Towards AI

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

Author(s): Umair Ali Khan, Ph.D. Originally published on Towards AI. How AI decision models offer a fast and cost-effective approach to turning unstructured text into decisions Most of the organizational data is unstructured, such as incident reports, support tickets, maintenance logs, call-center transcripts, and customer reviews. Image source: https://stock.adobe.com (Licensed)The article explains why large language models are often an inefficient, high-latency, and sometimes unreliable way to

Author(s): Umair Ali Khan, Ph.D. Originally published on Towards AI. How AI decision models offer a fast and cost-effective approach to turning unstructured text into decisions Most of the organizational data is unstructured, such as incident reports, support tickets, maintenance logs, call-center transcripts, and customer reviews. Image source: https://stock.adobe.com (Licensed)The article explains why large language models (LLMs) are often a poor fit for “structured extraction” when the downst

Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. Anthropic’s SDK 0.128.0, released with Claude Opus 5.5, can hand the model a new tool mid-run without touching tools[]. It needs one beta flag the runner won’t add for you. If your Claude agent picks up a new tool halfway through a long conversation, the way you add it decides how much of the next request can still come from the prompt cache: 98.7% of it with the new runner.addTools(), none if you edit tools[]. After in

Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. Anthropic’s SDK 0.128.0, released with Claude Opus 5.5, can hand the model a new tool mid-run without touching tools[]. It needs one beta flag the runner won’t add for you. If your Claude agent picks up a new tool halfway through a long conversation, the way you add it decides how much of the next request can still come from the prompt cache: 98.7% of it with the new runner.addTools(), none if you edit tools[]. The arti

Author(s): Dave R | Microsoft Azure & AI MVP ☁️ Originally published on Towards AI. How each pattern handles identity, routing, and private networking, and which Foundry features stop working when a gateway sits in the path. Microsoft Foundry model availability is regional, so the model or Foundry Agent Service feature you need can live outside the region your project was approved for. Foundry supports four ways to reach it, and they differ in who owns identity, routing, and the network path. On

Author(s): Dave R | Microsoft Azure & AI MVP ☁️ Originally published on Towards AI. How each pattern handles identity, routing, and private networking, and which Foundry features stop working when a gateway sits in the path. Microsoft Foundry model availability is regional, so the model or Foundry Agent Service feature you need can live outside the region your project was approved for. Foundry supports four ways to reach it, and they differ in who owns identity, routing, and the network path. On

Author(s): Quan Huynh Originally published on Towards AI. Build an AI Agent Evaluation with JEV Build a small eval harness for a tool-using AI agent: code checks the work it did, and JEV judges the words it wrote. One run of my incident agent told me a checkout slowdown was caused by a config deploy that shrank the database pool from 50 connections to 5. It was right. The explanation was clear; it cited four tools, and it even ruled out a payment-provider warning that showed up later in the logs

Last Updated on September 25, 2026 by Editorial Team Author(s): luisacsfreitas Originally published on Towards AI. Confidence Comes From Experience: What XConf Changes About How We Measure LLM Confidence Anyone who has put an LLM in charge of a real decision knows the question that comes right after the demo: when do we trust it? Routing an email to the right team, approving a patch, answering a customer without review. In all of these we need a number that says “this one can go, this one goes t

Last Updated on September 25, 2026 by Editorial Team Author(s): Ankit Agrawal Originally published on Towards AI. Qwen-Image-2.1 tops both blind image arenas among downloadable models. Here is the VRAM, the speed on a 4090, and the licence. Qwen-Image-2.1 is 7 billion parameters. The download is 33 gigabytes. It runs in 15 GB of VRAM. The parameter count, the download, and what actually sits on your card are three different numbersThe article explains how to reconcile Qwen-Image-2.1’s seemingly

Last Updated on September 25, 2026 by Editorial Team Author(s): luisacsfreitas Originally published on Towards AI. Jev Doesn’t Write, It Decides: Games Today, Company Data with Care, Computer Vision Next Most of what we build with LLMs is not writing. It is deciding. Which team gets this email. Is this message spam. Which move should the bot make. Is this action allowed. We use a text generator for these decisions because it is the tool we have, and then we spend time parsing its prose, fixing i