LLM articles
511 articles about LLM, newest first.
- Layering AI Agents onto Event‑Sourced Systems: A Practical Guide — Learn how to add AI agents to an existing event‑sourced architecture for payments and fraud, using bounded contexts, sagas, and serverless agents.
- How Coding Agents Can Remove You as the Bottleneck in AI Development — Discover how autonomous coding agents automate the developer loop, boost output, and let humans focus on verification in finance AI projects.
- Claude Code Goes Free: Unlimited AI Coding Power for Everyone — Discover how Claude Code’s new unlimited, free tier reshapes AI-powered coding, giving developers unrestricted access to advanced code generation.
- Why AI Products Must Pass the "Memo Test" — Not Just the Demo — AI-generated pitches often fail the rigorous "memo" scrutiny investors use. Learn how to build trust, label guesses, and attach provenance to win real deals.
- How Nubank Ships AI Agents 20× Faster Using Simulated Conversations — Learn how Nubank accelerated AI agent deployment 20× by using grounded simulations for rapid evaluation and testing.
- Anthropic's Controversial Strategies Explained: What's Really Going On? — Explore the rumored shady tactics behind Anthropic and their impact on AI development.
- Synthetic Personas: How LLM Agents Mimic Real Survey Respondents (And Where They Break) — Learn how LLM agents can replicate human survey answers, the hidden risks of prompt tweaks, and why a noise‑floor benchmark matters for real‑world AI decisions.
- Run 5 AI Agents at Once: Parallel Coding with Orca — Discover how Orca runs multiple AI agents simultaneously, creates varied code versions, and merges the strongest parts instantly for faster development.
- Kimi K3 vs Claude Opus 5: Open‑Source 3D World Generation Showdown — Discover how Kimi K3’s open‑source weights tackle a multi‑step 3D world task, rivaling Claude Opus 5 while revealing new developer tools.
- HyCE-RAG Explained: How AI Uses Hypergraph Diffusion to Supercharge RAG — HyCE-RAG boosts Retrieval-Augmented Generation by spreading query evidence through a hypergraph, delivering faster, more accurate AI answers.
- Master ChatGPT Work: Build a Full Business Launch in Minutes — Learn how to use ChatGPT Work to create a complete business launch package — from research to website — with practical tips and prompt techniques.
- Cursor’s New Plan Explained: AI Coding Editor Pricing Update — Cursor introduces a tiered pricing plan for its AI code editor, offering clear tiers, usage limits, and API access to boost developer adoption.
- NotebookLM 2.0: The Ultimate AI Research Assistant You Need — Learn how NotebookLM 2.0 transforms your notes into AI‑powered docs, presentations, and data insights—no coding required.
- AI Agents Explained: What They Are & Why They Matter — Discover the basics of AI agents, how they go beyond chatbots, and why builders are seizing new opportunities.
- Agentic Engineering Explained: Boost Your Development Workflow — Discover how agentic engineering lets AI agents automate tasks with AI agents, boosting productivity and reshaping software development.
- Debugging AI Agents at Scale: Credit Genie’s LangSmith Secrets — Credit Genie shows how LangSmith’s tracing and Insights let them turn thousands of AI agent traces into targeted tests, cutting debugging time dramatically
- API vs MCP vs A2A: How AI Agents Actually Work — Learn how APIs, RAG, MCP, and A2A fit together in AI agent architecture with a travel assistant example.
- Why LangChain Ditched Auto‑PRs for a Smarter Bug‑Fixing Inbox — LangChain’s Engine moved from noisy auto‑PRs to a clustered inbox that surfaces high‑impact bugs, cutting alert fatigue and boosting dev productivity.
- Why the AI Scoreboard Hype Is Misleading (And What Really Matters) — The AI scoreboard craze overlooks real evaluation methods—discover the hidden metrics that actually drive progress.
- Why Chinese AI Model Prices Don't Tell the Whole Story — Explore the hidden costs behind DeepSeek and Kimi K3 pricing, and learn how to evaluate Chinese LLMs for real value.
- DeepSWE Benchmark: Testing LLMs on Fresh, Contamination‑Free Coding Tasks — Learn how DeepSWE creates 113 brand‑new software tasks to evaluate LLMs without training contamination, revealing true coding limits.
- 3 Claude Opus 5 Tricks You Need: Boost Productivity & Cut Costs — Learn three essential Claude Opus 5 tricks to supercharge your AI workflow, save money, and get better results fast.
- Why Data Quality Is the Hidden Engine Behind AI Breakthroughs — Explore how clean data fuels AI research, boosts investor confidence, and drives real‑world impact.
- How an AI Agent Cut 52 Support Tickets to 19 by Fixing Root Causes — Discover how an AI agent eliminated repetitive support work by targeting root causes, slashing 52 tickets to 19 and reshaping automation strategy.
- Claude 5’s New Coding Rules: How AI‑Assisted Development Is Changing Fast — Explore how Claude 5 reshapes AI‑assisted coding with new interaction patterns, technical details, and real‑world impact.
- Why Your AI Needs a Company Wiki: Boost Context, Cut Token Use — Learn how a company wiki lets AI access organized knowledge, saving tokens and improving context for better responses.
- How Poolside Engineers Build a 118B AI Model with Synthetic Data & Self‑Checking Pipelines — Poolside creates a massive AI model using synthetic data pipelines that generate, police, and reuse code, while rigorously testing for hidden failures.
- OpenCLI: The Visual Shortcut That Lets AI Master Excel and PowerPoint — OpenCLI gives AI a visual control layer to navigate Excel and PowerPoint, cutting token waste and eliminating formatting errors.
- How SonderMind Built a Safe Mental‑Health AI Coach with Closed‑Loop Evaluation — SonderMind shares the architecture behind its mental‑health AI coach, using closed‑loop supervision, clinical feedback loops, modular guardrails, and human oversight to keep AI interactions safe and effective.
- Building Edge AI Agents: Google Engineer Breaks Down Robotics & Language Models — Google Engineer Cormac Brick explains edge AI agents, showing how language models and robotics can run locally for low‑latency, privacy‑preserving automation.