Thanks for this great summary @Adtrades18! We're thrilled to announce our new opensource project for BlindML, which enables #scikit-learn style algos on fully encrypted data. Benchmarked at around 50 seconds per million records. GitHub.com/blind-insight/bli…

Check it out... We made @scikit_learn for encrypted data. Benchmarked at 45s training on 1M records.

The most valuable data is often the data you can't afford to expose.

What if businesses never had to choose between using data and protecting it? That’s the problem Blind Insight is solving. Today, organizations collect enormous amounts of sensitive information, but using that data often means exposing it. The more accessible the data becomes, the greater the security and privacy risks. Blind Insight takes a different approach, the company enables organizations to search, analyze, and extract insights from data while it remains encrypted. In simple terms, businesses can get answers from their data without ever fully exposing the information itself. Think about the possibilities: * A hospital could analyze patient trends and treatment outcomes without revealing personal medical records. * A bank could detect fraudulent transactions and suspicious activity without exposing customer account details. * An e-commerce company could understand customer purchasing behavior while keeping personal information protected. * Government agencies could collaborate across departments using sensitive datasets without increasing the risk of data leaks. * Even multiple organizations could work together on shared insights without handing over their raw data to one another. This is becoming increasingly important as data breaches, privacy concerns, and regulatory requirements continue to grow worldwide. Companies want to use data to make smarter decisions, but they also need to protect the people behind that data. Blind Insight bridges that gap by making privacy a built-in feature rather than an afterthought. The vision is simple: data should remain secure, private, and protected while still being useful. In a world where information is one of the most valuable assets, Blind Insight is building the infrastructure that allows organizations to unlock insights without sacrificing trust. The future of data isn't just about collecting more information. It's about using information responsibly. And that's exactly what Blind Insight is working to achieve. #DataPrivacy

Check out our new open source project for BlindML ! scikit-learn for encrypted data! GitHub.com/blind-insight/bli…

Claude code source code has been leaked via a map file in their npm registry! Code: pub-aea8527898604c1bbb12468b…

Most people don’t realize the real bottleneck in AI isn’t compute it’s data access. You can scale GPUs infinitely, but the moment sensitive data is involved, everything slows down: compliance, trust, security reviews. That’s why approaches like encrypted data processing from @GetBlindInsight are interesting making data usable without exposing it. We’re exploring a similar direction on the compute side with @jungle_grid : running AI workloads across distributed nodes without requiring trust in the compute layer. Feels like the future of AI infra is: secure data × trustless compute. Curious to see where this goes.

Excited to announce that aggregation functions have shipped on @GetBlindInsight! We now support sum, avg, min, max, and count!

🔐 Meet Blind Insight—a complete solution for working with sensitive data in real-time. Blind Insight's searchable encryption is up to billions of times faster than FHE. Fine-grained access controls streamline compliance and minimize exposure. #PrivacyTech #CyberSecurity #AI

Blind Insight Founder @JackiePeters sat down with @markjeffrey today to discuss the lessons learned from the recent #Coinbase breach and how organizations can be more proactive about their data stewardship. #DataProtection #DataSecurity #CoinbaseBreach

Who wants to see what 1MM records looks like?

Based on this, here are projections on 1MM records and comparisons vs. #FHE on GPU and CPU. Will release a public @github repo so you all can try it out at home!

Hot take: #FHE set encryption-in-use back 5 years by overhyping and underdelivering, and convinced a lot of smart people that #encrypted #ML 'isn't ready yet.' Meanwhile, we just trained a #fraud model on 50K encrypted records in 90s. On standard hardware. #Demo in comments👇

They said it couldn't be done... we were underestimated and under resourced at every turn. But as they say, necessity is the mother of invention, and I have always loved a good challenge. So proud of our team for pulling this off, and I had so much fun building the example on top of our platform.

Today is my last day at Anthropic. I resigned. Here is the letter I shared with my colleagues, explaining my decision.

Founder wisdoms from Ram Dass

While it is easy to condemn this as a personal failure, we should also look at the incentives created by venture capital. When the market rewards growth at any cost, it invites founders to stretch the truth to stay relevant. Roy Lee is responsible for his choices, but this situation reflects a broader culture that prioritizes optics over operational reality. Can the tech industry build a sustainable model where transparency is valued as much as a high multiple?