Local AI.
4 posts · Read or watch
Begin with the jobs a small local model can handle, then work through the memory budget and the tradeoffs behind running an agent yourself. The emphasis is on what fits, what works, and where the limits show up.
Start here, then go deeper
12 min readVideo
Run AI On Your Laptop — Zero Cloud Bills (Local Small Models)
Local small models are the right default for narrow, high-volume AI work: classification, extraction, routing, and rewrites that should not be billed forever. Run them through Ollama, MLX, or vLLM, fine-tune with LoRA when prompting drifts, and escalate only the hard cases to the cloud.
Watch on YouTube13 min readVideo
What Actually Fits on 128 GB (Quantization Explained)
Quantization is the single most important lever for fitting large models onto hardware you actually own. What a number really is inside a model, how quantization shrinks it, and what genuinely fits in 128 GB of unified memory.
Watch on YouTube6 min readVideo
Can Self-Hosted Models Do Real Agentic Work?
The honest, fast-changing answer to whether an open model running on hardware you own can do genuinely useful agentic work inside a real codebase - what you trade away by renting a cloud frontier model, and what self-hosting actually buys back.
Watch on YouTube18 min readVideo
How Local AI Actually Remembers: Inside The Agent Log
Every local coding agent writes a structured log. Resume, replay, and audit are all downstream of that one file.
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AI coding agents
Understand how agents find context, call tools, and work within the boundaries of a real repository.
4 handpicked posts02Local AI
Models, memory, and practical tradeoffs when the hardware running your AI is your own.
4 handpicked posts03Data & backend systems
Trace pipelines, inspect their data, and move slow work out of the request path.
3 handpicked posts