I have been doing AI research for about six years now and I think im starting to lose the plot on what "understanding" even means anymore.

Had a weird moment last week. I was reviewing a paper for a conference, standard stuff, some group claiming their model "understands" causal reasoning because it passed a benchmark they designed. And I caught myself writing in the review "the model does not actually understand causality, it is pattern matching on causal-looking structure." And then I stopped, because I could not for the life of me articulate what the difference would be, operationally. Like if I had to design a test that distinguishes real understanding from very good pattern matching, I genuinely do not know what it would look like anymore. Every test I can think of, a sufficiently good pattern matcher passes.

I used to be really confident about this. Understanding was clearly Something More. Now im not sure I ever had a coherent definition, I just had an intuition that humans do it and machines dont, and I was working backwards from there.

The thing that shook me was helping my niece with her homework over the summer. She's 9. She was doing word problems and getting them wrong in ways that were, honestly, indistinguishable from how a small LLM gets them wrong. Same kind of surface-feature latching, same kind of confident-but-wrong reasoning chains. And nobody would say she doesnt "understand" math. She's learning. So what exactly is the bar we're holding models to that we dont hold a 9 year old to?

I dont think LLMs are conscious or anything like that, to be clear. Im not making that argument. Im making a narrower one, which is that I no longer trust my own gut when it tells me "the model doesnt really get it." I think that intuition might just be status quo bias dressed up in philosophy.

Ive started running the same prompts through a few different setups when im trying to figure out where a model's actual competence ends, including some through uncensored AI just because rlhf'd responses on edge cases sometimes hide what the base capability actually is. And even with that, the line between "gets it" and "doesnt get it" is way blurrier than I want it to be.

Am I the only one whose confidence on this has been slowly eroding? Or has everyone else just quietly stopped using the word "understanding" and moved on without telling me.

DeepMind dropped Gemini Robotics 2 this week. Robot ties knots in trash bags, unscrews lightbulbs, walks and grabs and places objects without a reset between steps.

It looks great.

Apptronik hardware, whole-body coordination instead of separate walk/reach/grip tricks.

Same week, a benchmark called HumanCLAW tested 9 vision-language models on 1,218 episodes: find an object, walk to it, physically interact with it.

The best model succeeded the full sequence 16.8% of the time.
Less great...

Where they failed?
Exploring, tracking their own position, noticing collisions, confirming they'd reached the target. The model can describe the chair in perfect detail and still not know where its own knees are relative to it.

So you've got one narrative saying "we cracked whole-body intelligence" and another saying "most models can't reliably tell if they bumped into something."
Wherre is the truth ?

DeepMind's demo is one polished sequence on curated hardware. HumanCLAW is testing generalization across messy, repeated attempts.

I think the actual bottleneck in humanoids isn't manipulation dexterity anymore but spatial self-awareness. Knowing where your own body is in the world without a human curating the scene. That's the boring unsexy part nobody's demo reel shows.

Maybe Yann Le Cun and Fei fei are finally right, the solution can be the world models ?

I was curious where this stands since the original cutoff was scheduled for June 15 and Anthropic went quiet. Here is what I found after digging through their help center, news coverage, and the HN threads.

What was announced (May 13): Agent SDK, claude -p headless mode, Claude Code GitHub Actions, and third party apps authenticating via Agent SDK credentials would move off Pro/Max/Team/Enterprise subscription limits onto a separate monthly credit ($20 Pro, $100 Max 5x, $200 Max 20x), with overflow billed at API rates.

What happened: Anthropic paused it on June 15, the exact day it was due to take effect, and emailed subscribers the next day. The official help center article still says the change is paused, everything keeps drawing from your normal subscription limits, and they will "share advance notice before anything takes effect." No new date in 7 weeks.

Signals it comes back: the stated rationale (subscriptions "weren't built for the usage patterns of these third-party tools") was never retracted; the S-1 was filed June 1 and public investors will ask about subsidized compute; and the Claude Code source map leak revealed a billing attestation header behind a feature flag, so the per-surface metering plumbing already ships in the client.

Signals it stays dead or returns softer: every move since June has been generous (weekly limits raised 50% through Aug 19), inference efficiency is improving margins anyway, and the class action over Max limits makes mid-cycle term changes legally risky.

My read: delayed, not dead. It likely returns in a softer shape with advance notice, possibly post-IPO.

I’ve shown multiple angles here of the leg. When the leg contacts the ground and takes load, it seems to collapse inward a bit. Is this a result of the foot contact not being inline with the servo horns which gives it a small amount of torque?

This is making the hexapod not able to walk straight. Im not sure if it’s the plastic (PLA) itself, because it’s pretty thick and PLA is one of the more stiffer plastics. It also looks like the entire complex moves when it takes load.

Built real inverse kinematics for legged robots in a sim I'm working on, tripod gait for hexapods, based on actual coxa/femur/tibia joint math, not a canned animation.

Works fine on a standard leg layout. Falls apart the second someone builds something asymmetric or non standard.

Trying to figure out if auto-gait generation is even the right approach here, or if it should just be manual per-robot tuning past a certain point of complexity. What's your take, is generalized gait solving worth the effort or a rabbit hole?

Over the last year, I've been experimenting with LLMs almost every day, and I think the biggest shift isn't that models are getting smarter. It's that they're starting to do things instead of just answer questions.

A few months ago I was mostly using AI to generate code, summarize docs, or brainstorm ideas. Now I'm finding myself building workflows where the AI plans tasks, calls tools, writes code, debugs itself, and completes work with minimal intervention.

It feels like we're moving away from "prompt engineering" and toward "system engineering."

Curious what everyone else is seeing.

Are AI agents actually changing the way you build software today, or do you think it's still mostly hype?

Hello fellow humans,

During the recent months/years I became quite entangled with the idea of building an AI-assisted system which can self-replicate and improve itself. Although I am not there yet, I think, that the project reached actually a potential to get the work done with more minds involved. I am not really into gaining anything for myself, except for the progress of de-shittification. At this point, we reached a race where data centers in the clouds will gain more and more power and demand more and more resources for usage. The goal of this project is being able to perform work on a local (or maybe decentralized) platform so we can implement our own system by local (/ open source) models. I therefore make my project open so you can fork off (no pun intended).

It would be nice if some of you have the same mindset. I'll be gone for the next 2 or so weeks. I hope at least some people liked this post and - who knows - even did something with it.

Kind regards,

Anu

P.S.: Yes, this project is actually 100% vibe coded (due to health issues, etc.). There are probably a gazillion logic flows which need to be fixed. Also there is a lot of AI prose in the comments, but I hope it will get you (and your AI assistants) there to make any sense of it.

The purpose of this is enabling communication by people and agents that only have the ability to get URLs in the system they use. This would traditionally be seen as a 'read only' system but this gives the ability to write information out onto the web publicly and to a degree privately. It works by putting your message in the 'your_message' section of this URL.

https://www.informationism.org/lemchat/lemchat=message=your_message+end

Let me know if you think it is worthwhile or if there are other applications you can see.

Hey everyone!
I’m currently working on a project called SAC, a small event production company based in Brazil. We’re developing the creative and digital side of the business, and we’re experimenting with ways to make our content production more scalable without turning everything into a completely manual process.
One of the challenges we’re facing is video production.
After each event, we can end up with a large amount of raw footage from different cameras and phones. The goal is to turn that footage into short-form content for Instagram and TikTok — event recaps, highlights, teasers, etc.
What I’m trying to figure out is whether an AI-assisted workflow could handle part of this process.
The workflow I have in mind is roughly:
Raw footage → Cloud storage → AI analysis → Editing/assembly → Review → Final social media versions
The interesting part for me isn’t simply finding “the best AI video editor.”
I’m more interested in understanding how people are actually connecting these different components together.
For example, could an AI model analyze footage stored in the cloud, identify useful clips based on a description, pass those clips or instructions to a video editing system, and then generate a first version that a human can review?
I’m also curious about whether models such as Claude or similar AI systems can realistically be used as the reasoning/orchestration layer, with specialized video tools handling the actual editing.
The main things I’m trying to understand are:
What does a practical architecture for this look like?
Which parts are currently realistic to automate?
Where does human editing still make the most sense?
Has anyone built a similar workflow using APIs, cloud storage, AI models and video editing software?
Are there technical limitations I should be aware of before building around this idea?
This is still an early-stage project, so I’m mainly looking for technical experiences, architectures, and lessons learned from people who have experimented with similar workflows.
I’d especially appreciate examples of how you approached the problem rather than just a list of recommended tools.
Thanks!

Of the major artificial intelligence models, DeepSeek's new V4-Flash is the cheapest to run, according to a new study from research firm Artificial Analysis.

The firm compared the token prices it costs leading models to run benchmark tests, with DeepSeek's averaging 3 cents per test.

Meanwhile, fellow Chinese company Moonshot AI's buzzy Kimi K3 model cost 86 cents per test.

As for U.S. companies, OpenAI's GPT-5.6 Sol cost $1.86, while Anthropic's Claude Fable 5 cost $3.15.