When I built clive, the core insight was deceptively simple: don't give the agent a list of tools. Give it an environment.An API is a call-response primitive. You invoke it, it returns something, the state lives somewhere else. An environment is a thing you inhabit. You enter it, act inside it, leave it different than...
Category: Technology
Clive Can Now Modify Itself
We've been building clive — a CLI Live Environment that gives an LLM a terminal and a keyboard. The core idea is simple: instead of wrapping tools in APIs and schemas, you let the agent read the screen and type. The terminal becomes the interface. The loop becomes the protocol. We've added something that takes...
The Terminal as Agent Interface: Introducing clive
There is a quiet assumption embedded in most agent infrastructure: that agents need structured interfaces. APIs, schemas, tool definitions, protocols like MCP or A2A. The assumption is that the gap between an LLM and a system needs to be bridged by something machine-readable. clive starts from a different assumption. LLMs are excellent at reading text...
Your Agent Framework Is a Monolith. The Browser Doesn’t Care.
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,...
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....
