https://research.meta.ai/blog/muse-spark-open

I made a plugin that adds Claude Code's /goal command to OpenCode v2. You give the agent a finish line like "/goal npm test passes with 0 failures" and it keeps working turn after turn until the goal is actually met, no need to keep typing "continue".

After every turn an evaluator checks the conversation and decides if the goal is met, not met or impossible. If it's not met, the agent gets the reason and keeps going on its own.

It's token efficient: the evaluator reuses the session's cached context, so each check costs little more than a short answer.

It also has safety rails (pauses when the agent stops making progress, retries on network errors, turn limit) and a status indicator in the TUI.

Tell me what you think about it and feedback is appreciated.

Here's the repo and npm package:

https://github.com/EL4CTEO/opencode-goal

https://www.npmjs.com/package/@el4cteo/opencode-goal

This project used to be called Finny. It's called Scout now, and since this sub is basically where it started, I wanted to write up what actually changed in the fork. Most of it wasn't what I expected going in.

The problem with a stock coding agent doing trading research is that it's very good at producing something that looks right. Ask for a momentum strategy and you get clean Python and a backtest with a great Sharpe. When I dug into those results, the number almost always came from one of three places: lookahead (deciding on a bar using that same bar's close), picking the best run out of a parameter sweep, or the model already knowing how that ticker moved. Nothing in a normal coding loop tells the agent any of that is wrong. The code runs, so as far as the agent is concerned it's done.

So most of my changes are about getting in the agent's way.

1. A fixed strategy contract. Every strategy is a class with an on_bar method. On the current bar it should only decide using bar["open"], and orders fill on the next bar. To be clear, that's a contract the agent writes against, not something the engine enforces. Still, giving the model one simple rule to write against was the cheapest fix I found, and it makes lookahead much easier to spot when it does show up.

2. A validation gate before anything runs. It checks syntax and class shape, and blocks os, subprocess, socket, requests, shutil and exec/eval inside strategies. There's no override flag. I went back and forth on that and decided an override would just get used every time.

3. A skeptical default. After the base backtest it runs walk-forward, Monte Carlo and lookahead checks, and the default verdict is research_only. Most ideas stay there, which honestly is the most useful thing it does.

The part that's probably most relevant here: I ran my own small-sample benchmark, ScoutBench, comparing Scout with stock OpenCode, Claude Code and Codex using GPT-5.5 in each harness. Across 5 tasks, 20 checks per task and 3 trials, the mean scores were Scout 53.3, Claude Code 46.3, Codex 44.1 and OpenCode 38.6. These are benchmark scores, not trading results.

Most of that gap comes from two tasks: repairing a lookahead bug (70 vs 51.7 for Claude Code, 45 Codex, 35 OpenCode) and an end-to-end BTC strategy build (81.7 vs 55). So the domain changes help where they're supposed to and not much anywhere else. Treat the results as directional: the sample is small, Scout lost on numeric discipline (30 vs 45), and no harness scored above 28.3 on hallucination resistance, mine included. OpenCode scoring lowest mostly says the tasks are narrow and it's general-purpose; it was a really good base to build on.

The full method, task breakdown and results are in my benchmark write-up: https://www.polarline.io/blog/finnybench-agent-benchmark

Disclosure: I built this, I'm the founder of Polarline. It's source-available (not open source). The CLI command is still finny from before the rename.

Has anyone else bent OpenCode toward a domain where "the code runs" isn't the same as "the answer is right"? Curious how you handled verification.

Title

OpenCode stops to ask before running a command or when it has a question, and if I'm away from the Mac it just sits there waiting. So I built Charly, a Mac app for OpenCode and Claude Code with an iPhone and Apple Watch app.

When an agent asks for permission, it shows up on the iPhone (Dynamic Island) and on the watch, and you approve it with a tap or a double tap on the watch. You can also answer its questions or send it the next task by voice from the watch, and when it finishes you get a summary of what it changed. It works with local projects and with servers over SSH, and it uses your own model provider or subscription. Charly doesn't charge for the AI.

How I built it: I came from React Native and had never built a watch app. I learned Swift, SwiftUI and watchOS while building this with coding agents. Once the first version worked, I started using Charly to build Charly, approving builds and test runs from the watch while I was away from the desk.

It's free to try, with an optional subscription. I'll give a free year to the first 20 people who try it and tell me what's missing for their workflow, in the comments or by DM.

getcharly.dev

App Store: https://apps.apple.com/us/app/charly-code-agents/id6816675386

hey guys by chance does anyone haave a referral links for me to try claude. been using chat gpt lately but want to get into claude code. Does anyone with a max plan happen to have referral links, you can check by typing /passes only works for max users. gives me 7 days to try and you also get $10. thank you guys itd really appreciate it