... launches, or is that section mostly reserved for makers and their network? Should I upvote anything that looks cool, or save upvotes strictly for products I would genuinely use myself? Right now I'm just scrolling and clicking whatever catches my eye, but I keep worrying I'm missing some unwritten rule or etiquette. Also, how is everyone catching

... agents. They're good at the task in front of them and they have no opinion about what the repo already does, so every session starts from a blank slate even when the codebase doesn't. Bad code you catch in review. A second correct implementation of something you already have reads fine in a diff, and it only shows up later when a change has to be made in four places. What's helped is a 40 line conventions ... ... HTTP, dates, queues and logging, pasted in at the start of anything structural. New duplicates mostly stopped. It does nothing about the ones already in there. Has anyone found a way to detect these after the fact? Grepping imports catches

... workflow? Comment from Rabnoor Singh(@rabnoor_s): the thing id gate isnt the refund, its the ratio. an agent that can issue refunds is either switched on or switched off, and both settings are useless in practice. what actually catches your example is checking the amount against the order it came from, because 1000 against a 100 order is impossible rather than just large, and impossible is something you can test without a model in the loop. the other half ...

... empty is genuinely correct. Logging every network response, which doubles the log volume and still needs me to know what I am looking for. A manual pass on a real device before every submission, which caught this one and catches nothing that needs a specific account state. So the question I keep coming back to: how do you catch

... correct your agent's output because you don't trust the first draft. Do the same for your own state. A five-minute end-of-day check, what actually happened, what I made up, what I'll do tomorrow, catches the spiral before it compounds, the same way a code review catches

... Before opening a PR, I started pasting my code into Claude for a quick review first, just to catch anything obvious before it goes to the lead. A few times it caught things clearly worth fixing: a missed null case, a variable name that didn't match the rest of the file's convention, a catch

... allowed to do. Then the handler credited the customer twice. Why it slipped through The bug wasn't on any line. It was the assumption between the lines: every event arrives exactly once. Reading the diff can't catch that. My tests didn't either. I wrote the fixtures, so I never thought to send the same event twice. My assumptions were just checking my assumptions. What actually caught it Running the failure. Deliver the event, deliver it again, watch ... ... layer instead of an application check. the part I keep thinking about is detection, not prevention: how did you actually notice this in the first place, before a customer emailed you? was it a support ticket, a reconciliation job catching

... more confident than a month ago. What we shipped? Five integrations now recognize the bugs people actually hit and prove the fix with a real before/after: Stripe, Paddle, Clerk, AgentMail, Descope. Not "we support the API," but "we catch the specific way it breaks." Chaos-order proof for billing webhooks. One of you asked whether we could prove a Paddle subscription survives webhooks arriving in any order. We built it, and then went deeper, to the failure devs actually fear ... ... take on faith. Why we're confident (the part that matters)? We tested recognition against real developer questions, harvested from GitHub/Stack Overflow, phrased by strangers, not us. When recognition was thin, the numbers said so (one integration went from catching

... reviewer will actually do it, which is the part that decides whether any of it works. One thing your version made me notice that I do not think you covered: who resolves the fields decides which failure you catch. If the step resolves them from what it read, you catch

... Unity 6 pipelines. If you've shipped a game, you know the pain: naming conventions drift, LODs get misconfigured, code reviews miss the same engine-specific mistakes over and over, and performance regressions sneak in until QA catches them. ShintTools catches

... Product Hunt We re building CatchDex, a private collection game for anglers who already take catch photos but want something more useful and fun than a camera roll. The idea is simple: Log a real catch

... Product Hunt We re building CatchDex, a private collection game for anglers who already take catch photos but want something more useful and fun than a camera roll. The idea is simple: Log a real catch

... what I want to see as well. That said, I wonder if there's another layer to this. A launch isn't just for the people who already know they have the problem. Sometimes it also needs to catch the attention of those who aren't a perfect fit or don't even realize they need the product yet. Polished demos and slick UI might not answer "what problem," but they do lower the barrier for discovery. They make people ... ... that's a very important distinction. That could also explain why you're attracting more men, who are looking for a real solution vs

... more about what happens after the catch: you log a real catch

... breaking, and so on. - Vaizle - I work with Vaizle, but it has very easily emerged as my fav platform. Not even lying. I can keep track of ad performance, check what competitors are posting on social. And best part is that I can feed it my own website + competitors

... agent decides to step outside the fence mid-run. Catching it at the decision costs one ping. Catching

... been building agents for a while and kept hitting the same problem. fix a failure, change the prompt or model, same failure comes back quietly. nobody catches it until a user does. built replayd to solve this. captures failed agent runs as regression tests and replays them before you deploy. if the same failure returns after a prompt, model, or tool change, it catches

... many found they were invisible. But here's what scared me more: Of those who WERE visible, 43% had incorrect information in AI answers. Not "suboptimal." Not "could be better." Wrong. Outdated pricing. Missing features. Wrong founder bios. Competitors credited for your work. And the average error sticks around for 4 6 months . Today, I'm sharing the full dataset: 1,000+ SaaS products, 75,000+ AI answers, and the hard truth about what's being said about you when ... ... analyzed 50 brands that discovered errors and tracked the impact. Error Type Average Revenue Impact Pricing error (higher than actual) 47,000 Pricing error (lower than actual) 23,000 (leakage) Missing critical feature 38,000 Wrong positioning (enterprise vs

... your narrative. What You Can Control But you're not helpless. Here's what works: 1. Monitor constantly You can't fix what you can't see. Daily tracking of AI answers across platforms is the only way to catch errors early. 2. Update aggressively AI favors recent content. Pages updated within 90 days are cited 2.3x more often and more likely to reflect current truth. 3. Create authoritative sources AI learns from patterns. If your site consistently says ... ... responses correctly cited their SOC2 status. Time to full recovery: 3 months Cost of lost deals during recovery: ~ 400k The Opportunity Hidden in the Risk Here's the contrarian take: If you're monitoring AI answers while competitors

... Product Hunt! I m the Project Lead for Lensmor . We ve all been there: You have a folder full of competitor screenshots or a spreadsheet of pricing changes. But the tricky part isn t collecting the data it s knowing what to do with it (and doing it fast enough to matter). That s why we are building Lensmor. It s the first AI agent that doesn't just alert you to competitor