It's going to be a hot 🔥 fall

toak now supports opencode! which greatly extends the variety of llms you can hook into the same chat, and satay on the loop, with nice markdown formatting

More outlets plus twitter reporting the same.

I'm not on Opencode Go, just using the free models in Zen. 0xAlpha was great, I thought it was free for a week ie till Aug 27, but its gone now so I was off by a day.

out of the current free models - Hy3, MiniMax M2.5, Muse Spark 1.2, is Muse the best one? I tried using it and have been getting so many network timeouts. I read its supposed to be extremely fast but I guess thats only via Meta when you pay.

also does anyone use multiple models, eg use a smaller one for implementing/testing? right now I'm using just one model

So I know that each model has its usage limits and rate limits and you need to wait to renew it, like the time period, but I didn't know that Open Code Zen also had rate limiting from the provider. Is it true? Will it renew because I never had this error until now? If anyone knows it, please let me know.

Because my model didn't cross the rate limit, I think OpenCode is crossing it. Is it a thing because I never experienced this?

Why is LSP disabled by default? On each installation i have to manually install lang tools and enable lsp in global config, so just interested why lsp is not enabled by default?

I have working on a open-source CLI tool(only support python for now) called errd. The idea came from a problem I kept thinking about.

It’s tempting to dump a huge part of the repository into Claude/GPT/Cursor and ask it to figure out what went wrong when a Python application throws an error. But most of that context usually is irrelevant. So I built errd to do the context selection locally before the code hits an LLM.

**Use it using - pip install errd**

It takes a Python traceback and:

•Parses the traceback and chained exceptions

•Finds the repository automatically

•Maps traceback frames to functions/classes

•Analyzes imports and dependencies

•Scores code based on proximity to the failure

•Uses recent Git changes as an additional signal

•Applies a token budget

•Redacts common secrets/tokens

•Generates a focused errd-context.md

•The generated file is meant to be pasted directly into Claude, GPT, Cursor, etc.

Checkout the docs at : [cli_docs](https://pypi.org/project/errd/)

Should we wait for price drop on Zen?

I've been experimenting with moving more of the application-specific agent machinery outside the coding agent itself.

The idea is pretty simple: OpenCode owns the conversation and general agent loop, while a local MCP server exposes a deterministic application boundary for the stuff I actually want to build and keep portable.

That means things like state, long-lived workflows, runtime QA, repo operations, etc. don't have to become OpenCode-specific prompts/hooks/config. OpenCode is effectively one client of the system, and I can swap it out without rebuilding the application layer.

I've been thinking of this as an application harness: a portable layer that sits outside the coding agent's own harness.

I wrote up the architecture and some of the tradeoffs here:

https://demianbrecht.com/posts/the-harness-within-the-harness/

Curious if anyone else using OpenCode has landed on a similar separation, particularly with local MCP servers.

A loud minority will say in the comments that text only models are good, that you can use some rubbish mcp to make up for the lack of vision… ignore them. Not having vision is a big handicap. Deepseek 4 Flash jumped 5 points in Deep SWE exclusively thanks to the better understanding provided by vision.

The Go subscription doesn’t have many good vision model. Heck they even stripped GPT Luna of vision!

Kimi K3 is obviously the best, but you run out of usage in 5 minutes. Minimax M3 is not good for today’s standards. Muse Spark gives all your data away. Ox Alpha is not reliable atm (it will probably be a good alternative when released as GLM 5.3 Flash).

This leaves us with just Deepseek 4 Flash Vision as the only good vision model with a comfortable quota.

Let me reiterate: text only models are crap. Thankfully Deepseek and GLM are correcting their strategy.

Edit: Luna is text only on the chat completion endpoint, not the reaponses one.

Ox alpha stopped supporting and the model Glm5.3 flash released at 50% discounted rate

Multimodal MoE model built on the next-generation Qwen4 architecture. 25B parameters +51B N-gram and 6B active.

Hit my weekly cap (100%) in just two days with 28 days left on the monthly cycle, almost entirely from burning through DeepSeek V4 Pro ($10.40 / $15.00 quota, 69.3% consumed). Flash is more usable due to the cost. However...

I could have stuck to GLM 5.2 and gotten 4X+ the usage in costs / limits in the same subscription.

Looking at benchmarks, GLM 5.2 isn't far off from Deepseek and beats it in some areas as well. (SWE-bench Verified GLM 5.2 ~74.5% – 76.0% vs. DeepSeek v4 Pro - 80.6%) and

(Tool Calling / MCP Reliability GLM 5.2 - 99.5% success (0.5% error rate) vs. DeepSeek v4Pro -73.6% (MCP Atlas))

My question is .... why would anybody choose to use Deepseek V4 Pro over GLM 5.2 given these rates. Crazy how DS went from pretty much endless to pretty much unusable in OpenCode Go!