... tried a lot of workflows over the years: editing local JSON files hardcoding mock states running proxy tools setting up mock servers intercepting requests with external apps But I still felt the workflow was slower than it should be especially when debugging edge cases like: empty states failed requests

... more the merrier. I don't want to go and register for all of them + to pay monthly fee, if I am going to use them very rarely. Is there a service who would provide that - pay per request? Good luck! Bogo Comment from Alper Tayfur(@alpertayfurr): Yes OpenRouter is probably the closest fit here. It gives you one API for many models, pay-as-you-go billing, and no need to register with every provider separately. If you want ...

... first. As agents start composing multiple services together, that becomes a bottleneck. How it works The gateway implements a payment negotiation pattern using HTTP status codes. Here is the full sequence: Step by step: The agent sends a deploy request to POST /agent/deploy If the agent has credits, the deployment proceeds normally (200) If not, the server returns HTTP 402 with a JSON body containing: the price, the token address (USDC or USDT), the recipient wallet, and the chain ... ... agent sends the ERC20 transfer on-chain to the specified address The agent retries the original request

... engineers. One dedicated product manager. Endless meetings about "enterprise readiness." We launched the tier. Sent emails to our biggest users. Ran LinkedIn ads targeting "Head of IT" and "VP of Infrastructure." Zero. Not one signup. Not even a demo request from an enterprise account. The real cost Let me put numbers on it. Cost category Amount Engineering time (3 people 6 months) $180,000 Product management $60,000 Marketing (ads, content, emails) $25,000 Opportunity cost (features we didn ... ... thought we knew We assumed enterprise customers wanted the same things as our small business users, just more of it. More security. More control. More features. We never talked to them. We read blog posts. We looked at competitor

... months, we were building features based on what users said they wanted. Feature requests. Sales calls. "It would be great if you added X." We built X. Nobody used it. So we stopped trusting what people said and started tracking what they actually did. The dataset We pulled 12 months of usage data from Rankfender. 1,247 active users. 27 distinct features across 8 modules. We looked at three metrics for each feature: Adoption rate % of users who tried ... ... least once Retention rate % who used it again after 30 days Request

... months, our most requested feature at Murror was a chat function. Users wanted to talk to the AI the way they talk to a friend. It seemed obvious. Every competitor

... months ago, the number one feature request for Murror was a detailed mood analytics dashboard. Charts, graphs, weekly trends, monthly breakdowns. People wanted to see their emotions visualized like a stock ticker. So we built it. And almost nobody used it more than twice. Here is what we realized: people do not actually want to analyze their feelings. They want to feel understood. There is a huge difference between those two things. The dashboard turned something deeply personal into ...

... users have been asking for this integration since day one, especially after Krisp added an AI note taker to its Voice AI app. In fact, it got 6x more votes than the second most-requested integration. Which naturally leads to the next question: why did it take us so long to build? Comment from Abdullah Mohamed(@abdullah_mohamed14): @asti_pili   Ha, so close. Now I'm really curious what beat Notion if it's sitting at #2 with ... ... copy-pasting after every call anyway. This makes the whole thing actually usable as a complete workflow. Comment from Asti Pili(@asti_pili): alright we have guessers that it's @Notion but Notion was only #2 in requests

... noticed something strange. Our API costs had spiked. Claude was making 3x more calls than necessary not because the code was wrong, but because it didn't understand pricing implications. It called external APIs in loops where a batch request would have been fine. No error. Just expensive. Day 4: The silent failure. We asked Claude to optimize our database queries. It wrote better SQL. Things ran faster. Then user emails started coming in. "Where did my old data ... ... simplify the requirements to make it work Stop and ask for guidance Explain the conflict clearly Communication Guidelines When in doubt: ASK FIRST before implementing Check if approach is correct Don't make assumptions Verify against actual code files Request

... working for a 20-person startup. @Linear is our product development system, which we really enjoy. Currently, our operations team receives support requests via their personal email and then publishes requests

... Would love to get feature requests from active users. Thanks! Comment from Navya(@navya_555): Would be great to have strong support for extracting and editing structured data (tables, fields, etc.) from PDFs. We re actually building a tool (Docuct) focused on this with human-in-the-loop validation still early and would love to learn from your approach ...

... outputs, no subscription Annual plans available on all tiers. ============================================================================ WHAT'S RUNNING UNDER THE HOOD (the stuff that proves it's not just smoke and mirrors) ============================================================================ WHY CLAUDE? =========== Munch runs on Claude Anthropic's flagship model, not a cheaper alternative. We made a deliberate choice not to cut corners on the AI layer because the AI layer is the product. Long-form content repurposing requires a model that can hold narrative across 800 words, match a brand voice consistently across ... ... different formats, and write something you'd actually publish not something you spend 20 minutes editing into shape. We tested the alternatives

... already established. At that point, there are more users, and their expectations start to differ. So how do you decide which features will, or won t, be implemented? The most common approaches I ve personally seen are: Whether the request comes from paying vs

... usually provide generalised advice. But when it comes to a specific case or a tailored strategy that requires a personal approach, that s where it becomes paid. Comment from Yisak Mebrate(@yisak_mebrate): I usually stick to the "How vs. What". The "What" is free, but the "How" is paid Comment from Michael Walker(@meatythemuncher): It all should be free until you want to use... Pretty simple Comment from adam badi(@adam_badi1): The tension here is real. From what ... ... that moment. what changed everything for me was caching aggressively. i cache outputs with vector similarity search so if someone requests

... with summarize the last 30 50 messages. Then I kept adding just one more thing : include prior decisions keep continuity across runs include relevant earlier context make it more detailed That makes results feel smarter, but it turns every request into a bigger prompt. The tricky part is it still feels like the same task, so you don t notice the cost drift until the number is already big. Tool output bloat snowballed I let tool outputs flow straight into ... ... loop I had cron-ish jobs that ran, went idle, then ran again. If your setup effectively re-establishes a big prompt footprint on each run, you keep paying the setup cost repeatedly. It s not one catastrophic request

... plays a significant role in how products appear in Google search results. What surprised us was that AI assistants like ChatGPT were rarely citing Product Hunt in product recommendations. AI assistants such as ChatGPT and Gemini rely on reviews, alternative lists, and structured product information gathered across the web. Product Hunt is strong across all three. In theory, this should make Product Hunt a natural source for AI-driven product recommendations and comparisons. In practice, it was not happening ... ... many tools which systematically run prompts against several AI models daily, and measure visibility of a product within prompts. We're using a new tool called @Gauge . We chose because a) their citation tracking is more useful than the alternatives

... mobile dashboard. At a system level, Snowmatic enables users to: View all connected controllers in one place Turn devices ON or OFF remotely Monitor power usage and operating duration Receive alerts for faults and abnormal behavior Submit service requests directly from the app From this Idea Usher review , it is clear that Snowmatic behaves more like infrastructure software than a lifestyle smart-home app. Reliability and clarity take priority over customization or automation gimmicks. Why This Use Case Needed ... ... profile Devices are added using a Device Key or Wi-Fi provisioning All controllers appear on a single dashboard User monitors status, power cycles, and alerts Devices are controlled remotely as needed Issues are reported through in-app service requests

... Today I m shipping RustAPI v0.1.300 with a new flagship feature: Replay (Time-Travel Debugging) a complete Record Replay Diff system for HTTP requests. Why it matters When a bug only happens in production, try to reproduce it becomes the bottleneck. With Replay, you can record real requests

... Since launching SignDeck, we ve been focused on reducing friction in document requests rather than adding flashy features. Recent updates: Pay-as-you-go credits (no forced subscriptions) Request

... recently noticed a disturbing pattern with one of our most promising Intern/Junior Engineers. Month 1: They were shipping features daily. Fast. Hungry. Month 2: The velocity slowed down. Month 3: Silence began.. They hadn't opened a Pull Request in 2/3 days, even though their tasks were marked 'In Progress'. I assumed the worst: Burnout? Disengagement? Maybe they are overemployed? I pulled them into a 1:1 to ask what was blocking the work. The answer floored ... ... that if they pushed it, the team would find another 'dumb mistake' and judge them. We weren't losing speed because of technical complexity. We were losing speed to Anxiety. The Problem: PRs are Public Performances We treat Pull Requests

... settings. I also noticed 10 uninstalls on Chrome Web Store and those are mostly from non-english speaking countries (China, Spain, Russia, but mostly China). What all these feedbacks have in common? It's not enough! 1 dark mode request 1 language output request

... agents to "actively use" those profiles in every conversation. So I rebuilt GetProfile as an LLM proxy and made it open source. Now the flow looks like this: 1. Your app or agent sends a normal OpenAI-style request but to the GetProfile proxy. 2. GetProfile loads the user s profile and relevant memories from its database. 3. It injects a compact profile summary, traits, and selected memories into the prompt as a system message. 4. It forwards the enriched ... ... request

... After 50 interactions: "Got it! User prefers Model A for emails, Model C for creative writing" After 100 interactions: "I know exactly what you like" Next time, it automatically picks the best model for you: Before LLM Arbitrage: User request Random model selection Hit or miss suggestion After LLM Arbitrage: User request

... everyone I built a platform called TraderRoute.com , where companies from all around the world can post buying & selling requests for global trade (import/export). Companies can share their requests

... Major update alert! Context Sync v0.6.0 just dropped with some incredible new features: What's New: VS Code + GitHub Copilot support (our most requested