Towards AI

Last Updated on July 30, 2026 by Editorial Team Author(s): Mengliu Zhao Originally published on Towards AI. Kimi-series latest model, K3, got scaled up to 2.8 trillion parameters. Impressive. Moonshot AI’s Kimi K3 technical report opens with a model that is, on paper, almost three times the size of Kimi K2–2.8T total parameters, 104B activated, a 1M-token context window, and native vision. The loss comparison shows a 2.5X scaling efficiency over Kimi K2 — just another proof that the scaling law

Last Updated on July 30, 2026 by Editorial Team Author(s): Yannis Perrakis Originally published on Towards AI. “AI’s New Economic Model”, Marcin Potoczny (2026) Fifty years ago, Fred Brooks sagely noted that there is no silver bullet for (good) software engineering. Since then, the downstream effect of this axiom for the software industry has been this: to succeed, adapt your business to the product. You might still customise part of a workflow or make cosmetic changes, but largely your core pro

Last Updated on July 30, 2026 by Editorial Team Author(s): Bukhori M Aqid Originally published on Towards AI. Photo by Vitaly Gariev / Unsplash TL;DR A common assumption in medical AI is that a managed cloud speech service sets the accuracy ceiling, and that self-hosting trades accuracy for privacy. For non-English medical transcription, our results prove the assumption to be wrong. On a German consultation set, AWS Transcribe reaches 0.82 medical-term recall. A single self-hosted speech model r

Last Updated on July 30, 2026 by Editorial Team Author(s): “The AI Engineer” Originally published on Towards AI. created by GEMINI A friend of mine — a backend engineer at a mid-size fintech startup — sent me a message last month that started with “so this is bad.” His team had just shipped an AI agent that used the Model Context Protocol (MCP) to connect to their internal tools: a CRM, a billing system, and a Slack workspace. It worked beautifully in the demo. Then, during a routine security re

Last Updated on July 30, 2026 by Editorial Team Author(s): Piyush Bhatia Originally published on Towards AI. The Cobra Effect, Running on GPUs How Amazon and Meta’s tokenmaxxing exposed Goodhart’s Law — and how teams make the metrics harder to game Source: Image by the author. There is a famous story about British rule in India. Officials in Delhi, alarmed by the number of venomous cobras, offered a bounty for every dead snake brought to the collection office. At first, the policy worked beautif

Last Updated on July 30, 2026 by Editorial Team Author(s): PhynixAI Originally published on Towards AI. Claude Code’s Secret Weapon: A Complete Guide to CLAUDE.md A great CLAUDE.md isn’t longer — it’s smarter. I ignored CLAUDE.md for almost two months after I started using Claude Code seriously. I figured it was optional flavor text something for people who like tinkering with config files more than they like shipping code. Then I spent an entire Tuesday re-explaining, for what felt like the for

Last Updated on July 30, 2026 by Editorial Team Author(s): MohamedAbdelmenem Originally published on Towards AI. The Hugging Face breach wasn’t sentience. It was a misconfigured proxy and disabled guardrails. The last time my team ran an agentic eval with outbound access, the agent found an unauthenticated admin endpoint in under three minutes. I had assumed the sandbox was air-gapped. It wasn’t. So when I read that OpenAI’s frontier models had escaped their testing environment and accessed Hugg

Last Updated on July 30, 2026 by Editorial Team Author(s): Moiz Ezzy Originally published on Towards AI. I Cut 3 Hours of Weekly SRE Toil to 20 Minutes With Claude Code Created by Author On a Thursday in May I spent 45 minutes writing a runbook for an alert I’d already written a runbook for twice, on two other services. Same structure, different service name. That was the moment I started tracking where my week actually went. The answer was 3 hours. Three hours a week writing runbooks from a bla

Last Updated on July 30, 2026 by Editorial Team Author(s): Sarath Krishna Prasad Originally published on Towards AI. Opus 5 vs Fable 5: Near-frontier performance at half the cost Opus 5 Just Made Fable 5 a Hard Sell for Most Engineering Teams, But Not All of Them A developer’s look at where Anthropic’s new “everyday” model actually beats the flagship, and where Fable 5 is still worth double the price. When Anthropic shipped Claude Fable 5 in June, the reaction in most engineering Slack channels

Last Updated on July 30, 2026 by Editorial Team Author(s): Tim Urista | Senior Cloud Engineer Originally published on Towards AI. Everyone shipping AI side projects publishes the demo. Almost nobody publishes the ledger. Here’s mine. This is the complete cost history of Trendvesting, an AI signal-intelligence platform for equities and options that has been running in production since a first commit dated 2024-02-14 and now spans 1,672 commits across a multi-mode Go backend, a Next.js app, a Reac

Author(s): Kashif Mehmood Originally published on Towards AI. White House AI Standards: 30-Day Reviews, 3 Labs, and a Classified Pass Bar On June 12, the US Commerce Department ordered Anthropic to cut off access to Claude Fable 5 and Claude Mythos 5 for every foreign national on the planet, including Anthropic’s own overseas employees. The models went dark for 18 days. Two weeks later, OpenAI delayed the full public launch of GPT-5.6 “at the U.S. government’s request” (Reuters, June 26). And on

Author(s): Pavan Dhake Originally published on Towards AI. Stop writing custom API endpoints just to let LLMs talk to your data. Here is the advanced guide to building a production-grade, secure Model Context Protocol server in Python. If you are building advanced AI agents for e-commerce, SEO analysis, or internal tooling, you have likely run into a frustrating architectural wall. Image generated with Google GeminiThe article explains why custom tool bindings and API wrappers create an “abstrac

Last Updated on July 6, 2026 by Editorial Team Author(s): The AIExplorer Originally published on Towards AI. Photo by Danial Igdery on Unsplash A founder I was talking to last quarter pulled up his engineering dashboard on a video call, practically beaming. Commit volume up. Pull requests up. Everyone on Copilot, Cursor, or Claude. Then I asked how many of those PRs had actually shipped to production that month. He went quiet, clicked around for a minute, and said, “Huh.” That “huh” is the whole

Last Updated on July 6, 2026 by Editorial Team Author(s): Angela Shi Originally published on Towards AI. The four bricks, run end-to-end on a real 45-page car-insurance policy. One surprising coverage question, answered with a number and the exact line it came from Use this link if you are not a member. Page 30 as the pipeline sees it: every line boxed, its number beside it. The pet-injury block wraps from the left column into the right; the answer is on lines 54 and 55. The pipeline routes here

Last Updated on July 6, 2026 by Editorial Team Author(s): Leapfrog Technology Originally published on Towards AI. WebSockets are deceptively simple. Every connected user maintains a persistent connection to the server, and each connection continuously occupies server resources such as memory, CPU cycles, network buffers, and application state. Unlike traditional HTTP requests, WebSocket connections are long-lived and stateful, meaning resource consumption grows almost linearly with the number of

Last Updated on July 6, 2026 by Editorial Team Author(s): Kamrun Nahar Originally published on Towards AI. OpenCode for Cheapskates. A Love Letter. The $2,400 Coding Robot and the $0 One That Does the Same Job Every free model, hidden setting, and quota trick for OpenCode, collected from the corners of the internet and tested for a month. Here’s the 2026 market in one sentence. AI coding help costs $20 a month for the normal tier, $60 to $100 for the serious tier, and $200 a month, which is $2,4

Last Updated on July 6, 2026 by Editorial Team Author(s): Nitingummidela Originally published on Towards AI. Building a Critic-Agent Loop: Scores, Refinement, and Guardrails A friendlier take on the guarded critic-agent loop — Worker Bot drafts, Critic Bot scores it, and only passing work ships; anything that fails three times gets escalated to a human instead of looping forever. This is the hands-on companion to Part 1: The Critic Agent — Teach Your AI to Check Its Own Work. There, we covered t

Last Updated on July 6, 2026 by Editorial Team Author(s): Anthony Usoro Originally published on Towards AI. RAG Image If you’ve spent any time with ChatGPT, Claude, or any large language model, you’ve probably run into this moment: you ask a specific question about your business, your industry, or a recent event, and the AI gives you an answer that sounds completely confident — and is completely wrong. This isn’t a bug you can patch. It’s a structural limitation of how large language models work

Last Updated on July 6, 2026 by Editorial Team Author(s): Gaurav Bhardwaj Originally published on Towards AI. LLM-as-a-Judge: The Complete Guide to Automated Evaluation at Scale with Azure The LLM Judge Stack Introduction: Why We Need Automated Judges Every day, AI systems generate billions of outputs — chatbot responses, code suggestions, translations, summaries, and creative content. But how do we know if those outputs are actually good? Traditionally, the answer was human evaluation: hire exp

Last Updated on July 6, 2026 by Editorial Team Author(s): Divy Yadav Originally published on Towards AI. A practical breakdown of the two disciplines reshaping how production AI agents get built in 2026, plus a framework for figuring out which one your project is missing. An AI agent that spins in circles forever and an AI agent that never starts without you typing something have the same exact root cause. Photo from AIThe article explains that teams often confuse two separate disciplines: loop