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to scale quality by increasing recurrence rather than purely scaling data, opening up an efficient frontier for training memory-constrained on-device models. Traditional scaling laws tell us that to achieve the best performance, we need to scale FLOPs, often with more parameters or data. But as models move to the edge and inference costs skyrocket, we wonder: Can we scale quality without inflating memory footprint? To that end, we’ve been exploring looped architectures, models that increase com

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s ways to do this are individual empowerment and making sure democratic system stays in control. It is important that the democratic process remains more powerful than companies. Laws and norms are going to change, but we have to work within the democratic process, even though it will be messy and slower than we’d like. We want to be a voice and a stakeholder, but not to have all the power. A lot of the criticism of our industry comes from sincere concern about the incredibly high stakes of this

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ors, we need to quantify it. In our recent paper , we introduce ConvApparel , a new dataset of human-AI conversations designed to do exactly that. ConvApparel exposes the hidden flaws in today’s user simulation and provides a path towards building AI-based testers we can trust. To capture the full spectrum of human behavior — from satisfaction to profound annoyance — we employed a unique dual-agent data collection protocol where participants were randomly routed to either a helpful "Good" agent

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atch() — creates a minimal empty image, useful as a lightweight filesystem to mount into a Sandbox; Sandboxes now accept include oidc identity token=True for OIDC-based auth (e.g. AWS federation); Finally, we’re introducing a few breaking changes and beginning to enforce some deprecations of pre-1.0 APIs, including the removal of backwards compatibility for the old autoscaler configuration (keep warm, concurrency limit, etc.), removal of vestigial namespace parameters, and more 📖 Content Roundup

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nBench thereby converts the optimization task from a statistical computation into a semantic reasoning problem, in which the LLM analyzes the plan's topology to identify logical flaws in join ordering. To implement these fixes safely, we instruct the LLM to avoid regenerating an entire physical plan from scratch; instead, it applies targeted edits to DataFusion’s existing physical plan, reducing the risk of syntax errors or invalid plan topology. Concretely, the LLM generates JSON Patches (RFC 6

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onal signage is presented in Appendix 3. Page 26. EAZA Best Practice Guidelines. Asian giant forest tortoise (Manouria emys). Date: Dec 4, 2025 [64] https://s3-us-gov-west-1.amazonaws.com/cg-654ebf73-8576-4082-ba73-dd1f1a7fe8dc/uploads/USTDA-Indo-Pacific-Resource-Guide-EDIT 0.pdf Title: [PDF] USTDA Indo Pacific Resources Guide EDIT 0.pdf - Amazon S3 Snippet: Sabah Oil and Gas Development Corporation Sdn Bhd (SOGDC) is a wholly-owned company of the state of Sabah designated as the vehicle to own,

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o be accelerating. We don't have access to the internals of companies. But every now and then something slips through to some news reporter. Like this supposed AI caused outage at AWS. Which AWS immediately "corrected". Only to then follow up internally with a 90-day reset. Satya Nadella, the CEO of Microsoft, has been going on about how much code is now being written by AI at Microsoft. While we don't have direct evidence, there sure is a feeling that Windows is going down the shitter. Microsof

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paring realistic RL sandbox workloads against AMD Zen 5, Vera with SMT delivers, on average, 30% greater throughput per CPU. For this comparison, the AMD Zen 5 baseline was run on AWS, where only physical cores are enabled. For memory, we ran a dynamic load latency test designed to simulate the demands of a system running many sandboxes concurrently. The results demonstrate two key strengths: Vera makes full use of its memory bandwidth with relatively few streaming cores, and memory latency stay

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between the original and ablated outputs. To discover influential interactions with a tractable number of ablations, we have developed SPEX (Spectral Explainer). This framework draws on signal processing and coding theory to advance interaction discovery to scales orders of magnitude greater than prior methods. SPEX circumvents this by exploiting a key structural observation: while the number of total interactions is prohibitively large, the number of influential interactions is actually quite

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nd Transparent Audit Trail: A developer can command LocalCowork to scan a folder for leaked secrets. The model immediately uses the security scanner to identify credentials (e.g., AWS, Stripe API keys) and logs all actions. A subsequent request for the "audit trail" triggers a call to a different server, which rapidly provides a complete log of the scan, including what was found and when. This showcases successful, sub-second chaining of two tool calls across two distinct Model Context Protocol

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reimplementing all of the features of Slay the Spire and Balatro. The first run for both agents didn’t yield an impressive result – the Slay the Spire implementation had obvious flaws around game progression, and the Balatro implementation had a broken user interface that made it unplayable. Appendix: Token Usage Table 1: Token usage for the Slay the Spire run. Table 2: Token usage for the Balatro run. The token usage adds up to around $20 for the Slay the Spire run, and around $6 for the Balatr

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dition, they included this “FAQ”: What if the government just changes the law or existing DoW policies? Our contract explicitly references the surveillance and autonomous weapons laws and policies as they exist today, so that even if those laws or policies change in the future, use of our systems must still remain aligned with the current standards reflected in the agreement. In an “AMA” (Ask Me Anything) on x.com, OpenAI CEO Sam Altman was asked about this by user @deredleritt3r: …could you ple

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prompt and settings are in our open-source agent at github.com/browser-use/browser-use. We are moving toward a fully closed loop: an automated infinite self-improvement cycle where the agent evaluates itself, finds its own flaws, writes its own patches, and proves its success statistically. To see how these models actually compare, check out our definitive model benchmark. For the full technical report on our earlier WebVoyager results, see our SOTA technical report. Published February 23, 2026

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o an existing product, you use the APIs to call a foundation model running in the cloud and the users don’t know or care what model you used. No-one using Snap cares if it runs on AWS or GCP. When you buy an enterprise SaaS product you don’t care if it uses AWS or Azure. And if I do a Google Search and the first match is a product that’s running on Google Cloud, I would never know. That doesn’t mean these APIs are interchangeable - there are good reasons why AWS, GCP and Azure have very differen

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s deployed against it. If RL scaling is log-linear and broadly applicable, if there’s no secret sauce, just scale and data and well-designed reward signals, then the same scaling laws apply to specialised fine-tuning. An open-source model fine-tuned with RL on domain-specific tasks benefits from exactly the same log-linear improvements that frontier labs see on their general RL training. The algorithms aren’t proprietary. The scaling behaviour isn’t proprietary. The only proprietary ingredient i

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uilding. The shop appears to be a local bookstore or library. A low resource Indic Language output The image depicts a busy street scene in an urban area. There are two auto-rickshaws, one black and one green, driving on the road. Several people are walking on the street, and there are various shops and buildings on either side of the street. The sky is blue with some clouds. For the above image, the model was prompted to describe the scene in Santhali (a low-resource Indian language). Instructi

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I, and others are governed by safety and security obligations under California’s SB 53, New York’s RAISE Act, Illinois’ SB 315, and the frontier AI part of the EU’s AI Act. These laws establish incident reporting requirements, model evaluation standards, safety and security mitigations, internal governance practices, and whistleblower protections. This document summarizes key provisions from these laws, though it is not a substitute for the official legal text. Overview California’s SB 53 applie

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a metric that reflects whether models can truly remember user-constructed characters’ relationships; Knowledge: We ensure the model adheres to the immutable physical and magical laws of the specific setting Diversity: We detect single-pattern phrasing, repetitive plot beats, stagnation, and low-information filler; Content Logic: It measures narrative coherence and OOC (out-of-character) breaks AI Speaks for User: Reflects whether the model oversteps boundaries; AI Ignores User: Captures whether

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subtle reproducibility bugs; Operational overhead and idle time: Scaling inference would mean manual orchestration, days of setup, and paying for idle clusters “I used to wrangle AWS, Google Cloud, and Azure, but when I was dealing with raw instances and volumes I frequently felt like I was operating at the wrong level of abstraction. With Modal, building infrastructure has shifted from being an imperative task to a declarative one. You add a few lines of code specifying what you want to do and

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Modal runs a globally distributed, autoscaling GPU worker pool by sourcing compute from all cloud giants: AWS, GCP, Azure, OCI. We’ve scaled the worker pool to well over 20,000 concurrent GPUs, and launched over four million cloud instances in the last couple years. At this scale, you see almost every GPU reliability problem there is. Today, we’re sharing our GPU reliability system as both a demonstration of our commitment to Modal customers and as a guide for fellow travelers renting hyperscale

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