Artificial Intelligence

Author(s): Rittika Jindal Originally published on Towards AI. Choosing between a script, RAG, a fixed workflow, and an AI agent using four requests to the same support team. Imagine you’re automating a billing-support inbox. Four customers send four different requests: Does this ttask actually need an Agent?The article explains that whether you need an LLM, RAG, a fixed workflow, or an AI agent depends on separating message understanding from subsequent actions, and on what evidence is required

Last Updated on October 29, 2026 by Editorial Team Author(s): Sheqon Originally published on Towards AI. Reading, Writing, Sharing, and Interacting with the World through Video A Video Is a Trace of a World TL; DR. We treat a video as a partial, lossy, time-ordered observation of a world that keeps evolving whether or not the camera sees it. Four operations connect the two — reading (video → world), writing (world → video), sharing (one model does both), and interacting (the world advances under

Last Updated on October 29, 2026 by Editorial Team Author(s): Kasrasadatsharifi Originally published on Towards AI. Understanding the architectural decisions behind ConvNeXt, advantages and disadvantages over Vision Transformers and older CNNs. For years, CNN models dominated computer vision tasks such as classification, object detection, segmentaiton, etc. In 2020, with the introduction of Vision Transformers (ViTs) the focus shifted. They challenged CNNs dominance by introducing the global con

Last Updated on October 29, 2026 by Editorial Team Author(s): Hayanan Originally published on Towards AI. Calling everything AI is a little like giving every variable in a program the type any. It saves us the trouble of being specific. It also hides the mistakes we most need to catch. I would be worried about a codebase in which every variable was declared as any. Cover image — John McCarthy, one of the authors of the proposal that gave the field its name. Photograph: Tom Varco, Wikimedia Commo

Last Updated on October 29, 2026 by Editorial Team Author(s): Senthil Originally published on Towards AI. How the LLM (and the World Around It) Changed Forever — 2022 to 2026 Before we talk about tokens, let’s set the stage — because 2022 feels less like four years ago and more like a different geological era. The geopolitics: In February 2022, Russia launched its full-scale invasion of Ukraine — the largest land war in Europe since 1945. The world responded with sanctions, and energy prices wen

Last Updated on October 29, 2026 by Editorial Team Author(s): Hubert Nakielski Originally published on Towards AI. Five ways to wire memory into an AI agent, from vector RAG to temporal graphs to nightly “dreams”, drawn inside the whole harness, plus a 60-exchange demo that counts how long each one keeps the old answer. Every agent memory, whether a vector store, a graph or a folder of markdown files, can answer “what did the user tell me?”. They come apart when the user tells them something dif

Last Updated on October 29, 2026 by Editorial Team Author(s): David Pradeep Originally published on Towards AI. Building Crash-Safe Agent Workflows with Pi Durable and SQLite Moving autonomous coding agents out of interactive terminal loops and into unattended background workers exposes a brutal operational reality. When your Node process receives an unhandled SIGKILL, suffers an out-of-memory crash, or restarts during a multi-file refactoring sequence, standard stateless agent runtimes leave be

Last Updated on October 29, 2026 by Editorial Team Author(s): Montasir Mahmud Originally published on Towards AI. How vector search finds “I’m vegetarian” from “suggest dinner,” why it can’t tell a fact from its contradiction, and how to wire it into a LangGraph store Suppose a user named Priya asks your agent, “Suggest something for dinner tonight.” Somewhere in its long-term memory sits the single most relevant fact it has: “Priya is vegetarian.” The two sentences share no words. A keyword sea

Last Updated on October 29, 2026 by Editorial Team Author(s): Montasir Mahmud Originally published on Towards AI. Facts, past experiences, and self-written instructions each need their own storage, write trigger, and retrieval. Here are all three in one LangGraph agent. Picture the long-term store of an agent a week after launch. Under one user’s namespace sit diet: vegetarian, diet_update: eats fish now, and a one-off restaurant booking from last Thursday. They are all flat key-value pairs in o

Last Updated on October 29, 2026 by Editorial Team Author(s): Dhanya Shanbhag Originally published on Towards AI. Image generated with AI The accuracy of my regex and co-ordinate lookup based named entity recognition system broke suddenly from 94% to 67%. Only then did I realize that the purchase orders that were coming as input to my system were actually of different layouts. The header labels like the buyer name, delivery date, purchase order id etc. were all sitting at different places. The c

Last Updated on October 6, 2026 by Editorial Team Author(s): Codebook Fusion Originally published on Towards AI. Learn the three files that make Claude Code smarter: CLAUDE.md, settings.json and SKILL.md. Real examples, setup steps, and honest tradeoffs. For my first few weeks with Claude Code, I did something embarrassing. I typed the same instructions at the start of every session. Use pnpm, not npm. Run the tests before you say you’re done. Leave the migrations folder alone. Then I got annoye

Last Updated on October 6, 2026 by Editorial Team Author(s): Muhammad Soliman Originally published on Towards AI. Jev, Laya, Kev and Nimble in one place — plus a short, open proof of concept that puts one in front of a LiteLLM proxy and picks the model for every request. Ask your coding agent to rename a variable, and it will load a few hundred billion parameters to do it. Is it worth to pass everything to LLM as is and waste hundreds of thousands of tokens for simple questions/routing/decisions

Last Updated on October 6, 2026 by Editorial Team Author(s): Muhammad Soliman Originally published on Towards AI. The Number That Matters in Cloudflare’s Clef System-One Model Isn’t 38.8 ms — Jev and Laya compared Cloudflare just open-sourced Clef and Clef-flash, two “System One” decision models built on Qwen. The headline is speed. I went in to prove the fast one couldn’t say “none of the above.” I ran it on my laptop against Laya, and that hypothesis didn’t survive. What broke instead was more

Last Updated on October 6, 2026 by Editorial Team Author(s): “The AI Engineer” Originally published on Towards AI. Subtitle You asked the model for JSON. You wrote “return valid JSON only” in capital letters. You added an example. You added a second example. For 99 calls out of 100, it worked. image generated by GEMINIAfter the lead, the article explains the difference between prompting and constrained decoding: prompts shift probabilities via context but cannot force invalid tokens to zero, whi

Last Updated on October 6, 2026 by Editorial Team Author(s): Dave R | Microsoft Azure & AI MVP ☁️ Originally published on Towards AI. An AB-100 case study: read the guardrail annotation, find the category and severity, and relax one threshold while Self-harm stays strict. An oncologist asks a clinical notes assistant built on Microsoft Foundry to summarize the risks of a high-dose chemotherapy plan, and the app gets back an HTTP 400 with the error code content_filter. The same assistant still de

Last Updated on October 6, 2026 by Editorial Team Author(s): Satish Kumar Originally published on Towards AI. The developer-to-production pipeline for Snowflake’s Cortex Agent GA enhancements — Personal Database sandboxes, temporary agents, COPY GRANTS, and the cost math on Cortex Search suspension. Part 2 of 2 — Part 1: Your Cortex Agent Specification Is Visible to Every Role That Can Invoke It From Personal Database to Production: Building Secure, Governed & Cost-Efficient Cortex Agents Part 1

Last Updated on October 6, 2026 by Editorial Team Author(s): Enzo Lombardi Originally published on Towards AI. Directional Edits in DS4, or why a slider can do what a fork of the weights cannot Most of what you change about a language model’s behaviour you change from the outside. You write a system prompt, you pick a sampling temperature, you ask for brevity in the last line of the message. The model reads those words like everything else and decides how much to care. There is a second lever th

Last Updated on October 6, 2026 by Editorial Team Author(s): Programming India Originally published on Towards AI. Five simple ideas that make ML tutorials, code and errors finally make sense The first time my model scored 98% accuracy, I took a screenshot. I felt like a genius for about ten minutes. After the opening anecdote, the article lays out five beginner-friendly “machine learning basics” that explain why tutorials can fail in practice: start with data quality rather than algorithms, und

Last Updated on October 6, 2026 by Editorial Team Author(s): Kamrun Nahar Originally published on Towards AI. See why the area grows at the curve’s height, why plus C cancels, and where it finally breaks. Next time you’re in a car, look at the dashboard. There are two numbers there that most people never think about together. Watch the rectangles shrink until the area is exact, which is the whole idea of an integral in one loop.After the dashboard hook, the article develops calculus as “area in

Last Updated on October 6, 2026 by Editorial Team Author(s): Prachi rise Originally published on Towards AI. Deepseek-V3: Multi-Token Prediction — Part 3 This is the full series of Deepseek-V3 technical report, where i explain all the technical details in simpler words with code implementation and explanation. Deepseek-v3 MTP(Multi-token prediction)The article introduces multi-token prediction (MTP) as an extension of conventional next-token autoregressive modeling, explaining how training with