AI articles
535 articles about AI, newest first.
- Fei‑Fei Li Reveals the Hidden Skill Gap in AI‑Driven Jobs | What Developers Must Know — Fei‑Fei Li explains the overlooked human skill behind AI job demand and why developers need to master context, ethics, and storytelling.
- Build Your Own AI Personal Advisor That Knows Your Principles, Goals, and History — Learn how to create a private AI advisor that stores your history, goals, and values, then gives you personalized advice. The video walks through data collection, fine‑tuning, and practical steps.
- Training AI Like a Skill: Peter Yang Explains the New Learning Paradigm — Peter Yang reveals how teaching AI through structured practice, feedback, and skill‑first training transforms LLM development and efficiency.
- The Missing Piece in AI That Nobody's Talking About — Discover the overlooked context layer that could transform how LLMs understand and generate information.
- Run Your Entire Business With a Single AI Agent—No Code Required — Learn how the Hermes AI agent automates proposals, invoicing, and daily tasks using no‑code tools. Perfect for founders wanting to scale without hiring.
- From Tokens to Cells: Building AI Foundations for Single‑Cell Biology — Learn how engineers adapt massive AI models to decode single‑cell biology data, turning raw gene activity into actionable insights.
- How Claude's AI Update Shook the Stock Market Overnight — Claude's latest AI upgrade triggered a market ripple, showing how a single model can drive real‑world stock moves.
- How a Frozen LLM Rewrites Its Own Workflow on the Fly — A frozen LLM reuses execution traces to reshape context, tools, and memory — no retraining needed. It lets AI agents adapt their workflow before answering.
- Local AI 201: Build Your Own Local LLM Setup in Minutes — Learn how to set up a local AI workstation, run models offline, and serve them via a simple API — perfect for developers and hobbyists.
- Why Your AI Agent’s Hidden Assumption Breaks Production: Lessons from Rate Limits — Discover how hidden design assumptions in AI agents cause production failures and what to do about them.
- How Medical AI Can Give Right Answers Without Visible Reasoning — Medical AIs can deliver correct diagnoses without showing their reasoning steps, challenging the need for explicit explainability.
- Run AI Offline: Keep Sensitive Files Private and Secure — Learn how to run AI models locally on sensitive documents, stay offline, and avoid data leaks — Microsoft’s enterprise approach explained.
- How Meituan’s LongCat 2.0 Trains a 1.6T MoE Model Without NVIDIA GPUs or TPUs — Meituan’s LongCat 2.0 shows a 1.6‑trillion‑parameter MoE model can be trained without NVIDIA GPUs or TPUs, using n‑grams, sparse attention and custom ASICs to cut cost and hardware reliance.
- World Model Collapse Explained: A Phase Transition in AI — Discover how AI world models can suddenly flip into chaotic behavior, what it means for LLMs, and why developers should care about this breakthrough.
- Why Renting AI Inference Doesn’t Pay: Own Your Cognitive Infrastructure — Discover why paying for AI inference is costly, and how owning the inference stack cuts costs, avoids lock‑in, and gives you full control.
- ChatGPT’s New Feature Redefines AI Automation – What It Means for Developers — Discover how ChatGPT’s latest breakthrough feature transforms AI automation, offering developers powerful new tools and real‑world impact.
- Why Auto‑Improvement Loops Need Domain Experts First — Discover how precise target functions and expert hand‑offs unlock efficient AI self‑improvement while saving tokens.
- Connect Claude and ChatGPT for Free with Frenemy – No API Keys Needed — Learn how the free Frenemy tool lets you link Claude and ChatGPT locally, no API keys or daemons required.
- Design AI Apps Users Love: UX Tips from Kathryn Grayson Nanz — Learn how to craft AI‑powered apps that users trust and enjoy, with practical UX patterns, transparency tricks, and developer insights.
- Autonomous Agents That Drive Real Scientific Discovery — How AI Hypotheses Are Built — Autonomous AI agents generate and test scientific hypotheses using real data, ontologies, and failure‑driven learning — unlocking faster discovery.
- Train AI Models to Be Self-Aware: Understanding Introspection Fine‑Tuning (IFT) — Explore how Harvard and MIT researchers teach small LLMs to introspect, turning blind guessing into self‑awareness. Learn the breakthrough technique reshaping AI transparency.
- Froglet: Verifiable Agent Commerce with Receipts, Not Just Tool Calls — Learn how Froglet lets AI agents trade services with signed receipts, cutting custom glue code and proving work across models and markets.
- Why Developer Experience Is the Real Secret Sauce in AI Tools — Discover how a smooth developer experience, not flashy frameworks, drives effective AI tool adoption and innovation.
- Can a Tiny Model Outsmart the Giants? How Fine‑Tuning Beats Frontier LLMs on Narrow Tasks — A fine‑tuned Qwen3 235B beats frontier models on investor filtering tasks, delivering higher accuracy at far lower cost. Learn why data strategy, not size, is the real moat.
- Fable 5 Returns: Safety Routing and Export Controls Shape the Next AI Wave — Fable 5 is back after a month of AI turbulence, introducing safety‑first prompt routing and new export controls that affect how models are released and deployed.
- Stop Building AI Agents the Old Way: 7 Proven Components for Reliable Long‑Running Agents — Learn the 7 core components to design AI agents that run reliably for hours or days, with practical workflow, observability, and memory tricks.
- Feature Flags for AI Agents: How to Ship Safely Without Breaking Everything — Learn how feature flags let AI teams safely roll out prompt, model, and tool changes, preventing user‑wide outages.
- The Geometry Behind AI Reasoning: How DiscoLoop Fixes Representation Drift — Explore how DiscoLoop aligns embeddings to fix representation drift in Transformers, boosting multi‑hop reasoning and out‑of‑distribution generalization.
- Claude's Long-Horizon Power: Building Reliable AI Agents — Discover how Claude handles extended tasks by separating planning from execution, self-verifying results, and learning on the fly — practical tricks for building robust AI agents.
- Kimi K3: The Open‑Weights Frontier for Agentic Coding — Kimi K3 is a 3‑trillion‑parameter MOE model with 1M context, native vision, and strong coding benchmarks, aiming to set a new open‑source frontier for specialized AI agents.