Stop wasting hours trying to learn AI.One list.Zero confusion.No fluff.I’ve already done the hard work for you Complete AI Learning Documenthttps://t.co/DENBbHC0tNWhat’s inside:VideosLLMs, Agentic AI, real-world breakdowns (Stanford + more)GitHub ReposGenAI agents, prompt engineering, hands-on LLMs, beginner → advancedGuides & WhitepapersGoogle, Anthropic, practical agent designCoursesHugging Face, MCP, Vector DBs, end-to-end agent systemsBooksFrom fundamentals to LLM engineeringResearch PapersReAct, Generative Agents, Toolformer, and moreNewslettersStay updated without doomscrollingEverything is curated, sequenced, and practical.No random bookmarks. No hype.Repost for your networkLike · Save

Agentic-first RL training framework from NVIDIA! Molt is a PyTorch-native RL training framework built for agentic research. Three components make up the entire stack: Ray for placement and async queues, vLLM for rollout, and NVIDIA AutoModel with FSDP2 for training. That's it. The agentic-first design means reward is any Python you write. A mathematical grader, an LLM-as-judge, a multi-turn tool evaluator, a VLM environment. You define what "good" means inside an 'Env' or 'ChatAgent' class. No pretrained reward model required. Two agent interfaces cover different workflows. The 'Env' interface is Gymnasium-style - the framework owns the LLM loop and your `step()` returns a reward. The 'ChatAgent' interface gives you the loop via the OpenAI or Anthropic SDK, so any external harness that already speaks those APIs works without modification. The stack is intentionally minimal. One trainable actor, optional KL reference workers, fully async rollout and training overlap. Small enough to read end-to-end and understand every gradient. The same script that trains an 8B model scales to 1T-class MoE. DeepSeek-V3-class actors run at `ep_size 256` with TP, EP, and CP parallelism plus Adam CPU offload. No rewrite between scales. Supported algorithms: REINFORCE, REINFORCE baseline, RLOO, GRPO, DR-GRPO, GAE (PPO), and on-policy distillation. Key capabilities: • Reward is any Python function - graders, LLM-as-judge, multi-turn tools, VLM environments • Fully async rollout, training, and weight sync overlap • Scales from 8B to 1T-class MoE with the same script • Two agent interfaces: Env (Gymnasium-style) and ChatAgent (OpenAI/Anthropic SDK) • REINFORCE, RLOO, GRPO, DR-GRPO, GAE, on-policy distillation 100% open source I've shared the link in the replies!

Someone built a deep research system for Claude Code.It’s called HyperResearch.Give it one prompt. It searches the web, checks sources, finds conflicts, and builds a detailed report.→ Uses a 16-step research process→ Checks whether citations support each claim→ Runs four critics against every draft→ Saves sources in a searchable research vault→ Can resume a research run after a crash→ Supports quick reports and long research projectsThe developer says it leads an internal DeepResearch-Bench test. Third-party validation is still pending.It requires Claude Code and Python 3.11 to 3.13.The repo has over 1,000 GitHub stars and an MIT licence.

Anthropic wrote about a way to decrease RAG retrieval error rate by 67%. It’s called Contextual Retrieval and it improves RAG by adding meaningful context to each chunk before embedding and indexing with BM25. First, for each chunk of your document, a concise explanation of key details is generated. For example— In a medical study database, a chunk "Patient A showed symptoms of fatigue" becomes -- "In a 2022 clinical trial, Patient A (Group 1, receiving Drug X) showed symptoms of fatigue." These details situate every single chunk within the whole document. While creating the vectordb, embeddings are calculated on these contextualised chunks. Then, BM25 is also applied to capture exact keyword matches. BM25 is a ranking function that improves text retrieval by measuring word relevance using TF-IDF while also accounting for doct length and saturation effects. At query time, both methods are used to retrieve relevant chunks, which are ranked, deduplicated, reranked and passed to the model. Contextual Retrieval reduces retrieval failures by 49%, and combining it with reranking boosts the reduction to 67%. ♻️ Share it with anyone who works with production-ready RAG pipelines :) P.S. Liten AI writes for you on any website – emails, forms, DMs, Liten takes care of it all. Try a 10-sec online demo here: https://www.liten.tech/ Link to the full article: https://lnkd.in/gubfPfvN #AI #LLMs #RAG

𝗦𝗼𝗺𝗲𝗼𝗻𝗲 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗽𝘂𝘁 𝗮𝗹𝗹 𝗼𝗳 𝗟𝗟𝗠 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗶𝗻 𝗼𝗻𝗲 𝗽𝗹𝗮𝗰𝗲. ━━━━━━━━━━━━━━━ Inference knowledge is scattered. Half a paper here, a vendor blog there, the real answer buried in a GitHub issue from eight months ago. Modular open-sourced an LLM Inference Handbook that pulls it together. Free, no signup. ━━━━━━━━━━━━━━━ The calculators are the part I'd actually use: → GPU memory calculator — VRAM for a given model and config → KV cache calculator — the number that decides your batch size → Batching simulator — static vs dynamic vs continuous → Chunked prefill scheduler — see a long prefill stall your decodes → Quantization memory visualizer → GPU ↔ model matching table, NVIDIA and AMD Plus the written coverage: TTFT, TPOT, goodput, prefix caching, prefill-decode disaggregation, GPU architecture. ━━━━━━━━━━━━━━━ Two notes: → Append .md to any URL for markdown. There's an llms.txt index — feed the whole thing to your coding agent. → It's Modular's, so deployment framing leans their way. Fundamentals don't. Bookmark it. You'll come back to the KV cache calculator. 🔗 Link in the comments 👇 ♻️ Repost so it reaches someone sizing a GPU cluster this week.

🚀 Excited to share one of my recent AI projects!I built an End-to-End AI Voice RAG Assistant that automatically ingests documents from Google Drive, generates embeddings with OpenAI, stores them in Pinecone, and allows users to interact naturally via voice or text using Telegram.The solution combines n8n, OpenAI, Pinecone, Sarvam AI (STT & TTS), Google Drive, and RAG to create a fully automated knowledge assistant capable of semantic search and multilingual voice conversations.This project strengthened my experience in:🤖 AI Agents🧠 Generative AI & LLMs📚 Retrieval-Augmented Generation (RAG)🎙️ Voice AI (Speech-to-Text & Text-to-Speech)⚡ Workflow Automation with n8n📂 Vector Databases & Semantic SearchI'm continuously exploring ways to build intelligent AI solutions that solve real-world business challenges.I'd love to hear your thoughts and feedback! Thirumurugan R Manojkumar Vasudevan

ou don’t need to build every agent loop and tool integration from scratch. OpenManus is an open-source framework for building general AI agents for developers who want a configurable agent they can run themselves. It helps you turn a terminal prompt into a tool-using workflow by connecting an LLM to Python execution, browser control, file editing, human input, and optional MCP tools. Key features: • Terminal workflow – run one prompt from the command line or enter requests interactively • Built-in tools – execute Python, control a browser, edit files, and request human input • Model configuration – set the model, API endpoint, token limit, and temperature in TOML • MCP support – connect additional tools through stdio or SSE servers • Multi-agent path – an explicitly unstable flow adds planning and an optional data-analysis agent It’s open-source (MIT license). 🔗 GitHub: https://lnkd.in/dU-zkh8b ⸻ ♻️ 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

The three specific use cases (turning messy thoughts into posts, fast explanations, no judgment on questions) are concrete, personal, and plausible experiences — not generic AI hype.

I spent some time inside HuggingFace's speech-to-speech repo this week, and it's the best hands-on way I've found to learn how voice agents actually work.The repo builds a voice assistant as a pipeline of four steps:1. VAD (Voice Activity Detection): a tiny model that listens for when you start and stop talking2. STT (Speech to Text): turns your speech into text3. LLM: generates the reply as text4. TTS (Text to Speech): turns the reply back into audioYour voice goes as in as the input, the agent's voice is received as the output. That's the whole architecture.The key lessons for me were:- Speed is everything. The LLM is the slowest step, so nothing waits for anything to finish. As soon as the LLM produces the first sentence, the TTS starts speaking it while the rest is still generating. That's how these systems feel instant.- The hardest part isn't the AI, it's the turn-taking. Deciding whether you paused or actually finished talking comes down to the small VAD model and a few millisecond thresholds. Get this wrong and the agent talks over you.- Every step is swappable. You can pick your speech recognizer, point the LLM at OpenAI or at a model running on your own laptop, and choose your voice output. A fully local, fully open voice agent is a few flags away.This isn't a toy either. The same pipeline runs in production behind thousands of Reachy Mini robots.If you've been curious how tools like ChatGPT's voice mode work, cloning this repo and running it locally will teach you more than any explainer post.Github Repo Link in the comments. Connect/Follow for more such daily AI engineering insights, practical learnings, and deep dives.

LangChain is not the thing you need to learn. The architecture behind it is. I've interviewed people who could explain: • Chains • Agents • Memory • Retrievers • Tools But when I asked: "How does a user query actually travel through a RAG application?" The answers got vague. That's the problem. Many developers memorize LangChain components. Few understand how they work together. A production AI application is not: Prompt → LLM → Done It's usually: Document Loader → Text Splitter → Embeddings → Vector Store → Retriever → Prompt → Model → Output Parser → Memory → Monitoring Every component exists for a reason. Bad chunking? Retrieval quality drops. Wrong embedding model? Relevant documents disappear. No memory? Your chatbot forgets everything. No observability? Good luck debugging production issues. The biggest advantage of learning LangChain isn't the framework itself. It's learning the architecture of modern AI applications. Because once you understand: → Retrieval → Context Management → Tool Calling → State Management → Agent Workflows You can build the same systems with any framework. LangChain. LangGraph. LlamaIndex. DSPy. Or your own code. Frameworks change. Architecture stays. This carousel is one of the best visual breakdowns of how all LangChain components fit together into a real RAG application. Save it. You'll come back to it more than once. 👉 Looking to build AI solutions for your business? 📞 Book a Free Discovery Call: https://lnkd.in/gD6EwEYt ♻️ Repost if this helped. ➕ Follow AI Coach John for practical AI engineering, RAG, agents, MCP, system design, and production lessons.

The moment your LLM reaches production...Everything you learned from tutorials stops being enough.That's when LLMOps begins.The interview stops being about frameworks.It becomes about engineering decisions.Questions like these are becoming common:→ Why don't traditional ML metrics work for LLMs?→ How do you detect prompt drift before users notice?→ How do you test a non-deterministic system?→ What should you version besides the model?→ Why is retraining usually the last option in LLMOps?→ How do you separate retrieval failures from generation failures?→ How do you reduce token costs without hurting quality?→ When do you choose a chatbot, RAG, or an AI agent?These aren't trivia questions.They're designed to test whether you've built systems that survive production.Because in real-world AI systems, the biggest problems aren't model quality.They're prompt drift.Retrieval failures.Silent model updates.Evaluation.Observability.Cost.Reliability.That's what LLMOps is really about.This carousel contains 20 production-focused LLMOps interview questions that every AI Engineer should know.Not memorized answers.Questions that teach you how to think like someone building AI systems in production.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

Excited to share one of my recent AI projects! I built an End-to-End AI Voice RAG Assistant that automatically ingests documents from Google Drive, generates embeddings with OpenAI, stores them in Pinecone, and allows users to interact naturally via voice or text using Telegram. The solution combines n8n, OpenAI, Pinecone, Sarvam AI (STT & TTS), Google Drive, and RAG to create a fully automated knowledge assistant capable of semantic search and multilingual voice conversations.This project strengthened my experience in: 🤖 AI Agents 🧠 Generative AI & LLMs 📚 Retrieval-Augmented Generation (RAG) 🎙️ Voice AI (Speech-to-Text & Text-to-Speech) ⚡ Workflow Automation with n8n 📂 Vector Databases & Semantic SearchI'm continuously exploring ways to build intelligent AI solutions that solve real-world business challenges. I'd love to hear your thoughts and feedback!#AI #GenerativeAI #LLM #RAG #AIAgents #OpenAI #n8n #Pinecone #SarvamAI #VoiceAI #WorkflowAutomation #MachineLearning #Python #ArtificialIntelligence #Innovation

today marks the biggest upgrade to the mcp spec since its initial release!it’s stateless and much simpler to serve, plus a bunch of other improvements. incredibly proud of the work our team put in alongside many other members of the community.

🚨🚨🚨AWS just went live with the full deployment guide for Kimi K3!!!!Two paths, both landing on an OpenAI-compatible endpoint:1. SageMaker HyperPodCreate a cluster orchestrated by EKS, keep default Helm charts so the Inference Operator installs itself, add a worker group on `ml.p6-b300.48xlarge`. Then one `InferenceEndpointConfig` manifest and `kubectl apply -f kimi-k3.yaml`. The operator handles the Hugging Face pull, scheduling, health checks, endpoint readiness.2. Self-managed EKSTerraform modules from the AI on EKS project, NVIDIA device plugin, vLLM via Helm, exposed on port 8000.The real gate isn't the YAML. It's capacity: 8× B300 Blackwell Ultra per node, reserved through Flexible Training Plans or EC2 Capacity Blocks. No reservation, no deployment.Tip: sync weights to S3 first. Loading 2.8T parameters from HF is not a fast cold start.Full guide in the comments

Text is not the cheapest way to get text into a model.DeepSeek-OCR showed a rendered page costs roughly 10x fewer tokens, and 97% of the words still come back out.Here's what that changes about how you feed documents to an LLM:(The numbers are silly when you see them side by side).1,000 words as text: about 1,300 tokens.The same page rendered to an image: about 100 vision tokens.Under 10x compression DeepSeek-OCR decodes 97% of the text correctly. At 20x it still holds around 60%. You get to pick how much accuracy you trade for room in the window.This is not just a paper thing. It's already running in stuff you use.Gemini treats every PDF page as an image and bills it at a flat 258 tokens, no matter how many words are on the page. Glyph (ACL 2026) renders long text to images for 3-4x compression and about 4x faster prefill, which lets a 128K model take on million token workloads. Computer use agents have been reading screenshots instead of scraped text for a while now.The idea people are chasing next: downsample old conversation turns to blurrier images, so agent memory gets vaguer with age while it stays in the window.Where it breaks: anything that needs token level precision. API keys, exact quotes, code you plan to run, long IDs. Keep those as text and compress the prose around them.Try it out. Take a doc you keep pasting in, screenshot the pages, send those instead, and compare the token count against the answer quality.Would you let your agent keep a blurry version of turn 3, or does fuzzy memory break the whole thing for you?

While Generative AI has spent the last couple of years transforming IT, corporate offices, and marketing, the "Operational Technology (OT)" layer has largely remained a tough nut to crack. Traditional Gen AI is passive, it waits for a prompt. But industrial operations require autonomous action, absolute safety, and real-time reasoning. That is exactly where Agentic AI comes in. By evolving from passive chat systems to goal-driven "agents," AI is finally ready to bridge the gap between abstract digital reasoning and physical OT execution. I just published a deep-dive article breaking down the exact blueprint for making this happen.

While Generative AI has spent the last couple of years transforming IT, corporate offices, and marketing, the "Operational Technology (OT)" layer has largely remained a tough nut to crack. Traditional Gen AI is passive, it waits for a prompt. But industrial operations require autonomous action, absolute safety, and real-time reasoning. That is exactly where Agentic AI comes in. By evolving from passive chat systems to goal-driven "agents," AI is finally ready to bridge the gap between abstract digital reasoning and physical OT execution. I just published a deep-dive article breaking down the exact blueprint for making this happen.

OpenAI JUST open-sourced Codex Security CLI 🔥 It finds, validates, and fixes security vulnerabilities in your code: It's a CLI and TypeScript SDK under @𝗼𝗽𝗲𝗻𝗮𝗶/𝗰𝗼𝗱𝗲𝘅-𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆. Point it at a repo and it does four jobs: → Find: scans for security bugs, each rated by severity and confidence → Validate: separates real findings from noise, with evidence and location → Fix: suggests remediation and re-checks it on the next scan → Track: labels findings as new, persisting, reopened, or resolved across runs It runs three ways: local scans, a pre-commit hook that blocks high-severity bugs, and CI checks that fail the build past a severity threshold. It also reports its own coverage on every scan (complete, partial, or unknown) and lists what it skipped. And since it runs on a model, scans cost tokens, so there's a --𝗺𝗮𝘅-𝗰𝗼𝘀𝘁 flag to cap spend per run. Early beta. Needs Node 22+ and Python 3.10+. See what it can do: npx @openai/codex-security@latest --help Repo + npm + docs in the comments 👇 -- P.S. We’re building the best AI English tutor in the world. Try it to improve your speaking - it’s 15× cheaper than a human one → GetFluently.app

𝗞𝗶𝗺𝗶 𝗞𝟯 𝗶𝘀 𝗮 𝟮.𝟴 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗼𝗽𝗲𝗻-𝘄𝗲𝗶𝗴𝗵𝘁 𝗺𝗼𝗱𝗲𝗹. 𝗧𝗵𝗲 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝗲𝘃𝗲𝗿 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱.Moonshot AI dropped the weights last night, a day ahead of schedule.MoE, 1M-token context, native vision. Available on API since July 16 — now downloadable.━━━━━━━━━━━━━━Where it actually lands:→ #3 on Artificial Analysis Intelligence Index — behind only Claude Fable 5 and GPT-5.6 Sol Max, and cheaper than both→ #2 on the Vals AI index→ #1 in Frontend Code Arena→ Beats Claude Opus 4.8 and GPT-5.5 on coding and agentic evalsNot the best model in the world. Comfortably the best one you can download.━━━━━━━━━━━━━━The architecture is the story, not the parameter count:→ Kimi Delta Attention — hybrid linear attention→ Attention Residuals→ Stable LatentMoE — 16 of 896 experts active per token, about 1.8% of the pool→ MXFP4/MXFP8 training→ Roughly 2.5x the scaling efficiency of K2, per Moonshot2.8T total. A fraction of it firing on any given token.━━━━━━━━━━━━━━And they opened the infrastructure too:Attention kernels, the MoE communication library, and tooling for running agent environments at scale.That's the part that compounds. Weights get superseded in six months. Kernels and comm libraries get built on.━━━━━━━━━━━━━━Now the part nobody posting about this will tell you.You cannot run this.2.8T parameters at 4-bit is roughly 1.4TB of weights before you allocate a single token of KV cache. An 8×H100 node gives you 640GB. You need multi-node, high-speed interconnect, and disaggregated serving to make it work at all — Moonshot uses their own Mooncake stack for exactly this."Open weights" and "self-hostable" are not the same sentence. For most enterprises this is an API model that happens to be downloadable.If you want genuinely deployable, the 30B-class open models are still where on-prem lives.━━━━━━━━━━━━━━Two things to check before you build on it:→ Confirm the LICENSE file in the repo yourself. The previous Kimi release was Modified MIT. Terms do not automatically carry forward.→ There is an active US policy dispute around Moonshot, including public discussion of export controls. If you're deploying for regulated or government clients, factor that into procurement now, not later.━━━━━━━━━━━━━━The gap between the best closed model and the best open one is now measured in weeks, not generations.That changes the build-vs-buy maths for everyone.We help teams work out what's actually deployable inside their own infrastructure — and what only looks like it is.DM me if you're weighing open weights against an API for a regulated deployment.♻️ Repost if this is useful to your network🔔 Follow me for open model and on-prem deployment breakdowns

Most AI interview preparation goes wrong for one reason. People memorize answers. Interviewers don't care if you can define RAG, Docker, or LangGraph. They want to know whether you understand why, when, and how these technologies are used in production. After months of preparing, taking interviews, and creating AI engineering content, I realized I kept writing the same explanations over and over. So I decided to organize everything into one place. Introducing Roy's AI Lab – AI Engineer Interview Q&A 🧠 A free GitHub repository covering interview questions from fundamentals to production-level AI engineering. Inside you'll find: 📘 Python → Core concepts to advanced topics → OOP, decorators, generators, async → Interview-focused explanations ⚡ FastAPI & Backend → APIs, dependency injection, validation → Async programming → Production-ready backend concepts 🤖 AI & Machine Learning → Machine Learning → Deep Learning → Transformers → RAG → AI Agents & Agentic AI 🔗 LLM Frameworks → LangChain → LangGraph → Real-world interview scenarios → Production design questions ⚙️ Engineering & DevOps → Git & GitHub → Docker → Kubernetes → MLOps → AWS for AI 🎯 Every topic includes → 15–20 interview questions → Basic → Advanced progression → Production-level discussions → Code examples where relevant The goal isn't to help you memorize answers. It's to help you understand the reasoning behind them because that's what interviewers actually evaluate. I'll continue adding new topics and updating existing ones as I learn. ⭐ If you find it useful, consider starring the repository. 💬 Link to the repository 👉 https://lnkd.in/gFbAPTym Follow Ritesh Rai & Roy's AI Lab for more AI Engineering content. #Day154 of Documenting my Learnings & Building Meaningful Connections on LinkedIn.