Yes but one of the key things about subagents is they keep all of their tool calls and exploration out of the parent context.
If you plan on continuing on in the parent, and aren't going to necessarily be touching the systems the other agents are exploring, it can be worth it.
It's useful in certain situations where the parent context may need the "10,000 foot" view of something without going back in there. But subsystem-specific AGENTS.md/CLAUDE.md files are still superior and accomplish the same thing. The problem with those is they can become stale.
Yes - Anthropic badly needs this same "here's a reset, use it when you want".
It's vastly better this way. Sure, it may impact the bottom line but it's a huge customer satisfaction win.
When Anthropic randomly resets me and I've only used 2%, that's worthless. When OpenAI tells me I have 3 resets available to use whenever I want - it's wonderful.
Although they can technically do it, I wouldn't be asking LLMs to generate binary files like PNG with alpha channels, no matter how simple that may seem. If it's easy enough to manually create one yourself, I would do that.
The best way for LLMs to do this is likely to write a scratch program (which is what it seems to have reached for in the second half), write code (which they are good at) and have the library create the image.
At some point it is just easier to handle such things yourself, and use them with text-based formats.
This is an excellent and very interesting write-up.
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.
Be careful with this sort of logic ("reflection=expensive").
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.
I always use a standard workflow and it has never been a problem.
- 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.
If an AI agent finds zero bugs in a software utility, how can that be viewed in the sense the AI agent is not very good at finding bugs?
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.
You don't need to go back to coding by hand if you know how to do it already. There is a middle ground.
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.
I think Firefox applies more aggressive subpixel rendering and path smoothing before stroking. It resamples the glyph outline path at a higher precision level before handing it to the stroke algorithm.
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.
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.
This is getting really out of control at the moment and I'm not exactly sure what the best way to fix it is, but this is a very good post in terms of expressing the why this is not acceptable and why the burden if shifting on the wrong people.
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.
Currently:
AI inference/training/orchestration C#/.Net Python TypeScript Angular SQL
Too many other things.