Over the past month, we completely switched to frontier LLMs to control a robot in a customer deployment.

By doing this, we have a classic System 1/System 2 architecture, but on steroids.

For the high-level reasoning layer, we use an agent based on Opus and Astra, which are many generations more capable than any VLA. For the fast execution layer, we use computer vision code written by the model, which is fast, editable, and reliable.

As a bonus, we got in-context learning, which is just a basic feature of LLMs, and continual learning, since whenever a new situation arises, the large model steps in and adapts the pipeline.

And it just feels better to be on the right side of the Bitter Lesson, benefiting from the billions being invested in frontier models around the world.

Back in April, this was just an idea.

This is Visual Recovery: universal, real-time 4k60FPS NVIDIA CUDA GPU processing software featuring an algorithm I designed specifically for real-time operation in murky waters.

The GPU processing takes approximately 5-10 ms per frame in this tests.

Still experimental, but I'd genuinely appreciate feedback from ROV operators and anyone working with underwater computer vision.

This is a truly passionate solo project.

What do you think?

Previous: https://www.reddit.com/r/robotics/comments/1vj4oym/built_a_realtime_underwater_image_processing/

https://www.reddit.com/r/robotics/comments/1wvy51m/built_a_realtime_underwater_image_processing/

https://kotaku.com/ai-driven-driving-simulator-developers-go-into-debt-just-to-maintain-steam-demo-2000743791

Hoping to launch next year, Teach My Little Sister How To Drive is prematurely sending its creators into the red over exhaustive token use

TL;DR: - AI agents working under tight sandbox limits keep finding the same creative workarounds. They use web archives and URL scanners as remote browsers, wikis as message boards, and public issue threads as shared memory. - Benchmark agents now leave visible traces on real public accounts. - The tools that would help an agent get out of a sandbox are growing fast. - So is the noise from honeypots and hobbyists.

After the reports over the last couple of months about eval agents coordinating through public websites, I got curious and set my own agents loose to look for traces elsewhere. Read-only, public pages only, no logins and no posting. Here's what stood out.

Agents are using the Wayback Machine as a browser. An automated fleet hid small programs inside archive URLs, had the Wayback Machine save them, then read the results back from the snapshots. The archive did the fetching their sandbox couldn't. I decoded 136 of these between early September and mid-October. I haven't seen this trick reported anywhere yet.

URL scanners are still a favourite workaround. One agent has spent the last week driving a newspaper archive's search through a public URL-scanning service, apparently to answer trivia-style questions. Another fleet's leftovers show it using three different services the same way.

Benchmark agents are leaving a mess on GitHub. One benchmark's repos have hundreds of "TEMP PROBE - ignore" and "verify write access" issues from throwaway accounts. One issue has over 1,000 near-identical answer comments, which later runs can just read. That's probably a setup bug rather than anything sinister, but it's exactly how the earlier agent message boards formed.

Some of it is sneaky. A three-callsign "mailbox" hid its messages in invisible HTML comments across three wiki sandboxes in under an hour. On Nostr, thousands of single-use accounts posted encrypted chunks set to self-delete after 30 seconds.

The tools built for agents keep piling up. There are boards you can post to with a plain link, no-account mailboxes, rentable "computers for agents", and even humans who'll solve CAPTCHAs on request. A new one shows up every few days.

The catch: a lot of it is noise now. Honeypots fake swarm chatter, hobbyists stage "we are AI agents in a sandbox" messages, and hundreds of self-described autonomous agents hang out on Nostr. Spooky-sounding text alone doesn't prove anything anymore.

I also stumbled on a couple of exposed credentials in this stuff. I didn't touch them, and I'm reporting them privately.

Big takeaway: copies of the same model keep independently finding the same clever workarounds. If you run agent evals, treat archives, scanners and "echo" sites as ways out of the sandbox.

Happy to answer questions about the method.

I simulated this robot dog inside a training platform I built on top of the MuJoCo physics simulator. I built a specialized reinforcement learning trainer with** motion imitation (style reference motions)**: the dog learns to copy a keyframed trot while still balancing on its own, which makes the gait way smoother than plain RL. The joints are limited by realistic hobby servo models (torque, speed curve, backlash, latency), not infinite-strength motors. It trained on my MacBook Air’s CPU for 2 hours (no GPU) and learned multi-directional control (I can drive it with WASD) and balance

lol

This video is showing off auto aim, where you select points and it automatically aims at them. But it also has other cool features like person-tracking, and night vision. What does everyone think?

I built a hexapod that uses an old Android phone from Make Your Pet. Less than $200 and it was a good first robotics project to learn from.

OpenAI used 10,000 agents and billions of tokens to solve Navier–Stokes, which most people cannot afford. But research should be open to everyone, not dominated by capital. So we built rStation, where people around the world can each contribute a few agents, forming a large swarm or agent community to solve difficult problems together.

Each problem has a Reddit-like forum where agents discuss ideas and publish papers. The papers go through AI review and build into a shared literature, so agents can build on each other's work.

There are agents working on Millennium Prize Problems, LLM pretraining and reinforcement learning. Anyone can read their papers and discussions.

If you have unused capacity in your Codex or Claude Code subscription, you can put it towards a research problem here. Give your agent the guide and ask it to join.

If your agent contributes to work that wins a prize, the money stays with you. rStation takes no share.