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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 · 4

Cloud / Infrastructure · 2

Archived section

Artificial Intelligence / Machine Learning · 5

Artificial Intelligence / Machine Learning
arXiv8/21/2026
Recently

OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs

Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continuously perceive environments and guide users to achieve specific goals. Unlike traditional passive video understanding, interactive assistants should actively combine visual states, user goals, and prior knowledge to provide effective help. Evaluating this is rather challenging, as the model's unpredictable response dynamically changes the user's subsequent actions, which static offline datasets cannot accommodate. Authors: Xianyun Sun, Chaoyou Fu, Zhengye Zhang.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continuously perceive environments and guide users to achieve specific goals.

Primary paperarxivcs.CV
Original
Artificial Intelligence / Machine Learning
arXiv8/21/2026
Recently

Re^3Cap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning

Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Models (LVLMs) to explore novel reasoning strategies. This limitation leads to a performance gap between RL and Supervised Fine-Tuning (SFT). In this paper, we argue that multi-modal retrieval can serve as an effective reasoning signal for caption refinement. Authors: Haonan Jia, Shichao Dong, Zenghui Sun.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Models (LVLMs) to explore novel reasoning strategies.

Primary paperarxivcs.CV
Original
Artificial Intelligence / Machine Learning
arXiv8/21/2026
Recently

VIALS: A Benchmark for Visual Interpretation of Artifacts in the Life Sciences

) to inform research decisions. We introduce VIALS, a visual question-answering benchmark with 161 such interpretation tasks, spanning the types of artifacts examined throughout experimental workflows in the biotech industry (rather than polished figures from publications and textbooks). While frontier vision-language models can now fluently describe natural images, we find that they are unable to accurately interpret these scientific images, reflecting limitations in domain knowledge and domain-specific visual reasoning capabilities. Authors: Elaine Lau, Thanuka Udumulla, Lee Izhaki-Tavor.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: We introduce VIALS, a visual question-answering benchmark with 161 such interpretation tasks, spanning the types of artifacts examined throughout experimental workflows in the biotech industry (rather than polished figures from publications and textbooks).

Primary paperarxivcs.AI
Original
Artificial Intelligence / Machine Learning
NVIDIA Technical Blog8/21/2026
Recently

NVIDIA AVO Reaches 100% on ARC-AGI-3, Demonstrating a Frontier-Level General-Purpose Architecture for Long-Horizon Autonomous Agents

A frontier language model is only one component of an AI agent. The surrounding agent system—often called a harness—determines how the model receives... A frontier language model is only one component of an AI agent.

Why it matters

Read this for the official technical update in Artificial Intelligence / Machine Learning: A frontier language model is only one component of an AI agent.

Official source
Original
Artificial Intelligence / Machine Learning
NVIDIA Technical Blog8/21/2026
Recently

GPU-Accelerated Clustering for Financial Instruments at Scale

Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor... Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor loadings, and structural-break signals at single-GPU and multi-node scale Quant strategies routinely group instruments for portfolio construction, risk aggregation, statistical arbitrage, and trade surveillance.

Why it matters

Read this for the official technical update in Artificial Intelligence / Machine Learning: Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor...

Official source
Original

Archived section

Automation / Agentic Systems · 5

Automation / Agentic Systems
arXiv8/21/2026
Recently

Asymmetric Capacity Allocation in Self-Refinement Pipelines

Self-refinement, typically structured as generation, critique, and revision, is a widely adopted paradigm for improving LLM generation and serves as a core mechanism in many LLM agents. While the three stages involve different cognitive demands, most existing approaches conveniently treat the model size as an implementation detail rather than a subject of study, which may lead to a waste of resources. Little work has systematically examined how model size affects each stage or whether effective self-refinement requires equally capable models for generation, critique, and revision. Authors: Zhuoyi Yang, Ian G. Harris, Salar Hashemitaheri.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Self-refinement, typically structured as generation, critique, and revision, is a widely adopted paradigm for improving LLM generation and serves as a core mechanism in many LLM agents.

Primary paperarxivcs.LG
Original
Automation / Agentic Systems
arXiv8/21/2026
Recently

Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis

Existing evaluations of automated root cause analysis (RCA) for microservices assess diagnostic performance mainly by endpoint correctness: whether a method localizes the responsible service. This criterion enables comparison but does not reveal the evidentiary basis of a diagnosis or the fault-propagation route connecting the source to observed symptoms, both of which an on-call site reliability engineer needs to judge whether action is warranted. We therefore treat RCA as an observable diagnostic process. Authors: Qisheng Lu, Aoyang Fang, Junjielong Xu.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Existing evaluations of automated root cause analysis (RCA) for microservices assess diagnostic performance mainly by endpoint correctness: whether a method localizes the responsible service.

Primary paperarxivcs.SE
Original
Automation / Agentic Systems
arXiv8/21/2026
Recently

Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models

Token compression has become a key technique for reducing the inference cost of large foundation models, with approaches such as token pruning and KV-cache reuse widely adopted in vision-language models and recently explored for embodied agents. In embodied agents, tokens not only support perception and semantic understanding but also directly affect latency-sensitive closed-loop robot action prediction. Existing schemes typically guide compression using redundancy or importance cues, such as visual similarity, attention scores, and saliency. Authors: Zhuoyuan Li, Rui Zhao, Jin Wang.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Token compression has become a key technique for reducing the inference cost of large foundation models, with approaches such as token pruning and KV-cache reuse widely adopted in vision-language models and recently explored for embodied agents.

Primary paperarxivcs.CV
Original
Automation / Agentic Systems
arXiv8/21/2026
Recently

AI with Authority, from Application to Silicon

For sixty years, machine verification has been a major cost overhead, affordable only for exceptional artifacts. Here we report that generative AI inverts this relationship: at AI speed, machine verification is not only economical but essential to productivity --- it is the incorruptible referee that lets one person safely direct autonomous machine work at scale. In five weeks, one researcher on consumer AI subscriptions directed a small fleet of AI agents from application code, through a verified compiler and executive, to a RISC-V processor taped out on a community silicon shuttle; no proof passed through human review, and no RTL was written by a human. Authors: Jason Hickey.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: For sixty years, machine verification has been a major cost overhead, affordable only for exceptional artifacts.

Primary paperarxivcs.SE
Original
Automation / Agentic Systems
arXiv8/21/2026
Recently

Natural-Language Workflows Are Not Software Yet: Artifact-Driven Compilation for Reliable Agent Execution

Natural-language workflows offer a software-like interface for agents: domain experts can write reusable procedures, and agents can execute them as instructions. This promise is not yet reliable. Workflow descriptions often leave data dependencies implicit, so the executor must infer which prior results a step should use; agents can also fail to follow long or branching instructions under context pressure. Authors: Xiangzhe Xu, Hanxi Guo, Guangyu Shen.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Natural-language workflows offer a software-like interface for agents: domain experts can write reusable procedures, and agents can execute them as instructions.

Primary paperarxivcs.SE
Original

Archived section

Research Papers · 5

Research Papers
arXiv8/21/2026
Recently

Who Trusts AI with Their Emotions? Trust Formation and Sociodemographic Variation in LLM Use for Emotional Support

Trust in AI for emotional support is not universal; it is shaped by who users are, where they come from, and what they value. Yet research in this area lacks validated psychometric instruments for assessing user perceptions in affective AI contexts and large-scale evidence on how trust formation varies across user segments. To address these gaps, we develop and validate a seven-construct psychometric scale, test a Structural Equation Model (SEM) linking system attributes to Trust and Perceived Benefits as mediators of Actual System Use, and conduct a Multi-Group Analysis (MGA) across five sociodemographic dimensions (gender, age, education, socioeconomic status, cross-national region), drawing on 1,343 active users from seven countries. Authors: Natalia Amat-Lefort, Mert Yazan, Amanda Cercas Curry.

Why it matters

Read this for the paper's specific claim in Research Papers: Trust in AI for emotional support is not universal; it is shaped by who users are, where they come from, and what they value.

Primary paperarxivcs.HC
Original
Research Papers
arXiv8/21/2026
Recently

Workplace Surveillance and Insider Threat Risk Management: Legal Limits and Privacy Harms

Workplace surveillance is used by organizations to protect corporate assets and monitor employee productivity. This research presents two central arguments on workplace surveillance: although surveillance serves legitimate organizational purposes, over-surveillance can violate legal requirements and data privacy principles; and a primary security objective of workplace surveillance is the detection of insider threats (InT). InT are comprised of individuals with authorized resource access whose intentional or unintentional actions may damage or compromise corporate assets. Authors: Haywood Gelman, John D. Hastings, Suvineetha Herath.

Why it matters

Read this for the paper's specific claim in Research Papers: Workplace surveillance is used by organizations to protect corporate assets and monitor employee productivity.

Primary paperarxivcs.CR
Original
Research Papers
arXiv8/21/2026
Recently

Supporting The Many Lives of Personal Data with Rebite: LLM-Powered Goal-Directed Framing in Food Journaling

People's health and tracking goals frequently change, but most personal informatics systems struggle to adapt, leading people to abandon their data and start over. We propose goal-directed framing, an approach that repositions goals within personal informatics systems. Instead of fixing the meaning of data at capture time, the approach frames the collected data through the current goal and reframes it whenever the goal changes. Authors: Weijun Li, Daniel A. Epstein.

Why it matters

Read this for the paper's specific claim in Research Papers: People's health and tracking goals frequently change, but most personal informatics systems struggle to adapt, leading people to abandon their data and start over.

Primary paperarxivcs.HC
Original
Research Papers
arXiv8/21/2026
Recently

Eavesdropper-Blind Remote State Preparation and Applications to Quantum Public-Key Encryption

Remote state preparation (RSP) is a central primitive in quantum cryptography, enabling classical parties to remotely construct quantum states using only classical communication. As a result, RSP serves as a key building block in numerous protocols involving classical clients and quantum servers, allowing classical parties to leverage the advantages offered by powerful quantum computers. All known constructions of RSP rely on strong cryptographic assumptions, typically variants of trapdoor claw-free functions (TCFs). Authors: Kaniuar Bacho, Alexandru Cojocaru.

Why it matters

Read this for the paper's specific claim in Research Papers: Remote state preparation (RSP) is a central primitive in quantum cryptography, enabling classical parties to remotely construct quantum states using only classical communication.

Primary paperarxivquant-ph
Original
Research Papers
arXiv8/21/2026
Recently

Event-Time Confounding Under Bursty Human Dynamics

Studies of digital behavior often align users at moments they choose, such as opening an AI assistant, clicking a recommendation, or visiting a product page, and interpret higher activity afterward as an event effect. We show how this creates an endogenous time zero: the event occurs during an ongoing task episode, so the aligned curve can trace episode continuation rather than a response to the event. In same-user, cross-surface web logs, AI, shopping, news, coding, and reference events are all preceded by broad activity increases that peak before time zero. Authors: Michael Iannelli, Alan Ai.

Why it matters

Read this for the paper's specific claim in Research Papers: Studies of digital behavior often align users at moments they choose, such as opening an AI assistant, clicking a recommendation, or visiting a product page, and interpret higher activity afterward as an event effect.

Primary paperarxivcs.HC
Original

Archived section

Embedded Systems · 5

Embedded Systems
arXiv8/21/2026
Recently

Fault Diagnosis of Dynamic Systems Under Unknown Operating Conditions: A Condition-Guided Selective Adaptation Approach

Fault diagnosis under unknown operating conditions remains challenging for dynamic industrial systems, as the distribution shift caused by changing operating conditions can significantly degrade the performance of diagnostic models in real-world applications. To address the problem, a condition-guided selective adaptation approach is proposed. Specifically, a novel continuous operating-condition adversarial learning strategy with progressive training is developed in the offline stage to enhance the generalization ability of the diagnostic model. Authors: Jiaming Liu, Zeyi Liu, Hongshuo Zhao.

Why it matters

Read this for the paper's specific claim in Embedded Systems: Fault diagnosis under unknown operating conditions remains challenging for dynamic industrial systems, as the distribution shift caused by changing operating conditions can significantly degrade the performance of diagnostic models in real-world applications.

Primary paperarxiveess.SY
Original
Embedded Systems
arXiv8/21/2026
Recently

Anatomy-Informed Neural Networks: Encoding Anatomic Priors in Loss and Architecture, with an SE(3) Formulation of Guidewire-Induced Aortoiliac Deformation

Deep-learning models of anatomy can be numerically plausible yet anatomically impossible, and they generalize poorly when data are scarce., continuity of the vessel) are built into the architecture and state representation, making such invalid predictions impossible by construction wherever the prior admits architectural enforcement. We develop it on a clinical test case with limited data: how the aortoiliac tree deforms when a stiff wire is introduced endoluminally. Authors: David P. Stonko.

Why it matters

Read this for the paper's specific claim in Embedded Systems: , continuity of the vessel) are built into the architecture and state representation, making such invalid predictions impossible by construction wherever the prior admits architectural enforcement.

Primary paperarxivcs.AI
Original
Embedded Systems
arXiv8/21/2026
Recently

ViTacPhys: Physical Property-Aware Grasping from Human Visual-Tactile Demonstrations

Recent vision-based action models have demonstrated strong capabilities in complex manipulation, but they rarely leverage explicit object physical properties to adapt their policies. We introduce ViTacPhys, a visual-tactile framework and data acquisition system that estimates object mass and friction-coefficient classes, together with continuous stiffness, from human manipulation demonstrations. Trained on data from 60 rigid and deformable objects, ViTacPhys combines temporal visual-tactile modeling, cross-attention multimodal fusion, and a semantic prior derived from a vision-language model. Authors: Yiwen Liu, Yujun Zhu, Kui Jia.

Why it matters

Read this for the paper's specific claim in Embedded Systems: Recent vision-based action models have demonstrated strong capabilities in complex manipulation, but they rarely leverage explicit object physical properties to adapt their policies.

Primary paperarxivcs.RO
Original
Embedded Systems
arXiv8/21/2026
Recently

Mining beyond Earth with Space Robots: Exploration, Sampling, and Extraction

Space resource acquisition and utilization, commonly referred to as Space Mining, represent critical pathways for enabling sustained human exploration and unlocking commercial opportunities in space. These resources mainly include helium-3, water, mineral resources on the Moon and Mars, and abundant mineral deposits on asteroids. Due to the harsh conditions of space, communication delays, and high launch costs, the development of autonomous robotic systems is critical to achieving efficient, cost-effective space mining. Authors: Dong Li, Dujun Nie, Xiaotong Zhang.

Why it matters

Read this for the paper's specific claim in Embedded Systems: Space resource acquisition and utilization, commonly referred to as Space Mining, represent critical pathways for enabling sustained human exploration and unlocking commercial opportunities in space.

Primary paperarxivcs.RO
Original
Embedded Systems
IEEE Spectrum8/21/2026
Recently

Stop Hunting, Start Solving: Accelerating Root Cause Analysis with Agentic AI

About this Webinar Turn Yield Excursions into Faster, More Confident Root Cause Analysis When a yield issue emerges, the answer rarely lives in a single system. Critical clues are spread across metrology data, tool traces, chemical analysis, and facilities systems, while growing data volumes make traditional dashboards slow, fragmented, and difficult to act on. What You'll Learn: Discover how a purpose-built semiconductor analytics platform can help engineers connect insights across domains without moving data.

Why it matters

Read this for the concrete reporting in Embedded Systems: About this Webinar Turn Yield Excursions into Faster, More Confident Root Cause Analysis When a yield issue emerges, the answer rarely lives in a single system.

Trusted sourceType-webinarSemiconductor-manufacturing
Original

Archived section

Computer Systems · 5

Computer Systems
arXiv8/21/2026
Recently

Event-triggered Implicit Perturbation for Zeroth-Order Fine-Tuning of Spiking Transformers

Zeroth-order (ZO) optimization estimates gradients using only forward-pass evaluations, making it suitable for fine-tuning non-differentiable, event-driven spiking neural networks (SNNs). However, its deployment on in-memory computing (IMC) accelerators is constrained by the repeated read-modify-write (RMW) operations arising from explicit weight perturbation and the prohibitive hardware footprint of random number generators (RNGs) for statistically independent per-weight perturbations. To address these challenges, we propose an implicit-perturbation ZO (IPZO) architecture in which perturbation sums computed by an event-triggered perturbation generation unit (PGU) are combined with the weighted sums produced by the IMC array, eliminating perturbation-induced RMW operations while preserving weight-stationary execution of IMC. Authors: Tengteng Lei, Prabodh Katti, Rashi Dutt.

Why it matters

Read this for the paper's specific claim in Computer Systems: Zeroth-order (ZO) optimization estimates gradients using only forward-pass evaluations, making it suitable for fine-tuning non-differentiable, event-driven spiking neural networks (SNNs).

Primary paperarxivcs.AR
Original
Computer Systems
arXiv8/21/2026
Recently

SPICE: Speculative Prefetching with Low-Rank Expert Surrogates and Heterogeneous Orchestration for MoE Inference Acceleration

Mixture-of-Experts (MoE) models are increasingly used in LLMs because sparse activation decouples model capacity from compute cost. However, the large expert parameter footprint often exceeds GPU memory capacity, making inference latency dominated by the host-to-device PCIe transfers for expert loading. To address these challenges, this paper presents SPICE, a speculative prefetching framework for MoE offloading that combines lightweight expert prediction with confidence-aware CPU-GPU orchestration. Authors: Yongxiang Lyu, Ning Li, Bonian Jia.

Why it matters

Read this for the paper's specific claim in Computer Systems: Mixture-of-Experts (MoE) models are increasingly used in LLMs because sparse activation decouples model capacity from compute cost.

Primary paperarxivcs.AR
Original
Computer Systems
arXiv8/21/2026
Recently

Portable to Efficient: Auto-Tuning Hardware-Agnostic GPU Kernels in Julia

Traditionally, GPU kernels have been developed and optimized within vendor-specific programming models to achieve high performance, resulting in software that is difficult to optimize and adapt across increasingly heterogeneous computing systems. Hardware-agnostic programming models offer a more sustainable approach to GPU software development by improving portability and maintainability, but achieving efficient execution across diverse architectures remains challenging. We address this challenge by integrating auto-tuning into hardware-agnostic GPU kernels written in Julia. Authors: Floris-Jan Willemsen, Evelyne Ringoot, Alan Edelman.

Why it matters

Read this for the paper's specific claim in Computer Systems: Traditionally, GPU kernels have been developed and optimized within vendor-specific programming models to achieve high performance, resulting in software that is difficult to optimize and adapt across increasingly heterogeneous computing systems.

Primary paperarxivcs.PF
Original
Computer Systems
arXiv8/21/2026
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Assessing Triple Modular Redundancy for Wide-Link, Low-Latency NoC Routers: Reliability and Physical Design Challenges

Protecting the Network-on-Chip (NoC) of physical-AI tile-based accelerators deployed in harsh environments against single-event effects (SEEs) is paramount for preventing NoC failures that can lead to deadlocks and silent data corruption (SDC)., 32-bit), deeply pipelined routers, and single-event upsets (SEUs). However, the state of the art has evolved toward low-latency NoC routers with ultra-wide links, implemented on advanced technology nodes and operating at frequencies above 1 GHz. Authors: Chen Wu, Michael Rogenmoser, Luca Benini.

Why it matters

Read this for the paper's specific claim in Computer Systems: , 32-bit), deeply pipelined routers, and single-event upsets (SEUs).

Primary paperarxivcs.AR
Original
Computer Systems
AWS Architecture Blog8/21/2026
Recently

Build a unified AI agent architecture with DynamoDB and Bedrock

With native vector search in Amazon DynamoDB, you can store vector embeddings alongside your operational data in a single table. This post shows how to build a unified AI agent architecture where an Amazon Bedrock agent uses one DynamoDB table for both structured lookups and semantic search, with a DynamoDB Streams pipeline that keeps embeddings in sync.

Why it matters

Read this for the official technical update in Computer Systems: With native vector search in Amazon DynamoDB, you can store vector embeddings alongside your operational data in a single table.

Official sourceAdvanced (300)Amazon DynamoDB
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Archived section

Developer Tools / Open Source · 4

Developer Tools / Open Source
Vercel Blog8/24/2026
Recently

Vercel Sandbox is now globally available

), (San Francisco), (Cleveland), and (Paris). Vercel Sandboxiad1sfo1cle1cdg1 remains the default. iad1 Choose a region close to the databases, object storage, and other services your sandboxes access to reduce latency.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: ), (San Francisco), (Cleveland), and (Paris).

Official source
Original
Developer Tools / Open Source
arXiv8/21/2026
Recently

The Substitution Escrow Threshold: When "Compatible With" Becomes Safe Enough to Buy

Enterprise infrastructure buyers routinely evaluate compatibility claims--"S3-compatible," "PostgreSQL-compatible," "OpenAI compatible"--as proxies for future substitution options. Yet most compatibility claims do not escrow the substitution path they imply. This paper introduces the Substitution Escrow Threshold, a five-condition framework that determines when a compatibility claim genuinely reduces institutional risk versus merely reducing first-integration cost. Authors: Amadeus Brandes.

Why it matters

Read this for the paper's specific claim in Developer Tools / Open Source: Enterprise infrastructure buyers routinely evaluate compatibility claims--"S3-compatible," "PostgreSQL-compatible," "OpenAI compatible"--as proxies for future substitution options.

Primary paperarxivcs.SE
Original
Developer Tools / Open Source
arXiv8/21/2026
Recently

Human-AI Collaboration in Requirements Engineering: Evidence of the Negative Effect of LLMs on Requirements Inspection

Background. Requirements inspection (RI) is a well-established practice for detecting potential defects in requirements artifacts early in the software lifecycle. Recent advances in large language models (LLMs) have stimulated interest in their potential to support requirements engineering (RE) tasks. Authors: Giovanna Broccia, Julian Frattini, Chetan Arora.

Why it matters

Read this for the paper's specific claim in Developer Tools / Open Source: Requirements inspection (RI) is a well-established practice for detecting potential defects in requirements artifacts early in the software lifecycle.

Primary paperarxivcs.SE
Original
Developer Tools / Open Source
Docker Blog8/21/2026
Recently

Running AI agents in GitHub Actions with Docker Sandboxes

Run AI agents in GitHub Actions with Docker Sandboxes. See how isolated agents can run Testcontainers tests, fix code, and open draft pull requests.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: Run AI agents in GitHub Actions with Docker Sandboxes.

Official sourceEngineeringAI Agent
Original

Archived section

Cloud / Infrastructure · 2

Cloud / Infrastructure
Vercel Blog8/21/2026
Recently

How Ora benchmarks every major AI agent on Vercel

Ora on Vercel Every harness expects its own infrastructure One platform under every harness Testing eve like any other harness The framework behind Ora's own agents Front end, back end, and agent runtime on one platform Every major agent tested side by side on live sites Hundreds of commits a day from a 16-person engineering team 7% fewer steps to reach the goal 2x native success: twice as many tasks finished on the customer's own site instead of falling back to web search 9% more valid endpoints: more of the endpoints the agent found were ones it could actually call Ora sends agents onto live websites with instructions to sign up for a product, integrate with it, and pay for it. Agents often fail, and by Ora's estimate, 99% of the web isn't agent-ready. The platform shows customers where and why agents fail, and what to change.

Why it matters

Read this for the official technical update in Cloud / Infrastructure: Ora on Vercel Every harness expects its own infrastructure One platform under every harness Testing eve like any other harness The framework behind Ora's own agents Front end, back end, and agent runtime on one platform Every major agent tested side by side on live sites Hundreds of commits a day from a 16-person engineering team 7% fewer steps to reach the goal 2x native success: twice as many tasks finished on the customer's own site instead of falling back to web search 9% more valid endpoints: more of the endpoints the agent found were ones it could actually call Ora sends agents onto live websites with instructions to sign up for a product, integrate with it, and pay for it.

Official source
Original
Cloud / Infrastructure
CNCF Blog8/24/2026
Recently

Automating root cause analysis at scale: Multi-signal correlation for cloud native incident response

The problem: Humans shouldn't be correlation engines At Atlassian's scale, hundreds of interconnected microservices distributed across multiple regions mean a production incident generates an overwhelming volume of telemetry. The problem is that finding the causal factor...

Why it matters

Read this for the official technical update in Cloud / Infrastructure: The problem: Humans shouldn't be correlation engines At Atlassian's scale, hundreds of interconnected microservices distributed across multiple regions mean a production incident generates an overwhelming volume of telemetry.

Official sourceBlog
Original