... Over the last year, AI copilots exploded. They write code. They summarize docs. They generate content. But here s the uncomfortable question: Are they actually improving deep work Or just helping us produce more surface-level output? While building around Claude, we noticed something interesting: Most AI tools are optimized for speed . Very few are optimized for: Structured thinking Long-context continuity Multi-step execution Real collaboration memory That s why we started building an AI coworker layer around Claude ... ... focused on deep workflow support instead of quick responses. Not another chatbot. More like: A persistent reasoning partner for research, coding, and complex tasks. We launched it on Kickstarter to validate whether people actually

... been talking to dozens of PMs over the last few weeks who prototype with Lovable, Bolt, Figma Make, V0, etc. Same frustrations keep surfacing. Output looks a bit generic: looks like a demo, not your actual product Context loss: explain your product in ChatGPT/Claude, then re-explain in Lovable, then again somewhere else No edge case thinking: AI executes prompts literally, doesn't challenge or expand on them The core issue I keep seeing: these tools are interface builders. They ... ... great when you already know exactly what to build. But the hard part, thinking through the flows, the states, the edge cases, where users will actually

... GEOScore, we've noticed a lot of discussion lately about how difficult it s becoming to decode why certain pages dominate search results while others, which seem just as good, get buried.   We built the Competitors feature in GEOScore specifically to take the guesswork out of that process, changing how you approach competitor

... real or not, and your whole team burns two days chasing it. I've watched this happen over and over at studios of every size. The fix isn't "listen harder," it's having someone who actually knows if a complaint represents five people or five hundred before the team scrambles. Someone posts in your Discord server: "The animation system is completely broken." A developer sees it while grabbing coffee. They share it in your team Slack. Now two engineers ... ... half the team is context-switching into a problem that might affect twelve people. I call this a feedback witch hunt. One person surfaces something that sounds urgent. It spreads internally before anyone can figure out if it's actually

... assistant - Now pulls full document context instead of keyword matches. Reads across multiple policy sections to catch exclusions and limits. Redesigned perk tracker - Built for people with redundant benefits (2 Priority Pass memberships, overlapping coverage). Shows what you're actually using vs

... product still isn t done. Not even close. Because the stuff that s left doesn t show up in the old evals. The real challenges are squishier now. They re about taste. Context. Judgment. Whether the AI is actually aligned with what you care about, not just what you asked for. Here are three big areas that keep coming up for us. 1. Does the AI know what we care about as a business? Two companies can look ... ... gross margin by product line. Another cares about sales cycle length by segment. The SQL queries can be perfect and still miss the point because they're optimizing for a generic idea of analysis, not your idea of what actually

... creators and small teams especially: No real task ownership No clear project structure Progress is invisible unless you manually track it Context lives in 5 different files Planning = copy/paste + hope They re static. Projects aren t. Why specialised tools actually help (even solo) A lot of people think project management tools are overkill unless you re a big team. In reality: Solo creators benefit most from clarity Small teams need less friction , not more tools Specialised tools give you: Tasks ... ... that actually

... spent 6 months building what I thought people wanted. Clean UI. Fast performance. All the features competitors had and some unique ones. Launched it feeling confident. Got 100 signups in week one. Felt amazing. Week two? 3 people were still using it. Everyone else bounced. That hurt. But what hurt more was I had no idea why. Here's what I learned the hard way: People don't leave because your product is bad. They leave because it doesn ... ... into their actual

... enough), we re opening this up as a shared walkthrough on how to research GEO properly and turn it into measurable action . Here are the exact questions people have been asking us: How is GEO actually different from SEO? What signals do LLMs care about? How do you know which prompts are worth optimizing before spending time or money? How do you separate being mentioned from being trusted or recommended ? How do you track impact when models are non-deterministic ... ... drift over time? How do actions like Reddit posts, blogs, or LinkedIn content actually

... outreach isn t to the buyer, but to someone who can open the door. Personalization isn t optional. Company context, role, recent updates. Generic gets ignored fast. Channels are chosen by output, not comfort. We double down on what actually converts. The first message rarely works. Conversations usually start around the third or fourth touch, if there s value each time. Timing matters more than volume. Funding news, hiring, social posts. Showing up when the problem is top of mind ... ... Context before calls helps. If someone engages multiple times, the conversation feels very different. Signals matter. Engagement often tells you when to reach out, not just who. None of this is complex. It s just deliberate. Curious what s actually

... game streamer discovering TataLaLa for the first time, you might be asking a fair question: What exactly does TataLaLa do? Not in a buzzword sense. Not in a vague AI-powered way. But in a way that actually matters to your stream. This article is written to answer that question clearly, honestly, and professionally. TataLaLa Is Not a Chatbot. It s a Growth System. TataLaLa was built for one specific purpose: To help game streamers grow revenue and subscriptions without ... ... subscriptions feels awkward, forced, or mistimed Most tools focus on either engagement or automation. Very few tools help you answer the hardest question in real time: Is now a good moment to push subscriptions? That decision is where growth actually

... tell you that 2 + 2 = 4. But the calculator still wins every time. I m starting to think that the real winners won t be AI-powered everything , but systems where AI is used only where the problem is actually probabilistic or ambiguous. So I m curious how others see this: Do non-AI solutions become less valuable just because AI could solve the same problem? Or do simple, deterministic solutions actually

... retention, and scale in education and content-driven products. This is not a launch post or a promo pitch. It s a transparent breakdown of what we built, why we built it that way, what went wrong, and what actually worked the kind of information I personally look for when browsing Product Hunt discussions. If you re building in edtech, gamification, communities, or subscription products , this may be useful. Why We Built Quaha in the First Place Most learning apps ... ... easy to exploit Premium content leaked through screenshots and screen sharing Traffic spikes caused slow navigation and broken flows Offline users dropped off entirely They didn t want another quiz app. They wanted a learning system that people would actually

... built a platform to validate a hypothesis: If we lower the barrier to entry for using and productizing agents, will normal users actually like them? I m looking for builders to test it out. You can take your existing agent code, fill out a simple manifest, run a few CLI commands, and have a shareable, web-accessible agent in 10 minutes. Links at the bottom. Happy New Year, everyone. I assume most of you here are tech professionals or enthusiasts ... ... past year, AI Agent has been the biggest buzzword. It s reached a point where hearing someone say I m building an AI Agent almost gives me a visceral reaction lol. But looking back, despite the hype, the actual

... everyone, I ve been thinking a lot about churn lately and realized something uncomfortable: most teams say they track churn, but very few actually understand why users leave in a structured, actionable way. so, I built a small MVP called Churnalysis to test the idea. The concept is simple: collect cancellation feedback in a structured way run short AI-powered exit interviews analyze patterns instead of reading random comments or spreadsheets Right now this is nearly-finished ... ... selling anything yet. I m trying to validate: whether this is a real problem for founders / SaaS teams what insights people actually

... think before it builds Instead of jumping straight into code, Capacity forces one missing step: product understanding. Before writing code, the AI: Clarifies requirements Structures features Defines data models & flows Aligns everything into a spec the AI can actually follow No more rewrite the whole thing prompts. What you get out of the box AI-generated product & feature specs Shared context that persists across builds Fewer hallucinated decisions Cleaner, more predictable code Works even ... ... know how to code When should you use Capacity? Use it if: You re tired of fighting your own AI after day 2 You want something you can actually

... over again: Financials that looked fine on the surface but fell apart under diligence Founder-dependent operations with no documented systems No visibility into operational maturity or transferability Sellers who wanted top-quartile multiples without understanding what buyers actually underwrite The problem isn t deal marketing. It s deal readiness. MainSquare is a value-creation platform that diagnoses value gaps and connects to vetted operators, advisors, and specialists who can actually

... rough edges, but people still engaged with it. A clear emotional hook mattered more than technical depth. If people immediately understand why it exists, they are far more likely to care. Overthinking features slowed me down more than actual coding. Constraints helped. Feedback came faster once something real existed, even if it was imperfect. I am curious how others here approach this tradeoff: When you ship something quickly, what do you intentionally leave out? How do you decide when good ... ... enough is actually

... Product Hunt! I m Simona a finance & business consultant who spent the last 10 months building a full software product solo , without a developer background . Not for fun. But because the product beauty professionals actually needed didn t exist. TL;DR: I built a simple finance & pricing app that shows beauty pros their real profit and tax in real time. I come from a background in business management, finance, and startup consulting NOT engineering. So learning everything from ... ... overwhelming.  But I leaned into it.  ChatGPT was my best friend, while Claude, Zencoder, Codex and Kilo Code all got a fair share of cursing at. But we got there in the end. So what did I actually

... this week, I m trying something different: I m optimising for rest . I ve spent the last few days intentionally stepping away from the screen. Not to plan the next pivot or to "reflect on KPIs," but to actually let my mind go quiet. I m finding that the better I use this week to truly recharge my own energy, the more excited I feel about 2026. It s a strange feeling to not be "productive" for a few days ... ... hustle" even for a few days? I'd love to hear how you manage your personal energy during these breaks! Comment from Tony Hsieh(@tony_hsieh2): This really hits. The distinction between re-syncing and actually

... work 50+ hours a week and still want to be healthy. No BS, no unrealistic expectations, just effective routines that fit into 15-20 minutes daily. First, I need your help. I'm conducting research to understand what ACTUALLY works for busy professionals: Why do you quit wellness apps? What would you actually

... data collection tool. Not as another way for tech companies to harvest user behavior. But as **infrastructure for developers.** You contributed to the future of AI engineering. You made it more user-oriented. More accessible. Less marketing BS, more actual utility. **That's legacy stuff right there.** --- ## Here's what we're actually

... Most reviews answer: Did someone like this? Did it go viral? Is it popular right now? But shoppers are usually trying to answer something else: Is this right for me? Does this match my preferences, needs, or habits? What actually matters in this product category? That s where decision fatigue kicks in online just as much as offline. Instead of adding more opinions, we started thinking about: extracting the essence of a product surfacing only what helps you decide reducing ... ... comparison to what actually

... maintain an evolving user profile. Not just everything they ever said , but: - What s this user s communication style? - How advanced are they in this domain? - What are their stable preferences? - What events in their history actually matter? In other words: I wanted something that looked less like a blob of text and more like a structured, typed profile that evolves over time. So I pivoted GetProfile AI to become exactly that. From profiling API to LLM proxy with memory ... ... 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 to your actual

... just wrapped the Orbit Awards for AI Dictation and now we re moving to the next category: AI Automation. This one is for the tools that actually do work for you clearing chores, running workflows in the background, or quietly taking over a chunk of your week without turning into another dashboard you have to babysit. We re digging into the AI Automation category based on traction and reviews, not hype. So we d love your help: Drop your favorite ... ... automation tools in the thread Add a quick note on what they actually