Loop Engineering + Graph Engineering: Building a Compounding Second Brain with OKF-based LLM Wiki
Author(s): Rick Hightower Originally published on Towards AI. From Session Loops to Persistent Graphs: OKF, the Second Brain, and Compounding Agent Systems Summary: This article shows how to stop paying for the same reasoning twice. It covers three foundations that decide whether …
Practical IP-Level Unsupervised Classification Using HDBSCAN and K-Means
Author(s): Bassem Essameldin Omar Originally published on Towards AI. Practical IP-Level Unsupervised Classification Using HDBSCAN and K-Means A recurring challenge in network analytics arises when two distinct consumer brands are operated by the same legal entity and share a single Autonomous System …
The AI Feature That Works in the Demo and Breaks in Production
Author(s): EMMANUEL NWANGUMA Originally published on Towards AI. Vendors sell a fix for it. I ran 833 tests across 6 AI models to see whether the fix actually works. Mostly, it didn’t. Imagine you hire a very fast, very well-read assistant to …
MCP Just Dropped 13 of Its 31 Methods. Why Did the Schema Still Grow 48%?
Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. MCP Just Dropped 13 of Its 31 Methods. Why Did the Schema Still Grow 48%? Every summary of the 2026-07-28 Model Context Protocol release says the same thing in the …
Matrices — Mathematics of Perceptions
Author(s): Wuiii Originally published on Towards AI. Matrices — Mathematics of Perceptions Note: This article assumes you have read about matrices but didn’t understand its geometric meaning or significance or didn’t intuite about it. The article helps understand while revisiting your already …
26 Important Concepts that you should learn in Linear Algebra for Machine Learning — PART II
Author(s): Rajendran S Originally published on Towards AI. Linear Algebra, being one of the most important subjects, is essential for anyone who’s working with data and predictive algorithms. This is Part II of the two-part linear algebra series. You can read Part …
26 Important Concepts that you should learn in Linear Algebra for Machine Learning — PART I
Author(s): Rajendran S Originally published on Towards AI. Machine Learning is the most fascinating field in the era of AI development. While many people put effort into learning machine learning, most of them fail to understand the core mathematics behind predictive models. …
The AI Agent Failure That Never Throws an Error
Author(s): Nick Hystax Originally published on Towards AI. The AI Agent Failure That Never Throws an Error Loops, drift, and recursion don’t crash your system. They just spend. Here are the three patterns and the four numbers that catch them. An agent …
The Evaluation Stack: Metrics That Predict Production Quality
Author(s): Armin Norouzi, Ph.D Originally published on Towards AI. The Evaluation Stack: Metrics That Predict Production Quality The hardest engineering problem in LLM deployment is not latency or cost — it is knowing whether your model got better. Metrics that seem rigorous …
The Right Way to Do AI Evals in 2026 (With Real Examples)
Author(s): Remy B. Originally published on Towards AI. The Right Way to Do AI Evals in 2026 (With Real Examples) Teams watch their AI more than they test it. Eval-driven vibe coding closes that gap with no eval platform and no research …
Ace the AI Engineer Interview: LLM Fundamentals
Author(s): Bixing Yan Originally published on Towards AI. Ace the AI Engineer Interview: LLM Fundamentals 0. Prep Principals Give a man a fish and you feed him for a day; teach a man to fish and you feed him for a lifetime. …
Your Test Suite Doesn’t Need a Frontier Model. It Needs a Cheap One That’s Fast.
Author(s): Shoaib Ahmed Quraishi Originally published on Towards AI. The QA model-routing problem: why the best AI model isn’t always the right model — and how to route testing work by reasoning complexity, cost, and failure risk. Imagine a regression pipeline finishes …
Data Engineering for RAG: Building Reliable AI with Better Data Pipelines
Author(s): GOWRI SHANKAR RAJU Originally published on Towards AI. Data Engineering for RAG: Building Reliable AI with Better Data Pipelines Introduction Retrieval-Augmented Generation (RAG) is a practical method for connecting Large Language Models to enterprise information. Rather than relying solely on training …
DeepSeek Didn’t Cut Prices. It Raised Them and Called it a Discount.
Author(s): allglenn Originally published on Towards AI. DeepSeek’s ‘discount’ will cost you 4x more. Here’s how to fight back. Before August 16, 2026, DeepSeek-V4-Pro output cost a flat $0.87 per million tokens. After August 16, the cheapest version of that same output, …
Tuning vLLM: What Every Setting Does to the Arithmetic
Author(s): Satsawat Natakarnkitkul (Net) Originally published on Towards AI. Tuning vLLM: What Every Setting Does to the Arithmetic The serving stack with the engine layer lit. Part five of seven. Where the previous parts turn into command-line arguments. Part 1: Start Here: …
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