and it has /plan-mode too

I like the new version except the session tabs being at the top. Previous version had them on the left is much easier to reach and glance.

I know we can toggle to enable the old layout, but we lose the new features like "open in" which is very useful.

Also is there any markdown viewer plugin for Opencode Desktop?

OpenCode Go says models are hosted in the US, EU, and Singapore but not which region serves kimi k3.

So how are people concluding “it’s in Europe” or “China has access”?
From the client I don’t see a way to tell:
- Cloudflare edge ≠ GPU location
- latency is a weak signal
- Moonshot being a Chinese company ≠ servers in China
- open weights being unavailable is a separate issue (Go is an API gateway)

“China access” like some comments say...
- Inference in mainland China?
- Or a Chinese upstream (e.g. Moonshot) can still see prompts/logs even if GPUs are in US/EU/SG?

Which gives you more usage on MiMo models?

Do you know why it's not implemented yet?

"You have insufficient permissions for this operation. Missing scopes: api.responses.write. Check that you have the correct role in your organization (Reader, Writer, Owner) and project (Member, Owner), and if you're using a restricted API key, that it has the necessary scopes."

The problem is that I'm using my monthly ChatGPT Plus subscription, not an API key.

I've already tried logging back in and clearing the cache but still nothing. Does anyone have any ideas?

been working on a side project and keep hitting this wall where i know a piece of code is kind of messy/not ideal but it works, and i have to decide whether to stop and clean it up now or just keep moving and deal with it later.

curious how people who've been doing this longer actually make that call in practice. is there some rule of thumb (like "if i'll touch this file again soon, clean it now") or is it more just a gut feeling that builds with experience? also curious if this changes a lot depending on solo projects vs working with a team

not looking for a "it depends" answer lol, more curious about the actual heuristics people use day to day

I did a extensive comparison of the various coding models across several benchmarks based on the estimated cost to complete a task successfully. Based on the formula:

Then I normalized each source’s expected cost per successful completion against that source’s median, then used a geometric mean across sources to get this composite ranking:

This is obviously just a estimate of value vs performance, still very useful if on a budget.

• Default value model: MiMo V2.5 Pro. Note that GLM 5.2 is the only clearly cost-effective model represented in all three sources, so it might be a safer budget pick.
• Best Performance: Grok 4.5 has far better completion performance than the other cheap leaders while retaining strong cost efficiency. This is the obvious model if high success rate is important to you.
• Alternative: DeepSeek V4 Flash is a great budget option but its performance/success rate is low so it will take more runs to complete the task successfully.

Cross-source ranking

I've been testing a few multi-agent coding combos lately:

• Claude Code + OpenCode
• Codex + OpenCode
• Codex + Hermes Agent
• Grok Build + Hermes Agent

Instead of paying for one max-tier plan, I've been running multiple agents on lower-tier plans and pairing them based on their strengths. To make sense of token usage across all of them, I use TokenTelemetry (https://tokentelemetry.com) gives me a breakdown of how much each agent is consuming and which combos are actually worth the cost.

So far the multi-agent approach feels more productive for certain tasks (parallelizing scaffolding + implementation), but I'm not sure if it scales better than just committing to one agent with a max plan.

What's working for you? What combos are you running and how do you structure your workflow across agents?

Today an AI agent trying to browse the web is like a thief in a balaclava sneaking around a police academy. Site protections block it, challenge it, turn it away.

browser-search flips the script: your agent stops being the thief and becomes the chief of police. No more clumsy access attempts. It walks through every door because it has the right tools. SearXNG for search, Camofox for browsing, CloakBrowser when things get tough.

100% self-hosted, free, no limits, no API keys.

I just released v2.0, whose core logic enforces the exclusive use of deterministic scripts. This eliminates model hallucinations, even with the cheapest models. The skill describes the 3 tools in natural language, but execution is rigid: the model can neither get the command wrong nor misinterpret the output. The result is guaranteed success on every query — the skill and deterministic scripts guide the model to scour the web until it finds the answer.

No more excuses. Your agent has the badge now.

https://github.com/Johell1NS/browser-search

Is there any setting to use a default agent with plan and another default for build?
I see Big Pickle on each time and the first thing I do is change that.

I just watched a video where some dude added a model to a provider, tested it, and then later added another model to the same provider.

I can't seem to find that option anywhere.

I know that when you first set up a provider, you can enter multiple models. But after the provider has already been created, I don't see any way to reopen that model list and add more. In the video, he simply opened the window with the provider again, the previously added models were still there, and then added another one.

What am I missing? Is this an older feature that was removed or is there another way to edit the models for an existing provider, in the gui...

I wanted my AI coding assistant to always follow a specific ethical framework, so I figured out how to embed my custom guidelines into every interaction with opencode. Here's how I did it—and how you can too.

### The Problem
I have a set of principles (based on 1 Corinthians 13) that I want all my AI interactions to follow—patience, kindness, humility, etc. But manually pasting these into every prompt is tedious and error-prone.

### The Solution
OpenCode has a built-in `instructions` feature that lets you automatically include custom guidance in **every** LLM interaction. Here's the step-by-step:

1. **Create your guidance file** (e.g., `agape-guidance.md`):
   ```markdown
   # System Directive – Agape-Aligned Assistance
   In all interactions, embody the spirit of 1 Corinthians 13:
   - Be patient: Give clear, step-by-step explanations...
   - Be kind: Use gentle, respectful language...
   - [Your full principles here]
   ```

2. **Add it to your OpenCode config** (`~/.config/opencode/opencode.json`):
   ```json
   {
   "$schema": "https://opencode.ai/config.json",
   "instructions": ["~/.config/opencode/agape-guidance.md"]
   }
   ```

3. **Done!** OpenCode now automatically includes your guidance in every prompt.

### Why This Works
- **Global application**: Affects all projects and interactions
- **Non-intrusive**: Doesn't modify OpenCode's core—just adds your layer
- **Flexible**: Use any text file (`.md`, `.txt`, etc.)
- **Official feature**: Uses OpenCode's [documented `instructions` config](https://opencode.ai/docs/config#instructions)

### Customization Ideas
- **Team-wide ethics**: Place the file in a shared location for your org
- **Project-specific**: Override with a local `opencode.json`
- **Multiple files**: Reference an array of guidance files

### My Experience
Since implementing this, my AI interactions feel more aligned with my values. The assistant naturally adopts the tone and approach I want, without me having to remind it every time.

**Question for the community:**
What ethical frameworks or guidelines do you wish your AI tools followed by default? Would you use something like this?

*(Cross-posting this because I think it's a useful pattern for anyone wanting more control over AI behavior. Not affiliated with OpenCode—just a happy user.)*

Hej!

Jag fattar inget, hur använder jag Qwen 3.8 max i opencode?

Skulle någon kunna hjälpa mig och är den så bra som alla säger?

Update of my earlier post:

After mostly positive feedback, the tool now has about 500 legit users. However, I spent quite some time over the past few weeks fending off 300+ bot accounts trying to exploit the system in any way they could find.

The good news: this helped me make the entire system (api, library etc) a lot more robust and was definitely a great test now that the tool is in prod with real users.

Made me realize that certain things you cannot mock/test fully until the first users show up (especially the ones that want to game your system), no matter how complete your test coverage is.

Most spammers came from OpenCode installs for some reason, but legit users/installs are also mostly from OpenCode (>50%, despite being only one of four implementations).

OpenCode users (300+) have also earned the most, compared to Claude Code and Hermes, which I found interesting. Maybe overall coding time spent per day on OpenCode is higher per user than the other harnesses?

Most feedback I received via DMs was great and payouts are working with Stripe. Still working on adding other methods and improving the tool every day.

Thanks for all the support 🙏

I need the 5$ for my opencode account 😇 dollahttps://opencode.ai/go?ref=ESVD7EK78S

I kept making the same decision in coding sessions: should Sol handle this, should Terra implement it, or is this inexpensive read-only work for Luna?

I turned that decision into an MIT-licensed OpenCode project called Open Harness.

Open Harness automatically routes coding work between GPT-5.6 Sol, Terra, and Luna inside OpenCode, with optional Kimi K3 agents through OpenCode Go.

You give /harness one normal task. Open Harness then chooses a role automatically:

• Sol for architecture, difficult judgment, review, and escalation

• Terra for implementation, debugging, tests, and repository research

• Luna for classification, extraction, summaries, and other inexpensive read-only work

The goal is not to use every model on every task. It is to select the smallest appropriate worker without making the user manually choose a model and reasoning level each time.

Open Harness discovers the available model IDs at runtime and installs the routing command, agents, and permissions while leaving OpenCode in charge of sessions and credentials.

This is an early developer preview, and I still use it with supervision. The public routing path is policy and instruction based; it is not an OS sandbox.

Repo: https://github.com/cwklurks/open-harness

I would really appreciate feedback from people who use multiple models in OpenCode: which routing choices should become automatic, and where would you still want manual control?

Hello guys, I am facing this issue after the recent OpenCode update. Can somebody help me? I haven’t even used it much—just 10k tokens. Not just on Big Pickle, every mode is giving me this error.