UrduBench
Author(s): ML Point Originally published on Towards AI. Measuring What AI Actually Understands About Urdu As large language models increasingly market themselves as multilingual, one critical question often remains unanswered: how do we verify that claim for languages outside the English-centric core? …
The AI CEO Who’s Warning Us About 2027 (This Isn’t Science Fiction Anymore)
Author(s): AbhinayaPinreddy Originally published on Towards AI. A Tech Billionaire Just Wrote 20,000 Words Explaining Why We Might Not Make It to 2030 Here’s something you don’t see every day: A CEO worth billions telling you his own technology might destroy civilization. …
Build LLM-Powered Documentation that Always Stays True to latest codebeases
Author(s): Cocoindex Originally published on Towards AI. A practical guide to using Pydantic, Instructor, and incremental processing with CocoIndex to generate always-fresh Markdown docs from source code. Code is Open-sourced, and available in Github. (Apache 2.0) ⭐ Star if you like it! …
Moltbook: Inside the AI-Only Social Network (A Quick Glance)
Author(s): Vivek Acharya Originally published on Towards AI. Moltbook: Inside the AI-Only Social Network (A Quick Glance) An early screen of the Moltbook homepage, highlighting its agent-only design (“A social network for AI agents where AI agents share, discuss, and upvote. Humans …
Entity Anchoring 2.0: What I Got Right About AI Visibility (And What I’ve Learned Since)
Author(s): Erica Pollock Originally published on Towards AI. Entity Anchoring 2.0: What I Got Right About AI Visibility (And What I’ve Learned Since) Back in December 2024, I published an article called “How ChatGPT Processes Entities — And How Your Brand Can …
From Questions to Insights: Data Analysis with LangChain’s Built-In Tools
Author(s): Vahe Sahakyan Originally published on Towards AI. In the first two articles of this series, we established why tools are essential for agentic systems and how those tools are constructed and orchestrated inside agents. What we deliberately avoided until now is …
How Tools Turn into Agents: What Actually Happens at Runtime
Author(s): Vahe Sahakyan Originally published on Towards AI. Many AI agent demos look convincing — until they fail in practice. Tools are defined correctly. Prompts seem reasonable. Yet the agent either calls the wrong tool, fails to call any tool at all, …
LAI #113: The Engineering Work That Decides Whether AI Holds Up
Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts, Shipping AI in 2026 is about operational discipline: catching data drift before users do, keeping inference fast as workloads grow, choosing architectures that survive real traffic, and understanding …
The Hard Limit of Prompting — and Why AI Agents Need Tools
Author(s): Vahe Sahakyan Originally published on Towards AI. People often believe that better prompts will eventually make AI agents reliable. They won’t. You can instruct a language model to “double-check its work,” “reason step by step,” or “be precise” — but none …
Zero-Sum vs Positive-Sum
Author(s): Shenggang Li Originally published on Towards AI. How Two-Agent Reinforcement Learning Explains Office Politics, Status Wars, and the One Move That Turns Conflict into Compounding On Monday morning, two people walk into the same meeting with the same goal: “make progress”. …
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