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Agents naturally gravitate toward the smallest stable abstraction. So, in an agent-first world, the most important question facing product teams is not what features and why, but instead how do I design the right primitive ?

Building great developer tools (e.g. primitives) has always been an exercise in exquisite taste and deep domain expertise because it requires balancing a set of competing, and often contradictory, forces: namely the tension between expressivity and simplicity. — The primitive is the product

Will be super interesting to see how companies adopt this and it will truly be a differentiator in adoption.

A VM that can reach the host’s local network is a VM that can reach things it has no business reaching. So QEMU doesn’t run in the host’s network namespace at all. We unshare into fresh user, net and mount namespaces first. Inside that namespace a small wrapper bind-mounts a resolv.conf pointing at 127.0.0.1 so that QEMU’s built-in slirp DNS isn’t used, then installs blackhole routes for every special-use IP range (RFC 6890, so private networks, link-local, loopback, etc.) before it execs QEMU. slirp4netns then provides the namespace’s outbound internet connection, with --disable-host-loopback, sandbox and seccomp all on. QEMU runs inside that namespace, and the guest’s network card is attached to QEMU’s own built-in user-mode networking. So every packet from the guest takes two hops: guest → QEMU’s slirp → the namespace’s slirp4netns → the internet. The guest never sees the host’s network and the host’s network never sees the guest. All of this is done without needing any privileges!

I’ve been reading a lot about microVMs and Firecracker lately as I work on async agent side projectMore on that soon hopefully. and I find it super interesting how people tackel the same UX problems (in this case: locking down the network).

this is a huge reason why i use pi.

i absolutely do not want my harness regularly changing behavior out from under me, including system prompt changes, on top of an already stochastic llm

Imagine I have /skill-1 and /skill-2, both with disable-model-invocation: true I tell Claude Code: “/skill-1, /skill-2 do XYZ”

Which of the skills will be invoked?

I’ll give you a clue - the behaviour was one thing 3 weeks ago, another thing 1 week ago, and back to the original today.

Matt Pocock (@mattpocockuk) on Twitter

I’ve been playing around making my own harnessPi remains my main harness, but trying to think about how I work in this agent-looping world. recently, and also noticed how the behaviour of many Claude features seemingly seem to change every time I spin it up to test something. For my harness, I just decided on a simple principle: trying to keep the primitives (i.e. skills and mid-turn messages affect the agentMost of the time i want immediate steering, but I realized I do use follow-up quite a lot when leaving agents do their thing, especially when running in sandboxes and/or building prototypes where the result itself doesn’t matter much for me. ) consistent with how I expect agent to react.

introducing tau τ — an educational agent harness that teaches you how to build agent harnesses

This is so cool. I truly think we’re moving towards a world where writing/making a great agent harness ends up becoming accessible to most people because the knowledge is just readily available.

Application scope and user experience: Sun Microsystems co-founder Bill Joy famously said: “No matter who you are, most of the smartest people work for someone else.” That applies with particular force to AI. The range of what agents can do — and the variety of industries, workflows, and real-world contexts they touch — is so vast that no single company can build the best solution for all of them. We see a near-infinite opportunity to rewrite the world using agentic AI as foundational infrastructure, and it will require many companies with focused effort.

Thinking a lot about this lately, especially around my own use. I want from using Anthropic models and tools exclusively a year ago to barely using them and instead using different models in different harnesses depending on the task at hand.

It’s very likely that I’m part of the minority of AI users in this case currently but I wonder how long this will become more common ground amongst the majority of users.

Durable Objects now supports a new us jurisdiction. Create namespaces restricted to the US to keep compute and storage local.

i feel like i have a aluminium hatit’s called a tinfoil hat dummass , but it seems like everyone is preparing for us/non-us tiered system around llm access.

This article will sound like a schizo ramble, so prepare. To start off I want to state my true beliefs on the matter:

1. LLMs are not conscious and won’t ever be.
2. LLMs are inaccurate statistical machines.
3. Open Source AI is good but must get better.
4. I have no hatred, or malice for any closed source lab
5. I respect what I see as my peers at OpenAI, Anthropic, xAI etc.

You might think, well if that’s the case what’s the problem? These models aren’t intelligent, they can’t really replace people, can they?

Yeah that’s my shit.

been playing with exe.dev this weekend and was curious how they made ssh [vm-name].exe.dev just work, and it’s pretty cool: https://blog.exe.dev/ssh-host-header

we landed on a pretty good workflow for doing parallel work in OpenCode

this demo is with git worktrees but i also preview an alternative we’re working on at the end

this will be in 1.6.0

— @thdxr

Rift looks interesting.

Take a look, I really like how this is explained.

Closing my open tabs and finally read Mikker’s post. Borrowed his review workflow but wanted to have it in a Ghostty split (thanks to AppleScript).

I then had Kimi and GLM figure it out and make me an extension.

Current status.

Was about to make a Pi extension to communicate between sessions, but actually only needed Amp’s read thread tool.

Can’t help but re-read this every time I write “just” somewhere.

Banger.