Safer Filesystem Tools for AI Agents Using MCP and S3
Author(s): Luna Originally published on Towards AI. Claude Code style file operations backed by S3, with strict scoping, audit logs, and ETag concurrency guards. Most agents need file access to do real work. But “just mount my laptop” is a risky default. …
SEMANTIC-WORM: Studying Information Propagation Patterns in LLM-Based Agent Networks
Author(s): Antares Originally published on Towards AI. Abstract As LLM-based autonomous agents move from research prototypes into production workflows, understanding how information propagates through multi-agent systems becomes a critical safety concern. We introduce SEMANTIC-WORM -a controlled experimental framework for studying how semantic …
What I Learned Building a Job-Matching System in Hebrew: Reversed Text, I/O Psychology, and When to Ditch the LLM
Author(s): Tom Ron Originally published on Towards AI. This is Part 2 of a series on building job-matching systems. Part 1 covered why job matching is fundamentally harder than it looks. This post is the technical deep-dive. In Part 1, I wrote …
How to Run Coding Agents in Parallel
Author(s): Eivind Kjosbakken Originally published on Towards AI. Get the most out of Claude Code In the last few years, coding agents have become more and more prevalent. Initially, coding agents could only auto-complete specific lines of code. We then experienced how …
The Roadmap of Mathematics for Machine Learning
Author(s): Tivadar Danka Originally published on Towards AI. A complete guide to linear algebra, calculus, and probability theory Understanding the mathematics behind machine learning algorithms is a superpower. Here’s the full roadmap for you.This article presents a comprehensive curriculum that guides readers …
I Built a Job-Matching Algorithm. Now I Understand Why LinkedIn Struggles.
Author(s): Tom Ron Originally published on Towards AI. Why job recommendations are so bad, and why fixing them requires psychology, not just better embeddings. I recently built a job-candidate matching system from scratch. Not a toy. A production system that takes real …
The Complete Guide to RAG: Why Retrieval-Augmented Generation Is the Backbone of Enterprise AI in 2026
Author(s): Faisal haque Originally published on Towards AI. How a simple architectural pattern became the $11 billion standard for making AI actually useful — and how you can master it Eighteen months ago, a VP at a Fortune 500 company asked me …
3 Game-Changing Tools for Modern Data Science
Author(s): Mohamed Abdelsalam Originally published on Towards AI. Introduction The rise of LLMs facilitates “vibe coding,” making it fast to generate initial Python scripts. However, this ease creates a false sense of progress. Building professional-grade data products requires more than quick scripts; …
The Rise of Synthetic Labor
Author(s): Sam Okoye Originally published on Towards AI. Abstract Advanced economies are entering a sustained structural labor deficit driven by demographic decline, aging populations, and persistent sector-specific shortages. Traditional automation, including robotic process automation and narrow task-based systems, has delivered productivity improvements …
Ollama vs vLLM vs Unsloth: A Detailed Comparison from an AI Engineer’s Perspective
Author(s): Neel Shah Originally published on Towards AI. As an AI engineer, choosing the right tool for deploying or fine-tuning large language models (LLMs) is crucial for balancing performance, ease of use, and hardware constraints. Among the many options, Ollama, vLLM, and …
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