The harness, not the model: how to make a weak or local model reliable enough to ship

Agentic coding on a Strix Halo laptop with Qwen3.8-27B and Flash-Next, the LlamaStash and Pi setup behind it, and the tuning

Honest comparison of fine-tuning options across Anthropic, OpenAI, and open-weight models. When fine-tuning wins, when RAG wins, and when prompt engineering is enough - from an Anthropic Partner shipping production ML.

Ran Qwen 3.8 27B 4-bit quantization benchmarks on RTX 4090 and M-series Mac for local AI agents. Don't fall for 1-bit — here's why.

MTP (Multi-Token Prediction) is a technique that lets a language model predict several tokens ahead...

Qwen3.8-Flash-Next

One region out of 33 can fine-tune Llama 3.3 70B on Bedrock. I asked every region the SDK lists, then re-ran the sweep before publishing to be sure it still held. Also in here: every model you can customise in us-east-1 belongs to Amazon, …

Discover the top 4 permissive open-weight coding models for local developer workstations: Muse Spark 1.2, Qwen3.8-27B, Muse Glimmer, and Qwen3.8-Flash-Next.

Qwen4 still isn’t officially here, but Qwen3.8-Flash-Next gives us something more useful than...

I have always liked RSS because it gives me a easy way to decide for myself which sources I want to...

How to build a production-grade drafting agent on local SLMs (Llama 3.2 3B) using deterministic Python verification, tri-state guardrails, QWK-calibrated evals, and continuous few-shot learning.