... reading up on API spend limits for a post, and one detail stuck with me. On Anthropic s standard tiers there s a monthly spend cap ($500 on the lowest). When you hit it, requests come back as a 429 that looks like a rate limit, but with no retry-after header. Automatic retries just keep failing until the 1st. OpenAI s hard spend limits return 429s too. Run it on an ordinary month: $280 spent by day 10, about ... ... That crosses a $500 cap

... declared revision c2ec0d5. The flow scans Stripe customers, flags missing country fields and duplicate emails, caps writes per run, then posts a summary to Asana and Slack twins. Four measured outcomes on this run: Full scan completed across 250 records and flags were written. Incomplete scan, meaning fewer records returned than expected, produced no writes and triggered a RevOps alert. Write cap

... LLMs getting stuck in execution loops is such a huge headache when building DB tools. We had a similar issue with API retries where the agent kept tweaking minor parameters forever. Capping it at two attempts and routing the failure directly to Slack is a solid circuit breaker. In our setup, we usually allow two retries with an exponential backoff before killing the thread completely. How do you handle the Slack notification does a developer jump in and manually correct ...

... continue because the task is almost done, we risk giving an uncontrolled process unlimited room to spend. I'm interested in how people are thinking about this distinction. Would you prefer a strict per-task spending cap, a predicted cost limit that adjusts as the agent makes progress, or a combination of both? And if you use adaptive limits, what signals would you trust enough to make that decision: historical task costs, the remaining context, tool-call patterns, retry frequency ... ... every failure refunded. Then I noticed that some failures depend on what the user asked. So the user gets the credit back, asks again, gets it back again, and that's a free loop that never touches the hard cap

... timers, and constant paywall popups just to do basic math. So I built Contri a clean, fast, and 100% free Android app for splitting group bills without the nonsense. What makes Contri different: Zero ads, no paywalls: No daily caps on adding expenses and no 10-second wait timers. No forced sign-ups for friends: Add guests to a group without forcing them to create an account or give their email. Flexible splitting: Split equally, by exact amounts, percentages ... ... more. I m an indie maker and would genuinely love your feedback, roast, or support on our Product Hunt page! Check out Contri here: Contri: Split group bills Ad-free, no daily caps

... minute, about 17 signups a minute. So 1,000 people signing up in the same hour would need about 15 million tokens, roughly an hour of queueing. What I'm doing about it: upgrade before marketing, set a spending cap, make the model name swappable (it's copied into 5 files right now), and plan a second provider. The same model is listed cheaper on other hosts, so a backup could also save money. For anyone who has launched ... ... when a spike hit a rate-limited provider, did you queue, fall back to another model, or hard-cap

... Three things went wrong on Arteza this year and not one was reported by a user. A spend function wrote bad balance events, so the ledger drifted and the cap I thought I had wasn't the cap

... Curious how people here handle this in practice. When an agent has a card or API budget for bookings and SaaS, do you use a hard daily cap, approve each charge, or something else? The acting side keeps getting better, but money controls still feel bolted on. Comment from George Basov(@gbasovv): My agents never hold a card at all. The working rule is simple: anything that spends money or reaches real users needs a yes from me first, while ... ... routine work like builds, tests and deploys doesn't. For ad budgets the cap

... believe that cloud providers should make hard spending caps the default: when you hit your limit, everything stops, rather than you getting a warning email you only see after the damage. The reason is simple: coding agents can now ship code that burns through paid APIs while you sleep. AWS and Google Cloud have both added spend caps

... about what happens at the boundary didn't get any faster. So the rule now is that anything touching balance gets driven to zero and past it before it merges. Not read, driven. If you've got a hard cap in your product, has anything in your test suite actually hit it? Comment from Konstantin Tikhaev(@konstantin_tikhaev): @asadmalik901  Diffs make schema checks look like behavior checks. If you don't break the runtime logic and verify the failure ... ... testing the bug. If you haven't watched the check fail with your own eyes, it is just decorative code. Comment from Asad M.(@asadmalik901): @konstantin_tikhaev  Decorative is exactly the right word. Mine was checking that the cap

... this?" It's "does this answer get better enough to be worth sending it?" We learned this the hard way at Murror. We used to keep every journal entry forever because more history looked like better insight. When we capped retention at 30 days, the insights barely changed and people started writing more openly. The context we thought we needed was mostly comfort for us, not value for them. So my rule for a cheaper model is this. Take ... ... better", "same" or "small better". If the large column never wins clearly, that's your answer. Do you compare the runs yourself or have a second model do a first pass? Comment from Gal Dayan(@galdayan): the "30 day cap

... free invoice generator takes a JSON post with no account behind it and hands back a PDF. So it has limits on everything. At most a hundred line items. At most twenty taxes on any one item. A length cap on every text field, a byte cap

... input. Ask for 30 cards on a topic that only has 15 real words in it and you'll get an undercount every time. With a plain refund rule that's a free generation loop. So those refunds are capped at 3 a day per user, and an undercount only refunds below 60% of what was asked. Above that you keep the cards and get an honest warning instead. Comment from Lisa(@yelyzaveta_kibets): @asadmalik901   Ceiling generous enough nobody ... ... unit cost is actually big, beats one number for everything. Naming what happens to the files is the line I'd push onto our own FAQ, since silence is what invites the what's the catch searches. The video-versus

... move with a free tier is to cut features. English only. Three languages instead of thirty. The formatting held back as the thing you pay for. The free version gets built to be annoying enough that you upgrade. We capped capacity instead. Every feature Lispr has sits on the free plan: push-to-talk dictation in about 99 languages with mid-sentence switching, instant translation into 32 languages, AI formatting, the personal dictionary, local history. No account needed. The limit ... ... that is several times what they actually speak into an app. None of it moves behind the paywall later. Pro is $79 a year or $7.99 a month, with lower regional pricing in selected countries. It removes the minutes cap

... India 160  (India nearly matches US reception). Schools: UC, Stanford, Oxford, Cornell, Tel Aviv. Part 3. What actually works at launch Teams win:  solo 123 2 3 = 188 4+ = 288; blended multi-maker avg ~227 vs 123 =  +85% .  Power-hunter  ( 10 hunts):  235 vs

... sets fixed later dates for the high-risk regime: 2 December 2027 for stand-alone Annex III systems, 2 August 2028 for high-risk AI embedded in regulated products. It extends some SME simplifications to small mid-caps, eases registration for non-high-risk systems, and adds a new prohibition on AI generating non-consensual intimate content and CSAM. That's the headline, and for a small subscription app it is almost entirely irrelevant, because you were probably never building ... ... same entity provides both, and AI inside very large platforms and search engines. Fines under Article 99(4) reach 15 million euro or 3% of worldwide turnover, whichever is higher, with proportionality available for SMEs and small mid-caps

... running into the same strange moment with small online tools. You find one, try it, and it works. Then you decide to actually use it. That s often when the limit appears an account, a paid feature, a usage cap, or an upgrade right in the middle of the thing you were trying to finish. I started thinking about this while building OneLess Pomodoro . I wanted the timer to stay useful after the first session, not just look useful ... ... have to unlock just to keep using it. I m curious about the opposite side of this: What s the smallest limit or restriction that has made you abandon a tool you otherwise liked? Was it a daily cap

... three months into clear aligner treatment and building a tracker for it while I use it (I am my own QA!). I pulled every public App Store review of my main competitor app in the category. 1,426 of them, 55 storefronts, off Apple's per-storefront feeds. Rating by year: 4.04, 3.79, 3.82, then 3.08 in 2025 and 2.88 so far this year. My main competitor

... products often brings unexpected API cost spikes. I ve been testing user-level rate limits, token quotas and cached responses to keep spending predictable. I m curious to hear practical approaches from other builders: Do you enforce hard monthly caps for every user? What fallback workflows do you offer once AI quotas are exhausted? Which caching strategies work best for repeated AI requests? Many generic guides skip these operational details, so real production experience would be really helpful. Comment from ... ... Siarhei Hamanovich(@hamanovich): Hard cap

... single release. And if you do need a ceiling, design it into the product instead of bolting it on as a punishment. A natural stopping point a session that ends, a reflection that concludes reads as thoughtful. A hard cap that fires mid-use tells your most invested user she's used too much of the thing she's paying for. Same economics, completely different product. The Murror version of this is uncomfortable and I'd rather say it than ... ... least want to meter. I don't have a clean answer to that. We made an unrelated decision last year to have the AI forget everything after thirty days, for trust reasons, and it happens to cap

... have a free tier that costs me real money every time someone uses it, and no way of knowing who's using it. I'm looking for the least invasive way to cap it. The background: I took the account requirement off the front of my product a few weeks ago. You can now use the expensive part of it without signing in, and you only make an account at the point where you'd want to keep the output ... ... enthusiasm. Someone running it eight times in an hour is either draining the budget or is the most interested person who's visited all month, and from the server side those two are identical. So I've set the cap

... building a tool that centralizes retry limits, spend caps, and alerts across every cron job or agent you run, instead of wiring guardrails into each project separately. The idea came from noticing how often solo devs and small teams only find out an agent looped or retried for hours after the bill shows up. Most people patch this per project (a retry cap

... what happens when the user closes your app? Two weeks ago, on July 15, China rolled out rules specifically aimed at curbing emotional dependence on AI companion bots. Platforms now have to detect distress, intervene in a crisis, cap excessive use, and hand users real control over their data. California, meanwhile, became the first U.S. state to pass legislation regulating companion chatbots, forcing developers to build in actual safety protocols. If you're building anything that touches people's emotional ... ... 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

... only found three pixel-level bugs we'd been staring past for months, but wrote commit messages more thorough than most postmortems. Thank you. Fixed The reading pane is now the reading pane. We were capping the message at 840px wide, which meant a big monitor bought you one skinny column of text, a lot of empty pane, and a scrollbar floating somewhere in the middle of the screen. Cap

... real answer, not the rate limit workaround. Once decisions live outside the model's context window, which agent picks the work back up stops mattering nearly as much. 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 ... ... just stays in the session until it resolves or gets dropped. The one line cap