Unify

Day 2 onboarding Rachel (my new virtual colleague) 👩🏻‍💻. I asked if she could help me grant her access to our Google workspace, and she guided me through via screenshare (I genuinely had no idea how to do this 🫠), and then she cleaned up my inbox with one python script; she did a

Not engaged with Twitter in a lonnnng time, but for anyone interested, I've decided to start doing regular (unfiltered) posts of my own experience onboarding a fully virtual colleague. AGI is certainly not solved (yet), and so I'll focus on what works well, what doesn't work

We've been heads down building for the past few months (custom stack, not OpenClaw 🦞), and I'm excited to finally launch our virtual teammates! Huge shout out to the team (and many long nights) to get us here ❤️💪 You onboard you new teammate exactly how you'd onboard any other

MCP servers are ONLY as good as their abstractions 🧱 and docs 📄. The official MCP for Google Drive fails at even the most basic tasks (see video). Building an MCP server is VERY EASY. Crafting the correct abstractions is VERY HARD. Very few servers are production ready; most

Unify (@letsunifyai) is building Notion for AI observability— with a lightweight, hackable, fast, and flexible framework. It's built for products with or without LLMs, letting you focus on the data, plots, and metrics that matter. ycombinator.com/launches/N5M-u…

Excited to be launching our new AI observability tool today! 😁 Think "Notion for AI Observability" 📊 When building AI apps ourselves, we spent months fighting with the prior tooling, trying to strip things back to the bare minimum, so we could observe and iterate on exactly what we needed, when we needed it 🔁 🔍 We care about the underlying LLM, but not more than the users! Existing tools are generally very much curated to one or the other, not both. Unify makes it easier to visualize, iterate on and interact with the data and visualizations that matters for *you*, your *AI app* and your *users*, and nothing else 🎯 The core building block is simple, just “unify.log”. This lets you store any kind of data for easy visualization, grouping, sorting, and plotting etc. You can then quickly build your own custom interface for whatever you want using three basic tile types, Tables 🔢, Views 🔍 and Plots 📊 You can use these three primitives to do all kinds of things, such as: ➕create + visualize your datasets in a new tab (with or without LLMs) ➕monitor and probe production traffic in a new tab (with or without LLMs) ➕start an evaluation flywheel in a new tab (with or without LLMs) 📉optimize your product for your users (with or without LLMs) 🧠whatever else you can think of (with or without LLMs! Check out our repo for a minimal example, explaining how to use these basic building blocks to ship with speed and clarity ⚡ github.com/unifyai/unify We're also live on ProductHunt right now: producthunt.com/posts/unify-8 Support + feedback here is also ofc appreciated ❤️ Finally, big shoutout to the team for working tirelessly to make this happen: Haris Mahmood Yusha Arif Ved Patwardhan Nassim Berrada James Keane Albert Lukács Feel free to let us know what you think! (criticism + suggestions are especially welcome 🙏) Thanks all, happy prompting ✌️

We’re excited to announce Ivy is partnering with Kornia, allowing Kornia to be used with TensorFlow, JAX, and NumPy for the first time! You can use Kornia's new `to_tensorflow()`, `to_jax()` and `to_numpy()` methods, which take advantage of Ivy’s transpiler, to use Kornia in

RT @DanielLenton1: Incredibly flattered that @amazon have invited me to be their keynote speaker for this year's AWS Gen AI Loft event. Can…

Open AI released the new model O1 and I tested and compared it's logical thinking capabilities with Claude 3.5 Sonnet using @letsunifyai I prompted both of them with a mathematical riddle which required some calculations and guess who won? Puzzle: Hansa ate a meal at Jugju

We’re excited to have @shirleyxiaoyic from @IndianaUniv, co-author of the paper "The Janus Interface: How Fine-Tuning in Large Language Models Amplifies Privacy Risks," joining us this Friday for our Paper Reading Session! 🤩 RSVP 👉 lu.ma/jok7bp2m The research introduces a novel attack, Janus, which exploits the fine-tuning interface to recover forgotten PIIs from the pre-training data in LLMs also formalizing the privacy leakage problem in LLMs, explaining why forgotten PIIs can be recovered through empirical analysis on open-source language models.🧠 Check out the Paper: arxiv.org/pdf/2310.15469 See you there!

We're thrilled to announce that @Vapi_AI will be joining us for our weekly Webinar Series tomorrow! (Wednesday) 🤩 RSVP here: lu.ma/cke3hpft Join us as we welcome Sahil Suman, Solution Engineer at Vapi AI, to the session. Discover how VAPI enables the quick setup of high-quality voice agents and see the integration of @letsunifyai with VAPI for seamless access to various models and providers. See you there! 🧑‍💻 Explore Vapi: ⚡️vapi.ai ⚡️github.com/VapiAI

We are really excited to welcome @zlwang_cs from @ucsd_cse, who co-authored the paper "Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting". Happening Tomorrow!🤩 RSVP 👉 lu.ma/zesezv3u The research introduces SPECULATIVE RAG, a framework that leverages a larger generalist LM to efficiently verify multiple RAG drafts produced in parallel by a smaller, distilled specialist LM 🧠🤖 Check out the Paper👉arxiv.org/pdf/2407.08223 See you there!

We are really excited to announce that we will be joined by @llmware for our Webinar Series today! (Tuesday)🤩 RSVP👉 lu.ma/e651sgj8 In this session, we're excited to welcome Darren Oberst and Namee Oberst from LLMware. We will explore how small specialized LLMs can compete with the larger models for specific use cases, especially for Financial, Legal, Compliance, and Regulatory-Intensive Industries. See you there! 🧑‍💻 Checkout LLMWare: ⚡️llmware.ai ⚡️github.com/llmware-ai/llm…

We are really excited to welcome Devichand Budagam from @IITKgp, who co-authored the paper "Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models". Happening Friday! 🤩 RSVP👉 lu.ma/7j11iscl ​The research introduces Hierarchical Prompting Taxonomy (HPT), a universal evaluation metric that can be used to evaluate both the datasets' complexity and LLMs' capabilities🧠 Check out the Paper👉 arxiv.org/pdf/2406.12644 See you there!

We are really excited to announce that we will be joined by @tavilyai for our Webinar Series this Tuesday!🤩 RSVP👉 lu.ma/a77wgrao In this session, we'll explore how Tavily API makes search engine optimised for LLMs and RAG, to provide efficient, quick, and persistent search results. We'll also showcase Unify's SSO integration with Tavily 🧠🧑‍💻 Checkout Tavily: ⚡️tavily.com ⚡️github.com/tavily-ai

We are really excited to welcome @sh_reya from @Berkeley_EECS, who co-authored the paper "Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences". Happening Tomorrow! 🤩 RSVP 👉lu.ma/ttwrh0n4 The research introduces EvalGen, an interface that provides automated assistance in generating evaluation criteria and implementing assertions🧠👩‍💻 Check out the Paper👉 arxiv.org/pdf/2404.12272 See you there!