Learn Center: v2 roadmap (the gap-to-88 ledger)

This is the honest “not-measured / not-yet-built” ledger for the Learn Center, in the spirit of the Evaluating AI Honestly track. The v1 build was rated by a decorrelated Codex + Gemini panel across four rounds; both raters agree the content is accurate and the prose is strong, and both place the remaining gap to a 90s score in product depth, not correctness. Those depth items are a deliberate v2, scoped here.

Where v1 landed

What would close the gap to a 90s score (v2)

  1. Richer interactive widgets (highest leverage). The current exercises are MCQ-shaped (predict_output / prompt_repair / spot_the_bug / agent_trace). Add applied formats that need new engine widgets: a confusion-matrix / discrimination calculator, a temperature-sampling demo, a tokenizer playground, an embedding-distance widget, and ordering tasks. This is the single most-requested upgrade from both raters.
  2. Diagrams and visual learning. The site reads like dense notes. Add inline SVG diagrams for tokens, attention, the context window, the RAG retrieve-augment-generate loop, the agent loop, and a confusion matrix. Wire the existing tracks.json icon ids to real SVGs.
  3. More breadth. Classical ML (supervised vs unsupervised, train/dev/test, core metrics), LLM-as-judge rubric design, inter-rater reliability, red-teaming, tool permissioning, current benchmark families, and production cost/latency tradeoffs.
  4. Navigation depth. A persistent lesson sidebar, prev/next controls in the lesson view, level/track filters inside search, and a “resume where I left off” affordance.
  5. Link hygiene at scale. Add last_checked metadata per link and a CI link-checker so the library ages honestly; widen sources so the same few links are not reused across lessons.
  6. Source discipline everywhere. Extend the source metadata beyond frontier lessons to the agent-security, eval-methodology, and vibe-coding claims that age quickly.

Known limitations (carried, not hidden)

v2 shipped (2026-06-18) + where it landed

v2 added the depth both raters asked for: three applied interactive widgets (a live confusion-matrix / discrimination calculator, a temperature->softmax visualizer, ordering tasks), six inline SVG diagrams, and four breadth lessons (classical ML, LLM-as-judge, inter-rater reliability, production cost/latency). 49 -> 53 lessons. The goal-loop ran two more rounds: Codex 78->82->80, Gemini 83->86->84.

The loop then plateaued: a re-rate of a strictly-improved build moved both scores down, which means the rubric scoring is noise-dominated at this quality level. Exited per anti-inflation discipline. Both raters confirm the content is accurate and dense throughout; the gap to a stable 88+ is structural.

v3 (would push past the mid-80s plateau)