EMM_386

Agreed. I went from 20+ years of Firefox (back to the earliest days with the .zip files and just dumping binaries into a folder) to Brave a couple years ago.

I'm the same way. Turn off crypto by default (is it these days?) and that's pretty much it.

I like how they host selected Manifest v2 extensions themselves so uBlock Origin latest continues to work and is actively maintained.

Speed of Chromium with privacy focus. Works for me.

If you're on a Claude plan and have a lot of tasks at the moment that don't require the frontier, Sonnet is a good model to do that since you get more usage out of it.

Sonnet 5 was not a good model though - hopefully Sonnet 5.5 makes the leap that Opus 5.5 did.

I have used Unity and Blender MCPs and I am not sure I am seeing why this is not a needed concept (or is a bad implementation).

If Unity updates the version (and the MCP server with it) - my agents immediately see the new surface. They weren't trained on the features of the new version of Unity, but they know about them as soon as they are added.

Context bloat is a thing - but you only turn on the MCP servers for what you are actually working with. If I am only in Blender, I don't need the Unity MCP. So it can be disabled.

These are just two example pieces of software but the concept applies to all of them with MCPs.

When you call via the API and want it to roleplay as a pirate or fix broken YAML in the coding harness - it doesn't need to know about the sports scores lookup tool or the recipe creation tool.

There are different use cases for the same underlying model.

They also can't tell Opus it might be a Fable handoff when Fable didn't exist when Opus was created. They need to be able to change them.

> but you don't see "load bearing" nearly as often in prose written by people

Unfortunately, we're starting to now.

Thanks to Claude.

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.

"I don't see them be making the next great song"

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.