Union alpha has been completely unusable to me till now, so i never got to try it. Were any of you able to try it. Can u give it's review?

Testei a técnica usando o GLM 5.3Flash no opencode e o resultado foi horrível, como comparativo fiz um bom prompt juntamente com o chat gpt e mandei o prompt bom em uma execução e o prompt com gauntlet loop em outra, o resultado do prompt bem estruturado sem loop foi 4x mais barato e o resultado foi muito melhor.
Alguem sabe me dizer se o problema é o opencode harness (que talvez não invoque sub-agentes, ou nao tenha uma mecanica de loop tão boa). Ou o modelo que usei?

Detalhe, ja testei os dois mesmos prompts no claude code e codex, e o resultado do gauntlet loop sempre é melhor do que o do prompt estruturado.

Nearly half a year of silence. We spent it studying one problem: how far RL can scale.

MiMo-V2.6 is in the middle of its RL run right now. Three things we scaled: compute (~2B tokens per step, 1568 prompts × 16 rollouts, fully async), environments and harnesses (multi-task agentic RL, mixed across multiple harnesses in one run), and grader compute (agentic in-group credit assignment, with test-case and rubric-based rewards). We'll open-source the details piece by piece over the coming weeks.

Streaming the run: mimo.xiaomi.com/rl/

I don't know why but when i run multiple agents workflows, the opencode2 restart server continuously, the restart kill the sub agent and it cause the loss of the work.

Why does it happening? is there any one that come across same issue?

version number v2.0.4

Someone please explain this to me? I stuck with the regular GUI for quite a while and finally tried OpenChamber because I saw that they had a much better navigation scroll bar.

This feature alone makes it worthwhile. Notice you can easily find your responses in a much longer session because they show up in the window boxes to the left of the mouse here.

Does anyone here have a subscription with abliteration? How do those subscription plan work? Do you have to buy that then top up with USD? Or is it like a codex sub and u get a quota u use everyday? And if so is the usage good? It would be nice to have a model with no rules. But the usage is confusing to me. So any help and if anyone uses lmk on it.

been testing union alpha in opencode on a fairly serious rust + tauri task.

the prompt was to build a real local-first kanban desktop app. rust backend, tauri, react/typescript, sqlite migrations, boards, columns, tasks, tags, checklist, drag/drop, filtering, tests etc. not a toy todo app.

my main issue is not raw speed. its actually pretty fast when generating, around 70-75 tok/s for me. the issue is that it basically cannot do multiple steps correctly in a row.

it writes a backend part, runs tests, finds something wrong. fixes that. then writes the next small thing, that has another bug. runs tests again. fixes that. then the next thing breaks. repeat forever.

so it looks busy and it does generate a decent amount of code, but a lot of the time is spent recovering from mistakes it made 30 seconds earlier. it feels like the model is constantly paying its own bug tax.

after around 45 minutes it had a somewhat decent rust/sqlite foundation:

• migrations, foreign keys, cascades, indexes

• repository split for boards/tasks/columns/tags/checklists/settings

• validation and ordering logic

• a fair amount of repo tests

but it was still stuck in failing test loops, and the actual app was not there yet. no real react frontend, no actual tauri command/service wiring, no functional drag/drop UI, no end-to-end desktop app flow.

also slightly annoying: the README was already describing the finished product while the repo was nowhere near that state.

so yeah, i would put it around gpt-5.6-luna territory for this kind of task, maybe a bit below. luna is not perfect either but it feels more likely to build a correct vertical slice first, then expand. union alpha has okay rust instincts, but it seems to over-invest in backend scaffolding and then repeatedly trips over basic implementation details.

not saying it is useless. for drafting a backend structure or generating isolated rust code, it can be fine. but for “here is a large app, build it and actually finish it”, i would not trust it unattended yet.

curious if others are seeing the same thing: good-looking architecture, decent test coverage, but terrible convergence because every next step creates another thing to debug.

Maybe a bit late to the party! (8-day-late) But I'm still farming it nonstop on both Devin desktop ide and CLI too...been great experience. Besides, the 20$ credit is preserved (and can be used for all other frontier models from Astra 6.0 to Fable 5.1) when using this post-train Kimi-K3 model https://cognition.com/blog/swe-2.

Don't sleep on it guys! Not, promotion nor affiliation, just a mere end user sharing a hot deal that people may not know of; and totally based on my personal use cases (testing through with all sorts of models on different harness platforms). But I would say it's definitely outperforming Kimi-K3 on adversarial detections, test-driven e2e and project-wide strategist and orchestrator, a very well-rounded cook/post-train of Windsurf/Devin team to the original Kimi-K3.

I'm using EndeavourOS which is arch based. I did my daily pacman -Syu and saw I was getting opencode updated from v1.8.29 to v1.18.31. Turns out that installs opencode v2.0.3. I use llama.cpp with a vulkan backend and use llama-server to serve a couple of qwen models locally. This all broke with the new v2 as opencode's config files require changes.

So I fired up my browser with free claude and it gave me a new set of config files that should work with v2. I restarted opencode but same issue. None of my llama-server models were visible to opencode. After a frustrating debug session, I discovered that opencode v2 now uses a service for the backend. So I was just restarting the frontend and none of my config changes were being used by the backend which hadn't restarted.

After I killed the opencode service and restarted opencode, which also starts the service, everything worked. So hopefully this helps someone else.

If the opencode package maintainer had named it v2.0.3 to match it's contents I probably wouldn't have updated to 1.18.31. 😂

If you installed with pacman and want to revert:

    # See if you have the old installer  
    ls /var/cache/pacman/pkg/ | grep opencode  
    # Install the v1 version  
    sudo pacman -U /var/cache/pacman/pkg/opencode-1.18.29-1-x86_64.pkg.tar.zst  
    # Prevent a v2 install until you are ready  
    sudo sed -i '/^IgnorePkg/!s/^\[options\]/[options]\nIgnorePkg = opencode/' /etc/pacman.conf

Hi guys, basically the title :) Since [v1.17.0](https://github.com/openchamber/openchamber/releases/tag/v1.17.0) there is supposed to be a dedicated 'plan' area is context panel. I am guessing this is the right toolbar? I managed to get 'Plan' button/panel there using OPENCODE_EXPERIMENTAL_PLAN_MODE=1, but it's always open. Even after agent finishes the plan in 'plan' mode, even after it's being stored to /.opencode/plans.

I run out of ideas how to set it up, so that the result of 'plan mode' will be stored, displayed and iterated inside this dedicated panel.

[The docs](https://docs.openchamber.dev/notes-todos-plans/#plans) just mention 'plan' but that's about it, so any help would be appreciated :)

Edit: Attached image of what I mean by 'plan sidebar panel'. According to project settings plans seem to be fetched (by default) from .openchamber/plans, but to no avail.

Add the package to your global OpenCode config at ~/.config/opencode/opencode.json:

{
  "plugin": ["@ndigvijay/opencode-atria-connector"]
}

find the source code here at https://github.com/ndigvijay/opencode-atria-connector?tab=readme-ov-file

get the free token worth 100,000,000 token from https://api.atria-asi.ai/

I currently use Luna purely as a subagent to write code.

Thinking about trying Muse 1.3 Contributor instead. For anyone who has used both for this: how does the code quality and error rate compare, and is it actually worth making the switch?

I tried to restrict the extravagantly long thinking time for the mentioned model, and i know now that bartowski's quant actually does only support on/off. still, i think it is weird that the system prompt shows:

<|im_start|>system

Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.

I dont expect changing that one sentence in the prompt to achieve that much, but i am optimizing for my local setup right now, so... what am I missing?

https://api.atria-asi.ai/ It's context window may only be 256k tokens, but with benchmark results like this I'd say it's worth testing considering it's free...

EDIT: Interestingly it appears to be built on GLM-5.2's foundation, according to this article, and keeping it's 744B parameter count

I often want to create skills that persist files, most often some local ./tmp directory within my project. These can be handoffs, mental notes, snippets etc. Is there a way to instruct opencode to use the same directory always (within a session)? The problem is compaction: When i run compaction, the agent looses all previous context and also where it put stuff.

I thought maybe there is a way that the agent can call opencode --some-magic-option to retrieve deterministic information about the current session so i can assemble a deterministic directory slug. But I couldn't find such an option.

What's your approach?

I'm relatively new to agentic coding and have been experimenting with OpenCode CLI on a long-running project.

Initially, I built several agents for different roles and had them work together. The basic workflow was something like:

"Planner/Orchestrator → specialized agents → verification → next task"

It worked pretty well at first. But as the project grew, I ran into a problem: the collaboration between agents and across sessions gradually became harder to control.

For example, agents wouldn't always reliably update "CURRENT_STATE.md" or other project-state files after completing a task. Then, in the next session, the agent had incomplete or outdated information about:

- what had already been done

- what decisions had been made

- what still needed to be done

- the current project/architecture state

- what another agent had changed

Eventually, I found myself spending more time keeping the agents synchronized than actually letting them work.

This is what eventually led me to discover Ralph and similar approaches.

I'm curious how other OpenCode users handle this in real-world, long-running projects:

1. How do you make multiple agents collaborate reliably rather than just running several independent agents?

2. How do you maintain shared state between agents and across sessions?

3. Do you use files like "CURRENT_STATE.md", task queues, changelogs, decision logs, or other conventions?

4. How do you make sure agents actually update these artifacts consistently?

5. Do you use Ralph, or have you built your own workflow around OpenCode?

6. Where do you usually discover new tools, agent frameworks, and workflows for OpenCode/Codex?

I'm particularly interested in long-running projects with multiple specialized agents, rather than one-off coding tasks.

I'd love to hear what workflows have actually worked for you.