Last week, I dropped an LLM agent into a plain HTML file. No Node.js. No Webpack. No Docker container running a Python backend that proxies to another Python backend. One <script type="module"> tag, a couple of imports, and an agent that reasons, calls tools, writes code, and delegates to sub-agents — all orchestrated entirely client-side,...
Category: Technology
The Web Has No API for Agents – Agentic Microformats
In February 2026, we pointed a browser-embedded AI agent at a demo e-commerce store and asked it to buy a laptop stand. It read the site's discovery file, parsed the page metadata, extracted six products with prices and availability, added three items to the cart via API, updated a quantity, removed one item, checked that...
The AI That Pauses to Think: How Interleaved Reasoning Is Reshaping Autonomous Agents
When Moonshot AI demonstrated its Kimi K2 model tackling a PhD-level mathematics problem in hyperbolic geometry, according to examples published in their technical documentation, the AI didn't just compute an answer. It embarked on a 23-step journey: searching academic literature, running calculations, reconsidering its approach based on results, querying databases again, and iterating until it...
The LLM Whisperers: How Cloudflare and Anthropic Cracked the Code on AI Agent Efficiency
There's a delicious irony at the heart of modern AI development. We've spent years training large language models on every scrap of code humanity has ever written—Stack Overflow answers, GitHub repositories, programming textbooks, documentation—teaching them to become fluent in Python, JavaScript, TypeScript, and dozens of other languages. Then, when it comes time to actually use...
AI Hallucinations: Why They Happen and How We’re Tackling Them
AI hallucinations refer to instances where a model generates a confident response that sounds plausible but is factually incorrect or entirely fabricated . For example, an AI chatbot might cite a nonexistent legal case or invent a scientific-sounding explanation out of thin air. These aren’t intentional lies – they result from the way generative AI...
The Complete Guide to Running LLMs Locally: Hardware, Software, and Performance Essentials
For years, the language model arms race seemed to belong exclusively to cloud providers and their API keys. But something remarkable has happened in the past eighteen months: open-weight models have matured to the point where sophisticated, capable AI can now run entirely on consumer hardware sitting under your desk. The implications are profound. Your...
Claude’s Modular Mind: How Anthropic’s Agent Skills Redefine Context in AI Systems
If you've been building with large language models, you've hit this wall: every API call requires re-explaining your entire workflow. Financial reports need 500 tokens of formatting rules. Code generation needs another 300 tokens for style guides. Multiply this across thousands of requests, and you're paying twice—once in API costs, once in context window exhaustion....
OnPrem.LLM: Running private AI on your own terms—no cloud overlords required
The AI revolution has a dirty little secret: most organizations can't actually use it for their most important work. Sure, ChatGPT is great for brainstorming blog post ideas or debugging code snippets, but ask a hospital administrator if they'll send patient records to OpenAI's servers, or a financial services firm if they'll pipe proprietary trading...
The Probabilistic Revolution: How AI is Making Software Engineering More Like “Real” Engineering
For decades, software engineers have endured a peculiar form of professional imposter syndrome. While their colleagues in mechanical, civil, and chemical engineering designed bridges that probably wouldn't collapse and factories that mostly wouldn't explode, software engineers worked in a deterministic paradise where 2+2 always equaled 4, functions returned predictable outputs, and bugs were logical puzzles...
Agent2Agent Protocol Analysis: The Enterprise AI Interoperability Game Changer
The Agent2Agent (A2A) protocol launched by Google in April 2025 represents the most significant standardization effort in AI agent communication, with backing from over 50 technology partners and a clear path to becoming the "HTTP for AI agents." With production-ready implementations expected by late 2025 and projected market valuations reaching $2.3 billion by 2026, A2A...
