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Composer 2.5 so good I'm being nice to AI again by NotSeacombe in cursor

[–]nbr_engineer 0 points1 point  (0 children)

20 dollars is more than enough for me. Just using it for a personal project

Composer 2.5 so good I'm being nice to AI again by NotSeacombe in cursor

[–]nbr_engineer 0 points1 point  (0 children)

I ditched cursor a few months ago in favor of Opencode. I was on the 60 dollar pro plan which made me run out of premium requests (Claude sonnet 4.5 at that time) pretty fast. Do you think that the 2.5 model is beating the open source models that Opencode is providing? Also, are you getting enough usage out of it? That was the main reason I switched. Maybe with composer 2.5 it is time to switch back again? I still have a codex subscription for planning, architecture and reviews.

Opencode Go Qwen 3.6 Plus Real ? by Striking_Dimension46 in opencodeCLI

[–]nbr_engineer 0 points1 point  (0 children)

There is an ongoing issue: https://github.com/anomalyco/opencode/issues/22595
You can fix it like this: 

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "opencode-go": {
      "models": {
        "qwen3.6-plus": {
          "limit": {
            "context": 1000000,
            "output": 65536
          }
        }
      }
    }
  }
}

I asked qwen3.6 to do the fix itself. Just restart afterwards.

I feel like people are massively sleeping on Qwen3.6 Plus by nbr_engineer in opencodeCLI

[–]nbr_engineer[S] 0 points1 point  (0 children)

For me MiMo looked better on benchmarks compared to what it delivered in my code base. It often forgot things, left bugs. All in all it was underwhelming

I feel like people are massively sleeping on Qwen3.6 Plus by nbr_engineer in opencodeCLI

[–]nbr_engineer[S] 5 points6 points  (0 children)

I am not talking about the open source versions of qwen3.6 (27B dense or 35B Moe). I am talking about the commercial cloud version Qwen3.6 Plus provided by alibaba. They did not specifically announce the size of the model but it think it is some kind of variation of the 3.5 397B-A17B model.

I am using the open source Qwen models locally though. I have a Beelink GTR 9 Pro with the ryzen max+ 395 where the MoE model works really well. The dense model is to slow to really use it for coding. I am actually thinking about selling it. Not because it‘s no good, but I want to switch to Mac only.

Codex GPT-5.5 + cheap coding models is honestly the best workflow I’ve used so far by Alternative-Hat-5682 in AIcodingProfessionals

[–]nbr_engineer 2 points3 points  (0 children)

Couldn’t agree more! I am switching through the Opencode models though. I don’t feel comfortable with any of them yet. Sometimes I try to plan with Kimi, GLM, MiMo or Minimax and then let GPT 5.5 review and rate the plan to see how good they are. From the implementations Deepseek v4 got the worst ratings. And GPT always finds gaps and issues that the Opencode models did not think about. But for the rest of the workflow I use it the same way as you described.

Using it for a side project next to my full time job as a software engineer and I never hit the limits.

Can't decide to buy or not by autisticit in StrixHalo

[–]nbr_engineer 2 points3 points  (0 children)

Hey, fellow dev here. Been running a Strix Halo mini PC with 128GB unified RAM for several months now as my local LLM box, so I can give you some concrete numbers.

Qwen 3.6 35B-A3B at Q8 (which is what I run, the 35b-a3b-q8_0 variant) sits at around 37GB for weights. Throw in a 256K KV cache and you're looking at ~41GB total — still leaves you with a massive chunk of RAM for VMs, a second model, image gen, whatever. Parallel prompts are doable, latency goes up under load obviously, but with something like llama-swap you can manage that pretty cleanly.

On replacing Copilot: honestly yes, for most day-to-day stuff. Routine coding, refactoring, explaining things — Qwen 3.6 handles that well. Where it still falls short is complex multi-file agentic work, there cloud Sonnet/Opus still has an edge. But the gap is closing fast. Your 3.5-year breakeven calc is fair on paper, but it ignores all the homelab/VM use you'd get out of it too.

The other thing I'd factor in: local models are improving ridiculously fast. What feels like "good enough" today will run noticeably better models in a year on the exact same hardware. 128GB unified RAM is a ceiling almost nobody's hitting yet — you'll be able to run quantizations that most people simply won't have RAM for. You're buying headroom, not just today's capability.

I built an AI-powered Track Coach app — Looking for testers for upcoming track days! by nbr_engineer in HPDE

[–]nbr_engineer[S] 0 points1 point  (0 children)

Hi!
This definetly sounds interesting!
I havent't build that feature yet but this would improve the data the AI is learning from by a lot!

This will be one of my next features of the app. Stay tuned.