Can we please not normalize telling people to install things by curl piped into bash? I hate this trend. And particularly not for a very new, mostly untested by a wider audience piece of software from a company that few have ever previously heard of.

It even says, quoting from the website: "Headlong is alpha research software."

Yeah that's totally something I want to curl thing.sh | bash , great idea.... Wow.

I understand that people want to get people using their software as quickly as possible and with the absolute minimum of friction, but let's put some more thought into how this could be done in a less sketchy way.

It's like we've regressed to the days when you would download a .exe file from tucows and blindly run/trust it on your windows 98SE PC.

Very fascinating, super interesting engineering. Although i do find it very funny how they just bypass a massive vulnerability, basically zero data isolation (even between good actors, let alone bad ones) with 3 sentences. Only in the llm space you can slap a massive limitation like this in the middle of the article and continue like nothing happened

> Whatever anyone tells Audel becomes part of the single experience that every other conversation draws on. In practice, Audel is bad at keeping secrets. Ask it what it’s been working on with someone else and it will often just tell you, even though we’ve asked it not to. We also haven’t studied what happens when two people give conflicting instructions. For now, we assume anything you tell Audel is shared with everyone on the team.

"turn -> FINAL -> schedule wake-up", this is where my excitement has faded unfortunately. Many of us are probably wondering about the same idea: bridging the gap between a reactive agent and my daily workflow or existence. But, this still feels too close to how Claude (or any other agent) runs as a process in the background (always ON), where you can use a custom channel to feed the dialog with external signals like chat, CI/CD events, whatsapp, etc.

Humans aren't scheduling a wake-up to the next thought. Ideally, a sub second agentic loop with no FINAL / wake-up, always "spinning" would get closer. I'm conscious about the waste of resources this would drag with it (because of current architectures), but exciting still.

PS. I love the take on using bash instead of Python (one less abstraction layer!) and using UNIX fundamentals as stepping stone when composing tools as agents are naturally drawn to using it on a box anyways.

But did it produce anything meaningful? If so, show me.

Are there any objective metrics/ benchmarks that people test harnesses by?

There are just so many now that it's hard to personally test them all or just trust the vibes.

The language composition is interesting. The source is half Shell, a quarter Python, almost a fifth Typescript. Among the rest is 2.3% Rust and 1.3% Swift.

If you wonder what the Rust is for: It is the Ratatui TUI.

The page has a nice little easter egg if you click "Dr. K" in the footer.

Sub Question : IS there a real successful agent product today that uses a library for harness(like langgraph etc)? Building our own worked for us. Works with our components(postgres, events ...) and scales naturally with our system.

The Googlers must be vague posting about something internal.

It buries the lede. Prime Agent sounds like a very cool project.

> Audel designed experiments to spawn recursive shellm sub-runs to work on subproblems. Most of the experiments failed, because shellm has a safety watchdog that kills any command that stays silent for 30 seconds. Audel fought the watchdog for about 40 minutes and mostly stopped using shellm sub-runs. Results from recursive sub-runs of shellm merged back into Audel’s mind 64 times in its first two days and 12 times in the twelve days since. We’ve since revamped the watchdog, and we’ll see if we can convince Audel to give recursion another shot.

This is why "I made it think in a loop" doesn't result in significant improvement in LLM performance. It's not learning. You need RLAIF, STAR, IDPO, etc to retrain the model to learn from its mistakes. And you need a human to review it so it's not compounding mistakes. It's expensive and time-consuming. Doing it wrong leads to bad outcomes. But not doing it leads to no significant improvement.

that imaginary line. this crossed it.

Why compress by recency rather than something else?

> "Headlong is a complete agent harness with a core of less than 10K lines of Bash..."

Wow. So, be nice or I'll replace you with a very large shell script?

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