The video cuts to a new shot every couple of second instead of one continuous shot like all robotics research. This makes it hard to trust it. The robot probably failed a million team and they stitched the video from 100 experiments.
I think posthog is one of these businesses where the COSS model does not work well.
COSS works well when there is a large distribution advantage of being OSS. This could be bacuse a large portion of users (need to) self-host the solution. This is true for databases, people will always need to self-host dbs (e.g. as part of their docker compose in dev, etc...). These people are also hard core engineers that will 1) talk about the db and 2) contribute to the project. So an OSS db have a large network effects and distribution advantage.
Posthog had a distribution advantage from OSS in their beginnings -- their beachhead was the self-hosting oss community. Now, it does not add much value -- It's unlikely Github adds much for their distribution. So, it does not make sense for them to do much more than just maintain it lightly. In fact, they try to push you from self-hosting by having great free tiers and startup programs.
Evolution discovered a bunch of structural patterns at different layers (fragments, folds..) that are energetically favorable, versatile, easily foldable, robust to mutations and then kept reusing them. As a result it sampled more and more in these parts of the space. That's why the fold space is uneven.
Are there any folds and patterns that evolution evolution has not discovered that are also useful? I think Baker Group created a bunch of new folds. I'm not sure if they are as useful as the one discovered by Evolution. After all, Evolution had more compute power than us.
I think you are underestimating both the value of both projects (autoresearch and personal wiki) just because they are simple. I see both POCs for continuous learning / optmization on the harness layer, which in my opinion is a very interesting direction.
I think Andrej has the experience (and now ressources) to productionize this research into something very interesting.
p.s. called it
> Karpathy will help launch a new team focused on using Claude itself to accelerate pretraining research — an increasingly important frontier as AI companies race to automate parts of AI development.
(https://www.axios.com/2026/05/19/anthropic-openai-karpathy-a...)
Going to Kabuki was one of the most amazing experiences we had when visiting Japan. Although I am not a theater person, and the whole thing was in Japanese ,and we did not have the auto-translate tablets, we enjoyed it a lot. It was very beautiful, and funny.
I recommend to anyone visiting that part of the world.
This seems like a hit job by a competitor. Really ruthless.
> Two months ago, an email went out to a few hundred Delve clients informing them that Delve had leaked their audit reports, alongside other confidential information, through a Google spreadsheet that was publicly accessible.
Who leaked the audit reports? Who sent this email? Who is taking the time to write this analysis and kill the company?
In my opinion, the majority of the points in the article are no news. A compliance saas that offers templates for policies, all of them do. The AI is a chatbot, well who thought.
I think the main point is the collusion between delve and the auditors. Is the evidence for that clear?
Not sure I agree with the AI edited comments. Using AI to improve the readability and clarity is fine. Sometimes a well structured comment is much better than a braindump that reads like ramblings. And AI is quite good at it (and probably will get better). To make the point, here is how this comment would have looked if edited:
"I don't fully agree with banning AI-edited comments. Using AI to improve readability and clarity is a reasonable thing to do. A well-structured comment is often much better than a braindump that reads like rambling. AI is quite good at this, and it will probably get better. To illustrate the point, here is how this comment would have looked if edited"
I don't follow the need to write CLIs for the agent. Why not use simply the API and document it well? The token difference between using an API and CLI is not that much, and models are trained to use REST APIs and understand their patterns, compared to your random CLI.
It might simply be that it was not trained enough in Elixir RL environments compared to Gemini and gpt.
I use it for both ts and python and it's certainly better than Gemini. For Codex, it depends on the task.
I mean, no one is asking for artistic writing, just not some obvious AI slop. The fact that we all can now easily determine that some text has been written / edited by AI is already an issue. No amount of prompting can help.
I wonder why AI labs have not worked on improving the quality of the text outputs. Is this as the author claims a property of the LLMs themselves? Or is there simply not much incentive to create the best writing LLM?
Making this multi-player + creating the right representation to collaborate with agents is in my opinion the next bottlenecks. I wrote a small article about my thoughts there https://x.com/mmabrouk_/status/2010803911486292154
mahmoud[at]agenta.ai