350 karma · joined August 19, 2009
- Fell 330 hectares of forest for Data Center 1
- Fell 200 hectares of forest for Data Center 2
Lack of this capability makes automated principles or patterns update a recipe for more bloat.
A quick AI search confirms that both the YouTube video and Traces agree on the mechanism of the attack
Is it possible to create mirrors of every repo with no Microsoft dependency or until someone else acquires GitHub from Microsoft and makes it more reliable (maybe Elon Musk like he did with Twitter)? This can solve for commit/push/pull when GitHub is down, but not for Pull requests and reviews.
https://github.com/mattpocock/skills/tree/main/skills/produc...
The point is not that I’m learning Concurrency in a better way using LLMs, it’s that I can apply this style of learning using bite-sized, hands-on, visual explanations, quiz to any topic in the future. The lessons it generates are just code, and you can ask it to type check the examples, validate with recent libraries, use analogies to learn something better.
Try it before thinking it’s just ELI5 or Summarization or assuming it’ll be hallucinating without verifying facts.
https://github.com/mattpocock/skills/tree/main/skills/produc...
You can even take a stand that you’ll never look at the code and just ship. This is a perfect equilibrium for a Software factory, where the only input or feedback is a one-liner/specced requirement and the only output is outcome of whether the one-liner requirement worked or not.
For almost any other software that does not fit this criteria, software factories are yet to solve the Intent and Subjective Quality problem.
Software factories can implement anything given a one-liner requirement. That one-liner requirement can be a complete app/product, epic, feature, bug, design change or refactoring. But these one liner requirements are requirements coming from a human who has an intent or requirement or direction for the product to evolve in mind. Can Software factories manufacture intent that reflects the exact requirements of the person using it or their vision of how the product or software should evolve?
Implementation in any language is as easy as generating a summary for an LLM. If coding is only math and there is only one way to translate a requirement into an implementation, the problem boils down to just providing or verifying generated intent. But, there is definitely more than one way to implement a thing and one of the ways leads to a design and architecture that is coherent with rest of the system, design and architecture that is extensible and evolves, code that you can keep in your head when you want to change things next time or tomorrow, software that can serve millions of users and securely guarantee millions of dollars of revenue. There is a combinatorial explosion of ways and the one right way is also subjective of the person and the problem. Software factories can of course improve Quality by generating more unit tests, more integration tests, fix code violations, generate Proof of work as videos, screenshots etc, but there is no feedback loop or test suite to verify and correct the other subjective notions of Quality.
Last time I checked, Facebook is not a thing other than for watching AI generated content, Instagram is still a thing to watch mind-numbing content and get distracted from other problems by doomscrolling.
It’s same as language translators and RLHF annotators doing work to contribute to AI training data. Is Facebook or Instagram solving a problem for humanity that’s worth selling your soul? Won’t a job that can be automated with AI training data of clicks and typing, markdown files, next function prediction with no coherence be significantly worse than a job that requires creativity?
Is all the recent GitHub downtime entirely attributable to GitHub AI Copilot related development? How hard can it be to reduce the blast radius of new AI features to not affect the core parts of hosting repositories? Because of Copilot everywhere, The UX has become bad and I had to click all over the place and on my profile to find repositories.
This is similar to using OpenClaw for a specific usecase, but without most of the complexities or security implications of OpenClaw.
Agent-kernel has personality, persistent memory, self-modifying capability, using Skills is same as using Skills from Claude code.
1 - “It seems like 99% of society has agreed that code is dead. …It's the same as thinking storytelling is dead at the invention of the printing press. No you dummies, code is just getting started. AI is going to be such a boon for coding.“
2 - Another one comparing writing and coding, and explaining how Code is both a means and an end to manage complexity:
“we're confused because we (incorrectly) think that code is only for the software it produces. It's only partly about that. The code itself is also a centrally important artifact… I think this is a lot clearer if you make an analogy to writing. Isn't it fucking telling that nobody is talking about "vibe writing"?”
The agent sees the output of the service, it does not directly see the keys. In OpenClaw, it’s possible to create the skill in a way that the agent does not directly know about or use vault_get command.
vault_get.sh: https://gist.github.com/sathish316/1ca3fe1b124577d1354ee254a...
vault_set.sh: https://gist.github.com/sathish316/1f4e6549a8f85ac5c5ac8a088...
Blog about the full setup for OpenClaw: https://x.com/sathish316/status/2019496552419717390