LLM articles
511 articles about LLM, newest first.
- 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.
- Stop Trusting the Score: The Truth About 'Benchmaxxing' in AI — Explore why AI benchmark scores often lie and how 'benchmaxxing' creates a gap between paper performance and real-world utility.
- Stop Building 'Wrappers': The 5 Levels of AI Survival — Learn the 5 levels of AI building to ensure your product isn't wiped out by the next OpenAI or Anthropic update.
- Beyond AI Slop: How to Teach LLMs 'Taste' — Learn how Taste Labs is fighting 'collapse to the mean' by decomposing subjective design into trainable data to end generic AI output.
- Precision Over Intuition: Why 'Dumber' AI Might Be Your New Favorite Tool — Explore why ChatGPT 5.6 Soul's shift from intuitive guesswork to strict prompt adherence makes it a powerhouse for professional workflows.
- Beyond the Chatbot: The Rise of AI Agent Ecosystems — Explore the latest GitHub tools transforming AI from simple chat interfaces into autonomous agents that code, review, and automate workflows.
- MANTA: The AI Agents That Can Rewire Their Own Brains — Discover MANTA, a breakthrough in multi-agent systems that allows AI agents to dynamically change their communication network during inference.
- Stop Hoarding AI Skills: How Agent Capabilities Actually Work — Discover why adding too many skills to your AI agent can degrade performance and learn how to build efficient, high-impact agent tools.
- Why the Base Model Is No Longer a Mirror of the Web — What’s Next for LLMs — Varun Singh explains how modern LLMs shift instruction and synthetic data early, making the base model carry reasoning priors for RL, and why that changes model building.
- Emulated: Simulating Real Infrastructure for Autonomous Software Engineers — Emulated creates high‑fidelity simulations of production infrastructure so AI agents can learn to manage live systems, incidents, and scaling challenges.
- 3 Secret AI Systems That Generated $5M – How They Work — Learn the three hidden AI tools that powered a $5M profit, how they function, and why they matter for developers and entrepreneurs.
- Scaling Long‑Term Reasoning in LLMs: From Galactica to Real‑World Simulations — Explore how long‑horizon reinforcement learning and better simulation, not just larger context windows, unlock agents that stay coherent over hours.
- Curating Data & Environments: The Secret Sauce Behind Post‑Training LLMs — Post‑training LLMs succeed when we meticulously curate data and environments, not just scale compute. This article breaks down the curation stack and its real‑world impact.
- Esther Wojcicki Explains AI Tools That Power Modern Content Creation — Esther Wojcicki reveals how AI editors, document assistants, and video clipping tools reshape creators' workflows and why they matter for tech growth.
- How to Deploy Self‑Learning LLMs That Adapt on the Job — Raymond Feng explains how enterprises can train LLMs that continuously learn from real user interactions using reinforcement learning, with practical insights on deployment and reward hacking.
- Teaching AI to Hack: Ladders, Deterministic Graders, and Real Vulnerability Exploits — David Brumley shows how to train AI models to exploit real bugs using step‑by‑step sandboxed challenges and precise grading, warning against superficial security benchmarks.
- Building Deep Agents: From Concept to Production with LangChain — Learn how deep agents combine prompts, memory, tools, and MCP, and what changes when deploying them in production using LangChain and LangGraph.
- Verifiable Biology Benchmarks: How AI Learns to Do Real Science — LatchBio treats massive biological datasets as verifiable substrates, building benchmarks that force AI models to reason. Frontier models still struggle, but better measurement drives progress.
- Tau Crash Course: Building Tree‑Structured Coding Sessions in Python — Explore Tau, a minimalist Python harness that adds tree‑structured sessions, skills, and prompts to AI‑assisted coding. Perfect for developers seeking lightweight LLM integration.
- OpenClaw! How to Build Autonomous AI Agents That Do It All — Learn how HyperAgent lets you create self‑sufficient AI agents that monitor rivals, manage inboxes, create content, and improve via shared knowledge.
- How MiniMax Ships Open‑Source LLMs at Scale — MiniMax’s RL lead reveals the open‑source model stack, inference engineering, and multimodal tricks that power game‑building and computer‑use agents.
- Inside AI: Activations & LoRA Explained – What Fires Inside LLMs? — Learn the basics of AI activations and Low‑Rank Adaptation (LoRA) in under a minute, with simple analogies and technical depth.
- Run Untrusted AI Code Safely with CubeSandbox — Instant, Secure VMs — Discover CubeSandbox, a 60‑ms hardware‑isolated VM that lets AI agents execute code securely without compromising your infrastructure
- Fighting Slop with Slop: Building Reliable AI Agents — Turn messy AI output into trustworthy tools by layering detection agents, invariants, and type-safe boundaries.
- First Steps Toward Automated AI Research — Richard Socher’s Vision — Richard Socher outlines how AI agents can automate scientific discovery, compressing research cycles and showing early wins in model improvement, architecture search, and CUDA kernel optimization.
- Why LoRA Stumbles on Complex Reasoning Tasks — Explore why low‑rank updates like LoRA can't capture multi-step procedural knowledge and what it means for AI reasoning.
- Token Chase: Why AI Coding Agents Are Driven by Incentives, Not Skill — Explore how token economics and productivity dashboards shape AI coding agents, and why incentive misalignment matters for developers.