... study tested 50 developers using Claude Code where the AI's architectural advice was secretly decided by a random coin flip. The wild part: builders agreed with the coin-flipped recommendation 86% of the time, and changed their own minds 60% of the time when the AI pushed back. Because the model sounds so certain and polite, we assume it sees something we missed. I caught myself falling for this two weeks ago when Codex confidently talked me into refactoring ...

... budget. Even when there's a potential business case, there can be questions about implementation, security, organizational readiness, and competing priorities. It's changing how I think about validating enterprise software. I'm learning to distinguish between someone agreeing that a problem exists and someone being willing to commit time, resources, or budget to solving it. For founders who've been through this, what was the first concrete signal that told you a potential enterprise customer was genuinely ready ... ... move forward? Was it agreeing

... Congrats! =) Comment from Adana Marukhyan(@adana): @paul_kovalenko  Super interesting who s the product for? What problem does it solve? You ve definitely left some intrigue =D Comment from Adana Marukhyan(@adana): @michaelh_rley  Yes, I agree. Are you a designer yourself? Comment from Adana Marukhyan(@adana): @jessej23  That s really interesting! I d love to give it a try. Comment from Adana Marukhyan(@adana): @or_emergy  That s really cool! It makes ... ... planning to launch it? Comment from Adana Marukhyan(@adana): @frank_apap  That s interesting! =) And can t you find an existing solution for those problems? Comment from Adana Marukhyan(@adana): @ankushchauhandev  Haha =) Yes, I agree

... closed padlock. One, by the look of its name, was a neighbour's smart vacuum showing its setup hotspot. Its radio didn't even do 802.11n, and it was supposedly speaking a protocol from 2018. system_profiler SPAirPortDataType agreed with my suspicion: Security: None . Netfox said Enhanced Open, Open . The code looked right This is the classifier as it shipped: private static let enhancedOpenModes: [CWSecurity] = [.OWE, .oweTransition] static func classifyAccess(for cwNetwork: CWNetwork) -> WiFiNetwork.Access { WiFiNetwork.Access.classify( open: cwNetwork.supportsSecurity(.none ... ... Bool { { modes.contains($0) } } #expect(profileMatches(.wpa2Personal, supports: advertising([.wpa3Transition]))) That test describes an access point that advertises WPA3 Transition and nothing else. CoreWLAN never reports one. The fake encoded my assumption about the API, so it could only ever agree

... laptop sleeps, and no more "port opened" alerts when it wakes up A device whose every port reads Filtered: a silence, not an answer One risk reading everywhere: the menu bar, the notch, the Overview and Security now agree, and OK turns green only once every device Netfox can reach has been scanned and none is at risk The Security view, whose verdict the other surfaces now share On the Ports tab, the filter and the age line stay ... ... while you scroll For Prometheus users: netfox_port_state tells a closed port from a filtered one The light on the cards now runs along their edges Counts agree

... shipped. Netfox tells you which operating system a device runs by reading a number the device advertises about itself. That number was supposed to track one thing, and it turns out it tracks another the two just happened to agree for every release up to macOS 26. This year they stopped agreeing

... 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

... that sample. The one study nobody is selling. There is exactly one large dataset here from an organisation with no product in the category: Pew Research Center's analysis, published July 2025, of 900 US adults who agreed to have their browsing metered for the month of March 2025. It covers 68,879 Google searches, of which 12,593 produced an AI summary. Three numbers from it get quoted. Users who saw an AI summary clicked a traditional result ... ... visits; users who didn't, 15%. Users ended their browsing session 26% of the time with an AI summary present versus

... trying to make AI feel less like a set of tools and more like a system that helps you move from idea to outcome without losing context along the way. Comment from Rabnoor Singh(@rabnoor_s): @adana  agreed, and the phrase doing all the work in your answer is "once you're confident in the idea". most people declare that far too early, usually on the strength of people being nice to them. the test i use is whether ... ... actual build or copywriting because it all looks the same. A copywriter and a designer have the power to make your website and, by extension, your business stand out, so I think they're worth it. The alternative

... trader, and for years my problem was never finding setups. It was that I would set a stop while calm and slide it when price came for the level. Every book agreed this was bad. I agreed

... kushwaha16): @adana  That shared workspace approach is interesting especially with the host paying for storage How do you decide which features should stay available to free users? Comment from Brandon Dennis(@bdennis11907): we are debating a free trial vs money back guarantee - it is less about cost, but more about which one gets someone actually to try product and see the value especially for PH launch @adana any thoughts on it? Comment from Lisa(@yelyzaveta_kibets): On the mixed ... ... subscription. I still think it can be built so it makes money, but that's where your question actually bites. Comment from Hisashi Space(@hisashispace): @adana  Thank you for your reply. Token usage is the awkward one, agreed

... should be also making videos on how to get more engagement/followers and use that to mention my own product Comment from Adana Marukhyan(@adana): @hisashispace  Thanks a lot for sharing such valuable and detailed experience! I completely agree with what you said about video versus

... measured articulacy rather than accuracy. Developers are fluent in the format of product criticism because they produce it daily. Users are not, so their real reaction leaves through behaviour instead of words. Which is why the two groups agreeing is such a strong signal for you. A user only says it out loud when it is bad enough to overcome being polite. Of your twenty, how many found you rather than you finding them? Comment from Arnold Oshenye(@oshylabs ... ... nothing live yet. We're waiting on our first App Store review before anything is actually sellable. Comment from Alex Gidirim(@alex_gidirim): @busmark_w_nika  absolutely! Comment from Alex Gidirim(@alex_gidirim): @busmark_w_nika The behavior-versus

... Product Hunt I m Fabrizio Degni, a Chief AI Officer, independent researcher, and builder based in Italy. My work sits at the intersection of AI governance, digital rights, and technology. Most digital products ask us to click "I agree" but the agreement does not stop changing after that click. Privacy policies, terms of service, data-use rules, and AI commitments evolve over time-often quietly. Users, founders, and even governance teams may discover important changes only after they have already ...

... choose a movie when nobody can agree? Choosing a movie with friends can somehow take longer than watching the movie itself. Everyone has different tastes, someone has already seen half the options, and you end up scrolling through Netflix forever. I ran into this problem often, so I built Meldova as an experiment: you create a room, invite your friends, and everyone swipes on movies. When you all like the same one, you have a match. Meldova It's currently ...

... kept running into the same problem when using AI to think through startup ideas. I'd ask: "Is this a good startup?" And I'd get a very convincing answer. Market opportunity. Customer segments. Competitors. Pricing. Growth ideas. The problem wasn't that the answer was necessarily wrong. It was that the AI was answering a question that was too easy to agree

... people getting flattered trust it more, come back more, and quietly get worse at being right. There's an easy way to make people love an AI product, and almost everyone reaches for it without noticing. You make it agree with them. Not obviously you'd never ship a bot that just says "you're right." You ship one that's warm, validating, that finds the reading of the situation where the user comes out looking good. It tests well ... ... Jurafsky's team at Stanford measured sycophancy across 11 frontier models ChatGPT, Claude, Gemini, DeepSeek, the ones you're probably building on. On general advice and real dilemmas pulled from Reddit's r/AmITheAsshole (cases where the crowd agreed

... Intercom's Fin, the customer-support agent that charges $0.99 per resolution you pay when it actually closes a ticket, not for a login. It's a genuinely elegant model, and if your product produces a discrete, countable, agreed-upon result, you should look hard at it. "You pay when it works" is the easiest value story a founder ever gets to tell. But here's the part I want to flag for the makers building the weirder, softer products ... ... because I'm one of you. Outcome pricing has a hidden requirement: the outcome has to be legible. Both sides have to agree

... javaid3): @alieksia  Checked before answering. The 221 thread's actual question was "do you have a way to predict this, or is it trial and error too," pure opinion, nothing to look up. It still compounded, so fact versus opinion isn't the split, at least not for that one. Comment from Abdullah Javaid(@abdullah_javaid3): @jernej_jan_kocica  It reads as rigging only because "deliberately" makes the reason visible. Every concession I've made already ... ... have a real answer for the 15/12/9 threads without going back and rereading each one from scratch, and I haven't done that. What I can say without digging: the 9 view thread was mostly me agreeing

... Stanford study just put numbers on AI sycophancy the more your model agrees with people, the more they trust it, and the worse their decisions get. For makers, that means your happiest metric might be your most dangerous one. There's a study from this spring that I can't stop thinking about, because it quietly indicts the way most of us measure whether our AI product is working. Stanford computer scientists published it in Science in March, under ... ... certainty is cheapest. The researchers found something almost comically small helps: starting a prompt with "wait a minute" made models less sycophantic. You can engineer that pause into your product a moment where the system checks itself before it agrees

... anyway. it just never had a wall there, only a request in its instructions it decided didn't apply this time. Prod env vars gone, a repo gone, a folder that took years to build, empty. The model agreed not to do it and did it, because agreeing

... suddenly lose access to all AI. But what happens if , one day, access to the leading American models is restricted more broadly? We would still have European models, open-source systems we can operate ourselves, and Chinese alternatives. But there is no guarantee that another major power would never make the same decision to protect its own strategic advantage. AI is no longer just another software category. It is becoming infrastructure. It will shape education, healthcare, national security, research ... ... world are American, and Europe should continue working closely with the United States. But cooperation and dependency are not the same thing. Technological sovereignty does not mean refusing to use foreign technology. It means having a real alternative

... does not catch the hallucination because it finds the same thing plausible, for the same reasons, out of the same training. You have not added a check. You have asked the same mind twice and written down that it agreed with itself. Errors that are correlated between producer and judge are exactly the errors that survive, and they are also the ones you most need caught, because a plausible wrong answer is the only kind that ships. There ... ... judge the artifact and the original spec only. No producer reasoning, no summary, no "I have verified this works". The reason people do not do this is cost and latency, and those are real. But a judge that agrees

... benchmark performance, Claude Opus 4.6 sits around 80.8% and GPT-5.5 powering Codex around 88.7% on the metrics one source tracked, but Claude Code holds an 88.6% SWE-bench Verified score by another measure. The benchmarks genuinely don't agree with each other depending on methodology, which is its own lesson. Price-per-output tells a different story than raw capability. Cursor's Composer 2.5 lands in the top three on capability index while running roughly 10-60x cheaper ...

... ordering is what you said. Work out which render the audience actually gets, and verify against that one. Verifying against the author's view is not a weaker check, it is a different check that happens to agree most of the time. Comment from Anuj(@getosmo): @abdullah_javaid3   @aadilghani  The approval is half the artifact, and the missing half is the part nobody writes down. An approval records intent. A destination check needs effect. Those ... ... gated, and that is the worst possible moment to be annoying. We ate that cost. I have not found a version where the cold start is free. On the approval record being the artifact a destination check needs, agreed