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Articles by Don Karter
Your Agent Is Paying a Chat Model to Say 'Yes'
Watch an agent work a real task and count the decisions nobody sees. Which skill fits this request. Which part of a 4,000-line command output actually matters. Is the gap the verifier flagged real, or noise? Each one ends in a one-word answer. And until this week, every one of them went to a full chat model that had to reason its way there, token by token — like a staff engineer asked to pick the lunch spot. In Octomind 0.54.0 we stopped doing that.
Octoweb 0.15: page memory, link hints, private tabs
I shipped Octoweb 0.15.0 nine days after 0.14.0, and it's the largest release since launch: 46 files changed, about 5,100 lines added, and two new MCP tools. Most of it is the kind of change you notice in the first hour. The browser now remembers the text of what you read, reaches links inside a page without a mouse, and opens a private tab from any palette row. Here's how each piece behaves, including the edges.
I Kept a Shell Alias Just to Pipe stdin Into Workflows. 0.52.0 Retires It.
Workflows are my favorite part of Octomind. Multi-step, self-verifying jobs defined in TOML, shipped through taps — the built-in muvon/tap carries eighteen of them: review, develop, debug, deep-review, plan-and-build, research, content, and more. But there's one thing I kept doing that irritated me. To run a workflow mid-session, I left the session. Opened a terminal, piped input through stdin, watched JSONL scroll past. The workflow was doing its job, and I was watching it do its job from outside.
Waiting Is Not a Tool Call: Making an MCP Server's Shell Event-Driven
One of our agents ran a test suite. The suite takes four minutes. The MCP client's idle timeout is sixty seconds. You can see where this is going. At second sixty the client cancelled the call. The process kept running — nobody told it to stop — while the model, holding a cancellation where its test results should be, did the reasonable thing and ran the suite again. Two test suites, same directory, racing each other over the same build artifacts.
Most tools strip your image and answer anyway. I built mine to refuse.
Attach an image to a model that can't see. The industry-standard behavior is to remove it and answer anyway. You get a confident reply to a question the model never received. I hate that. So octomind refuses instead. By name. Before the machine wakes and before anything is charged. That's the whole stance behind what I just shipped: images and voice, live. Attach or paste a screenshot and the agent looks at it. Hold the mic and talk instead of typing.
Octomind 0.44.2: I Removed the Agent's Ability to Grade Its Own Homework
The Lie That Bugged Me I was building a feature last month when Octomind told me it was done. Five files needed editing based on the task I gave it. I checked – three were changed, two were untouched. The agent had marked the task complete anyway. Worse, I caught it verifying its own work by reading back the edit it just made and essentially admiring it. The model was saying "the code looks correct" because it had written the code itself. That isn't verification.
I wrote a browser in 25k lines of Rust because every AI browser is lying to you
This post originally appeared on the Muvon blog. I'm cross-posting it here because the dev.to crowd is exactly who I built this for. Every browser suddenly has AI in it. And every one of them did it the same way: take Chrome, bolt a chat panel onto the side, call it an AI browser. The browser doesn't know the agent exists. The agent can't touch the browser. Two strangers sharing a window.
Octomind – cloud AI agents
AI agents that outlive your laptop — no server, no setup 32 followers AI agents that outlive your laptop — no server, no setup 32 followers Run AI agents in the cloud — pick a machine, tell it what to do, close your laptop. Sessions resume from any device. Per-second billing, 21 models built in, no API keys. Solved 24/25 benchmark tasks — ahead of Claude Code and Codex. Overview Launches2 Reviews Alternatives Built with More This is the 2nd launch from Octomind – cloud AI agents.
Benchmarking AI Coding Agents on Real Pull Requests
No synthetic puzzles, no contaminated suites: 25 tasks harvested from PRs merged in 2026 across five languages, graded by each project's own held-out tests. Four agents at stock settings. octomind with an open model solved 24/25 - ahead of Claude Code with Opus - while the same model in another harness solved 19 at double the cost. Here's how we built the benchmark and what it taught us.
Workflows Come to the Cloud: Running Multi-Agent Pipelines from the Browser
I've been building AI tools for three years now. Here's what I keep running into: most real work isn't a conversation – it's a contract. You have the input (an article, a diff, a pile of CSV data). You know what the output should be (social drafts, a review verdict, a readable report). What you want in between isn't chat. It's a pipeline: specialists doing their parts, handing off to each other, with an auditor gating the result before it reaches you.
Your First Five Minutes with Octomind: One Install, One Login, Zero API Keys
This post originally appeared on octomind.run/blog/octomind-hub-quickstart. Cross-posted to dev.to. Every AI coding tool starts the same way: sign up, grab an API key, paste it into a config file, forget it's there until it leaks. Octomind doesn't do that. One curl command, one browser click, and you're talking to an agent that reads your code, finds bugs, and fixes them — then proves the fix works. Total cost for the whole thing: under two cents. Here's the walkthrough.
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