Introducing MLZero: A Multi-Agent System for End-to-End Machine Learning Automation

#Anthropic released #ClaudeHaiku45, a #scaleddown AI model offering similar performance to #Sonnet4 at a third of the cost and twice the speed. The new model is expected to be particularly appealing for free AI products, enabling new deployment styles and use cases, especially in software development tools. https://lnkd.in/ejZEaSZH #tech #media #news

𝗟𝗮𝗻𝗰𝗲𝗗𝗕'𝘀 🐰𝗥𝗮𝗕𝗶𝘁𝗤 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗕𝗹𝗮𝘇𝗶𝗻𝗴 𝗙𝗮𝘀𝘁 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵⚡ As embedding dimensions grow, the challenge of maintaining speed and accuracy in vector search becomes critical. Standard quantization methods can fall short. That's why we added support for #RaBitQ, 𝗮 𝗻𝗲𝘄 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲 𝗶𝗻 𝗟𝗮𝗻𝗰𝗲𝗗𝗕 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗲𝗱 𝗳𝗼𝗿 𝗵𝗶𝗴𝗵-𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘀𝗲𝗮𝗿𝗰𝗵 𝗼𝗻 𝗹𝗮𝗿𝗴𝗲-𝘀𝗰𝗮𝗹𝗲, 𝗵𝗶𝗴𝗵-𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗱𝗮𝘁𝗮. Unlike traditional IVF-PQ, RaBitQ offers: ✅ 𝗦𝘂𝗽𝗲𝗿𝗶𝗼𝗿 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Achieve up to 32x compression, significantly reducing memory and storage costs. ✅ 𝗙𝗮𝘀𝘁𝗲𝗿 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴: RaBitQ's indexing is faster as it doesn't require the costly training step for codebook generation. ✅ 𝗛𝗶𝗴𝗵𝗲𝗿 𝗥𝗲𝗰𝗮𝗹𝗹: Our benchmarks show improved recall, especially for embeddings with over 512 dimensions, making it ideal for modern multimodal models. In our latest blog post, we dive into the technical details behind #RaBitQ, share benchmark results on datasets like #DBpedia and #GIST1M, and show you how to start using it today. Read the Engineering Blog: https://lnkd.in/gTVAXkBv #LanceDB #VectorDatabase #AI #MachineLearning #DataEngineering #OpenSource #BigData #Quantization

🔥 Day 5 of #40DaysOfLangGraph — Working with Graph Nodes in LangGraph We’ve already learned how State keeps memory and how Memory Stores retain context across runs. Now, let’s get hands-on and explore the real building blocks of LangGraph — the Nodes :- 🧩 What are Graph Nodes? In LangGraph, Nodes represent units of logic or actions in your workflow. Each node performs a specific task — for example: - Calling an LLM - Querying a database - Generating a response - Making a decision Every workflow you create is basically a collection of nodes connected together! 🔁 How Nodes Work Together - You connect these nodes through edges, which define the data flow and decision logic. Think of it as: ➡️ Input Node → Processing Node → Output Node Just like a flowchart — but for AI! 🤖 💬 Example Use Case: In a customer support bot: One node handles user queries Another node fetches knowledge base info A third node generates the final answer Together, they create an intelligent conversation pipeline. ✨ Key Takeaways: Nodes = functional units of your workflow Each node performs one clear task Connecting nodes forms your AI pipeline Helps modularize, debug, and scale easily. #AI #ArtificialIntelligence #MachineLearning #Technology #Innovation #LangGraph #LangChain  #AgenticAI  #AIAgents  #SoftwareDevelopment #TechLeadership #ContentCreator #DeveloperLife #LearningInPublic #BuildInPublic #OpenSource Andrew Ng Krish Naik CampusX

Thrilled to share my latest computer vision project: Pill Counting & Instance Segmentation! The goal was to build a model that can accurately identify and count individual pills, a task with great applications in pharmaceutical quality control and healthcare. Using the Roboflow platform, I managed the entire end-to-end pipeline: Data Annotation: I meticulously annotated a dataset of images, drawing precise polygon masks (instance segmentation) for the "pill" class . Data Augmentation: To make the model more robust, I applied various augmentations like flips, rotations, and noise . Model Training: I trained a YOLOv7-seg model to perform the segmentation. Evaluation: The model performed exceptionally well, achieving a 99.4% mAP@50, 99.8% Precision, and 99.6% Recall! [Video: 2:24] This project was a fantastic hands-on experience in managing a computer vision workflow from start to finish. As a Data Science student, I'm passionate about building practical AI solutions and am always eager for the next challenge. But don't just watch the video. Want to see it in action? I’ve deployed the model to a public Telegram bot. Check the results yourself! Send it a photo of pills: 👉 https://t.me/PillAi1bot #DataScience #ComputerVision #MachineLearning #AI #Roboflow #YOLOv7 #InstanceSegmentation #DataAnnotation #DeepLearning #Portfolio #HealthcareTech

🚨 76,000 lines of code produced. More than 6 hours of continuous AI execution. That's what Eric (@realfunnyeric) has done on Replit, and its CEO confirmed it sets a record! I saw this over the weekend, and it hasn’t left my mind. Think about what it takes to make an AI run that long: - Decoupling features so they don’t break each other - Sequencing execution so dependencies don’t collapse - Documenting the future so the agent knows what’s next The hard part is definitely the architecture of intent: Plan, Design, and Anticipate. Impressive ... but I don't know yet if the product is working ☺ Link in the first comment below. #SoftwareEngineering #VibeCoding #IndustrializedCoding

I’ve successfully developed a Multi-Strategy Framework to tackle some of the most challenging Combinatorial Optimization problems — including the Travelling Salesman, Knapsack, and Graph Matching problems 🧠💡 This framework integrates multiple algorithmic paradigms to achieve high efficiency, scalability, and accuracy: 🔹 Greedy Heuristics 🔹 Divide & Conquer 🔹 Dynamic Programming 🔹 Backtracking Search 🔹 Branch & Bound Pruning I also conducted extensive benchmarking on real-world datasets, uncovering deep insights into the trade-offs between speed, accuracy, and resource utilization across different optimization strategies. A heartfelt thanks to Gaurav Mandloi Sir, Gurtej Singh , and our HOD Dr. Kamal Sutaria Sir, along with the byteXL team for their continuous guidance, mentorship, and support throughout this journey 🙏 Excited to build upon these learnings and explore how advanced optimization techniques can drive innovation in AI, logistics, and intelligent decision systems 💫 #InnovationInCS #AlgorithmEngineering #CombinatorialOptimization #DynamicProgramming #GreedyAlgorithms #Backtracking #BranchAndBound #AIOptimization #FutureOfAlgorithms #byteXL

Excitet to see the release of Tinker by Thinking Machines Lab — a fine-tuning API that gives researchers and developers control over algorithms and data, while abstracting away infrastructure headaches. Why this matters to me (and to anyone caring about quality in GenAI): More control over fine-tuning means we can enforce more rigorous validation, consistency checks, and safety constraints. Support is offered for switching from small to large models by simply changing one string and enabling scalabiltiy without the necessity of a new pipeline. Also standardised training will be oferred for better auditability -> Trustworthy AI is getting more accessible. #AIQuality #GenAI #ModelGovernance #FineTuning

In 2025, “smart AI” isn’t enough, it’s about strategic intelligence. Your RAG setup determines whether your system simply retrieves data… or truly understands it. Here are 3 advanced RAG strategies leading the next wave of GenAI: 🔹 Hybrid RAG – merges multiple sources for richer, context-aware outputs. 🔹 Corrective RAG – perfect for high-stakes use cases where accuracy must never fail. 🔹 Adaptive RAG – dynamically balances simplicity and depth across varying queries. AI success today isn’t one-size-fits-all, it’s about precision architecture. 👉 Which RAG strategy do you believe will redefine GenAI in 2025? #TriSeed #RAG #RetrievalAugmentedGeneration #GenAI #LLMs #MachineLearning #ArtificialIntelligence #AIfuture #DataScience #AITech

The Real Power of Engineering AI Isn’t in Code, It’s in Connection... AI engineering isn’t about just building models it’s about connecting data, workflows, and intelligence to solve real-world problems. I once built an AI data pipeline for a company drowning in reports. The result? Instead of daily Excel chaos, they got real-time insights that made decision-making 10x faster. Code smart, not hard. Engineering is valuable only when it drives measurable business outcomes. Engineers build things that never sleep then forget to sleep themselves. What’s the one business process you wish you could “engineer” once and forget forever? #AIAutomationExpert #AIEngineerExpert #AIAutomationSolutions #AIBusinessAutomation #AISmartEnterpriseAutomation #AIChatbotExpert #LLMExpert #SpargusSolutions #MuhammadZainArshad

How ignio tackles System Slowdowns with Precision? Ever faced sluggish systems and laggy applications due to high memory usage and swap delays? Traditional IT teams scramble to diagnose, tune vm.swappiness, or increase RAM - but what if AI could do this autonomously? Enter 𝗶𝗴𝗻𝗶𝗼'𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗳𝗼𝗿 𝗜𝗻𝗰𝗶𝗱𝗲𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - a purpose-built agent that perceives, reasons, acts, and learns across IT landscapes. Here's how it handles this challenge:  • its ability to perceive detects memory spikes and swap activity in real time using telemetry and logs.  • Its reasoning ability correlates these symptoms with historical incidents and system configurations, identifying root causes like aggressive dirty page thresholds or misconfigured swap settings.  • It's ability to act applies fixes autonomously:      1. Tune vm.swappiness to reduce swap dependency.      2. Trigger memory cleanup routines.      3. Recommend RAM upgrades if thresholds are consistently breached.      4. Accelerate dirty page write-back to disk to avoid swap delays. It learning ability continuously refines these actions by analyzing outcomes and feedback, ensuring smarter responses next time. 𝗕𝗼𝗻𝘂𝘀: All decisions are backed by Explainable AI, so IT teams can audit and understand every step taken. This isn’t just automation - it’s autonomous IT in action. With ignio, we move from reactive firefighting to proactive self-healing. Curious to see it live? Join our demo waitlist: https://lnkd.in/ga49st5M #AgenticAI #ignio #AIOps #AutonomousIT #DigitalTransformation #SelfHealingSystems #AIInnovation #EnterpriseAutonomy