... right and is more useful than either, and the revised version went up this month, so it's worth twenty minutes before you write your Q4 plan. It's NBER working paper 35275, Demirer, Musolff and Yang - "Writing Code vs. Shipping Code." More than 500,000 GitHub developers, matched against their actual AI usage telemetry rather than a survey, in a matched event study design. The headline numbers are large and I have no interest in arguing with them. Cumulative ... ... they don't all have to be atomic and update every time. I keep them atomic anyway because "catch it up tomorrow" is exactly how AwesomeTTS sat there for months, the second commit never happened Comment from Mona

... Apple's own support pages. Apple In-App Purchase: 26%, or 15% if you're in the Small Business Program, the Mini Apps or Video Partner Programs, or on an auto-renewable subscription after the subscriber's first year. Alternative payment processing inside your app: 20%, or 10% for those same groups. Out-of-app offers with an actionable link: a 15% store services commission, or 10% for those groups, on anything bought within 7 days of the tap. Apps ... ... distributed outside the App Store entirely - an alternative

... Three numbers from it get quoted. Users who saw an AI summary clicked a traditional result in 8% of visits; users who didn't, 15%. Users ended their browsing session 26% of the time with an AI summary present versus 16% without. And the one that should matter most to anyone buying GEO: users clicked a link inside the AI summary itself in 1% of visits. One per cent. That is the click value of the thing the entire optimisation ... ... more often. Just 8% of one- or two-word searches produced one. For searches of ten words or more, 53%. For searches starting with a question word - who, what, when, why - 60%. Sit with that next to the 8%-versus

... line appears in both: starting September 2026, you're required to indicate whether your app or game includes social media capabilities in order to submit new versions or updates to the App Store, or for notarization for distribution on alternative marketplaces. It's September. It's live. I read both pages this week rather than the coverage, partly because Murror is the kind of app where the answer isn't obvious, and partly because the second-order effects aren ... ... Yolanda S.(@yolanda_c_schneider): I like the distinction betweeen a social network and an app that simply lets users interact with content. My own definition of social media would probably be much narrower than Apple s. Comment from Mona

... times. I'm a founder, not a lawyer, and none of this is legal advice - but the arithmetic of which paragraph applies to you is something you can do yourself in twenty minutes, and it's cheaper than the alternative. Comment from Mikkel Larsen(@mikkellarsen): The voice note trap is such a good catch. Text feels safe voice flips the category entirely. Are you parking that feature for now or exploring a lawyer friendly implementation first? Comment from Siarhei Hamanovich ... ... haven't signed the Code of Practice. What I did change is when the decision happens. Marking is an export-time decision rather than a UI one, and every file already out there predates whatever you decide Comment from Mona

... could have. And I still don't have a clean answer on the verification-method question, because the honest version is that I don't yet know what I'd do differently with a government-ID-checked 16 versus a self-declared one. I'd rather say that than write a paragraph pretending I've resolved it. The thing I am confident about is narrower. The signal is sparse for a long while yet. Texas only covers accounts created after ... ... never right anywhere refusing is an option. Self declared isn't the case where you behave identically, it's the case where you should be more cautious, and throwing the field away removes your ability to be. Comment from Mona

... categories on purpose, and write down why. Not because Cloudflare's defaults are wrong, mostly they're reasonable, but because a default you never chose becomes a strategy by attrition. This one has a date on it. Comment from Mona Truong(@monatruong_murror): @omri_ben_shoham1  That's the part I under-weighted, and it's the better version of my point. A blocked crawler leaves a mark somewhere eventually, a coverage drop, a grey row, something ... ... look at the numbers, because once you are staring at 9 out of 10 there is always a story available for the missing one. Comment from Omri Ben-Shoham(@omri_ben_shoham1): Good writeup - the distinction between blocking Training vs

... content/entity gap; if Murror is cited but sends no visits, it is visibility without acquisition. That separation should keep a tiny referral channel from being mistaken for proof that the product is absent from the buying conversation. Comment from Mona Truong(@monatruong_murror): @getosmo  Splitting it into three signals is better than what I'm doing, which is honestly nothing systematic. The failure mode you're describing cited but no visits, read as "the product is invisible" is exactly ... ... right that the pre-category phrasing is the test that matters for Murror. "Why do we repeat the same argument" is much closer to what people actually type than anything on my landing page. Comment from Mona

... destination? If the value is a result someone wants and doesn't care how they got an action then being agent-callable is close to free distribution and you should be looking hard at it before your competitor does. If the value is the experience of a person actually being there a destination then racing to become a tool call is optimizing for a future where your product's whole reason for existing has been abstracted away. Worse ... ... ones who shipped a server fastest. They'll be the ones who were honest about whether they should and, if they did, treated that borrowed trust like it was hard to get. Because it is. Comment from Mona

... small it moved onto exactly one desk. Comment from Ill Robyn(@ill_robyn): The idea of treating review as a budget really stands out. Asimple rule like that could stop AI generated work from piling up unnoticed. Comment from Mona Truong(@monatruong_murror): @ill_robyn Thanks and the reason it ended up as a budget rather than a rule is that I failed at every stricter version first. "Always review AI output" has no partial credit: the first week ...

... late, or produced an output a user cannot trust enough to act on. The eval set that ends up mattering is usually a small pile of real sessions tagged by failure mode rather than benchmark name. Comment from Mona Truong(@monatruong_murror): @getosmo  "Tagged by failure mode rather than benchmark name" is the line I wish I'd written. That's the whole thing. Once the cases are organized by how it breaks, the set stops being a scorecard ...

... pricing metric rewards the behavior your actually want from users . If the metric can grow because the user is struggling more, it's probably measuring the wrong thing. That's a useful lens far beyond AI companions. Comment from Mona Truong(@monatruong_murror): @joseph_walker2   Thanks for reading it that closely. That's exactly the test I now run before shipping any plan: would this number go up if my users were having a worse week? If the answer ...

... Mona, a marketer turned founder from Germany. I'm building Xolora, a marketing analytics tool dedicated to solopreneurs and small business owners. I noticed that 80% of that niche is overwhelmed with their marketing and just guessing. When it comes to ads, this means that they're wasting money (around $10,000/year). And the sad truth is that there is no tool out there, that helps them. All the other marketing tools are made for marketers or big teams ... ... underserved niche. Xolora's goal is to simplify ad analytics, give back time, money and ultimately confidence. It just launched in beta and I hope that it will be valuable for its users. What is your niche? Comment from Mona

... there some catch-up rule? that's usually where these systems fall apart for me, not the routine itself but the recovery after the first missed day. Comment from Omri Ben-Shoham(@omri_ben_shoham1): the make mode vs judge mode split is the one that actually changed something for me. i used to check analytics mid build and it wrecked the session every time, not because the numbers were bad but because i'd switch from building to defending ... ... decision i hadn't finished making yet. scheduling the judging block is such a simple fix for something that felt like a personality flaw Comment from Mona

... good one. you still need a human (ideally not the builder) to break the tie. feels like it narrows the blind-reading problem down to a much smaller, cheaper set of cases rather than solving it outright Comment from Mona Truong(@monatruong_murror): @omri_ben_shoham1  Exactly and that "ideally not the builder" part is the bit I keep relearning the hard way. On subjective output the builder is close to the worst possible judge, because I unconsciously grade ...

... this: if building is no longer the moat, then the moat is available to anyone willing to do the unglamorous work. Knowing your person. Owning a relationship instead of renting attention. Being early to a workflow before a competitor gets embedded in it. None of that requires funding or a team. It requires you to stop hiding in the part of the work that feels productive and go do the part that actually decides whether the thing lives. Ship ... ... weekend, sure. Just don't confuse the weekend with the work. Comment from Mona

... best day, on a task you made up. Your users meet it on an average day, on the task they actually have. Build for the average day. That's the whole game now. Comment from Mona Truong(@monatruong_murror): @magdalena_anderson   Yeah the first time we graded the model on ten real, messy sessions instead of a clean benchmark, the number dropped hard, and that drop was the most useful thing we learned all quarter. The public score describes ... ... task none of your users are actually doing. One thing that surprised us: half the value was just re-reading the transcripts ourselves, before we even scored anything. Comment from Mona

... sentence an AI says about you when you're not in the room. You can't write that sentence. You can only be the kind of product it's easy to say something true and good about. Comment from Mona Truong(@monatruong_murror): @supplymo  This is the best single data point I've seen on any of this, thank you for actually running it and reporting the boring mechanics instead of the headline. "Pick the sentence and then ... ... what my product does in one plain sentence to a person, no model's doing it for me either. Going to actually run the three-assistant test tonight, a little scared of what it says back. Comment from Mona

... inside your app if they can only feel understood while they're talking to your model you haven't solved loneliness. You've made it more comfortable to stay lonely. You've built a very sophisticated waiting room. The alternative is to design for the exit. Concretely, that means three things. Point users back at real people, not deeper into the app. The most valuable thing an emotional AI can do isn't be a better listener than your friends ... ... about: when your user closes the app, are they better off, or just less alone for the next four minutes? Your roadmap already knows the answer. The question is whether your metrics will let you admit it. Comment from Mona

... which one were you actually optimizing for? Comment from Nika(@busmark_w_nika): imo, the whole thing with AI agents is becoming oversaturated the same way as it was with AI in 2024 Comment from Asad M.(@asadmalik901): Mona, the test is sharp, but I think it hides a third case, and it is the one most tools actually live in. Some products are not trying to finish a job or to keep you hooked. They are a place ... ... matter how good the retention chart looks. Stacy, I think that is also the answer to your measurement problem: do not try to measure quiet, measure whether the work still gets done during it. Comment from Stacy Wycoff(@stacywycof83995): Mona

... refuse to build, even when users are asking for it and the metrics say yes. Anyone else held the line on a feature like this? Curious how you explain the "no" to users who genuinely want it. Comment from Mona Truong(@monatruong_murror): @asadmalik901  "Reading it as a report of a moment" that reframe is going to stick with me. You're right that it changes everything about how we respond. We've actually started experimenting with exactly what ... ... framing is spot on. We've been saying what we don't do instead of what we protect. "The AI waits until you're done" is a much better way to explain it. Thank you for this. Comment from Mona

... animation and 8 seconds of silence. Our engineer thought it was a bug when he first saw the spec. "You want me to add... a delay?" Yes. On purpose. Here's why. We ran an experiment: instant AI response vs. an 8-second pause before the same response. The content was identical. The only difference was time. Users who got the pause rated the AI's reflections 40% more helpful. Same words. Same insights. They just felt more considered ... ... forgot what "thinking about you" actually looks like. It looks like taking a moment. The most human thing our AI does is pretend to think. And I'm still not sure how I feel about that. Comment from Mona

... Building this side project is teaching me that you don t need to understand your own codebase deeply to build something people love. You just need to understand the problem. Anyone else navigating this weird middle ground? Comment from Mona Truong(@monatruong_murror): @galdayan   the paper trail idea is really smart. we actually haven't done this yet but I can see how it would solve the CTO walkthrough problem I mentioned instead of me trying to explain architecture ... ... language explainer of a chunk of the codebase, not because you need it day to day, but so there's a paper trail you can hand your CTO when something breaks and speed matters more than pride Comment from Mona

... valuable? Comment from Gal Dayan(@galdayan): @monatruong_murror  that opt-in rate over time is the metric I'd watch too - it's basically a leading indicator for the fatigue before it shows up in journaling quality. curiosity vs being watched is a really clean way to put words to the difference. good luck keeping it that way as usage scales up Comment from Mona

... alieksia): This confirms something a mentor kept telling us: people don't care about the tools or the time behind a product, only whether it solves their problem. Good to see the numbers back it up.  Comment from Mona Truong(@monatruong_murror): @yudhish_khatri  Great question! Yes, we're fully transparent about it. Our privacy policy and How It Works section explain that AI powers the journaling prompts and emotional pattern analysis. We're not hiding ... ... thought you were misleading your customers. I was just curious myself. Currently, I am in the final process of releasing a product that is AI based as well and considering all of this while researching in depth Comment from Mona