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Guide – to help me get moving and keep the momentum going

Hacker News

Guide – to help me get moving and keep the momentum going

Hi HN, I built Guide (leaning almost exclusively on Gemini Pro 2.5) for myself initially to get over the blank-page-hurdle while starting on a goal. As well as to reduce the overwhelm from huge lists of to-do when I got into it. It's Local. Single file HTML+CSS+JS only You can see the code and try it out here: https://github.com/nextfiveinc/guide (just save to your phone / desktop and start playing;) What it does: Guide is a task management app based on CBT principles focused on getting you moving when you're stuck, then keeping that momentum going toward your actual goals. Few core ideas: 1. Start small to build momentum – Add quick wins (15-20 min tasks) to break the inertia. Either defined by based on your goal or suggested by App. Once you finish it, the domanine hit makes me want to do another. That's when you define bigger goals. 2. AI suggests next steps – When you set a goal but can't figure out the next concrete task, clicking "Guide me" suggests what to tackle next (optional; bring your own key - Claude / OpenAI / Gemini). Of course, ideal would be you define that next step. 3. Only shows 3 tasks at a time – Add as many tasks as you want, but Guide only surfaces the top 3. Rest remain in what I call Task Bowl - sort of to reduce the idea of a list and enforce the imagery of a bowl I can pick from. You read more at https://blog.nextfive.in/guide can try it out here: https://github.com/nextfiveinc/guide

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Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
75%75% 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: claude, openai, single · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
30%30% 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: exclusive · Missing: plus, platform, intuitive
29%29% 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
24%24% 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
17%17% 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.

Correct prediction on native model

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