AI articles and tutorials
In-depth explainers and tutorials on LLMs, AI agents, tools and the latest model releases.
- Testing Claude: From Zero to Autonomous Problem Solving — Parker tests Claude with increasingly hard tasks, showing how the model reads images, writes code, debugs, and knows its limits.
- Perception Agents: How AI Can See and Use Your Computer Like a Human — Antje Barth explains perception agents that can view UI, reason, and act with clicks — closing the gap between demo AI and real‑world collaborators.
- Why AI Coding Tools Fail When Architecture Is Ignored — Dex Horthy explains that AI coding tools succeed only when they’re trained to value clean architecture, not just test passes. Upfront design saves hours of later debugging.
- Mastering DSPy: Separate Task from Model with Signatures — Learn how DSPy’s Signature framework lets you define AI tasks by inputs and outputs, enabling flexible model swapping and self‑improving workflows.
- How AI Is Revolutionizing Web Testing: Automation, Speed, and Smarter Bug Detection — Discover how AI automates web testing, cuts testing time by up to 80%, and speeds up releases. Simple analogies, technical deep dive, and key takeaways.
- Should You Train Your Own LLM? Costs, Benefits, and Smarter Alternatives — Learn when training your own LLM makes sense, the resources required, and cheaper alternatives like fine‑tuning.
- How LLMs Turn Words Into Meaning: The Magic of Embeddings — Discover how embeddings turn words into numbers, letting LLMs understand context and meaning.
- AI Clones Lovable and Builds Notion in One Leap — Discover how a developer used two AI prompts to clone Lovable and then generate a functional Notion clone—showcasing AI‑driven self‑building software.
- Your Moat Is Your Data Model: How Gates Foundation Builds a Defensible AI Edge — Discover why the real AI moat is a well‑designed data model, as shown by Gates Foundation’s Neo4j knowledge graph powering agents.
- How monday.com’s Sidekick Turns Data into Real Workplace Context — Discover how monday.com builds a pre‑computed context layer with slow and fast engines to make AI assistants give smarter, timely advice.
- How OpenAI Built Its Data Agent: Architecture, Codex Integration & Engineer Takeaways — Explore OpenAI's data agent architecture, Codex integration, and five key lessons for engineers building AI systems.
- AI Slop & Detection: What Substack’s New Tool Can’t See — Substack’s AI detector flags slop but can’t gauge real thought—human intent remains the key gap.
- 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.
- Mastering Agent Auth: Secure Autonomous Access with Better Auth — Discover how Better Auth’s Agent Auth protocol lets AI agents negotiate permissions securely, simplifying authentication for developers.
- 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.
- Why Distillation Might Be Impossible to Stop: AI Convergence & Regulation — Explore how converging model architectures make AI distillation unavoidable and why regulators are watching.
- 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.
- How Bee Keeps Your Data Private Even Inside Amazon — Bee uses phone‑based keys, attestation, and confidential compute to keep your data private even from Amazon.