I saw a cool version of the Monty Hall game here recently: https://monty.donk.systems. This is really cool! But I had an itch I wanted to scratch: rather than manually test out the probabilities, I wanted to run two games side-by-side: one where a switch happened and one where a switch didn't happen. Then, running those over and over, we should see the overall win percentages converge to 66.7% for switching and 33.3% for not switching. So I coded up a simulation here to scratch my itch! Controls # To control the simulation, you can start or stop here. You can also adjust the speed. Speed (between 1-30): Score # Cumulative stats as the games go on. Scenario Plays Wins Win Percent Switch 0 0 - No switch 0 0 - Switch simulation # If we switch cards after the first goat reveal. +---+ +---+ +---+ | | | | | | +---+ +---+ +---+ No switch simulation # If we don't switch. +---+ +---+ +---+ | | | | | | +---+ +---+ +---+ { playBtn.setAttribute('disabled', 'true'); setTimeout(() => { playBtn.removeAttribute('disabled'); }, 3000); started = !started; playBtn.innerHTML = started ? "Stop simulation" : "Start simulation"; play(true); play(false); }) let playCount = 0; let switchWinCount = 0; let nsPlayCount = 0; let noSwitchWinCount = 0; const playCountDisplay = document.querySelector('#plays'); const winCountDisplay = document.querySelector('#wins'); const winPctDisplay = document.querySelector('#win-pct'); const nsPlayCountDisplay = document.querySelector('#ns-plays'); const nsWinCountDisplay = document.querySelector('#ns-wins'); const nsWinPctDisplay = document.querySelector('#ns-win-pct'); const getRandomIndex = () => { return Math.floor(Math.random() * 3); } const boardDoors = (isSwitch) => document.querySelectorAll(`#${isSwitch ? '' : 'no-'}switch-board span.card`); const boardGuesses = (isSwitch) => document.querySelectorAll(`#${isSwitch ? '' : 'no-'}switch-board span.guess`); const clearBoard = (isSwitch) => { for (let i = 0; i { speed = parseInt(e.target.value); }) const delay = (seconds) => { return new Promise(res => { setTimeout(res, seconds * 1000 / speed) }); } const doorIndices = [0, 1, 2]; const setStatus = (isSwitch, text) => { const statusArea = document.querySelector(`#${isSwitch ? '' : 'no-'}switch-board section`); statusArea.innerHTML = text; } async function play(isSwitch) { if (!started) return; setStatus(isSwitch, " "); clearBoard(isSwitch); // Start with all goats const doors = new Array(3).fill("G"); // Add prize randomly const winIndex = getRandomIndex(); doors[winIndex] = "C"; await delay(0.5); setStatus(isSwitch, "Initial guess") await delay(0.5); // Select random spot for guess let guessIndex = getRandomIndex(); boardGuesses(isSwitch)[guessIndex].innerHTML = "✔" await delay(0.5); setStatus(isSwitch, "Revealing a goat") await delay(0.5); // Reveal a goat const doorsThatCanBeRevealed = doorIndices.filter(el => { return el !== guessIndex && el !== winIndex }) const revealIndex = Math.floor(Math.random() * doorsThatCanBeRevealed.length); const doorToReveal = doorsThatCanBeRevealed[revealIndex]; boardDoors(isSwitch)[doorToReveal].innerHTML = "G" await delay(0.5); setStatus(isSwitch, isSwitch ? "Switching choice" : "Not switching choice"); await delay(0.5); if (isSwitch) { boardGuesses(isSwitch)[guessIndex].innerHTML = " "; guessIndex = doorIndices.filter(el => { return el !== guessIndex && el !== doorToReveal })[0]; boardGuesses(isSwitch)[guessIndex].innerHTML = "✔" } await delay(0.5); setStatus(isSwitch, "Reveal") await delay(0.5); // Reveal for (let i = 0; i

One concern I have heard from AI naysayers is that AI slop will make code reviews nearly impossible. AI churns out so much code, documentation, etc. that it's just impossible for any reviewer to keep up with it... right? Wrong! If you have good PR review processes, then reviewing AI-assisted code shouldn't be any more onerous than reviewing any other code. Here are some concerns I have heard and my response. AI generates tons of code # It's true that AI can sometimes generate tons of code—but your PR process shouldn't allow for massive PRs in the first place! If I get a 100 file PR today, I wouldn't review that. I'd respectfully ask the author to break the PR down into smaller, atomic pieces of work that can be reviewed more carefully. I'd ask this whether or not AI helped generate the code. As an aside, AI can actually generate small, digestible diffs! You just need to prompt in a way to do so. I have found being more methodical in walking AI through the problem step-by-step not only results in more digestible diffs, but also results in higher-quality code. AI generates low quality code # I don't quite know what to say to this one! If you're not reviewing PRs for quality in the first place, then that's a problem. Just apply your regular level of vetting to AI-assisted code as you would regular code. If you don't currently review PRs closely, then the problem isn't the quality of the code—it's that you're phoning it in during PR reviews. People just accept whatever AI outputs without understanding it # I tend to review PRs pretty closely and ask questions about anything I don't understand or think may be wrong. If you author a PR and are unable to answer the questions I have about your code, then it's not making it into the codebase. Again, this is as true today as it was 10 years ago. AI or not, I am going to make sure your code makes sense! AI can use outdated/vulnerable dependencies # If you add third-party dependencies in a PR, that should be considered a "bigger deal" than some folks treat it today (I'm looking at you, node ecosystem). Your PR review process should include evaluating what new dependencies are being added and a review of the installed version. Ideally, there should also be some consideration of whether an external dependency is even needed. Outside of the PR review process, you should ideally have automated dependency scanning (sonarqube, dependabot, etc.) that will detect vulnerable dependencies. Conclusion # If you're worried about AI "slop" making its way into your codebase, consider how your prevent human "slop" from making its way into your codebase. PR reviews are a critical tool for this—and should remain one as we explore this new AI-assisted world.

I'm kind of writing this to "past" me, who I assume is "current" you for a number of folks out there. For the rest of you, this might just sound like ramblings of an old fogey super late to the party. Yes, AI is over-hyped. LLMs will not solve every problem under the sun but, like with any hot new tech, companies are going to say it will solve every problem out there, especially problems in the domain space of the company. Startups who used to be "uber for farmers" are now "AI-powered uber for farmers." You can't get away from it. It's exhausting. I let the hype exhaustion get the best of me for a while and eschewed the tech entirely. Well, I was wrong to do so. This became clear when my company bought Cursor licenses for all software developers in the company and strongly encouraged us to use it. I reluctantly started experimenting. The first thing I noticed is that LLM-powered autocomplete was wildly accurate. It seemed like it "knew" what I wanted to do next at every turn. Due to my discomfort with AI, I just stuck with autocomplete for a while. And, honestly, if I stuck with just using autocomplete it would still have been a massive level up. I remember having a few false starts with the agent panel in Cursor. I felt totally out of control when it was making changes to all sorts of files when I asked it a simple question. I have since figured out how to ask more directed questions, provide constraints, and supply markdown files in the codebase with general instructions. I now find the agent panel really helpful. I use it to help understand parts of a codebase, scaffold entirely new services or unit tests, and track down bugs. As a former skeptic, I am a wildly more productive developer with AI tooling. I let my aversion to the hype train cause me to miss out on those productivity gains for too long. I hope you don't make the same mistake.

Generative AI will probably make blogs better. Have you ever searched for something on Google and found the first one, two, or three blog posts to be utter nonsense? That's because these blog posts have been optimized not for human consumption, but rather to entertain the search engine ranking algorithms. People have figured out the right buzzwords to include in headings, how to game backlinks, and research keywords to write up blog posts about things they know nothing about. Pleasing these bots means raking in the views—and ad revenue (or product referrals, sales leads, etc.). Search Engine Optimization (SEO) may have been the single worst thing that happened to the web. Every year it seems like search results get worse than the previous. The streets of the internet are littered with SEO junk. But now, we may have an escape from this SEO hellscape: generative AI! Think about it: if AI-generated search results (or even direct use of AI chat interfaces) subsumes web search as a primary way to look up information, there will be no more motivation to crank out SEO-driven content. These kinds of articles will fade into obscurity as the only purpose for their existence (monetization) is gone. Perhaps we will be left with the blogosphere of old with webrings and RSS (not that these things went away but they're certainly not mainstream anymore). This, anyways, is my hope. No more blogging to entertain the robots. Just writing stuff you want to write and share with other like-minded folks online.

Here we go again: I'm so tired of crypto web3 LLMs. I'm positive there are wonderful applications for LLMs. The ChatGPT web UI seems great for summarizing information from various online sources (as long as you're willing to verify the things that you learn). But a lot fo the "AI businesses" coming out right now are just lightweight wrappers around ChatGPT. It's lazy and unhelpful. Probably the worst offenders are in the content marketing space. We didn't know how lucky we were back in the "This one weird trick for saving money" days. Now, rather than a human writing that junk, we have every article sounding like the writing voice equivalent of the dad from Cocomelon. Here's an approximate technical diagram of how these businesses work: Part 1 is what I like to call the "bilking process." Basically, you put up a flashy landing page promising content generation in exchange for a monthly subscription fee (or discounted annual fee, of course!). No more paying pesky writers! Once the husk of a company has secured the bag, part 2, the "bullshit process," kicks in. Customers provide their niches and the service happily passes queries over to the ChatGPT (or similar) API. Customers are rewarded with stinky garbage articles that sound like they're being narrated by HAL on Prozac in return. Success! I suppose we should have expected as much. With every new tech trend comes a deluge of tech investors trying to find the next great thing. And when this happens, it's a gold rush every time. I will say I'm more optimistic about "AI" (aka machine learning, aka statistics). There are going to be some pretty cool applications of this tech eventually—but your ChatGPT wrapper ain't it.