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Pontus the Zero Trust AI Layer

Hacker News

Pontus the Zero Trust AI Layer

Hi HN, We’re @sumants and @rmehtany, working on Pontus. Pontus makes it easy to use AI with privacy embedded. We were concerned about the volume of personal data that goes to large LLM models without protection. We tried find an easy solution where didn’t change the simple apis given by LLM providers. However, most required you to invest significant engineering effort. We wanted privacy and LLMs to be easy, so we built Pontus. Through a declarative YAML, we orchestrate a microservice with the most common element of the LLM stack. - Anonymize Prompts before it hits LLMs, yet keeps context on your servers - Secure RAG that embeds documents safely - Semantic Cache that doesn’t store PII at Rest We did this not only because it is the right thing to do, but the regulation landscape makes it critical to build with privacy first. Recently, many companies have been fined and forced to delete their AI models [1][2] For us, Privacy is by design, not an afterthought. Please visit us here Our Open Core Repo: https://github.com/PontusAI/Pontus Our Website: https://pontus.so [1] https://www.ftc.gov/news-events/news/press-releases/2023/05/... [2] https://techcrunch.com/2023/05/10/clearview-ai-another-cnil-...

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best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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
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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