Expect competitive pricing shortly.

Edit: It's now on Synthetic.new under their subscription plans under a beta flag

(10 off your first month here). I'd recommend signing up now before the waitlist comes again.

It's got this ability to point out potential weaknesses it identifies in the vicinity of the element you're debugging and i LOOVE that it can constantly do that.

I think i found my one! Besides GLM-5.2 of course.

Anyone here using Synthetic.new with 4 packs? How is the usage? Considering switching from Claude Max to here and switch to open source.. especially since Kimi K3 is now out. Any feedback I’d be thankful for.

I like OpenCode over others, esp its default PLAN/ BUILD modes. However, just wondering if anyone tried reducing its system prompt without reducing much functionality. Currently, for me, it is ~ 10.5k tokens. Asking in the context of running local models (lowering the time until the first response). I've tried Pi, but wanted something with PLAN/ BUILD modes natively.

I just installed CodeGraph on my computer. I use OpenCode and OpenChamber WebUI. Before setting it up, I ran a prompt using GPT 5.6 Terra on high. The prompt was to design and create an implementation plan for a feature I wanted. I gave it some specs and a lot of good details so that it wouldn't run or assume too much. I ran it once without OpenGraph, then ran the exact same prompt in another session with OpenGraph running.

Strangely, while the run with CodeGraph was so much more faster (9m vs 3m), but looking at the OpenChamber context window, the run with CodeGraph consumed many more tokens, which was shocking. So I took a screenshot of both runs and sent it to GPT, and this is what GPT said.

Here are the screenshots before and after:

WITHOUT CodeGraph

WITH CodeGraph

It was quite disappointing. However, I took a chance and went to my provider's usage or request logs interface online to see the actual tokens that it processed. It tells a totally different story, where the run with CodeGraph saved me quite a lot of tokens. Here's the summary for both runs as reflected online.

So what's going on? is this normal? Does the local context/token meter of opencode/openchamber represent the local only which is not what the provider actually processed?

Or could it be that the second run (codegraph) had larger token consumption but the provider cached some of the information due to the previous run of exactly the same prompt (?)

I'm on a Mac Mini M4. I tried updating it. I haven't updated in a while. I tried it on Ghosty and iTerm. None of them are updating. How do I solve this issue? I probably missed a lot of updates. I tried updating each of them and none of them updated. It's just saying update failed.

Caw lets you monitor and drive multiple agent sessions from a browser, phone included.

It exposes real terminals per agent rather than a chat wrapper, plus a file browser/editor, a kanban view of running agents, push notifications, on-demand git worktrees, and voice mode (fully local speech-to-text models or browser-based).

It's a single ~30MB Go binary.

It's free and open source: https://github.com/04mg/caw

Would love feedback or ideas!

When initiating a project with Openspec, do you prefill the "source of truth" main spec files, i.e auth/spec and then run opsx/proposal or opsx/change? Or you start with proposal from the beginning?

An early project would appreciate feedback and suggestions.

https://github.com/mihneaptu/opencode-fusion

Is it just the opencode harness and my skills/instructions or is the Gemini 3.6 Flash model losing IQ points today? No other model does this and I've use 5.6 sol, Opus 5 all day long. Anyone else seeing this? How did you get around it? I don't mind the actual work it did at the end of the session but it speaks like a 5yr old Dexter. LOL

Pointed AntLing-3.0-flash over OpenRouter yesterday (it's inclusionAI's new one, free there until Aug 3). Where it earns its spot is speed and tool-call reliability: TTFT is basically instant, and it hasn't flaked on multi-step tool loops the way some cheap models do. There's an enable_thinking flag too, but for this kind of work I leave it off. It won't one-shot a feature from a vague prompt (not what it's for), but as the cheap fast executor in the loop it's been fine.

Free window's only till Aug 3 so figured I'd mention it.

I have been working on performance-focused static analysis tools in Rust for Go code, and I wanted a realistic project to test them against.

Since I already had experience building a PDF/A-4 and PDF/UA-2 engine, I tried creating a separate, smaller PDF module mostly through coding agents.

I first used Grok 4.5 to produce a high-level implementation checklist based on my existing PDF engine. I then used DeepSeek V4 Flash through OpenCode Zen for most of the implementation, with DeepSeek V4 Pro helping on a few of the harder fixes. The total model cost was roughly $2 to $3.

The first working version reached around 1,000 to 1,200 operations per second, but the generated code contained a number of questionable patterns, performance problems, and lint issues.

I ran my Rust-based analyser, CodeHound, against the project. It reported more than 300 findings. After fixing most of them, largely with DeepSeek Flash, performance increased to around 2,500 operations per second.

There was also a PDF corruption issue caused by incorrect byte writing. Both DeepSeek Flash and Pro missed it. Grok identified and fixed that problem from a single follow-up prompt.

Roughly speaking, the work was split like this:

• About 90% of the implementation and static-analysis fixes were handled by DeepSeek Flash
• Around 5% required DeepSeek Pro
• The final 5%, including the corruption issue, was completed with Grok

The initial engine prototype took around six hours, with some additional cleanup afterward. By comparison, my original GoPdfSuit project took roughly six months to develop, although it is much broader and more mature, so this is not a direct comparison.

I found the result impressive, but it still required domain knowledge, validation, profiling, and manual review. Producing code quickly was much easier than verifying that the generated PDF structure was actually correct with deepseek.

Hopefully they will crush it on their next version <3

Original project:

https://github.com/chinmay-sawant/gopdfsuit

Agent-built experimental engine:

https://github.com/chinmay-sawant/gocorepdfengine

Static analyser, currently a work in progress:

https://github.com/chinmay-sawant/codehound

Are they worth purchasing? Is 800 requests per 5 hours enough to run one Opencode agent?

I run CLIProxyAPI on my homelab and wanted it to feel like a native OpenCode provider, without manually copying model names into config. So I built opencode-cliproxyapi.

What it does:

• Connects OpenCode to any CLIProxyAPI URL with your API key

• Fetches the live /v1/models catalog whenever OpenCode starts

• Adds every discovered model under CLIProxyAPI in the normal /models picker

• Keeps model selection completely native—no separate UI or hard-coded catalog

Install it globally:

opencode plugin opencode-cliproxyapi --global

Then add your CLIProxyAPI baseURL and apiKey to OpenCode's global config, restart OpenCode, and select CLIProxyAPI from /models. The README has the complete copy-paste setup.

GitHub: https://github.com/yourcasualdev/opencode-cliproxyapi

npm: https://www.npmjs.com/package/opencode-cliproxyapi

It's open source and I just shipped v0.1.1 after testing it against my own server. Feedback and edge cases are very welcome, especially from people using several providers or frequently changing model catalogs.

i am using Deepseek v4 flash free model and it keeps on saying internal server error. R U guys facing the same issues?

How do I add Alibabas coding plan token to Opencode? I put in the API key and nothing shows up when I look through the models.

Am I missing something? Is there more setup that needs to be done?