What I learned building an opinionated and minimal coding agent

> from copying and pasting code into ChatGPT, to Copilot auto-completions [...], to Cursor, and finally the new breed of coding agent harnesses like Claude Code, Codex, Amp, Droid, and opencode

Reading HN I feel a bit out of touch since I seem to be "stuck" on Cursor. Tried to make the jump further to Claude Code like everyone tells me to, but it just doesn't feel right...

It may be due to the size of my codebase -- I'm 6 months into solo developer bootstrap startup, so there isn't all that much there, and I can iterate very quickly with Cursor. And it's mostly SPA browser click-tested stuff. Comparatively it feels like Claude Code spends an eternity to do something.

(That said Cursor's UI does drive me crazy sometimes. In particular the extra layer of diff-review of AI changes (red/green) which is not integrated into git -- I would have preferred that to instead actively use something integrated in git (Staged vs Unstaged hunks). More important to have a good code review experience than to remember which changes I made vs which changes AI made..)

> If you look at the security measures in other coding agents, they're mostly security theater. As soon as your agent can write code and run code, it's pretty much game over.

At least for Codex, the agent runs commands inside an OS-provided sandbox (Seatbelt on macOS, and other stuff on other platforms). It does not end up "making the agent mostly useless".

I've seen a couple of power users already switching to Pi [1], and I'm considering that too. The premise is very appealing:

- Minimal, configurable context - including system prompts [2]

- Minimal and extensible tools; for example, todo tasks extension [3]

- No built-in MCP support; extensions exist [4]. I'd rather use mcporter [5]

Full control over context is a high-leverage capability. If you're aware of the many limitations of context on performance (in-context retrieval limits [6], context rot [7], contextual drift [8], etc.), you'd truly appreciate Pi lets you fine-tune the WHOLE context for optimal performance.

It's clearly not for everyone, but I can see how powerful it can be.

---

[1] https://lucumr.pocoo.org/2026/1/31/pi/

[2] https://github.com/badlogic/pi-mono/tree/main/packages/codin...

[3] https://github.com/mitsuhiko/agent-stuff/blob/main/pi-extens...

[4] https://github.com/nicobailon/pi-mcp-adapter

[5] https://github.com/steipete/mcporter

[6] https://github.com/gkamradt/LLMTest_NeedleInAHaystack

[7] https://research.trychroma.com/context-rot

[8] https://arxiv.org/html/2601.20834v1

I dont know how to feel about being the only one refusing to run yolo mode until the tooling is there, which is still about 6 months away for my setup. Am I years behind everyone else by then? You can get pretty far without completely giving in. Agents really dont need to execute that many arbitrary commands. linting, search, edit, web access should all be bespoke tools integrated into the permission and sandbox system. agents should not even be allowed to start and stop applications that support dev mode, they edit files, can test and get the logs what else would they need to do? especially as the amount of external dependencies that make sense goes to a handful you can without headache approve every new one. If your runtime supports sandboxing and permissions like deno or workerd this adds an initial layer of defense.

This makes it even more baffling why anthropic went with bun, a runtime without any sandboxing or security architecture and will rely in apple seatbelt alone?

Pi has probably the best architecture and being written in Javascript it is well positioned to use the browser sandbox architecture that I think is the future for ai agents.

I only wish the author changed his stance on vendor extensions: https://github.com/badlogic/pi-mono/discussions/254

The solution to the security issue is using `useradd`.

I would add subagents though. They allow for the pattern where the top agent directs / observe a subagent executing a step in a plan.

The top agent is both better at directing a subagent, and it keeps the context clean of details that don't matter - otherwise they'd be in the same step in the plan.

Armin Ronacher wrote a good piece about why he uses Pi here: https://lucumr.pocoo.org/2026/1/31/pi/

I hadn't realized that Pi is the agent harness used by OpenClaw.

I did something similar in Python, in case people want to see a slightly different perspective (I was aiming for a minimal agent library with built-in tools, similar to the Claude Agent SDK):

https://github.com/NTT123/nano-agent

I really like pi and have started using it to build my agent. Mario's article fully reveals some design trade-offs and complexities in the construction process of coding agents and even general agents. I have benefited a lot!

Being minimalist is real power these days as everything around us keeps shoving features in our face every week with a million tricks and gimmicks to learn. Something minimalist like this is honestly a breath of fresh air!

The YOLO mode is also good, but having a small ‘baby setting mode’ that’s not full-blown system access would make sense for basic security. Just a sensible layer of "pls don't blow my machine" without killing the freedom :)

Glad to see more people doing this!

I built on ADK (Agent Development Kit), which comes with many of the features discussed in the post.

Building a full, custom agent setup is surprisingly easy and a great learning experience for this transformational technology. Getting into instruction and tool crafting was where I found the most ROI.

I always wonder what type of moat systems / business like these have

edit: referring to Anthropic and the like

An excellent piece of writing.

One thing I do find is that subagents are helpful for performance -- offloading tasks to smaller models (gpt-oss specifically for me) gets data to the bigger model quicker.

>The only way you could prevent exfiltration of data would be to cut off all network access for the execution environment the agent runs in

You can sandbox off the data.

Can I replace Vercel’s AI SDK with Pi’s equivalent?

"Also, it [Claude Code] flickers" - it does, doesn't it? Why?.. Did it vibe code itself so badly that this is hopeless to fix?..

As a user of a minimal, opinionated agent (https://exe.dev) I've observed at least 80% of this article's findings myself.

Small and observable is excellent.

Letting your agent read traces of other sessions is an interesting method of context trimming.

Especially, "always Yolo" and "no background tasks". The LLM can manage Unix processes just fine with bash (e.g. ps, lsof, kill), and if you want you can remind it to use systemd, and it will. (It even does it without rolling it's eyes, which I normally do when forced to deal with systemd.)

Something he didn't mention is git: talk to your agent a commit at a time. Recently I had a colleague check in his minimal, broken PoC on a new branch with the commit message "work in progress". We pointed the agent at the branch and said, "finish the feature we started" and it nailed it in one shot. No context whatsoever other than "draw the rest of the f'ing owl" and it just.... did it. Fascinating.

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