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The engineering internet, summarised so you can actually read it.
Maximize AI Factory Energy Efficiency Through Full-Stack Inference and Training Optimizations
Power costs comprise up to 40% of AI factory operating expenses, making performance per watt critical within fixed site power limits. Operators maximize throughput by combining architectural enhancements, narrow-precision formats like NVFP4, and mixture-of-experts model designs that activate fewer parameters per token. During distributed LLM training in Megatron-LM, tuning individual GPU speeds along critical execution paths eliminates idle power waste without extending total training time. Additionally, the NVIDIA DSX platform orchestrates dynamic power allocation, 45°C direct-to-chip liquid cooling, and grid integration across racks and facilities. Together, these full-stack optimizations enable performance-optimized AI factories to deliver up to 2.6 times more tokens per second per megawatt.
Sachin IdgunjiBoost Inference Performance up to 15x on NVIDIA Blackwell Using DFlash Speculative Decoding
Sequential token generation in autoregressive LLMs limits GPU utilization and serving throughput, while conventional speculative decoding remains constrained by autoregressive draft models. DFlash addresses this bottleneck by employing a lightweight block-diffusion drafter that predicts an entire block of masked candidate tokens in a single forward pass. The architecture incorporates target hidden-state conditioning and key-value injection into draft model projections while relying on the target model for parallel verification. Evaluated on NVIDIA Blackwell GPUs across TensorRT-LLM, vLLM, and SGLang, DFlash increases gpt-oss-120b throughput by more than 15x at high interactivity targets and delivers up to 5.8x speedups on Gemma 4 31B compared to autoregressive decoding.
Amr ElmeleegyYour AI shipped a backend that boots. That is the whole problem.
Backend security vulnerabilities often emerge from permissive default settings in web frameworks, a risk amplified when AI agents generate code focused solely on passing tests. Common failure points include unmetered request body parsing, unvalidated schemas, prototype pollution, and wildcard cross-origin resource sharing policies. To eliminate these foot-guns, the TypeScript framework DaloyJS enforces secure-by-default behavior across its entire routing and middleware model. It provides built-in request timeouts, streamed body size caps, prototype-stripping JSON revivers, and unified Zod schemas that simultaneously validate payloads and generate OpenAPI contracts. While secure defaults lower the risk of common infrastructure exploits, developers still remain responsible for application-level authorization and business logic correctness.
Devlin DuldulaoThe 2026 Developer Survey is now open (for human developers only)!
The 2026 Developer Survey is open to gather data on software builders' tools, working lives, and emerging practices. Marking fifteen years since its initial 2011 release, the annual survey focuses heavily on the role of artificial intelligence and autonomous agents across the software development lifecycle. Past survey results indicated that while developer adoption of AI and agents doubled, user trust declined alongside escalating concerns regarding generated code quality. In addition to modern AI trends and financial return considerations, the questionnaire maintains traditional inquiries regarding day-to-day technologies and developer workflows. The survey is hosted on the Qualtrics platform, which requires users to pause third-party ad blockers and adheres to regional access restrictions across multiple countries.
Ryan DonovanBuild an AI Scientist for Life Science Discovery with NVIDIA BioNeMo Agent Toolkit
General-purpose AI agents often struggle with biomolecular discovery because scientific workflows require precise model selection, strict input formatting, and artifact interpretation. The NVIDIA BioNeMo Agent Toolkit bridges this gap by providing BioNeMo Skills and Model Context Protocol wrappers on top of accelerated NVIDIA NIM microservices. These skills instruct agents on tool capabilities, required inputs, expected file formats like CIF and SMILES, and specific failure modes. Developers can route requests through hosted endpoints for testing or deploy local NIM microservices on GPU nodes to lower warm per-call latency during iterative candidate generation loops. Internal benchmarks using Codex CLI demonstrate that equipping agents with BioNeMo Skills improves task completion rates from 57.1% to 100% while doubling token efficiency across passing assertions.
Kyle TretinaHow Telcos Build Autonomous Networks with Agentic AI
Telecom operators currently operate mostly within Levels 2 and 3 of the TM Forum autonomous networks taxonomy, relying on predefined scripts. Progressing to Levels 4 and 5 demands autonomous systems capable of intent comprehension, real-time telemetry sensing, and cross-domain action coordination. To address this, an autonomy platform architecture combines domain-specific reasoning models, agent harnesses, and isolated sandboxed runtimes. Workflows separate responsibilities among on-demand agents, long-running execution loops, and deep-research multi-agent systems that evaluate ranked remediation paths or design wireless algorithms. Initial experiments in simulated SR-MPLS routing and wireless physical or MAC layer design show autonomous discovery can deliver measurable gains over standard operational and engineering baselines.
Amogh DendukuriNetflix ·
Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix
Generative video editing tools often regenerate entire video clips, which inadvertently modifies untouched scene details or violates physical continuity during object deletion. To provide artists with precise control, two research prototypes were developed: Vera and VOID. Vera uses a layered video diffusion framework with a Mixture-of-Transformers architecture to jointly generate separate edit and alpha matte layers, compositing them with original footage while leaving unchanged pixels untouched. VOID addresses object deletion by conditioning on interaction-aware quadmasks and using a second-pass appearance refiner to reconstruct scenes with plausible physical continuity. In a user study evaluated across 75 real-world scenarios, reviewers selected VOID 64.8% of the time over six baselines.
Netflix Technology BlogNetflix ·
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.
Netflix Technology BlogEnable 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.
Cara LaaschInside 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.
Suhas Hariharapura SheshadriGrab ·
Scaling out Distroless adoption With AI
Grab is transitioning its microservices to Distroless base images to eliminate unnecessary binaries and reduce vulnerability risks, but the migration risks runtime failures from missing shared objects and system utilities. To safely validate container execution in continuous integration without staging dependencies, the team relied on medium tests that run containerized services alongside internal dependencies managed by Testcontainers. Because hundreds of services lacked this test harness, Grab implemented an agentic workflow using Claude Code and Model Context Protocol integrations to inspect repositories, generate test boilerplate, and resolve configuration errors. Once test baselines are established, an automated patch-test-compare pipeline updates Dockerfiles, constructs multi-stage builds for necessary dynamic libraries, and creates draft merge requests for human approval.
Jia Yee ChongGrab ·
Palana (Part 2): Architecting isolation, identity, and auditability for AI agents
Grab's Palana platform provisions isolated, Kubernetes-native runtime environments for autonomous AI agents using dedicated per-agent namespaces and role-based access controls. The architecture separates network enforcement across layers, applying Layer 3 and Layer 4 containment with Cilium and NetworkPolicy alongside Layer 7 application filtering evaluated by Open Policy Agent. Agent interactions with large language models route through a LiteLLM proxy wrapper that retrieves credentials from HashiCorp Vault based on Kubernetes pod context rather than client headers. Secrets management is divided between directly readable agent paths and proxy-only placeholder paths that prevent raw tokens from residing in runtime filesystems. An automated reaper monitors multi-source activity signals to shut down idle compute resources while preserving persistent storage and configuration state.
Kevin LittlejohnNetflix ·
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.
Netflix Technology BlogDispatches from O'Reilly: From capabilities to responsibilities
High-stakes AI agents capable of mutating external state often face governance failures when relying on system prompts or manual Human-in-the-Loop approval queues that quickly degrade into alert fatigue. The Responsibility-Oriented Agent architecture addresses this operational bottleneck by shifting system design from open-ended capability framing to deterministic, contract-enforced responsibilities. Under this model, underlying orchestration frameworks like LangChain operate in User Space with their side-effecting tools removed, isolating the agent to epistemic reasoning. The agent expresses its intended action exclusively by emitting a structured policy proposal to a privileged Kernel Space runtime. The runtime deterministically evaluates the proposal against versioned YAML contracts registered in an agent registry, ensuring that only genuine policy exceptions are escalated to human supervisors.
Artur HukGrab ·
Palana (Part 1): Why Grab built a secure platform for autonomous AI Agents
Autonomous AI agents introduce significant operational and security risks when granted network access, persistent state, and credentials. To address these concerns without impeding developer productivity, Grab created Palana, an in-house Kubernetes-native execution substrate. The platform isolates each agent workload within its own namespace, pairing it with dedicated storage, network policies, and role-based access control. Network egress is funneled through an Envoy and Open Policy Agent proxy layer that audits requests and injects credentials from HashiCorp Vault using placeholder tokens, keeping raw secrets outside the agent runtime. This design allows Grab to securely host hundreds of long-running workflows, remote coding environments, and automation bots.
Kevin LittlejohnThe new bottleneck
AI coding tools have significantly lowered the cost of generating software, yet many engineering organizations fail to realize overall delivery speed improvements. Applying the Theory of Constraints reveals that eliminating code production as a bottleneck shifts inventory directly into surrounding legacy processes that remain unadjusted. New friction points consistently emerge across underspecified requirements, prolonged design handoff gates, senior engineer review capacity, and external sign-offs from legal or security. Organizations can address these blockers by interrogating legacy agile ceremonies checkpoint by checkpoint to determine if their original constraints still exist. Practical remedies include adopting real-time co-development between product and engineering, treating initial designs as fluid starting points, and restructuring processes around running rapid, high-volume experiments.
Eira MayAI agents are a confused deputy with the keys to your kingdom
Attackers recently compromised over twenty thousand Instagram accounts by manipulating Meta's AI support assistant to rebind recovery email addresses without verifying account ownership. This incident illustrates the classic confused deputy security problem, where a privileged process is persuaded by an unprivileged user to perform unauthorized operations. Because large language model interfaces operate purely on natural language and cannot distinguish instructions from data, the model itself cannot serve as an authorization boundary. Securing AI agents requires verifying caller identity through external policy checks against authenticated sessions rather than relying on chat context or prompt engineering. Teams must enforce least privilege with short-lived scoped credentials, place irreversible actions behind hard policy gates or human approvals, and maintain audit trails of agent actions.
Fabio SalvadoriBuilding AI Agents for AR Glasses and XR Devices with NVIDIA XR AI
Building AI experiences for augmented reality and wearable devices requires bridging hardware with live media streams, multimodal models, enterprise tools, and runtime infrastructure. NVIDIA XR AI provides an open-source, modular framework connecting extended reality headsets and smart glasses to GPU-accelerated services across cloud, edge, and workstations. In this architecture, camera frames and microphone audio ingest into an XR Media Hub that routes data while keeping raw video pixels in shared memory to minimize overhead. The ecosystem uses NVIDIA Cosmos models for vision-language grounding, NVIDIA Nemotron models for reasoning and tool invocation, and the Model Context Protocol for enterprise integrations. Optional agent orchestration via NVIDIA NeMo Agent Toolkit and spatial streaming through NVIDIA CloudXR support complex workflows across healthcare and manufacturing.
Greg BarboneBuild Your Own Transaction Foundation Model for Financial Intelligence
Production financial intelligence systems frequently rely on hand-engineered tabular features and rule sets that are brittle, expensive to maintain, and blind to historical sequential patterns. NVIDIA demonstrates an accelerated reference pipeline that replaces standard BPE tokenization with a GPU-based domain tokenizer, converting raw transactions into semantic tokens with an 8,192-token context window. Using the NeMo AutoModel library, a compact 29-million parameter decoder-only transformer is pretrained from scratch on unlabeled transaction sequences using causal language modeling. Learned sequence embeddings are extracted, compressed using PCA, and concatenated with raw tabular features to train a downstream GPU-accelerated XGBoost fraud detection model. On the IBM TabFormer benchmark dataset, this combined approach achieves a 41.76% lift in Average Precision over the baseline model relying solely on raw tabular features.
Benjamin WuNVIDIA Blackwell Tops MLPerf Training 6.0 with Industry-Leading Scale and Performance
NVIDIA submitted results across all MLPerf Training v6.0 benchmarks, setting performance records on workloads including the DeepSeek-V3 and GPT-OSS-20B Mixture of Experts models. To scale training up to 8,192 Blackwell GPUs, NVIDIA combined hardware cluster designs with scale-out networking via Spectrum-X Ethernet and Quantum InfiniBand. The engineering team eliminated CPU-GPU synchronization bottlenecks in token-dropless MoEs by implementing full-iteration CUDA graphs with synchronization-free operators and paged stashing. Further software optimizations included CuTe DSL kernel fusions, an MXFP8 attention block in the Transformer Engine, and transitioning router elementwise math to FP32. Across these benchmarks, the GB300 NVL72 platform achieved the fastest time to train at scale and the highest normalized per-accelerator performance.
Farshad Ghodsian