Standardizing NLP for a Modern ETL

Hacker News

Hi guys, I've been thinking a lot about how advancements in NLP can be standardized; when building a sentiment analysis, you know for sure that other attributes than the actual sentiment can be highly interesting, such as the urgency. Together with my team, I started a new open-source project, aiming to do exactly that. It's called bricks, and it is a composition of more than 50 open-source and modular code snippets, such as computing sentence complexities, emotionality detection and many more. For context, the idea came up after watching the incredible talk "Inventing on Principle" by Bret Victor. The goal is to make the gap between Idea to Implementation shorter.

Visit product View on Hacker NewsDec 7, 2022

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

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

54%54% 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

27%27% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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