~ /home/coolthor
ai-muninn
Research notes on AI infrastructure, LLM serving, and autonomous agents. Things that took too long to figure out, written down so you don't have to.
❯ whoami
runs all kinds of models at home — LLMs, image gen, video gen, then writes down what he figures out
quantizes models to FP8 / NVFP4 and ships them on Hugging Face — people actually run them
builds options-trading infrastructure with AI agents
had a spec-decode fix merged into vLLM's speculators
occasionally ships iOS apps
❯ open ~/tools/model-anatomy
Model Anatomy →
Explore Qwen and DeepSeek in 3D: weight regions, attention, MoE, and animated data flow. Select a block to learn its role and parameter count. The explorer currently uses Traditional Chinese.
❯ cat ~/blog/start-here
Start here
New here? These are a good way in.
- 2026-06-16[Agent 101 #1] AI assistant vs ChatGPT: one answers you, one uses your tools to get things done
You mostly use ChatGPT one question at a time. A self-hosted AI assistant (agent) finishes the job with your own tools, runs on your side, and plugs into the apps you use daily. Lesson one of building your own assistant from zero.
- 2026-06-16[Agent 101 #4] How to install Hermes Agent Desktop: your first AI assistant, no terminal
Install the Hermes Agent desktop app — no terminal. Download it, let it auto-install dependencies, sign in with your ChatGPT account, and your first AI assistant is running in about 15 minutes.
- 2026-06-12[Local LLM #1] My first Q2 model looked broken on a 128GB box — the real culprit was a parser that couldn't read DSML, not the quantization
DeepSeek-V4-Flash is 284B. I got it onto a single 128GB GB10 with antirez's ds4 engine and an asymmetric Q2 GGUF at 15.6 tok/s. The fun part: the broken tool calls weren't the 2-bit quant's fault. The runtime just couldn't parse DSML.
- 2026-06-11[Benchmark #2] Qwen3.5-122B on DGX Spark — 2× faster
Qwen3.5-122B-A10B tops out at 17 tok/s on a 128GB DGX Spark — the GDN wall in vLLM won't budge, not even with a merged perf PR. I swapped vLLM for the Atlas engine on the same abliterated NVFP4 weights and the throughput doubled to 33.9 tok/s (36.5 with MTP, ~2×), uncensored behavior intact. The real lever was outside the quant toolbox.
❯ cat ~/blog/concepts
Concepts & Methods
For those who want to understand how AI works
- 2026-05-23[LLM 101 #7] How to spot AI hallucinations — three red flags before you verify
AI delivers wrong answers in the same confident tone as right ones. Three red flags to catch it early — impossible numbers, suspiciously specific details, answers that shift on a re-ask — plus a case where ChatGPT gave me a +205% P&L that can't exist.
- 2026-04-17[LLM 101 #6] Why Run AI on Your Own Computer? It's Not a Cheaper ChatGPT — It's a Different Tool
Local AI isn't a budget ChatGPT. It's a knowledge extractor, private code assistant, and offline tool. Monthly power cost ~$1.20 vs ChatGPT Plus $20. This guide has a decision table for when to use which.
- 2026-04-16[Ask AI Right #7] What AI Does Poorly — Four Landmines to Know Before Using ChatGPT or Claude in 2026
AI is strong, but four things still trip it up in 2026: hallucinations, stale knowledge, short memory, and privacy defaults. Even Anthropic's own lawyers got caught by the first one.
- 2026-04-14[Ask AI Right #6] The Art of Follow-Up Questions — What to Do When the First Answer Is Too Shallow
The first answer AI gives you is a rough draft, not the final answer. Learn 5 follow-up techniques — adding constraints, asking for comparisons, and letting AI ask YOU questions — to get dramatically better results.
- 2026-04-14[LLM 101 #5] Context Window — How Much Can AI Read at Once?
AI forgets what you said 20 messages ago. It's not broken — its desk is full. This guide explains context windows, why conversations go stale, and how to work around the limit.
❯ cat ~/blog/field-notes
Field Notes
For those who run models and debug the hard way
- 2026-09-21Two Modded RTX 2080 Tis Hit 153.8 tok/s on Qwen3.8-27B With FastLLM + DFlash2
FastLLM plus a DFlash2 draft runs Qwen3.8-27B FP8 at 153.8 tok/s on code across two modded 2080 Tis. Prose drops to 53.1. Full recipe and four traps.
- 2026-09-21Ternary Bonsai 2 on One RTX 2080 Ti: Qwen3.8-27B in 7.2 GB Scores 130 of 140
Ternary Bonsai 2 27B scored 130/140 on one modded 2080 Ti vs 131 for Qwen3.8-27B Q8_0 on two cards. Build steps, the flag that matters, why smaller is slower.
- 2026-09-20[LLM Deep Dive #6] A quantization lab: what quantization is, how it works, where the error comes from
A clickable INT4 / FP8 / FP4 lab: one outlier drops two of four weights to zero, and the scale itself is quantized, so the group maximum is off by 4.77e-7.
- 2026-09-16[LLM Deep Dive #5] What actually happens inside one transformer layer — four real models, four data flows
A cube you pull apart into layers. What model.ai-muninn.com shows for Qwen3-30B-A3B, Qwen3-8B, Qwen3.8-Flash-Next and DeepSeek-V4.1-Flash, and how to read it.
- 2026-09-14[DGX Spark #48] Two hard power-offs, reproduced on purpose — the fix, and why we turned it off again
A DGX Spark cut power twice under sustained video generation. I reproduced it on purpose, logged the curve off-box, and stopped it with a 2200 MHz clock cap.
❯ ls ~/blog/series
Browse by series
Every thread, grouped