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How Netflix Simplified Batch Compute with Kueue
Netflix transitioned its managed batch compute infrastructure from a homegrown solution called Compute Managed Batch to Kueue on its Titus container platform. CMB previously relied on custom scheduling and admission-only fair sharing without preemption, making feature development cumbersome as the Kubernetes ecosystem evolved. To modernize the platform transparently, Netflix mapped internal tenants to Cohorts and leaf tenants to ClusterQueues and LocalQueues while routing jobs through a custom Kueue router. Kueue operates alongside existing Titus scheduling profiles rather than replacing the kube-scheduler, preserving cluster placement efficiency. The migration was completed in four weeks across millions of batch workloads, significantly increasing average resource utilization through preemption-based fair sharing.
Uber Advertising: Introducing Uber Marketing Manager, Creative Studio Expansion, and New Strategic Partnerships
Uber Advertising is introducing Uber Marketing Manager as a self-serve platform intended to unify advertising across Uber and Uber Eats, building on Ads Manager for restaurants. It also outlines Creative Studio expansion, new APIs, and a Mastercard Commerce Media partnership designed to connect campaign activation, offers, measurement, and real-world experiences across consumer journeys. Examples include Krispy Kreme’s Destination Offers campaign in Mexico, Molson Coors’s multi-format soccer campaign, and adidas’s pan-European Journey Takeover. Creative Studio will expand to Australia and Brazil in 2026, followed by continued expansion across LATAM, EMEA, and APAC throughout 2027. Uber says its connected approach is intended to help advertisers engage consumers across Uber, Uber Eats, and beyond, while a third-party validated Affinity Solutions study found a 27% incremental lift in sales across all channels and a 5% lift in brick-and-mortar sales for Sponsored Listings on Uber Eats.
DataOps Strategy for Modern Data Engineering
DataOps is presented as a collaborative practice that applies DevOps principles—continuous integration, automated testing, and rapid delivery—to the full data lifecycle, from ingestion and transformation through trusted data-product delivery. It treats data as a continuously delivered product, with technical and non-technical participants sharing an operational cadence to improve quality and shorten time-to-insight. The recommended operating model standardizes source onboarding, enforces schemas at ingestion, uses idempotent jobs, version-controls transformations, and promotes them through tested staging and production environments, with the Bronze, Silver, and Gold layers serving as quality gates. Automated unit, data-contract, and regression tests, alongside observability, anomaly detection, SLAs, lineage, and documented rollback procedures, support reliable delivery and faster incident diagnosis. Organizations with mature DataOps practices report up to 99% fewer data-downtime incidents and 30–50% less reactive maintenance.
CCCL Runtime: A Modern C++ Runtime for CUDA
NVIDIA CCCL runtime introduces modern C++ abstractions for fundamental CUDA programming concepts starting in CUDA Toolkit 13.2 and CCCL 3.2. Designed as an alternative to the traditional CUDA runtime API, the library eliminates implicit global state by requiring explicit dependencies, such as binding streams directly to specific device references. The API enforces strong typing through owning types and non-owning reference types, omits default stream usage to ensure all streams remain non-blocking, and handles errors via standard C++ exceptions. In addition, CCCL runtime simplifies kernel launches through kernel functors that enable automatic template argument deduction and automatically transforms owning buffer arguments into device spans.
Enable Real-Time AI for High-Speed Data Acquisition with DAQIRI
Scientific instruments and high-bandwidth sensors generate immense data volumes, but traditional architectures rely on a store-then-analyze model that bottlenecks real-time discovery and forces extensive data discarding. NVIDIA DAQIRI shifts data acquisition to a software-centric pipeline that connects streaming detectors directly to GPU computing. Built as part of the NVIDIA Holoscan Platform, DAQIRI uses DPDK to bypass the Linux kernel and move UDP and RoCE v2 traffic directly from the NIC into GPU DMA buffers at line rates exceeding hundreds of gigabits per second. The system automates packet reordering, flow steering, and on-GPU data conversions such as mapping wire int4 payloads into fp16 tensors using YAML configuration files and simple C++ or Python APIs. This enables real-time edge processing, filtering, and model inference across experimental pipelines like CERN ATLAS.
PP-OCRv6 on Hugging Face: 50-Language OCR from 1.5M to 34.5M Parameters
PP-OCRv6 is PaddleOCR’s latest universal OCR model family, targeting text detection and recognition across documents, screenshots, displays, labels, and scene text. It offers tiny, small, and medium tiers spanning 1.5M to 34.5M parameters, while the small and medium tiers support 50 languages. Its PPLCNetV4 backbone is shared across detection and recognition, with RepLKFPN for lightweight multi-scale detection and EncoderWithLightSVTR for recognition using local context and global attention. On PaddleOCR’s official in-house multi-scenario benchmarks, PP-OCRv6_medium reaches 86.2% detection Hmean and 83.2% recognition accuracy, improving over PP-OCRv5_server by 4.6 percentage points and 5.1 percentage points. PaddleOCR provides Paddle Inference, Transformers, and ONNX Runtime paths, and results can be saved as visualization images or structured JSON for downstream workflows.
Inside NVIDIA Halos for Robotics: A Full-Stack Functional Safety System for Physical AI
NVIDIA announced the launch of NVIDIA Halos for Robotics, a full-stack functional safety system designed for industrial robots, humanoids, and autonomous mobile robots. The platform extends NVIDIA's functional safety architecture from autonomous vehicles into robotics, utilizing NVIDIA IGX Thor and the NVIDIA Holoscan Sensor Bridge as its hardware foundation. On top of this hardware, Halos OS provides the base Halos Core safety operating system and application blueprints, including an Outside-In Safety Blueprint that leverages external worksite cameras and safety decision modules. Platform hardware safety features include an isolated IEC 61508 SIL 3 capable Safety Island alongside extensive fault-detection mechanisms. To accelerate compliance, the accredited NVIDIA Halos AI Systems Inspection Lab assesses partner integrations before final third-party certification.
The future of observability won’t be one proprietary AI agent. It will be thousands built by teams.
Observability vendors are increasingly betting on a universal SRE agent that combines a platform, telemetry, and natural-language investigation. The post argues that debugging depends on team-specific systems, trusted signals, failure history, runbooks, workflows, and institutional knowledge that cannot be packaged into one vendor’s agent. Agents can compare more time windows and pursue more hypotheses than human investigators, creating demand for low-latency query infrastructure and more complete, high-fidelity historical data. It proposes a more open model in which teams build specialized agents on headless observability foundations, with APIs, data storage, and an observability MCP, while persistent investigation surfaces preserve work for collaboration and reuse. Humans remain responsible for direction, judgment, and action, so the near-term future is presented as human-agent collaboration rather than autonomous incident resolution.
How Spyne simplified their CDC pipeline with ClickPipes and ClickHouse Cloud
Spyne’s CDC pipeline streamed changes from MySQL and MongoDB through self-managed Debezium, Kafka, and ClickHouse, but table onboarding required 1 to 1.5 hours of manual work and schema drift could silently exclude new columns. The team migrated to ClickPipes and ClickHouse Cloud, where pipelines can be configured in minutes and automated schema evolution applies upstream changes to corresponding ClickHouse tables without manual intervention or restarts. To manage snapshot pauses, Spyne isolated about 10 critical historical tables in dedicated ClickPipes and grouped lower-priority tables separately. The migration also required using FINAL in downstream queries to handle ReplacingMergeTree eventual consistency, while binlog gaps may still require full table resynchronization and higher costs. Spyne reports less operational toil, faster feature and analysis workflows, and improved data consistency, accepting reduced recovery control and query-time CPU trade-offs.
Uber
Uber’s Summer Weekly Deal distributes up to 100 promo codes per phase through its Weixin service account to eligible users of the Uber mini-program. Phase 1 offers 20% off the next trip, while Phase 2 offers 30% off each of the next two trips, with market-specific local-currency caps and validity windows in June 2026. The Chinese terms also list a Phase 3 on June 24 offering up to 100% off one trip, but the supplied English terms omit that phase and state that English prevails if the versions conflict. Codes expire without rollover, cannot be transferred or exchanged for cash, and require a +86-registered account with the code applied; one code may be used per trip. Uber may modify or end the Offer without notice and may suspend suspected fraudulent accounts.
Gas Savings for Delivery Drivers
Uber is expanding fuel discounts and rebates for U.S. drivers and couriers through June 30, 2026, in response to rapid increases in gas prices. During the promotion, the Uber Pro Card adds 5% cash back at any gas station, raising rates to 7% for Blue/Green, 8% for Gold, 9% for Platinum, and 11% for Diamond. Additional rewards can reach 15% at eligible Exxon or Mobil pumps, while Shell Fuel Rewards offers up to 21¢ off per gallon and Upside up to $1.00, with each offer combinable individually with the card. These benefits began March 27, 2026; after the promotion, card gas cash back returns to lower tier-specific rates, and the card’s refueling rewards are capped at $105 per month excluding Mastercard Easy Savings cash back.
Unpacking sandbox startup latency: why started ≠ ready
Sandbox startup benchmarks measure container boot time, but in production application-level initialization after boot dominates, making container boot the smallest variable. Modal defines a lifecycle with events Created, Scheduled, Started, and Ready, where Ready indicates completion of tasks like git pulls or dependency installs. The gap between Started and Ready can be multiple seconds, directly affecting user-perceived latency for coding agents needing cloned repos and running services. To reduce perceived latency, Modal recommends warm pools that pre-initialize sandboxes using a Queue, combined with Directory Snapshots for per-project customization without rebuilding the environment. Readiness Probes, now generally available, allow defining a shell command that exits 0 or a TCP port that accepts connections, with wait_until_ready() blocking until the sandbox is truly ready, simplifying warm pool producer code and providing dashboard events for observability.
Engineering a Vercel Chat Adapter
Vercel’s Chat SDK supports bots across multiple chat platforms, but those channels can be ephemeral and leave conversations buried in feeds and threads. Email offers a persistent, widely used channel, so Resend built an official adapter that treats email as bidirectional chat. Inbound mail arrives through Resend webhooks and triggers Chat SDK handlers, while outbound messages use the Resend API. The adapter reconstructs threads from In-Reply-To and References headers, tracking message-to-thread mappings in an in-memory Map and generating IDs from the sender address and a SHA-256 prefix of the root Message-ID. It also translates Chat SDK messages, including markdown and CardElement structures, into email HTML and plain-text content through React Email components, trading richer native chat semantics for email’s reach and simpler delivery model.
Measuring What Matters with Jules
Proactive coding agents need evaluation beyond task completion because existing public benchmarks such as SWE-Bench do not measure goal-oriented insight policies: deciding what matters, supporting evidence, and whether to interrupt or stay silent. To create preliminary ground truth, researchers clustered 705 bugs and 1,178 CLs from internal Google codebases using temporal proximity and semantic similarity, reverted each codebase to its pre-fix state, and gave the agent up to three exploration rounds. An LLM scored predicted insights from 1 to 5 against clustered bug targets, while success used average top score and Hit@K. With one exploration round, the agent averaged 4.5 out of 5; increasing the budget from two to three rounds raised Hit@5 from 33% to 57%, although the authors describe the findings as preliminary and are expanding the evaluation to public GitHub data and richer context streams.
Build Cross-Language Multi-Agent Team with Google’s Agent Development Kit and A2A
The post presents a contract-compliance pipeline in which a Python agent uses Gemini to extract contract terms and a Go service applies deterministic corporate-policy checks. It connects the services with the Agent2Agent (A2A) protocol and uses Google's Agent Development Kit (ADK), including RemoteA2aAgent, to expose the remote validator as a local sub-agent in a SequentialAgent. A2A supplies discovery through Agent Cards at /.well-known/agent.json, JSON-RPC 2.0 messaging through message/send, typed Message Parts, and task states such as submitted, working, completed, and failed. Pipeline state is shared through ADK's ToolContext.state, with failures transitioning to MANUAL_REVIEW for human legal review when the Go service is unavailable. The accompanying open-source application demonstrates live handoffs, policy verdicts, and simulated network faults, while the conclusion emphasizes language independence, decoupling, resilience, and deterministic auditing.
We got local models to triage the OpenClaw repo for FREE!*
OpenClaw receives hundreds of issues and pull requests daily, creating a triage problem for maintainers who need near-real-time filtering without consuming a paid closed-model quota. The system uses Gemma and Qwen local models inside a Pi-based agent harness: each item is normalized, enriched with GitHub context, classified into finite topic labels, and required to return structured output. For pull requests, the agent can inspect the repository through reposhell, a read-only command-restricted shell, while localpager, SQLite, a worker queue, and deterministic notification policy handle orchestration and Discord delivery. Tests described for gemma-4-26b-a4b and qwen3.6-35b-a3b report hundreds of tokens per second and good zero-shot classification accuracy without fine-tuning, while an LLM audit loop monitors errors; the authors present the pattern as agentic classification for high-throughput triage.
Extending Stripe’s network with stablecoins
Stripe describes integrating stablecoin settlement into its existing Global Payments and Treasury Network (GPTN), rather than creating a parallel system. GPTN models accounts, wallets, and payment rails as a directed graph, allowing routing, netting, cross-entity settlement, FX, reconciliation, and liquidity management to span fiat and blockchain flows. For a payout, Stripe records a pending stablecoin obligation, Bridge draws from a stablecoin liquidity pool to credit the recipient within seconds to minutes, and fiat replenishment proceeds asynchronously over traditional rails; reverse flows work similarly. A direct mode bypasses the usual few-minute planning cycle for urgent wallet funding, trading netting efficiency for speed, while standard flows continue batching. The live infrastructure handles real traffic, with ongoing work focused on forecast-driven liquidity, faster replenishment through RTP and SEPA Instant, and broader product and corridor coverage.
Scaling out Distroless adoption With AI
Grab is migrating from heavy base images to Distroless images to reduce security risks, despite runtime failures caused by missing shared objects, utilities, or expected directory structures. Its medium tests run services in Docker, leave internal dependencies unmocked through Testcontainers, mock external dependencies with a network proxy, and execute in CI to catch Dockerfile regressions before deployment. For services without this setup, an agentic workflow detects candidates, scaffolds tests, fixes repository-specific failures, raises GitLab merge requests, and monitors CI while processing batches in parallel. Claude Code uses MCP integrations for GitLab, Glean, and Sourcegraph, with guardrails limiting changes to test files and CI configuration and humans reviewing every draft merge request. In roughly 1.5 months, the agent raised more than 100 medium-test merge requests and reduced work for 400 services from 400–1,200 engineer-days to approximately 0.1 days per service; Distroless adoption also grew substantially.
Palana (Part 2): Architecting isolation, identity, and auditability for AI agents
Palana is Grab’s Kubernetes-native secure execution platform for autonomous AI agents, designed to provide isolated, attributable, and auditable environments. It provisions per-agent namespaces, service accounts, EFS-backed storage, ingress, policies, and proxy-mediated egress, while admission webhooks inject proxy settings and enforce pod restrictions. Human identity remains authoritative for RBAC, while sanitized identifiers are reserved for object names and Vault paths; secrets are split between agent-readable values and proxy-only credentials, and the LLM wrapper derives agent identity from Kubernetes context. Layer 3/4 controls provide containment, Layer 7 OPA-backed proxy policies govern host and method access, and structured signals support audit and idle shutdown that preserves /data and platform state. The production lessons emphasize platform-level controls, a safe self-service path, namespace boundaries, proxy-mediated authentication, and lifecycle management as prerequisites for useful autonomy.
The Data Canary: How Netflix Validates Catalog Metadata
A manual mitigation action during an incident corrupted a data feed for a subset of titles, causing playback issues and catalog service failures that existing code canary systems failed to catch. To protect streaming reliability, Netflix built an automated data canary system that validates transformed catalog metadata prior to publication. The architecture utilizes a dedicated orchestrator alongside permanent baseline and canary service clusters to coordinate validation using real production traffic. By leveraging custom chaos experiment thresholds, sticky session affinity, and Starts Per Second playback metrics, the system detects regressions in under ten minutes and blocks publication automatically. Controlled failure injection experiments routing approximately 0.2% of global traffic confirmed that issues could be identified in 2.5 to 4 minutes.
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