brucekimrok

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[brucekimrok @brucekimrok]

[Recap] Our team placed 2nd at the 2025 Embodied AI Hackathon hosted by (@seeedstudio @NVIDIARobotics @huggingface), and the work is now written up for @CarnegieMellon.
Grateful to collaborate with @_moem, @arrowlikeX , @IndraneelPati10.

https://t.co/MgvcZNIxrX

[brucekimrok @brucekimrok]

Another breakthrough incoming from Anthropic. An agent orchestrating isolated hardware interfaces through a single standard.

https://t.co/BG4ATiHyVb

[Anatoli Kopadze @AnatoliKopadze]

This is the best 2 hours on graph engineering ever recorded, Andrew Ng breaking down how to build agentic knowledge graphs from scratch:

9:14 - your first working agent
33:11 - how loops actually work
1:02:46 - turning loops into working graphs
1:30:15 - agents that improve their own code
1:49:05 - a system that runs the whole thing for you

I've seen $500 courses that teach less than this video alone.

Watch it, then take it further with my step-by-step guide on graph engineering below.

[brucekimrok @brucekimrok]

@KevinGmelin fascinating work!

[kevin gmelin @KevinGmelin]

I finally feel justified in my earlier predictions that gpt-level robotics is achievable within two years.

This feels reminiscent of gpt3, where the innovation was in few shot prompting.

Curious if the next step for robotics looks like chat gpt, or more like o1 reasoning.

[brucekimrok @brucekimrok]

@IndraneelPati10 nice!

[brucekimrok @brucekimrok]

Releasing Exynos capacities in the domain of Edge AI

[MLCommons @MLCommons]

MLPerf Mobile v6.0 introduces new generative AI benchmarks for running LLMs (Llama 3.1 & 3.2, including the new 1B and 3B models) natively on mobile devices.

Test your on-device inference performance. Available on GitHub, iOS & Android: https://t.co/GpId2Aa1iK

[MLCommons @MLCommons]

MLPerf Mobile v6.0 introduces new generative AI benchmarks for running LLMs (Llama 3.1 & 3.2, including the new 1B and 3B models) natively on mobile devices.

Test your on-device inference performance. Available on GitHub, iOS & Android: https://t.co/GpId2Aa1iK

[MLCommons @MLCommons]

MLPerf Inference v6.0 is here - our most significant benchmark update ever.
5 new/updated benchmarks. 24 submitting organizations. Industry-first tests for text-to-video and speculative decoding.
Full results: https://t.co/avj9D2onVG
#MLPerf #MLCommons

[Indraneel Patil @IndraneelPati10]

Our team of @trunc8s , @cyansding and @brucekimrok deployed a RL policy on a lerobot at the Physical AI Hack and also did some Sim to Real, Sim pre-training and RL post training experiments.

Thank you @DhruvDiddi and @GZinMetaverse for organizing! https://t.co/XUWqwwUBx9

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[Lukas Ziegler @lukas_m_ziegler]

humanoid robots shipments in 2025 per company

[brucekimrok @brucekimrok]

Sharpa Announces CraftNet - A Hierarchical VTLA Model for Fine Manipulation https://t.co/rkzgycj0Kn

[Simon Kalouche @simonkalouche]

This is the most dexterous task I’ve seen a humanoid do so far.

Fully autonomous powered by Sharpa’s CraftNet (VTLA) — using tactile feedback to continuously fine-tune the last-millimeter interaction.

[Sawyer Merritt @SawyerMerritt]

NEWS: Boston Dynamics has just released a new video of its upgraded next-generation humanoid robot called Atlas.

• 4 hour battery. Self-swappable for continuous operation
• 6 feet 2 inches tall
• Weight: 198 lbs
• 56 total degrees of freedom
• Now fully electric, ditching older hydraulic systems
• New lightweight mix of aluminum and titanium components
• 110 lbs weight capacity (66 lbs sustained)
• Can reach up to 7.5 ft
• Constantly evaluates its surroundings and adjusts its posture, balance, and grip in real time
• Hands that can reconfigure as needed. Tactile sensors feed data back into the system, helping apply the right amount of force
• Brain is powered by Nvidia chips