Av

Avatar – Talk to people modeled on their tweets

Hacker News

Avatar – Talk to people modeled on their tweets

Stumbling across an interesting person online and seeing decades of their writing history is always daunting to dig through to discover key ideas. While standalone foundational models can dig through most popular writings available on open web, there is long tail that needs individual specific models to synthesize with LLMs. Starting with twitter, Avatar intends to create a digital avatar for individuals while aiming to be factually correct. Please Try ~50 interesting accounts on twitter IMO and let me know of any feedback on its utility. RAG stack components: LLM (gpt-3.5-turbo), Embeddings (text-embedding-ada-002), Vector index (Chroma) More details: https://twitter-avatar.com

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, open · Missing: mac, agents, macos
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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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