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Real‑time tracking of candidates' promises in the Canadian election

Hacker News

Real‑time tracking of candidates' promises in the Canadian election

There is an election currently taking place in Canada, and the major parties had not released any formal platforms until very recently—just days before election day. I built an app that parses transcripts of political appearances to extract promises (defined as forward-looking statements that represent a commitment). Each promise is tracked with a timestamped link to the exact moment it was made. I’m using pgvector and semantic analysis to group similar promises together, effectively identifying when the same idea is repeated. This allows me to generate a timeline showing how each promise has evolved over time. A cron job updates the data nightly, uploading it to huggingface [1] and making it available for download [2] The most interesting technical challenge was accurately parsing timestamps and capturing the surrounding context that gives meaning to each promise. 1: https://huggingface.co/datasets/jevon/buildcanada-2025/tree/... 2: https://2025.buildcanada.com/data

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
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
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: context, using · Missing: mac, agents, macos
37%37% 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
34%34% 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 · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: time tracking · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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