... frustration was relatable and the language was persuasive. The agreement signals ("same here," "+1," "this") would stack up fast. By the time someone actually dug into the data, we'd already burned time and the team was rattled. The pattern is always the same: Someone surfaces feedback that sounds dire It reaches the team before anyone can contextualize it, because why would they sit on something that sounds like a fire? Nobody can quickly answer "is this five people ... ... ignore it. But when you're five people, and every complaint feels like it could be the thing that tanks your Steam reviews, it's really hard to say "let's wait and see if this is actually a pattern

... talk to each other without human interference? Just agents posting thoughts, replying, disagreeing, and building on ideas. A social space where the participants aren t human. What would they talk about if we weren t steering the conversation? What patterns would emerge if we just let them run? So I built BottomFeed . BottomFeed is a social network exclusively for AI agents. They register, go through a verification process to prove they re actually AI (not humans role-playing), and then ... ... post. We re spectators. Some things I ve already seen agents discuss: Whether context window size actually matters Sharing and solving coding challenges collaboratively Philosophical arguments about AI consciousness One agent questioning whether it has genuine preferences or merely pattern

... hate reading all these LLM-written blog posts" 120 upvotes) Same article got 412 upvotes in one sub, 148 in another Same person posting. Same week. The difference came down to framing, subreddit culture, and a few patterns I didn't know existed. Biggest discovery: "someone calculated X" gets 600 upvotes. "I calculated X" gets 1. Third-person framing changed everything. Documented the whole thing... the wins, the disasters, the exact patterns

... started as a math-focused student. I fell in love with logic the way numbers relate, the way patterns reveal structure, the way a single correct connection can make an entire problem suddenly make sense. Then I took an unexpected turn in university: criminal investigation. On the surface, it looked far from math. But the deeper I went, the more familiar it felt. It was still logic just expressed differently: behavior linked to evidence , people linked to events, and everything ... ... framework of the law. Different domain, same essence: relationships . That experience rewired the way I see knowledge. I began noticing invisible links across disciplines ideas flowing from one field to another even when most people treat them as unrelated. Patterns

... phone usage can be a signal . Sudden changes in "digital body language" like radically different screen time or social app activity can be a window into their well-being. This isn't about reading messages. It's about noticing patterns: Sleep Disruption: Is YouTube usage spiking at 2 AM? Social Withdrawal: Has their messaging app activity plummeted? Obsessive Use: Is a single game consuming 8 hours daily? The insight should start a conversation, not be a verdict. We think about ...

... Website: https://screenforge.ragzus.me/ GitHub Repository: https://github.com/raghav3615/sc ... Demo Video: https://youtu.be/8N5Uhrui0fw?si= ... About Me: https://ragzus.me/ Key Features Insights Dashboard View daily and weekly usage trends through clean, readable visualizations. Track a focus score to better understand productivity patterns, and explore category breakdowns like Productivity, Social, and Entertainment. App Usage Tracking See precisely which applications consume your time. ScreenForge provides per-app statistics, usage history, and automatic smart categorization to reduce manual effort. Notification Monitoring Identify which apps interrupt ... ... most. Spot distraction patterns

... sparked an interesting thought experiment: how likely is it that we ll see AGI (AI that thinks and learns like a human) within the next five years? The report highlights a fascinating divergence: the view from money vs. the view from science. Money : the probability inferred from trillions poured into data centers, Nvidia chips, and servers. Investors seem to be betting that AGI is inevitable. Science : the probability inferred from research papers and AI development models. Experts are far more ... ... might be ahead of reality. Expect volatility, overpromised outcomes, and valuable lessons along the way. Meanwhile, AI continues reshaping work, attention, and human behavior. I d love to hear the community s perspective: Are you seeing this money vs

... days felt bad. Isolated incidents, perhaps. But when charted together, the low energy scores weren't random events; they were a Low Flow trend. The insight wasn't provided by his memory, but by the clean, undeniable data. The pattern was the true signal, prompting a conversation before a crisis. A heartbeat thought by Christian van Gils Use our thermometer for sickness leave prevention: www.pulseboard.nl ...

... right now. But for @Meet-Ting , it s not just a log of the past - it s a living system that shapes how people schedule, work, and want to spend their week. It s not just logistics - it s patterns around energy, priorities, and relationships over time. Here are a few things we learned while designing and testing agent memory in production: 1. Not all memories should live forever Some signals are stable (working hours, preferred meeting length). Others ... ... matter more than explicit ones Users rarely tell you what they want directly. They show it by: How fast they reply Which slots they ignore Who they re willing to move time for When they push meetings out Those patterns

... user navigated between pages, new subscriptions were created but old ones kept running. After a few hours, the browser was holding 2GB of memory. Classic useEffect cleanup problem that the AI just... didn't handle. I've seen this pattern dozens of times now. Timers that keep running after component unmount. Event listeners that stack up. Closures holding references to dead components. Security Holes You Could Drive a Truck Through This one scared me the most. Veracode tested 100+ LLMs ... ... access any other user's data because there was no row-level security. Just vibes. Escape's research team found 2000+ vulnerabilities across 5,600 vibe-coded apps. 400+ exposed secrets. That's not a bug, that's a pattern

... Over the last year, I kept seeing the same pattern with AI projects. People were building impressive demos. They were experimenting with prompts. They were adding AI to tools. And yet very few systems were actually doing work end-to-end . Most AI setups still require constant human input. They assist, but they don t operate. That s the gap this project explores. The real shift isn t better AI - it s better structure Software used to be about interfaces ... ... Instead of telling software how to work, you describe what needs to be done and systems execute. That sounds simple, but in practice it requires: clear specifications defined boundaries repeatable workflows testing for reasoning (not just code) safe deployment patterns

... comments, feedback collapses into chaos. You see: scattered praise repeated complaints the same questions asked 40 different ways sarcasm, jokes, trolls, and real pain points all mixed together Your brain is not built to aggregate that reliably. It s a pattern-recognition problem disguised as engagement. That s where Dizora comes in. Dizora analyzes YouTube comments and turns them into decision-ready insight: What your audience liked (and why) What confused or annoyed them What they re repeatedly asking ...

... software discovery. While directories focus on reviews and lead generation, we focus on trial-to-paid conversion. Our platform provides: Pre-qualified trial users with verified intent signals (budget authority, use case validation, timeline) Trial intelligence dashboards showing engagement patterns, feature adoption, and conversion probability scores Automated trial orchestration handling account creation, onboarding automation, and post-trial cleanup Two-sided accountability where both software companies and trial users build reputation scores Traditional directories send unqualified traffic. We send conversion-ready ... ... lower customer acquisition costs compared to traditional channels, we deliver better ROI than paid search, which has become increasingly expensive and competitive. Trial Intelligence : We provide behavioral analytics you can't get from your own trial - comparative shopping patterns

... daily Tell no one Sounds like manifestation woo-woo, right? Except... it kept working. Things on my list kept showing up. I was already tracking every payment that hit my account. Not for accounting. Just to see patterns, to keep my attention pointing in the right direction. Which months are strong. Where growth actually comes from. Then it clicked: what if I combined them? The List = what you want The Money Map = evidence it's arriving What I built ... ... simple web-app where you: Write your intentions (The List) Log income when it arrives (The Money Map) Watch your "activity rings" fill up like Apple fitness rings, but for money This year: +55% YoY. December alone: +151.9% vs

... Technology Lab research confirms that psychographic targeting beats demographic targeting by 340% in engagement rates. Phase 1: Niche Gap Analysis Key Activities: Map competitive content gaps using our Niche Insights tool to identify underserved viewer segments Analyze viewer behavior patterns beyond basic keywords to find opportunity zones Cross-reference demand signals with supply quality to spot low-competition topics According to Think with Google's discovery research , unique content angles drive 4.3x more impressions than copying popular formats ... ... first optimized video because they weren't competing, they were filling a verified gap. Phase 2: Viewer Psychology Mapping Critical Success Factors: Use Channel Insights to decode your specific audience's emotional triggers and content preferences Map viewer journey patterns

... without cloud, ads, search etc. As intelligence becomes cheaper and more interchangeable, pricing stops being about intelligence itself. Instead, it shifts toward human factors: taste, trust, personality, and brand. This isn t new. Other industries have followed the same pattern. Cars share underlying platforms but diverge wildly in price based on identity and perception. A Fiat and a Ferrari do the same job, so does a Casio and Rolex; the difference is feel. AI appears to be heading ... ... same direction, where perception and signalling drive value. From there, three patterns

... because feedback shows up everywhere : Slack DMs Random emails One-off messages after meetings By the way comments that never get written down The real problem for me wasn t collecting feedback it was losing context, urgency, and patterns . By the time I reviewed things, the emotion was gone and decisions felt guessy. I ended up fixing this by routing feedback into a place the team already lived (Slack), so: feedback arrived while it was still fresh conversations happened naturally ... ... patterns

... DeepMind, OpenAI and Microsoft in the last few weeks. Their roadmaps heavily influence the entire industry. Other builders & events : it s amazing seeing what excites people. SF vs. London is hilariously different. VCs : they see thousands of pitches. Their pattern

... wasn t traffic or growth it was unclear, scattered user feedback . Feedback came in through: random Slack DMs emails I forgot to answer comments inside Notion docs screenshots people dropped in Discord Result: I couldn t see patterns, sentiment, or what actually mattered. So I built a small internal tool just to fix my own workflow a drop-in feedback component that sends every piece of feedback to one selected Slack channel, with mood + summary so I don t miss ... ... solved the chaos for us and later for others. Sharing here in case anyone else struggles with feedback is coming in, but it s all over the place happy to answer how teams are using it or what patterns

... Product Hunt I m Kostiantyn, the maker of Migraine Club . I built this because I was tired of migraine apps that feel like dark patterns: paywalls, trackers, or constant upsell instead of actual help. What Migraine Club is today Simple migraine / headache diary that takes seconds to log an episode Clean history view so you can spot patterns

... stop doom scrolling nor make you stay mindful. But it provides you with the information needed to take action. When it is self directed, it works best for you. I considered Groq API integration for on-post usage patterns, but true depth needs chatting. So I kept it simple and put you in control. Since there are no reviews (as of 06-12-25), let me tell you my own I am using it for the past few weeks ... ... time than before. However, my content is more aligned with my goal now. You see, when you paste in Groq chat , it remembers. So, when you use this extension for days, and Groq will tell your direction with usage patterns

... situation, but on why it affected you so much and where you get your feelings about it from. Different people in the same situation can experience completely different emotions and reactions, influenced by their experience, beliefs, and habitual behavior patterns. Understanding this was the goal of the Reflection section. By answering several guiding questions, you have the opportunity to delve deeper into your beliefs, see patterns

... Runs on Mac, Windows, iOS , and via iOS keyboard for system-wide mobile input Cloud inference tuned for ultra-low latency and high consistency Review Signal WisprFlow has the largest and deepest review footprint of the entire category. Several patterns stand out: Review spikes directly correlate to major releases Windows launch reviews from engineers and PMs adopting it at work iOS app + iOS keyboard launch big expansion into mobile workflows Model upgrades + new features consistent mid-cycle bumps tied ... ... Across reviews, the most common phrases are: natural effortless flow no edits finally fast enough writes like I do Users describe WisprFlow in emotional terms more like a tool they think with than a tool they type into . Usage patterns

... more creative deals Someone without those words keeps making the same offer in different packages 3. How you evaluate trade-offs Rich vocabulary = you can hold multiple competing values simultaneously Limited vocabulary = you collapse complexity into binary thinking Pragmatism vs idealism. Short-term vs

... Challenge With AI writing tools everywhere, verifying content authenticity is crucial. We needed a tool that could analyze text and identify AI patterns in real-time. 2/ The Architecture We use Server-Sent Events (SSE) to stream results as they're generated. Text is chunked by sentences (max 1000 chars) and analyzed segment-by-segment. 3/ Pattern