aa0c690a6f
Adds newsletter infrastructure: CLI commands (new, list, preview, publish, fetch, status) integrated into binder, Quarto archive site config for mlsysbook.ai/newsletter/, and 12-month editorial content plan. Drafts are gitignored for private local writing; sent newsletters are committed as the public archive. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2.9 KiB
Newsletter Content Plan — 2026
The Thesis
AI Engineering is the emerging discipline.
Every issue reinforces a single coherent message: AI is not magic — it is infrastructure, and infrastructure has laws. The newsletter charts the formation of this field from the perspective of someone building its curriculum, tools, and community.
The Three Layers
Every newsletter contains three layers:
- A Big Idea — A perspective on AI engineering (500-700 words)
- A Concrete Example — A system, tool, benchmark, or research trend
- A Community Signal — What the community is building (TinyTorch, workshops, contributors, hardware kits, course adoptions)
The 12-Issue Arc
| Month | Theme | Big Idea |
|---|---|---|
| Jan | The Rise of AI Engineering | Define the shift: AI is systems, infrastructure, deployment, and physical interaction — not just models and papers |
| Feb | Why AI Systems Matter | Models alone are not enough: deployment, latency, memory, edge constraints, system co-design |
| Mar | The Stack of AI Engineering | Map the stack: models, training infrastructure, inference systems, edge hardware, applications |
| Apr | The Edge AI Moment | TinyML, embedded AI, physical AI — connect to TinyML4D and hardware kits |
| May | What an AI Engineer Actually Does | Data pipelines, evaluation, deployment, optimization, hardware co-design |
| Jun | The Tools of AI Engineering | PyTorch, ExecuTorch, vLLM, Ray, Triton, TinyTorch — why tools matter |
| Jul | Benchmarking AI Systems | MLPerf, evaluation, reproducibility — our territory |
| Aug | Physical AI | Robotics, edge devices, sensors, on-device inference |
| Sep | Teaching AI Engineering | What universities are missing, how courses are evolving, the MLSysBook and TinyTorch |
| Oct | AI Engineering in Industry | Case studies: Tesla, NVIDIA, OpenAI, Meta, Qualcomm |
| Nov | The Global AI Engineering Movement | Workshops, universities, contributors, global adoption |
| Dec | The Future of AI Engineering | State of the field, reflections, what surprised us, where we're going |
Template Structure
1. Opening Essay (500-700 words)
A clear opinion or insight about AI engineering.
2. System Spotlight
A real system, tool, or paper examined through the lens of constraints.
3. What the Community Is Building
TinyTorch updates, workshop recaps, contributor highlights.
4. One Question for the Community
Invite responses and build engagement.
The Competitive Edge
Three things this newsletter does that others don't:
-
Build a field — Not just AI adoption talk; defining AI Engineering as a discipline with principles, curriculum, and community.
-
Connect research, industry, and education — Ecosystem view spanning labs, open source, industry systems, and university courses.
-
Show what is being built — TinyTorch, MLSysBook, hardware kits, benchmarks. Readers watch something grow, not just read about trends.