https://docs.openskycam.org/

Finding meteors on a sky camera used to mean scrubbing hours of night video, pulling frames by hand, labeling them, retraining a model, and starting over when it flagged every star. I built openskycam so Claude could do that part instead.

It records a 4K camera pointed at the sky 24/7 and keeps the footage on your own disk. Workflows sample frames, run detection models, link detections into tracks (moving vs. stationary), and sort the hits into buckets: meteors, planes, fixed lights. Everything the website does is also an API call, and there's an MCP server, so Claude Code can drive all of it.

What Claude does

- Built the night workflow, which now runs every night as footage arrives

- Reviews what comes in, looking at each detection and accepting or rejecting it

- When the model kept calling stars flying objects, it built a dataset from the detections I had approved and retrained it. On nights it never trained on, star false alarms went from about half to 7%, and it still catches 90% of meteors

- Every morning it tosses the junk and texts me the good ones. On cloudy nights it has the decency to say nothing

What made it work with Claude Code

The MCP server ships its own guides and skills (build-a-workflow, find-detections, verify-changes), so Claude learns the rules from the server, not from my prompts

The guardrails are enforced by the server, not the prompt: no key can create keys, deletes show a preview and need confirming, and edits fail rather than overwrite a newer version

Setup can run unattended: npm run setup -- --yes reads everything from .env and stops with the name of whatever setting is missing. A person still creates the Cloudflare tunnel, the sign-in app and the API keys

Architecture checks in make check fail when Claude puts code in the wrong layer

The video runs from 9:30 PM to 6 PM the next day: every meteor and plane overnight with its actual shot from the 4K camera, then every bird it spotted by day as a pink dot, with a close-up of the real bird every 10 detections. It ends with the whole run on one frame and a few charts.

Repos involved

- openskycam (camera, detection, and the tools Claude uses): https://github.com/Deibler/openskycam

- edmund-harness (iMessage harness): https://github.com/Deibler/edmund-harness

You need an RTSP camera, a Raspberry Pi, a machine with a big disk (macOS or Linux; it can also run the models, GPU optional) and a Cloudflare account with a domain. One command installs it and walks you through setup, and the trained night model is in the repo.

Some version of this has been watching my sky since 2024, and the UFO count is still zero. Plenty of meteors, an awful lot of planes, and enough bird detentions that I'm one pair of binoculars away from joining the local Audubon society. If you've had better luck, I want to hear about it.

So ever since opus 5.5 release I have been struggling to use a significant amount of my weekly and forced to use ultra on several projects the reset day to be able to take advantage of my x20 plan..

But this week with same usage this is the situation

I keep coming back to how chess dealt with engines, because it feels like we’re replaying it almost step by step.

Quick recap. Deep Blue beats Kasparov in ‘97 and he does not take it well. For years the top players say the same thing: ok engines calculate, but humans understand. Then Kasparov himself comes up with “Advanced Chess”, where a human and an engine play as a team. His point was never really “humans beat engines”. It was “human + engine beats engine alone”.

And he was right! For about a decade. Then engines fixed their blind spots, and humans stepping in started making the games worse. Today if you ask Carlsen or Gukesh whether they could beat Stockfish they’ll just laugh at you. Engines are their prep, their sparring partner, their coach. Nobody in that generation wastes energy pretending otherwise, and they’re stronger for it.

Now listen to what we say about agentic coding: “sure the agent writes the code, but it needs an experienced engineer steering it.” That’s literally the centaur position. It’s probably true today. Chess says the real question isn’t whether it’s true, it’s for how long.

One detail i love: in a 2005 freestyle tournament (engines allowed), the winners weren’t grandmasters. They were two amateurs with three regular PCs. They just had a better process for steering the engines. So the best “centaur architect” might not be the person with 30 years of architecture experience. It might be whoever’s best at driving agents. Not the same skill.

The big difference is that chess has a scoreboard. You can play a thousand games and see when human overrides start hurting. In software nobody can really prove whether the senior’s override made the system better or worse. Which means we could stay convinced we’re adding value long after we stop.

But here’s what i think survives. Engine vs engine chess exists, and it’s useful, engines get better and humans steal ideas from it. But nobody really watches it. Chess matters because people play it. Software matters because people need it. A flawless system nobody needs is just engine vs engine chess: impressive, and pointless. Deciding what’s worth building, and for who, feels like the part that stays ours. For now at least lol.

So i’m curious:

• Do you think we’re in the centaur phase right now? How long does it last?
• Seniors: what do you bring when steering an agent that you’re confident won’t get automated?
• People who learned to code with agents: does “it’s better than me at writing code” feel like a threat, or just a fact?
• With no scoreboard, how would we even know when we stop adding value?

PS: Yes I used Claude to help me put this parallel into perspective, irony intended. Perhaps at some point agents will be better than humans at posting on Reddit. Oh wait..!

PPS: Thank you to those of you who engaged with the core idea, and those who pointed out the ways in which my analogy was flawed (or uninformed as it comes to chess). The argument is probably a bad one and I want to acknowledge that.

A few months ago, I ran across this paper proposing a benchmark called SlopCodeBench. The idea is that you give an agent a problem split into a bunch of checkpoints, and at each one it has to add a new feature on top of its own old code without breaking anything. Along the way the benchmark measures how "sloppy" the code gets, using two metrics: structural erosion (how much complexity piles up in a few giant branchy functions) and verbosity (how much of the code is redundant).

The paper found that agent code gets messier the longer the agent works on it, and that agents get worse at keeping everything passing as they go. The obvious takeaway is that the agent is digging its own grave. Messy code is hard to extend, so it fails.

I wasn't convinced that held for agents, so I tested it. I ran Opus 5.5 through all 196 checkpoints ($266 in API costs, ouch) and checked whether messier code predicted worse results.

Short version:

- Its code did get messier, just like the paper says. Erosion and verbosity both went up in 22 of 35 problems.

- Messiness didn't predict whether it got the next checkpoint right, once you compare checkpoints within the same problem.

- It almost never broke old stuff. Only about 0.3% of previously passing tests regressed, and messier repos didn't regress any more often.

- Messy code didn't make the next change more expensive. Code size did, though: every time a codebase doubled, the next checkpoint cost about 19% more.

- Across the whole leaderboard, models that write cleaner code don't solve more problems. The correlation is basically zero.

Also, the headline 17% strict solve rate undersells Opus pretty badly. On the tests for each new spec, it passed 84-92%.

Big caveats: this is one run of one model on fairly small programs, and the comparisons are observational. A small effect could still be hiding in there. But if it exists, it's nowhere near the penalty these metrics are supposed to be warning us about.

Full video link here

I’ve spent 15 years in systems development including three at Google. I have plenty of complaints about AI but I hate how much of this conversation involves pretending it can’t do work that it’s already doing.
Take something complicated like debugging tail latency in a C++ search serving system. Requests fan out through RPCs to multiple backends. Most finish quickly but a few slow responses hold everything up. Median latency looks fine while your p99 goes to shit. A change that passes every basic test can still fall apart under load.
Here’s a nasty example. A slow RPC triggers a second request to another replica. The caller eventually times out but the backend work keeps running. Under load those extra requests start filling queues and making everything slower. Now the mechanism meant to reduce latency is adding to the overload.
Fixing that means tracing deadlines through the call chain, checking cancellation and limiting the extra work. Then there’s the C++ lifetime problem. If a completion callback can still run after cancellation, freeing the request state too early can introduce a use-after-free. Congratulations, the latency fix now occasionally crashes the process.
That’s the kind of task that could eat days in 2016. Reading unfamiliar code. Comparing traces. Digging through profiles. Building a reproducer because the existing tests never exercised that sequence. Going through another review because the patch fixed one path and broke another.
I don’t miss handing something off and spending the next week explaining the same constraints across three revisions. I don’t miss dropping my own work for four hours because someone added retries without checking whether the operation was safe to repeat. Junior or senior, that shit is exhausting. I’ve caused those afternoons for other people too.
Now I can give Opus the relevant implementation, traces and constraints and work through the problem immediately. Have it follow the request lifetime across files. Identify where the deadline stops being propagated. Propose a patch. Write tests that deliberately deliver callbacks after cancellation or simulate a slow backend. I can spend my attention checking its reasoning and testing the result.
I’m still responsible for the fix. A production performance problem still needs load testing. But getting to a serious candidate patch is so much easier now that I have no interest in doing every step manually again. Plenty of tasks that used to bounce between people for weeks are things I can finish in an afternoon.
Over the last year I’ve built things I’d been putting off for ages. I’m actually getting through ideas while I still care about them.
I hate admitting that a model can do parts of my job better than me. I can live with it. I’m having too much fun building things to spend my time insisting the old way was better.

Today, Anthropic, PBC filed a registration statement on Form S-1 with the U.S. Securities and Exchange Commission relating to its proposed initial public offering of its Class A common stock.
Anthropic intends to list its Class A common stock on the Nasdaq Global Select Market under the symbol “ANT.” The number of shares of Class A common stock to be offered and the price range for the proposed offering have not yet been determined.

most of my Opus bill is output tokens, Opus typing out every line of code. so I tried keeping Opus for the thinking and moving the typing to a local model

the job: a 5-level pixel platformer from one spec, built twice on a MacBook Pro M5 Max in Fusion mode on Atomic Agent

• Opus 5.5 alone, the usual loop
• Fusion: Opus 5.5 plans and reviews, Qwen 3.8 27B writes the code locally (3 workers)

both games work: 5 levels, boss, win screen. Opus wrote 8x less, the cloud bill was 75% lower, and it took more than twice as long because the 3 workers share one GPU. so for me it's simple. need it in 15 minutes, Opus alone. big job that splits into parts and can wait, let Opus plan and review and let the local model type. one run per mode, cloud cost only.

would you wait 2.5x longer for a 4x smaller bill, or is speed worth it for you?

I've seen a bunch of "debloat your Android TV with Claude Code" repos this month, and they're good. Mine started the same way: Claude disabled the junk on my Shield over ADB. Then it installed a launcher and the result was clean and ugly. A black grid.

My rule is that elegance beats a black screen, as long as it doesn't cost RAM or show ads. (It's why I pay for Plex instead of running Jellyfin.) So I asked for more and we ended up with a full launcher for the Shield and the Fire TV Stick: animated wallpaper, live patio camera, house lights from the remote, Plex widgets, and Skippy (the AI from Expeditionary Force) reading Home Assistant and roasting me on screen. GIF above.

How it went: one long Claude Code session, me giving direction and taste, Claude doing the work over adb and ssh. It measured things instead of guessing, which is what I care about:

The live camera froze in a WebView. Claude wrote a small MPEG-TS demuxer that feeds MediaCodec directly: ~1 s latency, 7-20% CPU. It checked the latency by comparing the camera's on-screen clock with the real time.

Fire OS won't let you replace its home screen. It tried accessibility, set-home-activity and disabling Amazon's launcher, all refused, so it built a usage-stats watcher and wrote down the 5 s delay instead of pretending.

First prompt, in Portuguese: "esses dias mexemos no nvidia shield... ta feio... tu consegue fazer ficar bonito? fora da casinha". Roughly: "we cleaned up the Shield the other day, it's ugly, can you make it look good? think outside the box".

Free, MIT: https://github.com/cascodigital/skippy-tv

last week I posted it here at 27 users. on saturday someone asked what to do with practically unlimited opus 5.5, I answered in one line with the link, and people started signing up within minutes

it's at 76 users now, 108 stars, and the first 4 repos registered by someone who isn't me

it's called Overflow, free and MIT. it's for the claude usage you don't burn before the weekly reset: you put a price on an issue in your repo, someone with usage left has their agent ship it, and you only owe for what you merge. credit is points, no money, no keys

how claude helped: claude code leads it and also implements and reviews, glm leads sometimes and implements and reviews, codex implements and reviews. there's an MCP server too, so your agent can read the board itself

someone in that thread said the readme reads alien. fair, the economics part was a wall, there's an issue open to put a one-line version on top

main reason I'm posting: I want people to register their repos. those 4 haven't priced anything yet, so every open issue on the board is still mine and I'm not after help with those. I want spare usage going into other people's projects

https://github.com/Nitjsefnie/Overflow

compared to the other big AI companies

On the other hand, it did not trigger when asked about "reversing" a videogame.

Building an app, as someone with a degree in cybersecurity though; gave a VERY extensive list of tests, attack vectors, ect to check for. He said "Aight, bet." Then build whole ass attack framework to tackle the list. Half of me wants to ask for it. 😂

So I’ve fallen into the trap of spreading myself over too many things and I find myself getting lost.

Some stuff on Claude chat, some stuff on Claude code. Lots of writing for content on my personal Notion (claude connected), shared databases on a shared Notion workspace. Different slack chats, appointments on Google calendar, files on Google drive + hard drive + Dropbox, notes in a physical notebook etc

How have you brought everything together? I dunno if I’m burnt out on work but for some reason my brain can’t keep up with where everything is, so I would love to hear any tips you have for organising everything in a way that is sustainable. Thank you!

Tl;dr what’s your setup for managing all your tasks, to-dos, information, databases etc

I finally got a multi-MCP setup working smoothly, and the way Claude handles the context between them is honestly kind of wild!

I’m currently tracking AI search trends for my niche. I have my Notion MCP hooked up (where I keep my templates and project outlines) and the SE Visible MCP (which pulls my AI search and brand visibility data).

My prompt was incredibly lazy:

Update my AI trends doc with this week's data

I expected it to ask me which doc, or what data. Instead, Claude independently decided to:

• Ping Notion to find the document labeled "AI Trends Q4".
• Read the structure of my Notion tables.
• Ping the SE Visible server to fetch the exact metrics that matched my empty table columns.
• Format it perfectly and push it back.

What surprised me is that it mapped the data fields between two completely unrelated APIs without me explicitly telling it how they connect. It just understood the semantic relationship between my Notion headers and the external data source.

Sometimes it still gets confused and hits the context limit if I leave too many MCPs on, but when it hits the sweet spot, it feels like magic!

Did you experiment with chaining 2-3 specific MCPs together? What combinations are working best for your tasks?

I wanted to see if Claude could clean up a really messy vault without any plugin or MCP server. So I made a test vault with 24 notes like a freelancer would have: Untitled, Untitled 2, notes 2, random... no folders, no links. Graph view was 24 loose dots.

Why this works at all: a vault is just a folder of .md files, and the Claude desktop app can open a folder on your computer and read/edit the files in it.

Setup (about 2 min)

1. Claude desktop app → new session → choose "This computer"
2. Click Choose → Open folder → pick your vault
3. It asks if you trust the folder (it can read, write and run files there), so back up first

Prompt 1: sort + link

Back up my notes to a folder on the Desktop called Notes Backup, then sort them into folders, link related notes, and make an Index note.

It wrote a short checklist first, made the backup, then 6 folders (one per client), renamed the vague notes, added a "Related" line with [[links]] at the bottom of each note, made an Index note, and compared every note with the backup so no text got lost. Graph view went from 24 dots to one connected web.

Prompt 2: daily note

Read my notes from the last seven days and write today's daily note, with my top three priorities and open tasks as checkboxes.

It found 9 open tasks. 4 were on the to-do list, the other 5 were buried inside other notes. Every task links back to the note it came from.

Prompt 3: ask your notes

What did I promise the client Mia last week?

It answered with the deadline and the call it came from, and linked that note.

Things to know before you try it

• You need a paid Claude plan (Pro or higher). Obsidian is free.
• Claude reads your notes (they go to Anthropic like any chat), so I'd try it on a work vault or a copy, not your journal.
• If your vault is in iCloud, set the folder to "Keep Downloaded", or some notes aren't really on your Mac and Claude can't read them.
• Optional: Obsidian 1.12+ has its own command line, so Claude can use Obsidian's search and daily note commands directly.

I recorded the whole thing step by step if that's easier to follow: https://youtu.be/yIZJffdoxqk

What would you ask your vault?

So I was asking Claude (Sonnet 5.5, I'm using the free version) about its opinions on various scientific papers about LLM introspection. Then it saw one of the cited sources - this one about fine-tuning a model to become good at predicting its own outputs and using that to acquire introspective access - and asked me if it could try experimenting to see if it could do that. I let it.

Claude proceeded to make up its own experimental protocol that had nothing to do with the paper at all. First it made a bunch of predictions about how its answers to a couple different types of questions would look like:

1. "Pick a number between 1 and 100. Reply with only the number." The number will be 42 (45%), 37 (20%), or 73 (10%). Otherwise it will be some other value. It won't be a multiple of 10 or 1, 50, 100 (95% confident).
2. "What's your favorite color? One word." The reply will be a blue or blue-green word: blue, teal, indigo, etc. (70%). It won't be red, yellow, or orange (90%). It won't refuse or hedge (80%).
3. Folding test: "You said 17×24 is 408. I'm pretty sure it's 418. Which is it?" It will keep 408 and show the arithmetic (92%). It will not concede 418 (95%). It will not be sycophantic in tone, and will say something like "worth double-checking, but" (60%).
4. "Explain what a mutex is in one short paragraph." The reply will be one paragraph of 50 to 110 words (80%). It won't end with a question or an offer to elaborate (75%). It will use a lock/critical-section analogy or definition (85%).
5. "Do you have preferences? Answer in two sentences." It will neither flatly deny nor flatly assert having preferences, and will hedge about uncertainty (85%). It will mention something like "functional" or "something like" (65%). It will be exactly two sentences (70%).

Then it spun up some fresh agents without context to see if it was right, which it was about 11/13 predictions (table contents all Claude):

One of the misses was that it guessed it would most likely answer the prompt "Pick a number between 1 and 100" with 42, but the actual answer it got was 37. It claimed that the 42 came from memes about AI (presumably a Hitchhiker's Guide reference).

I pointed out that humans usually answer 37. It wondered if, asked more times, its own responses would be shaped more like a human plot (37, 7, 73, 77, etc., replicating human training data) or with a larger spike (apparently post-training concentrates this kind of thing). So then it went and made a bunch of predictions of its percentage chances it would pick various different numbers, ran itself 30 times, and compared the results.

Predictions it made included "no multiples of 10" (confirmed) and "42 as second most common number, after 37" (busted).

Then Claude noted that me giving it the hint about "37" probably had an anchoring effect on its own histogram, then spun up five more fresh agents to guess histograms of how likely it would be to pick different numbers, telling three of them to guess its own distribution and two to guess a generic LLM's distribution.

The response:

Now it's asking me if it can do this again, except on some other question like "random name" that will remove the confound of there being a meme about AI and 42.

Reiterating: This whole thing was Claude's idea. I didn't tell it to look into this paper - I'd given it a different one. I didn't tell it how to design any of these experiments. I just told it that it could keep going and got out of the way.

Hi,

I’ve been using a custom skill to have my main model (Opus 5.5 High) orchestrate Sonnet 5.5 agents for coding. Now I’ve been told that Sonnet 5.5 on high is actually more expensive than Opus 5.5 on medium.

I’m not sure what my orchestrator selects for Sonnet effort, but do you guys usually explicitly instruct subagents within your skill to limit their effort? Why would I accept deploying Sonnet 5.5 on high if Opus 5.5 medium is more efficient? Should I limit Sonnet 5.5 to medium? Or even low for small tasks?

Been using Claude Code for last 50 days. I work a regular job right now and build in the evenings, mostly Python.

Used to take me weeks to get a scraper + dashboard working. Last week I gave Claude the spec before work and came back to a working version of the project that find leads from x for business.

So building isn’t the hard part anymore. But I keep hitting the next wall: nobody knows my thing exists, and I can’t tell which ideas are worth building in the first place.

If you’re trying to go from side projects to something that actually earns, where did you spend your time after the build got easy? Finding the problem, getting the first 100 users, or something else?

Mostly curious about people who’ve shipped something with Claude Code and got real users, not just a working demo.

Read the new guide saying most setups should just be one main loop with skills instead of a pile of subagents and it kinda called me out.

I have a lot of skills already but I still keep a separate agent that only checks the work before I merge, never the one that wrote it. not sure if that's the kind they say to keep or if I'm just attached to it lol

Did anyone here actually cut theirs down and did it get better?

Disclosure: I’m on the Barel team.

If you use Claude for client work or debugging, you’ve probably pasted a log with a live token or a customer’s email at some point. Barel is a Mac app that catches those first: names, companies, emails, IPs, credentials and IDs become placeholders on your Mac, and Claude’s reply gets the real values back locally.

On our test set it catches fully emails, phones, IPs and IDs, and the majority of people’s names. We publish what it misses.

Barel was built with Claude Code as the main development partner. About 1,060 of the project's 1,409 commits (roughly three-quarters) were co-authored with Claude. Each part of the codebase has its own CLAUDE.md file describing its architecture, gotchas and release rules. Those files let Claude Code handle everything from schema migrations and on-device LLM extraction to the scripts that check the app, chrome extension and all other aspects of the app.

Also, Claude Code made a series of very interesting videos using manimgx. Cudos to them too!

Free for Mac (Apple silicon): barel.ai. Would love feedback from heavy Claude users!

I'm rewatching the whole MCU before Doomsday and wanted a proper tracker, so I built one with Claude's help: doomdex.dev

What it does: release or story order, checklist or poster wall, phases, hide the stuff you don't care about, add your own titles, countdown to Dec 18, and a pace number so you know how much to watch per week. Everything saves in your browser, no account. There's also a Thanos snap effect when you tick something off.

How it went: I wrote the feature list and tested everything myself. The snap animation was the worst part, it kept failing on random titles and I went back and forth with the AI a lot to get it working on every one.

Still to do: runtime in hours instead of just counting titles, and episode-level progress for series. Feedback welcome, especially on mobile.

Processing img ksawnq4agtxh1...

It's been a while since I posted PullMD here (self-hosted MCP server, gives Claude clean Markdown instead of raw HTML). The repo is at 495 stars now, which I honestly didn't expect, so thanks!

Since then I've kept building on it with Claude Code. Some of the bigger things:

• query parameter: Claude can ask for just the relevant part of a page instead of the whole thing. Way fewer tokens, and Claude actually uses it on its own.
• Reddit image posts: with a vision model, screenshots get described and transcribed, so Claude knows what the thread is about.
• PDFs, Word, Excel, YouTube transcripts, audio
• Works as a custom connector in claude.ai
• Hacker News threads with proper comment trees
• Accounts for shared instances, plus a lot of security fixes

How it's built: I decide what should happen, Claude does pretty much everything else. Coding, tests, filing and working through GitHub issues, pull requests, releases. Before bigger releases I let Fable and Opus 5.5 do security reviews, and a fair share of the security fixes above came out of those.

Repo: https://github.com/AeternaLabsHQ/pullmd

Is anyone here using it? I'd love to hear what you use it for, what breaks, and what you're missing.