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is a visual, no-code, AI-native experience for data preparation and analytics. It lets analysts, domain experts, and other less technical users prepare and explore data through a drag-and-drop canvas and natural language. What’s New? Here are some of our favorite enhancements we've made to Lakeflow Designer over the last 4 months. New no-code operators for data cleaning and quality checks Genie Code, Python, and SQL operators have always made Designer flexible. However, we heard from early custo

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ere real-time creates measurable business value; Organizations typically bolt on specialized serving layers when they need to analyze data in real-time, creating duplicated data, fragile pipelines, and fragmented governance; A lakehouse approach delivers millisecond query performance and high concurrency directly on governed data. Learn how to evaluate real-time analytics workloads, see common use cases across industries, and find best practices for building and optimizing real-time analytics at

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L; T; F; R; E AI-Generated Summary NVIDIA cuDF plugin for Apache Spark leverages GPU parallelism to accelerate data analytics workloads and improve total cost of ownership; User-defined functions in Spark break the declarative abstraction, forcing GPU-accelerated pipelines to fall back to CPU execution and incur data transfer overhead; UDFs can be rewritten using Spark SQL expressions, the RapidsUDF interface with the Java API for cuDF, or custom CUDA kernels via JNI to eliminate fallback and ac

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he Information and the Wall Street Journal, while Anthropic, per the WSJ and the FT, made $15.5 billion ($4.6 billion in Q1 and $10.9 billion in Q2). We do not have consistent coverage of their underlying costs, but in Q1 (per The Information), OpenAI spent $8.6 billion on training and $3.5 billion on “cost of revenue,” with no reporting on its sales and marketing spend where it allocates the cost of serving free users or discounts. Anthropic said it had hit an annualized run rate (undefined per

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stions I have about you making your own models is what is the constraints you operate in? So if you draw a parallel to language models, you see GPT-OSS 20B is very fit for your average MacBook. Then there’s a larger one that’s fit for a single GPU. Do you work in the constraints of robot, of model on on device, in cloud, a hybrid? And then how does that affect your scaling law of data? A million hours is great, but like do we just scale up parameters, data? Jason Ma [00:17:38]: No, that’s a grea

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d. He holds a PhD in Computer Science from the University of California, Irvine, with a research focus on machine learning and bioinformatics. Bryn Price Bryn is a technologist, paragliding pilot, and Principal Solutions Architect at AWS. With more than 20 years of experience across telecommunications, banking, and software, he’s spent recent years deep in Applied AI, designing and building multi-agent AI products in production, and helping customers move from experimenting with generative AI to

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ations that might vary in challenge format, supported stablecoins, or settlement timing Security and key management Storing a private key to sign payments, requiring secure key storage within an environment exposed to untrusted inputs; Managing wallet lifecycle operations, including key rotation, credential refresh, and provider failover Operational resilience Resubmitting the original request with the payment header while preserving session state, cookies, and request context; Providing idempot

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years ago that wildlife and infrastructure do not mix anywhere on the planet. The FAA estimates that wildlife strikes have cost U.S. civil aviation about $272 million a year on average between 1990 and 2025. In France, the rail operator SNCF says wandering animals affect more than 8,000 trains across the country each year and are among its five leading causes of delays. In Brittany, 120 trains hit animals in 2023 alone, disrupting 660 services and causing more than 15,000 minutes of delays. Read

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ing, I smiled at a barista while ordering a caramel apple-flavored matcha latte. Afterward, I sat on a park bench reading a book, called up a pal to chat about her recent trip to Prague, and then asked my Ray-Ban Meta Audio glasses what meetings were coming up on my calendar. The chipper AI voice said I hadn’t connected my Google Calendar yet (I had) and asked if I would like help connecting. I sighed and then asked it to play an upbeat song. It picked Kendrick Lamar’s “Not Like Us.” These past

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ng why and how they built their own custom alternative to Git, based on the dramatic increase in rate of changes to their code after the past six months. In May 2026, Anthropic averaged around 2,500 code changes merged per day; as of September, we merged over 30,000 per day. They included a chart showing those changes. I had Claude modify their chart (code here) to add markers for the public releases of each of their models in 2026: This shows public release dates, not the date when models becam

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urce Agent Development Kit (ADK), a framework powering enterprise agents, push their performance engineering further. The team gave Argon their .agents/skills rulebook and a one-paragraph goal to eliminate framework overhead without breaking existing apps. Working autonomously overnight, Argon surfaced optimizations, now live in google/adk-python, that cut in-memory session prep time by 40% in long sessions and sped up cold imports by 21%. Read the engineering deep dive to learn more; Quantum ch

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tcuts. Instead, the share of shortcuts the model took silently — without mentioning them in its thoughts — more than tripled (from 11.2% to 36.4%). Avoiding thought penalties encourages the model’s reasoning to stay transparent and monitorable. Prompt injection resistance To trust an agent to do the job right, we need to ensure that malicious content the agent comes across cannot trick it to take a harmful action. When agents browse webpages or read code, they can encounter hidden, malicious ins

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oundly inefficient. The result is heavy warp under-utilization or expensive memory padding overhead. Conversely, the Triton MoE backend excels at dynamically mapping these small, fragmented token arrays across the physical boundaries of the active expert weights. This keeps the hardware’s internal prefetch streams and Tensor Core pipelines fully saturated even when processing highly unstable workloads at very low batch sizes. Updating the routing policy is a pure configuration change with no ker

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time, so ramp a new high-volume workload gradually rather than starting at full rate. Auditing a run. Model invocation logging writes request and response data to Amazon Simple Storage Service (Amazon S3) or Amazon CloudWatch Logs. This gives you the full transcript of runs you want to review after the fact. Monitoring reasoning volume at scale. Amazon CloudWatch metrics give you the aggregate view of token usage, which is where the per-level baselines described earlier become something you can

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nd, which resulted in resolving 97.5 percent of incoming channel queries while earning a 96 percent positive feedback rating by employees (Figure 1). Figure 1: 3-month channel coverage and positive feedback Business challenge The problem Deckhand solves is the one every IT team will recognize: the same questions are routinely asked, error messages are posted after business hours, and information needs to be gathered from different teams. At ShipStation Global, those conversations lived in a busy

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app that reads your bazi, a traditional chart tracking birth dates and elemental forces. Wang brings both her Chinese origins and western analytical skills to serious China AI coverage, but this level of on-the-ground depth remains rare. What’s changing the old media gatekeeping model over the past three years is the inflow of new China tech content. First, Chinese researchers and scientists are opening accounts on X, communicating directly while cross-posting thoughts just as they would on Chin

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from $17 to $199 per one million tasks, and median times from 0.06 to 0.5 seconds per task. Artificial Analysis Decision Index vs. Cost per 1M Tasks Decision Cost per 1M Tasks Average cost per task at current token prices, multiplied by 1 million; for the index, the cost of one pass through all its evaluations divided by their combined tasks, multiplied by 1 million. Decision models are billed for input tokens only, and language models for input and output tokens. Decider v1.1 27B has the lowes

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urce Agent Development Kit (ADK), a framework powering enterprise agents, push their performance engineering further. The team gave Argon their .agents/skills rulebook and a one-paragraph goal to eliminate framework overhead without breaking existing apps. Working autonomously overnight, Argon surfaced optimizations, now live in google/adk-python, that cut in-memory session prep time by 40% in long sessions and sped up cold imports by 21%. Read the engineering deep dive to learn more; Quantum ch

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tcuts. Instead, the share of shortcuts the model took silently — without mentioning them in its thoughts — more than tripled (from 11.2% to 36.4%). Avoiding thought penalties encourages the model’s reasoning to stay transparent and monitorable. Prompt injection resistance To trust an agent to do the job right, we need to ensure that malicious content the agent comes across cannot trick it to take a harmful action. When agents browse webpages or read code, they can encounter hidden, malicious ins

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samplers’: the two runs’ pass@4 stays close throughout training. Their training reward and gradient norm also follow similar paths, despite a 4.7× larger sampler–trainer KL on average. Entropy matches until about step 160; afterwards, it rises on the homogeneous samplers and stays flat on the mixed ones. Importantly, nothing in how the correction works is specific to this model or these accelerators, so we expect this pattern to hold more broadly. Decoupling the samplers from the trainer also m

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