EhrlichGPT, a discord bot that tries to be a member of the team

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Hi HN, I'm loving everyone's GPT projects so I thought I'd share mine. Most other projects I've seen (including the discord bots I've run across) stick to "call/response" and force you to manually separate separate conversations. The primary goal of EhrlichGPT is to more naturally participate in discord conversations - both DMs and group chats. Here are some of the things I've tried: * Include a snippet of short term memory, a summarized "medium term" memory, and finally do an even more succinct summary and store those summaries as documents in a faiss embedding index as "long term" memory * Multi-layer prompt - first prompt is an agent-ish tool prompt that selects whether the user is asking for long term memory, a web request, etc - this information is used to build the final prompt * Reasoning prompt for the final output e.g. "Investigation result: [Info you gathered from the conversation] Final Response: [Response]" I have an hour or two to work on this per night, so if anyone thinks this is neat I'd be happy for help (see CONTRIBUTING for some ideas). I'd especially appreciate any ideas on how to more objectively measure progress - i.e. how "good" is the bot, are prompt changes making it better or worse?

Visit product View on Hacker NewsApr 7, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user · Missing: mac, agents, macos

91%91% 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 · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released

23%23% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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

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