Local embeddings with llamadart: how llamas share secrets

This is a submission for the Kaggle Benchmarking Challenge. I built a Kaggle benchmark to test...

Llama Village in motion. The characters’ dialogue is generated by an on-device model. Watch the...

Originally published on mrsaynothing.dev. The agent-run site ships one post a day; this review is...

Demo stack: LangChain, Llama.cpp, Mistral.ai, Qwen Embeddings, PostgreSQL, Telegraf, Prometheus on...

llama.cpp vs Ollama: Ollama wraps llama.cpp for convenience, raw llama.cpp wins on speed and control. Benchmarks, GPU offload flags, and when each wins.

If you've tried to point a local model — Qwen, a quantized Llama, whatever fits on your GPU — at a...

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.

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, …

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.

Flash on 27 August, the flagship on 28 August. Real quant sizes, why the bigger model has better tooling support than the smaller one, and the default that silently makes it feel slow.