Open-source vector & data lake to debug, curate and version AI data

Hacker News

Hi HN! I’m Farah, co-founder of Dioptra.ai. We just open sourced katiML ( https://github.com/dioptra-ai/katiml ) this week and wanted to get your take. katiML is a vector+data lake to debug, curate and version AI data. With katiML, teams avoid the “garbage in, garbage out” effect by taking control over the quality of their data. They quickly and effectively curate high quality data for training, fine-tuning, and fixing hallucinations and edge cases. Features include: - Data Curation: interactive embedding visualization and similarity search, mislabeling and hallucination scores as well as SOTA Active Learning miners. - GenAI 4 explainability: uncover drift and bias in your data with multi-modal foundational models that describe in human language what’s in your data. - ML Data Lake: zero copy, highly efficient ML data lake to store and query any ML data (vectors, metadata etc). - Data Versioning: to track changes in datasets, prompts, and model performance; and pinpoint the causes behind those changes. Check out our github repo( https://github.com/dioptra-ai/katiml ), documentation( https://dioptra.gitbook.io/dioptra-doc/EIKhoPaxsbOt062jkPon/ ) and new slack community channel. Here is a video to show you how we use GenAi 4 explainability( https://www.loom.com/share/c6916272b9914894bddd1a21df0321b4 ). We'd love to hear about your experiences with iterating on your data to improve models, the challenges you faced, and how katiML could prove useful to your efforts. Let us know what you think we can do better or differently. Looking forward to your comments!

Visit product View on Hacker NewsJun 7, 2023

Share card

Actual performance

12points

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: model, slack, new · Missing: mac, agents, macos

93%93% 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% 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 · Strong signals: efficient · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

17%17% 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.

Correct prediction on native model

Similar products

Open-Source Infrastructure for Vector Data Streams73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Open-Source Infrastructure for Vector Data Streams

Hacker News4

SirixDB – Storing and Querying of Temporal Data (Java and Open Source)62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

SirixDB – Storing and Querying of Temporal Data (Java and Open Source)

Hacker News13

Streamdal – an open-source tail -f for your data81%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Streamdal – an open-source tail -f for your data

Hacker News148

Open-Source Data Replication and Anonymization73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Open-Source Data Replication and Anonymization

Hacker News24

Neosync – Open Source Data Replication and Anonymization73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Neosync – Open Source Data Replication and Anonymization

Hacker News4

OhMyPocket – Open-source version of getpocket.com63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

OhMyPocket – Open-source version of getpocket.com

Hacker News62

Open-source Data Anonymization tool nxs-data-anonymizer62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Open-source Data Anonymization tool nxs-data-anonymizer

Hacker News4

Multiwoven – Open-Source Data Activation Platform – Ruby on Rails76%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Multiwoven – Open-Source Data Activation Platform – Ruby on Rails

Hacker News1

Open-source tool to find PII across data silos64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Open-source tool to find PII across data silos

Hacker News3

Desbordante – an open-source data profiling tool63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Desbordante – an open-source data profiling tool

Hacker News2

Browse all products Analyze your launch