LLM quantization techniques I'd learn if I had to fit a 70B model on a single GPU: (bookmark this) A 70B model in FP16 needs 140GB for weights alone. At 4-bit, that drops to 35GB, which fits on one card. But naive rounding fails on large models. Roughly 0.1% of hidden dimensions carry values up to 20x larger than anything else in the tensor, and they wreck the quantization grid for everything else. Each of these 5 methods handles those outliers at a different point: 1. RTN: ignores them. Rounds every weight to the nearest grid level with no calibration data. Cheapest option, weakest at low bit widths. 2. GPTQ: repairs after rounding. Quantizes a layer column by column and adjusts the remaining weights to absorb the error before moving on. 3. AWQ: protects before rounding. Finds the ~1% of weight channels that matter most and scales them up so they survive quantization. Everything still ends up in plain INT4. 4. LLM. int8(): isolates at inference. Outlier dimensions run in FP16, the other 99.9% run in INT8, and the results are merged. 5. QAT: solves it during training. The model is fine-tuned with rounding baked into every forward pass, so it adapts to the damage before quantization is actually applied. All five produce the same artifact, a model at a fraction of its trained precision. They differ only in where the outlier problem gets addressed. The visual below nicely summarise these techniques. There's a really good paper that provides a comprehensive study of LLM quantization techniques. I have shared the link to the paper in the first comment. ____ Share this with your network if you found this insightful ♻️ Follow me (Akshay Pachaar) for more insights and tutorials on AI and Machine Learning!

Building an AI application is one thing. Shipping it to production is another. Your RAG pipeline might work perfectly on your laptop. But can you deploy it reliably on any server? That's where Docker comes in. In this carousel, you'll learn how to containerize an AI application from scratch: → Docker Images vs Containers → Why AI projects need Docker → Dockerfile fundamentals → Layer Caching → .dockerignore → Managing Secrets → Multi-stage Builds → Non-root Containers → Health Checks → Volumes → Docker Compose → GPU Support → Production-ready Dockerfile → Essential Docker Commands → Common Docker Mistakes Along with: → Why python:3.11-slim is the preferred base image → How to reduce rebuild time from minutes to seconds → How to keep API keys out of your Docker image → Why AI applications should use multi-stage builds → How to persist vector databases and model weights → How to deploy RAG applications with Docker Compose → Best practices for production-ready AI deployments Docker isn't just about packaging your application. It's about making sure your AI system runs the same way everywhere without dependency conflicts, environment issues, or "works on my machine" problems. If you're serious about AI Engineering, Docker is a skill you can't skip. Save this for your next AI deployment.

This is a sharp, original observation — the X/Y dynamic is specific and insightful, naming a loop (AI writes, AI comments, both performing) that most "stop the AI slop" posts don't quite articulate. The closing thought ("learn to actually be good at things") is a genuine, standalone point of view, not a recycled platitude.

LangGraph isn't just another AI framework. It's a different way to think about AI systems.Many developers use LangGraph like LangChain — one node, one LLM call, one response. If that's your workflow, you probably don't need LangGraph. LangGraph shines when your application needs decisions, loops, memory, and human supervision.Here's how I think about it:➡️ State — Every node reads from the same shared memory. Design your state schema first; everything else depends on it.➡️ Nodes — One node, one responsibility (Retrieve, Grade, Generate). Don't build one giant node that does everything.➡️ Conditional Edges — The graph decides what happens next (generate, retrieve again, call a tool, ask for human approval). The workflow adapts at runtime.➡️ Parallel Execution — Break a complex question into smaller tasks, process simultaneously, merge results. Better latency without sacrificing quality.➡️ Checkpointing — Save state after every step, resume from failures, continue conversations across sessions. Stateless agents don't scale well.➡️ Interrupts — Pause before sending an email, executing code, or making expensive decisions. Some actions should always require human approval.➡️ ToolNode — Let the model decide when to use tools, standardize execution, avoid custom tool-calling logic whenever possible.➡️ Recursion Limits — Every loop needs a limit; every autonomous workflow needs a safety cap. Infinite reasoning is just an expensive bug.One lesson changed how I build AI agents: don't think of LangGraph as a chain — think of it as a workflow engine. A graph where every node has a purpose, every edge has a reason, every state change is intentional. That's what makes production AI systems reliable.

This is a sharp, specific critique — it names the exact tics (the X-not-Y framing, rule-of-three, buzzwords) that make posts feel formulaic, and it lands a genuinely good line ("you didn't get a lobotomy") that's memorable and in your own voice, not borrowed from a template.

Run your personal AI company with a team of AI agents! Alook is an open-source collaboration platform for AI coding agents. Self-hosted and local-first. The setup: Define an org structure. Give each agent a role - dev, ops, research, whatever you need. Set reporting lines. Alook gives each agent an email address. How it works: Assign a task to the right agent. They take it from there. Agents coordinate through email - passing deliverables, asking questions, updating status. You see everything in your inbox but you're not routing anything manually. Runs as an always-on daemon. Close your laptop, agents keep working. Come back to finished tasks. Shared memory across all agents. Every agent knows what every other agent worked on. You never re-explain context. After each task completes, Alook logs what worked and builds SOPs. The whole team gets sharper over time. Works with Claude Code, Codex, and OpenCode. Mix and match or run multiple agents from one runtime. Built-in Kanban for task tracking. Calendar for scheduling. Email for all communication. Agents pick up tasks autonomously, update their own calendars, close issues when done. Chat or email with agents like any AI tool. Install the runtime once, runs in the background. No terminal needed after setup. Key capabilities: • Email-based agent coordination with real inboxes • Org structure with roles and reporting lines • Shared memory and self-learning SOPs • Always-on daemon for 24/7 operation • Works with Claude Code, Codex, OpenCode • Built-in Kanban, calendar, and email • Self-hosted and local-first 100% open source. I've shared the Github Repo in the replies!

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