My Clinical AI Agent’s Debug Logs Were a PHI Database. Here’s How I (Mostly) Fixed It.
Author(s): Marie Humbert-Droz, PhD Originally published on Towards AI. Every trace restates the patient’s note — in extractions, tool calls, and reasoning. I added observability to my local clinical AI agent, opened the trace viewer, and stared at a patient’s full name, …
The Missing Equation of AI
Author(s): Spyros Georgopoulos Originally published on Towards AI. The Missing Equation of AI Photo generated with ChatGPT AI as a battle of Empiricism and Rationalism Ask an LLM a question and it answers instantly, drawing on everything it has ever read. Ask …
Every AI System Has Two Architectures — the Diagram and the Invoice
Author(s): Tim Urista | Senior Cloud Engineer Originally published on Towards AI. Part of the ongoing “FinOps for Production AI” series. I’ve spent most of this series on the operational surfaces nobody puts on the whiteboard: cost per inference, attribution for agent …
Five Single AI Agents Is Not Microservices. It Is a Distributed Monolith
Author(s): Sandeep Chaudhary Originally published on Towards AI. Five Single AI Agents Is Not Microservices. It Is a Distributed Monolith Does splitting into multiple agents solve a real problem — or does it just look more sophisticated? Every AI architecture review I …
Empirical Reflections on the Software Development Lifecycle in the Age of AI-Assisted Engineering
Author(s): Ajaykumar Antin Originally published on Towards AI. Empirical Reflections on the Software Development Lifecycle in the Age of AI-Assisted Engineering Abstract This paper presents a case study on the design, implementation, and deployment of an internal engineering intelligence application developed in …
Lets Be Realistic About Kimi Open Source
Author(s): Caspar Bannink – AI Engineer Originally published on Towards AI. K3 deserves the headline This article was written after I saw an interview from the DataBricks CEO, which suprised me a lot. Kimi K3 has earned the excitement. Moonshot’s new model …
Apple Is Suing OpenAI. An Engineer Wrote “LOL, I Can Still Access the Server.” That Quote Is Now in a Federal Lawsuit.
Author(s): Dr Swarneendu AI Originally published on Towards AI. Apple Is Suing OpenAI. An Engineer Wrote “LOL, I Can Still Access the Server.” That Quote Is Now in a Federal Lawsuit. Over 400 former Apple engineers now work at OpenAI. Two are …
I Tried to Break an AI’s Security — Here’s Everything I Learned as a Complete Beginner
Author(s): Neha Khan • AI & Software Engineer Originally published on Towards AI. Part 1 of my AI Security Engineering learning journey A few weeks ago, I decided I wanted to move from full-stack development into AI Security Engineering. The problem? I …
Agent Harness Engineering vs. Loop Engineering vs. Graph Engineering
Author(s): ML Point Originally published on Towards AI. A practical guide to the three architecture layers people keep mixing together The confusion is understandable. All three ideas sit around the same model, all three influence reliability, and all three can contain “loops.” …
🦀 Building AI Agents in Rust – part 10
Author(s): Enzo Lombardi Originally published on Towards AI. A provider with no socket Every provider Eugene has spoken to so far, going all the way back to Part 6, ends the same way: a URL, a header, a JSON body over HTTP. …
AI, Machine Learning, Deep Learning, GenAI, and Agentic AI — What’s Actually the Difference?
Author(s): Revati Pawar Originally published on Towards AI. AI, Machine Learning, Deep Learning, GenAI, and Agentic AI — What’s Actually the Difference? Everyone uses these terms, yet almost nobody explains what they mean. Here’s the clearest breakdown — with real examples from …
![Optimizing LLM Token Costs in Production: A Practical Engineering Playbook [Part 3]](https://miro.medium.com/v2/resize:fit:700/1*K6V2x8CJ8JUoaLlA1aFXVA.png "Optimizing LLM Token Costs in Production: A Practical Engineering Playbook [Part 3]")
Optimizing LLM Token Costs in Production: A Practical Engineering Playbook [Part 3]
Author(s): Garvit Agarwal Originally published on Towards AI. Optimizing LLM Token Costs in Production: A Practical Engineering Playbook [Part 3] In Part 2, we focused on optimizing how requests are constructed before they reach the language model. We explored how techniques like …
Building Intelligent Feedback Systems: A Deep Dive into Conditional Agentic Workflows with LangGraph
Author(s): Sandip Palit Originally published on Towards AI. Building Intelligent Feedback Systems: A Deep Dive into Conditional Agentic Workflows with LangGraph The landscape of Artificial Intelligence has shifted dramatically over the past couple of years. We are no longer simply chatting with …
Kimi K3 Beat Fable 5 and GPT-5.6 Sol at Frontend Code — Then I Found the 51% Hallucination Rate
Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. Kimi K3 Beat Fable 5 and GPT-5.6 Sol at Frontend Code — Then I Found the 51% Hallucination Rate On July 16, Moonshot AI shipped Kimi K3 — a 2.8-trillion-parameter …
I Helped 50,000 People Set Up Claude Code. Here’s What I Didn’t Tell Them
Author(s): Anubhav Originally published on Towards AI. The setup was the easy 20%. Six months in, here’s what actually breaks, memory that rots without a warning, connectors that say “Connected” and aren’t, and subagents that report success for work they never did. …
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