How to choose an embedding model for your search system
Compare hosted APIs with models you run yourself, then choose dimensions, language and context length against your own retrieval workload.
Wildness 3/5 · Partly tamed
The Tamers · 6 sightings filed
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How model hosting services differ: routers, inference hosts and first-party APIs
A practical guide to model routers, inference providers, media APIs and model makers, with the trade-offs to check before sending production data.
Wildness 4/5 · Still wildQuantization, plainly: what 16-bit, 8-bit and 4-bit models trade
A practical guide to model weight precision, memory estimates, common quantization formats, quality checks and choosing a quant for your workload.
Wildness 3/5 · Partly tamedChoosing a local runtime for open-weight models: llama.cpp, Ollama, vLLM, and LM Studio
A practical guide to choosing a local model runtime, understanding GGUF and memory needs, and deciding when a desktop setup is enough or a server makes sense.
Wildness 4/5 · Still wildMCP gives AI applications a common way to connect tools and context
The Model Context Protocol standardizes how AI hosts connect to servers that provide tools, resources and prompts, with separate transports and security responsibilities.
Wildness 1/5 · TamedHow a voice agent turns speech into a spoken answer
A practical map of speech recognition, language models, speech synthesis, direct speech-to-speech models, streaming, and the latency measurements that matter.
Wildness 3/5 · Partly tamed
