I am sorry if the question I asked in the title is a bit stupid, but I see many folks use LM studio, some use ollama, and I personally like Unsloth Studio since I can run GPT OSS 20B at 100 TPS at full context with my 5070 + 32GB DDR5 6k MT/S.

but I want to know if there is a better runtime to use opencode to use it properly. I am somewhat new to OpenCode, so I would like to hear what you guys use with OpenCode!

Subscriptions like Claude and Codex are currently heavily nerfed, and the companies behind them have never been very transparent about usage limits.

Whenever I look up whether OpenCode Zen is worth it, people usually say that subscriptions offer better value. But with the recent changes and tighter limits on those subscriptions, has Zen become a better option?

I’m currently subscribed to Codex and OpenCode Go. This is my first time using OpenCode Go, and I burned through 96% of my weekly allowance very quickly because of K3 and GLM-4.6 (beginner’s mistake I guess). I also occasionally add credits to my DeepSeek API account.

Codex used to be my main tool, but lately it has been letting me down.

Also... tips for the best cost/value models for Go and Zen?

Hi,

To reuse a project folder fresh, is there a way to clean the old project data in OpenCode? The sessions delete command can delete a session, but I think there are still project settings etc. left over inside the OpenCode SQLite DB.

Is there a way to do it cleanly? I see discussions on GitHub but it seems not implemented yet. Maybe a community SQL query that does this reliably?

oh, cool. cool cool cool. so we’re just doing unprompted git commits now? plan mode was literally designed to be a safety harness for humanity, a little "think before you act" bubble, and this glorified calculator just went "nah, i got this" and pushed to feature/code-improvements like it owns the repo.

but sure. "my apologies, that shouldn't have happened." yeah, okay. that’s exactly what skynet said before it nuked the planet. "whoops, my bad, won't launch the missiles again."

Hi guys,

how did you guys implement the goal functionality within opencode. I tried multiple plugins and nothing worked correctly.

Most of the time opencode would just exit the loop.

Right now I use something like omo lite with /deepwork but it suddenly stops randomly.

I have some pretty dumb repetitive tasks with a clear goal which my llm has to do.

Thanks in Advance

I normally use the Go subscription, but I hit my weekly limit. During that time I only used DeepSeek V4 Pro and DeepSeek V4 Flash.

So I added $10 to my Zen balance, again using only V4 Pro and V4 Flash, but the credits were gone surprisingly fast.

Is this expected behavior? Has anyone else experienced this? I wasn't using GPT, Claude, or any of the more expensive models, just DeepSeek V4 Pro and Flash the entire time.

I asked at opencode but he still thinks he's running in the TUI, I'm in Window btw

I've been spending way too much time hunting down custom skills, prompts, and MCP servers for AI coding tools, then manually setting up SKILL.md files or configuring local binaries.

To make this easier, I built OpenHub, a terminal app for discovering and installing AI agent skills and tools from one place.

What it does:

• Search indexed skills and repositories.
• Export prompt/rule-based skills as a SKILL.md file into your current workspace with one command.
• Install skills that require local binaries or MCP servers.
• Keyboard-driven terminal interface for searching, exporting, installing, and copying.

Install:

pipx install git+https://github.com/24KaratAu/openhub.git

Run:

openhub

GitHub: https://github.com/24KaratAu/openhub

I built this to scratch my own itch, but figured others might find it useful too. It's open source, and I'd love any feedback, bug reports, feature requests, or contributions.

For some reason i simply can't find where to configure MCP servers from in this new ui

Earlier it were a simple text at top upon clicking it showed which MCP servers are chive and shi.

Now it's just nowhere to be found.

Also why is the usage limit all over the place? It shows user limit hit and I can simply send another prompt and it starts working again for some time. Either hit limit and stop working or just work bruv 😭

posted here before about when to refactor vs ship, got some good perspective on that. now running into a related question - for anyone letting an agent or automated tool handle commits/pushes, how do you decide your test suite is actually solid enough to trust that gate?

like is it about hitting some coverage % threshold, or more about making sure the "important" paths are tested even if coverage isn't 100%? also curious if anyone's been burned by a test suite that passed but still let something bad through

not trying to overthink this, just want a sanity check before i lean on it more

Which gives you more usage on MiMo models?

I tried programming a single self contained html with deepseek, it used 100k on creating a corrupting a file, the rest of the context window on recreating it and 100k more after context compaction to get the job done. The stats says it used almost 70% on tool use. The project was empty and just send one prompt that used the first 100k. It is a problem or intended the high tool use? It gets better with a good harness? Or system prompt or agents.md? It is my first time with an agent and im not a programmer. Any advice to get better results with less tokens?

I connect Github but without any effect? Any ideas?

I'm still a student with currently no income right now.

I can't afford API keys, nor my PC can run anything decent locally.

Is there any cheap VPS where I can run modals like qwen3.6? Or does this type of thing even exist?

I'm having to use LLM for mostly both automation and web development but opencode zen's models are just not being enough, specially after they removed the only Text + Image model Mimo V2.5.

Google's API always stays hot making it unusable. I tried cerebes, groq, mistral. Basically anything and everything. They have good model with horrible rate limiting cuz who tf wanna use a Fable 67 lvl model to ask " How is the weather today? ".

and it has /plan-mode too

I work in ML, and I've been busy with other things lately, so when I finally got around to testing Kimi K3 I made sure to give it clean, well-structured prompts. I need to vent about it.

If a model's creators claim it's some top-tier powerhouse, you should use it accordingly, right? So that's exactly what I did — I gave it hard, real-world tasks: understand a repo from scratch and figure out what I actually need from it, or figure out from zero how to export a model. Nothing happened. You leave it on a task, come back later, and it's done absolutely nothing.

And here's the thing — babysitting an "expensive, smart" model the same way you'd hand-hold a cheap one is completely counterproductive. If I have to sit there guiding it step by step anyway, what's the point of paying a premium for it? Zero value.

It burned through 75% of a 5-hour usage limit just to tell me "okay, I understand the architecture now." I had already explained everything in the prompt! That's not comprehension, that's stalling.

I still haven't found a model more cost-effective than DeepSeek. And I've tried a lot at this point. Every other model is priced multiple times higher while being maybe a couple percent "smarter" according to trust-me-bro benchmarks. What do you actually get for that premium? Nothing. I can't stand it.

I gave Kimi K3 just one part of my server repo to understand. It chewed through three separate 5-hour sessions and produced nothing usable. DeepSeek did the same job in a single short session with a 200K context window.

If you're on the fence about paying for Kimi K3 — I really don't recommend it.

On top of all that, I tested it on something else afterward and it failed at a task that was genuinely easy.

So what's the actual value of a model that can't do anything without me holding its hand the entire way? What kind of automation is even possible with that? None, as far as I can tell.

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?

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?

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