Puffin – an open-source NLP-powered Discord bot

Hacker News

I'm not a super experienced programmer or a deep learning wizard, but I've been wanting to make something like this for a long time. Puffin is a Discord bot that uses NLP and zero-shot learning as well as Google's Toxicity API to flag unsavory messages in servers. Each message is analyzed for sentiment, emotion, and custom zero-shot labels that moderators can provide. If scores for certain emotions are too high, the labels are true, or the message seems to be very toxic, the message is added to a moderation queue hosted in MongoDB. Moderators can view and delete this queue at their will, while not having the bot actually delete anything that shouldn't be deleted. The bot is currently hosted on a Raspberry Pi 5, is written in discord.py, and uses the Hugging Face pipeline. In general, from what I've seen, most auto-moderation solutions that use NLP are either paywalled or non-transparent, and I'm hoping to change that a little bit with this bot. Are there any things that I should change? Are there any features that I should add?

Visit product View on Hacker NewsJan 21, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, open · 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.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

68%68% 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 · Strong signals: google · Missing: mobile apps, ios, personal

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

23%23% 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

13%13% 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.

Incorrect prediction on native model

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