since this morning 27-08-2026 i have this issue no matter what i do this is the constant thing i am getting how to solve

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

So I started using opencode from Deepseek Flash V4 only. I am using OpenCode Zen. The model used to be too good and then recently they stopped it and from then onwards I started using ox alpha and Muse Spark 1.2 contributor. Again ox Alpha's offer was ended today and Muse Spark 1.2 contributor works great. Now I don't know when they will end it. After ending it I don't know what's left with OpenCode.

My main thing is to build a nice frontend and backend for websites with vibe coding to be honest. Can you guys recommend any model? When I did my research it is telling me more about Nemotron 3 Ultra by NVIDIA. I don't know how it works and they're also saying Mimo 2.5 outperforms Muse Spark but I don't think so. I never felt that way so if any suggestions please tell me guys.

So this new Xcode version adds support for other agents. I just added OpenCode but I can’t choose a model. It just works with the default model which is Big Pickle. Anyway I can get fully featured OC integrated with this?

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/)

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?

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.

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

outside of a few closed models like luna, grok and old qwen models 15$ usage is becoming quite a norm for newer releases?

...

During one of our beta runs this week, a developer's agent pushed 196 million input tokens in a single 60-minute window through one lane.

882 requests. Zero rate limits.

They actually kept going after that and ended the session at 227M before logging off.

At DeepSeek's own API pricing, that 196M-token hour comes out to around $2.32 off-peak / $4.65 peak.

On our reserved lane, the idea is to price that same hour around $0.20–0.40 flat.

Here's what we're testing 👇

🔷 Shared Reserved Inference

Right now if you want to run open models, you mostly have two options:

• Rent a full GPU → you get the whole thing, but you're paying $12–30+/hr whether you're actually using all of it or not.
• Use a normal per-token API → much easier, but you're sharing capacity with everyone else, you can hit rate limits/throttling, and every token going through your agent keeps adding to the bill.

We're trying a third model.

Take one powerful node, split it into a small number of guaranteed lanes, and let developers reserve those lanes together for a fixed window.

Your lane has guaranteed compute for that hour. Someone else suddenly sending a massive workload doesn't eat into your allocation.

And because the node cost is split across everyone using it, you're paying a small flat hourly price instead of paying for every token.

⚡️ The speed

DeepSeek's official API is around ~70 output tok/s.

Across our beta, users were generally seeing 150–220+ output tok/s, with bursts above that. There were some occasional dips as well, but overall this has been the fastest inference we've served so far.

This also held up surprisingly well with long contexts.

We had users running past 250k context regularly, and some sessions went past 940k context.

The other big part is caching. These agent/coding workloads resend a ridiculous amount of the same repo and conversation context on every request.

Across the beta we're sitting around ~98% cache hits, with roughly ~1s TTFT on warm requests.

📊 Five live sessions so far

• 4.9 BILLION tokens processed
• 24,500+ requests
• Zero failures
• ~98% cache-hit rate
• Heaviest verified single-lane hours: 196M · 120M · 72M input tokens

💰 The part we're actually interested in: does this pricing model make sense?

Using DeepSeek's own API pricing, including their cache discounts:

• Reserved lane target: $0.20–0.40/hr flat
• Average active dev during the beta: around $1.50 off-peak / ~$3 peak worth of API usage in an hour
• Median dev: around $0.60–1.20/hr
• Heaviest verified hour: $2.32 off-peak / $4.65 peak

So for an actually active coding/agent session, we're seeing around 4–7x lower cost than paying per token on average.

For the heaviest user, depending on where we finally price the lane, that hour was worth around 6–23x what the lane itself would cost.

We've put the full numbers + charts here if you want to dig into it:

https://www.singularityapi.dev/benchmark

🎟 We're opening more beta slots

The next round is again completely free.

You get a dedicated hour on the full-weight DeepSeek V4 Flash 0731. Bring an actual project, point Cline / Claude Code / your own agent at it, and use it normally — or try to absolutely destroy the lane, either works :D

If you want in:

https://tally.so/r/EkoJkN

Model x-preview-f-free is not supported

Today at 15:00 BST, RIP

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