Pebble Finance – Explain

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Show HN: Pebble Finance - Explain Hello HN, We're back! You might remember us, Pebble Finance, from when we introduced our dynamic investment theme construction tool ( https://pebble.finance/themes ). Today, we're excited to share with you the next phase of our journey—demystifying investment performance with Pebble Explain. Introducing Pebble Explain Pebble Explain is our newest product, developed using advanced summarization, clustering techniques, and powered by OpenAI's LLM. At its core, it seeks to answer a fundamental question: Why did a particular security perform the way it did? By sifting through price movements, financial news, documents, and more, Pebble Explain provides insights on the performance of securities over different timeframes—from a single day to up to six months in the past. For now, you can look into the explanation of a single security per timeframe. But, if you're eager to get a holistic understanding of your entire portfolio, simply connect your brokerage account via Plaid. We'll serve up insights for every security you hold. Current Scope and Future Plans At this stage, our focus is solely on US equities. For those of you keen on insights for ETFs, stay tuned—support for ETFs is on our roadmap! Challenges We’ve come across We faced issues primarily related to speed, quality, and token limits. Handling large date ranges required a careful selection process to eliminate unnecessary articles, focusing instead on the ones that best explain the company's stock movements. As we added more articles, not only did processing time increase, but we also edged closer to OpenAI's token constraints. While GPT-4 offers superior quality, its slower response led us to experiment with prompt tuning. This enabled us to achieve results similar to GPT-4, but by leveraging the GPT-3.5-Turbo-Instruct model which is faster and cheaper. We're eager to hear your feedback, thoughts, and suggestions. Dive in, explore Pebble Explain, and let us know what you think! Thanks for reading! The Pebble Finance Team

Visit product View on Hacker NewsOct 10, 2023

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

94%94% 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, new, openai · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% 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: month, way · Missing: mobile apps, ios, personal

56%56% 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

46%46% 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

9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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