@vicdefran

engineer @idealista

 Europe
Joined June 2012
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Victor retweeted
gtray@DeoibelTnadeFeb 9
Replying to @megaeth @realtime_defi
Mainnet has been activated. Initial network access is available. Details: mainnet-megaeth.net
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Youtube video transcription chunks and embeddings for semantic search through vector search, this starts to sound challenging
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The issue is that Supabase AI uses gte-small model, but I am using all-MiniLM-L6-v2 locally. I realised that they have different embedding dimensions and vector spaces, so they won't be compatible for similarity search.
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The weird thing is that although I have been working with this stuff with little understanding, I think I understand what's happening under the hood. Next steps need to fix the problem about embeddings similarity.
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In order to deploy the project and make the transition from local to production, I am going to try supabase.com/

Supabase | The Postgres Development Platform

Build production-grade applications with a Postgres database, Authentication, instant APIs, Realtime, Functions, Storage and Vector embeddings. Start for free.

supabase.com
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🎯 How It Works Now 1- Data ingestion: YouTube videos → Enhanced transcript extraction → Quality validation 2- Knowledge base: Transcripts → Vector embeddings → Supabase storage 3- User interaction: Question → Vector search → Context assembly → AI response
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💡 What Makes This Special - The system provides semantic understanding: users can ask natural language questions. - Context-aware responses: AI finds relevant information across multiple videos. - Quality Assurance: every transcript is validated for completeness and accuracy.
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👀Continued working on mechanic AI assistant. Enhance video processing pipeline with retry mechanisms, quality scoring and progress tracking. 🤖Start to include AI-powered knowledge capabilities.
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AI capabilities: - Generate embeddings for semantic search - Find relevant transcript segments for user questions - Build context from multiple video sources - Maintain conversation history - Provide source attribution
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📊 Current System Status Based on the validation report: ✅ 17 videos successfully processed ✅ 27 transcripts stored (multiple languages) ✅ 100% pass rate on quality validation ✅ Average quality score: 0.93/1.0 ⚠️ Minor timing accuracy issues (easily addressable)
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Just first iteration of youtube video transcriptor service 🎥 Pulls whole playlists—auto, batch 💾 Saves everything into SQLite 🔍 Searchable transcripts, right from the DB 📊 Analytics—see what topics & questions pop up most #buildinpublic #IndieDev #OpenSource #ai
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Next 📦 Fast JSON export for backups & integrations 🔧 Next: connecting these insights to an AI Q&A agent
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📚Learning with Copilot in VSCode about Python virtual env setup. 🤖Learning experience takes a different approach with assistants.
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Limit dependence on algorithms. Make your own decisions, do your own research, think for yourself.
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