distillation technique

Daily cross-source mention tracking for distillation: how many AI news articles, Hacker News stories, research papers and GitHub trending repos mention it. Deterministic whole-word counts of real stored items, updated daily.

11 mentions, last 7 days 29 mentions, all stored days tracked since 2026-06-19
Daily total mentions · 2026-06-19 to 2026-07-10
0352026-06-19: 1 mention2026-06-25: 5 mentions2026-06-26: 5 mentions2026-06-30: 1 mention2026-07-01: 1 mention2026-07-02: 2 mentions2026-07-03: 3 mentions2026-07-04: 1 mention2026-07-05: 1 mention2026-07-06: 1 mention2026-07-07: 3 mentions2026-07-08: 2 mentions2026-07-09: 1 mention2026-07-10: 2 mentions06-1907-0407-10
Per-source breakdown
SourceLast 7 daysAll stored days
News articles 1 1
Hacker News stories 0 0
Research papers 10 28
GitHub trending repos 0 0
Receipts · current items mentioning distillation
news Dell vs. Lenovo: I've tested dozens of laptops from both brands, and here's my pick Fri, 08 Ma paper OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators 2026-07-10 · external ↗ paper Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers 2026-07-09 · external ↗ paper TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training 2026-07-08 · external ↗ paper Weak-to-Strong Generalization via Direct On-Policy Distillation 2026-07-08 · external ↗ paper UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning 2026-07-07 · external ↗ paper dOPSD: On-Policy Self-Distillation for Diffusion Language Models 2026-07-07 · external ↗ paper DuoMem: Towards Capable On-Device Memory Agents via Dual-Space Distillation 2026-07-06 · external ↗
How this is counted

Each item counts once per day if its title (plus summary or description where available) contains distillation or one of its tracked aliases, matched case-insensitively on whole words. Heat on the Hype Meter board is scored as: raw = total mentions across sources in trailing 7 days; breadth = number of distinct sources with >=1 mention in those 7 days (1-4); heat = percentile rank of (raw * breadth) among all terms with raw>0, scaled 0-100, rounded. News and repo counts start the day tracking began; Hacker News and paper counts are backfilled from stored daily snapshots. No language model touches these numbers.

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