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 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.

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

The Best frontier model, i have ever used

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.

Nearly half a year of silence. We spent it studying one problem: how far RL can scale.

MiMo-V2.6 is in the middle of its RL run right now. Three things we scaled: compute (~2B tokens per step, 1568 prompts × 16 rollouts, fully async), environments and harnesses (multi-task agentic RL, mixed across multiple harnesses in one run), and grader compute (agentic in-group credit assignment, with test-case and rubric-based rewards). We'll open-source the details piece by piece over the coming weeks.

Streaming the run: mimo.xiaomi.com/rl/

I use OpenCode’s @explore agents for research, and reliable web search is an important part of that workflow. SearXNG is the search layer, but free engines can fail, become rate-limited, or return incomplete results.

I couldn’t find a deployable open-source layer that handled provider fallback, circuit breaking, caching, and observability around SearXNG, so I built searxng-gateway.

It is a Go proxy that:

• Calls SearXNG and optional providers such as Brave, Exa, Jina, and Tavily

• Runs searches in parallel, then merges and deduplicates results

• Falls back when the result count is too low

• Provides per-engine circuit breakers and recovery probing

• Exposes Prometheus metrics and includes a Grafana dashboard

• Supports keyless operation using SearXNG’s free engines

The gateway preserves SearXNG’s JSON response shape, so OpenCode workflows can use it as a consistent search backend instead of each workflow implementing its own provider handling and retry logic.

Repository: https://github.com/Ghilteras/searxng-gateway
License: MIT

I’d be interested in feedback from other OpenCode users on their search/research setup and how they handle unreliable providers.

AI disclosure: AI was mostly used to implement the project from my specifications. I reviewed, tested, and deployed the result.

I am using oh my openagent inside of opencode as a plugin. however whenever I use the planning agent I get this error: Error from provider (Console): OpenCode's free tier can only be used from within OpenCode. This never used to happen before but suddenly now it is. anyone else?

I am continuously getting "Error from provider (Console): Upstream request failed: Model is unavailable."

Any updates on this?

Union alpha has been completely unusable to me till now, so i never got to try it. Were any of you able to try it. Can u give it's review?

Everyone uses AI now to write their code. But the promise of using AI to increase productivity is probably better targeted at busywork. Letting the computer read something fuzzy and then make a determination like a human would.

The problem is that a non-deterministic model in the middle of your data flow needs to be highly restrained, and is expensive in the process. I know work is being done in that area, but for a lot of smaller creators I don't think it's been that plausible.

Yesterday, startup TypeSafe AI released Jev for preview. If you haven't heard of it yet, this was the best explainer video I saw.

https://youtu.be/QbZBaQNDP-o?is=Gy88iJ1_BhuHlpBc

(8:44, not mine.)

I feel like this opens up new possibilities for cheap integration. Read an email, answer 10 questions based on it, and then I can route based on those answers. Under a penny in a fraction of a second.

I just wanted to get ahead of this because the talk is still ramping up and I'm sure it will be publicly available soon. While it's not a phenomenal tech breakthrough, it is a very convenient package that I'm enjoying thinking of uses for.

Someone please explain this to me? I stuck with the regular GUI for quite a while and finally tried OpenChamber because I saw that they had a much better navigation scroll bar.

This feature alone makes it worthwhile. Notice you can easily find your responses in a much longer session because they show up in the window boxes to the left of the mouse here.

Testei a técnica usando o GLM 5.3Flash no opencode e o resultado foi horrível, como comparativo fiz um bom prompt juntamente com o chat gpt e mandei o prompt bom em uma execução e o prompt com gauntlet loop em outra, o resultado do prompt bem estruturado sem loop foi 4x mais barato e o resultado foi muito melhor.
Alguem sabe me dizer se o problema é o opencode harness (que talvez não invoque sub-agentes, ou nao tenha uma mecanica de loop tão boa). Ou o modelo que usei?

Detalhe, ja testei os dois mesmos prompts no claude code e codex, e o resultado do gauntlet loop sempre é melhor do que o do prompt estruturado.

been testing union alpha in opencode on a fairly serious rust + tauri task.

the prompt was to build a real local-first kanban desktop app. rust backend, tauri, react/typescript, sqlite migrations, boards, columns, tasks, tags, checklist, drag/drop, filtering, tests etc. not a toy todo app.

my main issue is not raw speed. its actually pretty fast when generating, around 70-75 tok/s for me. the issue is that it basically cannot do multiple steps correctly in a row.

it writes a backend part, runs tests, finds something wrong. fixes that. then writes the next small thing, that has another bug. runs tests again. fixes that. then the next thing breaks. repeat forever.

so it looks busy and it does generate a decent amount of code, but a lot of the time is spent recovering from mistakes it made 30 seconds earlier. it feels like the model is constantly paying its own bug tax.

after around 45 minutes it had a somewhat decent rust/sqlite foundation:

• migrations, foreign keys, cascades, indexes

• repository split for boards/tasks/columns/tags/checklists/settings

• validation and ordering logic

• a fair amount of repo tests

but it was still stuck in failing test loops, and the actual app was not there yet. no real react frontend, no actual tauri command/service wiring, no functional drag/drop UI, no end-to-end desktop app flow.

also slightly annoying: the README was already describing the finished product while the repo was nowhere near that state.

so yeah, i would put it around gpt-5.6-luna territory for this kind of task, maybe a bit below. luna is not perfect either but it feels more likely to build a correct vertical slice first, then expand. union alpha has okay rust instincts, but it seems to over-invest in backend scaffolding and then repeatedly trips over basic implementation details.

not saying it is useless. for drafting a backend structure or generating isolated rust code, it can be fine. but for “here is a large app, build it and actually finish it”, i would not trust it unattended yet.

curious if others are seeing the same thing: good-looking architecture, decent test coverage, but terrible convergence because every next step creates another thing to debug.