Two Kinds of Vibe Coding There are two categories of vibe coding. One is when you delegate little tasks to a coding LM while keeping yourself as the human “real programmer” fully informed and in control. The second type of vibe coding is what I am interested in. It is when you use a coding agent to build towers of complexity that go beyond what you have time to understand in any detail. I am interested in what it means to cede cognitive control to an AI. My friend David Maymudes has been building some serious software that way, and he compares the second type of vibe coding to managing

Everyone’s heard the line: “AI will write all the code; engineering as you know it is finished.”Boards repeat it. CFOs love it. Some CTOs quietly use it to justify hiring freezes and stalled promotion paths. The Bun acquisition blows a hole in that story. Here’s a team whose project was open source, whose most active contributor was an AI agent, whose code Anthropic legally could have copied overnight. No negotiations. No equity. No retention packages.Anthropic still fought competitors for the right to buy that group. Publicly, AI companies talk like engineering is being automated away. Privately, they deploy millions of dollars to acquire engineers who already work with AI at

Source: Your job is to deliver code you have proven to work

That judgment is the job of a senior engineer. As far as I can tell, nobody is replacing that job with a coding agent anytime soon. Or if they are, they’re not talking about it publicly. I think it’s the former, and one of the main reasons is that there is probably not that much spelled-out practical knowledge about how to do the job of a senior engineer in frontier models’ base knowledge, the stuff that they get from their primary training by ingesting the whole internet. Source: What happens when the coding becomes the least interesting part of the work | by Obie Fernandez | Dec, 2025 | Medium

Writing a full HTML5 parser is not a short one-shot problem. I have been working on this project for a couple of months on off-hours.Tooling: I used plain VS Code with Github Copilot in Agent mode. I enabled automatic approval of all commands, and then added a blacklist of commands that I always wanted to approve manually. I wrote an agent instruction that told it to keep working, and don’t stop to ask questions. Worked well!Here is the 17-step process it took to get here: Source: How I wrote JustHTML using coding agents – Friendly Bit

Junior developer—obsolete accessory or valuable investment? How does the genie change the analysis? Folks are taking knee-jerk action around the advent of AI—slowing hiring, firing all the juniors, cancelling internship programs. Instead, let’s think about this a second. The standard model says junior developers are expensive. You pay senior salaries for negative productivity while they learn. They ask questions. They break things. They need code review. In an augmented development world, the difference between juniors & seniors is just too large & the cost of the juniors just too high.Wrongo. That’s backwards. Here’s why. Source: The Bet On Juniors Just Got Better – by Kent Beck

If you’re building an AI product, your interface isn’t a nice-to-have. It’s your primary competitive advantage. Here’s what that means in practice: Make the first five minutes seamless. Users decide whether they’re staying or leaving almost immediately. If they have to think about where to click, you’ve already lost. Netflix auto-plays. TikTok starts scrolling. What does your product do the moment someone opens it? Source: UX Is Your Moat (And You’re Ignoring It) – Eleganthack

Most teams still run ideation sessions with a whiteboard, a problem statement, and a flurry of post-its. To be honest, I’ve always loved a good Design sprint, especially in person and I hope those don’t go away for anyone because they’re an awesome way to learn and connect together. But with AI, the way we generate, evaluate, and shape ideas has fundamentally shifted. You can collapse days of thinking into a focused 90-minute sprint if you know how to structure it well. This is the format designed to move fast without losing the depth. It blends design thinking, systems thinking, and agent-era AI capabilities into a repeatable flow you can

I’ve also built a bunch of agents over the years, of various complexity. For example, Sitegeist, my little browser-use agent, is essentially a coding agent that lives inside the browser. In all that work, I learned that context engineering is paramount. Exactly controlling what goes into the model’s context yields better outputs, especially when it’s writing code. Existing harnesses make this extremely hard or impossible by injecting stuff behind your back that isn’t even surfaced in the UI. Source: What I learned building an opinionated and minimal coding agent

I’ve started using the term HTML tools to refer to HTML applications that I’ve been building which combine HTML, JavaScript, and CSS in a single file and use them to provide useful functionality. I have built over 150 of these in the past year, almost all of them written by LLMs. This article presents a collection of useful patterns I’ve discovered along the way. Source: Useful patterns for building HTML tools

We propose adding a /llms.txt markdown file to websites to provide LLM-friendly content. This file offers brief background information, guidance, and links to detailed markdown files.llms.txt markdown is human and LLM readable, but is also in a precise format allowing fixed processing methods (i.e. classical programming techniques such as parsers and regex).We furthermore propose that pages on websites that have information that might be useful for LLMs to read provide a clean markdown version of those pages at the same URL as the original page, but with .md appended. (URLs without file names should append index.html.md instead.) Source: The /llms.txt file – llms-txt

When people talk about AI in design, they often picture flashy visuals or generative art. But my own lightbulb moment happened at a less glamorous place: in an effort to solve this accessibility challenge under pressure. At UX Scotland earlier this year, I shared how AI helped me transform a messy, time-consuming process into something lean, structured, and scalable. Instead of spending weeks tweaking palettes and testing contrast, I had an accessible design system up and running in just a few days. In this article, I’ll explain how I did it and why it matters. Source: AI and variables: Building more accessible design systems faster – zeroheight

In June 2025 Sam Altman claimed about ChatGPT that “the average query uses about 0.34 watt-hours”.In March 2020 George Kamiya of the International Energy Agency estimated that “streaming a Netflix video in 2019 typically consumed 0.12-0.24kWh of electricity per hour” – that’s 240 watt-hours per Netflix hour at the higher end. Source: A ChatGPT prompt equals about 5.1 seconds of Netflix

We’re at the same inflection point we saw with mobile and cloud, except AI is more sensitive to context quality. Loose contracts, missing examples, and ambiguous guardrails don’t just cause bugs. They cause agents to confidently explore the negative space of your system. The companies that win this transition will be the ones that treat their specs as executable truth, ship golden paths that agents can copy verbatim, and prove zero-trust by default at every tool boundary. Your tech stack doesn’t need to be rebuilt for AI. But your documentation, contracts, and boundaries? Those need to level up. Source: Is your tech stack AI ready? | Appear Blog

So my question is this: Why vibe code with a language that has human convenience and ergonomics in view? Or to put that another way: Wouldn’t a language designed for vibe coding naturally dispense with much of what is convenient and ergonomic for humans in favor of what is convenient and ergonomic for machines? Why not have it just write C? Or hell, why not x86 assembly? Source: If You’re Going to Vibe Code, Why Not Do It in C?

Over the past 18 months, the largest AI companies in the world have quietly settled on an approach to building the next generation of apps and services — an approach that would allow AI agents from any company to easily access information and tools across the internet in a standardized way. It’s a key step toward building a usable ecosystem of AI agents that might actually pay off some of the enormous investments these companies have made, and it all starts with three letters: MCP. Source: AI companies want a new internet — and they think they’ve found the key | The Verge

However, it has several problems that make it less suitable to develop Dillo anymore. The most annoying problem is that the frontend barely works without JavaScript, so we cannot open issues, pull requests, source code or CI logs in Dillo itself, despite them being mostly plain HTML, which I don’t think is acceptable. In the past, it used to gracefully degrade without enforcing JavaScript, but now it doesn’t. Additionally, the page is very resource hungry, which I don’t think is needed to render mostly static text. Source: Migrating Dillo from GitHub

Before diving into the subject at hand – and having read a great deal about it in preparation – I want to start with a point of clarification. Everyone asks, “Is there a bubble in AI?” I think there’s ambiguity even in the question. I’ve concluded there are two different but interrelated bubble possibilities to think about: one in the behavior of companies within the industry, and the other in how investors are behaving with regard to the industry. I have absolutely no ability to judge whether the AI companies’ aggressive behavior is justified, so I’ll try to stick primarily to the question of whether there’s a bubble around AI

When we onboard developers, we give them documentation, coding standards, proven workflows, and collaboration tools. When we “deploy” AI agents, we give them nothing. They start fresh every time. No project context, no memory of patterns, no proven workflows. So I compiled AI Coding Infrastructure, the missing support layer that agents need. Five components: Autonomous Execution (Ralph): Continuous loops for overnight autonomous development Project Memory (AGENTS.md): Your tech stack, patterns, conventions that agents read automatically before every response Proven Workflows (Skills): Battle-tested TDD, debugging, code review patterns agents MUST follow Specialization (Sub-Agents): 114+ domain experts working in parallel, not one generalist Planning Systems (ExecPlans): Self-contained living docs for complex features Source: How I Shipped 100k LOC

Engines, steam engines, were invented in 1700.And what followed was 200 years of steady improvement, with engines getting 20% better a decade. For the first 120 years of that steady improvement, horses didn’t notice at all. Then, between 1930 and 1950, 90% of the horses in the US disappeared. Progress in engines was steady. Equivalence to horses was sudden. Source: Horses

On September 14, 2015, our first publicly-trusted certificate went live. We were proud that we had issued a certificate that a significant majority of clients could accept, and had done it using automated software. Of course, in retrospect this was just the first of billions of certificates. Today, Let’s Encrypt is the largest certificate authority in the world in terms of certificates issued, the ACME protocol we helped create and standardize is integrated throughout the server ecosystem, and we’ve become a household name among system administrators. We’re closing in on protecting one billion web sites. Source: 10 Years of Let’s Encrypt Certificates – Let’s Encrypt

Source: Building a Social Media Agent | goose

Verification Engineering is the next Context Engineering AI can only reliably run as fast as we check their work. It’s almost like a complexity theory claim. But I believe it needs to be the case to ensure we can harvest the exponential warp speed of AI but also remain robust and competent, as these technologies ultimate serve human beings, and us human beings need technology to be reliable and accountable, as we humans are already flaky enough 😉 This brings out the topic of Verification Engineering. I believe this can be a big thing after Context Engineering (which is the big thing after Prompt Engineering). By cleverly rearranging tasks and

The purpose of this curriculum is to help new Elicit employees learn background in machine learning, with a focus on language models. I’ve tried to strike a balance between papers that are relevant for deploying ML in production and techniques that matter for longer-term scalability.

Domain knowledge is the only moat So where does that leave us? Right now there is still enormous value in having a human ‘babysit’ the agent – checking its work, suggesting the approach and shortcutting bad approaches. Pure YOLO vibe coding ends up in a total mess very quickly, but with a human in the loop I think you can build incredibly good quality software, very quickly. This then allows developers who really master this technology to be hugely effective at solving business problems. Their domain and industry knowledge becomes a huge lever – knowing the best architectural decisions for a project, knowing which framework to use and which libraries work best.