Towards AI

An AI Agent Turned a Net $784K Into $5.3M of Debt. One Diff Catches It.

Mean Squared Error (MSE): The Loss Function That Makes Big Mistakes Expensive

Loss Functions Deep Dive — Part 4 of 30Continue reading on Towards AI »

How I Fine-Tuned Laya to Solve an Important Problem in Scientometrics — With Claude Code

Step-by-step instructions for fine-tuning ConvAI’s 33ms “System 1” decision model on your own yes/no task, plus what happened when I pointed it at Institution Name Disambiguation and got 99.2% accuracy across 163 countries.

What’s So Imaginary About Complex Numbers?

Understanding complex numbers through pairs, rotations, and a different multiplication.Continue reading on Towards AI »

Azure Document Intelligence or Content Understanding? Route by Document Type

Both services share one OCR and layout layer and split at field extraction. Here’s how that split maps to forms, contracts, RAG…Continue reading on Towards AI »

AI-Native Tag Insights in SLR: Visualizing Patterns and Inconsistencies

AI-Native Tag Insights in SLR transform how systematic literature reviews (SLRs) move from raw study lists to actionable evidence maps. In traditional workflows, reviewers manually code study characteristics, then struggle to spot cross-study patterns or conflicting findings.

The Critic Who Cannot Plan

K. Popper asked not who should rule but how to limit the damage rulers do. I put a machine (Jev) that cannot write or plan through Dörner’s lab of how people fail in complex situations, then made it coach to an AI mayor, to see what an AI critic can do for policy and what only the world can.

Daily Scrum Explained: Why Scrum Teams Meet Every Day and What a Scrum Master Really Does

Ever wondered why your team meets every single morning, and who that “Scrum Master” person really is?Continue reading on Towards AI »

Self-Adjoint Matrices and the Geometry Behind Symmetry

What does the adjoint tell us about a linear transformation?Continue reading on Towards AI »

What Is Jev? What the Tests Show and Who Should Try It

A new model called Jev is built for this kind of job. You give it the ticket and the possible answers, and it returns a choice with a probability, nothing else. A startup called TypeSafe AI released it on September 15.

Transform Any LLM into a Decision Model

Stop generating labels; read decisions from the model.Continue reading on Towards AI »

[Day 28/100] LangGraph Checkpointing: Make Your Agent Survive a Restart

What a checkpoint actually stores, how to move from InMemorySaver to SQLite and Postgres, and how to rewind and fork a run when it goes…Continue reading on Towards AI »

The Secret to Getting Better AI Answers Might Be Hiding in a 1986 Aircraft Manual

How ASD-STE100, an aerospace writing standard, can improve AI prompting and make LLM responses clearer, more concise, and easier to understand.

Everyone Told Me to Learn Julia. Here’s What I Found After Looking at the Numbers.

Julia is faster than Python and almost nobody uses it for AI. I wanted to understand why, and whether you should learn it anyway.Continue reading on Towards AI »

Build a Jev-Style Decision Model from an LLM

Agentic systems make decisions all the time: which tool to call, which category a request falls into, where to route a message. Each of these is a choice from a fixed list that is known in advance. LLMs, however, are text generators.

Deploying Memory Management: Production Roadmap

A 90-day architectural blueprint for scaling from prototype to mission-critical availability.

Claude Haiku 5.5 Earned a Spot in My Real-World Workflows

Last week I was staring at my AI usage, wondering if $4 was too much to pay to complete my project. Cost-avoidance is a feature and flaw of my personality, and it’s made Anthropic a recurring thorn in my side.

Google Shrank Its Embeddings to Fit a Phone. A Day Later, Perplexity Gave Every Token Its Own.

EmbeddingGemma 2 squeezes a document into one vector as small as 128 numbers. Perplexity’s pplx-embed-v2-late stores 128 numbers for every…Continue reading on Towards AI »