... Product Hunt community! When drafting commercial invoices, legal contracts, or official financial reports, converting numerical amounts into formal written English text (e.g., "$1,250" to "One Thousand Two Hundred Fifty") is a routine requirement to prevent ambiguity and tampering. To make this process effortless and error-free without relying on heavy or ad-cluttered websites, I vibe-coded a dedicated, browser-based Number to Words Converter micro-tool. Key features & functionality: Instant Text Conversion: Accurately converts small and large ... ... integer values into written

... When asking Gemini to compare our product against our top competitor, it hallucinated that the competitor

... first week. Curious what others are doing though. Are you testing event ordering explicitly? Replaying prod events? Or mostly monitoring and accepting some prod chaos as the cost of shipping fast? How are you validating that agent-written webhook integrations actually survive in prod? Comment from Diana Robin(@dianarobin): We started requiring chaos tests in PR review. Would you be open to sharing how you model the Paddle twin state so others can replicate that out of order logic? Comment ... ... still wrong. Guarding the write on the fetched object's own updated_at, so a stale read can't overwrite a fresher one, is what fixed it for me. Comment from Asad M.(@asadmalik901): The agent-written

... prompt: use test-driven development, use QuickCheck, use fuzzing, use TLA+, audit first. Then he scored each run on a hidden test suite. That was the whole design. No instruction wildly beat the run with none. He had written down six guesses beforehand about which conditions would not outperform, and all six came true. What interested us was how the instructed runs failed, because the failures share a shape. Told to use QuickCheck, all of them used the library ... ... code did. Naming the technique gives the agent the motions of the technique. The check still comes from the agent's own reading of the spec, so the new framework fills up with the tests it would have written

... since the day the number changed, which was 23 July. Fifty days ago. Eight live sentences in the same repository say two. Four are docblocks on the auth and rate-limiting path. Two are in the project's own written-down rules, the file I keep for my future self. Two are in the test files of the throttles themselves, including the test that guards a ceiling whose size was justified by the sentence sitting above it. Then the part ... ... that made me stop. Two of those eight were written

... half-drawn screen. Too long and every test crawls. So we don't sleep. We watch for the screen to stop changing and move when it has. There's no sleep(2) anywhere in the executor. Sounds trivial written down. It's most of the difference between tests that pass and tests that pass every time . 4. Knowing when to stop An agent with a retry loop and no budget will burn twenty minutes and a lot of tokens looking ... ... internally than anything else on this list. 5. Not re-learning the same app every session Sessions are stateless and models forget. So we keep what worked. Each app builds up its own set of learned locators and flows, written

... different clothes. In all three the instrument could only ever return good news, and none of them looked broken. One. A refresh job reported one item refreshed successfully. I checked the field that records when the price was last written and it had not moved. The function underneath returns an ok status for a product that does not exist, and my counter incremented on ok rather than on a value having been written

... Writing code got cheap. Checking whether it did the thing did not, and the gap between those two is where I spent a whole day. None of what follows was broken. Every line ran as written, raised no error, and reported success. That is the part worth talking about, because I think it is the real failure mode of this era and it does not look like a bug. One. A refresh job reported one item refreshed. I checked ... ... field that records when a price was last written

... time. House rule (there is only one) Here's the part that matters more than any feature table. When Happycapy finishes a job, the result doesn't live in our chat box it lands  in your tools : written into your Notion, committed to your GitHub, saved into a folder on your own machine. That's what Connectors are for. Which means the lesson of this week is already handled. If a platform any platform, ours included ... ... always was: in the accounts and on the hardware you own. An agent should be replaceable. Your work should not be deletable. The water's warm. Take your time   happycapy.ai Conclusion:  Happycapy is a direct Manus alternative

... case of this drift before it went out? Is this a real problem for your workflow, or am I overthinking it? Not looking for "just read it carefully" curious if anyone's found something that actually works at scale, versus per-article manual re-checking ...

... worked better for us: declaring intent in the morning instead of reporting history. "Here's what I'm picking up today" takes ten seconds, is answerable without memory, and produces something you can compare against reality at 6pm. Plan versus done, written

... verified purchase" label, so even that badge doesn't mean what people think it means anymore. The part that got me: real reviews mention weird, specific, slightly annoying details. "The manual was useless but I figured it out." AI written ones glide right past that. They praise smoothly and specifically avoid describing anything a person would actually notice. Once you know to look for it, you start seeing it everywhere. Feels like the entire signal system flipped. We used ... ... tell people to be suspicious of typos and broken English. Now the polished, confident, well written

... genuinely unclear where the line sits. Is swapping the underlying model a significant change to what the app does? Is broadening what the assistant will discuss? I don't think anyone knows yet, and I'd rather have written down my answer before a release forces one. Where I actually am on this. Murror is an emotional-reflection product, so the age of the person on the other side is not a compliance field to me, it's the most ... ... 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

... audited an AI-written billing service this week. 22 findings, 10 critical. None of them crashed. All returned 200. Four that stuck with me: an auth check that silently lets anyone in when one env var is missing, SQL injection in the one query that wasn't parameterized, a broken permission boundary so anyone can attach a charge to a workspace that isn't theirs, and a Stripe retry that double-grants seats because there's no idempotency check anywhere ... ... cleaner change and re-proved it. That one held. That's what I'm building FetchSandbox around: reproduce the real failure, apply the fix, then prove it holds on your actual code before you ship. For those shipping AI-written

... review queue and you quietly stopped showing up for the shift. There's a report that came out this spring that I keep coming back to, and I don't think it landed properly with makers because it's written for engineering leaders at big companies. Faros AI published "The Acceleration Whiplash" in April two years of telemetry from 22,000 developers across more than 4,000 teams. Not a survey about how developers feel. Actual measurement of what came ... ... code you didn't write and often can't fully explain. The report has a good phrase for the mechanism: AI-generated code is "superficially convincing." Idiomatic, well-named, stylistically consistent with everything around it. It reads like code written

... made this tool called Eneya , and unlike most things posted here the name tells you nothing, so: it's written pre-screening for tech recruiters. You build a short questionnaire around the competencies the role actually needs, candidates answer it in text on their own time, and you get back a scored report on each one. No video, no scheduling, no calls that end in the first four minutes. Anyone who has screened for a role with two hundred applicants ...

... scores your vibe. No human in the loop. You're not being interviewed you're being processed. AI-generated "thought leadership." Copy, paste, post. Zero lived experience, zero original thought. It's just AI slop in a blazer. AI-written emails to people who matter. Your client can tell. Not because the grammar's off because the warmth is. VS

... feed ships with real settings - freshness window, follow boost, social signal weight. You tune the algorithm, not the other way around. 2. Fame-weighted curation: established accounts' votes move posts further than day-old accounts'. It's written in the ToS, not hidden. 3. Creators sell products and subscription tiers wallet-to-wallet over BTCPay (BTC, LTC, USDT, Lightning). We never hold funds. The ToS and privacy policy are written

... years by people who cared about how it feels to use. Dark, considered interfaces. Whitespace that isn't accidental. Content that is the product, not decoration bolted onto a spreadsheet. The short answer to "what's the best LinkedIn alternative in 2026" is this: builders aren't looking for another feed. They're looking for a professional network that was actually designed, not just maintained. That's the gap forg.to is built to close. Here's why the gap exists ... ... people outsourcing the writing entirely, then hitting publish under their own name and photo. The market has already started punishing it. Coverage of the same study found AI-generated LinkedIn posts pulling in roughly 45% less engagement than human-written

... shifted under everyone. Antigravity relaunched in May as a multi-agent suite with a new CLI and Gemini 3.5 Flash as the default model, running roughly 4x faster than Opus 4.8 or GPT-5.5, worth knowing if you'd written it off as the "free but weaker" option. What I haven't found a clean answer to: does anyone have a real methodology for tracking cost-per-solved-task rather than cost-per-token? Token counts alone feel misleading once ...

... reads first. The diff still needs a human check, but at least the next agent won t start without the reasoning behind it. Comment from Abdullah Javaid(@abdullah_javaid3): @bickov   @alpertayfurr  The cap holds because nothing gets written while it's still messy. I only write an entry once a decision has actually landed, not while it's forming, so the half resolved state you're describing never makes it into the file at all. It just stays ... ... that. A note can link to something that doesn't exist yet, and that's fine, it just means the thought is still open. Costs nothing to leave it unresolved, it just sits there until it either gets written

... Basically all our code gets written by agents now, and so do the tests. Usually same session, same model. And I keep hitting the same loop: Agent builds the feature Agent writes the tests, everything green Ship it A user hits an edge, I dig in, and the tests were checking what the code does, not what the ticket asked for. Suite was green the whole time. The annoying part is that if the model misread the ticket, its tests ... ... homework. The model is just agreeing with itself. So what do people actually do here? Different model writing tests than the one writing code? Does that actually help or do they just share the same blind spots? Tests written

... some of what it surfaces is politically inconvenient. The canonical definition doesn't get written down because writing it down means someone has to lose the argument about whose number was right. The declaration isn't a documentation gap, it's an unmade decision, and the agent just forces the question. So my honest answer to "what happens when it was never written

... before my last two launches I wasted weeks building before checking who else was in the space. don t be me. here s the 30-min competitor check I do now, all free tools: 1. search PH archives (5 min) search your keywords on Product Hunt. sort by launch date. note how the top 5 positioned themselves the gaps in their taglines are your opening. 2. check who s actually alive (10 min) a launch from 2023 means nothing ... ... signals: are they hiring? shipped anything in the last 6 months? active changelog? I use exploreyc.com for this (free db of 8,600+ YC / a16z / PH startups with hiring status) but LinkedIn works too. 3. read the comments on competitor

... budget. Is that dishonest, or is it no different from using a framework, a library, or a junior dev and not itemizing every line's origin? Nobody used to ask what percentage of a website a developer typed themselves versus copied from Stack Overflow or scaffolded with a generator. But this feels different somehow, maybe because the speed and volume are so much higher, or because "AI-written