📰

New Releases

Latest AI product launches and version updates

📰new-releases

Large model inference container – latest capabilities and performance enhancements

AWS recently released significant updates to the Large Model Inference (LMI) container, delivering comprehensive performance improvements, expanded model support, and streamlined deployment capabilities for customers hosting LLMs on AWS. These releases focus on reducing operational complexity while delivering measurable performance gains across popular model architectures.

Dmitry SoldatkinFeb 26, 2026
📰new-releases

Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)

We’re excited to announce the availability of Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 through Amazon Bedrock global cross-Region inference for customers operating in the Middle East. In this post, we guide you through the capabilities of each Anthropic Claude model variant, the key advantages of global cross-Region inference including improved resilience, real-world use cases you can implement, and a code example to help you start building generative AI applications immediately.

Hossam BasudanFeb 24, 2026
📰new-releases

Supercharge regulated workloads with Claude Code and Amazon Bedrock

The release of Anthropic Claude Sonnet 4.5 in the AWS GovCloud (US) Region introduces a straightforward on-ramp for AI-assisted development for workloads with regulatory compliance requirements. In this post, we explore how to combine Claude Sonnet 4.5 on Amazon Bedrock in AWS GovCloud (US) with Claude Code, an agentic coding assistant released by Anthropic. This […]

Bradley WymanFeb 16, 2026
📰new-releases

Customize AI agent browsing with proxies, profiles, and extensions in Amazon Bedrock AgentCore Browser

Today, we are announcing three new capabilities that address these requirements: proxy configuration, browser profiles, and browser extensions. Together, these features give you fine-grained control over how your AI agents interact with the web. This post will walk through each capability with configuration examples and practical use cases to help you get started.

Joshua SamuelFeb 13, 2026
📰new-releases

What’s next for Chinese open-source AI

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. The past year has marked a turning point for Chinese AI. Since DeepSeek released its R1 reasoning model in January 2025, Chinese companies have repeatedly delivered AI…

Caiwei ChenFeb 12, 2026
📰new-releases

NVIDIA Nemotron 3 Nano 30B MoE model is now available in Amazon SageMaker JumpStart

Today we’re excited to announce that the NVIDIA Nemotron 3 Nano 30B model with  3B active parameters is now generally available in the Amazon SageMaker JumpStart model catalog. You can accelerate innovation and deliver tangible business value with Nemotron 3 Nano on Amazon Web Services (AWS) without having to manage model deployment complexities. You can power your generative AI applications with Nemotron capabilities using the managed deployment capabilities offered by SageMaker JumpStart.

Dan FergusonFeb 11, 2026
📰new-releases

Structured outputs on Amazon Bedrock: Schema-compliant AI responses

Today, we're announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples.

Jeffrey ZengFeb 6, 2026
📰new-releases

GeForce NOW Brings GeForce RTX Gaming to Linux PCs

Get ready to game — the native GeForce NOW app for Linux PCs is now available in beta, letting Linux desktops tap directly into GeForce RTX performance from the cloud. Alongside the expansion comes ten new games, including The Bard’s Tale IV: Director’s Cut and The Bard’s Tale Trilogy for a leveled-up gaming weekend. And Read Article

GeForce NOW CommunityJan 29, 2026
📰new-releases

Retiring GPT-4o, GPT-4.1, GPT-4.1 mini, and OpenAI o4-mini in ChatGPT

On February 13, 2026, alongside the previously announced retirement⁠ of GPT‑5 (Instant, Thinking, and Pro), we will retire GPT‑4o, GPT‑4.1, GPT‑4.1 mini, and OpenAI o4-mini from ChatGPT. In the API, there are no changes at this time.

OpenAIJan 29, 2026
📰new-releases

Announcing OpenAI Grove Cohort 2

Applications are now open for OpenAI Grove Cohort 2, a 5-week founder program designed for individuals at any stage, from pre-idea to product. Participants receive $50K in API credits, early access to AI tools, and hands-on mentorship from the OpenAI team.

OpenAIJan 2, 2026
📰new-releases

2025 LLM Year in Review

2025 has been a strong and eventful year of progress in LLMs. The following is a list of personally notable and mildly surprising "paradigm changes" - things that altered the landscape and stood out to me conceptually. 1. Reinforcement Learning from Verifiable Rewards (RLVR) At the start of 2025, the LLM production stack in all labs looked something like this: Pretraining (GPT-2/3 of ~2020) Supervised Finetuning (InstructGPT ~2022) and Reinforcement Learning from Human Feedback (RLHF ~2022) This was the stable and proven recipe for training a production-grade LLM for a while. In 2025, Reinforcement Learning from Verifiable Rewards (RLVR) emerged as the de facto new major stage to add to this mix. By training LLMs against automatically verifiable rewards across a number of environments (e.g. think math/code puzzles), the LLMs spontaneously develop strategies that look like "reasoning" to humans - they learn to break down problem solving into intermediate calculations and they learn a number of problem solving strategies for going back and forth to figure things out (see DeepSeek R1 paper for examples). These strategies would have been very difficult to achieve in the previous paradigms because it's not clear what the optimal reasoning traces and recoveries look like for the LLM - it has to find what works for it, via the optimization against rewards. Unlike the SFT and RLHF stage, which are both relatively thin/short stages (minor finetunes computationally), RLVR involves training against objective (non-gameable) reward functions which allows for a lot longer optimization. Running RLVR turned out to offer high capability/$, which gobbled up the compute that was originally intended for pretraining. Therefore, most of the capability progress of 2025 was defined by the LLM labs chewing through the overhang of this new stage and overall we saw ~similar sized LLMs but a lot longer RL runs. Also unique to this new stage, we got a whole new knob (and and associated scaling

karpathyDec 19, 2025
📰new-releases

Addendum to GPT-5.2 System Card: GPT-5.2-Codex

This system card outlines the comprehensive safety measures implemented for GPT‑5.2-Codex. It details both model-level mitigations, such as specialized safety training for harmful tasks and prompt injections, and product-level mitigations like agent sandboxing and configurable network access.

OpenAIDec 18, 2025
📰new-releases

Introducing OpenAI Academy for News Organizations

OpenAI is launching the OpenAI Academy for News Organizations, a new learning hub built with the American Journalism Project and The Lenfest Institute to help newsrooms use AI effectively. The Academy offers training, practical use cases, and responsible-use guidance to support journalists, editors, and publishers as they adopt AI in their reporting and operations.

OpenAIDec 17, 2025
📰new-releases

Measuring AI’s capability to accelerate biological research

OpenAI introduces a real-world evaluation framework to measure how AI can accelerate biological research in the wet lab. Using GPT-5 to optimize a molecular cloning protocol, the work explores both the promise and risks of AI-assisted experimentation.

OpenAIDec 16, 2025
📰new-releases

Advancing science and math with GPT-5.2

GPT-5.2 is OpenAI’s strongest model yet for math and science, setting new state-of-the-art results on benchmarks like GPQA Diamond and FrontierMath. This post shows how those gains translate into real research progress, including solving an open theoretical problem and generating reliable mathematical proofs.

OpenAIDec 11, 2025
📰new-releases

Introducing GPT-5.2

GPT-5.2 is our most advanced frontier model for everyday professional work, with state-of-the-art reasoning, long-context understanding, coding, and vision. Use it in ChatGPT and the OpenAI API to power faster, more reliable agentic workflows.

OpenAIDec 11, 2025
📰new-releases

Update to GPT-5 System Card: GPT-5.2

GPT-5.2 is the latest model family in the GPT-5 series. The comprehensive safety mitigation approach for these models is largely the same as that described in the GPT-5 System Card and GPT-5.1 System Card. Like OpenAI’s other models, the GPT-5.2 models were trained on diverse datasets, including information that is publicly available on the internet, information that we partner with third parties to access, and information that our users or human trainers and researchers provide or generate.

OpenAIDec 11, 2025
📰new-releases

Auto-grading decade-old Hacker News discussions with hindsight

TLDR: https://karpathy.ai/hncapsule/ Yesterday I stumbled on this HN thread Show HN: Gemini Pro 3 hallucinates the HN front page 10 years from now, where Gemini 3 was hallucinating the frontpage of 10 years from now. One of the comments struck me a bit more though - Bjartr linked to the HN frontpage from exactly 10 years ago, i.e. December 2015. I was reading through the discussions of 10 years ago and mentally grading them for prescience when I realized that an LLM might actually be a lot better at this task. I copy pasted one of the article+comment threads manually into ChatGPT 5.1 Thinking and it gave me a beautiful analysis of what people thought + what actually happened in retrospect, even better and significantly more detailed than what I was doing manually. I realized that this task is actually a really good fit for LLMs and I was looking for excuses to vibe code something with the newly released Opus 4.5, so I got to work. I'm going to get all the front pages of December (31 days, 30 articles per day), get ChatGPT 5.1 Thinking to do the analysis, and present everything in a nice way for historical reading. There are two macro reasons for why I think the exercise is interesting more generally: I believe it is quite possible and desirable to train your forward future predictor given training and effort. I was reminded again of my tweets that said "Be good, future LLMs are watching". You can take that in many directions, but here I want to focus on the idea that future LLMs are watching. Everything we do today might be scrutinized in great detail in the future because doing so will be "free". A lot of the ways people behave currently I think make an implicit "security by obscurity" assumption. But if intelligence really does become too cheap to meter, it will become possible to do a perfect reconstruction and synthesis of everything. LLMs are watching (or humans using them might be). Best to be good. Vibe coding the actual project was relatively painless and to

karpathyDec 10, 2025
📰new-releases

Early experiments in accelerating science with GPT-5

OpenAI introduces the first research cases showing how GPT-5 accelerates scientific progress across math, physics, biology, and computer science. Explore how AI and researchers collaborate to generate proofs, uncover new insights, and reshape the pace of discovery.

OpenAINov 20, 2025
📰new-releases

GPT-5.1-Codex-Max System Card

This system card outlines the comprehensive safety measures implemented for GPT‑5.1-CodexMax. It details both model-level mitigations, such as specialized safety training for harmful tasks and prompt injections, and product-level mitigations like agent sandboxing and configurable network access.

OpenAINov 19, 2025