Miles Wiesenthal’s Post

Did you know the GLiNER model has a brother? GLiClass from Knowledgator offers compute efficient zero-shot text classification with cross-encoder level performance – perfect for topic classification, sentiment analysis, or RAG reranking. Check out my latest article in AI in Plain English for the details! https://lnkd.in/gMUwtyuC And yes, I’ve included a GitHub repo with everything you need for a scalable deployment - from Docker Compose to FastAPI, even a Streamlit UI. Just like last time, it’s all set up for you to deploy and experiment with. 🚀 Thanks to Urchade Zaratiana & Ihor Stepanov 🇺🇦 for the awesome work! GitHub: https://lnkd.in/gtQ5FUJ4

Nearly 100,000 users overnight 👀 Last week, I launched a little project called 🌴 Stretch My Time Off (https://lnkd.in/eVKiSARP) on Hacker News, and the reaction was... unexpected. The tool is designed to optimise vacation days by planning around weekends and public holidays. The secret to its traction? An opinionated algorithm that claims to calculate the “best” days to take off. Turns out, sparking debates about who plans their holidays better (and sprinkle in a few early bugs) really drives engagement 😅 Add in geolocation and local holiday data for every country, and it quickly became a hit—visitors from 158 countries so far. Built in about 8 hours using Cursor and GPT-4, this project started as an experiment to see how far AI tools could go. Watching it take on a life of its own has been an experience. Now, to make use of that time off... for the next one 🫡

🤖 😤 This video was entirely made with Code and AI (My Weekend Project) - AI generates the script and also image search query on Unsplash - Elevenlabs for audio - And I create an object and pass it into Remotion(a React framework) - Transitions, effects everything in React code - In the end I spit out the video

🤖💬 Building my own website has been an exciting journey, and when I discovered the ability to create a personalized GPT chatbot, I knew I had to incorporate it! 🎉 I wanted to leverage the power of the latest language models while infusing the chatbot with knowledge about myself and my services. The goal? To provide users with tailored, relevant answers that go beyond generic responses. Many people have asked me how they can build their own GPT and integrate it into their app or call it via API. Looking into it, I realized how more complex it was than expected, so I decided to share my experience and the lessons I learned along the way. In this blog post, I describe the different approaches I explored in detail, including the step-by-step process, obstacles, and solutions I discovered.

Looking for a comprehensive guide on AI Agents? 🔍 📚 Check this new blog that our expert, Haziqa Sajid published that covers everything from what AI agents are, how they work, and even dives into its technical implementation! 🚀 🔥 Some of the technical insights were really interesting and went right into building your own AI agent. Here’s a snapshot of what you can expect: 1️⃣ An overview of AI agents 🛠 2️⃣ Benefits like better efficiency, cost savings, high availability, and effortless scalability 🚀 3️⃣ Real-life applications 📖 4️⃣ Introduction to LLM agents, including concepts like reasoning and acting 🧠✨ 5️⃣ A deep dive into Planning, Memory, and Tools for LLM agents, with frameworks like ReAct, CoT, and Tree of Thoughts 🗃 6️⃣ Hands-on guide to building task automation with LangGraph 🤖 If you're interested in AI and automation, this guide is a must-read! 😊

Looking for a comprehensive guide on AI Agents? 🔍 📚 Check this new blog that our expert, Haziqa Sajid published that covers everything from what AI agents are, how they work, and even dives into its technical implementation! 🚀 🔥 Some of the technical insights were really interesting and went right into building your own AI agent. Here’s a snapshot of what you can expect: 1️⃣ An overview of AI agents 🛠 2️⃣ Benefits like better efficiency, cost savings, high availability, and effortless scalability 🚀 3️⃣ Real-life applications 📖 4️⃣ Introduction to LLM agents, including concepts like reasoning and acting 🧠✨ 5️⃣ A deep dive into Planning, Memory, and Tools for LLM agents, with frameworks like ReAct, CoT, and Tree of Thoughts 🗃 6️⃣ Hands-on guide to building task automation with LangGraph 🤖 If you're interested in AI and automation, this guide is a must-read! 😊

This keeps blowing my mind 🎉🚀 I quickly needed a capitalize-first-char pipe that ☘️ Was automatically implemented ☘️ Written to the right directory in my codebase ☘️ Was exported through the barrel file ☘️ Was unit-tested with jest ☘️ Was generated based on my personal prompts 🎉 Short review and POOF! It took me 30 seconds with this AI agent that is part of Nitrokit. This AI agent knows about my codebase, can refactor for me, generate, move, rename, change the scopes of projects, generate forms, etc, etc Check it out and drop me a comment if you want to know more 🤓

[GenAI] Arize AI looks very interesting. It's really cool and powerful how they are visualising and graphing the LLMs queries. Also check out this "Navigating the New Types of LLM Agents and Architectures" article from its co-founder and CTO Aparna Dhinakaran

From the blog - I continue to use GenAI for completely serious, enterprise reasons: "Using GenAI to Help Pick Your D & D Class" Makes use of Google Gemini 1.5 - their latest model. https://lnkd.in/e6NhV5jY

💗Hello 🔥XRec🔥! ✨ "Can recommender systems speak out loud? Using Natural Language to uncover your interests." 🗣️ 📣 🚀 Excited to introduce our 🌟#XRec🌟: an intelligent large language model that lets recommender systems converse in natural language to truly understand your interests! 🤖 ✨ "XRec: Large Language Models for Explainable Recommendation" ✨ - 💬 Integrates #LLMs with recommenders to offer comprehensive explanations - 🧠 Uses natural language for deeper understanding of user preferences - 🎯 Shows strong generalization in zero-shot recommendations 📚 The paper: https://lnkd.in/gFGn__sS 👨💻 The model and source code: https://lnkd.in/gfwkbXrQ