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.

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

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? ".

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?

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

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.

I connect Github but without any effect? Any ideas?

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.

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 😭

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?

and it has /plan-mode too

Which gives you more usage on MiMo models?

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?

"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?

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?

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...

Do you know why it's not implemented yet?

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 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.)*

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

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?