... prevent that: Zero Fluff: We audited every single step. If a question didn't directly improve the user's immediate experience in the app, we cut it. UX > Marketing: We had tough conversations about what data helps marketing versus what the user actually needs to get started. We kept the focus entirely on the user's momentum. Vibe is a Feature: We didn't just want a functional flow. We wanted to show off the musicality and tone ...

... community! I ve been working as a software architect for a while now, and if there s one recurring headache I see across dev teams using Azure DevOps, it s getting a clear, low-friction view of sprint capacity versus actual logged hours. Built-in ADO reporting can feel a bit rigid, and teams often end up maintaining manual spreadsheets or forcing overly complex add-ons just to answer two basic questions: Are we on track with our capacity ...

... tags, sources, and connection counts. More AI languages AI language support is much broader. You can choose from 62 languages for summaries, quizzes, and tags, including Traditional Chinese. Caption and transcript language matching is also more accurate for Simplified versus Traditional Chinese. New AI models More models are now available in the picker on the Max Plan, including GPT-5.6 Terra, GPT-5.6 Luna, and Grok 4.5. We will release more documentation on which model to use when ... ... Recall Watch the interview Improvements and Bugs Improved AI for tags, connections, summaries, and quizzes Tags reuse your existing tree more often Connections focus on the core topics in a card rather than incidental mentions Summaries better separate concise versus

... expertise from big tech, strategy consultancies, and unicorns, systematically cleaned, audited, and structured. The neural layer handles nuance and language. Neither layer works alone. What that means in practice: on rule-bound queries we run at roughly 10% hallucination versus 31.4% measured across real-world LLM use, and 82% lower error rate versus

... Been thinking about this after a few good conversations here lately: there's a real tension between building something that flags "this looks off" for you automatically, versus just surfacing the raw signal and trusting the human to interpret it. Too much automation and you risk false confidence, the tool says everything's fine, so nobody looks closer. Too little, and you're back to relying entirely on someone's attention, which doesn't scale past a certain team ...

... that's still being invented. We need to be honest. We need to learn. We need to experiment. And slowly, we will evolve. From SEOs to whatever shiny new title this world gives us . The future isn't human versus AI. It's humans who know how to direct systems versus

... probably saw the VS Code 1.117 story: "Co-authored-by: Copilot" silently added to commits by default, including for code that Copilot never wrote. Microsoft reverted it in 1.119. Good. But the revert is the wrong ending to the right story. The developer anger was about consent and accuracy both valid. What did not get picked up in the discourse was the architectural question underneath: even when Copilot does write code, a git trailer tells you almost nothing useful ... ... capture provenance at insertion time before the diff ever hits git. Each insertion generates a structured record with roughly 40 fields: four timestamps, the full prompt body, the raw model response, 10 named capability slots indicating what was captured versus

... When someone journals on Murror, our AI doesn't just process text. It reads emotional weight. It picks up on patterns the user might not see yet -- the way their language shifts when they talk about work versus family, the recurring themes they circle back to every few weeks, the gradual change in tone that might signal something deeper. We built this because it makes the product better. The AI can ask more relevant questions, create more meaningful reflections ... ... know when to give space versus

... from groups You can give a group its own AI context, and it now flows into chat, chart-editing, and automation agents whenever a member of that group is interacting. If different teams think about your data differently engineering versus sales versus

... reason we open-sourced the scoring algorithms in Open Wearables wasn't just philosophical. It's because conflicting scores are meaningless without context, and context requires transparency. When you can see exactly how a Resilience Score weights HRV versus sleep duration versus

... show up? and What's the traffic?" I'd smile. Say something confident. Go home and have no idea. It wasn't just that one question. It was a stack of them I couldn't answer. What are competitors actually running on Meta this week, what's the angle, what's working for them and what should we do? (I had setup one dedicated associate to analyze each competitor

... problem. The freelancers I built for aren't looking for a free tool they're looking for the right one. If 7 days isn't enough to know, the product isn't good enough yet. Your point about generalized vs tailored is exactly the right framework. The free tax calculator I built gives anyone a general estimate no signup, instant value. The paid dashboard is the tailored version your clients, your real numbers, your specific situation. Free = the concept. Paid ... ... with the price tag :D Comment from Nika(@busmark_w_nika): @dechefini  Feel free to share a link :) Comment from Dishant Singh(@dishantsinghdev): For my main tool I offer a free plan with features that most of my competitors

... Product Hunt community! I m currently in the process of finalizing the offer strategy for a new product, and I need your input. Specifically, I m torn between offering a trial period versus a refund policy for customers. I d love to hear your thoughts on both options. Trial Period Pros : Low barrier to entry for new users. Allows customers to test out the product before making a commitment. Builds trust and encourages engagement. Cons : Add noises for conversion ...

... 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 showing what keeps people watching versus bouncing Identify psychographic patterns unique to your community, not generic demographics The Channel Insights component addresses what researchers call the "relevance gap", the difference between what creators think resonates and what actually drives engagement. Creators who understand viewer psychology ... ... View Duration : Target 50%+ completion (YouTube's recommendation threshold is 40-50%) Click-Through Rate : Achieve 6-8% CTR within first 48 hours (our A/B testing drives 40% improvements) Audience Retention : Maintain above 60% at the 30-second mark versus

... raycast   @chrismessina  Interesting . Windsurf + Warp seems perfect for Raycast work. I m curious, what s your favorite part of building extensions versus traditional web apps? Comment from mina(@minach): @pasha_tseluyko  Good point , extensions in VSCode

... like button. Benchmarks, benchmarks, benchmarks Benchmarks should always be taken with a grain of salt . They are effectively a snapshot of a models capabilities under near perfect conditions. Sort of like the Big Mac you see on the ads vs the Big Mac you get in the bag. It gives you a good idea, but they're far from a perfect measure of real world usage. Math (AIME 2025, no tools): 94.6 percent Real-world coding (SWE-bench Verified ... ... Multilingual programming (Aider Polyglot): 88.0 percent Multimodal understanding (MMMU): 84.2 percent Medical reasoning (HealthBench Hard): 46.2 percent Graduate-level logic (GPQA without tools, via GPT-Thinking Pro): 88.4 percent In production, GPT-5 Thinking cuts hallucinations by 45 percent versus