The 2023 Holiday Update rolls out next week Here’s what’s coming... Custom Lock Sounds Replace the horn lock sound of your vehicle with another sound—like a screaming goat 🐐 LAN Party on Wheels Play your favorite games on the rear touchscreen 🎮 Rear Screen Bluetooth Headsets Parents, rejoice. Rear passengers can now use wireless Bluetooth headphones when watching shows or playing games on the rear screen Apple Podcasts Listen to millions of the world’s most popular podcasts @ApplePodcasts Tesla App Trip Planner Use the Tesla mobile app to plan a multi-stop trip and send it to your vehicle Speed Cameras on Route Navigation now includes speed cameras, stop signs & traffic lights Automatic 911 Calls Your vehicle will automatically call 911 if an accident triggers the airbags Blind Spot Indicators The blind spot camera will alert you with red shading when your turn signal is on and a car is detected in your blind spot More Live Sentry Cameras When viewing vehicle surroundings from the Tesla app, you'll now have access to the left and right pillar cameras for a total of 7 angles Light Show Enjoy a thunderous new Light Show called 'The Arrival' Castle Doombad Castle Doombad is now available in the Tesla Arcade—plus updates to Beach Buggy, Polytopia & Vampire Survivors High Fidelity Park Assist See a 3D reconstruction of your surroundings while parking *Availability varies by model and location

Doctors juggle a packed schedule, ten minutes per patient, and constant context-switching—while being expected to be the expert on absolutely everything. Hertility Health's AI assistant helps close that gap: pinpointing the most relevant information about each patient and suggesting a diagnosis and follow-up care doctors might otherwise miss. It's a second opinion for the doctor, and a way for the patient to finally feel taken seriously. 👉 Find a link to the full episode here: Spotify: Apple Podcast: YouTube:

NHS clinicians get ten minutes with a generalist, if they can get an appointment at all—leaving little room for the specific, personal questions women's health actually requires. Hertility Health made a longer intake mandatory before purchase, and something unexpected happened: conversion went up. Women finally felt heard before being handed a test kit—and clinicians got better information to work with, easing the pressure that drives burnout. 👉 Find a link to the full episode here: Spotify: Apple Podcast: YouTube:

Endometriosis can take up to ten years to diagnose—and even then, women often have to keep fighting for the ongoing care they need. Hertility Health built GynAI to close that gap. Every data point—an online health assessment, blood test results, scan images, even a clinician conversation—stacks into a clearer likelihood score, so doctors can refer women onward with confidence, much earlier in the process. 👉 Find a link to the full episode here: Spotify: Apple Podcast: YouTube:

🎙️Gate Ventures New Podcast Series — The RWA Stack “DeFi Doesn’t Have a Cycle Problem — It Has a Collateral Problem” Is DeFi really broken — or just missing the right collateral layer? Join us for a deep dive into RWA, liquidity, and the future of on-chain credit 👇 👤 Host: Tiffany Chang, Gate Ventures @hella_tifficult 👤 Guest: Sonya Kim, Co-founder of 3F @sonyasunkim We’ll cover: • Why DeFi yields are stuck at 2–4% • How RWAs are reshaping on-chain liquidity • The missing link between tokenization and real markets • 3F’s approach to unlocking capital efficiency 📅 April 30 🕚 11PM UTC+8 | 4PM CET | 10AM EST Live on X, replay on YouTube, Apple Podcast, Spotify. Stay tuned!

How do you build trustworthy AI diagnostic tools in one of medicine's most historically under-researched areas? In this episode of Just Now Possible, Teresa Torres talks with Tulsi Patel (Director of Product and Technology), Lorna Brightmore (Head of Data and AI), and Jack Pickard (Head of Engineering) at Hertility, a UK and Ireland-based women's health tech company. Hertility combines an in-depth online health assessment with at-home hormone testing and clinician-reviewed reports to help diagnose conditions spanning menstruation to menopause. Built on seven years of data linking symptoms, blood results, and pelvic ultrasound scans for over a million women, the team walks through two AI products in development: a Bayesian network that gives clinicians probability-based diagnoses instead of binary calls, and a scan automation pipeline that classifies ultrasound images, measures follicle counts and ovarian volume, and drafts clinical letters using an agentic loop that checks its own output against patient data before a human ever reviews it. You'll hear how the team guards against automation bias, builds clinician trust through transparency, minimizes PII before it ever reaches a model, and treats healthcare regulation as a design constraint from day one rather than a last-minute scramble. It's a detailed look at what it takes to bring AI into one of the most sensitive, tightly regulated corners of healthcare. Guests: - Tulsi Patel – Director of Product and Technology, Hertility - Lorna Brightmore – Head of Data and AI, Hertility - Jack Pickard – Head of Engineering, Hertility What we cover: - What makes Hertility's data set unique: seven years of linked symptoms, blood tests, and pelvic scans from over a million women - How uses a Bayesian network to give clinicians probability-based diagnoses instead of binary yes/no calls - Why showing clinicians the reasoning behind a diagnosis—not just the label—builds trust and speeds up triage - Guarding against automation bias with holdout sets and independent, fresh-eyes review - Inside the scan automation pipeline: classifying ultrasound images, detecting follicles, and measuring ovarian volume more precisely than manual methods - Using an agentic loop to check AI-drafted clinical letters against patient data and catch hallucinations before a human sees them - The infrastructure challenge of securely piping DICOM ultrasound images from third-party scan providers into Hertility's systems - How Hertility handles PII and PHI: pseudonymization, data minimization, and running models in-house on AWS Bedrock - Why treating healthcare regulation as a product requirement from day one makes AI products more scalable, not slower Key Takeaways: - Probabilistic, transparent AI outputs build more clinician trust than binary classifications. - Guardrails against automation bias are as important as the model itself. - Data minimization and in-house infrastructure make it possible to build AI responsibly with sensitive health data. - Treating regulation as a design constraint from day one makes AI products more defensible and scalable, not slower. Resources & Links: - Hertility — At-home hormone testing and reproductive health diagnostics for women in the UK and Ireland - AWS Bedrock — The platform Hertility uses to run LLMs in-house under its own governance and regulatory controls - PyTorch — The foundation for Hertility's in-house image classification and contouring models Chapters: 00:00 Meet the Team 00:13 What Hertility Does 01:51 How Customers Access It 04:06 A Unique Women's Health Dataset 07:03 Mission and Efficiency with AI 10:03 Why Long Assessments Convert 13:52 Before AI Workflows 16:52 Research Publications and Impact 18:48 GynAI Reducing Time to Diagnosis 21:21 Triage and Clinician Support 24:37 Keeping Patient UX the Same 26:12 Bayesian Network and Explainability 30:19 Multiple Diagnoses and Probabilities 32:37 Probabilistic Diagnosis Shift 33:50 Clinician Adoption and Workflow Fit 34:58 Communicating Medical Uncertainty 36:43 Scan Automation Overview 40:30 In House Image Analysis 44:25 DICOM Pipeline Engineering 47:30 Evals and Automation Bias 50:31 LLM Letter Guardrails 56:47 PHI Handling and Regulations 01:00:43 Infrastructure Choices and Wrap Up Listen on Spotify, Apple Podcasts, or watch on YouTube. Spotify: Apple Podcast: YouTube:

【Monthly Strategy Dialogue with Julius Baer】 Against a shifting macro backdrop, markets are responding to easing geopolitical tensions following a US-Iran peace deal, with oil prices falling sharply and Asian equities rallying. Within China, however, a stark divergence persists: Hong Kong’s “old tech” sector has lagged, while mainland “new tech,” particularly AI infrastructure, continues to attract strong inflows and outperform. In this episode, Richard Tang speaks with Hong Hao, Managing Partner and CIO of Lotus Asset Management, about China’s uneven market outlook. They discuss the prospects for a rebound in Hong Kong internet stocks, the sustainability of the AI rally and signs of froth, alongside liquidity risks from IPO activity and the outlook for gold amid shifting oil and interest rate dynamics. (00:30) - The old and new tech divide in China (01:45) - Will Hong Kong internet stocks see a rebound? (03:25) - Why is there a lack of stimulus this time? (07:05) - A “K-shaped” economy (07:40) - A strong run in A-share tech — is the AI rally getting frothy? (15:48) - The liquidity impact of the IPO glut (20:27) - Where might gold be headed? This episode was originally recorded on 16 June 2026. Apple podcast: Spotify: