AI articles and tutorials

In-depth explainers and tutorials on LLMs, AI agents, tools and the latest model releases.

• Herdr: Orchestrating a Herd of AI Coders in Your Terminal — Stop drowning in terminal windows. Learn how Herdr lets you manage multiple parallel AI coding agents through a single, clean interface.
• OpenAI Astra: The Model That's Solving 'Unsolvable' Math — OpenAI's unreleased Astra model has reportedly solved 10 open problems in math and CS, sparking a massive debate about the path to AGI.
• The Death of Monolithic AI: The Scaffold & Harness Paradigm — Explore the shift from giant, single-model AI to modular 'Agentic' frameworks that use scaffolds and harnesses to self-evolve and solve complex problems.
• The Dual-Judge Era: Why AI is Changing How Marketing Works — Discover how AI has shifted from a content creation tool to a gatekeeper that decides which brands get recommended and which disappear.
• Beyond the Prompt: The 9 Layers of Production-Ready AI Agents — Moving AI agents from demo to production requires more than a prompt. Discover the 9 architectural layers needed for reliability, safety, and scale.
• The Secret Formula for AI Reasoning: Joint Scaling Laws Explained — Discover how new research reveals a mathematical trade-off between pre-training and RL to optimize LLM reasoning capabilities.
• DeepSeek V4 Flash: Lightning Speed Meets LLM Intelligence — DeepSeek V4 Flash brings high-performance AI to the masses with insane speed and efficiency. Here is how it works and why it changes the game.
• The AI 'Catch-Up' Illusion: Why Benchmarks Lie — Discover why Chinese AI models seem to be catching up to the frontier, and why the real gap is hidden inside the labs.
• Beyond the Hype: What Actually Makes a Great AI Engineer? — Stop hiring for buzzwords. Learn the critical skills and mindsets that separate mediocre AI developers from world-class AI engineers.
• Stop Memorizing, Start Building: How to Actually Ace Your AI Interviews — Move beyond LeetCode and memorization. Learn why building end-to-end mini-projects is the secret to passing AI technical interviews.
• Stop Worrying: Does Pasting Data into ChatGPT Actually 'Train' the AI? — Learn the critical difference between training a model and using its context window, and why pasting data isn't the same as teaching the AI.
• Do We Actually 'Train' ChatGPT? Understanding the AI Learning Process — Ever wonder if ChatGPT learns from you in real-time? Discover the difference between pre-training, fine-tuning, and inference.
• Stop the Hack: Your Guide to LLM Security and Production Optimization — Learn how to protect your AI from prompt injection and optimize LLMs for production using guardrails, LoRA, and quantization.
• Slaying the Vector Cost Monster: How Turbopuffer Reimagines AI Storage — Explore how Turbopuffer leverages S3 to slash vector database costs from $100 to $1 per million vectors, making AI memory affordable.
• From Laid Off to AI Engineer: Building a Job Search Agent with Claude — Learn how Mads Lorentzen used Claude and a multi-agent AI workflow to automate his job search and gain 30K GitHub stars.
• Local AI Made Easy: Run Your Own AI Stack with ODS — Stop paying for API tokens and reclaim your privacy. Learn how to use the Open Deployment Stack (ODS) to run LLMs and AI agents locally.
• Qwen 3.8 Max: The 2.4 Trillion Parameter Powerhouse That Thinks in Long-Term Goals — Alibaba's Qwen 3.8 Max arrives with 2.4T parameters, crushing benchmarks and mastering agentic, long-horizon coding tasks.
• Metis: The New AI Transformer That Actually Remembers — Meet Metis, a groundbreaking transformer architecture that integrates memory directly into its attention mechanism, ending the need for external memory files.
• The AI Clock: Why Patience and Hardware Beat the Hype — Explore the tension between high-leverage AI bets and Apple's long-term hardware play, and why 'controlling the clock' is the secret to winning the AI race.
• Beyond the Wall of Text: Introducing MCP Apps — Discover how MCP Apps are transforming AI assistants from text-generators into interactive application hubs using the Model Context Protocol.
• Beyond Prompting: The Rise of Autonomous Problem-Finding AI — Stop perfecting your prompts. Learn why the future of AI is about letting agents find your business bottlenecks and solve them autonomously.
• Genspark: The AI Agent That's Replacing Your Search Subscriptions — Discover how Genspark moves beyond simple chat to create synthesized 'Sparkpages', changing the way we research and interact with AI.
• Building a Digital Finance Department: The Power of Multi-Agent AI — Learn how to replace manual financial workflows with a multi-agent AI system using Claude Code to automate revenue, spending, and reporting.
• Is Prompt Engineering Dead? Mastering the New Way to Talk to Claude Opus 5 — Stop using 'magic spells' and start using business briefs. Learn the 5 rules for high-performance prompting with Anthropic's Opus 5.
• Turn Your Folders Into a Brain: Mastering OKF, Gbrain, and Hermes Agent — Discover how to transform messy Markdown folders into a structured knowledge graph using OKF and Gbrain for smarter AI agents.
• Unlocking AI Agency: Understanding Agent Skills Repositories — Learn how 'Agent Skills' transform LLMs from simple chatbots into powerful autonomous agents that can execute real-world tasks.
• Attention Is All You Need: The Paper That Changed AI Forever — Discover how the Transformer architecture replaced old AI models and paved the way for ChatGPT and the modern LLM revolution.
• Beyond the Chatbox: Why MCP Tasks are the Secret to Reliable AI Agents — Discover how MCP Tasks move AI agents from simple request-response loops to durable, long-running workflows that survive crashes and network blips.
• Beyond the Prompt: Mastering Loop, Graph, and Harness Engineering for AI Agents — Stop relying on prompt engineering alone. Learn how Loop, Graph, and Harness engineering turn fragile AI demos into robust production-grade agent systems.
• The Math of Empathy: How Harvard and MIT are Giving AI a 'Theory of Mind' — Explore how Theory of Mind (ToM) allows AI to switch between social and non-social learning to become more socially intelligent.

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