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Pravko-Solutions/FlashLearn

Integrate LLM in any pipeline - fit/predict pattern, JSON driven flows, and built in concurency support.

Latest metrics (snapshot 2026-10-15)

stars607
forks39
issues0
pushed_at2025-03-10T11:40:52Z

History

History chart: 1 daily snapshot so far (archive started 2026-10-14). The line fills in as the daily archive grows; full series via the API.

DateGitHub stars
2026-10-15607

ADC Momentum: 17/100

ADC Momentum 17 Leaderboard

Growth velocity29
Consistency23
Recency0
Agent demand0
Basisbootstrap

ADC Momentum (0-100) = 45% growth velocity (stars/likes gained per day in our daily archive, relative and absolute, percentile-ranked within the category) + 20% consistency (share of recent days with growth) + 20% recency (latest push/update/release, decaying) + 15% agent demand (how often AI agents looked it up via our API/MCP this week). MCP servers use release cadence; LLMs use price cuts, context and recency. Recomputed daily; scores marked "bootstrap" use lifetime rates until the archive has enough days. Method

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Details

descriptionIntegrate LLM in any pipeline - fit/predict pattern, JSON driven flows, and built in concurency support.
urlhttps://github.com/Pravko-Solutions/FlashLearn
languagePython
licenseMIT
topicsagentic-ai-development
ai
ai-agents
ai-agents-framework
concurrency
etl-pipeline
llm
llm-agent
python
created_at2025-01-16T15:19:55Z
homepagehttps://flashlearn.tech/

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