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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
arXiv9/2/2026
Recently

SolarWM: Open Data and Scalable Training for Long-Horizon Video World Models

We introduce SolarWM, a fully open foundation for building interactive video world models from data preparation through long-horizon inference. Training across heterogeneous data sources and video backbones is challenging: datasets differ in temporal scale, camera geometry, visual quality, motion, and captioning styles, while video generators use distinct representations and architectures. Naive data mixing and model-specific implementations therefore produce inconsistent supervision and make results difficult to reproduce and compare. Authors: Junchao Huang, Guian Fang, Shengju Qian.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: We introduce SolarWM, a fully open foundation for building interactive video world models from data preparation through long-horizon inference.

Primary paperarxivcs.CV
Original
Artificial Intelligence / Machine Learning
arXiv8/31/2026
Recently

Aspire: Can Models Self-Evolve from Vague Goals?

" Learners must interpret the goal, identify capability gaps, decide how to learn, and determine whether they have actually improved. In contrast, existing work on LLM self-evolution typically begins with tasks and evaluation metrics specified by humans, reducing self-evolution to optimizing an explicit objective rather than deciding what and how to learn. We introduce ASPIRE, a benchmark for vague-goal-driven self-evolution. Authors: Yuhao Wu, Jingyuan Zhang, Jiajun Shi.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: " Learners must interpret the goal, identify capability gaps, decide how to learn, and determine whether they have actually improved.

Primary paperarxivcs.CL
Original
Artificial Intelligence / Machine Learning
arXiv9/1/2026
Recently

Beyond Scores: Understanding LLM-as-a-Judge Mechanisms in Summarization Evaluation

LLM-based evaluators of natural language generation (NLG) quality are widely deployed as scoring tools and as automated training signals, yet the internal procedure by which they assign a rating remains poorly understood. We investigate this procedure mechanistically through an eight-attack perturbation taxonomy across the Readability and Adequacy dimensions of NLG quality, a generation pipeline that produces paired clean and corrupt summaries with controlled error intensity and explicit token-level modification maps, and a four-experiment battery of causal tracing, logit-lens vocabulary projection, and attention-head knockout applied to Themis (Llama-3-8B) and Prometheus (Mistral-7B). Both evaluators implement a structured, coherent evaluation pipeline operating in two stages: below layer 15, attention performs local error comparison and routes the result to the final input position; above it, the MLP cascade integrates the signal and writes the rating, with the decision crystallizing in the residual stream at a sharp late layer (L = 26 on Themis, L = 25 on Prometheus). Authors: Himil Vasava, Ming Jiang.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: LLM-based evaluators of natural language generation (NLG) quality are widely deployed as scoring tools and as automated training signals, yet the internal procedure by which they assign a rating remains poorly understood.

Primary paperarxivcs.CL
Original
Artificial Intelligence / Machine Learning
arXiv8/31/2026
Recently

Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions

Efficient evaluation changes the protocol used to support claims about model behavior, yet it is rarely tested whether those claims remain stable after the evaluation itself is made cheaper. We stress-test conclusion robustness in responsible-AI benchmarking by evaluating three dense and mixture-of-experts models on BBQ and BBQ-V under seven conditions spanning batching, quantization, benchmark reduction, and their combinations. Rather than treating preserved aggregate accuracy as sufficient, we compare accuracy, bias severity and prevalence, reasoning quality, subgroup behavior, subset-membership stability, runtime, and measured GPU energy against a full-benchmark BF16 baseline. Authors: Ahmed El Kady, Aravind Narayanan, Rehana Noorani.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Efficient evaluation changes the protocol used to support claims about model behavior, yet it is rarely tested whether those claims remain stable after the evaluation itself is made cheaper.

Primary paperarxivcs.LG
Original
Artificial Intelligence / Machine Learning
arXiv8/31/2026
Recently

When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning

The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. 6 lineages, together with proprietary GPT release variants, using the OntoLearner retrieval-augmented generation pipeline. All models are evaluated with the same embedding model, retrieval configuration, prompt templates, decoding settings, datasets, and metrics on term typing, taxonomy discovery, and non-taxonomic relationship extraction across four biomedical and materials science and engineering ontologies. Authors: Hamed Babaei Giglou, Sören Auer, Jennifer D'Souza.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized.

Primary paperarxivcs.AI
Original

Archived section

Automation / Agentic Systems · 5

Automation / Agentic Systems
arXiv9/2/2026
Recently

Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis

Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing performance degradations in modern 5G and emerging 6G networks remains challenging due to complex cross-layer dependencies. While large language models (LLMs) offer promising capabilities for reasoning and knowledge integration, directly applying vanilla LLMs to telecom RCA often leads to hallucination, unstable reasoning, and poor alignment with structured network evidence. This work first reviews the evolution of telecom RCA from rule-based and machine learning (ML) approaches to emerging LLM-enabled techniques, and provides an overview of recent paradigms, including structured reasoning, retrieval-augmented knowledge grounding, agentic orchestration, and verifiable reasoning. Authors: Hao Zhou, Mandar Kulkarni, Hao Chen.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing performance degradations in modern 5G and emerging 6G networks remains challenging due to complex cross-layer dependencies.

Primary paperarxivcs.AI
Original
Automation / Agentic Systems
arXiv9/2/2026
Recently

EarlyEval: Cheaper Agent Evaluation via Early Outcome Prediction

Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a single pass of a frontier model over an agentic benchmark can cost hundreds to thousands of dollars, a price paid repeatedly across iterative development cycles. Prior efforts, centered on benchmark distillation, reduce the number of evaluation tasks but leave the cost of executing each retained task untouched. In this work, we introduce early outcome prediction, a complementary axis of efficiency that instead cuts cost within each task. Authors: Yuling Shi, Zhensu Sun, Junsen Dong.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a single pass of a frontier model over an agentic benchmark can cost hundreds to thousands of dollars, a price paid repeatedly across iterative development cycles.

Primary paperarxivcs.CL
Original
Automation / Agentic Systems
arXiv9/2/2026
Recently

Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills

Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and verification, but this architecture still leaves domain-specific know-how outside the agent. We call this missing layer operational knowledge, the know-how that separates knowing a method from making it work. Authors: Jianlyu Chen, Yuyang Hu, Hongjin Qian.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Autonomous agents are beginning to carry out machine-learning (ML) research end to end.

Primary paperarxivcs.AI
Original
Automation / Agentic Systems
arXiv9/2/2026
Recently

CORAL: An LLM-Native Harness for Production Recommender Systems

Production recommender systems shape what billions of people see, and sustaining their performance requires continual optimization: as content, user behavior, and upstream models shift, the choices governing retrieval, ranking, and serving must be revisited. Traditionally, human engineers test such changes through online experiments--a slow, reactive process limited by engineering effort, leaving parts of the system unrevised as conditions change. Although large language models have been applied to ranking, user modeling, and offline model development, few systems place an agent in a continual closed loop that acts on a live recommender and learns from the measured effects of its decisions. Authors: Muhammad Rafay Azhar, Yuhang Zhou, Gilbert Jiang.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Production recommender systems shape what billions of people see, and sustaining their performance requires continual optimization: as content, user behavior, and upstream models shift, the choices governing retrieval, ranking, and serving must be revisited.

Primary paperarxivcs.CL
Original
Automation / Agentic Systems
GitHub Blog9/2/2026
Recently

Decoding the new AI lingo: Loops, harnesses, squads, hill climbing… oh my!

From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations. The post Decoding the new AI lingo: Loops, harnesses, squads, hill climbing… oh my! appeared first on The GitHub Blog.

Why it matters

Read this for the official technical update in Automation / Agentic Systems: From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations.

Official sourceAI & MLagentic AI
Original

Archived section

Research Papers · 5

Research Papers
arXiv9/1/2026
Recently

Evaluating Usability in Biomedical Visualization: Rethinking Heuristic Evaluation for Spatial Omics and Multidisciplinary Research Platforms

Introduction: Clinical research informatics (CRI) platforms support biomedical discovery by integrating advanced computational tools into research workflows. Emerging technologies such as spatial omics and AI-enabled imaging expand research capabilities but introduce complex interfaces that increase cognitive burden and alter established analytical processes. Traditional usability frameworks identify general usability issues but often miss challenges specific to high-dimensional biomedical data. Authors: Yulia A. Levites Strekalova, Rachel Liu Galvin, Jessica M. Ray.

Why it matters

Read this for the paper's specific claim in Research Papers: Introduction: Clinical research informatics (CRI) platforms support biomedical discovery by integrating advanced computational tools into research workflows.

Primary paperarxivcs.HC
Original
Research Papers
arXiv9/2/2026
Recently

Large Language Model-Driven Context-Aware Eco-Feedback Generation and Evaluation

The objective of this study was to demonstrate the potential of generating eco-feedback that accounted for unique household contextual information, named as context-aware eco-feedback, through a large language model-integrated framework. Previous studies have introduced personalized eco-feedback, mostly relying on household energy use patterns; however, they frequently did not reflect distinct household characteristics, including their persona or non-negotiable routines, leaving eco-feedback ineffective and sometimes superficial. To address these limitations, we introduced a contextual engineering framework that generated eco-feedback using a self-consistency with chain-of-thought prompt that leveraged household energy analysis data, utility rate structures, and characteristic information. Authors: Wooyoung Jung, Prosper Babon-Ayeng.

Why it matters

Read this for the paper's specific claim in Research Papers: The objective of this study was to demonstrate the potential of generating eco-feedback that accounted for unique household contextual information, named as context-aware eco-feedback, through a large language model-integrated framework.

Primary paperarxivcs.HC
Original
Research Papers
arXiv9/2/2026
Recently

Card-Based Computation in the Virtual Player Simulation Model

Player simulation has recently emerged as a new direction in card-based cryptography, with protocols developed for simulating virtual players in physical card games such as Old Maid, UNO, and President. Unlike conventional card-based secure computation, player simulation imposes additional constraints: the cards represent a persistent game state, the remaining cards in a virtual player's hand must be preserved after each action, and it is desirable to represent each card in the game by a single physical card. In this paper, we study generic card-based computation in the virtual player simulation model. Authors: Suthee Ruangwises.

Why it matters

Read this for the paper's specific claim in Research Papers: Player simulation has recently emerged as a new direction in card-based cryptography, with protocols developed for simulating virtual players in physical card games such as Old Maid, UNO, and President.

Primary paperarxivcs.CR
Original
Research Papers
arXiv9/2/2026
Recently

Overcoming the Randomness-Utility Trade-off in Answering Differentially Private Linear Queries

We study the question of answering linear queries with differential privacy using few (expected) random bits. We provide a randomness-efficient analog of the \| * \|_K-norm mechanism of Hardt and Talwar [HT10]. For the \ell_\infty-error, our algorithm can answer d linear queries with O(d/\varepsilon) error using O(\log d) random bits, improving upon algorithms of Canonne et al. Authors: Surendra Ghentiyala, Pritish Kamath, Ravi Kumar.

Why it matters

Read this for the paper's specific claim in Research Papers: We study the question of answering linear queries with differential privacy using few (expected) random bits.

Primary paperarxivcs.CR
Original
Research Papers
arXiv9/1/2026
Recently

Better Situational Awareness in AR-HRC? A Comparative Study of Augmented Reality and Mobile Interfaces for Human-Robot Collaboration

Augmented reality (AR) facilitates human-robot collaboration (HRC) by enabling in-situ spatial visualizations of the robot and the joint task. However, in safety-critical HRC scenarios such as search-and-rescue, spatial visualizations may also reshape visual attention in ways that create competing situational awareness (SA) demands, potentially introducing new safety concerns. While prior AR-HRC work suggests potential benefits for SA, rigorous evaluations that jointly consider robot and environmental awareness across multiple levels of SA remain limited. Authors: Zhehan Qu, Christian Fronk, Jaewoong Jeong.

Why it matters

Read this for the paper's specific claim in Research Papers: Augmented reality (AR) facilitates human-robot collaboration (HRC) by enabling in-situ spatial visualizations of the robot and the joint task.

Primary paperarxivcs.HC
Original

Archived section

Embedded Systems · 5

Embedded Systems
arXiv9/2/2026
Recently

Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework

Autonomous robots powered by deep learning face a fundamental auditability challenge: when incidents occur, investigators cannot reconstruct why the system made specific decisions. This paper presents TRACE (Transparent Reasoning Architecture for Credible Execution), a decision framework that ensures every autonomous action can be traced back to sensor evidence through documented causal chains. The framework organizes decision-making into four auditable layers: Semantic Perception for evidence-grounded entity recognition, Belief Reasoning for probabilistic state estimation with causal graphs, Action Synthesis for constraint-aware planning with counterfactual documentation, and Execution Verification for compliance monitoring. Authors: Cagri Temel.

Why it matters

Read this for the paper's specific claim in Embedded Systems: Autonomous robots powered by deep learning face a fundamental auditability challenge: when incidents occur, investigators cannot reconstruct why the system made specific decisions.

Primary paperarxivcs.RO
Original
Embedded Systems
arXiv9/2/2026
Recently

Toward Robust LiDAR Semantic Segmentation for Real-World Deployment: Evaluation under Coarse Labels, Adverse Conditions, and Domain Shifts

LiDAR-based semantic segmentation is a core perception module for autonomous vehicles and mobile robots. Despite the strong performance of recent state-of-the-art methods on standard benchmarks, existing evaluation protocols remain focused on clean, single-domain settings and fine-grained label taxonomies, leaving deployment readiness largely unassessed. Real-world systems must handle safety-critical label semantics, degraded sensing conditions, and cross-domain variability, yet no unified protocol currently addresses all three aspects together. Authors: Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich.

Why it matters

Read this for the paper's specific claim in Embedded Systems: LiDAR-based semantic segmentation is a core perception module for autonomous vehicles and mobile robots.

Primary paperarxivcs.RO
Original
Embedded Systems
arXiv9/2/2026
Recently

Guidelines for the implementation of power oscillation damping controllers in power converters

One of the most effective ways to damp electromechanical oscillations in power systems is by means of supplementary controllers attached to the different devices in the power system is by means of power system stabilizers (PSS) in synchronous machines or by means of power oscillation damping (POD) controllers in facilities with power converters. In the recent years, the use of POD controllers in voltage source converters (VSCs) with grid-following (GFL) control has been investigated. Although the potential of POD controllers to help to damp inter-area oscillations in power systems is enormous, their correct implementation is not trivial, because their effectiveness is strongly linked to their settings. Authors: Javier Renedo, Macarena Martín Almenta, Sergio Martínez Villanueva.

Why it matters

Read this for the paper's specific claim in Embedded Systems: One of the most effective ways to damp electromechanical oscillations in power systems is by means of supplementary controllers attached to the different devices in the power system is by means of power system stabilizers (PSS) in synchronous machines or by means of power oscillation damping (POD) controllers in facilities with power converters.

Primary paperarxiveess.SY
Original
Embedded Systems
Hackster.io9/2/2026
Recently

AI-Powered Robot Learns to Run and Jump Just Like a Dog

Dogs do not need to be trained to learn a carefully planned sequence of movements to jump through a narrow opening. They can run toward it, adjust their approach, leap at the right moment, tuck their legs out of the way, and keep running after they land — it all just comes naturally. Researchers are now teaching quadrupedal robots to do much the same thing.

Why it matters

Read this for the engineering context in Embedded Systems: Dogs do not need to be trained to learn a carefully planned sequence of movements to jump through a narrow opening.

Trusted source
Original
Embedded Systems
Hackaday9/2/2026
Recently

Doubling Thermal Printer Resolution by Wiggling

Thermal printers are still extremely common today, using small heating elements in combination with temperature-sensitive paper to create a dot matrix-like effect without messing with ink ribbons and complex mechanisms.

Why it matters

Read this for the engineering context in Embedded Systems: Thermal printers are still extremely common today, using small heating elements in combination with temperature-sensitive paper to create a dot matrix-like effect without messing with ink ribbons and complex mechanisms.

Trusted sourceTeardowncalculators
Original

Archived section

Computer Systems · 5

Computer Systems
arXiv9/2/2026
Recently

GRADSOLVE: fast exact gradients for ODE ensembles on GPUs

Ordinary differential equations (ODEs) underlie models in science and engineering, and many applications need derivatives of their solutions with respect to parameters. Ensembles of independent trajectories suit graphics processing units (GPUs), but current GPU software forces a trade-off: the fastest ensemble solvers cannot be differentiated in reverse mode at the speed they solve, and the solvers built for differentiation solve more slowly. No single tool has yet offered a reverse-mode gradient at the speed of a fused-kernel solve. Authors: Alessio Spurio Mancini.

Why it matters

Read this for the paper's specific claim in Computer Systems: Ordinary differential equations (ODEs) underlie models in science and engineering, and many applications need derivatives of their solutions with respect to parameters.

Primary paperarxivcs.MS
Original
Computer Systems
arXiv9/1/2026
Recently

Performance Characterization of SPEC CPU 2026 on AMD EPYC 9755 Processor

SPEC CPU 2026 is the first major update to the industry-standard CPU benchmark suite since 2017. This paper presents the first microarchitecture based performance characterization of the new suite, conducted on AMD EPYC "Zen 5", also the first SPEC CPU characterization study on this microarchitecture. Using a multi-lens methodology spanning pipeline efficiency, control flow behavior, cache hierarchy pressure, and instruction mix, we analyze both SPECrate and SPECspeed suites. Authors: Kunal Kashyap, Rajiv Ramanathan, Shayantika Bhattacharya.

Why it matters

Read this for the paper's specific claim in Computer Systems: SPEC CPU 2026 is the first major update to the industry-standard CPU benchmark suite since 2017.

Primary paperarxivcs.AR
Original
Computer Systems
arXiv9/1/2026
Recently

Just Talk Once: Communication-Efficient Split Federated LLM Fine-Tuning on Edge Devices

Large language model (LLM) fine-tuning is increasingly shifting toward data generated on edge devices, where memory, computation, bandwidth, and connectivity constraints make conventional federated learning difficult to sustain. Split federated fine-tuning (SFT) improves client-side efficiency by offloading most model parameters and computation to the server but requires step-by-step bidirectional communication loop across the split interface and forces continuous client involvement throughout training. In this paper, we present L-shaped SFT, a split fine-tuning framework that removes this bidirectional bottleneck. Authors: Jiaxiang Geng, Xianhao Chen, Bing Luo.

Why it matters

Read this for the paper's specific claim in Computer Systems: Large language model (LLM) fine-tuning is increasingly shifting toward data generated on edge devices, where memory, computation, bandwidth, and connectivity constraints make conventional federated learning difficult to sustain.

Primary paperarxivcs.DC
Original
Computer Systems
arXiv9/2/2026
Recently

QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays

Semiconductor quantum-dot arrays are a compelling platform for scalable quantum technologies, yet their practical operation is hindered by the complexity of tuning large-scale devices. Existing automation tools rely on simplified physical models---such as constant-capacitance approximations and equilibrium Hubbard models---which assume instantaneous relaxation to a steady state. These frameworks fail in experimentally critical regimes where measurement rates exceed tunneling dynamics, necessitating more sophisticated non-equilibrium control strategies. Authors: Pranav Vaidhyanathan, Barnaby van Straaten, Alice Petrillo.

Why it matters

Read this for the paper's specific claim in Computer Systems: Semiconductor quantum-dot arrays are a compelling platform for scalable quantum technologies, yet their practical operation is hindered by the complexity of tuning large-scale devices.

Primary paperarxivcond-mat.mes-hall
Original
Computer Systems
arXiv8/31/2026
Recently

Local Private Information Retrieval for Graph-Based Replicated Systems

We rethink the definition of privacy in multi-server, graph-replicated private information retrieval (PIR) systems, by introducing a novel setting where the user's privacy is governed by the servers' storage structure. In classical graph-replicated PIR, the user retrieves a single message stored at the servers, while hiding the message index from each server. In our proposed privacy setting, the user is concerned with hiding the message index from a particular server, only if that server stores the message being retrieved, and privacy is not imposed otherwise. Authors: Shreya Meel, Mohamed Nomeir, Sennur Ulukus.

Why it matters

Read this for the paper's specific claim in Computer Systems: We rethink the definition of privacy in multi-server, graph-replicated private information retrieval (PIR) systems, by introducing a novel setting where the user's privacy is governed by the servers' storage structure.

Primary paperarxivcs.IT
Original

Archived section

Developer Tools / Open Source · 5

Developer Tools / Open Source
arXiv9/2/2026
Recently

Type Hints in Python Libraries and Frameworks: An Empirical Analysis of Adoption and Maintenance

Context: In Python, type hints allow developers to annotate variables and functions with explicit type information, improving code clarity and reliability. Although type hints are widely available, little is known about how they are adopted and maintained in libraries and frameworks. Objective: We investigate the adoption, usage, maintenance, and rationale of type hints in Python libraries and frameworks. Authors: Thiago Roberto Magalhães, Fabio Petrillo, João Eduardo Montandon.

Why it matters

Read this for the paper's specific claim in Developer Tools / Open Source: Context: In Python, type hints allow developers to annotate variables and functions with explicit type information, improving code clarity and reliability.

Primary paperarxivcs.SE
Original
Developer Tools / Open Source
GitHub Engineering9/2/2026
Recently

How we make AI coding more cost efficient without sacrificing task quality

Why shorter outputs can cost more, and how GitHub Copilot reduces wasted work across the complete coding task. The post How we make AI coding more cost efficient without sacrificing task quality appeared first on The GitHub Blog.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: Why shorter outputs can cost more, and how GitHub Copilot reduces wasted work across the complete coding task.

Official sourceAI & MLArchitecture & optimization
Original
Developer Tools / Open Source
Docker Blog9/2/2026
Recently

Building Reproducible AI Evaluation Workflows with Docker Sandboxes

Learn how Docker Sandboxes can make AI evaluation workflows more reproducible with consistent execution, structured artifacts, and runtime evidence.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: Learn how Docker Sandboxes can make AI evaluation workflows more reproducible with consistent execution, structured artifacts, and runtime evidence.

Official sourceCommunityAI Agent
Original
Developer Tools / Open Source
Kubernetes Blog9/2/2026
Recently

Kubernetes v1.37: Scale Workloads to Zero with HorizontalPodAutoscaler

37 includes API support for horizontal autoscaling of workloads down to zero replicas. This feature is now Beta and enabled by default. A HorizontalPodAutoscaler (HPA) that uses a suitable object metric or external metric can now scale a workload to zero replicas, then bring it back when the metric changes.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: 37 includes API support for horizontal autoscaling of workloads down to zero replicas.

Official source
Original
Developer Tools / Open Source
Vercel Blog8/31/2026
Recently

Claude Fable 5.1 now available on AI Gateway

1 improvements compared to previous Claude models are concentrated in long, multi-stage work like agentic coding, knowledge work, and research that takes several rounds of searching and following up. 1 with cybersecurity and biology safety classifiers enabled. Finding vulnerabilities in source code is allowed, but some routine coding and debugging may still be refused.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: 1 improvements compared to previous Claude models are concentrated in long, multi-stage work like agentic coding, knowledge work, and research that takes several rounds of searching and following up.

Official source
Original

Archived section

Cloud / Infrastructure · 5

Cloud / Infrastructure
AWS Architecture Blog9/1/2026
Recently

Hybrid cloud orchestration: Modernizing on-premises infrastructure management with AWS

Learn how to build a hybrid cloud orchestration solution that manages distributed on-premises infrastructure at scale using AWS serverless technologies and Amazon EKS Anywhere. Part 1 covers the core event-driven architecture patterns for automating server lifecycle and cluster management across hundreds of sites.

Why it matters

Read this for the official technical update in Cloud / Infrastructure: Learn how to build a hybrid cloud orchestration solution that manages distributed on-premises infrastructure at scale using AWS serverless technologies and Amazon EKS Anywhere.

Official sourceThought Leadership
Original
Cloud / Infrastructure
AWS Compute Blog9/1/2026
Recently

Scheduling email campaigns at scale with Amazon EventBridge Scheduler

Learn how to use Amazon EventBridge Scheduler to deliver email campaigns at per-recipient optimal send times. This post shows how to create one schedule per recipient with zero idle compute cost, scale schedule creation with AWS Step Functions Distributed Map, and deliver through Amazon SES.

Why it matters

Read this for the official technical update in Cloud / Infrastructure: Learn how to use Amazon EventBridge Scheduler to deliver email campaigns at per-recipient optimal send times.

Official sourceAdvanced (300)Amazon EventBridge
Original
Cloud / Infrastructure
CNCF Blog9/1/2026
Recently

Platform engineering maturity: From toolchain to self-service

Most platform engineering conversations tend to split into two rooms pretty quickly. The first room is full of teams who don't have a platform yet. Scattered scripts, tribal knowledge, and every team is doing the same...

Why it matters

Read this for the official technical update in Cloud / Infrastructure: Most platform engineering conversations tend to split into two rooms pretty quickly.

Official sourceBlog
Original
Cloud / Infrastructure
LWN.net9/2/2026
Recently

Incus 7.4 released

4 of the Incus container and virtual-machine management system has been released. Notable changes in this release include UEFI Secure Boot key management, "near-live" migration of containers between Incus instances, as well as burst I/O limits for disk and network devices.

Why it matters

Read this for the concrete reporting in Cloud / Infrastructure: 4 of the Incus container and virtual-machine management system has been released.

Trusted source
Original
Cloud / Infrastructure
AWS Blog8/31/2026
Recently

AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026)

The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON.

Why it matters

Read this for the official technical update in Cloud / Infrastructure: The news that interested me the most last week was the DuckLabs acquisition.

Official sourceAmazon EC2Amazon Elastic Container Service
Original