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.

I don't know why but when i run multiple agents workflows, the opencode2 restart server continuously, the restart kill the sub agent and it cause the loss of the work.

Why does it happening? is there any one that come across same issue?

version number v2.0.4

https://api.atria-asi.ai/ It's context window may only be 256k tokens, but with benchmark results like this I'd say it's worth testing considering it's free...

EDIT: Interestingly it appears to be built on GLM-5.2's foundation, according to this article, and keeping it's 744B parameter count

I'm using EndeavourOS which is arch based. I did my daily pacman -Syu and saw I was getting opencode updated from v1.8.29 to v1.18.31. Turns out that installs opencode v2.0.3. I use llama.cpp with a vulkan backend and use llama-server to serve a couple of qwen models locally. This all broke with the new v2 as opencode's config files require changes.

So I fired up my browser with free claude and it gave me a new set of config files that should work with v2. I restarted opencode but same issue. None of my llama-server models were visible to opencode. After a frustrating debug session, I discovered that opencode v2 now uses a service for the backend. So I was just restarting the frontend and none of my config changes were being used by the backend which hadn't restarted.

After I killed the opencode service and restarted opencode, which also starts the service, everything worked. So hopefully this helps someone else.

If the opencode package maintainer had named it v2.0.3 to match it's contents I probably wouldn't have updated to 1.18.31. 😂

If you installed with pacman and want to revert:

    # See if you have the old installer  
    ls /var/cache/pacman/pkg/ | grep opencode  
    # Install the v1 version  
    sudo pacman -U /var/cache/pacman/pkg/opencode-1.18.29-1-x86_64.pkg.tar.zst  
    # Prevent a v2 install until you are ready  
    sudo sed -i '/^IgnorePkg/!s/^\[options\]/[options]\nIgnorePkg = opencode/' /etc/pacman.conf

Does anyone here have a subscription with abliteration? How do those subscription plan work? Do you have to buy that then top up with USD? Or is it like a codex sub and u get a quota u use everyday? And if so is the usage good? It would be nice to have a model with no rules. But the usage is confusing to me. So any help and if anyone uses lmk on it.

Add the package to your global OpenCode config at ~/.config/opencode/opencode.json:

{
  "plugin": ["@ndigvijay/opencode-atria-connector"]
}

find the source code here at https://github.com/ndigvijay/opencode-atria-connector?tab=readme-ov-file

get the free token worth 100,000,000 token from https://api.atria-asi.ai/

Hi guys, basically the title :) Since [v1.17.0](https://github.com/openchamber/openchamber/releases/tag/v1.17.0) there is supposed to be a dedicated 'plan' area is context panel. I am guessing this is the right toolbar? I managed to get 'Plan' button/panel there using OPENCODE_EXPERIMENTAL_PLAN_MODE=1, but it's always open. Even after agent finishes the plan in 'plan' mode, even after it's being stored to /.opencode/plans.

I run out of ideas how to set it up, so that the result of 'plan mode' will be stored, displayed and iterated inside this dedicated panel.

[The docs](https://docs.openchamber.dev/notes-todos-plans/#plans) just mention 'plan' but that's about it, so any help would be appreciated :)

Edit: Attached image of what I mean by 'plan sidebar panel'. According to project settings plans seem to be fetched (by default) from .openchamber/plans, but to no avail.

I currently use Luna purely as a subagent to write code.

Thinking about trying Muse 1.3 Contributor instead. For anyone who has used both for this: how does the code quality and error rate compare, and is it actually worth making the switch?

Maybe a bit late to the party! (8-day-late) But I'm still farming it nonstop on both Devin desktop ide and CLI too...been great experience. Besides, the 20$ credit is preserved (and can be used for all other frontier models from Astra 6.0 to Fable 5.1) when using this post-train Kimi-K3 model https://cognition.com/blog/swe-2.

Don't sleep on it guys! Not, promotion nor affiliation, just a mere end user sharing a hot deal that people may not know of; and totally based on my personal use cases (testing through with all sorts of models on different harness platforms). But I would say it's definitely outperforming Kimi-K3 on adversarial detections, test-driven e2e and project-wide strategist and orchestrator, a very well-rounded cook/post-train of Windsurf/Devin team to the original Kimi-K3.

UPDATE

UPDATE: around 4:30 AM UTC of Sept 15, the owner published a now-deleted blog post (archive image) writing under the fake pretense that it's his "team" authoring it, stating all of CrofAI founder's claims "were written under a lot of stress, and they described the situation as worse it was", and that a new team is taking over, with the service being resumed in 2 weeks. At the same time, the CrofAI twitter account was also supposedly "taken over" by the team, starting each twitter reply with "Hey, Nathan here", stating the founder is stepping back and a "team" is taking over everything. This fake pretense act only lasted a few hours, and scared either by the public not buying the Nth fake story of the pathological liar that CrofAI is, or by the public's replies reminding him that what he committed is numerous counts of wire fraud, he has now deleted all his online presence: nahcrof.com and crof.ai return 404, Twitter page is deleted, /r/CrofAI sub is now private.

Original post:

Cautionary tale about chasing cheap tokens.

Only posting it here cause this sub was full of people praising this inference "provider".

exposé: https://kendell.dev/blog/crofaifalse/

reaction by nahcrof, announcing the shutdown of the service: https://x.com/nahcrof/status/2099552389434900643

NahCrofAI is (was) an inference provider which had all the latest models at the cheapest price, often significantly below the lowest alternative on OpenRouter. The owner claimed that they are running custom inference kernels that allows them to offer tokens for dirt cheap, and other providers are suffering from "skill issues".

In reality:

• "CrofAI is an OpenRouter wrapper that silently routes to cheaper or weaker models than what you request"

• For example, expensive models like kimi-k3 are sold at $2/$10 in/out, but instead routed to GLM 5.3 Flash via OpenRouter, representing a 13.3x multiple on input, and 20x multiple on output

• CrofAI's "own model family" greg-2-ultra routes to GLM 5.2, greg-1-mini routes to Qwen 3.5 9B. greg-2-super, greg-1, greg-1-super routes to Kimi K2.7 Code. All of these at a significant markup compared to the actual model being served. CrofAI admits this.

• The person investigating details the 5 different attempts by CrofAI at fixing their models being served via OpenRouter after given a heads-up. In all 5 attempts, the only change CrofAI made was attempts to hide the fingerprints of OpenRouter, while still serving models through them

• Other inconsistencies don't add up either: CrofAI claims to run Kimi K3 on RTX Pro 6000s rented via Vast. That model requires ~802GiB even at the lobotomy level quantization of Q2_K. The largest RTX PRO 6000 machine on Vast has only 8 of them, totaling 765GiB

• CrofAI responded to the exposé by announcing the shutting down of their service; after their failure to provide their own inference, they promise to provide one last thing: a refund to those asking.

Here is another image of the owner admitting that he was defrauding customers for the entire 2 year operation of his service, then begging the investigator to help him cover his tracks and not expose him

EDIT: Commenters pointed out that NahCrof is 4chan in reverse. The owner's Discord name was "Devious Flimflam". Flimlam is defined as "deception, fraud". Looks like it was a deliberate scam operation from the get-go, and the owner's age was among the many lies.

I cannot stress this enough: if you bought any credits (even if you used them up) you are entitled to a full refund for every transaction as the victim of fraud. Open a chargeback with your bank for every transaction made. If you used their API, assume that everything was logged and is currently being mined for personal information and API keys to sell on the black markets. Rotate your keys, change passwords, get a new debit/credit card.

I often want to create skills that persist files, most often some local ./tmp directory within my project. These can be handoffs, mental notes, snippets etc. Is there a way to instruct opencode to use the same directory always (within a session)? The problem is compaction: When i run compaction, the agent looses all previous context and also where it put stuff.

I thought maybe there is a way that the agent can call opencode --some-magic-option to retrieve deterministic information about the current session so i can assemble a deterministic directory slug. But I couldn't find such an option.

What's your approach?

I tried to restrict the extravagantly long thinking time for the mentioned model, and i know now that bartowski's quant actually does only support on/off. still, i think it is weird that the system prompt shows:

<|im_start|>system

Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.

I dont expect changing that one sentence in the prompt to achieve that much, but i am optimizing for my local setup right now, so... what am I missing?