How Jev Cut Our Classification Cost by 20×
We moved bounded classification decisions in Ari from general-purpose LLM calls to TypeSafe's Jev, at roughly one-twentieth the cost and 10–15× the speed.
14 stories
We moved bounded classification decisions in Ari from general-purpose LLM calls to TypeSafe's Jev, at roughly one-twentieth the cost and 10–15× the speed.
oats is public, so anyone can file an issue, and most of maintaining it is toil. GitHub Actions workflows run Claude Code on a self-hosted runner. Three save the most time: auto-classification of every issue; autofix for bugs, critical review findings, and tasks, which it declines without…
At Ariso, we each review tens of pull requests a day. A GitHub Action prepares the handoff: when a draft becomes ready for review, Claude Code assesses risks, blockers, regressions, deleted behavior, and specific human checks.
AI can write code quickly, but shipping reliable software remains a human endeavor. The operating model I've built at Ariso: sprint planning becomes a dashboard of to-dos, I rewrite each in my own words inside Ivan, Ivan opens draft PRs, and custom widgets surface the review queue.
A hot take sparked by a Reddit thread about AI burnout: junior engineers should still learn data structures, architecture, and system design, because none of it is tied to a specific language.
Every week an engineer I respect tells me they're worried a coding agent is coming for their job. Engineers spend about 30% of their time writing code, and the more senior you get, the less you write. The agents took the typing.
I opened 80 pull requests in a two-week sprint by assigning tasks to an agent and walking away from my machine. The tool is called Ivan, it's free and open source, and it runs natively in GitHub Actions.
We mentioned a bug in a meeting on Tuesday. This morning, Claude Code suggested fixing it the moment I opened my terminal — and twenty minutes later there was a PR. Here's how ari-hooks wires Ari into Claude Code, and why we don't use a ticketing system anymore.
If your standup is still a tour of what everyone — or everyone's coding agent — did yesterday, you're doing it wrong. Ari already knows what happened. Standup isn't the memory layer anymore; it's the steering layer. Here's how we run it at Ariso.
This isn't about autocomplete anymore, or a slightly smarter Clippy. We're talking about a bot that took a feature request from a chat message, understood the business context, scanned the entire codebase, and opened a production-ready Pull Request. It did it in 20 minutes.
Managing a coding CEO isn't about stopping them from coding. It's about building a padded room where they can do cool karate moves without breaking the furniture.
Move beyond simple tool-calling agents. LangChain's middleware API lets you inject user preferences before model calls and orchestrate complex workflows after tool execution—turning brittle super-tools into composable, testable layers.
Hidden context messages keep user-facing LLM chats clean while giving the model rich, per-message instructions—turning conversations into a predictable, tool‑driven workflow engine with a tiny amount of code.
Deep agents aren't like simple chat applications. A single task might make 10-50+ LLM calls, take minutes, and cost $1-10+. Here's what I wish I knew from day one about running them in production.
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