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The Builder Revolution: Why We're Shifting to Builder Advocacy
The team formerly known as Developer Advocacy, within Developer Success, has been renamed Builder Advocacy within Builder Experience (BX). The change responds to an expanded definition of who builds with technology: alongside traditional developers, AI builders and no-code or low-code automators are creating production-level experiences and mission-critical infrastructure. The organization says its advocacy will serve developers, prompt engineers, LLM integrators, AI-agent creators, ML practitioners, and automation-focused builders, while continuing support for traditional coders, engineers, and architects. The announcement cites GitHub’s 180 million developers, 36 million new developers in 2025, and Stack Overflow’s finding that 84% use or plan to use AI tools. The rename is effective immediately and begins broader investments in resources, content, and community programs for this wider builder community.
Clever ingests 200x more logs at the same cost with ClickHouse Cloud
Clever, an identity platform for K-12 education, faced a fragmented logging setup as its 400 services and 200 AWS Lambdas generated about 150 TB of uncompressed logs monthly. Datadog indexed only about 10% and retained it for three days, while the remainder was in Athena and required SQL. Clever migrated to ClickHouse Cloud on AWS, indexing all logs with 60-day retention and reporting 200× more searchable log volume at no additional cost, with 10× compression. Its design extracts frequently queried JSON fields into columns, stores remaining attributes in a map alongside raw JSON, uses skipping indexes, date partitions, a 60-day TTL, and reverse Unix timestamp ordering; this ordering produced an 8–10× query speedup. A custom query layer translates LogQL to SQL, while separate development and production services and multi-region deployment address isolation and availability.
How Jua delivers the world’s most accurate physics simulations 3x faster with ClickHouse Cloud
Jua uses ClickHouse Cloud to deliver data from EPT-2, its physics foundation model, to energy traders and grid operators faster than its previous file-based pipeline. Jua’s forecasts combine outputs from multiple models and add roughly four terabytes of new data daily, producing about one petabyte compressed and over 10–12 petabytes uncompressed. Replacing Zarr and other file-based storage plus manual pre-fetching with ClickHouse Cloud reduced flagship forecast delivery from one hour to 20 minutes, with about 20 seconds of upload overhead. The platform also cut compute costs by roughly a third, reduced two-year single-location historical queries from hours to seconds, and supports both point lookups and regional aggregations; Jua selected the managed service for operational simplicity, separated storage and compute, and cost efficiency.
Teen rides return to Los Angeles
Uber for teens is now available in Los Angeles, allowing teens to request their own rides while guardians can book rides on their behalf. The service lets guardians track trips live, sends realtime notifications, pairs teens with highly rated drivers, and keeps PIN verification and RideCheck enabled for every ride. Guardians and teens can also message the driver in a shared group chat, while on-demand pickups and dropoffs must remain within the designated service area; Reserve trips remain available outside it. To book, a guardian selects “Where to?”, changes the rider from “For me” to the relevant teen, and chooses “Uber for teens.” Trips to or from airports cannot currently be requested.
Washington: Background Check Criteria Disclosure
Uber’s Washington State driver-screening disclosure sets time-based limits on driving violations, criminal charges, and convictions for people seeking to provide rides on the platform. Drivers may have no more than three minor driving violations in three years, while major driving violations and pending criminal matters such as driving while unlicensed or uninsured are disqualifying during that period. A broader seven-year bar covers felonies and specified offenses including violence, theft, fraud, DUI-related violations, hit and run, reckless driving, and speeding over 100 MPH; the disclosure also bars serious offenses including sexual crimes, murder, kidnapping, and active warrants. Seattle and King County add a seven-year licensing-related rule, while Vancouver adds tighter recent-violation limits and separate felony and drug-offense lookbacks.
How to Govern Autonomous Agents in Enterprise AI Factories
Autonomous AI agents require governed environments to safely inspect code, execute tests, query enterprise systems, and handle sensitive business data. The NVIDIA Secure Agent Workspace Reference Design shifts agent execution away from user laptops to dedicated, single-user virtual machines managed on Red Hat OpenShift Virtualization or Microsoft Azure. Perimeter defenses enforce enterprise single sign-on access broker connections, default-deny network egress rules, and human approval gates for critical state-changing actions. Inside the workspace virtual machine, active sandboxing through NVIDIA OpenShell evaluates signed security policies before each tool invocation while credential proxies prevent direct access to raw secrets. GitOps workflows continuously reconcile infrastructure profiles and network configurations while all operational and security telemetry streams to centralized SIEM platforms in OCSF format.
Washington State Driver Information
This page outlines benefits, protections, rights, and eligibility information for drivers operating on the Uber platform in Washington state. Starting July 1, 2026, drivers may request a downloadable CSV report containing two years of Washington mobility trip receipt data through drivers.uber.com; an email with an access link will arrive within three business days, and the link expires after seven days. It directs drivers to multilingual notices about driver rights and Paid Sick Time, states that drivers may be entitled to Paid Sick Time and Unemployment Benefits, and provides contact information for appealing deactivation through Drivers Union. The page also describes background check criteria for operating on the platform, noting that different criteria may apply in Seattle and King County.
GenPage: Towards End-to-End Generative Homepage Construction at Netflix
Netflix traditionally constructs its structured two-dimensional homepage through a complex, multi-stage recommender pipeline that separates candidate generation and ranking across rows and entities. To simplify this architecture and optimize directly for whole-page user satisfaction, Netflix developed GenPage, an end-to-end generative transformer model that autoregressively builds the entire homepage from raw tokenized context. The system relies on a domain-specific tokenizer to compress engagement history, context injection for cold start, hybrid row decoding to minimize decoding steps, and reinforcement learning post-training. In online A/B testing against the production baseline, GenPage delivered statistically significant gains in core user engagement metrics while cutting end-to-end serving latency by 20 percent. Offline evaluations further showed that enriching context prompts improved recommendation quality more effectively than increasing model capacity in the current operating regime.
Agents hate friction: early thoughts on building for agents
Designing software for LLMs and agents requires more than extending human developer experience: a polished text-first interface can still produce an AX that is merely functional. The post frames an agent as a combination of model, harness, context, and skills, with behavior varying across providers, models, and harnesses; it focuses attention on commonly used combinations while testing beyond them. In a test setup, agents receive goals and different amounts of context, and model-harness combinations are evaluated by how they reason, which tools they call, how many steps they take, and how consistently they repeat a task. The resulting design questions include friction, context and token efficiency, familiarity with established tools, and the trade-off between fewer steps and higher context or cost; the central conclusion is that agents may abandon a product when friction persists, making testing necessary to expose that behavior.
Build from anywhere with Cursor for iOS
Cursor for iOS is available in public beta on all paid plans, bringing agent-based development to a native mobile app. Users can choose a repository, select a frontier model, provide voice input or slash commands, and launch agents in the cloud or control agents running on their computers through Remote Control. The app supports asynchronous workflows with Live Activities and push notifications, while cloud agents use isolated virtual machines and full development environments to test, verify, and produce demos, screenshots, logs, and merge-ready pull requests. Developers can review diffs, provide follow-up instructions, merge pull requests, and hand sessions between local and cloud environments, including sending a local plan to the cloud or returning a cloud session to the computer for local testing. Cursor says repo-less chats are planned, and offers 75% off Composer 2.5 runs in the mobile app through July 5, 2026.
Next.js already traces your requests. Here's how to export them with OpenTelemetry.
Next.js provides out-of-the-box tracing for incoming requests, fetch calls, middleware, and server-side rendering, but traces remain invisible without a configured exporter. Developers can use the @vercel/otel package inside an instrumentation file to initialize the OpenTelemetry SDK and transmit traces to any OTLP-compatible destination. While Next.js automatically creates spans for standard request lifecycles, developers can define custom active spans in the Node runtime to capture specific domain operations and contextual attributes. Exporting traces to backends like Sentry requires configuring standard OTLP endpoint and authentication environment variables. Choosing between direct OTLP export via @vercel/otel and the dedicated Sentry SDK depends on requirements, as direct export lacks browser tracing, error monitoring, and Edge custom spans.
Cursor Mobile App for iOS
Cursor for iOS is available in public beta for all paid plans, bringing agent management and development workflows to mobile. Users can choose a repository, launch a cloud agent with any frontier model, provide voice input, and guide it with slash commands; agents run in isolated virtual machines with full development environments for testing, verification, and demos. Sessions can move from local to cloud, while Remote Control lets people continue directing a computer-based agent from the phone, with an option to keep the computer awake; Teams and Enterprise administrators must enable that feature in Cursor Dashboard. Live Activities and push notifications report agent status, including completion, requests for input, and review readiness, and the app supports reviewing artifacts such as demos, screenshots, logs, and diffs, leaving instructions, and merging pull requests.
How Verifiable Digital Credentials Are Reshaping Trust Architecture
Verifiable Digital Credentials (VDCs) are presented as a new trust layer for applications that need to verify claims such as age, licenses, employment, or eligibility without collecting documents and excess PII. The model uses an Issuer-Holder-Verifier trust triangle: authorities sign credentials, users hold them in wallets, and applications request specific proofs, with selective disclosure limiting exposed data. The standards stack combines SD-JWT VC or mdoc credential formats, OpenID4VCI for issuance, OpenID4VP for presentation, and the W3C Digital Credentials API for browser-wallet invocation. Production verification also requires issuer trust lists, trust-framework integration, and key rotation, not merely signature checks. Because browser API rollout is still progressing, the recommended baseline is redirect-based OpenID4VP with the Digital Credentials API added as a progressive enhancement, starting with one bounded verification use case.
Benchmarks and Obscurantism: A “red” line that should not be crossed
Melvyn Peignon examines a Databricks keynote benchmark in which ClickHouse was shown “crashing” under workloads associated with Reyden, Databricks’ gated low-latency compute product. The post argues that the presentation omitted hardware, cost, configuration, cache settings, deployment details, and sufficient methodology for independent validation. Using the disclosed TPC-H SF1 dataset and Q6 query, ClickHouse was tested on a 30 vCPU, 120 GB node without tuning; it did not crash and sustained about 420 QPS at sub-second P90 latency. The post reports that reaching 15,000 QPS required straightforward sizing to roughly 30–40 untuned nodes, while further gains could come from settings such as thread counts, query cache, and projections. It concludes that benchmarks should be open, reproducible, and detailed enough to inspect rather than accepted as keynote claims.
How Databricks is turning video into searchable, actionable intelligence
Databricks presents video analysis as a data engineering problem for organizations with terabytes of footage that is difficult and expensive to review manually. An app accepts a video and a natural-language prompt, then triggers a Lakeflow job on Serverless GPU Compute to run Meta’s SAM3 segmentation model frame by frame and retain matching moments. Those clips preserve original timestamps and are sent through the Databricks Foundation Model API for summaries that can be written to tables or passed into downstream workflows. In one example, 26 minutes of traffic footage became one minute and 55 seconds of relevant video. The model-agnostic pipeline uses MLflow signatures to support interchangeable or custom models, while event-driven execution and independent serverless GPUs allow concurrent processing without cluster management or idle GPU costs.
A Decision Framework for ETL Migration to Databricks
An ETL migration with hundreds of stored procedures, schedulers, scattered permissions, and a warehouse renewal deadline needs workload-by-workload decisions rather than a single rewrite strategy. The framework assigns work among Databricks SQL, Spark Declarative Pipelines (SDP), and PySpark or Spark SQL notebooks. SQL tasks suit single statements, Unity Catalog-governed stored procedures handle procedural logic, SDP manages dependencies, retries, quality constraints, and batch-plus-streaming, while notebooks cover complex logic, ML feature engineering, integrations, and large or tightly controlled Spark workloads. It recommends four phases—assessment, quick wins, modernization, and optimization—using profiling, side-by-side validation, and parallel runs before retiring legacy systems. Migration tools can automate 60–80% of initial conversion, but architecture choices remain essential: the goal is consolidating orchestration, metadata, lineage, permissions, and validation rather than reproducing technical debt.
How the English Office for Students leverages Databricks to enhance higher education standards and drive better student outcomes
The Office for Students, which regulates more than 400 higher education providers in England, modernised its data and analytics environment after a legacy platform could no longer handle growing volumes, varied sources, or emerging analytical demands. Its data spans millions of student records collected over 15 to 20 years, and a workflow processing about 300 million records took eight hours. Moving to Databricks consolidated structured, qualitative, and near-live data with analytics and AI workflows, while Unity Catalog added lineage, access controls, and security patterns for governed use. Genie Code reduced a student segmentation analysis from at least two weeks for two analysts to half a day, and a provider-registration triage proof of concept flags missing submissions earlier. The organisation frames AI as decision support rather than decision-making, keeping humans responsible for regulatory judgments.
Deploy a Production-Ready NVIDIA AI-Q Blueprint on Oracle Cloud Infrastructure
The NVIDIA AI-Q Blueprint operates as an open-source reference architecture for long-horizon agents built upon LangChain Deep Agents and the NVIDIA NeMo Agent Toolkit. Deploying this multi-agent system on Oracle Cloud Infrastructure requires coordinating foundational cloud infrastructure with containerized application workloads across Kubernetes. The deployment methodology utilizes Terraform to provision network isolation, an enhanced OKE cluster, a flexible public load balancer, and an AES-256 encrypted OCI Vault. Helm manages the application layer on Kubernetes by pulling official images from the NGC registry to run a FastAPI agent backend, a Next.js web frontend, and PostgreSQL. This configuration establishes an extensible routing architecture where user queries route automatically between shallow search agents and deep planning sub-agents while maintaining clean infrastructure teardown commands.
The new product introduction process: How to make sure your Agent is ready every time you ship
Frequent product releases can leave an AI Agent’s knowledge base outdated, causing it to hand off launch-related questions to humans when support volume peaks. The NPI process described here makes Agent readiness part of shipping, with support involved early in product, engineering, and go-to-market planning. Before launch, teams prepare clear customer-language content with full context, review and retire affected or duplicate material, then test likely questions, edge cases, and answer quality in the customer environment. At Fin, release details, walkthrough notes, demos, and GTM briefs form an input package for Operator, which proposes content updates, drafts, FAQ tests, and fixes. Post-launch Monitors and Scorecards track resolution rate and CX Score for two or four weeks, depending on launch complexity, to guide improvements.
Creating the NVIDIA Nemotron 3 Ultra NVFP4 Checkpoint with NVIDIA Model Optimizer
Moving large model weights efficiently requires quantization, but mapping weights to NVFP4's eight positive values risks severe precision loss or outlier-induced underflow. To address this, NVIDIA quantized the 550B Nemotron 3 Ultra model using NVIDIA Model Optimizer with a multi-precision strategy rather than quantizing all layers uniformly. The recipe applies four-over-six adaptive block scaling to Mixture of Experts routed weights, while assigning FP8 to shared experts and retaining BF16 for sensitive attention linears and embeddings. This process reduced the checkpoint size from 1,121 GB down to 352.3 GB and enabled single-checkpoint execution across Hopper using W4A16 and Blackwell using native W4A4. As a result, the model achieves up to 5.9x higher inference throughput than the GLM-5.1 754B FP4 model on decode-heavy workloads while matching BF16 benchmark accuracy.
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