ContextForge is IBM's MCP gateway. Register your MCP servers, A2A agents and REST endpoints in one catalog, then bundle the tools one agent needs into a virtual server it reaches as a single endpoint. Tool lists are filtered by team before the client receives them, so scoping stops being each server's private decision. 4,185 stars, Apache-2.0.

LLM evaluation is where AI systems either become trustworthy or remain demos.From my experience across enterprise architecture, cloud, data, and security, one pattern is very clear:a good output once means very little.A reliable AI system needs to produce good outputs repeatedly, safely, and measurably across different users, prompts, workflows, and edge cases.That is why evaluation cannot depend on one metric.You need multiple evaluation methods because each one measures a different part of quality.𝗥𝗢𝗨𝗚𝗘 and 𝗕𝗟𝗘𝗨 are useful when you need reference-based comparison.They help measure overlap, recall, precision, and how close the generated output is to an expected answer.𝗕𝗘𝗥𝗧𝗦𝗰𝗼𝗿𝗲 goes deeper by looking at semantic similarity instead of only exact word overlap.This matters when two answers use different wording but carry the same meaning.𝗚-𝗘𝘃𝗮𝗹 and 𝗟𝗟𝗠-𝗮𝘀-𝗝𝘂𝗱𝗴𝗲 are useful when responses need to be scored against custom criteria or compared using a rubric.𝗛𝘂𝗺𝗮𝗻 𝗘𝘃𝗮𝗹 is still important where quality, judgment, nuance, and business context matter.𝗦𝗮𝗳𝗲𝘁𝘆 𝗘𝘃𝗮𝗹 checks bias, toxicity, harmful content, privacy leakage, and policy violations.𝗠𝘂𝗹𝘁𝗶-𝘁𝘂𝗿𝗻 𝗘𝘃𝗮𝗹 is critical for chat systems because the model has to maintain context across the full conversation.𝗟𝗟𝗠 𝗝𝘂𝗿𝗶𝗲𝘀 reduce single-judge bias by aggregating multiple independent model evaluations.𝗗𝗔𝗚 𝗘𝘃𝗮𝗹 helps when the response follows decision paths or branching logic.𝗧𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 becomes very important for agents because the final answer is not enough. The execution path also has to be correct.My view is simple:LLM evaluation is not only about scoring text.It is about measuring correctness, relevance, safety, reasoning, conversation quality, and execution behavior.That is how AI systems move from impressive demos to production-grade systems.This is just a high-level working diagram, but it gives a useful starting point for understanding how LLM evaluation can be structured.

Building an MCP server is easier than most developers think.The hard part isn't writing the code.It's understanding why MCP exists in the first place.Most tutorials jump straight into code.Without explaining the problem they're solving.This guide starts from first principles and ends with a working MCP server.You'll learn:→ Why LLMs can't access your apps by default→ Why copy-pasting data doesn't scale→ The M × N integration problem→ What MCP actually is→ MCP vs REST APIs vs Function Calling→ MCP Architecture (Host, Client & Server)→ The MCP Handshake→ Tools, Resources & Prompts→ Build a complete MCP Server with FastMCP→ Connect it to Claude Desktop→ Testing, Debugging & Security→ Production best practicesYou'll also learn:→ Why MCP is called the USB-C for AI→ How AI clients discover tools automatically→ Why your docstring is prompt engineering→ How the model decides when to call your tool→ Why you rarely write protocol code yourself→ Common mistakes that break MCP servers→ When not to build an MCP server→ How to take a local server to production with authentication, databases, and tracingOne of the biggest misconceptions is that MCP replaces your APIs.It doesn't.Your APIs still do the work.MCP simply provides a standard way for AI clients to discover and use them, eliminating custom integrations for every application.By the end, you'll understand that building an MCP server isn't about learning a new protocol.It's about exposing your existing tools in a way every AI client can understand.Save this guide for your next AI project.If you want to clear AI interviews 99% confidently, this Interview Kit is for you.Learn in depth → Practice → Perform → Crack the jobEnroll here: https://lnkd.in/guPzFkTe

You're wasting Claude's tokens without knowing it. Here's how to cut your usage 90%: 1. Convert files before uploading.A PDF page costs up to 3,000 tokens. I paste the text into a Google Doc, download as .md. Done. 90% cheaper. 2. Plan in Chat. Build in Cowork.Chat is cheap. Cowork is expensive. I figure out what I want in Chat first. Then I paste the plan into Cowork + Opus 4.6. Full guide here: https://lnkd.in/eyJ-SmrF3. Type "ask me questions" instead of a long prompt. My prompt is 30 words max:  "I want to [task] to [success criteria]. Read my folder. Ask me questions using AskUserQuestion before you start." 4. Edit your message. Never send a follow-up.Every "No, I meant..." gets stacked on top of the conversation. Claude re-reads all of it. The Edit button replaces the old message. Cleaner and cheaper. 5. Match the model to the task.Quick question → Chat with Haiku.Writing a report based on files → Cowork with Opus.Building a chart from data → Code with Sonnet.

Your iPhone can now run the largest local AI models. Borrowing compute from your MacBook 🤯LM Studio just shipped LM Link for iPhone.100% free. Works with every Open Source model you can run locally.The problem with local AI has always been hardware.Your MacBook or home server can run massive open models. Your phone can't. So the moment you leave your desk, you're back to cloud APIs and subscriptions.LM Link fixes that.Your models keep running on your Mac at home. Your iPhone connects to them securely from anywhere. You chat with your biggest local models from a coffee shop, a flight gate, the back of a cab.What you get:• Full access to your largest models, right from your phone• End-to-end encryption between your devices• All chats saved locally on your devices, nothing touches a cloud server• Works through the Locally app, now the official LM Studio mobile appSetup takes minutes. Install LM Studio on your Mac, turn on LM Link, download Locally on your iPhone, pair the devices.That's it. Your phone becomes a window into your home AI rig. Private AI used to mean being chained to your desk. Now your hardware travels with you.

Generative AI (Gen AI):Generative AI is revolutionizing the way we create, learn, and solve problems. From generating text, images, videos, music, and code to automating repetitive tasks, Gen AI is becoming a powerful tool across industries.📌 Key Highlights: ✅ Creates original content from user prompts ✅ Enhances creativity and productivity ✅ Assists in coding and problem-solving ✅ Automates workflows and saves time ✅ Powers applications like ChatGPT, Gemini, Claude, and CopilotAs AI continues to evolve, understanding Generative AI is becoming an essential skill for students, professionals, and businesses alike.

Excited to share my latest Gen AI project – StyleAI: AI-Powered Fashion Design Generator!While building StyleAI, my goal was to explore how Generative AI can make fashion recommendations more personalized, creative, and accessible.Here's how the system works:👗 Google Gemini AI powers intelligent outfit recommendations based on user preferences such as style, occasion, budget, and season.💻 Python & Streamlit provide a clean and interactive interface, enabling users to receive fashion suggestions in real time.⚡ Prompt Engineering is used to generate tailored recommendations, ensuring that every output aligns with the user's specific requirements.The project demonstrates how AI can transform traditional fashion discovery into a more personalized and engaging experience while showcasing the practical applications of Generative AI in solving real-world problems.A special thank you to Dr.Nanthini Mohan Mohan for her guidance and mentorship throughout this journey. Your sessions provided a strong foundation in AI concepts and inspired me to apply my learning through hands-on projects like this one.This project was developed as part of my learning journey during the Edunet Foundation AI Internship Program.

Code Llama 4 from ScratchThis lecture teaches how to code Llama 4 from scratch.Contents - Introduction to the course- Llama 4 Overview and Ranking- Course Prerequisites- Course Approach for Beginners- Why Code Llama from Scratch?- Understanding LLMs and Text Generation- How LLMs Predict the Next Word- Probability Distribution of Next Words- The Role of Data in Prediction- Probability Distribution and Word Prediction- Sampling Techniques- Greedy Sampling- Random Sampling- Top K Sampling- Temperature Sampling for Controlling Randomness- What are Tokens?- Tokenization Example: "Hello world"- How LLMs Learn Semantic Meaning- Token Relationships and Context- The Concept of Embeddings- Tokenization Challenges- Large Vocabulary Size- Handling Misspellings and New Words- Introducing Subword Tokens- Byte Pair Encoding (BPE) Overview- Understanding Vector Embeddings- Visualizing Embeddings- The Embedding Layer- Token Indexing and Swapping Embeddings- Coding Your Own Tokenizer- Implementing Byte Pair Encoding- Initializing Vocabulary and Pre-tokenization- Splitting Text into Words- Calculating Pair Frequencies- Merging Frequent Pairs- Updating Vocabulary and Tokenization Rules- Implementing the Merges- Encoding Text with the Tokenizer- Decoding Tokens Back to Text- Self-Attention Mechanism- Query, Key, and Value Vectors- Calculating Attention Scores- Applying Softmax- Weighted Sum of Values- Self-Attention Matrix Operations- Multi-Head Attention- Implementing Self-Attention- Masked Self-Attention- Rotary Positional Embeddings (RoPE)- Understanding Positional Information- How RoPE Works- Implementing RoPE- Feed-Forward Networks (FFN)- Linear Layers and Activations- Implementing FFN

Hello connections✨ Generative AI refers to a category of artificial intelligence algorithms and models that generate new content, such as images, text, audio, or video, based on patterns and examples in the data they are trained on. These models use techniques like deep learning and neural networks to understand and replicate the data distribution they were trained on, allowing them to create new, original content that resembles the input data.✨ AIMER Society - Artificial Intelligence Medical and Engineering Researchers Society#AIMERS#AI#text#audio#techInnovation

Everyone is sleeping on this new OCR model!(open-source, 91 languages, < 1B params)Datalab just released Surya OCR, which is just a state-of-the-art OCR model scoring 83.3% on the olmocr benchmark (top under 3B)- 650M params- supports 91 languages- 5 pages/s on RTX 5090- runs on CPU, GPU, MPS- 83.3% olmocr bench score (top under 3B)- Full layout information- Extracts + captions images and diagrams- Strong handwriting, math, form, table support

7 open source repos that do what cursor, perplexity, and v0 do combined zero waitlists. zero api markups. zero vendor lock in. save the list: » openhands // agentic coding platform. plans, edits files, runs terminal commands, browses the web on its own. mit https://lnkd.in/gmxhbTCH » zed // rust-native ai editor. gpu-accelerated, ships the open agent client protocol so any agent can plug in. 86k stars https://lnkd.in/g_w75viP » codex // openai's official terminal coding agent. runs in network-isolated containers for secure work. 94k stars, apache 2.0 github.com/openai/codex » scira (formerly miniperplx) // minimalist perplexity alternative. citations, focus modes, built on vercel ai sdk. 11.7k stars https://lnkd.in/g7KdmvQg » morphic // open source answer engine with generative ui. streams charts and comparisons inline with the answer. mit github.com/miurla/morphic » december // self-hosted bolt.new. $5 in api credits gets you 50+ full app generations. docker deploy in one command https://lnkd.in/gihiCf7B » libra // cloudflare-native v0 and lovable alternative. serverless, free tier friendly, byok https://lnkd.in/gVg9cPyU closed source is losing this race quietly. star them before your feed catches on.

384GB of VRAM. On your desk. Not in a datacenter. Autonomous, the company behind SmartDesk, just open sourced complete build guides for a personal AI computer that runs open models on hardware no one can switch off. It's called Autonomous Computer. Three configurations, one philosophy: a model you rent can be cut off overnight. A model running in your own house can't. → Home build: 2x RTX 5090 workstation → Team build: 4x RTX PRO 6000 Blackwell, 384GB VRAM → On-prem business build: 8x RTX 4090/5090, up to 256GB VRAM Every config ships with the full bill of materials, 3D printable and CNC housing files, wiring diagrams, BIOS tuning, and assembly photos. Software side covers OS setup, NVIDIA drivers, and serving open models through Ollama, vLLM, and llama.cpp. Fork it. Change it. Build it. Sell it. MIT License. 100% Opensource. Repo: https://lnkd.in/gA6z_E4k

You are very lucky if this is showing up on your feed before 4th July. 𝐃𝐨 𝐲𝐨𝐮 𝐡𝐚𝐯𝐞 𝐚 𝐡𝐚𝐫𝐝 𝐭𝐢𝐦𝐞 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐚 𝐫𝐞𝐟𝐞𝐫𝐫𝐚𝐥? 𝐈’𝐯𝐞 𝐜𝐫𝐚𝐜𝐤𝐞𝐝 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐡𝐢𝐫𝐢𝐧𝐠 𝐡𝐚𝐜𝐤. 𝐀𝐟𝐭𝐞𝐫 𝐜𝐨𝐮𝐧𝐭𝐥𝐞𝐬𝐬 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬, 𝐞𝐧𝐝𝐥𝐞𝐬𝐬 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐜𝐚𝐥𝐥𝐬, 𝐚𝐧𝐝 0 𝐣𝐨𝐛 𝐨𝐟𝐟𝐞𝐫𝐬. ❌ It’s not your resume ❌ It’s not your LinkedIn headline ❌ It’s not your “open to work” badge ✅ It’s your connections/networking I’ve prepared a sheet that includes all the openings along with LinkedIn profiles from Startups to MAANG and emails of more than 4500+ recruiters. If you are really looking for job Checkout: https://lnkd.in/gJ6gRzDN Or 💬 𝐂𝐨𝐦𝐦𝐞𝐧𝐭 "𝐒𝐇𝐄𝐄𝐓" 𝐚𝐧𝐝 𝐈'𝐥𝐥 𝐬𝐞𝐧𝐝 𝐲𝐨𝐮 𝐭𝐡𝐞 𝐬𝐡𝐞𝐞𝐭 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐭𝐨 𝐲𝐨𝐮𝐫 𝐃𝐌! #sde1 #sde2 #sde3 #sde4 #hiring #jobHunt #explore

I'm sooo bullish on open source AI. Was paying $15/month for a popular AI voice to text tool. But switched to an open source one where you download the model to your computer, and everything is done locally. It's better, faster, more private, and it's free! I wonder what other subscriptions I can get rid of?

Everyone's building a company brain. Almost nobody understands what that actually means.Karpathy's personal LLM knowledge base went viral. Garry Tan built GBrain. YC people are clearly paying attention to the category.But here's what most of these get wrong:A personal brain is small, fast, and permission-less. A company brain serves hundreds of people, enforces access controls, and has to heal itself when it goes wrong.Most tools are just personal brains scaled up. They break at the seams.Slite's thesis is different: → Company knowledge needs a verification layer → It needs to be legible to both humans and agents simultaneously → It needs to self-correct, not just storeAfter 10 years building knowledge bases and 2 years on AI search, they just shipped the first 𝐬𝐞𝐥𝐟-𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐢𝐧𝐠 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐛𝐚𝐬𝐞.The Slite Agent monitors 20+ connected tools, detects drift, proposes fixes, and queues them for human review. Claude, ChatGPT, Cursor - any MCP-compatible agent, can read and write to it directly.Accurate context at the company level is a different problem than accurate context at the personal level.Someone finally built for the right problem.The interesting part isn't that Slite added AI to docs.It's that they're treating company knowledge like infrastructure

Turn any GitHub repo into an RL training environment!Repo2RLEnv is an open-source tool from HuggingFace that synthesizes verifiable RL training data from any GitHub repository. Point it at any repo and it automatically generates tasks, verifies them, and pushes datasets directly to HuggingFace Hub.The core idea: every merged PR in any GitHub repo is a solved problem. Someone found a bug, fixed it, and the tests passed. Repo2RLEnv turns thousands of these real solved problems into structured RL training tasks automatically.The pipeline is end-to-end. It bootstraps a Docker environment for the repo, mines merged PRs, creates tasks with the broken codebase as the starting state, verifies each task passes the quality gate (tests fail before the fix, tests pass after), and pushes the final dataset to HuggingFace Hub.The reward is verifiable. Agent attempts a fix, tests run in the sandbox, pass means +1, fail means 0. No LLM judge needed for most pipelines.Nine synthesis pipelines cover different scenarios. PR diffs for fast text-only tasks. PR runtime for sandbox-verified real bugs. Mutation bugs for artificially injected failures. CVE patches for real security vulnerabilities. Equivalence tests for function-level synthesis. Each pipeline flows through the same QA gate before tasks are admitted.Works with any coding agent harness for evaluation. Claude Code, OpenHands, Codex CLI, Gemini CLI all supported out of the box via Harbor compatibility.Key capabilities:• Converts any GitHub repo into verifiable RL training tasks• 9 synthesis pipelines covering PRs, commits, bugs, CVEs, and refactors• Automated Docker bootstrap per repo, cached after first run• Verifiable rewards via test execution or diff similarity• Pushes datasets directly to HuggingFace Hub• Compatible with TRL, SkyRL, Prime-RL and other RL trainers• Works with private repos end-to-end

Your iPhone can now run the largest local AI models. Borrowing compute from your MacBook 🤯LM Studio just shipped LM Link for iPhone.100% free. Works with every Open Source model you can run locally.The problem with local AI has always been hardware.Your MacBook or home server can run massive open models. Your phone can't. So the moment you leave your desk, you're back to cloud APIs and subscriptions.LM Link fixes that.Your models keep running on your Mac at home. Your iPhone connects to them securely from anywhere. You chat with your biggest local models from a coffee shop, a flight gate, the back of a cab.What you get:• Full access to your largest models, right from your phone• End-to-end encryption between your devices• All chats saved locally on your devices, nothing touches a cloud server• Works through the Locally app, now the official LM Studio mobile appSetup takes minutes. Install LM Studio on your Mac, turn on LM Link, download Locally on your iPhone, pair the devices.That's it. Your phone becomes a window into your home AI rig. Private AI used to mean being chained to your desk. Now your hardware travels with you.

Your iPhone can now run the largest local AI models. Borrowing compute from your MacBook 🤯LM Studio just shipped LM Link for iPhone.100% free. Works with every Open Source model you can run locally.The problem with local AI has always been hardware.Your MacBook or home server can run massive open models. Your phone can't. So the moment you leave your desk, you're back to cloud APIs and subscriptions.LM Link fixes that.Your models keep running on your Mac at home. Your iPhone connects to them securely from anywhere. You chat with your biggest local models from a coffee shop, a flight gate, the back of a cab.What you get:• Full access to your largest models, right from your phone• End-to-end encryption between your devices• All chats saved locally on your devices, nothing touches a cloud server• Works through the Locally app, now the official LM Studio mobile appSetup takes minutes. Install LM Studio on your Mac, turn on LM Link, download Locally on your iPhone, pair the devices.That's it. Your phone becomes a window into your home AI rig. Private AI used to mean being chained to your desk. Now your hardware travels with you.

This shouldn't be free. Someone open sourced a fully local AI meeting notetaker that kills Otter, Fireflies, and Fathom in one Rust app. It's called Meetily. Every meeting gets recorded, transcribed, and summarized entirely on your own machine. Zero cloud. Zero API calls to a company you don't trust with your audio. The pitch isn't just privacy. It's speed too. → Whisper and Parakeet models both bundled in, 4x faster live transcription → Ollama handles summaries locally, or plug in Claude, Groq, OpenRouter, or any OpenAI-compatible endpoint → Captures your mic and system audio at the same time with intelligent ducking so nothing clips → GPU acceleration ships automatic. Metal and CoreML on Apple Silicon. CUDA on NVIDIA. Vulkan on AMD and Intel. → Import any old recording and re-transcribe it with a better model or a different language → Native app for macOS and Windows. Linux builds from source. The average data breach costs $4.4M. Every cloud notetaker on the market is a compliance risk waiting to fire. Meetily is what happens when engineers finally build the alternative. 13.2K stars. MIT License. 100% Opensource. https://lnkd.in/geyf7rsR

 the tool, its open-source MIT release, the speculative decoding mechanism, and the general performance direction are all confirmed.