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Artificial Intelligence / Machine Learning · 5

Automation / Agentic Systems · 5

Research Papers · 5

Embedded Systems · 5

Computer Systems · 5

Developer Tools / Open Source · 5

Cloud / Infrastructure · 5

Archived section

Artificial Intelligence / Machine Learning · 5

Artificial Intelligence / Machine Learning
arXiv7/21/2026
Recently

InstructMixup: Instruction-Guided Salient Patch Editing for Robust Data Augmentation

In image and video technologies, data augmentation is widely used to improve the generalization of deep visual models, and mixup-based strategies that interpolate between samples have become the dominant approach. However, computing informative mixing regions adds substantial overhead, and blending content across different images frequently disrupts the semantic integrity of the resulting sample. We propose \our, a data augmentation method that constructs challenging yet label-consistent training samples entirely within a single visual sample. Authors: Khawar Islam, Arif Mahmood, Xin Jin.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: In image and video technologies, data augmentation is widely used to improve the generalization of deep visual models, and mixup-based strategies that interpolate between samples have become the dominant approach.

Primary paperarxivcs.CV
Original
Artificial Intelligence / Machine Learning
arXiv7/20/2026
Recently

GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis

Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data. A growing landscape of pathology foundation models now spans diverse data sources, architectures, and downstream applications. However, most pretrained models operate only at the image-tile level, use restrictive licenses, and remain computationally expensive, limiting large-scale slide-level clinical and research use. Authors: Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data.

Primary paperarxivcs.CV
Original
Artificial Intelligence / Machine Learning
arXiv7/20/2026
Recently

EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database

Large-scale scientific simulations generate volumetric data at rates that far outpace advances in storage and network bandwidth, making effective lossy compression increasingly critical. However, conventional compressors often struggle to preserve fine structural details at high compression ratios (CRs), and implicit neural representations (INRs) require costly per-volume optimization and produce models with fixed CRs. To respond, we present EVOLVE, an autoencoder (AE)-based volume-compression framework that targets high CRs for offline compression, with three key contributions. Authors: Kaiyuan Tang, Maizhe Yang, Chaoli Wang.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Large-scale scientific simulations generate volumetric data at rates that far outpace advances in storage and network bandwidth, making effective lossy compression increasingly critical.

Primary paperarxivcs.GR
Original
Artificial Intelligence / Machine Learning
NVIDIA Technical Blog7/21/2026
Recently

Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale... What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale. These factories are now tasked with powering agentic workflows that reason, plan, use tools, verify intermediate results, and execute complex multistep tasks across vast contexts.

Why it matters

Read this for the official technical update in Artificial Intelligence / Machine Learning: What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale...

Official source
Original
Artificial Intelligence / Machine Learning
Apple Machine Learning Research7/20/2026
Recently

Accelerating Text-to-Video Generation with Calibrated Sparse Attention

Recent diffusion models enable high-quality video generation, but suffer from slow runtimes. The large transformer-based backbones used in these models are bottlenecked by spatiotemporal attention. In this paper, we identify that a significant fraction of token-to-token connections consistently yield negligible scores across various inputs, and their patterns often repeat across queries.

Why it matters

Read this for the official technical update in Artificial Intelligence / Machine Learning: Recent diffusion models enable high-quality video generation, but suffer from slow runtimes.

Official source
Original

Archived section

Automation / Agentic Systems · 5

Automation / Agentic Systems
arXiv7/21/2026
Recently

Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes

This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes. Rather than treating LangGraph, a low-level orchestration framework for stateful agents, as a model-quality benchmark target, we present three executable recipes -- SQL analytics with repair loops, agentic retrieval-augmented generation with evidence gating, and human-in-the-loop policy review with interrupt and checkpoint recovery -- to show how typed state, conditional routing, deterministic tools, retries, interrupts, checkpoints, and traces fit together. LangGraph is positioned by workflow-complexity fit, not as a universal default: simpler ReAct-style or plain SDK loops may be better for basic tool use, schema-first tools for structured extraction and validation, and DSPy when prompt or program optimization is the main goal. Authors: Daniel Pearson, Sidney Shapiro, Emiliano Sebastian Gonzalez Venegas.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes.

Primary paperarxivcs.AI
Original
Automation / Agentic Systems
arXiv7/21/2026
Recently

Agents in the Wild: Where Research Meets Deployment

Agentic systems large language model (LLM) based architectures capable of reasoning, planning, acting, and coordinating with tools and other agents are rapidly transitioning from research prototypes to production scale deployments across domains such as software engineering, scientific discovery, and finance. While academic work has emphasized benchmarks and algorithmic innovation, deployment raises new challenges around robustness, safety, and reliability. This tutorial brings together researchers and practitioners to explore advances in reasoning and planning, multi agent coordination, and evaluation, highlighting open challenges arising from deployment experience. Authors: Grace Hui Yang, Pranav N. Venkit, Hooman Sedghamiz.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Agentic systems large language model (LLM) based architectures capable of reasoning, planning, acting, and coordinating with tools and other agents are rapidly transitioning from research prototypes to production scale deployments across domains such as software engineering, scientific discovery, and finance.

Primary paperarxivcs.AI
Original
Automation / Agentic Systems
arXiv7/21/2026
Recently

ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D

As AI agents begin to automate AI R&D, we need ways to assess whether their outputs are safe to deploy, even when the agents themselves may be untrusted. AI control offers one such approach: rather than trusting the agent, it treats it as a potential adversary and uses a monitor to detect covert sabotage before deployment. We evaluate AI control for automated AI R&D with ResearchArena, a framework spanning four long-horizon tasks: safety post-training, capabilities post-training, CUDA-kernel optimization, and inference-server optimization. Authors: Lena Libon, Ben Rank, Jehyeok Yeon.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: As AI agents begin to automate AI R&D, we need ways to assess whether their outputs are safe to deploy, even when the agents themselves may be untrusted.

Primary paperarxivcs.AI
Original
Automation / Agentic Systems
arXiv7/21/2026
Recently

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation

Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable. We study a layered pipeline on the PaySim dataset that combines a gradient-boosted classifier, graph-derived structural features, an autoencoder-based anomaly signal, TreeSHAP explanations, and a bounded LLM investigation agent applied to cases the classifier scores uncertainly. Before any model comparison, we identify and remove a simulator-specific balance shortcut that would otherwise inflate baseline performance. Authors: Rahil Sharma.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.

Primary paperarxivcs.LG
Original
Automation / Agentic Systems
arXiv7/21/2026
Recently

BioSecBench-Surveillance: A Verifiable Benchmark for AI Agents in Pathogen Genomic Surveillance

As pathogen genomic surveillance scales, the bottleneck is shifting from data generation to analysis. We present BioSecBench-Surveillance, a verifiable benchmark of 100 evaluations testing whether AI agents can infer the right analysis pipeline from raw sequencing data and surveillance context. Each evaluation gives an agent only the data and context a human analyst would have, then grades its structured answer deterministically. Authors: Harmon Bhasin, Kevin Flyangolts, Dianzhuo Wang.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: As pathogen genomic surveillance scales, the bottleneck is shifting from data generation to analysis.

Primary paperarxivcs.AI
Original

Archived section

Research Papers · 5

Research Papers
arXiv7/21/2026
Recently

PTSan: A Practical Memory Safety Sanitizer for C/C++ with Pointer-Object Authority

Memory safety errors remain the dominant source of severe vulnerabilities in C and C++. Pointer-based sanitizers provide stronger guarantees than location-based tools such as LLVM's ASan, but their overhead and compatibility limitations have constrained production use. We present PTSan, an LLVM sanitizer that makes pointer-based checking practical by storing an object identifier in each pointer's high bits and its bounds in a fixed-size runtime table. Authors: Eli Davis, Eric Lahtinen, Michael Gordon.

Why it matters

Read this for the paper's specific claim in Research Papers: Memory safety errors remain the dominant source of severe vulnerabilities in C and C++.

Primary paperarxivcs.CR
Original
Research Papers
arXiv7/20/2026
Recently

CutBackdoor: A Circuit Cut Triggered Backdoor Attack on Variational Quantum Algorithms

Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical optimization across quantum chemistry, combinatorial optimization, and quantum machine learning. Since real-world VQA deployments routinely require circuits that exceed available hardware capacity, quantum circuit cutting has become an indispensable execution strategy, and pre-trained parameters are increasingly distributed through public repositories, introducing supply-chain security risks that have received little attention. Prior quantum backdoor attacks either introduce detectable circuit modifications or depend on device-specific noise, and none consider circuit cutting as an attack surface. Authors: Ahatesham Bhuiyan, Hoang Ngo, Cheng Chu.

Why it matters

Read this for the paper's specific claim in Research Papers: Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical optimization across quantum chemistry, combinatorial optimization, and quantum machine learning.

Primary paperarxivquant-ph
Original
Research Papers
arXiv7/21/2026
Recently

SoK: Adversarial Robustness of the Variational Quantum Eigensolver via Red-Teaming

The Variational Quantum Eigensolver (VQE) is a leading algorithm for estimating molecular ground-state energies on near-term quantum hardware, with applications spanning quantum chemistry, materials science, and drug discovery. As VQE workloads are increasingly deployed through cloud-based ``VQE-as-a-service'' pipelines, they become exposed to adversaries such as compromised service components, malicious co-tenants, or insiders in the transpilation stack, any of which can corrupt results before they reach the user. A range of attacks on variational quantum circuits has been proposed, but each has been studied in isolation: some on quantum classifiers with accuracy-based metrics, others on variational quantum algorithms with energy-error metrics. Authors: Ahmed Azaz Humdoon, Cheng Chu, Lei Jiang.

Why it matters

Read this for the paper's specific claim in Research Papers: The Variational Quantum Eigensolver (VQE) is a leading algorithm for estimating molecular ground-state energies on near-term quantum hardware, with applications spanning quantum chemistry, materials science, and drug discovery.

Primary paperarxivquant-ph
Original
Research Papers
arXiv7/20/2026
Recently

GARAGE: Characterizing the Automation Boundary in LLM-based Attack Graph Generation

While modern vehicle security depends on effective Cyber Threat Intelligence (CTI) synthesis, current automated tools struggle with unstructured data and automotive-specific architectural nuances. To bridge this gap, we introduce GARAGE, a RAG-powered framework that converts fragmented CTI into an actionable, domain-specific knowledge base for automated attack graph generation. 1 and Auto-ISAC ATM-compliant knowledge base. Authors: Daekwon Pi, Sangho Lee, Young Hun Lee.

Why it matters

Read this for the paper's specific claim in Research Papers: While modern vehicle security depends on effective Cyber Threat Intelligence (CTI) synthesis, current automated tools struggle with unstructured data and automotive-specific architectural nuances.

Primary paperarxivcs.CR
Original
Research Papers
arXiv7/20/2026
Recently

Hardware Robustness of Sample-Based Quantum Diagonalization

Sample-based Quantum Diagonalization (SQD) is a hybrid quantum-classical method that replaces variational optimization with a self-consistent recovery loop over QPU samples. Although SQD is considered robust to noisy samples and imperfect classical inputs, its robustness across practical deployment choices has not been systematically analyzed. As a result, shot budgets, qubit layouts, noise mitigation strategies, and the coupled-cluster singles and doubles (CCSD) amplitudes that initialize the ansatz are often chosen without clear empirical guidance. Authors: Ahatesham Bhuiyan, Cheng Chu, Qian Lou.

Why it matters

Read this for the paper's specific claim in Research Papers: Sample-based Quantum Diagonalization (SQD) is a hybrid quantum-classical method that replaces variational optimization with a self-consistent recovery loop over QPU samples.

Primary paperarxivquant-ph
Original

Archived section

Embedded Systems · 5

Embedded Systems
arXiv7/21/2026
Recently

Dataset on residential electricity load profiles in Switzerland

Residential electricity profiles are undergoing significant changes due to the increasing adoption of distributed renewable generation, heat pumps, and electric vehicles. Up-to-date real-world measurements are essential for characterizing these evolving consumption patterns and providing a timely empirical foundation for research. This paper presents a dataset of 15-minute smart meter measurements from 2,447 residential installations in Switzerland, covering the years 2023 and 2024. Authors: Katharina Kaiser, Markus Kreft, Marina González Vayá.

Why it matters

Read this for the paper's specific claim in Embedded Systems: Residential electricity profiles are undergoing significant changes due to the increasing adoption of distributed renewable generation, heat pumps, and electric vehicles.

Primary paperarxiveess.SY
Original
Embedded Systems
NVIDIA Technical Blog7/20/2026
Recently

Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps

Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and... Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and services they already use. Many of these workflows already depend on OpenUSD scenes, simulation-ready (SimReady) assets, Blender-based workflows, CAD pipelines, or domain-specific app stacks.

Why it matters

Read this for the official technical update in Embedded Systems: Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and...

Official source
Original
Embedded Systems
IEEE Spectrum7/20/2026
Recently

SEM-Guided Low-kV FIB Finishing for Leading-Edge Semiconductor Failure Analysis

Discover how the ZEISS Crossbeam 750 FIBSEM sets a new benchmark for precise TEM lamella prep, tomography, and advanced nanofabrication. This delivers better resolution, better SNR, larger usable FOV, and shorter acquisition times. Learn how uninterrupted FIB milling will reduce damage and rework, accelerate time to TEM, and increase first pass success—so your FA, yield, and materials teams make faster, confident data driven decisions.

Why it matters

Read this for the concrete reporting in Embedded Systems: Discover how the ZEISS Crossbeam 750 FIBSEM sets a new benchmark for precise TEM lamella prep, tomography, and advanced nanofabrication.

Trusted sourceType-webinarSemiconductors
Original
Embedded Systems
Hackaday7/20/2026
Recently

Sail Virtually Aboard the "Itanic" with IA-64 Emulator

Intel's Itanium architecture was an interesting experiment, but it has gone down in history as one of the chip giant's bigger flops, so much so that it earned the name …read more

Why it matters

Read this for the engineering context in Embedded Systems: Intel's Itanium architecture was an interesting experiment, but it has gone down in history as one of the chip giant's bigger flops, so much so that it earned the name …read more

Trusted sourceSoftware Developmentemulator
Original
Embedded Systems
Hackster.io7/21/2026
Recently

The Software Revolution Has Come for Optics

Software transformed electronics by making a single piece of hardware capable of performing countless different tasks. Researchers at the University of Stuttgart are now applying that same idea to optics with a programmable metasurface that can change how it manipulates light. Instead of manufacturing a separate optical component for every application, their prototype allows each optical pixel to be controlled independently, enabling the device to generate different holographic images on demand.

Why it matters

Read this for the engineering context in Embedded Systems: Software transformed electronics by making a single piece of hardware capable of performing countless different tasks.

Trusted source
Original

Archived section

Computer Systems · 5

Computer Systems
arXiv7/20/2026
Recently

uSTM: A Lightweight and Efficient STM Supporting General Types and Deferred Aborts

Software Transactional Memory (STM) systems allow developers to more easily exploit multicore architectures by wrapping arbitrary sequential code in transactions that are executed concurrently. In recent years, the performance of STM systems has approached that of hand-tuned data structures through techniques that avoid unnecessary aborts and exploit the semantics of underlying data structures. Despite achieving excellent performance, most STM systems do not fully address the concerns they targeted in the first place: safety, usability, and generality. Authors: Zachary Kent, Guy Blelloch, André Costa.

Why it matters

Read this for the paper's specific claim in Computer Systems: Software Transactional Memory (STM) systems allow developers to more easily exploit multicore architectures by wrapping arbitrary sequential code in transactions that are executed concurrently.

Primary paperarxivcs.DC
Original
Computer Systems
arXiv7/21/2026
Recently

A Flexible Sparsity-Aware FPGA Accelerator with Column-Wise Compression for Efficient CNN Inference

Efficient acceleration of convolutional neural networks (CNNs) on resource-constrained platforms remains challenging due to the irregularity of sparsity patterns and the associated hardware overhead. While unstructured sparsity offers high model accuracy, it introduces significant inefficiencies in hardware mapping, whereas structured sparsity simplifies execution at the cost of reduced flexibility. This paper presents SparHiXcel-v2, a cost-effective and highly configurable FPGA-based CNN accelerator that achieves an improved balance between sparsity flexibility and hardware efficiency. Authors: Amirhossein Zarei, Shervin Vakili.

Why it matters

Read this for the paper's specific claim in Computer Systems: Efficient acceleration of convolutional neural networks (CNNs) on resource-constrained platforms remains challenging due to the irregularity of sparsity patterns and the associated hardware overhead.

Primary paperarxivcs.AR
Original
Computer Systems
CNCF Blog7/22/2026
Recently

Multi-Cluster databases on Kubernetes: Architecture and deployment

Introduction Running a database on Kubernetes is well understood. Running one that survives a complete regional failure, a corrupted control plane, or a severed network requires a fault-resistant architecture. This post walks through how to build...

Why it matters

Read this for the official technical update in Computer Systems: Introduction Running a database on Kubernetes is well understood.

Official sourceBlog
Original
Computer Systems
arXiv7/20/2026
Recently

RRAM-DP: Device-Calibrated Differential Privacy for In-Memory Edge Learning

Edge Artificial Intelligence of Things (AIoT) systems often collect sensitive data in situ, raising serious privacy concerns. Resistive-switching random-access memory (RRAM) is an attractive substrate for efficient AIoT thanks to its multi-bit storage and compute-in-memory (CiM) capabilities, while its inherently stochastic write behavior provides a natural source of randomness that can be leveraged for differential privacy (DP) protection. Yet how to transform this device-level randomness-typically viewed as detrimental to accuracy-into a principled randomized mechanism while preserving model utility remains underexplored. Authors: Kwunhang Wong, Jichang Yang, Karl M. H. Lai.

Why it matters

Read this for the paper's specific claim in Computer Systems: Edge Artificial Intelligence of Things (AIoT) systems often collect sensitive data in situ, raising serious privacy concerns.

Primary paperarxivcs.CR
Original
Computer Systems
arXiv7/20/2026
Recently

Empowering On-Device Model Adaptation with an Edge AI Inference Accelerator

On-device model adaptation is essential to enable lifelong personalization on resource-constrained hardware, but compute, power, and memory limitations of such devices make end-to-end backpropagation impractical for modern deep neural networks. This work proposes a heterogeneous adaptation pipeline that repurposes a commercial edge AI inference accelerator, Hailo-8L, for frozen-backbone feature extraction during on-device training. The computational graph is partitioned so that the pre-trained backbone is quantized to INT8 and run on the accelerator, while only a lightweight FP32 classification head is fine-tuned on the host CPU, enabling frequent, energy-efficient in-field updates with most weights remaining fixed. Authors: Mateusz Piechocki, Alessandro Capotondi, Marek Kraft.

Why it matters

Read this for the paper's specific claim in Computer Systems: On-device model adaptation is essential to enable lifelong personalization on resource-constrained hardware, but compute, power, and memory limitations of such devices make end-to-end backpropagation impractical for modern deep neural networks.

Primary paperarxivcs.LG
Original

Archived section

Developer Tools / Open Source · 5

Developer Tools / Open Source
Vercel Blog7/21/2026
Recently

How Searchable ships customer-requested features in 30 minutes on Vercel

Searchable on Vercel 5x increase in development velocity 100+ billion tokens processed Customer-requested features shipped in as little as 30 minutes Zero model SDK implementation or API key rotation with AI Gateway AI SDK AI Gateway Read more

Why it matters

Read this for the official technical update in Developer Tools / Open Source: Searchable on Vercel 5x increase in development velocity 100+ billion tokens processed Customer-requested features shipped in as little as 30 minutes Zero model SDK implementation or API key rotation with AI Gateway AI SDK AI Gateway Read more

Official source
Original
Developer Tools / Open Source
arXiv7/21/2026
Recently

EmbeddedKittens: An Evaluation of Code Embeddings for Scratch

The trend of embedding source code for machine learning applications also enables new opportunities in learning analytics in programming education, but which code embedding approach is most suitable for learning analytics remains an open question. A common approach to embedding source code lies in treating the code as a token sequence similar to natural language when training large language models~(LLMs). However, in case of visual block-based programming languages like Scratch, this approach cannot be applied directly. Authors: Benedikt Fein, Gordon Fraser.

Why it matters

Read this for the paper's specific claim in Developer Tools / Open Source: The trend of embedding source code for machine learning applications also enables new opportunities in learning analytics in programming education, but which code embedding approach is most suitable for learning analytics remains an open question.

Primary paperarxivcs.SE
Original
Developer Tools / Open Source
Vercel Blog7/20/2026
Recently

Laguna S 2.1 is now available on AI Gateway

is now available on AI Gateway. 1 is an open-weight Mixture-of-Experts model that supports a context window of up to 1M tokens and runs in thinking and no-thinking modes. The model specializes in agentic coding and long-running tasks, including writing and debugging code, running tests, building browser-based tooling, and working on MLOps pipelines and AI research.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: 1 is an open-weight Mixture-of-Experts model that supports a context window of up to 1M tokens and runs in thinking and no-thinking modes.

Official source
Original
Developer Tools / Open Source
Hackster.io7/20/2026
Recently

Telling Time Through Literature

Most clocks tell the time with numbers, but GitHub user kapoorankush's clock tells time with stories. Instead of displaying "10:26," for example, it might show a passage from a novel reading, "It was twenty-six minutes past ten," pulling from a library of thousands of carefully selected quotes that each correspond to a specific minute of the day. 5-inch Waveshare e-paper display to create a literary clock designed for everyday use.

Why it matters

Read this for the engineering context in Developer Tools / Open Source: Most clocks tell the time with numbers, but GitHub user kapoorankush's clock tells time with stories.

Trusted source
Original
Developer Tools / Open Source
GitHub Blog7/20/2026
Recently

$100 million for open source: A milestone built by the community

Celebrating 100 million contributed by the community to the people who build and sustain open source every day. The post100 million for open source: A milestone built by the community appeared first on The GitHub Blog.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: Celebrating 100 million contributed by the community to the people who build and sustain open source every day.

Official sourceMaintainersOpen Source
Original

Archived section

Cloud / Infrastructure · 5

Cloud / Infrastructure
CNCF Blog7/21/2026
Recently

Platform engineering for the agentic enterprise: Managing applications, resources, and AI agents

Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era. As organizations embraced Kubernetes, microservices, GitOps, and distributed architectures, the complexity of building and operating software grew beyond...

Why it matters

Read this for the official technical update in Cloud / Infrastructure: Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era.

Official sourceBlog
Original
Cloud / Infrastructure
CNCF Blog7/20/2026
Recently

Why goodput matters more than throughput for LLM serving

When we benchmark an LLM serving setup, the number almost everyone reaches for first is throughput: how many requests per second the system can push through. It is easy to measure, easy to compare, and it...

Why it matters

Read this for the official technical update in Cloud / Infrastructure: When we benchmark an LLM serving setup, the number almost everyone reaches for first is throughput: how many requests per second the system can push through.

Official sourceBlog
Original
Cloud / Infrastructure
AWS Blog7/20/2026
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AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026)

Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul.

Why it matters

Read this for the official technical update in Cloud / Infrastructure: Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders.

Official sourceAmazon BedrockAmazon Cognito
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Cloud / Infrastructure
Vercel Blog7/21/2026
Recently

Introducing the new Vercel Agent

Today we're expanding. It started by triaging alerts and reviewing your pull requests. Vercel Agent Because Vercel Agent is built into the platform that deploys and runs your app, when something changes in production, it's your first responder.

Why it matters

Read this for the official technical update in Cloud / Infrastructure: It started by triaging alerts and reviewing your pull requests.

Official source
Original
Cloud / Infrastructure
CNCF Blog7/21/2026
Recently

Why your agent needs access to your documentation

What 1,192 agent conversations taught us about knowledge base search A few months ago we shipped an agent inside our own product. It lives in our web app and lets users ask questions about their deployment,...

Why it matters

Read this for the official technical update in Cloud / Infrastructure: What 1,192 agent conversations taught us about knowledge base search A few months ago we shipped an agent inside our own product.

Official sourceBlog
Original