Exa Labs opens first Asia office in Singapore to advance AI search infrastructure - TNGlobal
<a href="https://news.google.com/rss/articles/CBMiuAFBVV95cUxPMlZyQXlROHV4TlRGNHVtVi16cFpGcjY0LVRYWTFNcEg5N2llNk84R3pXa0FXYXJ1bnVUVzRLZDYxSEFPRWtNS0lMLVFCWF94cUxkSjk5YXBUS2pHVkNQMzJOZ3JNRFk1SmxMdHBhcmdNalBralJVRW9QeVpvVml2Yy00a0tTdE1vSmlyanB3aDhUMWFZdml4T1VaTml4MG83YVFFaWlaRnJpV0ZGM3hCRWRRUWdjZm1s?oc=5" target="_blank">Exa Labs opens first Asia office in Singapore to advance AI search infrastructure</a> <font color="#6f6f6f">TNGlobal</font>
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Cheaper/faster/easier makes for step changes (and that's why even current-level LLMs are transformative)
We already knew there's nothing new under the sun. Thanks to advances in telescopes, orbital launch, satellites, and space vehicles we now know there's nothing new above the sun either, but there is rather a lot of energy! For many phenomena, I think it's a matter of convenience and utility where you model them as discrete or continuous, aka, qualitative vs quantitative. On one level, nukes are simply a bigger explosion, and we already had explosions. On another level, they're sufficiently bigger as to have reshaped global politics and rewritten the decision theory of modern war. Perhaps the key thing is remembering that sufficiently large quantitative changes can make for qualitative macro effects. For example, basic elements of modern life include transport, communication, energy, comput
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[P] MCGrad: fix calibration of your ML model in subgroups
Hi r/MachineLearning , We’re open-sourcing MCGrad , a Python package for multicalibration–developed and deployed in production at Meta. This work will also be presented at KDD 2026. The Problem: A model can be globally calibrated yet significantly miscalibrated within identifiable subgroups or feature intersections (e.g., "users in region X on mobile devices"). Multicalibration aims to ensure reliability across such subpopulations. The Solution: MCGrad reformulates multicalibration using gradient boosted decision trees. At each step, a lightweight booster learns to predict residual miscalibration of the base model given the features, automatically identifying and correcting miscalibrated regions. The method scales to large datasets, and uses early stopping to preserve predictive performanc
your media files have an expiration date
A photo uploaded to your app today gets views. The same photo from two years ago sits in storage, loaded maybe once when someone scrolls back through an old profile. You pay the same rate for both. I have seen this pattern in every media-heavy application I have worked on. The hot data is a thin slice. The cold data grows without stopping. If you treat all objects the same, your storage bill reflects the worst case: premium pricing for data nobody touches. Tigris gives you two mechanisms to deal with this. You can transition old objects to cheaper storage tiers, or you can expire them outright. Both happen on a schedule you define. This post covers when and how to use each one. how media access decays Think about a social media feed. A user uploads a photo. For the first week, that photo a
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What next for the struggling rural mothers in China who helped to build AI?
Before autonomous driving freed up the hands of Beijing’s middle class, thousands of workers some 1,500km (930 miles) away in China’s southwestern Guizhou province clicked away at computer screens to teach AI about navigating traffic. In the mountainous city of Tongren, where incomes are less than half those in Beijing, the work of data labelling – marking residential buildings, pavements, roadways and traffic lights – shaped the artificial intelligence guiding those vehicles. The job required...
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