Artificial Intelligence

Author(s): N Selvaraj Originally published on Towards AI. The Verified Identity Agent Bridge Verify the human at the trusted edge, then carry that identity as explicit context to every downstream agent. Never collapse a user-initiated action into a shared service principal. The problem An enterprise wants its employees to reach an internal assistant from a commercial AI platform they already use every day. The assistant is a low-code agent: a Copilot Studio bot wired to an enterprise search inde

Author(s): Venkat Peri Originally published on Towards AI. You Can’t Prompt Your Away Your LLM Problems When an LLM feature breaks in production, the first instinct in the room is to open the prompt and reword it. We had that instinct too. Then we built a production assistant for financial advisors and kept a record of every LLM-related failure the system hit, along with the fix that actually closed each one. Across the whole build, almost nothing that mattered was fixable by editing a prompt. T

Author(s): Ramon Invarato Originally published on Towards AI. The Free Agent Trap Why agents that run on their own are still failing A standalone article from the series “AI and You”. You’ve been promised the same thing as everyone else: that artificial intelligence no longer just answers questions — it does the work on its own. You assign a task, step out for a coffee, and come back when it’s done. That’s the promise of AI “agents,” talked about everywhere. It sounds great. The problem is what

Author(s): Dr Swarneendu AI Originally published on Towards AI. After 500 cycles, a long-running agent is not the same agent that started. Its goal has shifted. Its constraints have eroded. This is measurable, preventable, and nobody has built the instrument. Until now. Fareed Khan’s long-running agent survived a host reboot. It survived context overflow. It survived 31 items over-scoped to 14. No image found in the provided HTML.The article explains why representational drift is mathematically

Author(s): Sarfaraz Merchant Originally published on Towards AI. Beyond Chat: Processing Images, PDFs, and Documents with the OpenAI Adapter in Oracle Integration Cloud Exploring File Uploads, Image Processing, and Document Extraction using the Native OpenAI Adapter in OIC. When Oracle introduced the OpenAI Adapter in Oracle Integration Cloud (OIC), most of the examples I came across focused on text generation and chatbot-style interactions. Naturally, I assumed that if I wanted to process files

Author(s): Enzo Lombardi Originally published on Towards AI. Skills as traits Two tools fit in a match statement. Three start to feel cramped. By six, the dispatcher is a swamp of clones, retries, error formatting, and case branches that all look almost but not quite alike. The agent loop is still simple. The space around it is not. The article proposes replacing the brittle match-based dispatcher with a typed trait approach: define each “skill” as its own trait implementation with associated in

Author(s): FS Stance Originally published on Towards AI. Self-Hosting Airflow at Home: Automating Stock Price Data Collection One of the main goals of creating my home lab is to gain a deeper understanding of Machine Learning Operations (MLOps) and how to productionalize AI workflows. Generally speaking, MLOps and productionalization deals with moving AI models from research into a real-life environment with automation and ability to handle errors gracefully. In my previous articles, I set up a

Last Updated on June 18, 2026 by Editorial Team Author(s): HyperDeep AI Originally published on Towards AI. The 76-Hour Frontier: How the Takedown of Claude Fable 5 Birthed the Military-Industrial-AI Complex On Tuesday, June 9, 2026, Anthropic released Claude Fable 5, the most capable artificial intelligence model ever shipped to a public API. By Friday, June 12, at 5:21 PM ET, it was completely gone. In a move that has sent shockwaves through Silicon Valley and permanently rewritten the boundar

Last Updated on June 18, 2026 by Editorial Team Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. I Trained a Markdown File to Boost GPT-5.5 by 23 Points — It Shouldn’t Work I did not fine-tune anything. I did not touch a single weight. I ran a training loop whose only output was a 1,400-token Markdown file, dropped that file into the context window of a frozen GPT-5.5, and watched its six-benchmark average jump from 58.8 to 82.3. That is +23.5 points from a text file

Last Updated on June 18, 2026 by Editorial Team Author(s): Services Ground Originally published on Towards AI. We Replaced ChatGPT With a Local AI Server. Six Months of Honest Data. This is not a “local AI is better” argument. It is a data argument. Six months ago, a number stopped me mid-scroll: Qwen 2.5 Coder 32B scored 92.9 on HumanEval. GPT-4o scored 90.2. HumanEval is the industry-standard coding benchmark — 164 programming problems across languages and problem types, designed to measure re

Author(s): Praveen Bhavani Originally published on Towards AI. Principal Component Analysis (PCA) is one of the most widely used techniques for dimensionality reduction and feature extraction. PCA transforms correlated variables into a smaller set of uncorrelated variables called principal components, while preserving as much information (variance) as possible. PCA is fundamentally a linear algebra and statistical method rooted in: Covariance structure analysis Orthogonal transformations Eigenva

Author(s): yooiken Originally published on Towards AI. Build a Zero-Cost Web Automation Pipeline With OpenRouter, OpenClaw, and MediaUse I have become less interested in whether a cheap model can “browse the web” and more interested in whether it can run a boring workflow correctly every morning. That is a different problem. Most low-cost or free LLMs fail at web automation because the model has to do too much at once. It has to understand the goal, inspect the page, decide where to click, recov

Last Updated on June 8, 2026 by Editorial Team Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. I Gave Qwen3.7-Plus a Screenshot and It Found the Exact Pixel to Click for $0.40 I uploaded a messy AWS console screenshot and asked one question: which pixel do I click to launch an instance? The model came back with click at (x=1147, y=283). I overlaid that coordinate on the image. It landed dead center on the orange "Launch instance" button. Then I checked the price: $0.

Last Updated on June 8, 2026 by Editorial Team Author(s): Suchit Majumdar Originally published on Towards AI. Beyond the Prompt: Why Autonomous AI Agents Are Replacing the Chatbot In May 2025, Sebastian Siemiatkowski — the same Klarna CEO who fifteen months earlier had told the world that one OpenAI-powered assistant was doing the work of 700 customer service agents — quietly started hiring humans back. Bloomberg got the quote: “Cost unfortunately seems to have been a too predominant evaluation

Last Updated on June 8, 2026 by Editorial Team Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. Why this matters right now A Chinese lab just shipped a terminal coding agent that does almost everything Claude Code does, released the entire thing under the MIT license, and pointed it at a model that costs $0.60 per million output tokens. Claude Code’s default model, Opus 4.8, costs $25 for the same million tokens. That is roughly 42 times more expensive on the part of

Last Updated on June 8, 2026 by Editorial Team Author(s): Satish Kumar Originally published on Towards AI. Connections, Roles, and Warehouses: Getting CoCo Desktop Production-Ready from Day One Snowflake COCO Desktop| Part 1 of 8 There’s a moment every data engineer hits when first opening Snowflake’s CoCo Desktop: the welcome screen looks clean, the interface is polished, and then the connect step appears. And if your organization uses SSO, has multiple accounts, or runs a non-default role setu

Last Updated on June 8, 2026 by Editorial Team Author(s): Anubhav Originally published on Towards AI. My First $5,000 Month Writing About AI Engineering on Medium In May, my Medium earnings crossed $5,000 from writing about AI engineering. A month earlier, the same account had done 10.9K views. Two months earlier, it was at 3.9K. The jump looks like an overnight success if you only look at the final revenue screenshot. The tempting explanation is that two posts exploded and carried the entire mo

Last Updated on June 8, 2026 by Editorial Team Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. Google Shrank Gemma 4 by 72% and Unsloth Fixed the 4-Bit Bug Nobody Else Caught on One 4090, and 4-Bit Shouldn't Be This Good A 26-billion-parameter model has no business fitting in 15GB of memory and spitting out 193 tokens a second on a single consumer GPU. That is laptop-and-gaming-rig territory, not a datacenter. Yet that is exactly what Google’s new Gemma 4 QAT checkpo

Last Updated on June 8, 2026 by Editorial Team Author(s): Atul Kumar Originally published on Towards AI. LangChain Explained: Understanding Models, Prompts, Chains, Memory, Indexes, and Agents Large Language Models (LLMs) such as GPT, Gemini, and Claude have made it easier than ever to build intelligent applications. However, developing production-ready AI systems often requires much more than simply calling an API. This is where LangChain comes in In this article, we’ll explore the core compone

Last Updated on June 8, 2026 by Editorial Team Author(s): Sourav Ghosh Originally published on Towards AI. Is JSON Finally Getting a Token-Efficient Alternative for LLMs? For years, JSON has been the default language for APIs, integrations, configuration files, event payloads, and all other types of application-to-application communications. It is an easy language to understand, it is very robust and developers can easily exploit it. But when we transition from traditional software systems to La