Package to parse SEC 10Ks into JSON and other fun things

Hacker News

Features: * parse textual filings into simplified html, interactive html, or structured json. * download sec filings quickly and easily Datasets: * 2024 Management Discussion and Analysis * 2024 10Ks in json format Datasets can be downloaded using the package, or here https://www.dropbox.com/scl/fo/byxiish8jmdtj4zitxfjn/AAaiwwu... All APIs are free and do not require auth, so you can use them directly :) e.g. I'm currently using the parser api with js to run my filing viewer demo: https://datamule.xyz/filings_viewer

Visit product View on Hacker NewsSep 14, 2024

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1points

Did not reach leaderboard

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

56%56% 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: using, apis · Missing: mac, agents, macos

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

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

39%39% predicted probability of success on Indie Hackers, 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 · Strong signals: active · 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 · Missing: web3, chat, crypto

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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