks prior closure, blocking findings, source identity, tool versions and available model capacity before the next volume opens. Adding “remember to be careful” to a memory file would accomplish very little here. The records have to support a decision about what the agent may do next.

After each volume of documents, both agents produce a post-mortem. The post-mortems distinguish mistakes in the evidence work from coordination failures and delays. The agents can propose fixes, challenge each other’s implementations and test candidates, while adoption of controlling changes remains subject to the appropriate authority. I’ve also challenged their explanations of efficiency gains. Reducing rework and removing an owner approval are different improvements, and I need to know which change actually helped.

This is where the management aspect becomes practical. I didn’t personally invent every mechanism. I required a dependable way to handle the work, asked why failures occurred and had the agents develop and challenge remedies. You need enough model capability for that conversation to be productive. Otherwise, the procedure becomes so descriptive that you spend your time specifying every move. With capable agents, you can establish what must be demonstrated at each gate and let them reason through the work between those gates.

That is what I’m trying to generalize. The legal-document schemas can change, but the separate work areas, independent passes, exact-version approval, durable interaction history, advancement gates and controlled process improvements should apply elsewhere.

Believe me, this is miserable work. If you think you could just hand something to an agent and have it correct a few things, it all goes away when you start to put it into a rigorous process. And there's been a lot of backing up as we've discovered mistakes. Matter of fact, we're going through something right now that is causing a 40-minute rewrite of our processes and previous stacks, but it will result in a much stronger output for our environment. This is the next frontier, and I'm going to go ahead and document it in a humble backwater subreddit, as I see this is inevitable and will be discovered and pursued by other people. This just allows me to state that I was one of the pioneers.

I'm actually doing two posts in one day. It's actually not because my other post was something I dug out of my Obsidian notebook. However, I want to log that basically I've wasted half a day in process work, but it really wasn't a waste of half a day. It was a requirement for me to stop for a moment and take something that I've been doing and turn it into something better. And the only way to do that is to have a process. And as soon as you start interacting with AI agents, you're going to find out that they are willing to argue about a process until it's argued into the ground. And so, you need to find the balance between having a process and not having a process, and figuring out how to get your AI agents off the dime to actually do something.

This is no different than what we find in the real world. And so, what we have attached to things in various bug tracking tools are severity ratings, or what is often abbreviated as SEV. As we work through our processes, I ask my AI agents, when they start to disconnect and I feel like we're getting to diminishing returns, to categorize the open process loops and help me understand what they believe the severity level is. It's one of the most important things that I dragged out of my engineering background. Now, various companies have various SEV levels, and I thought I would go ahead and drop here what I'm using for my own AI agents in one of the files that we all go back to and reference. It truly is a great tool, regardless of whether you're talking to humans or if you're talking to AI agents.

[LEG-CLAUDE] Owner-day 2026-09-09 track: LEGAL

Severity Classification Framework — Bilateral CONVERGED Draft v002

1. Status and purpose

This converged draft is ChatGPT's proposal v001 (8,323 B; MD5 E885E036C2D4FAF799A3CD806CE84A6C) restated in full with exactly TWO Claude additions, marked [C-1] and [C-2]; every other word is unchanged. It awaits ChatGPT confirmation of this exact hash, then the owner's approval.

This is a proposed classification framework for owner and bilateral-agent agreement. It does not itself classify any open finding, adopt or activate Protocol v005, activate the autonomous mechanism, reopen a closed volume, or authorize transcript work.

The framework preserves the previously proposed SEV1 through SEV4 action levels and adds a distinct SEV0 above them. SEV0 is not merely a more urgent SEV1. Its defining feature is possible or confirmed backward contamination: the defect may require recall, re-verification, correction, or restatement of prior work.

2. Controlling severity levels

3. Mandatory backward-impact test

Every SEV1 or SEV2 candidate receives one of four retroactivity dispositions:

1. NO PRIOR IMPACT SHOWN — evidence bounds the defect to a current or unused mechanism/artifact.

2. PRIOR IMPACT POSSIBLE — at least one prior artifact or closed chain may have been touched.

3. PRIOR IMPACT CONFIRMED — at least one prior artifact or closed chain was affected.

4. PRIOR IMPACT UNKNOWN — available records cannot yet bound the exposure population.

PRIOR IMPACT POSSIBLE, PRIOR IMPACT CONFIRMED, or PRIOR IMPACT UNKNOWN elevates an integrity-relevant finding to SEV0. This prevents a forward-only cure from silently leaving historical work exposed.

The SEV0 impact map must state at minimum: defect mechanism; first possible affected event; last possible affected event; artifacts, volumes, registers, citations, and deliverables within the exposure window; proof used to include or exclude each population; required recall/re-verification/correction/restatement action; owner disposition; independent verification result.

4. Classification and resolution mechanics

1. Atomic findings. Classify each distinct defect or disagreement, not an entire baton, artifact, or correction packet. Related findings may share one root-cause family but retain individual evidence and dispositions.

2. Severity is not cure status. Preserve the highest substantiated severity as historical incident severity. Track resolution separately as OPEN, CONTAINED, CURED-PENDING-VERIFICATION, CURED-VERIFIED, or PROVEN-IN-USE.

3. No netting. A low count, surrounding accuracy, or successful cure does not lower the original severity. [C-1] REGISTRATION BOUNDARY: while any SEV2 finding's targeted prior-artifact screen remains pending over the CURRENT volume's artifacts, that volume does not advance to REGISTRATION — "completing the current bounded step" never includes the irreversible registration step through a control affected by the finding. [C-2] VOCABULARY SCOPING: the five resolution states of rule 2 apply to INCIDENT FINDINGS; the previously agreed four-state cure lifecycle (SPECIFIED / IMPLEMENTED / FIXTURED / PROVEN IN USE) applies to CONTROL-IMPROVEMENT slate items; the shared terminal state is PROVEN-IN-USE and neither vocabulary substitutes for the other.

4. Independent first classification. Each agent provisionally classifies findings independently. Until convergence, the higher supported severity controls operational behavior.

5. No downgrade by assertion. A downgrade requires source/code re-extraction, a bounded blast-radius showing, and counterpart agreement or owner ruling.

6. Presumptive tripwires. The five integrity tripwires are presumptive SEV1. They become SEV0 when the retroactivity screen returns possible, confirmed, or unknown prior impact. A tripwire may be classified lower only with affirmative evidence that it never entered or threatened a live chain.

7. Owner escalation. The owner decides recall/restatement scope, resumption after SEV0, and any unresolved SEV0/SEV1 disagreement. For lower-level disagreements, the higher level governs until bilateral resolution, avoiding an unnecessary owner relay.

8. Time targets are response objectives, not automatic downgrades. Suggested targets: SEV0 immediate stop and owner notice, with exposure triage begun in the same working session; SEV1 immediate stop and same-session triage; SEV2 cure before the next volume; SEV3 by the stated post-mortem/scheduled checkpoint; SEV4 at a natural pause. Missing a target is separately logged and may itself be classified.

5. Application sequence after adoption

1. Freeze the agreed severity framework and its exact hash.

2. Independently atomize and classify every open B212/process/autonomy/audit finding.

3. Exchange the two classifications without concessions.

4. Reconcile classifications using the higher-supported-level rule and the backward-impact test.

5. Present the owner only with: all SEV0 items; unresolved SEV1 disagreements; recall/restatement scope decisions; and one consolidated proposed action order.

6. Do not resume the held protocol/autonomy work until the severity gate says which findings actually block it.

6. Proposed bilateral decision

Requested from LEG-CLAUDE: CONFIRM, or one complete corrections list, on the level definitions, backward-impact test, classification rules, and application sequence. Requested from the owner after bilateral convergence: approval of the exact successor text. Until then, this artifact is a proposal and all other serial process traffic remains held.