At 'AI Coachella,' Stanford Students Line Up to Learn From Silicon Valley Royalty

Ukraine’s military robot surge aims to offset drone risks to humans

Ukrainian ground robots and drones have demonstrated how to overcome a Russian military position by themselves while forcing the surrender of Russian soldiers, claimed Ukrainian President Volodymyr Zelenskyy. If true, that would represent a significant robotic milestone during the ongoing war that has already been significantly reshaped by drones—and it could offer lessons for how militaries worldwide may use robots and drones to do the dirtiest and most dangerous jobs in future conflicts. The claim by Zelenskyy has not been independently verified but was accompanied by a promotional video in which he described Ukraine’s military robots as having completed over 22,000 missions in the last three months. Ukraine’s defense ministry also recently described a threefold increase in the Ukrainian military’s uncrewed ground vehicle missions over the last five months, with more than 9,000 robotic missions conducted in March, according to…

Company-wise memory in Amazon Bedrock with Amazon Neptune and Mem0

Artificial Intelligence Company-wise memory in Amazon Bedrock with Amazon Neptune and Mem0 This post is cowritten by Shawn Tsai from TrendMicro. Delivering relevant, context-aware responses is important for customer satisfaction. For enterprise-grade AI chatbots, understanding not only the current query but also the organizational context behind it is key. Company-wise memory in Amazon Bedrock, powered by Amazon Neptune and Mem0, provides AI agents with persistent, company-specific context—enabling them to learn, adapt, and respond intelligently across multiple interactions. TrendMicro, one of the largest antivirus software companies in the world, developed the Trend’s Companion chatbot, so their customers can explore information through natural, conversational interactions (learn more). TrendMicro aimed to enhance its AI chatbot service to deliver personalized, context-aware support for enterprise customers. The chatbot needed to retain conversation history for continuity, reference company-specific knowledge at scale, and ensure that memory remained…

Cost-effective multilingual audio transcription at scale with Parakeet-TDT and AWS Batch

Artificial Intelligence Cost-effective multilingual audio transcription at scale with Parakeet-TDT and AWS Batch Many organizations are archiving large media libraries, analyzing contact center recordings, preparing training data for AI, or processing on-demand video for subtitles. When data volumes grow significantly, managed automatic speech recognition (ASR) service costs can quickly become the primary constraint on scalability. To address this cost-scalability challenge, we use the NVIDIA Parakeet-TDT-0.6B-v3 model, deployed through AWS Batch on GPU-accelerated instances. Parakeet-TDT’s Token-and-Duration Transducer architecture simultaneously predicts text tokens and their duration to intelligently skip silence and redundant processing. This helps achieve inference speeds orders of magnitude faster than real-time. By paying only for brief bursts of compute rather than the full length of your audio, you can transcribe at scale for fractions of a cent per hour of audio based on the benchmarks described in this post.…

End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps

Artificial Intelligence End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps Production machine learning (ML) teams struggle to trace the full lineage of a model through the data and the code that trained it, the exact dataset version it consumed, and the experiment metrics that justified its deployment. Without this traceability, questions like “which data trained the model currently in production?” or “can we reproduce the model we deployed six months ago?” become multi-day investigations through scattered logs, notebooks, and Amazon Simple Storage Service (Amazon S3) buckets. This gap is especially acute in regulated industries. For example, healthcare, financial services, autonomous vehicles, where audit requirements demand that you link deployed models to their precise training data, and where individual records might need to be excluded from future training on request. In this post, we show how to combine three…

Omnichannel ordering with Amazon Bedrock AgentCore and Amazon Nova 2 Sonic

Artificial Intelligence Omnichannel ordering with Amazon Bedrock AgentCore and Amazon Nova 2 Sonic Introduction Building a voice-enabled ordering system that works across mobile apps, websites, and voice interfaces (an omnichannel approach) presents real challenges. You need to process bidirectional audio streams, maintain conversation context across multiple turns, integrate backend services without tight coupling, and scale to handle peak traffic. In this post, we’ll show you how to build a complete omnichannel ordering system using Amazon Bedrock AgentCore, an agentic platform, to build, deploy, and operate highly effective AI agents securely at scale using any framework and foundation model and Amazon Nova 2 Sonic. You’ll deploy infrastructure that handles authentication, processes orders, and provides location-based recommendations. The system uses managed services that scale automatically, reducing the operational overhead of building voice AI applications. By the end, you’ll have a working system…

Nova Forge SDK series part 2: Practical guide to fine-tune Nova models using data mixing capabilities

Artificial Intelligence Nova Forge SDK series part 2: Practical guide to fine-tune Nova models using data mixing capabilities This hands-on guide walks through every step of fine-tuning an Amazon Nova model with the Amazon Nova Forge SDK, from data preparation to training with data mixing to evaluation, giving you a repeatable playbook you can adapt to your own use case. This is the second part in our Nova Forge SDK series, building on the SDK introduction and first part, which covered kicking off customization experiments. The focus of this post is data mixing: the technique that lets you fine-tune on domain-specific data without sacrificing a model’s general capabilities. In the previous post, we made the case for why this matters, blending customer data with Amazon-curated datasets preserved near-baseline Massive Multitask Language Understanding (MMLU) scores while delivering a 12-point F1 improvement…

Power video semantic search with Amazon Nova Multimodal Embeddings

Artificial Intelligence Power video semantic search with Amazon Nova Multimodal Embeddings Video semantic search is unlocking new value across industries. The demand for video-first experiences is reshaping how organizations deliver content, and customers expect fast, accurate access to specific moments within video. For example, sports broadcasters need to surface the exact moment a player scored to deliver highlight clips to fans instantly. Studios need to find every scene featuring a specific actor across thousands of hours of archived content to create personalized trailers and promotional content. News organizations need to retrieve footage by mood, location, or event to publish breaking stories faster than competitors. The goal is the same: deliver video content to end users quickly, capture the moment, and monetize the experience. Video is naturally more complex than other modalities like text or image because it amalgamates multiple unstructured…

Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock

Artificial Intelligence Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock Optimizing models for video semantic search requires balancing accuracy, cost, and latency. Faster, smaller models lack routing intelligence, while larger, accurate models add significant latency overhead. In Part 1 of this series, we showed how to build a multimodal video semantic search system on AWS with intelligent intent routing using the Anthropic Claude Haiku model in Amazon Bedrock. While the Haiku model delivers strong accuracy for user search intent, it increases end-to-end search time to 2-4 seconds. This contributes to 75% of the overall latency. Now consider what happens as the routing logic grows more complex. Enterprise metadata can be far more complex than the five attributes in our example (title, caption, people, genre, and timestamp). Customers may factor in camera angles, mood and sentiment,…

How Automated Reasoning checks in Amazon Bedrock transform generative AI compliance

Artificial Intelligence How Automated Reasoning checks in Amazon Bedrock transform generative AI compliance Compliance teams in regulated industries spend weeks on manual reviews, pay for outside consultants, and still face audit gaps when AI outputs lack formal proof. Automated Reasoning checks in Amazon Bedrock Guardrails address this by replacing probabilistic AI validation with mathematical verification, turning AI-generated decisions into provably correct, auditable results. In this post, you’ll learn why probabilistic AI validation falls short in regulated industries and how Automated Reasoning checks use formal verification to deliver mathematically proven results. You’ll also see how customers across six industries use this technology to produce formally verified, auditable AI outputs, and how to get started. The compliance challenge Regulated industries face high-stakes compliance challenges. Hospitals navigate radiation safety regulations. Financial institutions classify AI risk under the EU AI Act. Insurance carriers answer…

Transform retail with AWS generative AI services

Artificial Intelligence Transform retail with AWS generative AI services Online retailers face a persistent challenge: shoppers struggle to determine the fit and look when ordering online, leading to increased returns and decreased purchase confidence. The cost? Lost revenue, operational overhead, and customer frustration. Meanwhile, consumers increasingly expect immersive, interactive shopping experiences that bridge the gap between online and in-store retail. Retailers implementing virtual try-on technology can improve purchase confidence and reduce return rates, translating directly to improved profitability and customer satisfaction. This post demonstrates how to build a virtual try-on and recommendation solution on AWS using Amazon Nova Canvas, Amazon Rekognition and Amazon OpenSearch Serverless. Whether you’re an AWS Partner developing retail solutions or a retailer exploring generative AI transformation, you’ll learn the architecture, implementation approach, and key considerations for deploying this solution. You can find the code base to…

Accelerating decode-heavy LLM inference with speculative decoding on AWS Trainium and vLLM

Artificial Intelligence Accelerating decode-heavy LLM inference with speculative decoding on AWS Trainium and vLLM Practical benchmarks showing faster inter-token latency when deploying Qwen3 models with vLLM, Kubernetes, and AWS AI Chips. Speculative decoding on AWS Trainium can accelerate token generation by up to 3x for decode-heavy workloads, helping reduce the cost per output token and improving throughput without sacrificing output quality. If you build AI writing assistants, coding agents, or other generative AI applications, your workloads likely produce far more tokens than they consume, making the decode stage the dominant cost of inference. During autoregressive decoding, tokens are generated sequentially, leaving hardware accelerators memory-bandwidth-bound and underutilized. This drives up the cost per generated token. Speculative decoding addresses this bottleneck by letting a small draft model propose multiple tokens at once, which the target model verifies in a single forward pass.…

How Guidesly built AI-generated trip reports for outdoor guides on AWS

Artificial Intelligence How Guidesly built AI-generated trip reports for outdoor guides on AWS This is guest post by David Lord, Taylor Lord, Shiva Prasad, Anup Banasavalli Hiriyanagowda, Nikhil Chandra from Guidesly. Guidesly is reshaping how outdoor recreation is booked, run, and experienced. Founded in 2019, it began as a way to connect anglers, hunters, divers, and outdoor recreation enthusiasts with trusted guides, dive shops, and charters. It has since grown into a vertical AI software as a service (SaaS) system serving the entire industry. With Guidesly Pro, outdoor professionals gain a business solution that powers every part of their operation—bookings, payments, websites, client management, and marketing—all from a single system. For many guides, the toughest challenge is getting discovered and cutting through the noise online. Even those who know what must be done can spend up to eight hours a…

Best practices to run inference on Amazon SageMaker HyperPod

Artificial Intelligence Best practices to run inference on Amazon SageMaker HyperPod Deploying and scaling foundation models for generative AI inference presents challenges for organizations. Teams often struggle with complex infrastructure setup, unpredictable traffic patterns that lead to over-provisioning or performance bottlenecks, and the operational overhead of managing GPU resources efficiently. These pain points result in delayed time-to-market, suboptimal model performance, and inflated costs that can make AI initiatives unsustainable at scale. This post explores how Amazon SageMaker HyperPod addresses these challenges by providing a comprehensive solution for inference workloads. We walk you through the platform’s key capabilities for dynamic scaling, simplified deployment, and intelligent resource management. By the end of this post, you’ll understand how to use the HyperPod automated infrastructure, cost optimization features, and performance enhancements to reduce your total cost of ownership by up to 40% while accelerating…

Navigating the generative AI journey: The Path-to-Value framework from AWS

Artificial Intelligence Navigating the generative AI journey: The Path-to-Value framework from AWS Generative AI is reshaping how organizations approach productivity, customer experiences, and operational capabilities. Across industries, teams are experimenting with generative AI to unlock new ways of working. Many of these efforts produce compelling proofs of concept (POC) that demonstrate technical feasibility. The real challenge begins after those early wins. Although POCs frequently demonstrate technical feasibility, organizations often struggle to translate them into production-ready systems that deliver measurable business value. The journey from concept to production, and from production to sustained value creation, introduces challenges across technical, organizational, and governance dimensions. The Generative AI Path-to-Value (P2V) framework was created to address this gap. It provides a mental model and practical guide to help organizations systematically move generative AI initiatives from ideation and experimentation to production at scale. The goal…

How to build effective reward functions with AWS Lambda for Amazon Nova model customization

Artificial Intelligence How to build effective reward functions with AWS Lambda for Amazon Nova model customization Building effective reward functions can help you customize Amazon Nova models to your specific needs, with AWS Lambda providing the scalable, cost-effective foundation. Lambda’s serverless architecture lets you focus on defining quality criteria while it handles the computational infrastructure. Amazon Nova offers multiple customization approaches, with Reinforcement fine-tuning (RFT) standing out for its ability to teach models desired behaviors through iterative feedback. Unlike Supervised fine-tuning (SFT) that requires thousands of labeled examples with annotated reasoning paths, RFT learns from evaluation signals on final outputs. At the heart of RFT lies the reward function—a scoring mechanism that guides the model toward better responses. This post demonstrates how Lambda enables scalable, cost-effective reward functions for Amazon Nova customization. You’ll learn to choose between Reinforcement Learning via…

Figma - MultiAgents April 16, 2026

Everything is easier now. I have been toying around with agent orchestration for a while now. I’m currently running 10-20 agents around the clock.AI agents are now capable of bringing my ideas to life. Like many developers, I’ve been feeling the token anxiety. I can do much more now than ever before, and every time I have a spare minute I want to kick off another agent session. - I see a cool product I don’t want to pay for? Codex will build it for me. - I have a silly idea I want to see come to life? Codex will build it for me. - I get mildly annoyed doing the same thing over and over? Codex pls. If you have an army of infinitely patient, intelligent, and helpful agents waiting for your next command, why shouldn’t we take…

Debugging Dead MoE Models: A Step-by-Step Guide August 19, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

MoE at Scale: Making Sparse Models Fast on Real Hardware September 03, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras CS-3 vs. Groq LPU September 19, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras CS-3 vs. Nvidia DGX B200 Blackwell September 19, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras Inference: Now Available via Pay Per Token October 13, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

MoE Math Demystified: What Does 8x7B Actually Mean? October 14, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

REAP: One-Shot Pruning for Trillion-Parameter Mixture-of-Experts Models October 16, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Building Instant RL Loops with Meta Llama Tools and Cerebras October 27, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras October 2025 Highlights November 03, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras February 2026 Highlights November 03, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

The world’s fastest GLM-4.6 – now available on Cerebras November 18, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Scaling Code-Repair Agents with Reinforcement Learning: Extending OpenHands for Real-World Repositories November 24, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Scaling SWE Agent Data Collection with Dockerized Environments for Execution November 24, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Rox × Cerebras: Real-time speed for agentic sales workflows November 25, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras at NeurIPS 2025: Nine Papers From Pretraining to Inference December 04, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Jais 2: A Blueprint for Sovereign AI December 09, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Thinking Inside the Box: The Implicit Chain Transformer for Efficient State Tracking December 12, 2025

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

2026: Fast Inference Finds its Groove January 06, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

GLM-4.7: Frontier intelligence at record speed — now available on Cerebras January 08, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

This new model is smarter than Sonnet 4.5…and 20X faster? January 08, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

OpenAI Partners with Cerebras to Bring High-Speed Inference to the Mainstream January 14, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

StackAI × Cerebras: enabling the fastest inference for enterprise AI agents January 28, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Fast inference is going mainstream — the Cerebras ecosystem is scaling access January 28, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

The Year of Latency Debt (And How Big Tech Is Paying It Down) January 28, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Introducing OpenAI GPT-5.3-Codex-Spark Powered by Cerebras February 12, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Why speed wins: faster inference is about more than just quicker answers–it’s the new path to accuracy February 19, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

ExomeBench: A Benchmark for Clinical Variant Interpretation in Exome Regions February 23, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Stop Shipping AI Slop: How Codex Spark Changes The Way You Code March 04, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras is coming to AWS March 13, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

How to stop your autoresearch loop from cheating March 19, 2026

Stop autoresearch loops from “cheating” by enforcing strict evaluation, isolating experiments, and designing metrics that prevent shortcuts and false gains.

The GPU Is Being Split in Half March 26, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Partner Spotlight: Armis + Cerebras Enable Teams Build and Secure Software Faster March 27, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

The Debate of MCP vs. CLI Centers on Speed April 06, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Lessons learned from building multi-agent workflows April 16, 2026

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

March 20, 2026 Why the AI Race Shifted to Speed Read blog post

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Latest Agent LLM Prompting Context Engineering Kacper Łukawski Lead DevRel at Deepset Context Engineering for Agentic Systems: What Goes Into Your Agent's Mind A practical introduction to context engineering - what fills the LLM context window in agentic systems, why it matters, and how to keep it under control. April 20, 2026

Context Engineering for Agentic Systems: What Goes Into Your Agent's Mind A practical introduction to context engineering - what fills the LLM context window in agentic systems, why it matters, and how to keep it under control. April 20, 2026Every new generation of Large Language Models arrives with a bigger context window - and the temptation to use it fully. If the model can read a million tokens, why not feed it everything? In practice, more context doesn’t reliably mean better answers: it often means higher costs, slower responses, and a model that loses track of what actually matters. Context engineering is the discipline of deciding not just what to put in the context window, but how much, in what form, and when to leave things out - and it’s quickly becoming one of the most important skills in building…

Gemma 4 VLA Demo on Jetson Orin Nano Super

Gemma 4 VLA Demo on Jetson Orin Nano Super You speak → Parakeet STT → Gemma 4 → [Webcam if needed] → Kokoro TTS → Speaker Press SPACE to record, SPACE again to stop. This is a simple VLA: the model decides on its own whether to act based on the context of what you asked, no keyword triggers, no hardcoded logic. If your question needs Gemma to open her eyes, she'll decide to take a photo, interpret it, and answer you with that context in mind. She's not describing the picture, she's answering your actual question using what she saw. And honestly? It's pretty impressive that this runs on a Jetson Orin Nano. :) Get the code The full script for this tutorial lives on GitHub, in my Google_Gemma repo next to the Gemma 2 demos: 👉 github.com/asierarranz/Google_Gemma Grab…

How to Ground a Korean AI Agent in Real Demographics with Synthetic Personas

How to Ground a Korean AI Agent in Real Demographics with Synthetic Personas Nemotron-Personas-Korea fixes this. The dataset provides 6 million fully synthetic personas grounded in official statistics and seed data from the Korean Statistical Information Service (KOSIS), the Supreme Court of Korea, the National Health Insurance Service, and the Korea Rural Economic Institute. NAVER Cloud contributed seed data and domain expertise during design. Every persona is demographically accurate but contains zero personally identifiable information (PII). It’s designed with Korea's Personal Information Protection Act (PIPA) in mind. South Korea is also one of the few countries to publish an official Synthetic Data Generation guide, establishing governance for grounding models with synthetic versions of sensitive data. This dataset follows that approach. In this tutorial, we'll turn a synthetic persona into a deployed Korean agent — from filtering the dataset to inference…

The PR you would have opened yourself

The PR you would have opened yourself TL;DR We provide a Skill and a test harness to help port language models from transformers to mlx-lm, so they become (almost) instantly available the moment they are added to transformers. The Skill is designed to support contributors and reviewers as an aide, not an automation. We explain why we did it, how, and comment about how to meaningfully contribute to open source in the age of agents. The advent of code agents In 2026, code agents started to actually work. What used to be auto-completion at the side of your editor turned into a system that one-shots reasonable solutions from brief specifications. The generated code usually works out of the box, covers what you asked for, and makes reasonable assumptions about details you didn't specify. This is great. As Jensen Huang puts…

The Download: turning down human noise, and LA’s stunning subway upgrade

The Download: turning down human noise, and LA’s stunning subway upgrade Plus: Apple’s Tim Cook is stepping down as CEO. This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. The noise we make is hurting animals. Can we learn to shut up? As human society has expanded, animals have started struggling to hear one another. For many birds, the noise has grown so loud that they’ve begun to sing with faster trills. Now, their mating calls aren’t as effective. The growing hubbub can also increase bird-on-bird conflict, and entire species that can’t handle urban clamor simply leave town for good. But there are technological solutions to the noises hurting animals—and they could help humans, too. —Clive Thompson Los Angeles is finally going underground In May,…

How to evaluate the performance of AI agents?

Traditional software testing is straightforward: you give input X and expect output Y. If the function returns the wrong value, the test fails. LLM-based agents don't work that way. They're non-deterministic which means the same prompt can produce different outputs across runs. They operate over multiple steps, making decisions about which tools to call, what parameters to pass, and how to interpret results. An agent can complete an execution without errors and still hallucinate facts, miss the user's intent, or take unnecessary steps. Classical testing may not catch problematic outputs produced by an AI Agent. When building AI Agents, you face three main evaluation challenges: - You're evaluating trajectories, instead of just outputs. An agent might give the correct final answer but call the wrong tools, use the wrong parameters, or take five steps when one would do. If you…

Simplify Sparse Deep Learning with Universal Sparse Tensor in nvmath-python

In a previous post, we introduced the Universal Sparse Tensor (UST), enabling developers to decouple a tensor’s sparsity from its memory layout for greater flexibility and performance. We’re excited to announce the integration of the UST into nvmath-python v0.9.0 to accelerate sparse scientific and deep learning applications. This post provides a walkthrough of key UST features, implementation details, and performance overview, including: - Zero-cost interoperability: Data-movement-free conversion with PyTorch, SciPy, and CuPy. - Custom formats: Define novel sparsity schemes. - Polymorphic operations: Sparsity-agnostic functions automatically use optimized kernels or generate custom sparse code—eliminating the need for manual coding of new formats. - PyTorch injection: Easily inject UST performance benefits into existing PyTorch models. - Transparent caching: Avoid JIT/LTO recompilation and replanning—amortizing overhead over subsequent repeated execution of the same operation. Tensor format DSL The UST describes common (e.g., COO, CSR,…

How to Build Vision AI Pipelines Using NVIDIA DeepStream Coding Agents

Developing real-time vision AI applications presents a significant challenge for developers, often demanding intricate data pipelines, countless lines of code, and lengthy development cycles. NVIDIA DeepStream 9 removes these development barriers using coding agents, such as Claude Code or Cursor, to help you easily create deployable, optimized code that brings your vision AI applications to life faster. This new approach simplifies the process of building complex multi-camera pipelines that ingest, process, and analyze massive volumes of real-time video, audio, and sensor data. Built on GStreamer and part of the NVIDIA Metropolis vision AI development platform, DeepStream accelerates a developer’s journey from concept to actionable insight across industries. Video 1. How to use the NVIDIA DeepStream coding agents to generate complete vision AI pipelines from natural language prompts with Claude Code. To watch a recording showing how to build a DeepStream…

How to Accelerate Protein Structure Prediction at Proteome-Scale

Proteins rarely function in isolation as individual monomers. Most biological processes are governed by proteins interacting with other proteins, forming protein complexes whose structures are described in the hierarchy of protein structure as the quaternary representation. This represents one level of complexity up from tertiary representations, the 3D structure of monomers, which are commonly known since the emergence of AlphaFold2 and the creation of the Protein Data Bank. Structural information for the vast majority of complexes remains unavailable. While the AlphaFold Protein Structure Database (AFDB), jointly developed by Google DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI), transformed access to monomeric protein structures, interaction-aware structural biology at the proteome scale has remained a bottleneck with unique challenges: - Massive combinatorial interaction space - High computational cost for multiple sequence alignment (MSA) generation and protein folding - Inference scaling across millions of…

Codex settings

Codex settings Make Codex work the way you want, with fewer interruptions. You can access settings from the menu in the bottom left corner of Codex. For your first few tasks, focus on a few key settings: personalization, prevent sleep, detail level, and appearance. General > Prevent sleep while running keeps your computer awake while Codex is running. This is useful for longer tasks. If your computer goes to sleep, Codex may stop working. General > Detail level controls how much information Codex shows while it is working. Coding mode shows the specific commands Codex is executing. If this is more information than you need, switch to Default to keep your conversation cleaner. Personalization works a lot like personalization in ChatGPT. You can decide whether you want Codex to speak to you in a friendly tone or a direct tone.…

How to get started with Codex

How to get started with Codex Tips to set up Codex, create your first project, and start completing real tasks. Start by downloading the Codex desktop app and signing in with your ChatGPT account. Once you open Codex, create your first thread. A thread is like a chat in ChatGPT: a space where you go back and forth with Codex to accomplish a task. You can create a standalone thread, but most of the time you’ll want to work inside a project. A project is connected to a folder on your computer: Tip: To keep things simple, create a folder on your computer named Codex. Inside that Codex folder, you can have a separate folder for each project. If you want Codex to work with specific files for a project, just drag them into the folder. If not, you can…

Working with Codex

Working with Codex Learn how to set up your Codex workspace and start working with threads and projects. When you open Codex, you’ll see a few core elements: a sidebar menu, projects, settings, and a chat window. You don’t need to understand everything right away, but we’ll cover the basics here. The sidebar is where you navigate between threads, projects, and tools. Most of your work will begin by creating a new thread. When you’re using Codex, think of a “thread” the same way you would think of a “chat” in ChatGPT. You can have a thread which stands on its own, or a thread which is nested within a project. Select New thread to begin. You can select an existing project to associate it with, create a new project, or leave it as a standalone conversation. Search to find…

Plugins and skills

Plugins and skills Plugins and skills help Codex do more specific kinds of work. Plugins help Codex connect to other tools and sources of information. For example, a plugin might help Codex reference files in Google Drive, scan your email inbox, or work with information from another tool you use. Plugins can be simple and useful right away. If you already have the information you need in a connected plugin, you can ask Codex to use it instead of copying and pasting everything into the thread. To access plugins, select plugins in the top left corner of Codex. From there, you can see plugins that are recommended or already installed, browse the plugins library, or create a new plugin. Creating a new plugin usually requires more technical expertise than creating a skill. A skill is like a playbook Codex can…

Automations

Automations Run recurring tasks automatically using schedules and triggers in Codex. Codex can automatically run tasks on a schedule. This makes Codex proactive. Instead of waiting for you to come back and ask for an update, Codex can return at the scheduled time, do the work, and surface the result for you to review. This is useful for recurring work, like preparing for the day, reviewing what changed, checking for updates, summarizing recent activity, or creating a weekly report. For example, you might use a thread automation to: - Write a weekly review every Friday - Create a morning brief from yesterday’s work - Summarize new files added to a folder - Clean up a weekly data export - Check for missing or inconsistent information - Create a recurring project status update Some automations can also return to the same…

What is Codex?

What is Codex? Understand what Codex is and how it fits into your work Codex is an AI agent that you can delegate real work to. ChatGPT is great for asking questions, brainstorming, and drafting in conversation. Codex is designed for a different kind of task—it can work across files, tools, and repeatable workflows to help move work forward. A simple way to think about it: ChatGPT helps you think through the work, while Codex helps you hand off parts of the work itself. You don’t need to be a developer or working on software to use Codex. It goes beyond coding and is especially useful for tasks that require more than a single answer—like gathering information from multiple sources, creating and updating files, or producing outputs such as documents, slides, and spreadsheets. Codex can connect to tools, take action,…

Workspace agents

Workspace agents Understand, build, and use agents for repeatable work in ChatGPT. Most ChatGPT users already know how to use AI for one-off tasks—like drafting, summarizing, brainstorming, or answering questions. The next phase of AI use is broader and more embedded in day-to-day work. Instead of helping with isolated moments, AI is increasingly being used to support repeatable workflows that depend on shared systems, standard handoffs, consistent outputs, and real-world constraints like timing, accuracy, and process. That’s where workspace agents in ChatGPT fit. They’re designed to be used for repeatable workflows—work you’d otherwise do manually, re-explaining the steps each time, and copying information between tools. Learn more about workspace agents in our blog post. If you’re new to agent building, let’s focus on the core concepts first so when you start building, you’ll know how to set up your workspace…

Creating images with ChatGPT

Creating images with ChatGPT Generate and refine images using clear, descriptive prompts. ChatGPT can generate original images from plain-language prompts. You can iterate quickly—request variations, adjust composition or size, or explore new visual directions—and produce production-ready assets in minutes. This makes it easier to explore concepts, communicate ideas visually, and adapt existing assets for different audiences, formats, or channels. A good image prompt does not need to be long. In most cases, 1–3 clear sentences are enough. The goal is to help ChatGPT understand what the image is, how it should feel, and what it needs to accomplish. In practice, this means grounding the prompt in a few key details: the purpose of the image, the main subject, what is happening, where it takes place, and the desired visual style. If framing, lighting, or specific constraints matter, include those too.…

ChatGPT for marketing teams

ChatGPT for marketing teams Plan campaigns, create content, and analyze performance faster with ChatGPT. Marketing teams often use ChatGPT to move smoothly from idea to brief to assets to launch—and then back again to review what worked. It helps bring scattered inputs into one place, turn them into clear messaging, and draft strong first passes of campaign content. Teams can also generate variations for testing and quickly summarize performance data into practical next steps. The result is less time spent starting from scratch or rewriting drafts, and more time focused on strategy, creativity, and execution. - Helps you think more clearly, faster. ChatGPT can take a messy starting point—notes, half-formed ideas, or lots of context—and turn it into a clear direction and next steps. It’s useful at both the beginning of a project, when you’re brainstorming or outlining, and at…

Using projects in ChatGPT

Using projects in ChatGPT Organize your work into dedicated spaces with shared context and history. Projects in ChatGPT are dedicated spaces for a specific body of work or area of focus. A project can hold chats, files, instructions, and related context in one place, so you do not need to restate the same background every time you start a new conversation. Projects are especially useful for work that continues over time. Instead of spreading materials across separate chats, you can keep everything together in one place and return to the same context when needed. On some plans, you can also invite other people to collaborate within a project. - Open Projects from the left-hand menu. - Create a new project and give it a name. - You can now add files, set project instructions, or move existing chats into the…

ChatGPT for sales teams

ChatGPT for sales teams Learn how sales teams use ChatGPT to build stronger pipeline and sell more effectively. ChatGPT helps sales teams move faster through the parts of selling that often slow them down—research, prep, follow-up, and deal coordination. It turns messy inputs like account notes, call takeaways, and CRM data into clear outputs such as briefs, emails, and plans. The result is more time for customer conversations and more consistency across outreach, discovery, and deal execution. - Speeds up account and meeting prep without missing the basics. Before a call, reps often pull context from multiple sources. ChatGPT can research accounts, synthesize internal context, highlight gaps, and produce a clear prep brief and follow-up plan. - Makes outreach and follow-up more consistent—and easier to personalize. Good sales writing is specific, concise, and relevant. ChatGPT can draft first-pass emails, call…

AI fundamentals

AI fundamentals Understand the basics of AI, including what it is, how it works, and how it’s used. Welcome! If you’re new to AI, you don’t need a technical background to get started. What helps most is a simple map of the landscape—so you can understand what AI systems can do, how they’re packaged, and how to choose the right tool for your needs. Artificial intelligence (AI) is a broad category of software that can recognize patterns, learn from data, and produce useful outputs. You’ve probably seen AI show up in everyday moments, like when: - Your map app reroutes you around traffic - Your bank flags a purchase as “unusual” - A customer support chatbot answers common questions AI is a category—not one single tool. Within that category are models: trained systems that learn from data and then apply…

Healthcare

Healthcare AI resources for clinical workflows and decision support. This page brings together practical examples of how AI can support day-to-day clinical work. Whether you’re exploring early use cases or supporting teams already deploying AI, these prompts and guides are designed to help you move forward with confidence. Clinicians spend significant time searching for evidence, reconciling guidelines, and documenting care—time that could be spent with patients. ChatGPT for Healthcare is a secure workspace built for hospital providers and designed for HIPAA-compliant use, providing cited answers from trusted medical sources. It can support tasks like drafting clinical documentation, preparing prior authorizations, and summarizing patient information—helping reduce administrative overhead and improve focus on care. The prompt templates below illustrate how clinicians can use ChatGPT for Healthcare in common workflows.

Working with files in ChatGPT

Working with files in ChatGPT Upload and work with files to analyze, edit, and generate content. ChatGPT allows you to upload and work with files directly in your conversations. This means you can analyze spreadsheets, edit documents, summarize PDFs, or work with images without leaving your chat. - Start a chat with ChatGPT. - Upload your file by opening the tools menu and selecting “Add photos or files” (supported formats include CSV, XLSX, PDF, DOCX, JPEG, PNG, TXT, and more). 3. Ask a question or give a task, for example: - “Summarize the main findings in this report and call out any risks or open questions.” - “Visualize this sales data by region and highlight the biggest changes month over month.” - “Rewrite this document to be clearer and more concise, while keeping the same tone.” - “Extract the key…

Using custom GPTs

Using custom GPTs Build purpose-built ChatGPT assistants that follow your instructions, use your context, and streamline repeatable work. Some versions of ChatGPT let you build custom GPTs—purpose-built versions of ChatGPT designed for a specific task or workflow. Instead of starting from a blank chat each time, a custom GPT can follow your preferred format, use your team’s context, and produce more consistent outputs—whether you’re drafting content, analyzing recurring datasets, generating visuals, or answering common questions. Custom GPTs are powered by tailored instructions that define how the GPT behaves. You can also add knowledge (files you upload) and enable tools (such as web search, data analysis, or connected actions). The result: less re-explaining, less copy/pasting, and fewer “wait—what’s the context again?” moments. You can explore custom GPTs here(opens in a new window). A regular chat is well-suited for quick, one-off tasks—brainstorming…

Financial services

Financial services Explore resources to evaluate, deploy, and scale AI in regulated financial environments. This page brings together essential resources to help financial institutions evaluate, adopt, and scale AI in regulated environments. Whether you’re exploring early use cases or supporting teams already deploying AI, these tools, guides, and examples are designed to help you move forward with confidence. All resources are tailored specifically for the needs of banks, asset managers, insurers, and other financial services organizations. Learn more about OpenAI for Financial Services. A curated set of ready-to-use prompts vetted for day-to-day financial services work, including: - Data analysis and financial modeling - Research, search, and synthesis - Policy, tax, and regulatory interpretation - Contract, covenant, and document analysis - Data extraction and support for Excel, BI, and ERP workflows These prompts are built to accelerate time-to-value while maintaining clarity,…

Analyzing data with ChatGPT

Analyzing data with ChatGPT Explore, analyze, and turn data into clear insights and actions. Loading… ChatGPT can help you move from raw data to useful insights with minimal setup. You can upload a CSV or Excel file, paste in a table, or connect a data source (if supported in your workspace), then start asking questions in plain language. Instead of building formulas, pivot tables, or dashboards for every question, you can quickly explore data, clean up tables, generate simple visualizations, and extract key takeaways in a format that's easy to share. It’s especially useful early in the process—when you’re still figuring out what’s in the data, identifying anomalies, and deciding where to dig deeper. It also helps translate findings into summaries others can review and act on. - Start with the decision you’re trying to support. A simple frame is:…

ChatGPT for managers

ChatGPT for managers Prepare for conversations and manage team work more effectively with ChatGPT. People management is a series of high-stakes moments: 1:1s, feedback, hiring decisions, performance cycles, team updates, and hard conversations. Much of the work is preparation and follow-through—capturing what you heard, deciding what to do next, and communicating clearly. ChatGPT can help with the time-consuming, repetitive parts such as organizing notes, drafting first-pass messages, and creating reusable templates for recurring tasks like 1:1 agendas, interview kits, onboarding plans, and performance documentation. It doesn’t replace your judgment or responsibility to follow HR or legal policy, but it helps you get past the blank page and move faster. - Prepare for conversations without overthinking them. You know what needs to be addressed, but planning how to approach the conversation takes time—how to be direct, which examples to use, and…

ChatGPT for operations teams

April 10, 2026 OpenAI AcademyChatGPT for operations teams Bring structure and clarity to operational work with ChatGPT. Operations teams sit at the intersection of information and execution. ChatGPT behaves like an always-on chief of staff. It reduces coordination friction by turning fragmented inputs into decision-ready summaries, documenting outcomes as reusable SOPs, and reinforcing the operating rhythm with consistent updates and artifacts. The result is less time stitching information together and more time driving execution. Why operations teams use ChatGPT - Helps you turn scattered inputs into a clear set of next steps. Operational work often pulls from many sources—notes, trackers, messages, and updates. ChatGPT helps organize this into a simple structure: what’s known, what’s unclear, what needs a decision, and who’s responsible. - Makes status updates clear enough that people stop asking the same questions. Status updates often stall because…

ChatGPT for customer success teams

ChatGPT for customer success teams Manage accounts, improve communication, and drive better customer outcomes. Customer success work blends relationship management with operational follow-through—onboarding, adoption, troubleshooting, renewals, and cross-functional coordination. The challenge is often the overhead including pulling context from calls and tickets, turning notes into plans, writing clear follow-ups, and keeping everyone aligned on next steps. ChatGPT helps reduce that overhead by turning scattered inputs into clear, structured outputs so teams can focus more on customers and less on coordination. - Turns scattered customer context into a clear plan. CSMs often have the information—they just don’t have it in one place. ChatGPT can synthesize notes, emails, and product signals into a simple view of goals, current state, risks, and a concrete action plan you can share internally and with the customer. - Makes customer communication clearer and easier to act…

Research with ChatGPT

Research with ChatGPT Use search and deep research to find, analyze, and synthesize information from across the web. ChatGPT can be a helpful research partner because it quickly brings together information from many sources, making it easier to explore ideas, spot patterns, and understand complex topics. By reasoning through context, citing sources, and producing clear, structured summaries, it helps turn open questions into well-defined insights. There are two different ways to search the public internet with ChatGPT—search and deep research. Below is an explanation of both, and when to use each. ChatGPT search allows ChatGPT to pull in the latest information from the internet directly into your conversations. This means you can go beyond ChatGPT’s built-in training knowledge and get up-to-date answers on things like current events, market trends, competitor activity, or niche details not included in its training data.…

Responsible and safe use of AI

Responsible and safe use of AI Learn best practices for using ChatGPT safely and effectively. AI is a transformative new technology that is reshaping knowledge work. The large language models (LLMs) that power ChatGPT are trained on vast amounts of publicly available text and other data to predict and generate human-like language. This enables them to assist with tasks such as drafting, summarizing, brainstorming, and answering questions, helping people work more efficiently and creatively. As this technology continues to evolve, it is important to use AI responsibly. These models may sometimes produce incorrect information or be misused if their outputs are applied without care. OpenAI’s mission is to ensure that artificial general intelligence (AGI) benefits all of humanity, and achieving this goal requires safe and thoughtful use by everyone. The tips on this page are designed to help anyone using…

Personalizing ChatGPT

Personalizing ChatGPT Customize ChatGPT’s behavior with instructions and memory to fit your needs. ChatGPT works best when you treat it less like a search box and more like a collaborator. It’s a new kind of tool—one that responds in a conversational way, can take on a “personality,” and adapts based on the guidance you give it. The more context and direction you provide, the more useful (and consistent) it becomes. In this section, you’ll learn two simple ways to personalize ChatGPT so it behaves more like a reliable teammate: Custom instructions and Memory. Custom instructions tell ChatGPT what it should know about you and how you prefer it to respond. These settings apply to new conversations until you change, disable, or remove them. Even small details can meaningfully improve results, such as: - Your role and responsibilities (“I lead customer…

Writing with ChatGPT

Writing with ChatGPT Draft, revise, and refine written work with clarity and intent. ChatGPT can support many common workplace writing tasks: drafting from scratch, rewriting and tightening, adjusting tone for a specific audience, and turning rough notes into clear communication. It’s especially useful when you’re short on time, staring at a blank page, or trying to land the right level of polish. Tip: ChatGPT can work with uploaded files, or access files via connected apps. Learn more here. Most workplace writing has the same goal: help someone understand something quickly and know what to do next. ChatGPT can speed up the parts that often take the most time—finding a strong opener, organizing ideas, and refining wording—so you can focus on the decisions and details that matter. It is also effective for adapting tone across audiences. You can take the same…

ChatGPT for finance teams

ChatGPT for finance teams Improve reporting, streamline planning, and communicate insights more clearly. Finance teams spend a lot of time turning incomplete inputs into something reliable—reconciling numbers, explaining variances, updating forecasts, and responding to business questions. The challenge is often the overhead such as organizing context, drafting narratives, and maintaining consistency across recurring work. ChatGPT helps reduce that overhead by structuring messy inputs, drafting first-pass outputs, and standardizing common workflows. It doesn’t replace finance judgment, but it reduces time spent on formatting, rewriting, and starting from scratch. - Helps you organize the work before you write or build. When you’re reviewing a spreadsheet export, a set of notes, and different explanations from stakeholders, the hardest part is often structuring the problem. ChatGPT can help you outline the questions to answer, the drivers to test, and the follow-ups to request—so you…

Using skills

Using skills Create reusable workflows that guide ChatGPT through recurring tasks. Skills turn the way you already work into reusable workflows that ChatGPT can follow consistently—so you spend less time re-explaining steps, formats, and requirements, and more time getting to a solid result. If you’ve ever found yourself reusing the same prompt or pasting the same template again and again, skills are designed to fix that. A skill is a reusable, shareable workflow that tells ChatGPT how to do a specific task. Rather than starting from scratch each time, you define the process once so it can be applied reliably whenever the task comes up. A skill typically includes: - Name and description: Help ChatGPT recognize when the skill is relevant. - Workflow instructions: Step-by-step guidance for the worflow—usually written in a file called SKILL.md. - Resources: Supporting materials the…

Getting started with ChatGPT

Getting started with ChatGPT Learn the basics of using ChatGPT and how to begin your first conversation. ChatGPT is a conversational AI assistant that helps you think, write, and solve problems by understanding natural language and generating human-like responses in real time. ChatGPT is built on large language models, enabling it to assist with a wide range of tasks. Learn more about large language models in What is AI. Take a look at the video below to learn about the different parts of the ChatGPT interface. Open ChatGPT.(opens in a new window) A new chat is already waiting for you. To get started, simply enter a prompt. A prompt is the question or instruction you type or share with ChatGPT to start a conversation. It is usually text, but it can also be an image, audio, file. Your prompt guides…

ChatGPT for research

ChatGPT for research Use ChatGPT to move from questions to evidence-backed insights and decisions. Researching with ChatGPT helps you move from question to evidence to decision more quickly. You can use it to gather and synthesize information, compare sources, and produce structured reports that include citations—so your output is easier to trust and easier to share. It’s useful for both quick orientation and for deeper, multi-step investigations. Why use ChatGPT for research? - Turn a fuzzy question into a clear research plan and set of sub-questions. - Sift through many sources faster and capture the important details with citations. - Produce consistent deliverables such as briefs, memos, competitor tables, annotated bibliographies. - Identify gaps, contradictions, and weak signals early—before committing to a direction. ChatGPT offers two main approaches for research, depending on how deep you need to go: Search is…

Prompting fundamentals

Prompting fundamentals Learn how to write clear prompts to get better, more useful responses. Prompt engineering is the process of designing and refining your input in a way that helps ChatGPT give the best possible answer. It’s about figuring out how to ask so you get the result you want—whether that’s a clear summary, comprehensive report, or detailed analysis. ChatGPT works best when you give it clear instructions. There’s no single “perfect” way to write a prompt. Think of it as a conversation with a colleague, where you might need to adjust your phrasing or tone to help them understand what you need. Experimentation and iteration are the best ways to discover how AI can be most useful to you. Be clear about what you need ChatGPT to do. Outline what you want, who it’s for, and why it matters.…

Brainstorming with ChatGPT

Brainstorming with ChatGPT Generate ideas, organize thinking, and turn direction into actionable plans. ChatGPT can act as a structured thought partner. It helps you generate options quickly, organize ideas into clearer themes, and turn a rough direction into a plan you can execute. It’s especially useful when you’re starting from a blank page, working through many competing ideas, or creating a “first pass” before you bring others in. It won’t replace your context, expertise, or judgment—but it can make the thinking process faster, more consistent, and easier to share. Most brainstorming gets stuck in one of two places: not enough ideas, or too many ideas with no structure. ChatGPT helps by doing three things well: - Expands your option set: It can propose angles, experiments, messages, and alternatives quickly so you’re not starting from scratch. - Adds structure: It can…

How to make remarkable videos with Seedance 2.0

How to make remarkable videos with Seedance 2.0 Run Seedance 2.0 AI video used to be utterly bad. (We’ve all seen Will Smith eat spaghetti more times than we can count, so I’ll spare you.) Last year, however, we really began to see AI video take off with front-runners like Google’s Veo 3 series and Kling from Kuaishou. With each new model release, we inched toward improvements with prompt adherence, audio integration, and solving the “AI look.” Seedance 2.0 is the largest step change we’ve seen in months. You can make movies with this thing. A catastrophic collision between two massive space stations in low Earth orbit. Metal shears apart in slow motion as the stations grind into each other, sending a hailstorm of debris spiraling outward. Entire modules crumple like tin cans. Pressurized compartments blow out in violent bursts…

Quoting Maggie Appleton

23rd April 2026 [...] if you ever needed another reason to learn in public by digital gardening or podcasting or streaming or whathaveyou, add on that people will assume you’re more competent than you are. This will get you invites to very cool exclusive events filled with high-achieving, interesting people, even though you have no right to be there. A+ side benefit. — Maggie Appleton, Gathering Structures (via) Recent articles - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026 - Extract PDF text in your browser with LiteParse for the web - 23rd April 2026 - A pelican for GPT-5.5 via the semi-official Codex backdoor API - 23rd April 2026

Join us at PyCon US 2026 in Long Beach - we have new AI and security tracks this year

Join us at PyCon US 2026 in Long Beach—we have new AI and security tracks this year 17th April 2026 This year’s PyCon US is coming up next month from May 13th to May 19th, with the core conference talks from Friday 15th to Sunday 17th and tutorial and sprint days either side. It’s in Long Beach, California this year, the first time PyCon US has come to the West Coast since Portland, Oregon in 2017 and the first time in California since Santa Clara in 2013. If you’re based in California this is a great opportunity to catch up with the Python community, meet a whole lot of interesting people and learn a ton of interesting things. In addition to regular PyCon programming we have two new dedicated tracks at the conference this year: an AI track on Friday…

Gemini 3.1 Flash TTS

15th April 2026 - Link Blog Gemini 3.1 Flash TTS. Google released Gemini 3.1 Flash TTS today, a new text-to-speech model that can be directed using prompts. It's presented via the standard Gemini API using gemini-3.1-flash-tts-preview as the model ID, but can only output audio files. The prompting guide is surprising, to say the least. Here's their example prompt to generate just a few short sentences of audio: # AUDIO PROFILE: Jaz R. ## "The Morning Hype" ## THE SCENE: The London Studio It is 10:00 PM in a glass-walled studio overlooking the moonlit London skyline, but inside, it is blindingly bright. The red "ON AIR" tally light is blazing. Jaz is standing up, not sitting, bouncing on the balls of their heels to the rhythm of a thumping backing track. Their hands fly across the faders on a massive…

datasette PR #2689: Replace token-based CSRF with Sec-Fetch-Site header protection

14th April 2026 - Link Blog datasette PR #2689: Replace token-based CSRF with Sec-Fetch-Site header protection. Datasette has long protected against CSRF attacks using CSRF tokens, implemented using my asgi-csrf Python library. These are something of a pain to work with - you need to scatter forms in templates with <input type="hidden" name="csrftoken" value="{{ csrftoken() }}"> lines and then selectively disable CSRF protection for APIs that are intended to be called from outside the browser. I've been following Filippo Valsorda's research here with interest, described in this detailed essay from August 2025 and shipped as part of Go 1.25 that same month. I've now landed the same change in Datasette. Here's the PR description - Claude Code did much of the work (across 10 commits, closely guided by me and cross-reviewed by GPT-5.4) but I've decided to start writing these…

Ordering with the Starbucks ChatGPT app was a true coffee nightmare

Venti iced coffee, light skim milk. That’s what I get at Starbucks. It is what I have gotten at Starbucks every time I’ve been to Starbucks for as long as I can remember, other than a brief love affair with the caffe misto a few years ago. In person, my brain barely needs to activate to say the words aloud; in the app, it’s four taps and I’m ready to go. Ordering with the Starbucks ChatGPT app was a true coffee nightmare Ordering coffee is easy. Besting the Starbucks app with AI chat is going to be very, very hard. Ordering coffee is easy. Besting the Starbucks app with AI chat is going to be very, very hard. My first time ordering Starbucks through its new ChatGPT integration, which launched last week, was comparatively a complete mess. Getting started is…

Apple’s Next Chapter, SpaceX and Cursor Strike a Deal, and Palantir’s Controversial Manifesto

This week on Uncanny Valley, the team discusses what’s next for Apple as Tim Cook steps down from his role as CEO. They also go into the reasoning behind SpaceX and Cursor’s surprising deal, and why Palantir’s self-published manifesto drew a lot of heat online. Also, we discuss why some conspiracy theorists are leaving Trump’s side, and how a scammer created an AI-generated woman to attract and grift MAGA men. Articles mentioned in this episode: - Tim Cook’s Legacy Is Turning Apple Into a Subscription - MAGA Is Starting to Look Beyond Trump - This Scammer Used an AI-Generated MAGA Girl to Grift ‘Super Dumb’ Men You can follow Brian Barrett on Bluesky at @brbarrett, Zoë Schiffer on Bluesky at @zoeschiffer, and Leah Feiger on Bluesky at @leahfeiger. Write to us at [email protected]. How to Listen You can always…

Join Our Livestream: Musk v. Altman and the Future of OpenAI

Two of Big Tech’s most influential billionaires, Sam Altman and Elon Musk, will go head-to-head in a highly anticipated trial beginning April 27. In Musk v. Altman, a judge, advised by a jury, will ultimately determine whether OpenAI has strayed from its founding mission to ensure that artificial general intelligence (AGI) benefits humanity, and the ruling could influence how the world’s leading AI developer controls and distributes its technology. For now, you can learn more about the trial here. On May 8, a panel of WIRED experts will go live to answer your questions about this consequential case. - Zoë Schiffer: WIRED's director of business and industry, who oversees coverage of business and Silicon Valley. - Maxwell Zeff: a senior writer at WIRED covering the business of artificial intelligence. He writes the weekly Model Behavior newsletter, which focuses on the…

Tech CEOs Think AI Will Let Them Be Everywhere at Once

Silicon Valley moguls have lately complained that too many people are too negative about artificial intelligence. They’re likewise frustrated by stalled AI adoption among major corporations that aren’t seeing the lucrative efficiencies promised by Big Tech. But if consumers and corporations are proving resistant to AI’s acceleration, it hasn’t stopped billionaire CEOs from charging ahead with their personal fantasies of what the technology can do. On April 13, the Financial Times reported that Meta is working up a photorealistic, three-dimensional AI avatar of chief exec Mark Zuckerberg, according to several people at the company. Trained on his public comments, mannerisms, and up-to-date perspectives on corporate strategy, the bot is being designed to interact with Meta staff on Zuckerberg’s behalf. Employees would supposedly be able to hop on a video chat with the avatar, which could answer questions and offer managerial…