My favorite thing about agentic coding is still super mundane: I no longer have to resolve git conflicts and merge PRs. It does a great job of looking at git logs and attached issues and figuring even harder conflicts out.

I see no good coming from anthropomorphizing LLM behavior. LLMs do not reason. They do not decide, or collaborate, or attack. They do not make decisions. They do not weigh or evaluate. They are incapable of judgment. They do not think. Agents are programs in which a predictive model uses input text to generate the text that would naturally follow that input. That model sits in a loop (a program written by a human being) that parses the output text and does something that touches the internet based on the results. That same program usually takes any (text) responses that are returned from the internet and feeds them back into the input at the top of the loop. If that agent/program does not access the internet, the outside world is not impacted. All the bad behavior we've been reading about is entirely caused by a human not bothering to check whether the actions requested in the model's output are sensible and safe before executing them. It's the humans who wrote the code that impacts the outside world who are at fault here, not the "AI." Anybody who lets an agent write another agent without seriously and thoroughly vetting the resulting code is even more culpable. It's lazy, and it's dangerous. If something goes wrong, the human is to blame, not the LLM.

📢 I'm officially looking for a new job. You hiring? Full time, remote from Spain, paid via EOR, IC, TypeScript, no crypto, support for 3-4 conference speaking gigs/year. Share plz. 🙏 DMs open. Act now, while supplies last!

".

Please help me make MSW sustainable. It helps hundreds of thousands of engineers, countless companies from startups to enterprise to government institutions build better software. Consider becoming a GitHub sponsor: github.com/sponsors/mswjs

Looking for my next job. Staff/Principal TypeScript, Senior too: editors, canvas, component libraries, devtools (frameworks, SDKs), agentic systems and interfaces, or just strong product FE. Remote from Finland. Can overlap US hours, already do. DMs welcome.

¿Podría ser "Data Center Fornicator" la canción del verano?

Still working on this this: an open-source toolkit for defining agent logic as typed state machines/statecharts. Goal: make *any* agent loop/workflow/orch. pattern visualizable, reusable, testable, & framework/SDK agnostic. If you’ve used XState or similar (Langgraph etc.) w/ agents, I’d love your thoughts on what works, what doesn't, what's missing.

Early POC demo from a talk I gave recently: piped.video/uMvTAF280so?t=1401 This goes beyond "draw a workflow, run a DAG" It gives agents explicit models for decisions, planning, retries, interrupts, tool calls, memory, and human input, making the logic analyzable, visualizable, testable, and reusable instead of buried in prompts and glue code.

Like many Italians, I'm paying my 2025 taxes. Thankfully, 2025 has been an incredible year for me (I'm under Italy's regime ordinario). But there's something that genuinely makes no sense. First of all, more than 50% of my personal income goes to taxes and contributions (INPS, IRPEF, and others). But what really blows my mind is this: In Italy, you don't just pay the taxes for the previous year. You also have to prepay part of the following year's taxes. Here's how it works: If I make €100, I pay roughly: €50 in taxes for 2025. Another €25 as an advance payment for 2026. The catch? That €25 advance isn't based on what I'm earning in 2026. It's based on what I earned in... 2025. So imagine this scenario: 1. 2025 revenue: €100 2. 2026 revenue (so far): €10 You're still expected to pay €75 (€50 in taxes + €25 in advance), even though your current year's income has collapsed. How does that make sense? We're living in a world where governments have access to real-time financial data, AI, predictive analytics, and sophisticated forecasting tools. So why is the advance payment calculated solely on last year's revenue instead of using a reasonable estimate based on how the current year is actually going? I'm not even debating the tax rate here. I'm questioning a system that can create massive cash-flow problems for entrepreneurs and freelancers, simply because it assumes next year will look exactly like the last one.

Formal Complaint: GitHub Copilot Token-Based Billing Model @GitHubCopilot Subject: Critical Issue with New Token-Based Billing — Product Has Become Unusable Summary of the Issue I am writing to formally complain about the recent shift to token-based billing for GitHub Copilot, which was rolled out this morning. This change has fundamentally broken the value proposition of the product and is rendering it unusable for paying subscribers, including myself. Specific Problems Observed Within just a few hours of the new billing model going live, the developer community is already reporting alarming consumption patterns: Pro+ subscribers paying $39/month are reporting that 60% of their monthly credits were depleted in only 2 hours of normal usage. One user reported losing 20% of their entire monthly allowance from a single file review — no code generation, just a review. At this rate, a paying customer will exhaust their plan in less than a single working day, despite paying a premium subscription fee. This is not "normal usage at scale" — this is a broken pricing model that punishes the very developers who rely on Copilot daily for their work. Why This Makes the Product Unusable The core promise of Copilot was a predictable, always-available AI coding assistant integrated into the developer workflow. Token-based billing destroys that promise because: Developers cannot predict costs. Every keystroke, every file review, every refactor becomes a financial calculation rather than a productivity boost. The tool actively discourages use. Users will hesitate before invoking Copilot, defeating the entire point of an AI assistant. The $39/month Pro+ tier is misleading. Customers signed up expecting reliable access, not a pre-paid metered service that runs out mid-morning. Heavy users — your most loyal customers — are penalized the most. The Competitive Reality While GitHub Copilot is moving toward a restrictive metered model, competitors are moving in the opposite direction: Cursor offers Composer 2.5 with unlimited usage once token limits are reached on their plans, ensuring developers can keep working without interruption. Other tools (Windsurf, Cody, Continue) offer flat-rate or far more generous usage tiers. Developers will not stay on a platform that runs out of credits before lunch when alternatives offer uninterrupted productivity at the same or lower price point. My Demand If GitHub does not revise this licensing model, the product is effectively dead. I am requesting: Reinstatement of a flat-rate unlimited (or effectively unlimited) tier for Pro and Pro+ subscribers. Transparent, upfront communication of what each interaction actually costs in tokens. A grace period or credit refund for users who burned through their allowance under the new model without warning. A long-term commitment that core IDE-integrated features will not be metered into uselessness. Without these changes, I — along with a growing number of developers — will be canceling our subscriptions and migrating to Cursor or competing alternatives. The decision to monetize aggressively at the expense of usability will not be remembered as a successful pivot; it will be remembered as the moment GitHub Copilot lost its market. Please escalate this to the product and pricing teams immediately.

I don’t usually share things like this, but I think it’s important to be honest. I’ve been looking for a full-time role since September. I’m a senior iOS engineer + product designer with 10+ years experience, and I’ve spent that time building and shipping real products (most recently: @ateiq_app, @naturalis_app, @getuppapp). Despite interviews and ongoing work, I’m now about a month away from needing something stable for my family. If you know a team that values someone who can both design and build, I’d really appreciate an introduction. Thank you! ❤️

Italy’s "Piracy Shield" forces providers to block content in under 30 minutes without judicial oversight, which leads to overblocking (taking down legitimate websites alongside infringing ones). We're appealing a €14M fine to protect the Internet from automated censorship and ensure infrastructure providers aren't forced to overblock. cfl.re/4cMh0WA

I've published the first two chapters of a new guide to Agentic Engineering Patterns - coding practices and patterns to help get the best results out of coding agents like Claude Code and OpenAI Codex simonwillison.net/2026/Feb/2…

I build AI for a living. I believe in what we're building. But this kind of rhetoric makes my work harder and more dangerous. @sama, comparing human development to model training is tone-deaf, strategically reckless. People are losing jobs. They're getting angry. They're seeing AI as an enemy instead of a solution. Some are planning to destroy data centers and the people who build this stuff. That anger and backlash might not be reaching your floor but it reaches the engineers and builders doing the actual work. The CEO of the most visible AI company should not frame humans as inefficient compute units, should not be anti-human. Your role as a leader is to show how AI solves real problems for humanity. Not to reduce human life to an energy accounting problem from a comfortable position. If someone working in AI gets hurt because the public narrative turned hostile, leaders like you who chose dehumanizing framings bear responsibility for that too. I'm a techno-optimist. I believe AI enhances human capability. I work with this new form of intelligence every day. I genuinely respect what it is. It is real, significant, unlike anything that has existed before. But I also believe in human excellence. We have to accept that it's two fundamentally different forms of intelligence working together. IMHO the real techno-optimist position isn't "AI is cheaper than humans." It's "we now have two forms of intelligence on this planet, and the combination is more powerful than either alone." You're the leader of OpenAI, and whether you chose it or not, you represent everyone building in AI right now. Every word you say shapes how the world sees this technology and the people behind it. Please act like it.

He's not just defending AI energy use. He is smuggling in a whole anthropology where humans are basically inefficient meat computers that you have to pour food and years into before they become useful. And once you accept that, the next move is obvious. If people are just costly biological training runs, then burning mountains of electricity to build synthetic intelligence starts to feel not only equal, but superior, even if it negatively impacts actual humans. That is the dystopian. It makes human development sound like a bug in the system, and it makes sacrificing human and creational flourishing for more computational power sound logical. To him, the grid gets strained, prices go up, ecosystems get hit, but hey, humans eat too, so what's the difference? The difference is that humans aren't an inefficient line item. They're the point. If your worldview can look at a child growing into an adult and describe it as energy spent to train intelligence, you haven't said something profound. You've revealed a horrifically rotten worldview.

A human consumes about 2,000 calories per day. Over 20 years, that’s roughly 17,000 kWh of total food energy. Training GPT-4 consumed an estimated 50 GWh of electricity. That’s 3,000 humans worth of “training energy” for a single model run. And GPT-4 is already dead. OpenAI retired GPT-4o from ChatGPT on February 13th. The model that took 50 GWh to train got less than two years of flagship status before replacement. The human you spent 17,000 kWh “training” for 20 years produces economic output for the next 40 to 60 years. The amortization window on GPT-4 was shorter than a car lease. Now look at what replaced it. GPT-5.2, released December 2025, is OpenAI’s current default. The GPT-5 series consumes an estimated 18 Wh per average query according to the University of Rhode Island’s AI Lab, up to 40 Wh for extended reasoning. That’s 8.6 times more electricity per response than GPT-4. With 2.5 billion queries hitting ChatGPT daily and GPT-5.2 now the default model, the inference math gets staggering fast. Even at a blended average well below 18 Wh, you’re looking at daily electricity consumption that could power over a million American households. This is what Altman is actually doing. OpenAI hit $13 billion in annual recurring revenue but still isn’t profitable. They need you to think of AI energy consumption as natural and inevitable, the same way you think about feeding a child, because the alternative framing is that they’re burning through enough electricity to rival small countries while racing to build 1-gigawatt Stargate data centers. The food analogy makes the energy costs feel biological and unavoidable instead of what they are: an engineering and business choice that scales with every model generation. The comparison sounds clever at a fireside chat in India. It falls apart the second you do the arithmetic.

So, we'll agree we have no idea what to do with AI, right? #openai #Grok

Router frameworks are cool, but can fail short with microfrontends. All is good when apps are route based and everything sits use under the same router, but what about when they don't? Example: microfrontend is not integrated in the shell as a route @nextjs @tan_stack @remix_run

I never had to force a component to re-render other than when I setState. Not sure I understand the need. I haven't found a need for timeouts or intervals either. Am I building boring apps? #react #remix3