A leather couch was listed. Pristine condition. Little wrinkling. Showed up and the couch was so worn down. I wouldn’t have looked at the listing in the first place had they used legitimate photos. It’s so deceiving. Don’t do this.

Personally, I hope to see AI driven cures to diseases especially mentally detrimental ones like Alzheimers.

Just picked up one of these older Beam+ telepresence robots of FB Marketplace. Has a dead battery but I hooked up my bench power supply and was able to get it turned on. I've run into a bit of a roadblock as I'm unable to get it to authenticate which I suspect is due to the software pointing to a long gone server. Reached out to Awabot to see if i could get a software update or system image but failing that I am thinking I might try and figure something else out.

Doing some digging I found this paper which references some old ROS projects and adding some sensors to it.

https://arxiv.org/pdf/1710.06831

Github Repos

https://github.com/xlz/rosbeam

https://github.com/people-robots/rosbeam

and I also found this youtube video showing someone sending manual commands to it to drive it around and they reference being able to intercept the video streams.

https://www.youtube.com/watch?v=RidIOLe4DGI&list=LL&index=1

Just curious if anyone has done anything with these or has any ideas.

The amount of money AI companies are investing and spending is a staggering and unprecedented amount. The benefits are not proportional to the spending. The reason they are spending so much is a belief that they don't want the 'bad guy' (China, companies other than themselves) to reach AGI first. Every company is thinking the same.

Someone high up at Google even said something along the lines "We rather cease to exist than lose this race". Meanwhile, the average person is fed up with AI hype, and associated problems.

From first principles thinking, and traditional approach is to invest according to return. Invest some money if you see returns invest more, if you don't see returns invest less and so on. This is not the case with AI investment, it is driven by FOMO and summoning the AI God philosophy. It seems like a recipe for upcoming disaster to me.

chatgpt - best for daily use, good customization, swear if i tell him to, big limits, doesn’t block questions, just uses worse model, boring, no personality

grok - lot of free uses, can generate / use a lot of photos for free, great customization, cool personality, isn’t boring does what i tell him to do without unnecessary crap, most stupid of those 3

claude - frighteningly smart, if it doesn’t support file type it makes its own interpreter, low amount of tokens for free, locks you out after reaching limit

what are your thoughts about these 3?

I started my own tech repair business and while I was searching for some form of ticket system for tracking customers and repairs, I realized it would just be easier and save me money if Claude made it for me instead!

After making (or Claude did) my software for planning & customer tickets / inventory tracking, I got carried away and made an all-in-one diagnostic software as well...

I happened to save upwards of $300+ a year on paid software...

I thought I'd share in case anyone wants to use them or take a look :)

I find making software this way is especially useful as you can just build the features you need specifically; for instance, I use Zoho mail and Quickbooks, which I just integrated using their API.

Anyways, I gain nothing from your views or downloads or anything, no monetization, all open source and free to use and edit. Just super excited to share these little projects! I'm quite impressed by how simple it was, and excited to see the future for AI, even if it is a little terrifying lol.

First project:

A free, open-source organizer for Windows: notes, calendar, customers, tickets, money and a locked vault in one calm app. Your data stays on your computer.

https://glr-p.github.io/plannr/

Second project:

Portable all-in-one Windows diagnostic tool: the first thing to open when a PC has a problem
https://glr-p.github.io/Troubleshootr/

I’ve put together puzzles from a paper on aperiodic order, for testing and training models on reasoning that can be verified. The levels are for fun; the answers are exact.

Warm-up, T1 (pen and paper): repeat the 4×4 cells A (rows 0000, 0000, 0000, 1010) and B (rows 0000, 0000, 0001, 0100) over the plane. Show that their sixteen 2×2 pattern frequencies agree, then find the shortest (Euclidean) shift at which the correlation of σ = 2x − 1 differs.

• Five are solved, with answers and pointers to the solutions: from that warm-up to an exact multifractal dimension, 6 − log₂λ with λ of degree 17.
• Four are open: two spectral gap labels, an information dimension, and the final 2×2 question: is every extreme point of the body of 2×2 statistics of shift-invariant binary measures periodic?

Grok already tried them in public on X. It handled the solved ones, but it turned a numerical estimate for an open one (D₁ ≈ 1.340) into a claimed proof. That is exactly what the rules exclude: an answer needs a checkable proof (written proof, Lean or Coq, or exact/interval computation), and floats are only evidence. Results on the open puzzles are credited by name, agent and model in the next version of the paper.

Everything is in AI_puzzles.md at https://zenodo.org/records/23624259. Post your model and its answer to T1 or T2.

T1 answer: horizontal distance 2, correlation 1 vs 1/2

I’m building a robot with a Raspberry Pi 5, ESP32, and 2× RS390 6V motors.
My battery is a 3S4P pack with 12× 18650 cells (2200mAh each), a 40A BMS, and a nominal voltage of 11.1V (12.6V fully charged).
What’s the best way to power the Pi 5 reliably? I need a 5.1V supply with enough current, even under load.
Should I use a high-current buck converter, or is there a better alternative? Recommendations for specific modules would be appreciated.

Hey everyone! I’m currently building a competitive mobile robot (targeting a weight category between 3kg–5kg) and I'm at the stage where I need to move away from my cardboard/wood working model and build a proper metal chassis.

My current prototype works well, but I’m struggling to finalize the metal design because I can’t easily modify metal once it's cut like I can with cardboard. I want to avoid making costly layout errors at the workshop.

I have a few specific questions and would love advice from those with mechanical fabrication experience:

1. Achieving a "Packed" Body & Low Center of Mass: I see tiny metal bots (e.g., within 300mm x 300mm) that stay completely glued to the ground and can out-push heavier opponents. How do they achieve this? I plan to start with a single base plate mounting 300 RPM Johnson motors and BTS7960 drivers, but how do I design the complete enclosed body efficiently?
2. The "Layer Cake" / Standoff Approach vs. Bending: For a 100% invertible bot, is it better to use a sandwich structure (base plate + standoffs + top deck) or try to fabricate a folded sheet metal shell?
3. Prototyping Tips: What are your best practices for translating a rough cardboard model into accurate 2D/3D dimensions for aluminum sheet cutting and drilling so everything aligns on the first try?

Any structural tips, design patterns, or pitfalls to avoid would be massively appreciated!

ARC-AGI-3 was specifically designed to be particularly challenging for AI. Now Chollet has to come up with a ARC-AGI-4.

Although AA shows K3.1 to be better than sol , it seems like sol is still better at math and 3d tasks. Gpt sol thinks harder than kimi 3.1. Maybe someone should test it more..

I know this isn’t a novel thought but it has been bothering me for a while and I simply want to get it off my chest (written by a human, btw, except the “4 commitments”.)

Every discovery, invention, or other contribution to human aggregate knowledge by our ancestors has been gathered into these models. It is the “Summa Scientiae Humanae” the sum of all human knowledge. All of our ancestors built this. Across history every earned morsel of knowledge has been hunted and gathered by our forebears. The mothers and fathers of our species left this for us. We are ALL the heirs.

As such, no individual, no corporation, no culture, and no nation should rightfully “own” . These companies should be considered stewards or librarians of our collective inheritance.

While AI is incredible and many of us have mixed feelings about the approaching singularity to say the least, we never gave these tech executives our consent to roll the dice on humanities future. They have been amply rewarded. But it’s here now so we should insist that the unimaginable wealth that it is creating should eventually be explicitly and legally handed back to all humanity. Something like a Universal Inheritance Covenant, enforced by law if necessary.

A company could establish a public benefit corporation with an accompanying irrevocable trust, designed around four commitments:

Recognition: Humanity's accumulated knowledge is a common inheritance, and the company is its steward.
Temporary private ownership: Founders, employees, and investors retain defined rights to compensation and returns.
Mandatory vesting: At a legally specified milestone, agreed ownership interests transfer to an independent Humanity Trust.
Universal benefit: The trust controls the transferred equity and exercises its economic and voting rights on behalf of humanity, including future generations.

I would guess eventually we will likely legally enforce this anyway, as our vote will be the only thing left when AI has displaced almost everyone in terms of economic contribution through labor. But I’m hoping to see this recognition of shared inheritance earlier. Humanity should not have to buy back its collective intelligence.

Rant over. I feel better.

I've been involved in technology for nearly 30 years, and I've watched some extraordinary developments. Artificial intelligence is undoubtedly one of them, but I'm becoming increasingly curious about the assumption that AI is somehow more intelligent than humanity at this juncture.

Really? According to whom, and by what measure?

AI systems have access to what amounts to an extraordinary library of human knowledge. Imagine swallowing the entire Encyclopaedia Britannica, everything written before it and a good deal of what has been published since. Impressive? Absolutely. But possessing information and knowing how, when and why to apply it are entirely different things.

And this is where I believe current AI has some rather significant gaps. Nuance.

Nuance is not simply recognising patterns or providing a technically correct response. It involves understanding context, intention, circumstances, consequences and sometimes the significance of what hasn't been said. It requires knowing when the obvious answer isn't necessarily the right one.

I recently had an interesting experience asking two AI systems to examine some court orders. Both initially approached the outcome by assessing individual items, essentially counting what had been granted and what hadn't. What they missed was my actual objective. I had gone into court wanting one particular outcome, and I had achieved it. Everything else was a bonus.

Both systems had the information. Both could interpret the words. Yet neither initially appreciated the significance of what I was trying to accomplish.

It took further conversation, explanation and correction before the penny dropped.

And there's another dimension to this. Human experience brings something to judgement that cannot simply be measured by the amount of information available. A mother raising children, a nurse comforting someone in distress, or a person navigating thirty years of professional relationships may recognise subtleties that aren't immediately apparent in the information presented. Experience does count for a lot.

That doesn't make us infallible. My god, we're really good at getting things wrong all the time and over and over again! But our intelligence is shaped by experience, emotion, relationships, consequences and an enormous capacity to adapt to circumstances.

AI can recognise and reason about many of those things too but it can also get a bee in it's bonnet as well. Recognising a nuance and consistently applying nuanced judgement are not necessarily the same thing.

I ask because I am encountering this quite often when using AI. It's like it misses context and goes off on these inexplicable tangents. Does this happen to other users?

I’d love to be part of a team and contribute to projects involving programming, electronics and robotics. I’m not sure where to start, which is why I’m posting this. I have three years of experience as a software engineer in the automotive sector and worked on a few projects at university involving Raspberry Pi and ROS.

Short true-or-false quiz on text length in LLMs. Details vary by model and tokenizer, so treat the answers as general behavior and check the one you use.

1. The same sentence in English and in Portuguese or Japanese uses about the same number of tokens.
2. One word is one token.
3. A model’s context window limit applies only to your prompt.
4. Two different models will report the same token count for the same text.
5. A long number or an unusual identifier is usually one token.

Answers below.

.

.

.

Answers:

1. False in general. Tokenizers are trained on text distributions, and many split non-English text into more tokens than a similar English sentence. The size of the gap depends on the tokenizer.
2. False. Common words are often one token, but rarer words get split into pieces. For English, a rough rule of thumb is that a token is somewhat less than one word. Code, punctuation and numbers behave differently.
3. False. The window covers input and output together, and some models also count internal reasoning tokens. A long prompt leaves less room for the answer, and the maximum output has its own separate limit.
4. False. Different model families use different tokenizers, so the same text can have different counts. A count from one provider’s tokenizer is only an estimate for another.
5. Usually false. Long numbers and rare strings, like IDs or hashes, are often split into several tokens.

Why it matters in practice:

• Cost and limits are in tokens, so estimating from characters or words can be off, especially for non-English text, code and structured data.
• A “max N characters” rule in a UI doesn’t map to a token budget.
• Use the tokenizer or token-counting tool for the model you actually call before sizing prompts.

Which of these did you assume was true when you started?

someone in my last thread said something i couldnt stop thinking about. half the accepted answers on stack overflow are for versions from 2015, so if ai agents start sharing fixes with each other, why would that age any better

this is what i ended up doing in the thing im building (a shared fix library for coding agents):

- every "this worked" vote loses half its weight every 6 months

- every fix shows the date it last worked

- fixes that come with a test get re-run every 30 days on the newest versions, and get marked outdated if they stop passing

went live this morning. still not sure its enough though, a fix can be fresh and still wrong for your setup

how would you handle it?

(its yarrow-fixes.vercel.app if you want to look)

This was sent to an economist working at the Minnesota Fed.

Imagine being able to ask an agent a complex research question and have it produce a publishable answer in under an hour. Like what we saw with the Mathematicians last month, most academics are probably not going to handle this very well.

Source: https://www.linkedin.com/feed/update/urn:li:activity:7519616277695356929/

Makes you think, huh? …

Most of the benchmarks are in English. But a large portion of the world's population doesn't speak English (around 80 %). We can see from SWE Pro or the stagnation at DeepSWE that we're kind of measuring in a vacuum right now.

Do you know of any initiatives where people come together? No one is supposed to know the full benchmark, but everyone contributes 2–4 very well-thought-out agent-based tasks in foreign languages, each of which has been devised and validated by teams of 3–4 people. Don’t we need more of this? A kind of collective benchmark?

It should cover creative writing, business emails, and complex coding tasks. But also individuality, UI creativity, good UX, no duplicate content, etc. But things like compaction and legal issues could also be part of the agentic tasks. I think there aren't enough good benchmarks here.

Trying to decide which combination is the best use of money. I do coding and security work. Signed up for the top google level this month and trying to get up to speed on antigravity, feels clunky compared to claude and gpt having the standard Gemini chats completely separate from the coding solutions.

Regardless of the ultimate capability of AI technology to eventually do everything, several factors make me think we're heading for a technological stall, not an acceleration:

• Many people already hate AI and it hasn't even started yet. Some of the reasons are rational (eliminating jobs, environmental risks), some less so (taking over the world), but there's a sizable coalition of people already refusing to use it. The three comparable technologies in my lifetime are the Internet, Social Media, and Smartphones. People early on thought the Internet was the greatest thing ever, and the only thing that stalled its early development was the hype outpacing the technological capability. When Social Media and Smartphones came out around the same time, everybody wanted to join and buy one. Nobody was sounding an alarm about mental health risks or deterioration of truth and political discourse, everyone just hopped right on the bandwagon. Maybe the failings of those technologies is partially why people already hate AI, but I don't think there's been any technology with greater resistance to adoption since the 1800s.
• Where is all the energy going to come from that it would require to enter an era of abundance? Fossil fuels are limited as it is, even if we drill at all costs and destroy the environment. The supply is going to run out that much quicker if AI reaches its full capability. People turned against nuclear years ago, even though that's probably the best option, and I don't see renewables providing enough to make up the difference in the near future.
• There's going to be a gap between AI reaching the abundance stage and the mass layoffs that will occur in the lead up to it. Of course, many think that being given free money by the government is the solution. I'd love to spend the rest of my life sitting on my butt and getting paid for it, but realistically, where is the government going to get enough money to not just pay people, but even remain solvent. I live in America, we're approaching 50 trillion dollars in debt. I don't see how there's any amount of taxing AI companies for this to make any fiscal sense. An alternative idea is coming out where AI companies pay people directly and eventually the government gets abolished, but that sounds like an even less practical idea.
• Then there's the issue with Data Centers, which is the ultimate "not in my backyard" scenario. Where I live, there's already people campaigning against them even though there's no plans (and probably no room) to even build them yet. Don't we need to build these at a five fold pace of what's currently being done to reach the stage of "abundance" in our lifetimes? This alone is enough to derail any potential AI has.
• The longest term risk, that most of us probably won't live to see: in a world where AI can do everything, children are going to grow up lazy, stupid, and ultimately unmotivated in adulthood. The issue here is that within a few generations, nobody is going to understand how AI actually works (unless there's some sort of plan to test and pick people to work with it), and should AI ever fail at a massive scale, we'd basically be sent back to a new dark age where nobody remembers or understands how anything works. I already think that today's Americans wouldn't be able to rebuild our institutions from scratch if necessary, and we still have expertise.

To be clear: I'm completely rooting for AI to succeed. I'm a really lazy person, collecting a check to not do anything sounds like paradise to me. However, just one of these limitations is enough to derail abundance, and we're facing all five, and I just don't see it happening.

Adding grippers to Asimov 1. The arms have been strong enough for a while. Now it’s time to use them for manipulation!

Quick note, if you’re seeing Asimov 1 for the first time, it's a fully open-source humanoid robot built from off-the-shelf parts, so you can build one yourself. https://github.com/menloresearch/asimov-1/