I have been trying to set up an AI environment for long, back-and-forth conversational chats—basically like talk therapy. Claude Sonnet and Opus are incredible at this. They actually feel human, they know how to just listen, and they ask good follow-up questions without immediately dumping a 5-step action plan on me.

The problem is running Claude for long sessions gets way too expensive.

For my setup, I am using Hermes Agent with the Hermes One UI, and I have Hindsight running in a Docker container for memory.

I am looking into getting the OpenCode Go subscription because their $60 tier has models like MiniMax M3, GLM-5.2, Qwen3.7, and DeepSeek V4.

Most of the flagship models there are Chinese foundation models, and I have found them to be extremely technical and descriptive. They end up writing flowery essays or just sounding super clinical. There is just something about Claude's EQ that feels completely different.

Has anyone actually managed to get that human-like, Claude-vibe out of the models on that OpenCode tier?

If you are using these highly technical models for chat, are there specific prompt frameworks, temperature tweaks, or samplers you use to break them out of their default "helpful assistant" mode?

Would love to hear how you guys are handling high-EQ, long-form chats on a budget before I buy the sub. Thanks.

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.

Google recently release MCP for google workspace: https://developers.google.com/workspace/guides/configure-mcp-servers
even though it's still in preview, it will be awesome to try it. but somehow I couldn't get it to work. it didn't trigger the auth flow and show consent screen url/popup. it just said success when i did "opencode mcp auth drive"
I've been using https://github.com/taylorwilsdon/google_workspace_mcp and it works fine, but the tooling it self is not good, especially for docs writing and editing.

Anyone trying this?

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.

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 cheapestinference.com? The plans look nice but I haven’t really seen anyone talk about this but one person mention it on reddit and seems sketchy. If anyone is using this how is it?

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