live from @MIT where @ZeroEntropy_AI 's CTO is presenting our latest zElo paper and reranker model! @npip99

shout out to @ghita__ha and the @ZeroEntropy_AI team for organizing!

We just built a free tool to ask questions over the 2025 @NeurIPSConf research papers. Try it out at neurips dot zeroentropy dot dev No signup, no credit card, just the best way to learn more about this year's papers!

It’s always amazing to see small teams outperform companies with $100M+ in funding, and even more amazing when you get to be a part of it. 😅 Stoked that we were able to support @ZeroEntropy_AI on training their state of the art reranker model family! Read here about the

We are very excited to release zerank-2, @ZeroEntropy_AI 's newest reranker model. 🔥 It shows major improvement on the 5 most common RAG failure modes below. Existing rerankers consistently fail on seemingly “simple” tasks: 🔢 Comparing numbers and date: “Biggest deals closed

@ZeroEntropy_AI (Nicholas Pipitone, @ghita__ha) will talk about how they used RLAIF to train their SOTA rerank model:

come chat about smarter search and smarter AI

next week will be extra @elastic-packed in SF monday meetup: https://t.co/d4S5fLckWc * @ghita__ha, @ZeroEntropy_AI: search tools for efficient AI agents * jesse, @fintoolx: LLMs and the next generation of financial search * @joshnkeezy, @reductoai: building a vision-first RAG

The #1 bottleneck for software gen going forward is search / retrieval. Whether that’s open data sources (good web search) / personal data sources, it’s the layer that will become the biggest prereq to capturing value

@jeffreyhuber bullish on @ZeroEntropy_AI here

over 200 people have already RSVPd to join us along with the teams at @mastra_ai and @mem0ai to talk about engineering context for Agents. RSVP: https://t.co/7q8tPOcSwX @ZeroEntropy_AI

Join us with @mastra_ai (TypeScript framework for Agents), @mem0ai (the long-term Agent memory layer), and @zeroentropy_ai(fastest and most accurate rerankers) on Oct 17th for our first Context Engineering Webinar! https://t.co/3Kw8lllUee

most agents burn $$ reading 100 docs just to answer one question. a set up that works is @turbopuffer (fast, cheap, high recall hybrid search) with @zeroentropy_ai (fast, cheap, high precision reranker) tutorial + open benchmarks from the ZeroEntropy team

waking up to a team literally creating memes to show that @ZeroEntropy_AI is a "no-brainer" we want more memes please

Another team moved their prod reranking to @ZeroEntropy_AI Excited to support @mem0ai's billion-token scale retrieval reranking⚡️ With zerank-1: • Improved accuracy and cross domain calibration • Latency p50 75ms / p90 125ms / p99 238ms • One-line API swap • SOC2 + HIPAA

Excited to partner with @llama_index ! Here is how you can leverage both LlamaIndex and ZeroEntropy to build a powerful parse-retrieve-rerank workflow. Link in the comments

🚨 STOP using gpt-4o-mini or Gemini Flash as a reranker in your RAG pipeline! gpt-4o-mini is for example on average ~10x slower, ~6x more expensive in input tokens and significantly worse on NDCG@10 than zerank-1, our latest reranker.

You can now build a Deep Research Agent over your organization's data in literally 1 tool call using @ZeroEntropy_AI's MCP server! We just launched our MCP server, which you can connect to the @OpenAI Deep Research API! This demo shows to find ZeroEntropy's secret buried in

Many of our users doing RAG wanted other rerank providers! Lucky for us @ZeroEntropy_AI just launched a rerank model that pretty much destroys cohere, is way cheaper, and is fast AF. Thanks @ghita__ha and team for contributing this to Mastra!

Excited to announce our $4.2M seed round led by @Initialized and the release of our state-of-the art reranker zerank-1. zerank-1 was trained using a novel ELO-score inspired training pipeline, that treats query-document relevance like a ranking game (literally, just like Chess!