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Gossipdle – Daily Logic Puzzle with a New Timed Challenge Mode

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Gossipdle – Daily Logic Puzzle with a New Timed Challenge Mode

I built Gossipdle, a daily logic puzzle where you decide who is guilty or innocent in a 5×5 grid of 20 characters, using only logical clues (no guessing allowed). Each correct mark reveals a new clue, so you solve the puzzle as a chain of small deductions. Themes change daily (office gossip, campus drama, nightclubs, etc.), but the underlying puzzle is a clean Einstein-style logic grid. I’ve just shipped a Timed Challenge Mode: A separate puzzle from the main daily one (so you effectively get two puzzles per day) Difficulty-based time limits (Mon–Tue easier, Sat–Sun hardest, with more time) 3 attempts per day; when the timer hits zero, that run is over No hints in timed mode – pure reasoning under pressure State persists across refresh/close, and there are checks around New York midnight so you don’t start a run when there isn’t enough time left in the day If you like structured logic puzzles (Einstein riddles, nonograms, Sudoku-style deduction) and want a daily “thinky” game with a speedrun variant, I’d love feedback on: How the difficulty feels Whether the timed mode is fair/fun Any rough spots in the UX Link: [ https://www.gossipdle.com ]

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
71%71% 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.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, 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
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos
37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% 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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