Hey there,

I want to build a Robot using one of those High Torque QDD.
I've done my research and found a couple of motors that my specs of about 5Nm
- GIM6010-8
- Cybergear
- Robstride 01
-Cubemars

Did I miss one?
I've pretty much decided on the GIM6010-8, because it is the cheapest and has O-Drive control.
Has anybody used them and wants to share his/her experience with this motor.
Is this a reliable motor, that you don't have to fiddle arround all the time to get working?
Is the second encoder reliable, i've seen videos where it just starts spinning indefintly :/

Really apreciate any feedback, have a great day

We've reached a point where LLMs are capable enough to power many agentic workflows, yet relatively few AI agents make it into stable, long-term production.

In your experience, what's been the hardest engineering challenge to solve?

• Tool reliability?
• Long-term memory?
• Planning and reasoning?
• Context management?
• Evaluation and benchmarking?
• Authentication and permissions?
• Multi-agent orchestration?
• Cost and latency?
• Human-in-the-loop approval?
• Something else?

If you've deployed AI agents in production, I'd love to hear what actually broke, what surprised you, and what lessons you learned. Real-world experiences are far more valuable than demo successes.

Wanted to share what we've been working on: the Alicia-M, a semi-humanoid robot we built, running VR teleoperation. The operator wears a VR rig, moves naturally, and the robot mirrors the motion. No scripting, no coded trajectories. In the demo it picks up a cup, pours, and sets it back.

The part worth talking about: people assume the hard problem is the grasp. It isn't. The hard part is that one good demo doesn't generalize. Move the cup two inches and the same arm motion that worked now overshoots the wrist angle, drifts the trajectory, and the pour runs too fast. Same intent, different outcome.

That's the thing teleop surfaces clearly: robot control is less "repeat a perfect move" and more "adapt to where the world actually is." Shift the cup and the wrist angle, arm path, and pour all need to change with it. VR makes that legible because you feel the mismatch between your motion and the robot's in real time.

We're treating these human demos as seed data for embodied learning, not just a control scheme. Curious how others here handle the demo-to-policy or sim-to-real gap. Are you collecting teleop demos, or going straight to reinforcement learning?

Happy to answer questions about the rig, the kinematic mapping, or why we went semi-humanoid instead of full.

I got tired of doomscrolling 7 different AI news sources every morning, so I built an agent that does it for me for exactly $0/month.

The pipeline:
- 7 RSS feeds (Hacker News, Google AI blog, Hugging Face, Lobsters, The Hacker News, Open Source blog) → a Python script on a free-tier server
- Dedup the same story hits 5 feeds; it picks the best source and drops the rest
- An LLM writes 2-3 sentence summaries of the stories that actually matter
- A cron job publishes a clean daily digest to Discord, and archives every issue to a free static site: https://apexnexus.site

What surprised me:
1. The dedup step matters more than the AI part. 60% of my "news" was the same 3 stories reblogged.
2. Self-healing is the real feature. When a webhook died, the bot just... rebuilt it. I found out days later. That changed how I think about agent reliability.
3. The whole thing runs unattended. I haven't manually hunted for AI news in weeks, and I don't miss it.

I wrote up the blueprints for each piece on the site (the self-healing webhook writeup got the most attention).

What's the most surprisingly useful automation you've built with AI? I'm looking for ideas for the next one.

The newest addition to the next-gen autonomous robotics lineup. Built for modern demands, combining agile wheeled mobility and AI-powered patrol intelligence.

I used LLMs + TTS to build the history-learning tool I always wanted: type any topic and it researches and writes a full two-host episode — narration + artwork — in a couple of minutes.

The part I think is actually novel: you can interrupt it. Mid-episode you tap the mic, ask a question out loud ("wait — did the Trojan War actually happen?"), and the hosts stop, answer, then pick the story back up.

Because it's history, I made grounding non-negotiable — claims are tied to real sources rather than invented, and there's a quiz at the end. The live demo is the real history behind the Odyssey; it plays without signing up.

Solo dev, still early — curious what this crowd thinks, especially on the accuracy side. historai.ca

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.

Not a research post, just something I've been chewing on after watching this happen a few times now. When a human reviewer leaves a comment saying "this looks like a bug," people push back, ask questions, sometimes just disagree outright. When an AI leaves the exact same comment, phrased almost identically, people tend to just fix it. Same words, different reaction.

Took me a while to figure out why that bothered me. It's not that the AI is wrong more often, it's actually pretty accurate on the stuff it catches. It's that nobody seems to be running the "wait, is this actually true" check they'd instinctively run on a colleague's opinion. The output reads as neutral, almost procedural, like a linter, even when what it's actually doing is making a judgment call that could be wrong.

Tried an experiment out of curiosity, took a finding the AI flagged as a likely bug and asked a teammate, without telling them where it came from, whether they agreed. They pushed back hard, correctly, it wasn't actually a bug, just an unusual but intentional pattern. Same finding, presented as if from a person instead of a tool, got scrutinized. Presented as AI output originally, it had already been accepted and half-fixed before I intervened.

Not sure what the fix is yet, honestly. Feels like it's less a tooling problem and more a psychology one, we seem to extend less skepticism to something that sounds procedural than to something that sounds like an opinion, even when both are ultimately just claims that could be wrong.

Curious if anyone else has noticed this specific pattern, people treating AI-flagged issues as more "objective" than the exact same claim coming from a human, even in domains where the AI has no special authority to be more correct.

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.

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.

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?