amitunix @amitunix
Technology Optimist. Hyderabad, India Joined November 2008-
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BRILLIANT @GoogleDeepMind research. Even the best embeddings cannot represent all possible query-document combinations, which means some answers are mathematically impossible to recover. Reveals a sharp truth, embedding models can only capture so many pairings, and beyond that,…
This new DeepMind research shows just how broken vector search is. Turns out some docs in your index are theoretically incapable of being retrieved by vector search, given a certain dimension count of the embedding. Plain old BM25 from 1994 outperforms it on recall. 1/4
Muvera
Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by @neo4j Innovation Lead @akollegger, shows you how. Knowledge graphs are an important way to…
NVIDIA research just made LLMs 53x faster. 🤯 Imagine slashing your AI inference budget by 98%. This breakthrough doesn't require training a new model from scratch; it upgrades your existing ones for hyper-speed while matching or beating SOTA accuracy. Here's how it works:…
A team at DeepMind wrote this solid piece on how you must think about GPUs. Essential for AI engineers and researchers.
Continuing the journey of optimal LLM-assisted coding experience. In particular, I find that instead of narrowing in on a perfect one thing my usage is increasingly diversifying across a few workflows that I "stitch up" the pros/cons of: Personally the bread & butter (~75%?) of…
The Illustrated GPT-OSS New post! A visual tour of the architecture, message formatting, and reasoning of the latest GPT. Link in 🧵
Today we are making URL Context, my favorite Gemini API tool, ready for scaled production use 🔗 The model can now visit webpages, PDF's, images, and more when you provide the direct URL, and you simply pay for the tokens it processes, no additional tool cost!
I am (slowly) re-reading the Tolkien legendarium (of which Lord of the Rings is a small part). The whole body of work is so incredible and there's nothing else like it... it dilutes other worlds of fiction. Wait - your story doesn't have a comprehensive history/mythology spanning…
in light of claude hitting 1M context here is a 27min video showing how bonker insane it is in it I review most strategies from interpolation to chunking to linearization to hybridization to making gpu go brrbrr to just pray for Nikolai Savinov to make it happen please god
GenAI Traffic Share Update — 🗓️ 12 months ago: ChatGPT: 84.7% Gemini: 8.1% Claude: 2.2% Perplexity: 1.6% Copilot: 0.8% 🗓️ 6 months ago: ChatGPT: 79.7% DeepSeek: 9.2% Gemini: 4.9% Perplexity: 1.8% Claude: 1.4% Copilot: 1.2% 🗓️ 3 months ago:…
Introducing Jan-v1: 4B model for web search, an open-source alternative to Perplexity Pro. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. Use cases: - Web search - Deep Research Built on the new version…
GPT-4.1-mini level model right in your iPhone 🤯 @OpenBMB released MiniCPM-V4, only 4B model surpassing GPT-4.1-mini in vision benchmarks 🔥 allows commercial use as well!
Our secure agentic AI platform, North, is now widely available.
To guys diving into fine-tuning open-source MoEs today: check out ESFT, our customized PEFT method for MoE models. Train with 90% less parameters, gain 95%+ task perf and keep 98% general perf :)
To guys diving into fine-tuning open-source MoEs today: check out ESFT, our customized PEFT method for MoE models. Train with 90% less parameters, gain 95%+ task perf and keep 98% general perf :)
OpenAI has released a guide on how to finetune their open-source models: cookbook.openai.com/articles/gpt-o…
ColBERT and ColPali are amazing. But they're also bankrupting your infrastructure budget. Remember when multi-vector embeddings meant choosing between quality and your AWS bill? 𝗠𝗨𝗩𝗘𝗥𝗔 (Multi-Vector Retrieval via Fixed Dimensional Encodings) 𝗵𝗮𝘀 𝗺𝗮𝗱𝗲 𝗶𝗻𝘀𝗮𝗻𝗲…
Summary of GPT-OSS architectural innovations: 1. sliding window attention (ref: arxiv.org/abs/1901.02860) 2. mixture of experts (ref: arxiv.org/abs/2101.03961) 3. RoPE w/ Yarn (ref: arxiv.org/abs/2309.00071) 4. attention sinks (ref: streaming llm arxiv.org/abs/2309.17453)

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