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How AI Works
Published 2026-02-06
A plain-English explanation of what happens when a user sends a prompt and receives a response.
Open guideLearn
These pages are for business owners, operators, and software teams that need enough technical understanding to make better decisions. The goal is not jargon. The goal is clear ownership, safer AI workflows, and fewer expensive surprises.
Recommended Path
Start here
Published 2026-02-06
A plain-English explanation of what happens when a user sends a prompt and receives a response.
Open guideDeeper technical view
Published 2026-02-20
A full walkthrough of the AI pipeline from request intake, tokens, attention, sampling, safety checks, and streaming.
Open guideModel strategy
Published 2026-03-21
A practical guide to large models, small models, deployment constraints, training tradeoffs, and hybrid AI systems.
Open guideModel comparison
Published 2026-04-03
A practical guide to why the major AI assistants behave differently across architecture, context, alignment, and reasoning.
Open guideAgent systems
Published 2026-04-18
How AI systems go from answering questions to planning, using tools, observing results, and completing real work.
Open guideProduction agents
Published 2026-05-02
How to scope, design, guard, evaluate, monitor, and maintain AI agents that call tools and affect real workflows.
Open guideComputer vision
Published 2026-05-29
A practical comparison of Apple's Vision Framework, Core ML, TensorFlow Lite, ML Kit, and heuristic mobile vision pipelines.
Open guideMore Resources
The education pages support the main HerbDev offer. They help a visitor understand the work before asking for rescue, AI consulting, or maintenance ownership.
Published 2026-01-09
Common agent architectures for intake, retrieval, planning, scheduling, decision support, and human approvals.
Published 2026-06-27
How harness, API, and inference layers reduce repeated work in production agent loops.
Published 2026-03-07
How AI-assisted engineering changes context, specification, verification, team structure, and ownership.
Published 2026-08-09
Why Groq, Cerebras, and SambaNova are changing AI inference with the LPU, wafer-scale compute, and reconfigurable dataflow.
Published 2026-07-25
Knowledge distillation, teacher models, student models, soft labels, and deployable AI.
Published 2026-07-12
Why KV cache, long context, batch size, and decoding make model memory expensive.
Published 2026-06-13
Where LLMs fit in production search: catalog enrichment, query understanding, embeddings, and guardrails.
Published 2026-05-16
How to choose mobile AI architecture for cost, speed, privacy, offline use, and long-term maintenance.
Published 2026-05-29
Apple Vision Framework, Core ML, TensorFlow Lite, ML Kit, and heuristic mobile vision pipelines.
Published 2026-01-23
Estimate subscription app revenue across subscriber counts, monthly pricing, yearly pricing, and platform fees.
HerbDev Perspective
If a project is already stalled, unstable, or difficult to release, education is useful only when it leads to clearer ownership. The fastest path is usually a technical triage, a short list of blockers, and a practical rescue plan.