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So excited about this. We're rolling out a mechanism that protects Vercel customers even in a Binance-style attack, where your engineers get targeted with infostealer malware. With DBSC, renewing a session requires a key bound to your device, which means stolen cookies can't be practically re-used from remote Command & Control (C2) infrastructure. While this is a pretty major win, there's no silver bullet in security. Make sure your Vercel account has MFA enabled (both app-based TOTP and passkeys), your organization enables SAML / directory sync, your endpoints are protected, etc. (and stay paranoid!)

coke vs pepsi one of the greatest rivalries of all time in 1985, new coke beat pepsi in 200,000 taste tests and got pulled in 79 days in 2026, i run the same kind of test on my own products with synthetic users fathom calls → LLMs personas → browserbase → 30 simulated days in your product 50 customer calls become 50 personas, each gets a browser and uses the product for 30 days the stuff they love on day 1 and hate on day 10 gets cut i don't know why more people aren't doing synthetic user testing very cheap way to find out if you're on the right path

We’re seeing a lot of @Shopify store owners automating a lot of their business operations on Perplexity Computer, right from competitive analysis and audits, building web apps, running email and marketing campaigns on Klaviyo, and automating their ad spend on Instagram. The power is in closing the loop.

I just got this gift from a customer well played

This is currently my fav vlogger on YouTube. 19 year old kid just moved to NYC for a gap year. His vlogs are incredibly polished. A good mix of slow vibes with alot of cuts and good visuals and music. Each video feels like a mini documentary. 10th video on his channel and already at 14k subs. I predict he'll be at 250k subs in 2 months.

RT LlamaIndex 🦙 We got OCRia-1 generating tokens nearly 10× faster on a single B200. Getting there meant making much better use of the same GPU. We tuned how OCRia-1 runs for the demands of document parsing, testing every change for both speed and accuracy. Some improvements were only 2–5%. Seventeen smaller optimizations later, “just a few percent” starts adding up. Our infrastructure team wrote up what worked, how they tested the changes, and what they learned about making document models run faster. We also plan to contribute some of these optimizations back to the open-source vLLM project. Read the full breakdown → https://www.llamaindex.ai/blog/the-inference-stack-behind-ocria Watch the video below to see OCRia-1’s token generation before and after the optimizations.