Olivier Balais (overnetcity.bsky.social)

Tested #Gemini 2.0 Flash to convert recordings+audio into specs for Windsurf — mind-blowing. Now hoping for native support :)

A few weeks ago, I was blown away by @windsurf_ai IDE! Next step: a multimodal approach (video+text+audio) so we can "show" code while explaining logic—like a screen record in a tech issue.

@dunglas Amazing! Thanks for the pointer, @dunglas! I checked the page you shared but couldn’t find any mention of the BoltDB you referenced. Is there any other documentation or guidance you could point me to?

@overnetcity Hey, it’s built-in! #reconciliation" target="_blank" rel="nofollow noopener">mercure.rocks/spec#reconcili… The reference (FOSS) implementation uses BoltDB to achieve that. The commercial version also supports Redis, Kafka, Pulsar and Postgres as storage engine.

Hey @dunglas, hope all’s well! I’m exploring mercure.rocks for handling SSE auto-retries on network failure. Does Mercure have a built-in feature to store/buffer events for client auto-retry, or is it something we typically manage on the backend?

o3 just dropped and it’s a monumental leap in AI capabilities. From blazing code gen to near-human performance on the ARC AGI test (yes, that test), it changes what we thought AI could do. Exciting? Absolutely. Terrifying? A bit. Hardware is now the real bottleneck. Buckle up!

Our new models — fully integrated alongside our database — bring best-in-class retrieval to your applications: ✔️ Our new sparse embedding model — pinecone-sparse-english-v0 — boosts performance for keyword-based queries, delivering up to 44% and on average 23% better NDCG@10 than BM25 on TREC. ✔️ Our new reranking model — pinecone-rerank-v0 — improves search accuracy by up to 60% and on average 9% over industry-leading models on the BEIR benchmark. ✔️ @cohere's latest model — cohere-rerank-v3.5 — balances performance and latency for a wide range of enterprise search applications. Learn more by visiting our Model Gallery: docs.pinecone.io/models/overview

First-of-its-kind Pinecone Knowledge Platform Powers Best-in-class Retrieval for Customers 💠 Industry-leading vector database capabilities combined with proprietary AI models help developers build up to 48% more accurate AI applications: faster & easier prnewswire.com/news-releases/…

Bye bye Twitter. Let’s meet on Bsky: overnetcity.bsky.social.

I’ve been working and building products with generative AI for over 4 years now. It’s hard to impress me at this point. But damn, @windsurf_ai, your IDE is absolutely next-level! 🔥

New in Copilot Chat... enhanced links for any workspace symbols that Copilot mentions 🔗 These links appear in responses as little pills, letting you jump directly to definitions for better understanding.

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Pretty excited about this new RAG technique I cooked up 🧑‍🍳 A top issue with RAG chunking is it splits the document into fragmented pieces, causing top-k retrieval to return partial context. Also most documents have multiple hierarchies of sections: top-level sections, sub-sections, etc. This is also why lots of people are interested in exploring the idea of knowledge graphs - pulling in "links" to related pages to expand retrieved context. This notebook lets you retrieve contiguous chunks without having to spend a lot of time tuning the chunking algorithm, thanks to GraphRAG-esque metadata tagging + retrieval. Tag chunks with sections, and use the section ID to expand the retrieved set. Check it out github.com/run-llama/llam…

Text chunking now matches human reading patterns by detecting natural breaks in information flow. Meta-chunking, proposed in this paper, uses probability patterns to find natural segment boundaries in documents, just like humans do Original Problem 🎯: Text chunking in Retrieval-Augmented Generation (RAG) systems often fails to maintain logical coherence between segments, leading to incomplete or fragmented information retrieval. Current methods rely on fixed-length splits or basic semantic similarity, missing crucial logical connections between sentences. ----- Solution in this Paper ⚡: • Meta-Chunking: A novel segmentation technique operating between sentence and paragraph levels • Two key strategies: - Margin Sampling: Uses LLMs for binary classification to determine segment boundaries based on probability differences - Perplexity (PPL) Chunking: Analyzes perplexity distribution to identify natural text boundaries • Dynamic combination approach to balance fine and coarse-grained segmentation • KV caching mechanism for handling longer texts efficiently ----- Key Insights 💡: • Smaller models (1.5B parameters) can effectively perform chunking tasks • PPL distribution characteristics guide optimal threshold selection • Dynamic chunk sizing preserves logical integrity better than fixed-length approaches • Re-ranking performance improves significantly with Meta-Chunking ----- Results 📊: • Outperforms similarity chunking by 1.32 on 2WikiMultihopQA while using only 45.8% processing time • PPL Chunking with Qwen2-1.5B achieves 0.3760 BLEU-1 score on single-hop queries • Maintains consistent performance across both Chinese and English datasets • Shows 3.59% improvement in Hits@8 metric when combined with PPLRerank

Peur sur les pistes cyclables : gros pneus et coups de sonnettes, la menace de la « SUVisation du vélo » – via @lemondefr lemonde.fr/m-perso/articl…

It's true.

I don't want to say "I told you so", but I told you so. Repeatedly. I was heavily criticized for it. Mocked, even.

This project tries to implement a real-time replication of OpenAI’s groundbreaking O1 model. Exploring advanced reasoning capabilities and a specific "journey learning" mechanisms for AI. They propose a new approach: “journey learning”. This paradigm goes beyond the traditional focus on specific tasks and emphasizes continuous progress through learning, reflection, and adaptation.

🤯

Wow! @HeyGen_Official just released today ability to have an AI avatar join a Zoom meeting and interact. I invited one of their AI avatars into a Zoom room and recorded this clip. Time to build my own now