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Cursor described an “agent sandboxing” system that let local coding agents run freely inside a constrained environment and only asked for approval when leaving the sandbox (often for internet access).
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optimize_anything is a declarative API that optimizes any artifact representable as text. Users declare what to optimize and how to measure it, and the system handles the search. It consistently matches or outperforms domain-specific tools. A surprisingly wide range of problems can be formulated as optimizing a text artifact. If it can be serialized to a string and its quality measured, a large language model can reason about it and propose improvements.
When not using reasoning, repeating the input prompt improves performance for popular models without increasing the number of generated tokens or latency. It is interesting that tricks like this are still possible despite the amount of work being put into improving large language models. The discovery proves how much room for improvement there still is for current models.
Building on Google's Transformer architecture, the authors proposed training sequence-model agents against many different opponents so they learned to adapt within each game without hardcoded assumptions about how others learn.
OpenAI has a big user base, but it has limited engagement and stickiness and no network effect. The company doesn't have any unique technology. The incumbents have already matched the technology and are leveraging their product and distribution. This post takes a look at OpenAI's strategy and how the company can compete in today's landscape.
Prototype with the best and polish small gems. Use teams of agents as micromanagers, and experiment with different tools and workflows. Document everything to create improvement loops that improve success rates without manual intervention. Skills are easier to debug than code.
Sam Altman and Dario Amodei avoided hand-holding at an AI summit, highlighting their tense rivalry.
DuckDuckGo has launched AI-powered image editing on Duck.ai, allowing users to edit images without needing an account, though subscribers have higher daily limits.
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How can you best use AI to build better software? In 2026, this is the question on everyone's mind. At the Sonar Summit virtual conference [4], you'll get to hear answers from some of the most respected voices in tech.
Google announced that the Gemini app integrated Lyria 3, its latest music generation model, enabling users to create 30-second tracks from text or images in beta. The update expanded Gemini's multimodal capabilities into consumer-facing music creation.
World Labs secured $1 billion in funding from investors like AMD, NVIDIA, and Fidelity. They aim to advance spatial intelligence with products like MARBLE, which creates cohesive 3D worlds from images, video, or text.
LangChain's coding agent went from Top 30 to Top 5 on Terminal Bench 2.0 with just a harness change. This post discusses the team's approach to harness engineering. The goal of a harness is to mold the intelligence of a model for tasks you care about. Self-verification and tracing help a lot.
OpenAI published a detailed guide explaining how prompt caching reuses repeated prompt prefixes to skip prefill compute, reducing latency and input token costs. It outlines cache mechanics, routing considerations, KV reuse, and practical strategies for increasing cache hit rates in production workloads.
Ivan Zhao frames AI as the latest “tool of life” after language, markets, and the internet. Humanity uses culture, capital, and computation to impose its values on an indifferent universe, and AI extends this by accelerating idea discovery and coordination. In this view, AI is not separate from life but a continuation of life organizing matter and energy through intelligence.
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Give it a question, and it gets to work: Subagents deeply interrogate your topic, scour a wide range of credible sources, and package it all up into boardroom-ready reports, slides, docs, or websites [12].
EVMbench is a benchmark that evaluates the ability of AI agents to detect, patch, and exploit high-severity smart contract vulnerabilities. Smart contracts routinely secure hundreds of billions of dollars in crypto assets. As AI agents improve, it becomes increasingly important to measure their capabilities in economically meaningful environments. This also means using AI systems to defensively audit and strengthen deployed contracts.
GLM-5 technical report outlines the details of the model development, combining DSA-based efficiency improvements with asynchronous reinforcement learning to decouple generation from training.
Google researchers showed that randomly masking parameter updates can outperform dense adaptive optimizers, with a masked RMSProp variant exceeding recent state-of-the-art methods. Their proposed Magma optimizer uses momentum-aligned gradient masking to improve pre-training efficiency.
Charles Porch, a 15-year Meta and Instagram veteran, has joined OpenAI as Vice President of Global Creative Partnerships. Porch previously led major cultural initiatives at Meta, including high-profile onboardings like Pope Francis and digital strategy for Beyoncé. In his new role, he will focus on the intersection of AI and culture, collaborating with global creative communities to develop products for learning, exploration, and entertainment.
Amical has launched an open-source AI dictation app that offers privacy-focused, on-device speech recognition using models like Whisper. Unique features include context-aware formatting, custom prompts, and local data storage with a cloud fallback option for less capable devices. Available for Mac and Windows, Amical plans to expand to mobile and integrate with broader AI ecosystems.
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Agents don't fail because of prompts, they fail because of systems. Take Temporal's 8-question quiz to see if yours is ready for
David Silver plans to raise $1 billion for Ineffable Intelligence, focusing on reinforcement learning, not incremental LLM updates.
Saudi Arabia's Humain has invested $3 billion into Elon Musk's xAI, acquiring a significant minority stake that converts to SpaceX shares.
Compilers are deterministic, and large language models are stochastic.
Before developing or buying a security system for AI agents, consider what it monitors, what it was trained on, and how well it was tested.
* Discover risks, orchestrate agents, and monitor threats in one place
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You want AI to be more than a never-ending work in progress, and that means enabling no-code, low-code, and pro-code development without IT gatekeepers standing in the way. Yet that doesn't mean governance goes out the window.
Anthropic has released Claude Sonnet 4.6, upgrading coding, computer-use, planning, long-context reasoning, and knowledge-work performance while keeping Sonnet pricing unchanged. Sonnet 4.6 also introduced a 1M-token context window in beta and is now the default model for Free and Pro users in Claude's apps.
NotebookLM has rolled out Prompt-Based Revisions, a feature that lets users tweak, tailor, and tune slides just by prompting the revisions they want. It currently supports PPTX, with Google Slides support coming soon. A video demo of the feature is available in the thread.
Mistral AI agreed to buy serverless deployment startup Koyeb in its first acquisition, positioning Koyeb's platform and team as a core component of Mistral Compute.
Elon Musk recently went on a podcast to discuss alignment, AI data centers in space, robots, China, and more. Musk is very gung-ho about data centers in space, robots, and making his own fabs. He plans to make virtual humans and robots to turn on an 'infinity money glitch'. Otherwise, China will win - they're already more productive.
OpenAI's acquisition of OpenClaw marks a strategic shift from conversational AI to autonomous agents capable of executing tasks. OpenClaw's popularity stemmed from its unrestrained, robust functionality, combining tool access, sandboxed code execution, and integration with messaging platforms. This move signals a new phase for enterprise AI as companies race to develop secure, deployable versions of dynamic AI agents.
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Figma users can now bring work from Claude Code into the platform. They just need to install the Figma MCP, type 'Send this to Figma', and the browser's rendered state will be automatically translated to fully editable Figma layers. Workflows are changing, and it's easy to get lost in the momentum of creation. The new Claude Code to Figma Design integration aims to help designers escape that tunnel vision, zoom out, and explore the big picture.
Cohere Labs released TinyAya-Base (3.35B) and instruction-tuned TinyAya-Global plus regional variants, aiming for balanced quality across ~67 languages on consumer hardware. The drop also included a multilingual fine-tuning dataset, new benchmarks, and a technical report for reproducible multilingual experimentation.
ERL trains policies with an explicit attempt → feedback → reflection → revised attempt loop, then reinforced the successful revision back into the base model. The approach improves sparse-reward learning and tool-using reasoning performance while keeping deployment-time inference cost unchanged.
WebWorld uses a pipeline of 1M+ open-web interactions to simulate long-horizon (30+ step) browsing tasks, paired with a multi-metric WebWorld-Bench for intrinsic evaluation. Trajectories synthesized from the simulator boosted downstream web-agent performance and transferred to other domains like code, GUI, and games.
Cursor added plugin support so agents could connect to external tools and extend capabilities via packaged MCP servers, skills, subagents, rules, and hooks.
Meta plans to use millions of Nvidia chips in its AI data centers. The companies recently announced an expanded partnership to deliver personal superintelligence to everyone in the world. The financial terms of the deal have not been made public. Meta plans to spend up to $135 billion on AI this year.
Domain expertise is appreciating in value because AI can't easily replace it.
Benign outcomes from technological transitions have always been the product of deliberate institutional design: labor law, antitrust enforcement, public education, and social insurance.
Three things to consider when deciding what AI to use: Models, Apps, and Harnesses.
Simile, Anthropic, Runway, Goodfire, Fundamental, ElevenLabs, PaleBlueDot AI, Decagon, Flapping Airplanes, Baseten, Inferact, OpenEvidence, humans&, SkildAI, Deepgram, Arena, and xAI.
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95% of labeled data never gets used [4], and most teams spend 5-7 review cycles before datasets are production-ready.