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Deep Research Using Radpod.ai on Paul Graham's Essays

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Deep Research Using Radpod.ai on Paul Graham's Essays

We created RadPod to bring the latest in LLM Reasoning Agents (think Gemini/OpenAI "Deep Research") to your data. You upload possibly large datasets to Pods (handles a lot more than other products), providing context for the LLM agent, and start chatting. Under the hood we employ on-the-fly code generation and execution to answer arbitrarily hard questions with in-context citations to make answers verifiable and trustworthy. This is a lot more powerful than common Retrieval Augmented Generation (RAG)-based approaches In this example Pod, we uploaded 227 of Paul Graham's essays, which are inspirational to us as founders, and show-case some example threads (questions + responses). Among other things it was able to find that: - The most common topics are unsurprisingly Startups, Programming, and Investing - Lisp is his favorite programming language - There are many mentions of Mark Zuckerberg and Larry Page in the essays, but not as many as Jessica Livingston, who he really admires :) - He wrote more frequently in the period 2005-2009 It's fun to try, ask away! Check some other cool Pods at https://radpod.ai/spotlight-pods . We find RadPod works well on many domains, such as finance and legal documents.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, context · Missing: mac, macos, cursor
97%97% 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: created, gemini · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: answers, way · Missing: mobile apps, ios, personal
43%43% 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.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
15%15% 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
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