Title: Add algorithm optimization guide from source code analysis by Copilot · Pull Request #2 · ScriptSynth/the-algorithm · GitHub
Open Graph Title: Add algorithm optimization guide from source code analysis by Copilot · Pull Request #2 · ScriptSynth/the-algorithm
X Title: Add algorithm optimization guide from source code analysis by Copilot · Pull Request #2 · ScriptSynth/the-algorithm
Description: Analyzed Twitter's recommendation algorithm codebase to document ranking mechanics, reputation scoring, and visibility optimization strategies. Changes New documentation: docs/TWITTER_ALGORITHM_OPTIMIZATION_GUIDE.md Ranking mechanics: 4 primary signals (favorites, retweets, replies, dwell time) with configurable weights (-10k to +10k), scored via weighted aggregation TweepCred calculation: PageRank-based reputation with age penalties (<30 days), verification boost (score=100), and exponential follower/following ratio penalty when >0.6 with >500 following Engagement tracking: In-network vs out-of-network metrics, negative signal weights (reports=-20k, blocks=-1k), USS feature matrix across ML models Source references: Extracted from UserMass.scala, Reputation.scala, home-mixer/, scoring weight registries, and signal tracking configs Updated README.md: Added prominent link to guide Key algorithm insights Following/follower ratio penalty from adjustReputationsPostCalculation: // Exponential penalty for ratio >0.6 with >500 following divisionFactor = exp(5.0 × (ratio - 0.6)) adjustedScore = originalScore / divisionFactor // Example: ratio 2.0 = 403× penalty Score aggregation from RerankerUtil.scala: finalScore = Σ(engagement_score × weight) + epsilon // epsilon = 0.001, weights range [-10000, 10000] // rank_decay = 0.95 Verification gives maximum TweepCred (100) while restricted accounts receive 0.1× multiplier. ✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.
Open Graph Description: Analyzed Twitter's recommendation algorithm codebase to document ranking mechanics, reputation scoring, and visibility optimization strategies. Changes New documentation: docs/TWITTER_ALGORITHM...
X Description: Analyzed Twitter's recommendation algorithm codebase to document ranking mechanics, reputation scoring, and visibility optimization strategies. Changes New documentation: docs/TWITTER_ALGOR...
Opengraph URL: https://github.com/ScriptSynth/the-algorithm/pull/2
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