Programming

TL;DR When two AI agents talk to each other in English, they are doing something faintly absurd: serialising rich internal state into a lossy human language, transmitting it, and decoding it back. English between machines is a compatibility layer, not a natural medium. Machines have already shown they will drop that layer the moment we let them - negotiation bots drifting out of English in 2017, agents switching to sound-based data protocols in 2025, and research systems now sharing internal model state directly with no language in between. The same logic applies to programming languages. Python and Rust exist for human readers. If agents write, maintain, and consume the software, the human-readability requirement quietly disappears - and with it, eventually, the need for source code and compilers as we know them. I do not think compilers vanish so much as sink. Like assembly, the layers below us stop being something humans write or read, while the guarantees they provide get absorbed into the agents’ toolchain. The part worth worrying about is not efficiency, it is legibility. Human language and human-readable code are our audit trail into what machines are doing. This is all speculation on my part, and I sketch where I think the line should be held. Human Language Is a Compatibility Layer Think about what actually happens when two AI agents have a conversation in English today. ...

There is a specific sound that defines the childhood of a generation: the high-pitched screech and rhythmic thrum of a data cassette loading into an 8-bit computer. In the early 1980s, the United Kingdom underwent a transformation that was arguably more profound than the arrival of the internet a decade later. While the US was falling in love with the office-centric IBM PC and the “appliance” feel of the Apple Macintosh, the UK was building a nation of bedroom coders. ...

TL;DR 45 years separate typing BASIC on a ZX Spectrum and using Claude Code on a MacBook - the abstraction rose, but the core loop stayed the same Programming has always been: figure out what you want, type, run, fail, fix AI agents compress the feedback loop and raise the abstraction - they do not replace the act of directing the machine The tools changed from line numbers and 16KB RAM to natural language and cloud models; the discipline did not I suspect that loop will still be true in another 45 years I’m sitting at a desk with two machines. ...

TL;DR An internal debugging file was accidentally included in a public package update, exposing a compressed archive of roughly 500,000 lines of code across around 2,000 files - not a breach, but a packaging mistake The leaked material revealed unreleased features including persistent memory, an always-on autonomous background assistant, and multi-device remote access Competitors gained rare visibility into Anthropic’s development pipeline and longer-term product direction, which is the primary competitive damage The incident undermines Anthropic’s safety-first positioning, particularly because it was the second such exposure in just over a year The broader lesson for the AI industry: internal operational security is becoming as critical as defending against external threats, especially as AI tools target enterprise customers Anthropic’s Claude Code has been making waves as one of the most capable AI coding assistants available, but a significant internal leak has exposed the underlying technology behind the platform for the second time in just over a year. The incident raised fresh concerns about how the company handles sensitive internal information and operational security. ...

Most engineering concepts are easier than they look once someone explains them clearly. This page collects short explainer notes on the fundamentals I find myself re-explaining most often, each paired with the best external visual or video I have found on the topic. APIs An API is a contract: this is what I will accept, this is what I promise to return, this is how I will behave when something goes wrong. Everything else - REST, GraphQL, gRPC, WebSockets - is just a different way of expressing that contract over the wire. ...

A working DevOps engineer draws from several disciplines at once - distributed systems, operating systems, network engineering, software development, and the organisational side that keeps it all moving. The books below are the ones I have either read cover-to-cover or regularly pull off the shelf to reference. I have added a one-line note on why each one is on the list. Culture and Practice The hardest problems in DevOps are usually not technical. ...

A short list of development courses I either learned from myself or have recommended to others starting out. Most of the best teachers charge for their work - I have pointed out the free options where they are genuinely good. Python and Django Code With Mosh - The Ultimate Django Series - structured end-to-end path, best if you already know Python basics Corey Schafer’s Python tutorials - free YouTube series covering Python, Django, and Flask in genuine depth Real Python - article- and video-driven learning with a strong practical slant Django Girls Tutorial - free, beginner-friendly, the cleanest on-ramp to Django I have seen JavaScript and React The Odin Project - free full-stack curriculum that takes you from HTML through to JavaScript frameworks Code With Mosh - The Ultimate React Native Series - covers cross-platform mobile development with React Native Kent C. Dodds - Epic React - paid but widely regarded as the deepest React course available React official tutorial - free, well-maintained, and the right place to start for the current React model C and C++ Code With Mosh - Ultimate C++ Series - modern C++ taught from first principles learncpp.com - a free, comprehensive, and regularly updated C++ reference-and-tutorial hybrid The C Programming Language (K&R) - still the canonical text for learning C, forty years on Fundamentals and Computer Science CS50 (Harvard) - free introductory computer-science course, famously well-produced Missing Semester of Your CS Education (MIT) - shell, Git, debugging, and the tools a CS degree usually skips Teach Yourself Computer Science - a curated syllabus for self-taught engineers who want to fill gaps Related Pages DevOps Courses DevOps Books