Cr

Create a GPT3-powered Chatbot from any YouTube playlist

Hacker News

Create a GPT3-powered Chatbot from any YouTube playlist

Edit: here is a link to a Chatbot of all Huberman Lab podcast episodes. There are about 50 other Chatbots like Lex Fridman, All-in Podcast, Tim Ferriss, etc on our homepage. https://addcontext.xyz/search/huberman-lab Original text: Me and my friend wanted to do a project with all the new LLM tech that has been released and this is what we came up with. A little bit of background on how it works: 1. User enters a link to the playlist they want to chat with. 2. Download all videos in playlist and store in GCS. 3. Transcribe all videos using Whisper via replicate.com. 4. Break transcripts into ~300 token snippets and store in MongoDB. 5. Embed each snippet using OpenAI’s new embeddings and store results in Pinecone. 6. When a user asks a question to the Chatbot, we embed the query string via OpenAI and then query Pinecone to get the most relevant snippet ids, then query Mongo for the snippets. We pass these as context into a call to GPT-3 along with a prompt to answer the original query using the context provided. It’s working pretty well so far and we’ve had close to 50k queries run since we launched in December. Lots of inbound leads from people looking for related tech or curious about how this can be applied to other domains. We launched a self-service version that about 30 people have used. Still looking for other monetization strategies. The hardest part of this project was getting the YouTube download to work properly without hitting rate-limits. The actual “AI” work was extremely minimal. If I had to build it again I would use something like langchain.[1] We would love feedback from the HN community! You can reach us directly at (sam | joel)@addcontext.xyz. Thanks! [1] https://github.com/hwchase17/langchain

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, context · Missing: mac, agents, macos
89%89% 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
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
41%41% 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
38%38% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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