OpenHands: Capable but Requiring InterventionI connected my repository to OpenHands through the All Hands cloud platform. I pointed the agent at a specific issue, instructing it to follow the detailed requirements and create a pull request when complete. The conversational interface displayed the agent's reasoning as it worked through the problem, and the approach appeared logical.

When an agent doesn't deliver what you expected, the temptation is to engage in corrective dialogue — to guide the agent toward the right solution through feedback. While some agents support this interaction model, it's often more valuable to treat failures as specification bugs. Ask yourself: what information was missing that caused the agent to make incorrect decisions? What assumptions did I fail to make explicit?This approach builds your specification-writing skills rapidly. After a few iterations, you develop an intuition for what needs to be explicit, what edge cases to cover, and how to structure instructions for maximum clarity. The goal isn't perfection on the first try, but rather continuous improvement in your ability to delegate effectively.

One effective technique for creating comprehensive specifications is to use AI assistants that have full awareness of your codeba

A complete task specification goes beyond describing what needs to be done. It should encompass the entire development lifecycle for that specific task. Think of it as creating a mini project plan that an intelligent but literal agent can follow from start to finish.

we tend to underspecify because we're exploring, experimenting, and can provide immediate course corrections. We might type a quick prompt, see what the AI produces, and refine from there. This exploratory approach works when you're actively engaged

The fundamental rule for working with asynchronous agents contradicts much of modern agile thinking: create complete and precise task definitions upfront. This isn't about returning to waterfall methodologies, but rather recognizing that when you delegate to an AI agent, you need to provide all the context and guidance that you would naturally provide through conversation and iteration with a human developer.

The ecosystem of asynchronous coding agents is rapidly evolving, with each offering different integration points and capabilities:GitHub Copilot Agent: Accessible through GitHub by assigning issues to the Copilot user, with additional VS Code integrationCodex: OpenAI's hosted coding agent, available through their platform and accessible from ChatGPTOpenHands: Open-source agent available through the All Hands web app or self-hosted deploymentsJules: Google Labs product with GitHub integration capabilitiesDevin: The pioneering coding agent from Cognition that first demonstrated this paradigmCursor background agents: Embedded directly in the Cursor IDECI/CD integrations: Many command-line tools can function as asynchronous agents when integrated into GitHub Actions or continuous integration scripts

isn't just about saving time — it's about restructuring how software gets built.

several of these tasks running in parallel, each agent working independently on different parts of your codebas

why asynchronous agents deserve more attention than they currently receive, provides practical guidelines for working with them effectively, and shares real-world experience using multiple agents to refactor a production codebase.

asynchronous coding agents represent a fundamentally different — and potentially more powerful — approach to AI-augmented software development. These background agents accept complete work items, execute them independently, and return finished solutions while you focus on other tasks.

I have yet to try a local model that handles Bash tool calls reliably enough for me to trust that model to operate a coding agent on my device.

My excitement for local LLMs was very much rekindled. The problem is that the big cloud models got better too—including those open weight models that, while freely available, were far too large (100B+) to run on my laptop.

The year of programming on my phone # I wrote significantly more code on my phone this year than I did on my computer.

The year of vibe coding # In a tweet in February Andrej Karpathy coined the term “vibe coding”, with an unfortunately long definition (I miss the 140 character days) that many people failed to read all the way to the end:

There’s a new kind of coding I call “vibe coding”, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It’s possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard. I ask for the dumbest things like “decrease the padding on the sidebar by half” because I’m too lazy to find it. I “Accept All” always, I don’t read the diffs anymore. When I get error messages I just copy paste them in with no comment, usually that fixes it. The code grows beyond my usual comprehension, I’d have to really read through it for a while. Sometimes the LLMs can’t fix a bug so I just work around it or ask for random changes until it goes away. It’s not too bad for throwaway weekend projects, but still quite amusing. I’m building a project or webapp, but it’s not really coding—I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.

The year I built 110 tools # I started my tools.simonwillison.net site last year as a single location for my growing collection of vibe-coded / AI-assisted HTML+JavaScript tools. I wrote several longer pieces about this throughout the year: Here’s how I use LLMs to help me write code Adding AI-generated descriptions to my tools collection Building a tool to copy-paste share terminal sessions using Claude Code for web Useful patterns for building HTML tools—my favourite post of the bunch. The new browse all by month page shows I built 110 of these in 2025!

I love the asynchronous coding agent category. They’re a great answer to the security challenges of running arbitrary code execution on a personal laptop and it’s really fun being able to fire off multiple tasks at once—often from my phone—and get decent results a few minutes later.

Vendor-independent options include GitHub Copilot CLI, Amp, OpenCode, OpenHands CLI, and Pi. IDEs such as Zed, VS Code and Cursor invested a lot of effort in coding agent integration as well.

The major labs all put out their own CLI coding agents in 2025 Claude Code Codex CLI Gemini CLI Qwen Code Mistral Vibe

You need three things. A Mac with Xcode, which is free to download. A $99 per year Apple Developer account. And an AI tool that can write code based on your descriptions.

Writing a good CLAUDE.md