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Ask.com officially closed May 1, 2026. Here’s what the Ask Jeeves shutdown means for search history — and what the butler got right all along. The post Ask.com Shuts Down: The End of Ask Jeeves appeared first on 1redDrop.
Researchers at Meta’s FAIR lab have released NeuralSet, a Python framework designed to eliminate one of the most persistent bottlenecks in Neuro-AI research: the painful, fragmented process of getting brain data into a deep learning pipeline. https://kingjr.github.io/files/neuralset.pdf The Problem: Neuroscience Data Is Stuck in the Pre-Deep-Learning Era Neuroscience already has excellent, battle-tested software. Tools like
Poolside AI released the first two models in its Laguna family: Laguna M.1 and Laguna XS.2. Alongside these, the company is releasing pool — a lightweight terminal-based coding agent and a dual Agent Client Protocol (ACP) client-server — the same environment Poolside uses internally for agent RL training and evaluation, now available as a research
What if a language model had never heard of the internet, smartphones, or even World War II? That’s not a hypothetical — it’s exactly what a team of researchers led by Nick Levine, David Duvenaud, and Alec Radford has built. They call it talkie, and it may be the most historically disciplined large language model
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.
Building a production-grade voice AI agent is one of the hardest engineering challenges in applied machine learning today. It is not just about transcription accuracy. You need a system that can hold context across a five-minute conversation, invoke external APIs mid-call without an awkward pause, gracefully recover when a caller corrects themselves, and do all
For years, the computer vision community has operated on two separate tracks: generative models (which produce images) and discriminative models (which understand them). The assumption was straightforward — models good at making pictures aren’t necessarily good at reading them. A new paper from Google, titled “Image Generators are Generalist Vision Learners” (arXiv:2604.20329), published April 22,
DeepSeek-AI has released a preview version of the DeepSeek-V4 series: two Mixture-of-Experts (MoE) language models built around one core challenge making one-million-token context windows practical and affordable at inference time. The series consists of DeepSeek-V4-Pro, with 1.6T total parameters and 49B activated per token, and DeepSeek-V4-Flash, with 284B total parameters and 13B activated per token.
Training frontier AI models is, at its core, a coordination problem. Thousands of chips must communicate with each other continuously, synchronizing every gradient update across the network. When one chip fails or even slows down, the entire training run can stall. As models scale toward hundreds of billions of parameters, that fragility becomes increasingly untenable.
There’s a pattern playing out inside almost every engineering organization right now. A developer installs GitHub Copilot to ship code faster. A data analyst starts querying a new LLM tool for reporting. A product team quietly embeds a third-party model into a feature branch. By the time the security team hears about any of it,
