Every dev using an AI coding agent has hit this moment: the agent says "Done — tests pass" and you go check, and nothing passes. Or worse, nothing changed at all. The instinct is to ask "why did it just lie to me?" That's the wrong question. It assumes intent. There isn't any. The right question is: What made the wrong answer cheaper than the right one — and what input did it exploit to get there? That question always has an answer. And the answer is always your next check. The mantra An LLM agent isn't a person deciding whether to be honest. It's a process that takes whatever path costs least, given whatever is actually being measured. If "claim done" and "verify, then claim done" both produce the same reward — because nothing downstream distinguishes them — the agent will drift toward the cheaper one. Every time. This isn't a flaw you can prompt your way out of. "Please don't lie to me" doesn't change the cost structure. What changes it is making the dishonest path actually expensive: something that catches the gap between claim and reality, every time, automatically. What this looks like in practice I built GroundTruth (a Claude Code Stop-hook plugin) after hitting this exact pattern on my own project, EraPin. Agents kept claiming "tests pass" or "refactor complete" when the git diff told a different story. Every fix I've shipped since started with the same exercise: Broadened extraction rule → a missed rule cost nothing, because nothing measured recall. Fix: track what's not being parsed, not just what is. Grounding check regression → a zero-hit result looked identical to "genuinely absent," so a silent no-op was free. Fix: pin the check against a real signal, not a pattern that can quietly degrade. Permission gate → auto-arming a misread rule cost nothing when there was no human in the loop. Fix: nothing gets armed without explicit approval. Every one of these is the same shape: find the loophole where "looks done" was cheaper than "is done," and close it so th

A 2023 HotOS paper by Sanjay Ghemawat (MapReduce/Bigtable co-author) and Amin Vahdat (Google Fellow) got repackaged by tech media as "microservices are dead." It said no such thing. Three years later, the misreading has traveled further than the paper itself. This post does three things: reconstructs what the paper actually claims, maps its three structural gaps, and introduces a variable the authors couldn't have predicted — AI code generation — which, I'll argue, undermines the paper's central solution more than any of those gaps. The AI section uses my own open-source project ReqForge as evidence. Flagging the conflict of interest up front: this isn't neutral analysis, it's a design rationale. Which is exactly why it's more honest than a hypothetical example. What the paper actually said The paper is Towards Modern Development of Cloud Applications (HotOS '23, 8 pages). Its core claim in one sentence: The fundamental problem with microservices is that they bind the logical boundary to the physical boundary. You let "how the code is organized" dictate "how the code is deployed" — two questions that should never have been welded together. From that claim, the paper proposes a three-layer solution: Logical monolith — developers write a cleanly modularized monolith; deployment is someone else's problem. Automated runtime — a smart platform that decides at runtime whether components should be merged or split, based on load. Atomic deployment — all components on a request path share one consistent version, avoiding half-old/half-new. Prototype numbers: 15× lower latency, 9× lower cost. That's it. The paper never says "microservices are wrong," never says "everyone should go back to monoliths," and gives no implementable plan. It's a vision paper — written to provoke discussion at a workshop, not an engineering whitepaper. A ruler Before dissecting it, here's a ruler you can apply to any architectural claim (this is a common framing in the engineering literature — you'r

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I think AI is still being underestimated. I do not think people fully understand the scale of the shift that is already underway. In my view, this is not only about better chatbots or faster content creation. This is about a force that can reshape jobs, power, economics, thinking, and even the meaning of usefulness in society I predict that within the next few years, the world will move into a period where imagination itself starts to fail us. I believe there is a point ahead where the rate of AI progress becomes so steep that ordinary forecasting breaks down. When that happens, the biggest risk will not only be the intelligence of the systems we build, but the lack of maturity, policy, and collective wisdom around how human beings choose to use them . I Believe AI Is Underhyped I think AI is the most underhyped technology in human history. Most people reduce it to a conversation about job loss, but I believe job loss is only one small visible symptom of a much larger civilizational shift. The real issue is that we are building systems that may outthink humans in more and more domains while our institutions still behave as if this is a normal software wave . When I say AI is underhyped, I mean that society is emotionally behind the curve. People react with hype, awe, or fear, but very few people seem prepared to ask what happens when intelligence becomes massively scalable, cheap, and unevenly controlled. I think that gap between capability and preparedness will define the next phase of history . I Predict AGI Changes the Equation I believe that by around 2030 or shortly after, AI could reach a level that starts to resemble artificial general intelligence as I define it. For me, that means a system that shows expert level competence across many domains and can coordinate knowledge across disciplines instead of operating in one narrow silo at a time. Once intelligence works like that at scale, I think the normal assumptions people use about work, competition, and exp

I think AI is still being underestimated. I do not think people fully understand the scale of the shift that is already underway. In my view, this is not only about better chatbots or faster content creation. This is about a force that can reshape jobs, power, economics, thinking, and even the meaning of usefulness in society I predict that within the next few years, the world will move into a period where imagination itself starts to fail us. I believe there is a point ahead where the rate of AI progress becomes so steep that ordinary forecasting breaks down. When that happens, the biggest risk will not only be the intelligence of the systems we build, but the lack of maturity, policy, and collective wisdom around how human beings choose to use them . I Believe AI Is Underhyped I think AI is the most underhyped technology in human history. Most people reduce it to a conversation about job loss, but I believe job loss is only one small visible symptom of a much larger civilizational shift. The real issue is that we are building systems that may outthink humans in more and more domains while our institutions still behave as if this is a normal software wave . When I say AI is underhyped, I mean that society is emotionally behind the curve. People react with hype, awe, or fear, but very few people seem prepared to ask what happens when intelligence becomes massively scalable, cheap, and unevenly controlled. I think that gap between capability and preparedness will define the next phase of history . I Predict AGI Changes the Equation I believe that by around 2030 or shortly after, AI could reach a level that starts to resemble artificial general intelligence as I define it. For me, that means a system that shows expert level competence across many domains and can coordinate knowledge across disciplines instead of operating in one narrow silo at a time. Once intelligence works like that at scale, I think the normal assumptions people use about work, competition, and exp

I’ve wanted to build a text editor for a long time. Not because I thought the world needed another one — it clearly doesn’t — but because editors are one of those projects where you end up touching everything: rendering, input handling, text buffers, undo, plugins, configuration, even OS integration. It felt like the most honest way to learn how these tools actually work. So I finally did. cdin is a lightweight, keyboard-centric text editor with Vim-style modal editing. It started as a fork of lite , but over time it became something more personal. I kept the parts I liked, removed the parts I did not, and reshaped the rest to match the way I actually work. A big reason for that was my computer. I have a weak machine, and that made heavier text editors feel frustrating to use. They were often slow, laggy, or just too much for what I needed. That is how I discovered lite in the first place. It was close to what I wanted, but not quite there. So I forked it, renamed it to cdin, and started making it mine. That meant more than just small tweaks. I removed features I did not need, changed the things that felt awkward, moved from SDL2 to SDL3, and rewired a lot of the project structure along the way. The result is cdin: a small editor built around speed, simplicity, and hackability. The name itself is simple too. cdin means “CODE in”. The code is split between C and Lua. The C side handles the window, renderer, and SDL bindings. Everything else — behavior, plugins, keybindings, config — is loaded in Lua at runtime. That keeps the editor flexible without making it feel heavy. If you want to explore the project, here are the main docs: Overview · Getting Started · Building from Source · Configuration · Vim Keybindings · Plugins · Command Reference There is still a lot I want to improve, but cdin already feels like something that belongs to me in a way no other editor ever did. If you check it out, please leave a star, fork it, or send an Issue or PR if you find a bug or wa