Neum AI – Improve your AI's accuracy with up-to-date context

Hacker News

While building Gen AI apps, we found that passing context to the prompt is crucial. However, the context must be accurate and up to date. If you are doing search over a vector database and that DB is not updated, then the search results are not good and the quality of your context will suffer. With this, we set to create an easy-to-use UX and a set of APIs that help you manage and synchronize your data. With Neum AI, your context in prompts is always accurate and up-to-date. By leveraging vector syncing, you can create a pipeline that moves data from source to sink. (Think about FiveTran or Azure Data Factory). By connecting your data sources to automatically synchronize data into vector stores, you can populate context in your prompt.

Visit product View on Hacker NewsJul 21, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, context, apis · 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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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