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FlowTask – AI to bootstrap project setups

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FlowTask – AI to bootstrap project setups

For years, I was stuck. Trapped in a loop. Every new project, every team initiative – it began with the same soul-crushing setup. Hours. Gone. Not on the actual work , mind you. But on the meta-work. Defining tasks. Sketching forms. Mapping workflows. Hunting for that 'perfect' template that never quite fit. Just trying to get to zero. To a starting line where meaningful work could actually begin. Most project management tools? They're brilliant once things are up and running. Tracking, collaborating, reporting – they nail it. But that initial blank canvas? That moment you’re staring at an empty page, forced into a rigid template that doesn't quite fit your brain? They leave you cold. You're still the architect of the initial system , not just the manager of the tasks within it. That frustration? It boiled over. It forced us to ask: What if this initial setup wasn't a blank canvas? What if an AI didn't just suggest tasks, but built the entire damn structure for your workspace? That's the core idea behind FlowTask. Imagine a comprehensive blueprint, generated for you. We’re talking about a full task hierarchy descriptions, assignees, due dates, dependencies, all logically organized. Forms, too. Generated with the right fields text, numbers, dates, dropdowns and smart validation rules, context-aware. And workflows? Defined states, transitions, ready for your no-code builder or internal system. All of it. The 'how' is where it gets gnarly. And exciting. This isn't just tossing a prompt at an LLM and hoping for decent text. We're building a structured AI generation layer. Think of it less as a word spinner and more as a schema generator for project components. We're pushing fine-tuned models and sophisticated prompt engineering to ensure the output isn't just plausible, but structured . System-ready . It’s about killing the 'hallucination' problem by guiding the model towards concrete, defined outputs. We're taking that initial project 'cold start' problem and crushing it. Hours of administrative grind, cut to minutes. This isn't about replacing human smarts. It's about automating the truly tedious, non-creative crap. We've been grinding on this for a while. Now we need your unfiltered take. What are the landmines you see? How do you currently kick off a new project? What kind of structured output would actually save your ass? Let's talk. Happy to dig into the tech or specific use cases.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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