LLM articles
511 articles about LLM, newest first.
- Thinner Agents on a Smarter Substrate: How Ontology Powers Autonomous Data Agents — Emil Eifrem reveals a thin-agent architecture powered by a shared ontology layer that automates data discovery, trust, and learning across databases.
- Workflows vs Agents: The Complete Guide to Building AI Agents (2025) — Explore the shift from static workflows to autonomous AI agents, learn core components, MCP, reasoning models, and why this matters for developers.
- How Multi-Agent AI Systems Stop Hallucinations Without Human Oversight — Discover how a second AI agent can catch hallucinations in real time, creating self-checking systems that eliminate manual oversight.
- Why Most People Are Using AI Wrong (And How to Fix It) — Discover the most common AI misuse mistakes and quick fixes to boost productivity and avoid pitfalls.
- How LLMs Use HTML to Generate Deterministic Videos | HyperFrames Deep Dive — James Russo explains how plain HTML, CSS, and JavaScript let small LLMs create deterministic video outputs, bypassing heavy frameworks.
- Kimi K3: How a 2.8T Open Model Uses MoE, Delta Attention & More to Compete with GPT‑5 — Explore Kimi K3’s 2.8‑trillion‑parameter architecture, Mixture‑of‑Experts, Stable LatentMoE, Delta Attention, and Attention Residual, and why they matter for open‑source LLMs.
- Watch Every AI Agent Move: Trace Cursor in LangSmith — Learn how to hook Cursor into LangSmith so every AI agent step is fully traced, with step‑by‑step plugin install, env var setup, and real trace inspection.
- Barr Yaron on the 2026 State of AI Engineering — What It Means for Developers — Barr Yaron reveals how AI engineering is reshaping software teams, costs, and agent workflows in 2026.
- Build $10K Premium Websites with AI: The Ultimate Guide — Learn how to create high-value sites using AI, spend under $25, and monetize them—step‑by‑step for entrepreneurs.
- The Desktop Frontier: Frontier LLMs Come to Your PC — Frontier LLMs are getting so efficient they can run locally on high‑end GPUs, shrinking the hardware gap and giving users control over their own AI.
- Why AI Agents Should Do the Homework Before You Click — AI agents automate the tedious prep work—reading, sorting, assembling—so you only click the final button. Discover the real value behind the hype.
- Why Your Agent Architecture Should Outlive Every AI Trend — Discover the timeless building blocks of durable agents and how to design them so you never have to rebuild when new trends arrive.
- Why AI Models Can’t Self‑Check (And What It Means for Developers) — Explore why most AI models skip self‑verification, how that leads to errors, and what developers can do to build safer, more reliable systems.
- Master AI Model Switching: Keep Your Apps Running Smoothly — Learn how AI model switching saves money and keeps user experience smooth by automatically swapping in better models when others lag.
- Fable 5 vs. Cheaper Models: When Expensive AI Actually Pays Off — Testing Anthropic's top model on real business tasks reveals where Fable 5 justifies its high cost and where cheaper alternatives win.
- Why AI Security Is the New Frontier for Developers | Snyk’s Randall Degges Explains — Randall Degges reveals why AI security is the biggest hurdle to scaling AI‑powered software, covering insecure code, rogue agents, and model access politics.
- AI Agents Repeat Forgotten Dongle Mistake — Why “Halt and Explain” Matters — Aaron Stanley warns that today’s AI agents bypass constraints like his old forensic software mistake, urging ‘halt and explain’ guardrails before new EU AI rules.
- AI Agents Write Code at Scale – But Who Checks the Bugs? — AI agents generate code faster than we can secure it. Learn why a deterministic verification layer is essential to stop security debt.
- Why Small Voice Models Win: latency, scaffolding, and smart design — Discover how a tiny, fast model can power a smart voice tutor by offloading planning to deterministic scaffolding — latency beats model size.
- Why LLMs Can't Own Your Flow: Building Reliable AI Agents with State Machines — LLMs excel at generating text but shouldn't control multi-step workflows; a harness enforces contracts and state transitions for reliable AI agents.
- Why AI Agents Flip‑Flop and How to Turn It Into a Strength — Discover why LLMs sometimes give contradictory answers, how to spot the "gray zone" of ambiguity, and practical ways to use inconsistency as a learning signal without costly retraining.
- $2.1B AI CEO: How Grant Lee Turned a Crazy Idea Into a Billion‑Dollar Company — Grant Lee shares the journey from a risky startup idea to a $2.1B AI company, revealing key insights for founders and tech leaders.
- 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.