... long time, I delivered projects to my clients as freelancers, often with Supabase or Firebase as a backend. Today, with Aurabase, I am building the only European alternative to these platforms. The trigger came from the field. With each project, the same questions came up: where is my data, who can access it, what happens if the supplier changes its prices overnight. These are legitimate questions, which Firebase and Supabase, which are subject to the American CLOUD ... ... answer serenely for sensitive data. Three frustrations recur among those who build digital products today: Sovereignty. An "EU" region checked on a dashboard does not change the nationality of the company that hosts your data. The surprise bill. A spike

... them means automation. Concentrated on the newest one or two means people.Cross it against a surface only humans reach. My repo had 81 unique visitors while the registry was reporting 2,411 downloads. Thirty to one.Look for a spike with no correlated movement anywhere else. Mine had 1,542 downloads in a single day and the visitor graph for that day is flat. Nothing happened. A mirror woke up. Then work out what you cannot see at all, which ... ... thing with npm search on my own package and traffic from npm results has been close to zero for me for over a year now. Comment from Saman Baloch(@saman_baloch):   @destiny_elle  The 1,542 download spike

... always impressive. The latest one: 109 visitors, 41 signups, 13 paid trials in a weekend, $0 spent. I haven't tried it, so I can't tell how much is real and how much is the usual launch-week spike that dies by Wednesday. It doesn't feel crazy though. More of my clients' traffic is coming through AI tools every month, so distribution inside them has to start working at some point. Anyone here actually listed their product ...

... files, it suffers from rapid context degradation. Each successive edit accumulates dirty context from past steps. In a complex system, the probability of a critical failure compounds exponentially. Unsupervised autonomous coding quickly devolves into endless audit loops, explosive generation spikes, and massive token burn. Development costs can easily 10x without strict human supervision. What feels fast initially creates a mountain of hidden debt. This is why I stick to my own approach: tab-by-tab, file-by-file development with ...

... tool I used in those workshops was AICF, but the content was unbiased and educative. We got 500+ users from these workshops, and >80% of them are still active users today. 4/ Product Hunt This was the big spike. We launched on Product Hunt thinking it would be GREAT if we could somehow land in top 10. But sometimes, the universe helps you. We came #1 product of the day and this gave us a trickle of 700+ users ... ... next few weeks before the spike

... community! While working on local inventory optimization, we hit an interesting technical bottleneck: standard time-series models struggle with neighborhood-level demand spikes caused by micro-weather shifts, local events, and regional supply chain delays. To tackle this, we shifted our architecture to blend real-time sales signals with regional contextual data. A few key takeaways from our build process: Context over raw volume: Regional variables often weigh heavier than historical sales trends for short-term spikes

... happy with the growth. September Said 'largest PC manufacturer' went through a reorganisation. The opportunity disappeared. That was that. We kept building and reached 400 users. October A large AI community discovered Ting. An Instagram post caused a huge spike in traffic and brought a wave of VCs to us. We took the calls but told them we weren t raising. Another lesson: always be ready to raise, especially as an AI startup. These conversations can take months, and investor ... ... market: I speak Portuguese, Mariana is Portuguese and my wife is Brazilian. We ran a test in Portuguese. We recruited 100s of Brazilian users in three weeks, mainly thanks to Ting on WhatsApp. IYKYK . December Based on competitors

... without code or a game engine. We launched Ctruh Studio here yesterday (landed at #2, which still feels surreal). And funnily enough, the email-list lesson is one we're living in real time this week. The launch spike is loud and fun, but the quiet thing, the handful of genuine relationships built over months, is what actually showed up when it mattered. Same discipline you described: trust the quiet thing over the number that feels good today. Glad ... ... paths crossed here, Tiffany, this was a good one. Comment from Sharun Kanan(@sharun_kanan): @rohanrecommends  Thanks Rohan, and yeah, #2 feels good. Your vertical-vs

... When launching or scaling a product, server timeouts during sudden traffic spikes are one of the most frustrating things that can happen. While testing higher concurrent loads on our backend, we noticed our app was dropping connections not because the CPU was maxed out, but because the database couldn't handle too many open connection requests at once. Here are 3 quick tweaks we made to fix connection bottlenecks: 1. Implemented Connection Pooling: Managed and reused open database connections instead ... ... Graceful Fallbacks: Set up proper timeout limits and retry logic on the frontend so users get clean fallback UI instead of hanging load states. This reduced our database response times significantly and stopped server crashes during concurrent user spikes

... works if you answer it yourself first, otherwise it reads like a survey instead of an invitation. Comment from Abdullah Javaid(@abdullah_javaid3): For me it's not finding users, it's telling early which channel is actually compounding versus just spiking

... there's a distinction I keep thinking about: apps built with AI (Cursor, Claude, Copilot writing the code) vs apps that depend on AI APIs at runtime to function. I'm in the first camp. I used AI-assisted coding heavily to build Briskly (a cleaning app that scans your room and generates a timed task list), but the app itself makes zero LLM API calls. The scanning runs on Apple's Vision framework, entirely on-device. No tokens ... ... pricing volatility bitten anyone yet? Anyone shipped with local/on-device models (Apple's Foundation Models, Core ML, quantised open models)? How was the quality gap in practice? Where's the line for you? Which features genuinely need a frontier model vs

... paid conversion. 25%+ with PQL. Webinars (live): 22-33% CTA conversion. High-engagement webinars hit up to 69%. AI search (GEO/AEO): 4-5x higher conversion than traditional organic. One B2B study found AI-referred traffic converted at 7.12% vs 1.37% for organic. Another found 14.2% for AI traffic. Quora: 2-5% conversion. 35% of B2B decision-makers answer questions on Quora. Reddit: 0.3-1.5% post-to-customer conversion in fit subreddits. CPC of $0.50-$2.00 vs

... like topic research you did not commission. Google pairs annotations with checking whether an external update, such as rewriting a YouTube title or TikTok caption, affected search performance over time. It pairs the 24-hour filter with catching sudden spikes. Running those together is my idea rather than Google's guidance, and I want to see whether it holds up. Exporting several properties into one spreadsheet lets you compare how your content performs side by side across platforms rather than ... ... laid it out well, thanks. The part I'd push on is your annotation + 24h idea I think it holds for one big discrete change like a retitled video, but Google attributes over a window, so a spike

... evaluate tools for my own workflow: a lot of products look reasonably priced on the surface, but the actual cost/value only becomes clear after you're locked in auto-renewing annual plans marketed as "discounts," usage-based pricing that spikes unexpectedly, or feature-gating that pushes you into a much pricier tier than you expected. Curious to hear from other makers and users here: What's a pricing structure that seemed reasonable at signup but turned ... ... avoid this exact reaction from users? Would love this to turn into a useful reference for both users evaluating tools and founders designing their own pricing pages. Comment from Rittwika Roy Choudhury(@rittwika_roy_choudhury): @alieksia  The "$1.99 vs

... After the initial launch spike on Product Hunt, most early-stage founders face a critical fork in the road: whether to double down immediately on aggressive paid distribution or spend those first weeks refining onboarding based on launch feedback. From analyzing recent SaaS releases, a recurring pattern shows up: startups often see high top-of-funnel traffic during launch week, but bounce rates spike

... Google still largely shows traditional results. AI search is mostly eating informational traffic, not purchase-intent traffic. Second, the traffic that AI does send is higher quality. ChatGPT-referred visitors spend an average of 15 minutes on a website versus Google-referred visitors at 8 minutes. Sessions from AI referrals grew 527% year over year in the first five months of 2025. Third, only 16% of brands are systematically tracking their performance in AI search. That means 84% of your ... ... competitors

... been working on it for nearly 3 months late nights, endless bug fixing, polishing the UI until it felt just right. In my head, launch day was going to be this big moment where traffic spikes, people leave reviews, and feedback comes pouring in. Reality? Complete silence. For the first 48 hours, I refreshed my analytics page every 10 minutes. 42 visitors. 0 signups. No DMs, no comments, nothing. My first reaction was panic: "The product is garbage. The positioning ... ... honestly, I felt like getting a working product out first was crucial. Without something tangible for users to test right away, hype dies down fast and people lose interest. Comment from Sabber Ahamed(@sabber_ahamed): the friends-vs

... nika  Fair point, and yeah I overcomplicated it Simplest version: run more than one product, and most CRMs turn into a mess. LiftUp keeps each product's stuff cleanly separate so you always know what's what. Competitors? Plenty of CRMs let you add extra pipelines. The difference is they bolt it on as a setting, whereas LiftUp is built around it from the ground up. Whether that matters enough is exactly what I'm here to find ... ... would be: A stable core product A clear landing page and positioning Real users who have used the product before launch Basic analytics to measure what happens after launch A strong reason for people to choose this product over alternatives

... When a site suddenly introduces hundreds or thousands of new URLs, Google does not automatically expand its budget to accommodate them. It evaluates the site based on three factors: Perceived Inventory: The total volume of URLs Google believes exist versus what it actually deems useful. Demand: How much users and Google actually care about the topics you are publishing. URL and Domain Popularity: The baseline authority your site possesses to justify the processing cost. If you flood your site with ... ... thin or repetitive AI-generated pages, Google quickly realizes the demand and popularity do not justify the massive spike

... Everyone talks about launch day. The upvotes. The ranking. The traffic spike. But for me the best moment is quieter than that. It's when a stranger uses something you built and it just works for them. No help needed. No DM asking how. They just got value out of it and moved on with their day. That moment is why I build. And I think it's massively underrated in conversations about what makes building worthwhile ... ... unless the idea actually reached you. traffic tells you people showed up. someone getting it well enough to poke the right hole tells you you built something real. Comment from Rahil Pirani(@rahilpirani): @omri_ben_shoham1 Launch day comments vs

... sets cannibalizing each other. 7. Search term mining and negative keyword lists. Finds the negatives I should have added last month. 8. Landing page and tracking QA. Checks every URL and pixel fire in the account. 9. Competitor ad monitoring. Scrapes Meta Ad Library, sends me a weekly digest. 10. Anomaly detection and alerting. Spend spikes

... minute flow works (the user's journey): 1. Connect (30s) - User links a source: uploads a CSV/Excel, or connects a database, Google Sheet, Stripe, etc. 2. Understand (30s) - Your system profiles the data automatically: detects column types, dates, metrics vs. dimensions, and relationships. This is the step that makes the rest automatic. 3. Generate (45s) - It picks the right visualizations (trends over time, top categories, distributions, KPIs) and lays them out into a coherent dashboard - not just random charts ... ... Explain (15s) - An LLM reads the results and writes plain-language insights: "Revenue grew 18% MoM, driven mostly by the Enterprise segment; churn spiked

... what timeframe worked for you? Do you keep soft-deleted rows for analytics or GDPR reasons? Any retention windows you recommend? If you have automated detection , what signals did you use (IP clusters; identical metadata; signup rate spikes)? Cheers! Comment from Paul Doerwald(@pauldoerwald): The advice out there is to reduce friction on sign-up so that it's as easy as possible to create an account. We did the opposite. At Clockk added just a little bit of friction ... ... part that bites people, because late-confirmers exist and you can't get them back. We soft-delete (flag + deleted_at) and just exclude unconfirmed from active counts, then purge much later. For detection, identical metadata + signup-rate spikes

... whole career around social media content and how it travels, which is probably why this particular problem has bugged me for years. Recently the thing that got me: I kept watching friends launch great products, get a nice spike, and then go completely quiet weeks later. Not because the product got worse, they just put their heads back down to build and stopped sharing, and the momentum died in that silence. So we built Spira for Product Hunt Makers ... ... seems the connectionto tiktok doesn't work! There is an eternal spinning wheel. Comment from Rahul Bhavsar(@rahulbhavsar): The momentum problem is very real. A lot of founders do the launch, get the spike

... There are so, so, many email apps out there: Superhuman, Hey, Slashy, Cowork gmail plugin, Spark Mail, Proton mail, Spacemail, Titan, Shortwave, Alfred, Canary, Spike, Tuta, Skiff, Icloud, Zoho, Yahoo (if it counts even). But has any one of us tried it before? Why/Why not? Comment from Peter Shu(@peterz_shu): @scarlett_massey  Good method which maximizes discovery and minimizes cost for you. so does that mean the ones that are non plug-in, you don t use? Also ... ... plug-in to Gmail and you re welcome to go start the trial there :) Comment from Roberto(@brajn): I just fine with Gmail Comment from Ghost Kitty(@ghost-kitty-9775800): Comment Deleted Comment from Sarvesh Chidambaram(@sarveshsea): I try alternatives