Progressor – coach that breaks down big goals into actionable steps

Hacker News

I built Progressor to help with a problem I kept running into: setting ambitious goals but getting stuck in planning, motivation, or knowing what to do next. You start by describing your goal — the more detailed, the better. Progressor then asks a series of targeted questions to understand your situation. Based on your answers, it creates a personalized step-by-step plan with small, focused daily tasks. Each task comes with relevant guidance and resources. You can adjust the plan at any point, and Progressor sends reminders to help you stay on track. This is not a habit tracker or to-do list — it’s a structured way to move forward on goals that usually feel too big or vague (e.g. launching a product and reaching €10k MRR, switching careers, finishing a personal project). Would love feedback from anyone who’s ever struggled to push a long-term goal over the finish line.

Visit product View on Hacker NewsJun 22, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: tasks · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, answers, way · Missing: mobile apps, ios, entrepreneurs

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: mrr · Missing: arr, revenue, profit

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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