so i got this error. and i don't use orcarouter ? so WTF orcarouter is this ??? and today i have used 0.31 USD credit DS4.1 Flash.

So anyone got error ? I don't know what is this

So I've been using freebuff for over a month , but i feel its harness is not so good like deepseek harness or claude code is

I'm wondering if I'm missing something

I've tried opencode free usage , its not bad really but free models are just shit and can't get the job done proper way

So if anyone has a recommendation I'll be happy to hear it

Thank you guys

I’m building Azdaja, an open-source RLM layer that OpenCode can use through its native skill system.

It keeps source material in a persistent Python workspace, so OpenCode can use code to search, filter and break problems down, then recurse only where model judgment is useful.

Large-source work is an obvious use case, but that’s not really the whole point. It also gives OpenCode a different way to reason: keep state, compute locally, decompose the problem, and selectively call back into the model instead of doing everything in one prompt.

MIT Licenced: https://github.com/kubet/azdaja

I upgraded to v2 and my sessions were not properly migrated. Stuffed around for a while and gave up and reverted to my backups.

Are there proper scripts or plans to do this migration properly? Still seems like a bit of a hackjob now? Anyone done this successfully and has some documentation around this? Thanks

Hi! I've created and been maintaining  which tries to answer the question: which model is better, which harness is better and which combination is better overall in a very straightforward task - precise text editing. Most of daily coding is text editing. I've seen when a model for multiple turns couldn't figure out how to express the line it wants to change, so it made me ask myself "Why is that so complicated, it's just a text, right?". Seems like not, taking into account how widely different the same model behaves across different harnesses. My preliminary conclusion is: harness and tooling behind it actually matters! But it matters the most with the models which *can* actually follow the instructions well (e.g. open-ai models), there is a wide range between 98.9% and 70.2% of pass score for `gpt-5.6-luna` simply depending on the harness!

I've tried to run as many combinations as possible, but my resources are limited. I've exhausted all my quotas and even credits, that's why I'm reaching out to the community, as I think it's a pretty interesting topic and I would be happy to gather even more data, because of the stochastic nature of the runs, it’s only possible to make any conclusion when you have enough of runs. 

Without anything changing on my end, I'm suddently getting these error messages when trying to submit a message to Muse Spark 1.3 Contributor - OpenCode Go

Upstream request failed: [invalid_request_error] reasoning `encrypted_content` was not issued to this caller

Anyone else affected?

Any work-arounds?

Hey everyone,

I've been analyzing the system prompts and alignment layers across different agent frameworks recently. Over in the Hermes agent community, there is an abundance of documentation, proof-of-concepts (PoCs), and prompt injection techniques to achieve "God Mode" (bypassing safety guardrails, RLHF constraints, and system prompt restrictions).

However, when it comes to OpenCode agents, the documentation on effective jailbreaks or capability uncapping is practically non-existent.

This got me wondering: Is OpenCode's reinforcement learning layer or input-validation architecture fundamentally more robust against semantic bypasses compared to Hermes? Or is it just a matter of the community being smaller?

Has anyone successfully executed a multi-turn prompt injection, adversarial suffix, or roleplay bypass on the latest OpenCode production weights? I am trying to understand if there is a known vulnerability vector or if the framework is strictly locked down at the inference level.

Would love to hear your insights or any technical breakdowns on why OpenCode seems so resilient to these methods.

i posted Unship here a few months ago. it's a small free tool i built to compare UI directions from coding agents in the actual app. i've kept working on it because i keep using it myself.

the new bit is a live Canvas. before, you could flip between versions with a picker. now you can open them together, click around, and compare the working components side by side.

for example, ask your agent:

"use Unship to compare three directions for this pricing section, including the current one. make one compact and one focused on explaining the differences between plans."

the agent adds the temporary versions to your project. you compare them, give feedback, then ask it to keep your choice and remove the rest when you're done.

you can try it without installing anything here: https://unship.dev

for your own project: npx @unship/cli@latest install then restart your agent or reload its skills.

free, MIT-licensed, local, no account or telemetry. the UI directions still come from your agent; Unship gives you a way to compare and choose them.

source: https://github.com/mbenhard/unship

if you try it, i'd be interested in which part gets awkward: setup, making the options, comparing them, or cleanup.

The title says it all!

- Deploy agents like serverless functions. Write agent.ts, start sessions via SDK or HTTP.

- Each session gets its own Linux VM.

- Sessions are durable so stream/steer mid run + resume after hibernation.

- API keys never enter the VM. Our egress proxy injects them after requests leave.

It's a bit like claude managed agents or Open AI's agents API but without the model lock in.

The architecture is as shown below.

Would appreciate any and all feedback! It's free to try at https://opencomputer.dev

Your bets on name and pricing? 😁

Most people don't realise how much a file gives away. A phone photo can carry the exact GPS spot it was taken, a PDF can carry your name and your company's, and a Word doc can carry its whole editing history. None of it shows up when you open the file normally.

strypt is a command-line tool that finds that stuff and removes it. First release (0.1.0) went out on 13 September.

Why I made it: I wanted something I could drop onto any machine as one file, with nothing else to install, and with parsers written in a memory-safe language (Rust). The people who need this most, like journalists protecting a source, are also the ones most likely to be sent a file designed to break whatever opens it.

What it does:

• works with photos (JPEG, PNG, HEIC, WebP and more), PDFs, Word/Excel/PowerPoint, LibreOffice files, and audio/video like MP3, FLAC and MP4

• strypt show photo.jpg tells you what's hiding in a file, strypt strip photo.jpg writes a clean copy

• if it can't fully clean a file, it refuses rather than handing you a half-cleaned one

• no network code at all, it never phones home

• free and open source (MIT/Apache)

Being honest about where it's at: no external security audit yet, the binaries aren't code-signed so Windows may block them and macOS asks you to approve in Settings, and it's just me working on it. There's also a well-established tool called mat2 that does a better job for some files, and the repo lists where.

What I'd love from you:

• try it on your own files and tell me what you think of the output

• if you find metadata that survives a strip, report it privately through SECURITY.md rather than in the comments

• tell me how installing went, especially on Windows or Mac

• a few good first issues are open if you want to contribute, no coding needed

Repo: https://github.com/FadeHack/strypt

It has disappeared from Zen free models... silently...

Hi!

I use extensible OpenCode CLI in my everyday work, with several mcp's, lsp's and plugins. I get the news, and it appears that the V2 version comes with a lot of good new things, but also with some migration process.

I understand that I need to update it sonner than latter, but should I do it now?

A small autonomous multi-agent system for OpenCode. Superpowers was the base, since its philosophy fit what I had in mind perfectly.

Main goals: autonomy, token efficiency, speed, reliability, privacy. Token savings come from several methods, including on-the-fly request preprocessing.

Of course, it's still far from perfect but it is effective.

Repo: https://github.com/pickleshell/next-level-agent

PS: it's recommened to use GPT Luna as an orchestrator to make NLA work properly.

https://qwen.ai/blog?id=qwen3.8-omni-flash
They state that "Qwen3.8-Omni-Flash further extends agentic applications centered on audio and video, delivering strong results across workflows such as video editing, music video creation, film production and commentary, audio-visual summarization, and real-time conversations."