From killing your chatbots to optimizing your prompts, here are the best ways to go full AI native and conquer the new world. Sam Liang is appalled as I confess my technique for recording an interview: running the Voice Memos app on an iPhone and transferring the transcript manually to a Google Doc. The CEO of Otter, a transcription service for analyzing meetings, looks at me as if I tried to call into our video chat using a rotary phone. He believes, naturally, that I should switch to Otter. Time-saving productivity tools like next-gen note-takers, task-based agents, and chatty inbox assistants are exploding in popularity as they invade every nook and cranny of our digital lives.

Play behavior is not all'marshmallow science,' and more play can equal better health. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. From by Heide Island, PhD, to be published on 4/28/26 by Avery, an imprint of Penguin Publishing Group, a division of Penguin Random House, LLC. From behind a stand of frozen lupine, Patches, Crest, and Slash emerge onto the wetland. Moonshine reflects off the newly fallen snow, illuminating the predawn hour with a supernatural brightness. They halt beside a corrugated metal culvert, side by side, until Patches lurches forward and leaps onto the bank of Admirals Lake. Her landing fractures the frozen lakeshore, stamping an otter-sized divot. The two girls follow behind her, each landing with a loud crunch, leaving star-shaped bull's-eyes in the ice. The otters are out early, exploiting the cold; an icy lake makes for sluggish fish.

Environment Animals Wildlife Endangered Species Earth's largest otters have chocolate bar-sized babies Chester Zoo celebrates the birth of giant otter triplets. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. While they only weigh 7.1 ounces as babies, giant otters can grow to six-feet-long and weigh up to 71 pounds. Breakthroughs, discoveries, and DIY tips sent six days a week. It turns out that giant otter () newborns are actually quite small, weighing just around 7.1 ounces.

However, such energy advantage is often unrealized because inference requires evaluating a temporal decay function and subsequent multiplication with the synaptic weights. We fabricated a custom indium oxide optoelectronic synapse, showing how its natural physical decay directly implements the required temporal function. By treating the device's analog output as the fused product of the synaptic weight and temporal decay, optoelectronic synaptic TTFS (named Otters) eliminates these expensive digital operations. To use the Otters paradigm in complex architectures like the transformer, which are challenging to train directly due to the sparsity issue, we introduce a novel quantized neural network-to-SNN conversion algorithm. This complete hardware-software co-design enables our model to achieve state-of-the-art accuracy across seven GLUE benchmark datasets and demonstrates a 1.77 improvement in energy efficiency over previous leading SNNs, based on a comprehensive analysis of compute, data movement, and memory access costs using energy measurements from a commercial 22nm process. Our work thus establishes a new paradigm for energy-efficient SNNs, translating fundamental device physics directly into powerful computational primitives. Large language models (LLMs) have demonstrated remarkable capabilities, yet their immense computational and energy costs hinder their deployment in resource-constrained environments such as edge devices (Lin et al., 2023; Jegham et al., 2025). This critical challenge has spurred research on more efficient, brain-inspired architectures, with spiking neural networks (SNNs) emerging as a promising candidate (Tang et al., 2025; Xing et al.).

Popular zero-shot models suffer due to artifacts inherited from pretraining. One particularly detrimental issue, caused by unbalanced web-scale pretraining data, is mismatched label distribution. Existing approaches that seek to repair the label distribution are not suitable in zero-shot settings, as they have mismatching requirements, such as needing access to labeled downstream task data or knowledge of the true label balance in the pretraining distribution. We sidestep these challenges and introduce a simple and lightweight approach to adjust pretrained model predictions via optimal transport. Our technique requires only an estimate of the label distribution of a downstream task.

Otter is an AI-powered transcription service and app, and I use it every time I interview someone. Even in a group setting, it's the perfect tool for a journalist: it records and transcribes what people are saying, identifies the speaker, and allows me to click on the transcribed text and hear the recorded audio, just to check up on it. Otter even offers AI services, so I can see an AI-generated summary of the conversation and what needs to happen next. Yesterday, my wife complained that the secretary of a non-profit she volunteers at had quit, forcing her to record the minutes of a meeting. So, why should you use Otter?

The next time you join a video call, Otter.ai is hoping its new AI tool will help make things run smoother. On Tuesday, the company introduced the Otter Meeting Agent. It's part of a suite of three new AI helpers designed to assist a variety of different users. The first of those, the voice-activated Meeting Agent, can schedule follow-up calls and draft emails for you. It can also answer questions based on information it finds in your company's meeting database.

Vision-Language-Action (VLA) models aim to predict robotic actions based on visual observations and language instructions. Existing approaches require fine-tuning pre-trained visionlanguage models (VLMs) as visual and language features are independently fed into downstream policies, degrading the pre-trained semantic alignments. We propose OTTER, a novel VLA architecture that leverages these existing alignments through explicit, text-aware visual feature extraction. Instead of processing all visual features, OTTER selectively extracts and passes only task-relevant visual features that are semantically aligned with the language instruction to the policy transformer. This allows OTTER to keep the pre-trained vision-language encoders frozen. Thereby, OTTER preserves and utilizes the rich semantic understanding learned from large-scale pre-training, enabling strong zero-shot generalization capabilities. In simulation and real-world experiments, OTTER significantly outperforms existing VLA models, demonstrating strong zeroshot generalization to novel objects and environments. Video, code, checkpoints, and dataset: https://ottervla.github.io/.

While there has been plenty of work on generating tests from existing code, there has been limited work on generating tests from issues. A correct test must validate the code patch that resolves the issue. In this work, we focus on the scenario where the code patch does not exist yet. This approach supports two major use-cases. First, it supports TDD (test-driven development), the discipline of "test first, write code later" that has well-documented benefits for human software engineers. Second, it also validates SWE (software engineering) agents, which generate code patches for resolving issues. This paper introduces Otter, an LLM-based solution for generating tests from issues. Otter augments LLMs with rule-based analysis to check and repair their outputs, and introduces a novel self-reflective action planning stage. Experiments show Otter outperforming state-of-the-art systems for generating tests from issues, in addition to enhancing systems that generate patches from issues. We hope that Otter helps make developers more productive at resolving issues and leads to more robust, well-tested code.