🚨 The #1 problem with local AI is now solved.There’s a new free tool called CanIRun AI that checks your hardware and tells you which models will actually run well before you download anything.So instead of guessing and hitting out-of-memory errors…it grades every model against your machine.What it does (right in your browser, no install):→ detects your setup (RAM / CPU / GPU / VRAM)→ scores each model for fit, speed, and context length→ grades every quantization level (Q4_K_M, Q6_K, Q8_0, etc.)→ labels what runs great vs okay vs too heavyIt covers most of the open-weight stack, Llama, Qwen, Gemma, Mistral, DeepSeek, Phi and more, pulling requirements from llama.cpp, Ollama, and LM Studio.Link: canirun.ai Screenshot 2026-08-24 at 6.17.11 PM100% opensource

You can now run a 2.8 TRILLION parameter model on a 4GB GPU.It’s called AirLLM, an open-source tool that uses "Layer-wise Inference." It only loads one layer onto your GPU at a time. so the VRAM you need depends on the layer size, not the model size.No quantization. No distillation. No pruning.→ DeepSeek-V3 (671B) on 12GB→ Llama 3.1 405B on 8GB→ Kimi K3 (2.8 TRILLION params) on under 4GB→ works with almost every open modelThe biggest model on it needs the LEAST VRAM. K3 is sparse MoE, so it streams only the experts a token actually routes to instead of a whole dense layer.2.8 trillion parameters running in less VRAM than a 70B.

Hey everyone!Just brushed up on my SQL fundamentals again while sitting through 8-hour-LONG!!!! tutorials. 😭I’ve noticed that even some paid courses don’t cover very basic concepts despite having 6+ hours of content.So, I decided to create a single ".ipynb" file that covers SQL from fundamentals to advanced concepts, with hands-on examples throughout.Here’s the link: https://lnkd.in/gsHP_wx9Let me know what you think And if you ever have doubts while learning, feel free to irritate me with DMs. 😂Hope it helps someone!

The University School of Computing, Rayat Bahra University, Mohali successfully organised the IEEE International Conference on Secure Information Systems and Technologies (ICSIST 2026) from 17–19 August 2026 at Rayat Bahra University.The conference provided a vibrant international platform for researchers, academicians, industry professionals, scientists, and practitioners to exchange ideas and present cutting-edge research in cybersecurity, secure information systems, artificial intelligence, blockchain, IoT, cloud and edge computing, digital forensics, privacy, and emerging technologies.The event featured keynote and invited talks, technical paper presentations, expert sessions, knowledge exchange, and opportunities for international research collaboration, bringing together distinguished experts from academia and industry across the globe.I would like to thank our program chair, organising chair, keynote speakers, session chairs, technical programme committee, speakers, reviewers, authors, delegates, volunteers, and the entire organising team for their valuable contributions in making ICSIST 2026 a successful academic event.🌐 IEEE ICSIST 2026📅 17–19 August 2026📍 Rayat Bahra University, Mohali, Punjab, IndiaCongratulations to the entire University School of Computing, Rayat Bahra University team! 👏Dr. Sahil Verma, B.T, M.T, Ph.D.(C.S.E) Postdoc-3, SMIEEE,ACM,IAENG#ICSIST2026 #IEEE #RayatBahraUniversity #UniversitySchoolOfComputing #CyberSecurity #SecureInformationSystems #ArtificialIntelligence #EmergingTechnologies #Research #Innovation #InternationalConference #AcademiaIndustryCollaboration #IEEEConference #RBU

OpenWorker -- an open source agent that doesn't just chat but completes tasks on your laptop -- just released a new version with many features for security workflows. After our initial release, many users found it especially useful for cybersecurity. Attackers are already using AI; OpenWorker is committed to giving defenders the same leverage. Running an agent requires both (i) A model and (ii) A harness (the software around the model). Because the OpenWorker harness is fully open source, security teams can audit it to make sure we haven't built any backdoors that exfiltrate your code and data to some company or even a foreign adversary.OpenWorker now comes with built-in cybersecurity agents for (i) Scanning your code for vulnerabilities. (ii) Scanning dependencies for supply chain injections. (iii) Checking your cloud security configuration for attack surfaces. This enables developers to do much more security work before deployment (part of what's called the "shift left" movement).You choose the model: you can run open weight models fully locally so sensitive code never leaves your machine. This helps with legitimate security work (like reproducing a known exploit to defend against it) that can trigger refusals in leading closed models. Or use your ChatGPT subscription, or stealth preview models like Ox Alpha, or any model via API key.Thanks also to all the open source contributors!Join work with Rohit Prsad so please follow him too to get more frequent updates. Try it out: https://openworker.com/Code: https://lnkd.in/gvZYspRv

Asked in Senior GenAI Interview Interviewer: Your LLM supports a 128K-token context window, but answer quality drops significantly when prompts exceed 50K tokens.Question:Why can performance degrade despite the model supporting 128K tokens? How would you diagnose and improve long-context performance?Explanation:1.Context window ≠ quality guarantee: 128K means the model can accept that many tokens, not that it can use every token equally well.2. Lost-in-the-middle: Important information placed in the middle of a very long context may receive less attention.3.Noise: Too many irrelevant documents can dilute the useful evidence and confuse the model.4. Attention cost: Longer sequences increase attention computation and can increase latency/memory pressure.5.Diagnose: Test different context sizes, position of relevant information, retrieval Recall@K, answer accuracy, and token usage.6.Improve: Use better retrieval/reranking, remove redundant chunks, summarize older context, prioritize relevant information, and use hierarchical/targeted retrieval instead of sending the entire 128K context.______________________________________Preparing for an ML or AI interview or Looking for Transition in AI?Check out the resource below, it covers key concepts with 1000 interview question Answers, Roadmap, Projects and practical topics to help you prepare with confidence.

Holy 😳 : OpenAI says its first custom inference chip is already beating Nvidia GB200 and GB300 systems on speed and efficiency.Across GPT‑OSS 120B, DeepSeek R1 670B, and Kimi K2.5 1T, Jalapeño delivered 1.5–1.9× more AI work per watt at peak throughput and 1.7–3.6× lower end-to-end latency in OpenAI’s own InferenceX testing!For highly interactive workloads, OpenAI reports 2.1–4.1× higher performance.The chip is rated at 700 watts, but remained at or below 550 watts during the tested workloads.OpenAI plans to begin deploying Jalapeño by the end of 2026. Gen 2 is already deep in development, with Gen 3 taking shape.Probably thats why Tibo said that in 1-2 years 750token/s will be the defaultREPOSTED

London, New York, LA and San Francisco offer the WORST value on earth 💸Valerii Emelianov mapped 100 cities on cost of living vs quality of life using Numbeo data:▫️ Migration magnets rank worst: high costs, expensive housing, services that stop matching expectations▫️ Porto, Valencia and Prague have held the top of the value ranking for 7 years, and all three keep drifting right as costs climb▫️ Europe has the widest spread on the planet, from Switzerland to the Balkans▫️ Sun Belt and Pacific Coast cities are climbing fast on domestic migration▫️ Some cheap winners are pockets of comfort for elites and tourists rather than the median residentQuality of life here means purchasing power, safety, healthcare, housing, traffic, pollution and climate

As a mom to a 3-year-old, I recently started researching schools in Charlotte — and quickly found myself jumping between school websites, maps, academic data, and lots of browser tabs.That sparked a question: 𝗖𝗼𝘂𝗹𝗱 𝗜 𝘂𝘀𝗲 𝗔𝗜 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘀𝗶𝗺𝗽𝗹𝗲𝗿 for parents?So I built 𝗖𝗵𝗮𝗿𝗹𝗼𝘁𝘁𝗲 𝗦𝗰𝗵𝗼𝗼𝗹 𝗦𝗲𝗮𝗿𝗰𝗵. 🎓What started as a simple school finder evolved into three parts:📍 𝗙𝗶𝗻𝗱 𝗦𝗰𝗵𝗼𝗼𝗹𝘀 — discover nearby schools on an interactive map and filter by level and type.📊 𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝗦𝗰𝗵𝗼𝗼𝗹𝘀 — compare schools using official NC performance, growth, math, and reading data.🤖 𝗔𝗜 𝗦𝗰𝗵𝗼𝗼𝗹 𝗔𝗱𝘃𝗶𝘀𝗼𝗿 — ask questions in plain English and get answers grounded in the same school data.For now, the app is intentionally restricted to Charlotte-Mecklenburg Schools (CMS) and relies on official CMS and NC DPI data wherever possible.What makes this project special to me is that it started with a real question in my own life — how do I make a thoughtful school decision for my daughter? — and became an opportunity to explore how AI can make real-world information easier to navigate.The goal was to build a practical application that helps parents explore Charlotte-area schools through a streamlined interface instead of manually piecing information together.🔗 Try it here:https://lnkd.in/eFz-23e3I’d love feedback, especially from Charlotte parents: What else would you want to know when researching a school for your child?A big shout out to Aishwarya Srinivasan, Arvind Narayanamurthy and The Gen Academy for demystifying so many of the buzzwords around AI.#AI #AgenticAI #GenAI #EdTech #Python #Streamlit #CharlotteNC #BuildInPublic

Open-source framework for building real-time voice AI agents!Pipecat is a Python framework for orchestrating audio, video, AI services, transports, and conversation pipelines. Voice-first architecture with pluggable components.What you can build: voice assistants, AI companions, multimodal interfaces, interactive storytelling, business agents (customer support, intake), and complex dialog systems.The framework handles speech recognition, text-to-speech, conversation logic, and real-time interaction. WebRTC and WebSocket transport built in. Ultra-low latency for natural conversations.Why Pipecat:• Voice-first: Integrates STT, TTS, and conversation handling in one framework • Pluggable: Supports multiple AI service providers for each capability • Composable pipelines: Build complex behavior from modular components • Real-time: Low-latency interaction with streaming audio/videoSupported services:• Speech-to-Text: Deepgram, AssemblyAI, OpenAI Whisper, Groq, Azure, AWS, Google, and more • LLMs: OpenAI, Anthropic, Gemini, Groq, Mistral, Ollama, AWS, Azure, and more • Text-to-Speech: OpenAI, ElevenLabs, Deepgram, Cartesia, Azure, AWS, Google, and more• Speech-to-Speech: OpenAI Realtime, Gemini Multimodal Live, AWS Nova Sonic, Ultravox, Grok Voice AgentI've shared link to the repo in the comments!

neo4j-labs/agent-memory gives agents three memory layers in one graph: conversation history, a long-term entity graph on the POLE+O model, and reasoning traces of what tools it called. Ships an MCP server and LangChain adapters. The audit trail links a reasoning step to the entities it touched, but only for tool calls mapped by hand; an unmapped call disappears from that query. 435 stars, Apache-2.0.#KnowledgeGraphs #Neo4j #AgentMemory #GraphDatabase

Your private AI agent shouldn’t require Docker and a terminal.Skales is a local-first desktop AI agent for people who want an agent to work with their files, browser, calendar, email, and code.It helps you delegate multi-step work from your own computer by combining background goals with desktop tools, coding workflows, and a choice of hosted or local models.Key features:• One-click desktop setup – install it on Windows, macOS, or Linux without Docker or terminal setup• Background goals – hand it a multi-step goal, close the chat, and let it continue until completion or a decision is needed• Built-in code workflows – bind a folder, review inline diffs, run tests, and undo file changes• Flexible model access – start with the built-in trial, connect 15+ providers, or run offline with Ollama or LM Studio• Mobile access – pair Android or iOS with your desktop via QR to use its tools remotelyPublic GitHub repo; the current Skales app is closed source under BSL 1.1.🔗 GitHub: https://lnkd.in/dYtj5U99⸻♻️ Share this with your network if you found it useful or insightful.✉️ If you’re into AI, ML, agents, and building real systems, join my newsletter (it’s free): dankornas.substack.com

I wasted 3 months building agents with LangChain before realizing LangGraph exists😭😭Here’s why I switched.LangChain works. You ship agents, it’s fine. But managing state between agent calls? You’re writing custom logic. Agent runs, you parse output, decide what’s next, route manually. Flexible but messy. One bad parse and your whole flow breaks.LangGraph is different. It’s designed specifically for agent orchestration.Instead of imperative loops, you define nodes (agent steps) and edges (transitions). State flows automatically through the graph. Agent A outputs to Agent B. Agent B loops back to A if needed. Routing logic? Built-in. State management? Built-in. Retry logic? Built-in.Practical example: I had an agent that needed to re-evaluate its own output. In LangChain, I manually tracked iteration count and state. In LangGraph, I defined a cycle in the graph. Clean.Another advantage: visualization. You see your agent flow as an actual graph. “Why didn’t Agent B get called?” Look at the graph, find the bug in 30 seconds instead of 30 minutes in logs.Quick decision tree:• Simple chains or chatbots? LangChain is fine. • Multiple agents, complex routing, state management? LangGraph wins. Less boilerplate, cleaner abstractions.For agentic AI, LangGraph is the better choice.#GenAI #AgenticAI #LangChain #LangGraph #LLMEngineering #SystemDesign #Python

Recently, I faced an interview for a hashtag#Generative hashtag#AI Engineer role, and the interview was heavily focused on RAG, LLMs, and production-grade AI systems.I thought of sharing some of the important RAG questions that were discussed during the interview. Hopefully, these will be helpful for anyone preparing for GenAI / AI Engineer / LLM Engineer roles. 🚀🔥 hashtag#Important hashtag#RAG Interview Questions🔹 What is RAG and why do we need it?🔹 RAG vs Fine-tuning — when would you choose which?🔹 Explain the complete RAG architecture.🔹 How do you decide chunk size and chunk overlap?🔹 What are embeddings and how do they work?🔹 How does vector similarity search work?🔹 How do you choose Top-K?🔹 What is Hybrid Search?🔹 What is Hybrid RAG and how is it different from Hybrid Search?🔹 Why do we need reranking?🔹 What is Query Rewriting and why is it useful?🔹 How do you improve poor retrieval quality?🔹 How do you reduce hallucinations in RAG?🔹 How do you handle questions when relevant information is not available in the knowledge base?🔹 How do you evaluate Retriever performance?🔹 How do you evaluate the final LLM response?🔹 How do you identify whether an issue is with Retrieval or Generation?🔹 How would you handle a large context window?🔹 How do you reduce latency in a production RAG system?🔹 How would you scale RAG from 100 queries to 100K+ queries?🔹 How would you scale a vector database from 10K to 1M+ documents?🔹 Where would you use caching in RAG?🔹 How do you monitor a RAG system in production?🔹 What are common security risks in RAG?🔹 What is Prompt Injection and how do you prevent it?🔹 How do you implement Guardrails in a RAG application?🔹 How would you design a multi-tenant RAG system?💡 Key takeawayModern GenAI interviews are going beyond just asking “What is RAG?”Interviewers are increasingly focusing on how you would build, evaluate, optimize, secure, monitor, and scale RAG systems in production.If you're preparing for a GenAI / LLM / RAG Engineer interview, I hope this list helps you identify the areas you should focus on.I’ll be covering these questions with practical explanations and real-world architecture examples in upcoming posts.What RAG topic do you find the most challenging?

Microsoft just open-sourced one of the best AI data analysis tools out there.It’s called data-formulator. Connect any source, CSV, Postgres, Bigquery, even a live URL, then build charts with a mix of drag-and-drop and plain english. The AI agent writes the SQL and transforms underneath and hands you a chart you can actually edit, not a code dump.- drag fields onto x/y/color and it builds the chart- anchor a cleaned result so follow-ups don't drift back to raw data- branch any chart to explore a variation - connect live data with auto-refresh

Hey everyone!Just brushed up on my SQL fundamentals again while sitting through 8-hour-LONG!!!! tutorials. 😭I’ve noticed that even some paid courses don’t cover very basic concepts despite having 6+ hours of content.So, I decided to create a single ".ipynb" file that covers SQL from fundamentals to advanced concepts, with hands-on examples throughout.Here’s the link: https://lnkd.in/gsHP_wx9Let me know what you think And if you ever have doubts while learning, feel free to irritate me with DMs. 😂Hope it helps someone!

You can now run a 2.8 TRILLION parameter model on a 4GB GPU.It’s called AirLLM, an open-source tool that uses "Layer-wise Inference." It only loads one layer onto your GPU at a time. so the VRAM you need depends on the layer size, not the model size.No quantization. No distillation. No pruning.→ DeepSeek-V3 (671B) on 12GB→ Llama 3.1 405B on 8GB→ Kimi K3 (2.8 TRILLION params) on under 4GB→ works with almost every open modelThe biggest model on it needs the LEAST VRAM. K3 is sparse MoE, so it streams only the experts a token actually routes to instead of a whole dense layer.Link: https://lnkd.in/exD-jrwE2.8 trillion parameters running in less VRAM than a 70B.

Recently, I faced an interview for a #Generative #AI Engineer role, and the interview was heavily focused on RAG, LLMs, and production-grade AI systems.I thought of sharing some of the important RAG questions that were discussed during the interview. Hopefully, these will be helpful for anyone preparing for GenAI / AI Engineer / LLM Engineer roles. 🚀🔥 #Important #RAG Interview Questions🔹 What is RAG and why do we need it?🔹 RAG vs Fine-tuning — when would you choose which?🔹 Explain the complete RAG architecture.🔹 How do you decide chunk size and chunk overlap?🔹 What are embeddings and how do they work?🔹 How does vector similarity search work?🔹 How do you choose Top-K?🔹 What is Hybrid Search?🔹 What is Hybrid RAG and how is it different from Hybrid Search?🔹 Why do we need reranking?🔹 What is Query Rewriting and why is it useful?🔹 How do you improve poor retrieval quality?🔹 How do you reduce hallucinations in RAG?🔹 How do you handle questions when relevant information is not available in the knowledge base?🔹 How do you evaluate Retriever performance?🔹 How do you evaluate the final LLM response?🔹 How do you identify whether an issue is with Retrieval or Generation?🔹 How would you handle a large context window?🔹 How do you reduce latency in a production RAG system?🔹 How would you scale RAG from 100 queries to 100K+ queries?🔹 How would you scale a vector database from 10K to 1M+ documents?🔹 Where would you use caching in RAG?🔹 How do you monitor a RAG system in production?🔹 What are common security risks in RAG?🔹 What is Prompt Injection and how do you prevent it?🔹 How do you implement Guardrails in a RAG application?🔹 How would you design a multi-tenant RAG system?💡 Key takeawayModern GenAI interviews are going beyond just asking “What is RAG?”Interviewers are increasingly focusing on how you would build, evaluate, optimize, secure, monitor, and scale RAG systems in production.If you're preparing for a GenAI / LLM / RAG Engineer interview, I hope this list helps you identify the areas you should focus on.I’ll be covering these questions with practical explanations and real-world architecture examples in upcoming posts.What RAG topic do you find the most challenging?#GenerativeAI #GenAI #RAG #LLM #ArtificialIntelligence #MachineLearning #AIEngineer #LLMEngineer #GenerativeAIEngineer #InterviewPreparation #TechInterview #Python #VectorDatabase #HybridSearch #LangChain #LangGraph

Microsoft just open-sourced one of the best AI data analysis tools out there.It’s called data-formulator. Connect any source, CSV, Postgres, Bigquery, even a live URL, then build charts with a mix of drag-and-drop and plain english. The AI agent writes the SQL and transforms underneath and hands you a chart you can actually edit, not a code dump.- drag fields onto x/y/color and it builds the chart- anchor a cleaned result so follow-ups don't drift back to raw data- branch any chart to explore a variation - connect live data with auto-refreshLink: https://lnkd.in/es58xgrjbring your own model key and it runs locally.

Attended a demo by Logan Markewich who walked through LiteParse a genuinely clever piece of engineering worth breaking down.What makes it different: there's no LLM involved at all. Just clean markdown or JSON across PDF, DOCX, PPTX, XLSX and scanned images, at roughly 2ms per page.For anyone building agent pipelines, that's the real value. Reading a document no longer requires a model call, which strips latency, cost, and a whole category of hallucination risk out of the ingestion step. Fast pipelines stay fast, and the agent gets usable structure immediately.RAG ingestion is the obvious application. The more compelling one, in my view, is real-time agent tool calls where speed and determinism are exactly what the workflow demands.