u/dogan_karadas

I’ve been testing Qwen 3.8 27B as a long-running coding agent on my RTX 5090.

I used GPT-5.6 Sol to help write a short plan md for FloodLayer, then let Qwen execute the build in DeepSeek Harness.

FloodLayer is a small 3D AEC sandbox where water follows the actual floor slope, moves toward drains, pools, and can escape through thresholds.

What finally worked well for me was 131K context, full GPU offload with ngl 99, Flash Attention, Q8 KV cache, parallel 1, and MTP with draft max 2.

In DSH I set contextWindow to 131072 and maxTokens to 16384.

Loading the model directly instead of using router/preset mode was also much more stable for me.

Without MTP I was getting around 54 tok/s. With MTP I’m seeing roughly 70–100 tok/s depending on context length and draft acceptance.

Auto-compaction is working now too, so it can keep going for much longer without constantly needing manual continue.

Still testing the long-run behavior, but this is the first setup where local Qwen genuinely feels useful as a serious coding agent.

Just got my first Mac Air m2 15", 24 GB RAM / 1TB.
still have my Windows PC and I’m very happy with Windows, but damn… macOS feels kind of magical and different.

Battery health is at 90% with around 340 cycles. Is there anything I can do to maximize the battery’s lifespan from here?

I’ve been experimenting with Qwen 3.8 27B locally on an RTX 5090 (32GB) using DeepSeek Harness, mainly to see how far it can go as an autonomous coding agent.

I’ve tried a few different setups:

• Ollama + Qwen 3.8 27B → ~100–120 tok/s, but eventually Harness would stop with “Output token limit reached” and I had to manually send continue.
• llama.cpp + Q6, ~114K context → ~44–45 tok/s, 95–99% cache hit, but it eventually hit n_tokens = 114431, truncated = 1. Again: manual continue.
• llama.cpp + Q6, 262K context, Q8 KV, 1 slot → no context limit yet, but speed dropped to ~20–25 tok/s and VRAM sits around 31.4/32.6GB.

The current test is a fairly complex little physics/Three.js project. After ~1.5 hours Harness had done ~41 agent steps, ~3.3M cumulative input tokens and ~119K output tokens with 98% cache hit. It’s still autonomously debugging the physics solver rather than just blindly following the markdown plan.

What I’m trying to understand is the compaction behavior.

I’ve seen people running Qwen + DSH for 5–10 hours, with automatic compaction and no babysitting. In my 114K setup, it seemed to reach the llama.cpp hard context limit before Harness compacted, so I repeatedly had to type continue.

I also found reports of 5090 setups using roughly Q6 + 131K context + Q8 KV + MTP and getting around 100–110 tok/s. That seems like a much better trade-off than my current 262K / 22 tok/s experiment.

For people running Qwen locally with DSH: what setup actually works best for long unattended runs?

Especially interested in:

• context size
• KV quant
• MTP/speculative decoding settings
• whether DSH auto-compaction reliably happens before the backend context limit
• Ollama vs llama.cpp
• whether ~131K is the sweet spot on a 5090 rather than trying to push 262K

I built a small open-source app called TraceLayer. It puts a transparent layer on your screen that sits on top of whatever app you’re already using, basically the same as laying tracing paper over a drawing.

You put transparent paper over something, sketch on it, compare, review, think, without touching the original. I wanted that on a computer too, so I made a desktop overlay for it.

I started with Revit, since that’s what I work with: quick redlines, review notes, and visual checks, without building an actual Revit plugin.

Then I just tried it on other things too. In the demo video it’s running over Revit drawings, Google Maps, stock charts, dashboards, sketching, and AI image generation. It works the same everywhere because it doesn’t know or care what’s underneath it.

No plugin per app, no new workflow, just a layer on top of whatever’s on screen.

It started as a small Feble 5 experiment, but it made me think there might be more small, app-agnostic tools like this worth building, instead of everything living inside one app.

Next I want to add some AI on top: automatic review notes, drawing analysis, visual comparisons, that kind of thing.

First version is up now, repo is open if you want to try it, break it, or suggest something.

What would you use this for?

Repo: https://github.com/Dogancanka/TraceLayer