The 6 Optimization Algorithms: How AI Learns to Learn 10× Faster with 50% Less Memory
Author(s): TANVEER MUSTAFA Originally published on Towards AI. The 6 Optimization Algorithms: How AI Learns to Learn 10× Faster with 50% Less Memory You’re training a language model with 175 billion parameters. Image generated by Author using AIThis article explores six optimization …
OpenAI Hires OpenClaw Creator: The Illusion of the “Open” Agentic Future
Author(s): Mandar Karhade, MD. PhD. Originally published on Towards AI. When the architect of the open source agent revolution joins the closed source giant, we have to ask if innovation is being fostered or fenced in This is a fast one, short …
The Admin Work Killing Your Practice Has a Simple Fix You’re Probably Ignoring
Author(s): Bobby Tredinnick Originally published on Towards AI. Article Authored By Bobby Tredinnick LMSW-CASAC; CEO & Lead Clinician at Interactive Health Companies including Coast Health Consulting & Interactive International Solutions Created By OpenAI Clinicians across the field are exhausted. Not the kind …
Practical Local RAG with .NET and Vector Database
Author(s): Nagaraj Originally published on Towards AI. A complete guide to implementing Retrieval-Augmented Generation using .NET, LM Studio embeddings, and local vector storage — no cloud required. Did it never occur to you that ChatGPT could answer queries regarding your company’s documents …
GPU and CPU Utilization While Running Open-Source LLMs Locally using Ollama
Author(s): Muaaz Originally published on Towards AI. Large Language Models (LLMs) are powerful, but running them locally requires significant hardware resources. Many users rely on open-source models due to their accessibility, as closed source models often come with restrictive licensing and high …
Inside the Forward Pass: Pre-Fill, Decode, and the GPU Economics of Serving Large Language Models
Author(s): Utkarsh Mittal Originally published on Towards AI. Why Inference Is the Endgame Pre-training a frontier large language model typically consumes somewhere between 15 trillion and 30 trillion tokens. That sounds like an enormous number — until you do the arithmetic on …
WebMCP: Transforming How AI Agents Interact with the Web
Author(s): Jageen Shukla Originally published on Towards AI. WebMCP: Transforming How AI Agents Interact with the Web Imagine asking an AI assistant: “Book me a flight to New York for next Monday.” webmcp-agent-mcpWebMCP is a proposed web standard that aims to streamline …
You Don’t Need GPT-5 for Agents: The 1.2B Model That Beats Giants
Author(s): MohamedAbdelmenem Originally published on Towards AI. Forget GPT-5 for agent tasks. LFM 2.5 runs at 359 tokens/sec in 900MB. Here’s why it works and how to fine-tune it for your use case. 1400x overtraining. 900MB memory. 359 tokens/sec. Three lines of …
The boring AI That Keeps Planes in The Sky
Author(s): Marco van Hurne Originally published on Towards AI. One of the ways I keep myself busy in the AI domain is by running an AI factory at scale. And I’m not talking about the metaphorical kind where someone prompts an AI …
First-Principles Statistics for Cognitive Bias
Author(s): Shenggang Li Originally published on Towards AI. A practical, model-based way to stop getting fooled by “simple health rules” online Why do “one simple habit” posts feel so convincing? Photo by Teena Lalawat on UnsplashThis article explores the pitfalls of oversimplified …
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 …
I Analyzed 5,000 DAX Measures. Here Are The 5 Patterns That Kill Performance.
Author(s): Gulab Chand Tejwani Originally published on Towards AI. 18 seconds for one measure. The dashboard was unusable. I analyzed 5,247 DAX measures to find what kills performance. 78% had these 5 patterns. Fix one, get 14x faster. He clicks “Refresh” on …
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 …
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