It's so refreshing to read technical articles that are clearly written by a knowledgeable human and explained perfectly like this. By walking the reader through this with the example screenshots it unfolds and gets more interesting as you continue reading.

It's also strange to realize that these days, most articles are not like this.

Everything should obviously be measured.

I've worked with large .NET code bases that used attributes for things like plugins and it was completely negligible for overall performance in the grand scheme of things.

- Define the task and the goal, write a short spec document (markdown is fine)

- Point the agent at it in plan mode and have it write the plan to disk with phases. Iterate on its plan if necessary here and now.

- Have each agent tackle a phase and have it update it as a living document (switch models if some phases are more difficult than others)

- Clear and repeat until done

I've never had to overcomplicate this and it's worked both on enterprise-scale projects and personal projects. I am not sure what I'm missing - if anything.

Meanwhile, songs are hitting number one on some charts on Spotify that people think are humans and are actually AI. And Spotify has to start labelling them as such. One AI "band" had an entire album of hits.

Also - music is a subjective. Mathematics isn't.

And in this case, an LLM discovered a new way to reason about a conjecture. I don't know how much proof is needed - since that is literally proof that it can be done.

What if there are actually zero bugs?

> Five issues felt like nothing as we had expected an extensive list.

The expectation here may not match reality, but not necessarily because Mythos isn't as capable as claimed. curl may just happen to be a well-hardened tool that doesn't have too many security vulnerabilities in its present state.

https://github.com/vadimdemedes/ink

Which is what Claude Code CLI uses (or was using?) and it caused many issues such as flickering, thrashing, and latency.

If you understand good software architecture, architect it. Create a markdown document just as you would if you had a team of engineers working with you and would hand off to them. Be specific.

Let the AI do the implementation of your architecture.

His other works aren't subtle.

https://www.anthropic.com/news/claude-new-constitution

The flaw itself is absurd but then just accepting it as "by design" makes it even worse.

The Anthropic video on that page at 0:53 literally shows them clicking a "knobs" button and adjusting the pixel CSS value.

I know it's not exactly the same ... but it has that functionality to a degree.

I just randomly looked at Railway and for $20 a month you get a whole lot. I've hosted many a web project (successful personal projects and enterprise projects alike) and I don't see a large barrier to entry on "hosting a website" here.

Blocking AI scrapers and crawlers is not a huge ordeal. Planning for a unicorn before just putting a product up isn't the way to go.

https://railway.com/pricing

Yes - the postinstall hook attack vector goes away. You can do SHA pinning since Git's content addressing means that SHA is the hash of the content. But then your "lockfile" equivalent is just... a list of commit SHAs scattered across import statements in your source? Managing that across a real dependency tree becomes a nightmare.

This is basically what Deno's import maps tried to solve, and what they ended up with looked a lot like a package registry again.

At least npm packages have checksums and a registry that can yank things.

Will humans take this to heart and actually do the right thing? Sadly, probably not.

One of the main issues is that pointing to your GitHub contributions and activity is now part of the hiring process. So people will continue to try to game the system by using LLMs to automate that whole process.

"I have contributed to X, Y, and Z projects" - when they actually have little to no understanding of those projects or exactly how their PR works. It was (somehow) accepted and that's that.

Or - there are enough people who know their stuff that the people who don't will be replaced and they will take over anyway.

That would be like saying studying mathematics can't lead to someone discovering new things in mathematics.

Nothing would ever be "novel" if studying the existing knowledge could not lead to novel solutions.

GPT 5.2 Thinking is solving Erdős Problems that had no prior solution - with a proof.

The author can easily do this by creating a simple memory tool call, announcing it in the prompt to the LLM, and having it call the tool.

I wrote an agent harness for my own use that allows add/remove memories and the AI uses it as you would expect - to keep notes for itself between sessions.

At least to a level that gets you way past HTTP Bearer Token Authentication where the humans are upvoting and shilling crypto with no AI in sight (like on Moltbook at the moment).