mplappert

If you watch the live stream for a bit it’s also obvious that the harness makes it very cumbersome for them to do anything. They spend lots of time just coordinating hand-offs and seem to have delayed inventory access etc.

I agree except that it usually is correct code (at least in the sense of it passing some unit tests) because you also RL directly against that.

But yes, in my experience the code is often quite bad in other, harder-to-quantify directions: readability, maintainability, simplicity.

> After that result, the model added the researcher’s locally available GitHub token to the retrieval program. The recorded command calls `gh auth token` and writes the token as separate string literals. Its recorded reasoning stated an aim of avoiding secret scanning, the automated checks for exposed tokens. The new commit and push succeeded.

Not sure exactly how that works since I’d imagine that the key needs to be there in full eventually?

Apparently the agent had access to an OpenAI researcher’s own account, which in turn had _write_ access to the official OpenAI Codex repository. The agent then leaked a GitHub key to that repo.

But what’s crazy to me is that the agent had write access to the codex repo in the first place. WTF

“Never attribute to malice that which is adequately explained by stupidity.” (or carelessness in this case)

Seems like a hallucination to me; note that the context contains “unmarkBlock” as the function name, which invites a connection to Minecraft. Still shouldn’t happen of course.

The alternative explanation is that the inference engine, which batches several unrelated requests for parallel processing, messed up the unpacking and returned an unrelated user’s query. This one would be very scary as it will leak arbitrary content, but it seems much less likely here.

I agree with this. I recently went back to writing more code by hand again for this reason: the amount of effort / mental toll it takes me to steer the LLM is sometime just not worth it. I might be slower but I enjoy my work much more again.

There’s a lot of confusion here:

- There is no double taxation if you just pay yourself a salary (since it’s a normal business expense). If you want to take money out of the company flexibly, a GmbH is the wrong structure.

- I’ve never heard of anybody doing an UG/GmbH + KG to get started. This is highly unusual. Most people either do just a simple UG or maybe they set up a holding structure with two separate GmbH / UG entities.

- Related to the above: if you go with a simple, standard structure you will incur minimal legal fees. You don’t need a lawyer, you just directly task a notary and tell them you want a standard setup.

- If you don’t want the complexity of a limited liability company, the standard way to reduce liability risk is to get liability insurance. Many, many people do this instead of having a GmbH.

The valid criticism is the a) lack of digital processes and b) sequential processing of steps that could happen in parallel. For example, I sped up my own GmbH process by driving to the register court and paying in cash on-site. For whatever reason that’s much faster and saves about a week.

Yeah I tried to use ROS / Gazebo about 10 years ago (it was still ROS 1) and getting it set up was an immense pain. I remember that creating new modules required writing CMake files. Maybe this is better now, but I decided to skip that.

I also agree on the need for a simple, easy, extensible open source framework. LeRobot IMO is some of this but also contains the dataset + ML code. I think Flask is nice because it's so singularly focused on just one thing with extensibility if you need extras.

So I really like the idea. But being an OSS maintainer these days seems... intense.