Edge Curriculum · Vol. 2 · No. 14
Independent notes on the AI credentials, programs, and learning paths that actually matter.
A reference publication covering AI micro-credentials, university programs, and the self-taught paths producing the next generation of builders. Edited from independent contributors, with a working library of program notes, credential reviews, study plans, and a side-by-side comparison tool.
Working tools
Interactive tools for assembling your own credential plan, tracking progress, and comparing programs side-by-side.
Tracker
Course tracker
Mark programs as Want to take, In progress, or Completed. Browser-only — your tracking stays on your device.
Plans
Study plans
Pre-built credential sequences for common goals — AI engineer in 6 months, founder-track AI literacy, AI safety primer, and more.
Compare
Credential comparison
Side-by-side comparison of major AI credentialing programs across cost, duration, prerequisites, format, level, and recognition.
Credential references
Long-form reference pages on the AI credentialing programs we get the most questions about.
Reference
Google AI Micro-Credentials Overview
Edge Curriculum's standing reference page on Google's AI micro-credential offerings: program structure, delivery surfaces, and how the credentials function in hiring.
Reference
Harvard AI Micro-Credentials Overview
Edge Curriculum's standing reference page on Harvard's AI micro-credential offerings: program structure, pricing tiers, how to choose, and how the credentials function in hiring.
Latest reports
Reported essays, profiles, and field notes on AI learning paths.
The Hugging Face Course at Year Four — Assessment of the Open-Source Curriculum
The Hugging Face course is in its fourth year of public availability. The curriculum has expanded substantially. This piece is a working assessment of where the course is now, what it has become, and where its remaining gaps are.
Stanford CS231n vs Berkeley CS182 — Picking Your Computer Vision Foundation
Stanford's CS231n is the canonical computer-vision deep-learning course. Berkeley's CS182 is the rising challenger. This piece works through the structural differences in their curricula and the question of which one suits which candidate.
The fast.ai Problem — Why the Best AI Course Is Getting Fewer Students Every Year
Jeremy Howard and Rachel Thomas's fast.ai is, by most working practitioners' accounting, the best practical deep-learning course on the open web. It has also seen declining enrollment year over year since 2022. This piece works through why.
DeepLearning.AI's MLOps Specialization — What Employers Actually Look For
The DeepLearning.AI Machine Learning Engineering for Production specialization is the most-recognized MLOps credential in 2026. This piece walks through what the credential actually attests to, what hiring managers say they look for behind it, and how to pair the credential with the shipping evidence that does the real signal work.
Andrej Karpathy's Zero-to-Hero — A Review by Completion Rate
Andrej Karpathy's Neural Networks: Zero to Hero playlist is the most-rigorous free AI curriculum on the open web. It is also one of the lowest-completion. This piece works through why.
Stanford AI Index 2025/2026 — What Every Founder Should Know
The Stanford HAI AI Index is the closest thing the field has to a referee report. We pulled out the dozen findings from the 2025 and 2026 editions that every founder and operator should be tracking — framed for builders, not for academics.
Self-Taught AI Founders — How They Actually Built Their Curricula
A working reference on how the cohort of self-taught AI founders actually assembled their learning paths — stacked micro-credentials, open-source contribution, and real shipping. Pieter Levels, Anton Osika, João Moura, Amjad Masad, and Paul Klein IV as worked examples.
DeepLearning.AI's Agentic AI Course — Field Review
A working review of Andrew Ng's Agentic AI course on DeepLearning.AI — syllabus walkthrough, what you actually learn, who should take it, and how it stacks up against the rest of the agentic-AI curriculum landscape in 2026.
AI Credentials Worth Stacking — Q2 2026 Map
A reference map of the five credentials we recommend most often as the spine of a working AI stack in Q2 2026 — cost, time, recognition value, prerequisites, and what comes next. Honest evaluation, with the trade-offs.
AI Credentials vs. Real-World Shipping: What Employers Actually Weight
An interview-driven essay on how hiring managers actually weight AI credentials versus shipping evidence in 2026 — and what the data tells us about the difference between resume signal and hire decision.
How to Build an AI Career Without a CS Degree
A practical guide to building an applied AI career without a four-year computer science degree. Stack-pattern, shipping evidence, and the credential choices that actually move the needle.
The New Polymath Curriculum
An essay on the curriculum the emerging cohort of polymath builders is actually assembling — technical credentials plus artistic practice, treated as two surfaces of one learning project.
Beats we cover
Topic landing pages. Each topic collects our reference pages, reported essays, and field notes for a single beat.
Topic
Credentials
Reference pages, rankings, and reporting on individual AI credentialing programs.
Topic
Career paths
How candidates assemble credentials, shipping evidence, and signal into hiring-ready stacks.
Topic
Self-taught founders
Profiles and essays on founders building outside the traditional credentialing track.
Topic
Edtech landscape
Annual landscape maps of the AI-education category.
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Other paths and programs we are tracking
A secondary index of programs and pathways we cover in shorter form.