We’re launching the Google DeepMind Accelerator program in Asia Pacific to tackle environmental risks.

We’re launching the Google DeepMind Accelerator program in Asia Pacific to tackle environmental risks.

OpenAIMay 25, 202624Views 0Likes 0Comments

The Asia-Pacific region is a global engine for economic growth, but it's also highly vulnerable to climate change. While green technologies are gaining momentum, a recent report shows they aren’t scaling fast enough to keep up with the region’s rising environmental risks. To help innovators tackle these environmental challenges, we’re launching an inaugural Google DeepMind…

SAP Sapphire 2026: The Complete Breakdown

UncategorisedMay 21, 202631Views 0Likes 0Comments

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How to Get the Most Out of Claude Cowork

Data AnalyticsMay 20, 202633Views 0Likes 0Comments

# Introduction to Claude Cowork Most people who use Claude like a smarter search engine. Type something in, read the response, copy it somewhere useful, then come back and do it again. That loop works fine for quick questions. It does not work for the kind of deep, file-heavy, multi-step knowledge work that…

Simulate real-world places with Project Genie and Street View

OpenAIMay 20, 202633Views 0Likes 0Comments

Street View: ground your worlds in real places When creating imaginative worlds in Project Genie, you can now also base them on real places. Just tap the Maps pin to choose a place in the U.S. and optionally select a style for your world, like “Desert Sands” or “Stone Age.” Then, describe your character —…

Time-Series Feature Engineering with Python Itertools

Data AnalyticsMay 15, 202636Views 0Likes 0Comments

# Introduction Time series feature engineering doesn't follow the same rules as tabular data. Observations aren't independent, row order isn't incidental, and the most useful features are rarely individual readings. You'll have to identify patterns across time like rates of change, lag comparisons, deviations from a rolling baseline, and more. Building lags, sliding…

Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference

AI NewsMay 15, 202630Views 0Likes 0Comments

Liquid AI just released LFM2.5-VL-450M, an updated version of its earlier LFM2-VL-450M vision-language model. The new release introduces bounding box prediction, improved instruction following, expanded multilingual understanding, and function calling support — all within a 450M-parameter footprint designed to run directly on edge hardware ranging from embedded AI modules like NVIDIA Jetson Orin, to mini-PC…

Google DeepMind’s most advanced forecasting model

OpenAIMay 15, 202627Views 0Likes 0Comments

Weather predictions need to capture the full range of possibilities — including worst case scenarios, which are the most important to plan for. WeatherNext 2 can predict hundreds of possible weather outcomes from a single starting point. Each prediction takes less than a minute on a single TPU; it would take hours on a supercomputer…

AI Bias in Hiring: Disasters & Fixes That Work

Human AiMay 15, 202634Views 0Likes 0Comments

Ever built a model that nailed 95% accuracy… but tanked for half your users? Yeah, me too. Spent weeks on data. Trained overnight. Launched with hype. Then complaints rolled in. “Why does it always pick the same type?” Ouch. That’s bias sneaking in. Not some abstract tech term. It’s when systems spit out unfair results…

Stop Wasting Tokens: A Smarter Alternative to JSON for LLM Pipelines

Data AnalyticsMay 10, 202632Views 0Likes 0Comments

# Introduction JSON is great for APIs, storage, and application logic. But inside large language model (LLM) pipelines, it often carries a lot of token overhead that does not add much value to the model: braces, quotes, commas, and repeated field names on every row. TOON, short for Token-Oriented Object Notation, is a…

Meta AI Releases Sapiens2: A High-Resolution Human-Centric Vision Model for Pose, Segmentation, Normals, Pointmap, and Albedo

AI NewsMay 10, 202631Views 0Likes 0Comments

If you’ve ever watched a motion capture system struggle with a person’s fingers, or seen a segmentation model fail to distinguish teeth from gums, you already understand why human-centric computer vision is hard. Humans are not just objects, they come with articulated structure, fine surface details, and enormous variation in pose, clothing, lighting, and ethnicity.…

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