... closed the tab. That's the moment Serpent API started in my head. Not "what if we built something better" just "what if we charged for what it actually costs to run this instead of what the market will bear." What we built SERP data from Google , Bing , Yahoo , DuckDuckGo and Brave . Local rank tracking city by city. AI citation monitoring across Gemini , ChatGPT , Perplexity and Claude . News search with last-hour freshness. YouTube , Instagram , LinkedIn data . Reddit , TikTok ...

... Current problems totally sabotage my life not only in work, but unfortunately, in general. Comment from Peter Jenkins(@peter_jenkins1): @adana  Sometimes there have been. I advise clients I'm experiencing some personal difficulties and ask them to bear with me. It eases the pressure a little bit and helps me to deal with whatever is going on and then get back to it a little bit later. Or I can push ahead, knowing I've managed the client ...

... your app has social media capabilities. Say yes and Apple applies a minimum 13+ rating and drops you into a Time Allowance parents can cap. The definition is broader than having a feed, and your secondary category is load-bearing too. If you've shipped an update in the last few days you've already met this question. If you haven't, you will on your next one, and I'd rather you met it here than at 11pm with ... ... 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

... 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

... English is not my main language, so bear with me :) I used to be a daily user of PH. Not as a contributor, but a lurker. I loved to discover new apps, services, ideas... However, after a couple of years of not using the platform, I returned only to find that an overwhelming majority of the products are just... AI. Not necessarily saying I'm against AI products. That's a debate in itself, and not the point ...

... only if users complain? Or is there a minimum level of finish below which people won't even try it? And more practically: how do you tell the difference between a side branch that's actually load-bearing and one that just feels urgent because you're staring ...

... 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 load-bearing context I 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 ... ... versus

... While building an AI-powered interview platform, I initially found myself adding more and more AI features because they seemed useful individually. Eventually, i had to think much more carefully about which features genuinely improve the user's experience vs which ones simply make the product look more impressive. For other makers building AI products, how did you decide which AI features to keep, simplify, or remove? was there a particular signal from users,usage patterns,development experience, or something ... ... most. people click a shiny AI button once out of curiosity and that shows up as "engagement" in every dashboard. what actually separated real features from decoration was turning them off for a week and seeing who complained versus

... observation watching them use it is often more valuable than asking them what they think. people are bad at predicting their own behavior, but great at showing it. Curious did you notice any patterns in what users called okay vs. what experts flagged as broken? that gap is usually where the biggest product opportunities hide. Comment from Konstantin Tikhaev(@konstantin_tikhaev): @busmark_w_nika  Metrics must have clear failure conditions. Plus real validation only comes when you mesure specific ... ... demand question, and favour testers cannot answer it at twenty or at two hundred. Of the people testing now, how many arrived on their own rather than being asked? Comment from Arnold Oshenye(@oshylabs): Bharat's point about views versus

... back to is that a run which fails your own quality check shouldn't bill at all. It moves the judgement onto you instead of making the customer fight to get it back. Not clever, just cheaper than the alternative. So if you're pricing on credits, what does your refund path actually look like? Most founders I ask haven't decided. They find out during their first bad week. Comment from Serdar D(@serdarships): Founder here, and I sell ... ... that they did not like. Worth having, just do not expect it to retire the refund conversation, because that conversation was never mostly about the failures your checker can see. And the warning. The moment the gate is load-bearing

... biases, or tell us what they think we want to hear. AI removes some of that discomfort. But if it mostly agrees with our framing, have we really solved the problem? Because ultimately, neither your advisor nor your AI bears the cost of being wrong. You do. Curious how others handle this: when you use AI for an important decision, how do you make it genuinely challenge you? Comment from Vineeth Nair(@vineeth_n1): I started this thread because ...

... second drawing engine requires having read nothing, so it's a cheap check that catches exactly this. The scarier version isn't the duplicate, it's a duplicate with slightly different behaviour, because six weeks later both are load bearing and neither one is wrong ...

... 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 ... ... single step qualifying is a totally different, much harder problem, and it's usually the one people forget to build for after they ship the bright-line version Comment from Gal Dayan(@galdayan): @monatruong_murror  the load-bearing

... never needs review." Separately, I've also been leaning hard into OTA-first shipping (Expo + `eas update`) specifically so JS/UI fixes don't need to wait on app-store review at all that's been more load-bearing than I expected, for reasons unrelated to convenience. Curious what stacks/workflows others here are running for solo or small-team AI-assisted development especially anyone else who's handed an agent real production access and how you scoped the guardrails. (For context ...

... looking like this: That's the whole thing I actually read. Four sentences a  user  would recognise, in the order they'll ship. I comment until it's right "drop revoke for v1, it's not load-bearing" and the agent regenerates the list. No code has been written. No branch has been cut. I'm editing outcomes, not architecture, and it costs me a coffee's worth of attention. Get the blueprint right and you've done ... ... train, the roadmap keeps moving. The babysitting tax goes to zero, because there's nothing running on the thing I'm carrying around. Every milestone fixes itself before it reaches me This is the load-bearing

... survived precisely because you committed to one blunt sentence everywhere, so the model had no competing version to average against. Makes me think the real work isn't "write good copy," it's "decide which five sentences are load-bearing and then never paraphrase them." Genuinely grateful you wrote this up. Comment from Liam Cai(@supplymo): Ran your test on Perplexity before writing this. Context first: pre-payment sourcing checks on Chinese factories, me in Yiwu on the build side ... ... about. Import duty gets decided by an officer at clearance, so nobody can promise you a rate in advance. I fully expected the model to flatten that into "they tell you your duty." It didn't. It put who bears

... rather than hold the line forever. A stalled job that took the cautious path is recoverable. A stalled job that just sat there is a support ticket. Comment from Dale Mooney(@dalemooney): @alex_amancio_candoa_   @aadilghani  Constant versus invariant is the useful thing here and I am taking it. One addition: you cannot separate them by observation, but you can by deliberately violating the constant once. A property that survives a test faking an unattended caller ... ... luck, it is the other side of the same trade. You avoid my loop and you get a plan that may be out of date, which is the staleness problem wearing a different coat. That makes typed refusal load bearing

... nicely summarized tool output gets passed into a shell pipeline expecting the original format. I ve started thinking about context in three categories: Disposable context Repeated search results, duplicated docs, verbose logs. Usually safe to filter or compress. Load-bearing context Tracebacks, file paths, exact values, patch anchors and acceptance criteria. Losing a few tokens can break the task. Executable context JSON, shell output, patches, CSV or anything another program will consume. This often needs to remain byte-for-byte ... ... intact. The important part is that these aren t permanent labels. The same search output might begin as disposable, become load-bearing

... from AutoScaled I wanted to actually introduce myself properly instead of just dropping a one-liner and disappearing, so bear with me. Yes, I'm solving a problem (like most people here), but more about that later. What I'm hoping to get out of being here isn't just visibility for the launch. I genuinely want to talk to people who live in RevOps, sales, or GTM day to day, hear what's broken in your current process ...

... carelessness. They violate it because it was never written anywhere they could read. Mart n's point about making it structurally hard to bypass the source of truth is right, and I don't think it's an alternative to a review layer so much as the other half of it. Structural prevention covers the constraints you thought to encode. The drift that actually hurts is the one nobody had articulated as a rule yet, because if you could state ... ... cleanly you'd already have enforced it. The two fail in opposite directions, which is an argument for both rather than either. The six weeks is the part I'd sit with. By week six the decision is load-bearing

... bugs surface what my red-team didn't think of, before prod does. a genuinely novel failure class still slips until someone hits it, which is exactly why the "tell me when it didn't work" ask is load-bearing, not politeness. Comment from Raj Nagulapalle(@rnagulapalle): @omri_ben_shoham1  it's me, personally .. I read what the adversarial model surfaces and decide what is a real class vs

... goldfish with a bigger bowl. So I built memory that works more like a brain: Consolidates related facts into clean summaries, sharper over time, not bloated Decays stale memories on an Ebbinghaus forgetting curve, but keeps the load-bearing stuff Hash-chained audit trail so you can actually trust what's stored MCP-native (Claude/Cursor/any client), open source, self-hostable. It's a relaunch, I shipped v1, learned where it fell short, and rebuilt it. Would genuinely love this community ...

... Cursor: coding Supabase: backend and auth Vercel: hosting GitHub: version control Google Search Console: SEO Campix: my own project, camp software, yes I'm biased Comment from Hans Desjarlais(@ismaelyws): Gmail Calendly Claude Code Google Docs & Sheets VS Code Comment from Bill Davis(@billspaced): @nolan_vu  Slack, not because I love it but because everything dies without it. YESSSSSSSSSSSSS Comment from Bill Davis(@billspaced): @riya_pariyar   @benfleming   @asapx  same question... never heard ... ... solo founder, these are the five tools I use almost every day: ChatGPT VS

... convince 12 people to install something new" problem is genuinely the hardest part of any group coordination tool, and you've already identified the right answer: don't make the players pay or install anything heavy. the captain bears the friction so nobody else has to. that asymmetry is what makes group adoption actually possible. distribution is still the grind, but the model is solid. Comment from Nolan Vu(@nolan_vu): @shaheen5  thanks for sharing this, Shaheen! the analysis ... ... generic to actually tailor against meaningfully? That's usually where automated resume tools fall apart in my experience. Will check out the link, sounds genuinely useful for the right audience. Comment from Nolan Vu(@nolan_vu): @thenameisarian  Interesting, alternative

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