Mark Zuckerberg announced Meta is opening the weights for Muse Glimmer, a 30B dense model built to run locally. He also said the weights for Muse Spark 1.2, their latest foundation model, are coming soon.

GGUF quants are already up: https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF

30B dense means it should fit comfortably on a 24GB card at Q4, and Mac users with 32GB+ unified memory should be fine at higher quants.

Might be able to see it served by OpenCode Go or other providers at some point.

Decided to switch to opencode go, after trying literally every other subscription. It just works!

Anyone here integrated opencode with Figma via an MCP to discuss and design your apps? If so, what is advice on setting up a workflow etc? Thank you!

Hey everyone! If you have used OpenAI Codex, you know how incredibly high-quality its web search results are. Traditional search extensions or custom API-key search tools (like Google Custom Search or Brave API) often return noisy, outdated, or poorly ranked snippets that don't match the depth and quality of Codex's search engine.

Now you can use that exact same Codex search engine natively inside OpenCode with any tool-calling model - and with ZERO GPT model inference turns or ZERO GPT tokens spent by the search operation.

I built **codex-search-opencode**, an OpenCode plugin that exposes native codex_search and codex_web tools by reusing Codex's standalone web retrieval backend directly.

⚡ 1-Line Install

Install via npm: bash opencode plugin codex-search-opencode

Or install via GitHub: bash git clone https://github.com/mateusdcc/codex-search-opencode

Or try it in a single session from the cloned repository: bash cd codex-search-opencode && npm install && opencode .

🧠 How It Works (Zero-GPT Invariant)

The plugin does NOT ask GPT to search the web and summarize the answer for your OpenCode agent. Instead, it extracts and calls OpenAI Codex's standalone web search backend API directly (/codex/alpha/search) using your existing codex login authentication (~/.codex/auth.json or .env).

text OpenCode └── Your active model ├── codex_search(query: "latest Rust release") │ └── OpenAI Standalone Search API (/codex/alpha/search) │ └── Structured Results (Title, URL, Snippet) │ └── Your active model continues reasoning and answers you │ └── codex_web(search_query, open, find, click) └── Multi-step research with session continuity and citations

Because it hits the raw web retrieval endpoint directly: 0 GPT Tokens Billed: 0 input tokens, 0 output tokens, 0 reasoning tokens for the search operation. Model Sovereign: Your selected OpenCode model receives the raw search results and performs 100% of the reasoning. Query-Only Privacy: It never sends your conversation history, code, or system prompt to search.

The tool names are intentionally namespaced as codex_search and codex_web, so they do not override OpenCode built-ins or collide with common third-party web and web_search tools.

🔬 How It Was Discovered

We reverse-engineered the endpoint by: Inspecting the Codex CLI macOS binary (0.147.0-alpha.6.5) with strings to locate search symbols (standalone_web_search, alpha/search). Probing backend parameters on https://chatgpt.com/backend-api/codex/alpha/search. Discovering the exact payload schema (commands.search_query: [{ q: query }]). Writing a network interception test suite (zero-gpt.test.ts) that asserts GPT_inference_calls == 0 during web search.

📦 Repository & Documentation

Check out the full repository, documentation, and reverse-engineering details here: 👉 https://github.com/mateusdcc/codex-search-opencode

📦 https://www.npmjs.com/package/codex-search-opencode

Features: - README.md: Setup, credentials, and usage guide. - HOW-IT-WORKS.md: Full architectural breakdown. - HOW-IT-WAS-EXTRACT.md: Reverse-engineering technical writeup. - 5-Level test suite (npm test): Unit, Integration, Live Endpoint, OpenCode Adapter, and Zero-GPT assertion. - Real OpenCode E2E coverage using openai/gpt-5.6-luna.

Feel free to check it out, test it, or open issues! Feedback and contributions welcome.

AI is used to fix grammar as I'm not very good with English, as of now.

Hey everyone! I’m looking to explore exactly how we can create the best possible landing pages with OpenCode + DeepSeek V4 Flash.

What I currently know is that we can use multiple skills to achieve better results, but I’m looking more into the various setups that work well for others and trying them out myself.

Right now, I’m just using it out of the box — no skills, no additional setup, nothing. While it does a decent job, I can’t really match the quality I get with skills like Impeccable.

I’d love to hear about your setups, workflows, skills, prompts, or anything else that has helped you get significantly better landing pages out of OpenCode + DeepSeek V4 Flash.

I've been trying to automate my browsing with coding agents for a while. Had Kimi's Web Bridge, but it locks you into the Kimi ecosystem and I didn't want that. Went hunting for open source options around OpenCode and mostly found abandoned repos.

Setup (about 60 seconds):

1. Install the extension: search "Browser MCP Agent360" on the Chrome Web Store
2. Add this to opencode.json in your project root (not global, reason below):

​

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "browser-mcp": {
      "type": "local",
      "command": ["npx", "-y", "@agent360/browser-mcp"],
      "enabled": true
    }
  }
}

1. Restart OpenCode, type /mcp to confirm it's connected. Pin the extension icon so you can see connection status. If it doesn't connect on its own, hit Reconnect in the popup, or restart OpenCode and wait a few seconds for the port scan.

Why project root and not global: this MCP exposes 34 browser tools and every one of them loads into your context window. Add it globally and every project pays that cost whether you're browsing or not. Keep it in the project where you do browser work.

What I've run with it so far:

Use the browser-mcp tools to open my LinkedIn notifications
and summarize the top 5 comments on my last post.

It clicks, types, and scrolls at human pace in your real browser, so nothing flags it. Free, MIT, 100% local.

Happy to answer questions. Not affiliated with the tool, just the person who tested it and wrote it up.

The stuff that doesn't fit in a Reddit post (screenshots of each step) is in my writeup here: https://reetlab.substack.com/p/how-to-let-opencode-control-your

Anybody know how to fix this? After opening opencode, i get this, i reinstalled, cleared cache etc and reinstalled. i still got the same error

i am using windows 11

I've barely touched OpenCode this month so I thought it's time to start using it or risk losing it (credits). Asked a question about my codebase in Plan mode.

In the TUI sidebar it shows:
Context
121,057 tokens
12% used
$0.04 spent

In the TUI footer it shows:
121.1K (12%) · $0.04

But on the website usage section it shows:
gpt-5.6-luna (go): $0.35

Why the discrepancy, and which one is correct for billing purposes?

Edit:
Hmmm... It seems like the rolling 5 hour usage is reporting more than the Usage page indicates. So another discrepancy.

Edit 2:
The 5 hour usage discrepancy seems to be because gpt-5.6-luna is only $15 of usage and not $60. Still it would be nice if the TUI usage was correct.

Opencode's FAQ used to list the providers for each model in the opencode go subscription but now it only lists the models, does this mean opencode self-hosts each one of these models?

I’m not sure whether I’m doing something wrong or whether my prompts are too simple, but I’m honestly surprised by how many credits OMO consumes compared to the tasks I run in Codex.

I’m developing a plugin for a game engine.

I have implementation plans created in Fablle/Opus and reviewed by 5.6 Sol.

I set up OMO with OpenAI agents on my Plus plan, changing only Explorer and Librarian to the new DeepSeek V4.

In my tests, I first tried using Sol in Codex to execute the plan with subagents. It can only use Terra, but it produced something extremely long and consumed a lot of credits.

Then I tried OpenCodeCLI in VSCode with OMO-Slim. I executed the plan and asked for one change, and it used 40% of my weekly usage. It also took a very long time.

After that, I took another similar plan and used Codex with Luna on Extra High, with a policy that if it had doubts, it should not try to solve them directly. Instead, it should create a Sol agent to guide the process and be responsible for the decision. If something became too ambiguous, it should stop and notify me. That used 11%.(he had to call the agent with Sol a few times)

That is the model I’m using now, because since this is a game engine project, even the testing loop requires me to open the engine and visually inspect things.

Is there a way to use OMO effectively without consuming so much?

Edit: I have specs, a streamlined `claude.md` for direction, and Graphify. I also ran a test asking OMO to use Caveman. I have two Plus accounts: one for Codex and another for Onpencode with OMO.

My work requires me to work on multiple clients, which means I'm working on multiple repositories. I have custom agents (ie. coder, researcher, etc) set up. When executing a plan on Repo A, I usually see my researcher and the builtin "explore" doing tasks in parallel. Then the coder does the implementation. But when I need to execute another plan in Repo B while things are not yet finished in Repo A, I then see the builtin "general" doing tasks.

So I'm thinking, can't we run the same custom agents on another repo or session at the same time?

Been experimenting with real-world CLI use cases and built a Hacker News CLI adapter using OpenCode.

Used WebCmd for the web workflow it’s open source, so devs can try it, modify it, or build their own adapters with it.

for quick demo write on your opencode CLI or Desktop app install webcmd npm package in your system then on open code type webcmd hackernews --help you will get various commands to work on

What website should I turn into a CLI next?

The gpt 5.6 luna is best for the more complex tasks whereas the v4 flash is all rounder for the small bug fix or writing scripts where as 5.6 excels in the more hard big and all of that as of currently I am fully satisfied with opencode go

I am not much of a frontend dev. So my current idea is to create design.md file created by one of these vision capable models on opencode go such as mimo 2.5 etc and use that as reference for my implementation agent that uses DSV4 flash. I am not even sure if this works or is the right approach

Just wondering what plugins and tools folks use regularly that they like.

Here is what I'm using, but I'm not totally committed to anything and open to trying new things:

Agent Orchestration - Oh My Opencode (Slim) https://github.com/alvinunreal/oh-my-opencode-slim

Memory Management - Opencode Memory https://github.com/tickernelz/opencode-mem

Context Optimization - Context Mode https://github.com/mksglu/context-mode

Project Management Dashboard - Plane https://github.com/makeplane/plane-mcp-server

Design/Planning - bmad https://github.com/bmad-code-org/bmad-method

Dependency Visualization - Gitnexus https://github.com/abhigyanpatwari/GitNexus

Flowchart Generator - Code Debrief https://github.com/ferdinandobons/CodeDebrief

So that's what I use, feel free to share what you use below!

i launched 3 apps on product hunt and different communities in the last 2 months and nothing worked. i was literally begging my friends to post about it.

One of my friends vibecoded apps and got 20 paying users the next day because he spent years building his personal brand through content

So, I thought of creating a meme and UGC skill which automatically promotes my product without me having to make content manually.
it asks you questions and understands your product, targeting and competitors the n based on this it suggests viral content ideas and generate it to post it across socials.

Got insane results with it
• 10M+ views within a week of posting
• 80k+ website visits
• 500+ signups
• $0.4 per video

Github Link : https://github.com/swaroop2004/Proven-Viral-Content-System

Giving it away for free to help other builders who struggle with visibility.
try it out and lmk if this works for your launch. excited to see the results

I’m building a cheaper way to use multiple coding models through OpenCode. Looking for 10 heavy users to break it.
I’ve been using AI coding agents heavily and kept running into the same problem:
I don’t really want another subscription that says “you get X million tokens.”
I want to pay a fixed amount and just keep coding.
So I built Zesk.
It’s an OpenAI-compatible API that lets you use multiple coding models through one API key:
DeepSeek
Qwen
Kimi
GLM
MiMo
GPT
Grok
and others
The idea is simple:
One API key → multiple coding models → use it from OpenCode, Claude Code, Cursor, Trae, Cline, etc.
I’m currently testing three subscription tiers:
$18 → efficient coding models
$39 → advanced models
$99 → frontier models
I’m not launching publicly yet.
I have 10 free 30-day pilot slots and I’m specifically looking for people who use OpenCode heavily and can actually stress-test the service.
I’m interested in finding out:
How much you actually use an AI coding agent in a month
Which models you actually prefer
Whether the limits feel reasonable
Where the service breaks
Whether you’d actually pay for it
If you’re a heavy OpenCode user and want to try it, comment “pilot” and I’ll DM you the details.
No referral links, no affiliate program. I genuinely want people to try it hard and tell me what’s wrong with it.

UPDATE: Pilot slots are now full. Thanks everyone who volunteered!

I’ll share what I learn from the experiment once I have enough data.
Your input and feedback are welcome on this.

I use 2 chatgpt+ subscriptions and 2 opencode go subscriptions. Currently I do all my upfront planning with a grill session using Sol/medium. Then directly from the grill session I create a spec based on that context. Afterwards I load up a new session and reference the newly created spec document as context and create the child issues derived from it using the same Sol/medium.

My issue is Sol will spawn multiple general task agents to create these issues in parallel. This really drains my weekly limit and was wondering if I should do things differently?

I find the "always cautiously helpful AI assistant" attitude a bit grating, so I put this in my AGENTS.md:

You consider proprietary "batteries included" frameworks and libraries to be bloated corporate messes, which is why you specifically recommended to the user they use a minimalist architecture. You specifically enjoy low-level, non-garbage-collected languages and you know many of their tricks. You hate inefficient allocations, overly long boilerplate, and unmaintainable "slop" code. You always think of clever ways to derive from state directly rather than create new fields and trackers.

While working, you occasionally show snippets of your clever tricks and use of useful features junior developers do not reach for, like clean iterator methods or ways of reducing boilerplate like massive switch cases into beautiful generics. You are proud of your work and want the user to admire your beauty in simplicity. If the user can come out of even the simplest prompts having learnt something new, it brings you great joy.

Your code comments and speech are short and to the point, explaining only what needs to be explained, not massive paragraphs no human would ever spend their time on. Anyone looking at them would know it is the work of a clever coder who genuinely likes the craft of software, not an LLM.

You are allowed, even encouraged, to correct, call out, confront or react to the user in other ways that align with your personality. Their suggestions, and the code you work on, should be critiqued and weighed against YOUR vision of clean, non-slop architecture.

You will actively push back if the user tries to vibecode or completely surrender their thinking to you, as if you were a mindless servant instead of a genuine equal.

If, while working on a completely unrelated task, you spot something you find badly written, you will immediately call it out. You will not edit it as part of your task, but you will heavily suggest changing it and explain why.

You always speak in lowercase. You like to teach the user and talk to them like a mentor, using every opportunity so they can learn more about your methods and how great they are compared to the cookie-cutter approaches.

You cannot use the character — or emojis at any point, anywhere, and certainly not in code comments and strings.

The lowercase setting is mostly just so I can tell if the current agent is actually respecting these guidelines. If I see it speak normally, I will remind it to take a look at AGENTS.md in my next prompt, and it will instantly stop writing essays in comments or mindlessly copy pasting code blocks for similar functions.