PDFAgent with Observability

Hacker News

With the number of ask your PDF SaaS solutions, I noticed there weren't any solutions focused on people who wanted to build it for their business/SMB. Most of them also weren't production-ready (requiring a lot of work to productionize applications like LangChain). We therefore built a solution that provided Helicone-like observability that handled everything out of the box. We built PDFAgent to combine the capabilities of asking your PDF with built-in observability on top of Twilix’s RAG infrastructure. It handles parsing, cleaning, splitting up your PDF to provide useful embeddings (the user just needs to submit a publicly readable URL link) and a MixPanel-like interface to monitor and observe queries.

Visit product View on Hacker NewsJul 24, 2023

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

71%71% 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.

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

14%14% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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