LLM topic

Daily cross-source mention tracking for LLM: 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.

51 mentions, last 7 days 133 mentions, all stored days tracked since 2026-06-19
Daily total mentions · 2026-06-19 to 2026-07-10
09182026-06-19: 3 mentions2026-06-20: 3 mentions2026-06-21: 3 mentions2026-06-22: 3 mentions2026-06-23: 6 mentions2026-06-24: 7 mentions2026-06-25: 8 mentions2026-06-26: 6 mentions2026-06-27: 3 mentions2026-06-28: 2 mentions2026-06-29: 6 mentions2026-06-30: 5 mentions2026-07-01: 13 mentions2026-07-02: 9 mentions2026-07-03: 5 mentions2026-07-04: 4 mentions2026-07-05: 3 mentions2026-07-06: 7 mentions2026-07-07: 10 mentions2026-07-08: 4 mentions2026-07-09: 5 mentions2026-07-10: 18 mentions06-1906-3007-10
Per-source breakdown
SourceLast 7 daysAll stored days
News articles 10 10
Hacker News stories 6 10
Research papers 28 106
GitHub trending repos 7 7
Receipts · current items mentioning LLM
news What is Mistral AI? Everything to know about the French OpenAI competitor Sat, 04 Ju hn Agentic test processes, LLM benchmarks, and other notes on agentic coding fr 2026-07-09 · external ↗ paper Teaching LLMs a Low-Resource Language: Enhancing Code Completion in Pharo 2026-07-10 · external ↗ repo unclecode/crawl4ai 2026-07-10 · trending today news How memory tools can make AI models worse Wed, 10 Ju hn ZML/LLMD alpha – cross platform LLM server 2026-07-08 · external ↗ paper JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications 2026-07-09 · external ↗ repo langfuse/langfuse 2026-07-10 · trending today
How this is counted

Each item counts once per day if its title (plus summary or description where available) contains LLM 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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