AWS Professional Services compressed engagement timelines from months to days by fundamentally rebuilding its delivery process, not just adding AI tools. This post shares how they became a frontier team and the practices that enabled it.

Imagine a world with more birdsong and less Nigel Farage. If this is the future, bring it on. Unpopular opinion incoming: there’s cool stuff brewing in the world. Microbots might one day mend spinal cords, a petri dish of brain cells can already play video games, and now the prospect of a new wonder: according to a New Yorker article on misophonia, a team of miracle workers are using machine learning to develop headphones that can quickly target and eliminate irksome audio. This project, led by Shyam Gollakota of the University of Washington’s Mobile Intelligence Lab, aims to develop headphones that selectively filter out triggering noises, leaving or enhancing the good sounds. Gollakota offers the example of sitting on a park bench, oblivious to loud talkers next to you but able to hear birdsong.

From local experiments to production AI systems, mastering key Python concepts is essential.

Anthropic is throttling its new Mythos model for certain tasks while building apps that directly compete with its largest customers. Customers, partners, and investors are pushing back.

OpenAI is acquiring Ona, formerly Gitpod, a startup founded in Kiel, Germany in 2020 that specializes in AI agents and secure cloud development environments for software development.

OpenAI introduces three Academy courses that help people build practical AI skills, create repeatable workflows, and apply agents in everyday work.

Databricks introduces Lakehouse Federation to connect to external data sources without migration, allowing natural language queries via Genie while maintaining governance through Unity Catalog.

Moonshot AI has introduced Kimi Work, a local desktop AI agent for macOS and Windows that runs a swarm of up to 300 sub-agents on your machine. It drives your logged-in browser via WebBridge, reads local files, and schedules background jobs with a built-in cron engine. Based on the Kimi K2.6 MoE model (≈32B active parameters, 256K context), it targets knowledge workers by keeping data and execution local.

A Guardian cartoon by Stephen Collins humorously depicts the inner workings of OpenAI headquarters, blending AI, life and style themes.

A new poll shows 8 in 10 Americans are worried about AI, but it's being forced into their pensions and portfolios anyway, tying their futures to tech moguls' risky AI race.

SpaceX will become publicly traded on Friday with a valuation of $1.77 trillion, the largest IPO in history. Elon Musk, founder and CEO, holds a significant stake and could become the world's first trillionaire.

This article outlines nine settings adjustments in Google Messages to enhance privacy, reduce distractions, and improve the texting experience, including disabling sensitive content warnings, limiting profile sharing, turning off Gemini, and more.

Zyphra has released Zamba2-VL, a family of open vision-language models at 1.2B, 2.7B, and 7B parameters. The models use a hybrid Mamba2 state-space and Transformer backbone, shipping under Apache 2.0. They stay competitive with comparable Transformer VLMs while cutting time-to-first-token by about an order of magnitude.

Customer segmentation is the practice of dividing existing customers into groups based on shared characteristics to tailor marketing and services. This guide covers types, methods, importance, challenges, and how AI is transforming segmentation.

Gemini Omni integrates video generation directly into the Gemini multimodal AI assistant, enabling users to create videos from text or images, animate static pictures, and edit existing videos. The article demonstrates its capabilities through hands-on tests, while noting limitations such as usage quotas, video length caps, and restrictive content policies.

This tutorial builds an end-to-end 3D medical image segmentation pipeline using MONAI to segment the spleen on the Medical Segmentation Decathlon Task09 dataset. It covers volumetric CT processing, medical imaging transformations, training a 3D UNet with mixed precision and DiceCE loss, sliding-window inference, validation, and qualitative visualization.

Location scans from the globally popular augmented reality game have helped train AI to recognise and interpret physical spaces.

Apple's software chief Craig Federighi says the new Siri won't be sycophantic like other chatbots, and is designed to be helpful, not to form emotional connections.

Qursor lets you point at any UI element to send its exact context to your AI assistant, streamlining interactions. Launched on Product Hunt.

The article discusses the emerging trend of designing loops to drive AI agents instead of manual prompting, covering key figures' insights, Anthropic's Fable 5 rollout controversy, automated research systems, data infrastructure bottlenecks, inference speed optimizations, and agent tooling developments.

Sparse2Act is a pretraining framework that uses task-space end-effector actions as geometric supervision to align sparse point-cloud encoders with observations. It achieves 86.9% success on LIBERO-10, 73.4% cross-domain on Meta-World-5, and 72.5% on real-world tasks.

EquiDexFlow is an SE(3)-equivariant flow-matching model that jointly predicts wrist pose, joint angles, fingertip contacts, surface normals, and contact forces from an object point cloud. By projecting contacts onto the object surface and forces into the Coulomb friction cone by construction, it ensures placement and friction compliance without loss penalties. Experiments show wrist residuals below 0.04° over 200 rotations, zero joint deviation, zero friction violations, and the best composite score among ablations. On a physical robot, retargeted grasps successfully complete open-loop pick-and-hold trials on all six test objects.

EWAM is a closed-loop online adaptation architecture built on a frozen Cosmos3 backbone. It uses four lightweight neural layers for inference-time co-reasoning, enabling zero-shot task adaptation without fine-tuning or extra demonstration data, significantly reducing the amount of deployment data needed for new task layouts.

A new arXiv paper introduces DARRMS, an algorithm that reduces computational resource demands by allowing agents to limit their attention radius dynamically, improving coordination and scalability in uncertain environments while maintaining decision-making robustness.

EgoEngine is a scalable framework that transforms egocentric human manipulation videos into high-fidelity robot observation videos and executable action trajectories, bridging the visual and action gaps between human and robot. It enables zero-shot dexterous policy learning without real-robot demonstrations.

FlowPilot is a mapless navigation policy for long-horizon sidewalk navigation using only a monocular RGB camera. It employs anchored flow matching for pre-training on large-scale robot fleet data and a human-in-the-loop preference learning scheme to improve social compliance and counterfactual reasoning. In simulation, it achieves 42% success rate and 66% route completion; real-world experiments show a 40.0% reduction in intervention rate and 52.1% reduction in non-intervention rate over the base model.

This paper presents G-MAPP, a GPU-accelerated framework for reactive motion generation that achieves up to 5x speedup by parallelizing world modeling and planning on GPU, enabling real-time perception-action coupling in dynamic environments.

Foresight is a test-time framework that uses a finetuned Vision-Language Model to iteratively propose and critique motion plans for mapless navigation from sparse language instructions. It learns a reward model from human feedback and post-trains the VLM with reinforcement learning, achieving 37% higher task success and 52% fewer interventions in real-world environments.

A new pretraining framework called AEM learns compact temporal representations from vision-action history for robot manipulation, outperforming baselines in simulation and real-world tasks.

This work shows that end-to-end imitation learning with vision-language-action (VLA) models can support collaborative manipulation. It identifies demonstration action leakage as a failure mode causing premature assistive behavior, and proposes an inference-time steering method. A 16-participant user study on a long-horizon assembly task demonstrates that steering enables longer execution horizons, faster collaboration, and fewer failures.