Analysis
Opinion, explainers and guides from people worth reading.
I tested different Qwen 3.8 27B quants
There is a lot of discussion which quant to use.
VideoTeaching Agents to Search with NVIDIA Data Designer — Dhruv Nathawani, NVIDIA
Dhruv Nathawani uses that funnel to explain how NVIDIA builds synthetic data that teaches a model to search, rather than answer from memory.
I trained a 414k-parameter transformer to fly a boids flock, then tested whether the rules a probe can read are the ones it uses [P]
I wrote a small boid simulator (12 birds), recorded it flying, and trained a transformer to predict each bird's next move without it knowing about any boid rules.
Building prompts with LLMs
I’ve been into AI image and video generation for a while, and I’ve been a bit obsessed with H3 for the past two months.
ArXiv caps submissions at two per month as AI paper flood overwhelms the preprint server
Starting October 2026, arXiv will cap submissions at two per person per month.
Byte Language Models: Scaling, Emergent Abstractions, and Information Allocation
The paper challenges the assumption that language models need explicit tokenizers to be efficient demonstrating that standard flat Transformers can process raw byte sequences and actually outperform traditional subword models as parameter sizes scale.
Reverse Engineering w/ Local?
Can a local model like Qwen 3.8 27B reverse engineer games and programs?
[P] Pecision models that score every allowed label from the logits: Jebadiah v2.1 (27B, 9B), open weights and self-run benchmark results [P]
I've been building open models that treat a decision as a closed-set scoring problem rather than text generation.
I trained a 102M recursive BitNet-v2 model from scratch: 64K context, trained on less than 5B tokens
Hiya, I’m releasing Recursive BitNet N-Gram 102M, a small experiment combining ternary weights, shared transformer layers, and hashed n-gram embeddings, trained with a whooping budget of 100€
Qwen 3.8 27B Q5 vs Qwen 3.8 Next Q3_S for document analysis
So far I've been using Qwen 3.8 27B Q5 with a 150K context window, but I'm wondering whether I should switch to Qwen 3.8 Next Q3S, since it has much more knowledge and could extract data much better than the 27B.
Reminder: try probabilistic MTP if you missed it. Decode +14% on prose
Optimal draft-n-max / draft-p-min seem to be in line with greedy sampling.
Running Next Flash IQ3_XXS at ~70 tok/s with 100k context or 2 instances of Qwen 3.6 35B A3B IQ4 at ~145 tok/s with 256k all on $500 of ex mining BC-250 boards
This will be my third update on the bc-250 cluster and for my first forray into local ai I have been having a blast.
Heretic or Abliterated?
Looking for recommendations for local LLM to convert an image into a usable prompt for Krea2 with max accuracy.
OMG! If you have a Mac with 64GB, try Qwen3.8-Flash-Next-oQ4e-mtp with oMLX!
I was able to run Qwen3.8-Flash-Next-oQ4e-mtp on M3Max 64GB with oMLX!
M5 Ultra 256GB or 2x DGX Spark? Which one?
Currently contemplating adding either M5 Ultra 256GB or dual DGX Spark in addition to existing 5090.
Krea2 Turbo Distill 2 step LoRA - FINAL checkpoint released (chk51195)
Krea 2 Turbo — 2-Step Distillation LoRA (FINAL Version)