... 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 slow APIs malformed payloads role-based responses Most solutions either require backend changes, extra setup, or switching context away from DevTools. So recently I started experimenting with a Chrome DevTools-based approach where API responses

... ReplyRight writes personalized, AI-powered responses to your Google, Yelp, Facebook, and TripAdvisor reviews automatically. Most local businesses ignore 80% of their reviews. Not because they don't care, but because responding to every one takes time they don't have. ReplyRight fixes that. How it works: Paste in a review (or we monitor Google automatically) AI generates a personalized response

... back. I have over 650,000 followers across my "Big Trains" and aviation brands, and I couldn't spend 8 hours a day in the DMs anymore. The Question: What s your take on using AI context to automate responses while you stay in charge? Is it better to spend your time on content creation and let the AI handle the engagement "grunt work," or do you think automation loses the soul of the page? I d love to hear ...

... this. There are tools that generate replies, post on schedule, analyze sentiment, even mimic your brand voice. But I don't use them. Here's why. A 2024 study on community engagement across 500 brands found that personalized responses drive 3.2x higher retention and 4.7x more repeat interactions than automated replies. People can tell when a response

... code. How do I display levels in my application? We've added new APIs to support fetching levels and thresholds from Trophy to support powering UI elements within web and mobile apps. // Fetch all levels and current thresholds const response = await trophy.points.levels("xp"); // Fetch level summary data (levels + user counts in each) const response

... execution easier to design, inspect, and debug with full context. You can check it out on Product Hunt here Comment from Rohan Chaubey(@rohanrecommends): How does the conditional branching works? Can you set up multiple if/else paths based on response data? :) Comment from Arnold Oshenye(@oshylabs): @janasundar  exactly! Once u can branch on response

... Most AI visibility tools show you a dashboard. They tell you where you're invisible. They show you competitors winning. Then what? You stare at the screen, knowing there's a gap, but no idea how to close it. RAIVE 2.2 (Rankfender AI Visibility Engine) fixes that. It's not just a tracking engine. It's a prescription engine. It analyzes where competitors

... users quickly grasp their brand s overall performance and trends within generative engines. 1.1 Four Core Metrics The Overview page focuses on four key metrics: Visibility , Citation , Share of Voice , and Sentiment . Visibility: Indicates the proportion of relevant AI responses that actually mention your brand. A higher value means your brand is seen more frequently by AI within this domain. Citation: Measures, among all responses

... monitor everything at 3am. I've been building in the reputation management space for a while now, and one pattern kept showing up when I talked to comms directors and PR leads: the problem wasn't their response it was the lag between when a threat emerged and when someone actually noticed it. A critical video starts circulating at 11pm. A journalist publishes a thread with wrong information on a Sunday. A mid-level employee's comment gets picked ... ... aggregator and starts spreading before anyone on the team is even awake. By the time the response

... useful here. It's an open-source CLI tool called AI SOV Analyzer . The idea: AI models like ChatGPT are increasingly where buyers discover products. This tool lets you measure how often your brand shows up in AI responsesvs your

... days ago, I shared the 10k mistake product owners make with AI search. The response was overwhelming. Since then, we've more than doubled our dataset at Rankfender. And many found they were invisible. But here's what scared me more: Of those who WERE visible, 43% had incorrect information in AI answers. Not "suboptimal." Not "could be better." Wrong. Outdated pricing. Missing features. Wrong founder bios. Competitors

... everyone I'm Lincoln. I used to run local SEO campaigns, and I'll be honest, the prospecting process almost broke me. Most of my week was glorified data entry: clicking through page 5 of Google Maps, checking review response rates one by one, hunting for unclaimed Google Business Profiles, and copy-pasting business info into spreadsheets. All of that just to find maybe 10 qualified leads. And then the worst part: I'd send those leads a generic ... ... they have an optimized website, and if they are showing up in AI search results like ChatGPT, Google AI Overview, and Perplexity. Each business gets a Need Score (0-100) so you instantly see who's desperate for help vs

... launched Rankfender 48 hours ago, and the response has been overwhelming. Thank you to everyone who checked us out, upvoted, and shared feedback! One question kept coming up: "How do I actually win in AI search?" So I dug into our data. Rankfender has now tracked over 50,000 AI-generated answers across ChatGPT, Google SGE, Perplexity, and Gemini. We've analyzed millions of citations, thousands of brands, and hundreds of industries. Here's what we learned and what ... ... schema 71% 19% Cite data sources 68% 27% Part 3: What content wins AI citations? This is the question everyone asks. Here's the data: Top 5 content types by citation rate: Comparison pages (X vs

... Today we re shipping Streaming ! A faster, smoother way to get AI responses. Video here Responses

... this problem. What Is Socra? Socra is an AI learning platform built around the Socratic method the 2,400-year-old teaching philosophy that uses questions, not answers, to build genuine understanding. Where most AI tools give you a response and send you on your way, Socra turns every session into an interactive dialogue that challenges you to think, articulate, and truly understand what you're working through. The result? Knowledge that sticks. Thinking that improves. Understanding you actually ... ... that?" "Can you explain that in simpler terms?" "What would happen if that assumption were wrong?" "What's the connection between this idea and what you said earlier?" These aren't filler questions. They're precisely calibrated to your responses

... boost, use the new boosts tab on the points page of the Trophy dashboard. Or to create a boost for a particular user, use the new admin API methods. // Create a new active boost for a specific user const response = await trophy.admin.points.boosts.create({ systemKey: "xp", boosts: [ { userId: "user-123", name: "2X boost just for you!", start: "2026-02-23", end: "2026-02-25", multiplier: 2, }, ], }); Displaying Boosts We've added new APIs and SDK methods to support displaying boosts in your ... ... application: // Fetch all active boosts const response

... database scaling "your data never leaves your machine" resonates with people trusting you with their financial data. Tradeoff is update distribution, but Tauri handles that. Solo dev, happy to chat if useful for your research! Comment from Vihaan Kodiganti(@vsync_66): @ryan_hendrickson  Hi! My ultimate vision is a mental health app that does the following: Solves the problems of: 1. People not having anyone they trust/want to talk to 2. People not having (or not trusting) immediate access ... ... users feel distressed, the following steps are taken: 1. Coping activities are suggested 2. Leo shares the pain with the user/responds in a heartwarming way (unlike clinical apps) 3. Leo asks the user to get professional help. Leo's response

... PATCH /api/v1/memory/policies/:id DELETE /api/v1/memory/policies/:id GET /api/v1/memory/policies/resolve SDK Example Default policies are automatically applied // npm: @mnexium/sdk import { Mnexium } from "@mnexium/sdk"; const mnx = new Mnexium({ apiKey: process.env.MNX_KEY, openai: { apiKey: process.env.OPENAI_API_KEY }, }); const alice = mnx.subject("user_123"); const response = await alice.process({ content: "Remember that I prefer concise weekly summaries.", model: "gpt-4o-mini", learn: true, recall: true, memory_policy: "mem_pol_support_assistant", }); console.log(response.content

... activation energy of starting from scratch on 30+ replies. Having a pre-written draft sitting there changes the task from "compose a thoughtful response" to "review and tweak." Way less cognitive load. We also added confidence scoring to each draft so you can quickly triage which ones are ready vs

... manual effort, but they introduce a different challenge: control complexity . Property owners and facility managers often deal with: Multiple snow-melting controllers across large areas No unified visibility into device status Manual checks to confirm systems are running Delayed response to faults or missed cycles Energy waste from over-running devices This Idea Usher review identifies Snowmatic s core problem as device sprawl . The hardware exists and works, but managing it efficiently at scale is where most users struggle. Snowmatic ... ... systems running correctly right now? Feature Breakdown (What Actually Matters) Centralized Device Dashboard All connected snow-melting devices are visible on a single screen. This is not just a UI convenience. For properties with multiple zones, centralized visibility reduces response

... Everyone! Our app is designed to be used at site and I always felt that response speed is critical for seamless app use It appeared to be a compromise. This specially relates to audio and TTS we use. We can use very naturally sounding TTS models, but they may take about 1 minute to respond. Or we can use something which responds in 5 seconds but quality will be lower. How much do you think a user can wait ... ... response

... answers has become harder not easier. Most Q&A platforms were not designed with long-term knowledge preservation in mind. They are optimized for engagement, discussion, or real-time interaction, not clarity or durability. Qeeebo was built in response to this problem, with a fundamentally different philosophy about how answers should be created, stored, and accessed. The Traditional Q&A Model Most popular Q&A platforms share a similar underlying model: Questions are posted by users. Answers ... ... also introduces several systemic problems that become more severe at scale. Answers can become outdated as technology, facts, or best practices change. Threads often drift off-topic. High-quality answers may be buried under newer but less accurate responses

... exciting if they turn out useful for other indie devs. I built Rewo because replying to app store reviews is repetitive, easy to procrastinate on, and yet really important. Don t get me wrong: reviews are important, they deserve responses, and you NEED to get the customer insight , but doing it fully manually becomes painful. I tried templates and copy/paste (boring, generic, slow) and even first-pass AI prompts but that approach didn t work. It still required too much ... ... built Rewo : a proper tool that uses AI under the hood, but with full configuration, automation, and control . It s not just paste a prompt and hope for the best , it automatically pulls in reviews drafts thoughtful responses

... Express apps often end up with scattered error handling. Try catch in routes, inconsistent responses, duplicated logic. Centralizing this logic can make apps more predictable and easier to maintain. I published a open source error handling library called ds-express-errors that tries to address these issues in a structured, minimal way. Here is what it provides: Centralized error middleware added after all routes, so error handling logic is not duplicated across the app. Preset standard HTTP error classes like ... ... that can be thrown directly from handlers. Automatic mapping of common library errors such as Zod, Joi, Prisma, Mongoose, Sequelize and JWT into consistent HTTP responses

... Netflix uses for recommendations). Here's the flow: USER FEEDBACK LOOP Write AI Suggests Accept/Deny/Retry System Learns Model Stats DB Model A: 85% Success rates Model B: 72% Model C: 91% Use this one! Thompson Sampling Algorithm Balances exploration vs exploitation Try new models Use proven models (10% of time) (90% of time) What's Thompson Sampling? Think of it like this: The Casino Analogy: You walk into a casino with 5 slot machines. Some pay out more than others ... ... make. Just write. 4. Privacy First SourcePilot track which models you like (stats only). SourcePIlot never store your actual writing content. Here's exactly what it tracks: Tracked: - Model ID (e.g., "gpt-4", "claude-3") - Your feedback (accept/deny/retry) - Response