# backend

Published articles for backend.

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## Podcast: Distributed LLM Inference on Kubernetes with llm-d, Wasm, & TornadoVM

DevFeed: [Podcast: Distributed LLM Inference on Kubernetes with llm-d, Wasm, & TornadoVM](<https://devfeed.tech/articles/podcast-distributed-llm-inference-on-kubernetes-with-llm-d-wasm-tornadovm-123025.md>)

Original publisher: [Read original article](<https://www.infoq.com/podcasts/distributed-llm-inference-kubernetes/>)

Author: Edoardo Vacchi

Published: 2026-11-09T11:00:00Z

Content type: news

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [LLM optimization](<https://devfeed.tech/topics/llm-optimization.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Local AI](<https://devfeed.tech/topics/local-ai.md>), [TornadoVM](<https://devfeed.tech/topics/tornadovm.md>), [LLM observability](<https://devfeed.tech/topics/llm-observability.md>), [Wasm components](<https://devfeed.tech/topics/wasm-components.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-ml-data-engineering](<https://devfeed.tech/tags/ai-ml-data-engineering.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [backend](<https://devfeed.tech/tags/backend.md>), [batch](<https://devfeed.tech/tags/batch.md>), [cache](<https://devfeed.tech/tags/cache.md>), [cloud-computing](<https://devfeed.tech/tags/cloud-computing.md>), [clusters](<https://devfeed.tech/tags/clusters.md>), [containers](<https://devfeed.tech/tags/containers.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [distributed-ai-inference](<https://devfeed.tech/tags/distributed-ai-inference.md>), [distributed-llm-inference-kubernetes](<https://devfeed.tech/tags/distributed-llm-inference-kubernetes.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [gpu-utilization](<https://devfeed.tech/tags/gpu-utilization.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infoq](<https://devfeed.tech/tags/infoq.md>), [java](<https://devfeed.tech/tags/java.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [kubernetes-clusters](<https://devfeed.tech/tags/kubernetes-clusters.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-d](<https://devfeed.tech/tags/llm-d.md>), [llm-inference](<https://devfeed.tech/tags/llm-inference.md>), [ml-data-engineering](<https://devfeed.tech/tags/ml-data-engineering.md>), [podcast](<https://devfeed.tech/tags/podcast.md>), [the-infoq-podcast](<https://devfeed.tech/tags/the-infoq-podcast.md>), [tornadovm](<https://devfeed.tech/tags/tornadovm.md>), [wasm](<https://devfeed.tech/tags/wasm.md>), [web-development](<https://devfeed.tech/tags/web-development.md>), [webassembly](<https://devfeed.tech/tags/webassembly.md>)

### AI overview

llm-d extends vLLM's high-throughput inference from a single machine to distributed deployments, balancing requests across instances while favoring cache locality for conversational KV and prefix caches and avoiding overload. It also provides flow control and request prioritization, and can run on bare metal as well as Kubernetes. The discussion describes batch inference during idle periods to use otherwise underused hardware, and semantic classification to route prompts to right-sized models based on factors such as topic or cost. Wasm is presented as useful for sandboxing and portable workloads, including smaller CPU-suitable models, while larger parallel inference generally benefits from GPUs. TornadoVM translates specially written Java code into GPU kernels; it does not run arbitrary Java frameworks such as Spring on the GPU.

### Source excerpt

In this episode, Edoardo Vacchi discusses the evolving landscape of WebAssembly on the backend, running Java workloads on GPUs using TornadoVM, and optimizing distributed AI inference using llm-d and vLLM on Kubernetes. Edoardo explains how llm-d manages cache locality (KV and prefix caches), request routing, and batch inference across Kubernetes clusters to maximize expensive GPU utilization. By Edoardo Vacchi

## AWS Launches TorchNeuron to Run PyTorch Models on AWS Trainium Without Modifications

DevFeed: [AWS Launches TorchNeuron to Run PyTorch Models on AWS Trainium Without Modifications](<https://devfeed.tech/articles/aws-launches-torchneuron-to-run-pytorch-models-on-aws-trainium-without-modifications-123022.md>)

Original publisher: [Read original article](<https://www.infoq.com/news/2026/11/aws-torchneuron-trainium-pytorch/>)

Author: Sergio De Simone

Published: 2026-11-09T11:00:00Z

Content type: release

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [AWS Trainium](<https://devfeed.tech/topics/aws-trainium.md>), [neuron](<https://devfeed.tech/topics/neuron.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [AWS AI chips](<https://devfeed.tech/topics/aws-ai-chips.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-chips](<https://devfeed.tech/tags/ai-chips.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [ai-ml-data-engineering](<https://devfeed.tech/tags/ai-ml-data-engineering.md>), [amazon](<https://devfeed.tech/tags/amazon.md>), [annapurna-labs](<https://devfeed.tech/tags/annapurna-labs.md>), [apis](<https://devfeed.tech/tags/apis.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-torchneuron-trainium-pytorch](<https://devfeed.tech/tags/aws-torchneuron-trainium-pytorch.md>), [aws-trainium](<https://devfeed.tech/tags/aws-trainium.md>), [backend](<https://devfeed.tech/tags/backend.md>), [capacity](<https://devfeed.tech/tags/capacity.md>), [conference](<https://devfeed.tech/tags/conference.md>), [developers](<https://devfeed.tech/tags/developers.md>), [development](<https://devfeed.tech/tags/development.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [drug-discovery](<https://devfeed.tech/tags/drug-discovery.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [ml-data-engineering](<https://devfeed.tech/tags/ml-data-engineering.md>), [native](<https://devfeed.tech/tags/native.md>), [news](<https://devfeed.tech/tags/news.md>), [no-code](<https://devfeed.tech/tags/no-code.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [pytorch-conference](<https://devfeed.tech/tags/pytorch-conference.md>), [torchneuron](<https://devfeed.tech/tags/torchneuron.md>)

### AI overview

Amazon introduced TorchNeuron, a native PyTorch backend for AWS Trainium that aims to reduce migration work: with PyTorch automatic device detection, the text says switching from CUDA requires replacing `.to('cuda')` with `.to('neuron')`. It supports distributed training APIs including DDP, FSDP2 and DTensor, Torch streams and events, and custom performance-critical operators through the Neuron Kernel Interface. Amazon says it validated 100,000 popular models and more than 800 architectures. The backend is also presented as enabling shared hardware capacity for training and inference. The excerpt does not establish production performance or cost; cited observers say workload-specific performance, cost per token, reproducibility, and total cost of ownership remain to be tested.

### Source excerpt

At the recent PyTorch Conference North America 2026, Amazon introduced TorchNeuron, a native PyTorch backend that enables developers to run existing PyTorch code on AWS Trainium AI chips with virtually no code modifications. By Sergio De Simone

## The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility

DevFeed: [The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility](<https://devfeed.tech/articles/the-silent-hyperparameter-quantifying-the-impact-of-inference-backends-on-llm-reproducibility-123156.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2605.19537>)

Author: David Pape, Jonathan Evertz, Lea Sch\\"onherr

Published: 2026-11-09T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [reproducibility](<https://devfeed.tech/topics/reproducibility.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [llama.cpp](<https://devfeed.tech/topics/llama-cpp.md>), [LLM optimization](<https://devfeed.tech/topics/llm-optimization.md>), [CUDA Graphs](<https://devfeed.tech/topics/cuda-graphs.md>)

Tags: [abstract](<https://devfeed.tech/tags/abstract.md>), [adoption](<https://devfeed.tech/tags/adoption.md>), [alter](<https://devfeed.tech/tags/alter.md>), [analyze](<https://devfeed.tech/tags/analyze.md>), [arithmetic](<https://devfeed.tech/tags/arithmetic.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [backend](<https://devfeed.tech/tags/backend.md>), [backends](<https://devfeed.tech/tags/backends.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [caching](<https://devfeed.tech/tags/caching.md>), [can](<https://devfeed.tech/tags/can.md>), [choice](<https://devfeed.tech/tags/choice.md>), [comparisons](<https://devfeed.tech/tags/comparisons.md>), [cost](<https://devfeed.tech/tags/cost.md>), [critical](<https://devfeed.tech/tags/critical.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [impact](<https://devfeed.tech/tags/impact.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [reproducibility](<https://devfeed.tech/tags/reproducibility.md>)

### AI overview

Inference backend choice can materially affect LLM benchmark results even when model weights, decoding parameters, and hardware are held constant. Across five engines and open-weight models from 4B to 30B parameters, the study reports score shifts of up to 16.6 percentage points and frequent output disagreement. It attributes divergence to backend-specific optimizations and defaults, including prefix caching, CUDA graphs, custom kernels, and logit processing. A survey found 200 distinct engines, while analysis of 35,000 ML publications found that inference stacks are rarely reported. The authors argue that standardized reporting of inference stacks would improve benchmark reproducibility and interpretability.

### Source excerpt

arXiv:2605.19537v3 Announce Type: replace Abstract: Progress in LLMs is increasingly measured through standardized benchmarks, where state-of-the-art improvements are often separated by fractions of a percentage point. At the same time, the computational cost of evaluating modern LLMs has driven widespread adoption of specialized inference backends, software systems that execute trained models efficiently at inference time. While critical for scalability, system-level optimizations, such as custom CUDA kernels and reduced-precision arithmetic, can alter token probabilities and introduce non-determinism, possibly cascading into divergent generation. In this work, we first survey the inference landscape, identifying $200$ distinct engines, and analyze $35,000$ ML publications, finding that the specific inference stack is rarely reported despite this widespread diversity. We then present a systematic empirical study of how inference backends affect LLM benchmark results. Holding model weights, decoding parameters, and hardware constant, we evaluate five widely used inference engines, including vLLM, SGLang, and llama.cpp, across multiple open-weight models spanning 4B-30B parameters and established benchmarks. We show that the choice of backend alone can shift benchmark scores by up to $16.6$ percentage points and induce high rates of output disagreement. By isolating backend optimizations and tracing the execution pipeline, we find this divergence is driven by system-level optimizations such as prefix caching and CUDA graphs, custom kernels, and engine-specific defaults in logit processing. Our findings identify the inference backend as a previously unreported yet consequential hyperparameter in LLM evaluations and advocate standardized reporting of inference stacks to improve the reproducibility and interpretability of benchmark comparisons.

## Building Reliable iOS Apps With AI Agents

DevFeed: [Building Reliable iOS Apps With AI Agents](<https://devfeed.tech/articles/building-reliable-ios-apps-with-ai-agents-122637.md>)

Original publisher: [Read original article](<https://hackernoon.com/building-reliable-ios-apps-with-ai-agents?source=rss>)

Author: Yehor Chernenko

Published: 2026-11-08T16:39:14Z

Content type: article

Language: en

Sources: [HackerNoon](<https://devfeed.tech/sources/hackernoon.md>)

Topics: [iOS](<https://devfeed.tech/topics/ios.md>), [agentic workflows](<https://devfeed.tech/topics/agentic-workflows.md>), [Loop Engineering](<https://devfeed.tech/topics/loop-engineering.md>), [Swift](<https://devfeed.tech/topics/swift.md>), [SwiftUI](<https://devfeed.tech/topics/swiftui.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>), [Xcode](<https://devfeed.tech/topics/xcode.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [agents-md](<https://devfeed.tech/tags/agents-md.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-coding](<https://devfeed.tech/tags/ai-coding.md>), [ai-coding-agents](<https://devfeed.tech/tags/ai-coding-agents.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [api](<https://devfeed.tech/tags/api.md>), [app](<https://devfeed.tech/tags/app.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [async](<https://devfeed.tech/tags/async.md>), [auth](<https://devfeed.tech/tags/auth.md>), [automation](<https://devfeed.tech/tags/automation.md>), [await](<https://devfeed.tech/tags/await.md>), [backend](<https://devfeed.tech/tags/backend.md>), [bash](<https://devfeed.tech/tags/bash.md>), [blocking](<https://devfeed.tech/tags/blocking.md>), [c4-model](<https://devfeed.tech/tags/c4-model.md>), [claude-code](<https://devfeed.tech/tags/claude-code.md>), [design](<https://devfeed.tech/tags/design.md>), [harness](<https://devfeed.tech/tags/harness.md>), [ios](<https://devfeed.tech/tags/ios.md>), [ios-development](<https://devfeed.tech/tags/ios-development.md>), [swift-concurrency](<https://devfeed.tech/tags/swift-concurrency.md>), [system](<https://devfeed.tech/tags/system.md>), [xcode](<https://devfeed.tech/tags/xcode.md>)

### AI overview

AI agents can build, run and check iOS apps, making prototypes and MVPs largely feasible, but reliable products still require human ownership of system design and the quality harness. The article recommends documenting architecture in discoverable C4 diagrams under docs/architecture/, linked from a short AGENTS.md and detailed only as needed; a skill and Claude Code Stop hook can refresh affected diagrams after Swift changes. For quality, it prioritizes automated tests, CI and linters, then agentic review, then careful human review of small diffs. Use focused skills with good and bad examples for issues such as Swift concurrency, and keep context lean because loaded rules and docs consume tokens on every turn.

### Source excerpt

AI agents can build iOS apps end to end. For reliable apps you still own two things: the system design, kept in C4 diagrams, and the harness around the agent.

## Pulse: Heart Rate Zones From the Watch to the Trainer's Screen

DevFeed: [Pulse: Heart Rate Zones From the Watch to the Trainer's Screen](<https://devfeed.tech/articles/pulse-heart-rate-zones-from-the-watch-to-the-trainer-s-screen-122452.md>)

Original publisher: [Read original article](<https://www.codenameone.com/blog/launch-your-own-gym-app/>)

Author: Shai Almog

Published: 2026-11-08T00:00:00Z

Content type: article

Language: en

Sources: [CodeName One](<https://devfeed.tech/sources/codename-one.md>)

Topics: [Full Stack Development](<https://devfeed.tech/topics/full-stack-development.md>), [streaming data analytics](<https://devfeed.tech/topics/streaming-data-analytics.md>), [Sports](<https://devfeed.tech/topics/sports.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [app](<https://devfeed.tech/tags/app.md>), [apple](<https://devfeed.tech/tags/apple.md>), [apple-watch](<https://devfeed.tech/tags/apple-watch.md>), [backend](<https://devfeed.tech/tags/backend.md>), [beats](<https://devfeed.tech/tags/beats.md>), [class](<https://devfeed.tech/tags/class.md>), [dashboard](<https://devfeed.tech/tags/dashboard.md>), [full-stack](<https://devfeed.tech/tags/full-stack.md>), [gym](<https://devfeed.tech/tags/gym.md>), [health](<https://devfeed.tech/tags/health.md>), [java](<https://devfeed.tech/tags/java.md>), [live](<https://devfeed.tech/tags/live.md>), [pass](<https://devfeed.tech/tags/pass.md>), [pulse](<https://devfeed.tech/tags/pulse.md>), [screen](<https://devfeed.tech/tags/screen.md>), [studio](<https://devfeed.tech/tags/studio.md>), [template](<https://devfeed.tech/tags/template.md>), [trainer](<https://devfeed.tech/tags/trainer.md>), [watch](<https://devfeed.tech/tags/watch.md>)

### AI overview

Pulse is a Java studio template that collects class heart rate through HealthKit on iOS or Health Connect on Android and sends live zones to a trainer's phone. It can also connect to Bluetooth chest straps using the standard Heart Rate profile, with auto-reconnect; strap readings enter the same workout session and health history. Straps report individual beats, while watches provide platform workout statistics. The backend keeps only each member's latest reading in memory, calculates zones from heart rate relative to an age-based estimate, and does not store individual readings in the database. Completed workouts are retained and saved to the member's health store; history from other apps can also feed progress features. The template requests only the health data it uses, but its implementor's guide identifies privacy and compliance work required before release. The excerpt also describes bookings, passes, trainer tools, and owner dashboards.

### Source excerpt

Pulse is a full-stack gym and studio template in Java: bookings, a door pass, workouts and heart rate from HealthKit and Health Connect, a live class view and an owner's dashboard.

## PyTorch 2.15 tackles the 'one bad GPU' problem

DevFeed: [PyTorch 2.15 tackles the 'one bad GPU' problem](<https://devfeed.tech/articles/pytorch-2-15-tackles-the-one-bad-gpu-problem-122495.md>)

Original publisher: [Read original article](<https://www.devclass.com/devops/2026/11/07/pytorch-215-tackles-the-one-bad-gpu-problem/5308234>)

Author: Joab Jackson

Published: 2026-11-07T18:38:00Z

Content type: news

Language: en

Sources: [DevClass.com](<https://devfeed.tech/sources/devclass-com.md>)

Topics: [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [Multi-GPU](<https://devfeed.tech/topics/multi-gpu.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [apple](<https://devfeed.tech/tags/apple.md>), [backend](<https://devfeed.tech/tags/backend.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [bottlenecks](<https://devfeed.tech/tags/bottlenecks.md>), [cluster](<https://devfeed.tech/tags/cluster.md>), [clusters](<https://devfeed.tech/tags/clusters.md>), [communication](<https://devfeed.tech/tags/communication.md>), [communications](<https://devfeed.tech/tags/communications.md>), [computing](<https://devfeed.tech/tags/computing.md>), [data-movement](<https://devfeed.tech/tags/data-movement.md>), [deadlocks](<https://devfeed.tech/tags/deadlocks.md>), [devops](<https://devfeed.tech/tags/devops.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [distributed-computing](<https://devfeed.tech/tags/distributed-computing.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [job](<https://devfeed.tech/tags/job.md>), [ml](<https://devfeed.tech/tags/ml.md>), [nccl](<https://devfeed.tech/tags/nccl.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

PyTorch 2.15 rewrites its NCCL backend to improve diagnosis and recovery in multi-GPU jobs. The former ProcessGroupNCCL could create many communicators per process group, contributing to intermittent deadlocks and unpredictable performance; the new backend limits each group to one communicator at initialization, so programs needing parallelism must dispatch more groups. It also supports easier reconfiguration when a GPU is slow or fails: a third-party orchestrator can exclude it and assign a replacement worker. A new flight recorder stores operation snapshots for inspection after failures. The legacy backend remains available as a rollback in 2.15 and is slated for removal in 2.16 if the replacement works as expected. Other reported additions include an asynchronous one-way Python transport API and parallel-processing improvements for Apple hardware and Nvidia Blackwell GPUs.

### Source excerpt

ML admins will be happy to learn that one bad GPU may no longer mean starting an entire training job ...

## Coding Challenge #145 - Disk Analyser

DevFeed: [Coding Challenge #145 - Disk Analyser](<https://devfeed.tech/articles/coding-challenge-145-disk-analyser-122224.md>)

Original publisher: [Read original article](<https://codingchallenges.substack.com/p/coding-challenge-145-disk-analyser>)

Author: John Crickett

Published: 2026-11-07T09:01:03Z

Content type: tutorial

Language: en

Sources: [Coding Challenges](<https://devfeed.tech/sources/coding-challenges.md>)

Topics: [Tool](<https://devfeed.tech/topics/tool.md>), [Application Development](<https://devfeed.tech/topics/application-development.md>), [Learning](<https://devfeed.tech/topics/learning.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [app](<https://devfeed.tech/tags/app.md>), [backend](<https://devfeed.tech/tags/backend.md>), [build](<https://devfeed.tech/tags/build.md>), [cache](<https://devfeed.tech/tags/cache.md>), [challenge](<https://devfeed.tech/tags/challenge.md>), [cleanup](<https://devfeed.tech/tags/cleanup.md>), [coding](<https://devfeed.tech/tags/coding.md>), [directories](<https://devfeed.tech/tags/directories.md>), [directory](<https://devfeed.tech/tags/directory.md>), [disk-space](<https://devfeed.tech/tags/disk-space.md>), [space](<https://devfeed.tech/tags/space.md>), [tool](<https://devfeed.tech/tags/tool.md>)

### AI overview

The challenge is to build a disk analyzer that scans a chosen directory read-only, measures allocated disk usage separately from apparent file size, and visualizes the results as a navigable treemap. It emphasizes counting hard-linked storage once, not following symbolic links, handling unreadable directories with warnings, and keeping scans responsive with concurrent work and live progress. The tool should surface item sizes, ages, largest files, and likely cache or build-output candidates, while treating reclaimable space as an estimate and leaving cleanup under user control. Cleanup proceeds through staging and review, protects system paths and anything outside the scan root, rechecks staged paths before acting, and moves confirmed items to recoverable Trash. The challenge calls for reporting failures and distinguishing space moved to Trash from actual free-space changes, since Trash may not immediately free disk space.

### Source excerpt

This challenge is to build your own disk space analysis tool.

## Ubuntu Summit 26.10: Less than a week away!

DevFeed: [Ubuntu Summit 26.10: Less than a week away!](<https://devfeed.tech/articles/ubuntu-summit-26-10-less-than-a-week-away-121738.md>)

Original publisher: [Read original article](<https://canonical.com//blog/ubuntu-summit-26-10-less-than-a-week-away>)

Author: Graham Morrison

Published: 2026-11-06T16:43:41Z

Content type: news

Language: en

Sources: [Canonical](<https://devfeed.tech/sources/canonical.md>)

Topics: [Ubuntu](<https://devfeed.tech/topics/ubuntu.md>), [On-device AI](<https://devfeed.tech/topics/on-device-ai.md>), [Local AI](<https://devfeed.tech/topics/local-ai.md>), [AI Integration Strategies](<https://devfeed.tech/topics/ai-integration-strategies.md>)

Tags: [accessibility](<https://devfeed.tech/tags/accessibility.md>), [ai](<https://devfeed.tech/tags/ai.md>), [astra](<https://devfeed.tech/tags/astra.md>), [backend](<https://devfeed.tech/tags/backend.md>), [cad](<https://devfeed.tech/tags/cad.md>), [coding-agent](<https://devfeed.tech/tags/coding-agent.md>), [delivery](<https://devfeed.tech/tags/delivery.md>), [design](<https://devfeed.tech/tags/design.md>), [development](<https://devfeed.tech/tags/development.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [event](<https://devfeed.tech/tags/event.md>), [exoplanet](<https://devfeed.tech/tags/exoplanet.md>), [hax](<https://devfeed.tech/tags/hax.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [one](<https://devfeed.tech/tags/one.md>), [summit](<https://devfeed.tech/tags/summit.md>), [ubuntu](<https://devfeed.tech/tags/ubuntu.md>), [ubuntu-summit](<https://devfeed.tech/tags/ubuntu-summit.md>)

### AI overview

Ubuntu Summit 26.10 is scheduled for November 12-13, with a live, interactive stream from London and local watch parties. The twice-yearly schedule is presented as a way to surface current work and ideas; attendees can ask speakers questions and join activities remotely. The first morning's listed sessions cover open-source observatory software for exoplanet detection, scaling ntfy to four million daily push notifications, integrating AI coding agents into Unix workflows, locally processed AI for Ubuntu accessibility, and using LLMs and open-source tools to design a robot.

### Source excerpt

Ubuntu Summit 26.10: Less than a week away! Here in London, the long summer has stepped aside for cooler nights and damp, misty mornings. The trees are turning amber and the sunlight is low on the horizon, which can only mean one thing: the next Ubuntu Summit is just around the corner. In fact, it's [...]

## Yelp Combines Native and Server-Driven UI for Its AI Assistant

DevFeed: [Yelp Combines Native and Server-Driven UI for Its AI Assistant](<https://devfeed.tech/articles/yelp-combines-native-and-server-driven-ui-for-its-ai-assistant-121237.md>)

Original publisher: [Read original article](<https://www.infoq.com/news/2026/11/yelp-assistant-hybrid-ui/>)

Author: Leela Kumili

Published: 2026-11-06T14:25:00Z

Content type: article

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [server-driven-ui](<https://devfeed.tech/topics/server-driven-ui.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-architecture](<https://devfeed.tech/tags/ai-architecture.md>), [ai-assistant](<https://devfeed.tech/tags/ai-assistant.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [architecture-design](<https://devfeed.tech/tags/architecture-design.md>), [backend](<https://devfeed.tech/tags/backend.md>), [challenges](<https://devfeed.tech/tags/challenges.md>), [chaos](<https://devfeed.tech/tags/chaos.md>), [chat](<https://devfeed.tech/tags/chat.md>), [chatbots](<https://devfeed.tech/tags/chatbots.md>), [configuration](<https://devfeed.tech/tags/configuration.md>), [cross-platform](<https://devfeed.tech/tags/cross-platform.md>), [data](<https://devfeed.tech/tags/data.md>), [development](<https://devfeed.tech/tags/development.md>), [event-driven-architecture](<https://devfeed.tech/tags/event-driven-architecture.md>), [faster](<https://devfeed.tech/tags/faster.md>), [framework](<https://devfeed.tech/tags/framework.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [mobile-development](<https://devfeed.tech/tags/mobile-development.md>), [native](<https://devfeed.tech/tags/native.md>), [news](<https://devfeed.tech/tags/news.md>), [pilot](<https://devfeed.tech/tags/pilot.md>), [platform](<https://devfeed.tech/tags/platform.md>), [rendering](<https://devfeed.tech/tags/rendering.md>), [server](<https://devfeed.tech/tags/server.md>), [server-driven-ui](<https://devfeed.tech/tags/server-driven-ui.md>), [server-side-rendering](<https://devfeed.tech/tags/server-side-rendering.md>), [ui](<https://devfeed.tech/tags/ui.md>), [yelp](<https://devfeed.tech/tags/yelp.md>), [yelp-assistant-hybrid-ui](<https://devfeed.tech/tags/yelp-assistant-hybrid-ui.md>)

### AI overview

Yelp's AI Assistant uses CHAOS for server-driven conversation rendering while keeping navigation and message input native. The backend sends a view configuration when a chat starts, then sends conversation events that update the existing view without refetching that configuration. Events can carry persistent messages, temporary typing indicators and quick replies, links, or actions such as disabling input when a project request is complete. This separation lets supported presentation changes reach users without a new app release and helped Yelp reuse its Android Assistant for the Biz Ask Anything pilot. The client still handles message state and interactions; recoverable errors trigger retries, while non-recoverable failures disable input and ask users to try again later. Yelp reports coordination and learning costs, and shared implementations may still behave differently across platforms.

### Source excerpt

Yelp combines native mobile controls with server-driven conversation rendering in its AI Assistant using the CHAOS framework. The architecture separates view configuration from conversation data, supports backend-driven chat interactions, and enables faster UI updates without new app releases. Yelp also discusses cross-platform challenges and reuse for its Biz Ask Anything pilot. By Leela Kumili

## Decentralized AI Inference Needs a Boring Job Queue

DevFeed: [Decentralized AI Inference Needs a Boring Job Queue](<https://devfeed.tech/articles/decentralized-ai-inference-needs-a-boring-job-queue-121014.md>)

Original publisher: [Read original article](<https://hackernoon.com/decentralized-ai-inference-needs-a-boring-job-queue?source=rss>)

Author: William Onyejiaka

Published: 2026-11-06T08:33:30Z

Content type: article

Language: en

Sources: [HackerNoon](<https://devfeed.tech/sources/hackernoon.md>)

Topics: [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Job](<https://devfeed.tech/topics/job.md>), [AI Architecture](<https://devfeed.tech/topics/ai-architecture.md>), [DePIN](<https://devfeed.tech/topics/depin.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [ai-jobs](<https://devfeed.tech/tags/ai-jobs.md>), [backend](<https://devfeed.tech/tags/backend.md>), [bill](<https://devfeed.tech/tags/bill.md>), [bullmq](<https://devfeed.tech/tags/bullmq.md>), [decentralize-ai-hackathon](<https://devfeed.tech/tags/decentralize-ai-hackathon.md>), [decentralized](<https://devfeed.tech/tags/decentralized.md>), [decentralized-ai](<https://devfeed.tech/tags/decentralized-ai.md>), [distributed-computing](<https://devfeed.tech/tags/distributed-computing.md>), [duplicate-billing](<https://devfeed.tech/tags/duplicate-billing.md>), [engineer](<https://devfeed.tech/tags/engineer.md>), [fail](<https://devfeed.tech/tags/fail.md>), [fault-tolerant-systems](<https://devfeed.tech/tags/fault-tolerant-systems.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [gpu-inference](<https://devfeed.tech/tags/gpu-inference.md>), [idempotency](<https://devfeed.tech/tags/idempotency.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-api](<https://devfeed.tech/tags/inference-api.md>), [job](<https://devfeed.tech/tags/job.md>), [jobs](<https://devfeed.tech/tags/jobs.md>), [queue](<https://devfeed.tech/tags/queue.md>), [queues](<https://devfeed.tech/tags/queues.md>), [remote](<https://devfeed.tech/tags/remote.md>), [retries](<https://devfeed.tech/tags/retries.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>)

### AI overview

The article proposes a reliable backend path for decentralized GPU inference, centered on job queues, persistent job records, retries, and idempotency. An Express.js and TypeScript API would return a job ID; BullMQ and Redis would coordinate work, while PostgreSQL preserves job and execution history. A provider adapter would normalize common operations, starting with one provider such as Nosana while retaining provider-specific differences. On timeouts, the system should check execution status and record uncertainty rather than blindly resubmit potentially billable work. Idempotency keys and billing safeguards can prevent duplicate logical jobs and records, but cannot guarantee a provider executes only once. Results need basic validation; hashes show file integrity, not answer correctness. This is an unbenchmarked proposal: the author plans to test duplicate submissions, worker crashes, recovery, and added latency. Multi-provider routing, escrow, permanent storage, and advanced verification are deferred.

### Source excerpt

A backend engineer's proposal for reliable decentralized GPU inference using job queues, retries, and idempotency, so remote AI jobs don't fail or bill twice.

## Wave-Based Dispatch for Circuit Cutting in Hybrid HPC-Quantum Systems

DevFeed: [Wave-Based Dispatch for Circuit Cutting in Hybrid HPC-Quantum Systems](<https://devfeed.tech/articles/wave-based-dispatch-for-circuit-cutting-in-hybrid-hpc-quantum-systems-121253.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2604.15279>)

Author: Ricard S. Raigada-Garc\\'ia, Josep Jorba, Sergio Iserte

Published: 2026-11-06T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Distributed Computing](<https://devfeed.tech/sources/arxiv-distributed-computing.md>)

Topics: [High-Performance Computing](<https://devfeed.tech/topics/high-performance-computing.md>)

Tags: [backend](<https://devfeed.tech/tags/backend.md>), [backends](<https://devfeed.tech/tags/backends.md>), [circuit](<https://devfeed.tech/tags/circuit.md>), [circuits](<https://devfeed.tech/tags/circuits.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [code](<https://devfeed.tech/tags/code.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cs-dc](<https://devfeed.tech/tags/cs-dc.md>), [dispatch](<https://devfeed.tech/tags/dispatch.md>), [execution](<https://devfeed.tech/tags/execution.md>), [failover](<https://devfeed.tech/tags/failover.md>), [fragment](<https://devfeed.tech/tags/fragment.md>), [fragments](<https://devfeed.tech/tags/fragments.md>), [framework](<https://devfeed.tech/tags/framework.md>), [hpc](<https://devfeed.tech/tags/hpc.md>), [hybrid](<https://devfeed.tech/tags/hybrid.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-systems](<https://devfeed.tech/tags/quantum-systems.md>), [recovery](<https://devfeed.tech/tags/recovery.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

DQR makes circuit fragments first-class schedulable units so HPC centers can manage hybrid HPC-quantum workloads with existing resource policies. It uses backend-agnostic fragment descriptors and a wave-based coordinator with non-blocking polling, and was implemented on CESGA's Qmio system with local QPU and remote IBM Torino backends. In tests on a 32-qubit Hardware-Efficient Ansatz circuit, it improved makespan over a monolithic CPU baseline and rerouted fragments from the local QPU to classical simulators after hardware incompatibilities without restarting the pipeline. For deeper circuits, coordination residual was 5% of total execution time. These reported results suggest low coordination overhead in that evaluation; the supplied abstract does not give broader benchmark details.

### Source excerpt

arXiv:2604.15279v2 Announce Type: replace Abstract: Hybrid High-performance Computing (HPC)-quantum workloads based on circuit cutting decompose large quantum circuits into independent fragments, but existing frameworks tightly couple cutting logic to execution orchestration, preventing HPC centers from applying mature resource management policies to Noisy Intermediate-Scale Quantum (NISQ) workloads. We present DQR (Dynamic Queue Router), a runtime framework that bridges this gap by treating circuit fragments as first-class schedulable units. The framework introduces a backend-agnostic fragment descriptor to expose structural properties without requiring execution layers to parse quantum code, a wave-based coordinator that achieves pipeline concurrency via non-blocking polling, and a production-ready implementation on the CESGA Qmio supercomputer integrating both QPUs local on-premises (Qmio) and remote cloud (IBM Torino) backends. Experiments on a 32-qubit Hardware-Efficient Ansatz (HEA) circuit demonstrate not only makespan improvements over a monolithic CPU baseline but also transparent per-fragment failover recovery-specifically rerouting tasks from the local QPU to classical simulators upon encountering hardware-level incompatibilities-without pipeline restart. For deeper circuits, the coordination residual accounts for only 5% of the total execution time, highlighting the framework's scalability. These results show that DQR enables HPC centers to integrate NISQ workloads into existing production infrastructure while preserving the flexibility to adopt improved cutting algorithms or heterogeneous backend technologies.

## PlayGen2: Learning Real-Time Playable Worlds with Explicit 3D State

DevFeed: [PlayGen2: Learning Real-Time Playable Worlds with Explicit 3D State](<https://devfeed.tech/articles/playgen2-learning-real-time-playable-worlds-with-explicit-3d-state-120673.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.07867>)

Author: Jihai Zhang, Yuhan Zhou, Junyou Li, Deheng Ye, Qi Dou, Yu Cheng

Published: 2026-11-06T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [World models](<https://devfeed.tech/topics/world-models.md>), [real-time 3D](<https://devfeed.tech/topics/real-time-3d.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [backend](<https://devfeed.tech/tags/backend.md>), [completion](<https://devfeed.tech/tags/completion.md>), [control](<https://devfeed.tech/tags/control.md>), [cs-cv](<https://devfeed.tech/tags/cs-cv.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [discovery](<https://devfeed.tech/tags/discovery.md>), [doom](<https://devfeed.tech/tags/doom.md>), [evolution](<https://devfeed.tech/tags/evolution.md>), [extensibility](<https://devfeed.tech/tags/extensibility.md>), [fixed](<https://devfeed.tech/tags/fixed.md>), [framework](<https://devfeed.tech/tags/framework.md>), [game](<https://devfeed.tech/tags/game.md>), [gameplay](<https://devfeed.tech/tags/gameplay.md>), [generation](<https://devfeed.tech/tags/generation.md>), [grid](<https://devfeed.tech/tags/grid.md>), [identity](<https://devfeed.tech/tags/identity.md>), [learning](<https://devfeed.tech/tags/learning.md>), [memory](<https://devfeed.tech/tags/memory.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [state](<https://devfeed.tech/tags/state.md>), [worlds](<https://devfeed.tech/tags/worlds.md>)

### AI overview

PlayGen2 models a playable world through persistent 3D state rather than feeding generated frames back into its predictions. Its memory stores static geometry in a volumetric occupancy grid and tracks the player and other entities with identity-indexed 3D states. An action-conditioned model updates entity dynamics, completes geometry, and discovers entities in a local region; a separate neural renderer produces first-person views from depth and semantic projections. In Doom tests, it learned geometry-aware movement and player-NPC interactions. The authors report about 160 ms backend latency for the full pipeline on a MacBook Pro with an M4 Pro chip, zero-shot deployment on unseen Doom maps, multiplayer interaction by composing single-player models, and rule-based interventions for programmable gameplay. The evidence is an abstract and does not provide further evaluation details.

### Source excerpt

arXiv:2611.07867v1 Announce Type: cross Abstract: Building a playable world requires more than generating plausible video: it demands responsive control, persistent spatial structure, and coherent interactions with dynamic entities. Using generated frames as the sole recurrent world representation ties these requirements to view-dependent appearance synthesis, making it difficult to maintain long-term spatial and temporal consistency in generation and interaction. We present PlayGen2, a real-time interactive world model that separates learned 3D state evolution from neural rendering. PlayGen2 maintains a persistent world memory comprising static geometry represented by a volumetric occupancy grid and identity-indexed 3D states for the player and other entities. Within a fixed-size local region, its world-state model jointly learns action-conditioned entity dynamics, geometry completion, and entity discovery, committing updates to a persistent world memory. A separate neural renderer synthesizes first-person observations from depth and semantic projections of the world state, without feeding generated images back into state prediction. Using Doom as a testbed, we demonstrate that the model successfully learns world dynamics including geometry-aware player motion and player--NPC interactions. The framework is computationally efficient, enabling the complete simulation and rendering pipeline to achieve approximately 160ms backend latency on a MacBook Pro with an M4 Pro chip. Beyond single-player interaction, we demonstrate zero-shot deployment on unseen Doom maps, multiplayer interaction through the composition of single-player models, and programmable gameplay through rule-based state interventions. These capabilities demonstrate the transferability and extensibility of the learned game-world model. Code and pretrained model weights are publicly available.

## Embedded Java: 20x Smaller Than Chrome, Faster Than Rust, Royalty Free

DevFeed: [Embedded Java: 20x Smaller Than Chrome, Faster Than Rust, Royalty Free](<https://devfeed.tech/articles/embedded-java-20x-smaller-than-chrome-faster-than-rust-royalty-free-120961.md>)

Original publisher: [Read original article](<https://www.codenameone.com/blog/embedded-linux-ui-in-18-mb/>)

Author: Shai Almog

Published: 2026-11-06T00:00:00Z

Content type: article

Language: en

Sources: [CodeName One](<https://devfeed.tech/sources/codename-one.md>)

Topics: [Embedded Systems](<https://devfeed.tech/topics/embedded-systems.md>), [Chromium](<https://devfeed.tech/topics/chromium.md>), [Rust](<https://devfeed.tech/topics/rust.md>), [Linux Kernel](<https://devfeed.tech/topics/linux-kernel.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [Cross-Compilation](<https://devfeed.tech/topics/cross-compilation.md>), [Lvgl](<https://devfeed.tech/topics/lvgl.md>), [Test automation](<https://devfeed.tech/topics/test-automation.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>), [buildroot](<https://devfeed.tech/topics/buildroot.md>), [Zephyr RTOS](<https://devfeed.tech/topics/zephyr-rtos.md>)

Tags: [1-3](<https://devfeed.tech/tags/1-3.md>), [1-4](<https://devfeed.tech/tags/1-4.md>), [backend](<https://devfeed.tech/tags/backend.md>), [chrome](<https://devfeed.tech/tags/chrome.md>), [chromium](<https://devfeed.tech/tags/chromium.md>), [codename](<https://devfeed.tech/tags/codename.md>), [embedded](<https://devfeed.tech/tags/embedded.md>), [embedded-linux](<https://devfeed.tech/tags/embedded-linux.md>), [faster](<https://devfeed.tech/tags/faster.md>), [free](<https://devfeed.tech/tags/free.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [headless](<https://devfeed.tech/tags/headless.md>), [java](<https://devfeed.tech/tags/java.md>), [linux](<https://devfeed.tech/tags/linux.md>), [memory](<https://devfeed.tech/tags/memory.md>), [more](<https://devfeed.tech/tags/more.md>), [one](<https://devfeed.tech/tags/one.md>), [rust](<https://devfeed.tech/tags/rust.md>), [ui](<https://devfeed.tech/tags/ui.md>), [with](<https://devfeed.tech/tags/with.md>)

### AI overview

Codename One presents an embedded Linux Java/Kotlin stack for device UIs, headless services, companion apps and cloud backends, with shared classes across targets and built-in hardware APIs. In its gauge benchmark, the software renderer used 18 MB without a GPU; the article reports Chromium at 400 MB and Flutter's software path at 112 MB and 1.4 CPU cores. In a separate two-core service benchmark, it reports 379,900 requests per second, 1.3 times Rust axum's peak, while noting Rust and Go use less memory. These tests ran in a Debian arm64 container on Apple Silicon, not on an embedded board. The product currently supports embedded Linux, with board profiles, SSH deployment and a simulator; other operating systems would require porting. It is GPL 2 with the Classpath exception and has no per-device royalties. The article says long-term maintenance and backports for certified product branches are available as paid support.

### Source excerpt

Codename One now targets embedded Linux: a touch UI in a twentieth of a Chromium kiosk's memory with no GPU, and a headless backend that served more requests than Rust's axum on two cores. Royalty free.

## Hardening Production Multi-Container Topologies with Docker Compose v2

DevFeed: [Hardening Production Multi-Container Topologies with Docker Compose v2](<https://devfeed.tech/articles/hardening-production-multi-container-topologies-with-docker-compose-v2-119322.md>)

Original publisher: [Read original article](<https://hackernoon.com/hardening-production-multi-container-topologies-with-docker-compose-v2?source=rss>)

Author: Siva Sai Krishna Suryadevara

Published: 2026-11-05T15:31:57Z

Content type: article

Language: en

Sources: [HackerNoon](<https://devfeed.tech/sources/hackernoon.md>)

Topics: [Docker Compose](<https://devfeed.tech/topics/docker-compose.md>), [container-networking](<https://devfeed.tech/topics/container-networking.md>), [container escape](<https://devfeed.tech/topics/container-escape.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [backend](<https://devfeed.tech/tags/backend.md>), [blueprint](<https://devfeed.tech/tags/blueprint.md>), [compose](<https://devfeed.tech/tags/compose.md>), [container](<https://devfeed.tech/tags/container.md>), [container-hardening](<https://devfeed.tech/tags/container-hardening.md>), [container-security](<https://devfeed.tech/tags/container-security.md>), [docker](<https://devfeed.tech/tags/docker.md>), [docker-compose](<https://devfeed.tech/tags/docker-compose.md>), [docker-networking](<https://devfeed.tech/tags/docker-networking.md>), [docker-secrets](<https://devfeed.tech/tags/docker-secrets.md>), [docker-security](<https://devfeed.tech/tags/docker-security.md>), [hackernoon-top-story](<https://devfeed.tech/tags/hackernoon-top-story.md>), [hardening](<https://devfeed.tech/tags/hardening.md>), [health](<https://devfeed.tech/tags/health.md>), [least-privilege](<https://devfeed.tech/tags/least-privilege.md>), [network](<https://devfeed.tech/tags/network.md>), [production](<https://devfeed.tech/tags/production.md>), [race-conditions](<https://devfeed.tech/tags/race-conditions.md>), [startup](<https://devfeed.tech/tags/startup.md>), [v2](<https://devfeed.tech/tags/v2.md>), [yaml-configuration](<https://devfeed.tech/tags/yaml-configuration.md>)

### AI overview

The excerpt's production Compose blueprint makes startup readiness, network access, secrets, and resource limits explicit. Long-form `depends_on` gates the API on a TCP health check for Postgres, then on a one-shot migration's successful exit; this avoids startup races and concurrent migration runs when scaling the app. Separate `dmz` and internal backend networks leave only Nginx's port published and isolate the data stores. File-sourced Compose secrets stay out of environment variables and `docker inspect`, though outside Swarm their host file permissions control container access. The blueprint also applies CPU, memory, and process limits, non-root users, read-only filesystems, and dropped capabilities. It notes named-volume compatibility with image ownership, bind-mount ownership pitfalls, Redis cache headroom, and rootless Docker's restriction on publishing ports 80/443. It recommends validating the rendered configuration, service health, migration exit status, network isolation, and applied limits.

### Source excerpt

Eliminate silent startup failures, race conditions, and network exposure in multi-container stacks using Docker Compose v2 health gates and least privilege.

## Frontend vs Backend Explained for Beginners 2026

DevFeed: [Frontend vs Backend Explained for Beginners 2026](<https://devfeed.tech/articles/frontend-vs-backend-explained-for-beginners-2026-119805.md>)

Original publisher: [Read original article](<https://talent500.com/blog/frontend-vs-backend/>)

Author: Ekakshar

Published: 2026-11-05T10:22:21Z

Content type: article

Language: en

Sources: [Talent500](<https://devfeed.tech/sources/backend-archives-talent500-blog.md>)

Topics: [Back end](<https://devfeed.tech/topics/backend.md>), [Front end](<https://devfeed.tech/topics/frontend.md>), [Web Development](<https://devfeed.tech/topics/web-development.md>), [Learning](<https://devfeed.tech/topics/learning.md>), [Full Stack Development](<https://devfeed.tech/topics/full-stack-development.md>), [backend frameworks](<https://devfeed.tech/topics/backend-frameworks.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [Web Accessibility (a11y)](<https://devfeed.tech/topics/web-accessibility.md>), [Tool](<https://devfeed.tech/topics/tool.md>), [performance-optimization](<https://devfeed.tech/topics/performance-optimization.md>), [Node.js](<https://devfeed.tech/topics/node-js.md>), [Vite](<https://devfeed.tech/topics/vite.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [api](<https://devfeed.tech/tags/api.md>), [api-design](<https://devfeed.tech/tags/api-design.md>), [api-development](<https://devfeed.tech/tags/api-development.md>), [backend](<https://devfeed.tech/tags/backend.md>), [backend-developer-skills](<https://devfeed.tech/tags/backend-developer-skills.md>), [backend-development](<https://devfeed.tech/tags/backend-development.md>), [beginners](<https://devfeed.tech/tags/beginners.md>), [career](<https://devfeed.tech/tags/career.md>), [development](<https://devfeed.tech/tags/development.md>), [frameworks](<https://devfeed.tech/tags/frameworks.md>), [frontend](<https://devfeed.tech/tags/frontend.md>), [frontend-and-backend-frameworks](<https://devfeed.tech/tags/frontend-and-backend-frameworks.md>), [frontend-and-backend-programming-languages](<https://devfeed.tech/tags/frontend-and-backend-programming-languages.md>), [frontend-developer-skills](<https://devfeed.tech/tags/frontend-developer-skills.md>), [frontend-development](<https://devfeed.tech/tags/frontend-development.md>), [frontend-vs-backend](<https://devfeed.tech/tags/frontend-vs-backend.md>), [frontend-vs-backend-explained-for-beginners-2026](<https://devfeed.tech/tags/frontend-vs-backend-explained-for-beginners-2026.md>), [full-stack-development](<https://devfeed.tech/tags/full-stack-development.md>), [languages](<https://devfeed.tech/tags/languages.md>), [programming](<https://devfeed.tech/tags/programming.md>), [programming-languages](<https://devfeed.tech/tags/programming-languages.md>), [roles](<https://devfeed.tech/tags/roles.md>), [tools](<https://devfeed.tech/tags/tools.md>), [web](<https://devfeed.tech/tags/web.md>), [web-development](<https://devfeed.tech/tags/web-development.md>), [web-development-career-paths](<https://devfeed.tech/tags/web-development-career-paths.md>)

### AI overview

Frontend development builds the interface people see and use, typically with HTML, CSS, and JavaScript; backend development processes requests, manages data and business rules, and enforces security on servers through APIs and databases. They work together: the frontend sends input to backend services and presents their responses. Browser-side checks can improve usability, but sensitive validation and permissions must be enforced by the backend because client code can be bypassed. The article recommends starting with the area that best matches your interests: interface design and interaction for frontend, or logic, data, and system behavior for backend. Learn one area deeply before expanding; basic knowledge of the other helps collaboration, and full-stack work adds breadth. It lists example stacks and skills, while noting that hiring demand varies by region and employer and neither path guarantees an easier first job.

### Source excerpt

Confused about Frontend vs Backend development? Understand how these two sides of web development work together to create functional websites and applications. Explore their roles, programming languages, frameworks, tools, key differences, and career paths to find where your interests fit best. The post Frontend vs Backend Explained for Beginners 2026 appeared first on Talent500 blog.

## Smartphone-Aided VR Exergames for HIIT: Gamifying Fitness with Real-Time Pose Estimation

DevFeed: [Smartphone-Aided VR Exergames for HIIT: Gamifying Fitness with Real-Time Pose Estimation](<https://devfeed.tech/articles/smartphone-aided-vr-exergames-for-hiit-gamifying-fitness-with-real-time-pose-estimation-118503.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.06734>)

Author: Abhiram Kothagundu, Pheelip Pritipal Raipure, Abhijit Chandrakant Patil, Himangshu Sarma, Christian Eichhorn, David A. Plecher, Johanna Pirker

Published: 2026-11-05T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>)

Topics: [Frontend & real-time](<https://devfeed.tech/topics/frontend-real-time.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [algorithm](<https://devfeed.tech/tags/algorithm.md>), [anthropic](<https://devfeed.tech/tags/anthropic.md>), [backend](<https://devfeed.tech/tags/backend.md>), [camera](<https://devfeed.tech/tags/camera.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-hc](<https://devfeed.tech/tags/cs-hc.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [feedback](<https://devfeed.tech/tags/feedback.md>), [fitness](<https://devfeed.tech/tags/fitness.md>), [focus](<https://devfeed.tech/tags/focus.md>), [form](<https://devfeed.tech/tags/form.md>), [full](<https://devfeed.tech/tags/full.md>), [gpt](<https://devfeed.tech/tags/gpt.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [model-context-protocol](<https://devfeed.tech/tags/model-context-protocol.md>), [model-context-protocol-mcp](<https://devfeed.tech/tags/model-context-protocol-mcp.md>), [motion-capture](<https://devfeed.tech/tags/motion-capture.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [smartphone](<https://devfeed.tech/tags/smartphone.md>), [vr](<https://devfeed.tech/tags/vr.md>)

### AI overview

The system uses a tripod-mounted smartphone camera and real-time pose estimation to track 11 HIIT combo moves, then analyzes biomechanical data through an MCP backend and GPT-4o to provide personalized feedback on form, efficiency, intensity, and potential injury risks. In a within-subject study of 20 participants aged 18-25, VR sessions scored higher than non-VR sessions for enjoyment, immersion/focus, and likelihood to recommend; confidence to continue did not differ significantly, and perceived intensity was comparable. Feedback agreed with one certified trainer's evaluations in 87.5% of 10 sessions.

### Source excerpt

arXiv:2611.06734v1 Announce Type: cross Abstract: This paper introduces an innovative virtual reality (VR) exergame system designed to gamify High-Intensity Interval Training (HIIT) workouts through 11 predefined combo moves, including squat-punch combinations, bilateral arm strikes, and diagonal stretch combos. Unlike conventional tracking methods that often rely on wearable motion capture suits or limited headset-based sensors, our system uses a standard smartphone camera placed on a tripod to capture full-body movement. This approach offers a cost-effective and scalable alternative, particularly well-suited to the fast, dynamic motions typical of HIIT routines. The smartphone footage is processed in real time using a pose estimation algorithm, with extracted biomechanical data analyzed by a backend server built on the Model Context Protocol (MCP), Anthropic's open standard for connecting AI models to structured data sources and tools, and a GPT-4o large language model. The system generates personalized assessments of form accuracy, movement efficiency, exercise intensity, and potential injury risks, tailored specifically to high-impact, time-sensitive exercises. A within-subject study involving 20 participants (ages 18-25) compared experiences in VR and Non-VR HIIT sessions. Results indicated significantly higher enjoyment (Mean = 3.5 vs 2.8, p = 0.02), improved immersion/focus (Mean = 3.2 vs 2.8, p = 0.03), and increased likelihood to recommend (Mean = 3.2 vs 2.7, p = 0.04) under the VR condition. Confidence to continue did not differ significantly between conditions (p = 0.15), while perceived intensity remained comparable. The MCP system showed strong agreement with expert evaluation (87.5%, N = 10 sessions, one certified trainer), supporting its reliability as a feedback tool.

## Performance Portability of Out-of-Kernel Optimizations on GPUs: A Molecular Docking Case Study

DevFeed: [Performance Portability of Out-of-Kernel Optimizations on GPUs: A Molecular Docking Case Study](<https://devfeed.tech/articles/performance-portability-of-out-of-kernel-optimizations-on-gpus-a-molecular-docking-case-study-119110.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.06207>)

Author: Gianmarco Accordi, Leonardo Beltrame, Theresa Pollinger, Jens Domke, Davide Gadioli, Gianluca Palermo

Published: 2026-11-05T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Distributed Computing](<https://devfeed.tech/sources/arxiv-distributed-computing.md>)

Topics: [GPU optimization](<https://devfeed.tech/topics/gpu-optimization.md>), [Distributed Systems & Parallel Computing](<https://devfeed.tech/topics/distributed-systems-parallel-computing.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>)

Tags: [amd](<https://devfeed.tech/tags/amd.md>), [applications](<https://devfeed.tech/tags/applications.md>), [backend](<https://devfeed.tech/tags/backend.md>), [batching](<https://devfeed.tech/tags/batching.md>), [case-study](<https://devfeed.tech/tags/case-study.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cs-dc](<https://devfeed.tech/tags/cs-dc.md>), [cs-pf](<https://devfeed.tech/tags/cs-pf.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [execution](<https://devfeed.tech/tags/execution.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [gpus](<https://devfeed.tech/tags/gpus.md>), [hip](<https://devfeed.tech/tags/hip.md>), [intel](<https://devfeed.tech/tags/intel.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [memory](<https://devfeed.tech/tags/memory.md>), [methodology](<https://devfeed.tech/tags/methodology.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [optimizations](<https://devfeed.tech/tags/optimizations.md>), [performance](<https://devfeed.tech/tags/performance.md>)

### AI overview

For irregular multi-kernel GPU workloads, tuning decisions outside kernels--batching, worker concurrency, and per-worker memory allocation--can improve throughput across platforms. The authors implement explicit resource bounds in muDock's manager/worker model, where host workers share a GPU through independent streams, and evaluate NVIDIA, AMD, and Intel GPUs with CUDA, HIP, and SYCL. The memory-bounded formulation beats the occupancy-guided baseline's peak throughput across all tested platform/backend pairs; fully tuning the methodology improves end-to-end throughput in every tested configuration, with a reported average speedup of 1.53x. The evidence provided is the abstract, so it does not specify per-platform results or tuning details.

### Source excerpt

arXiv:2611.06207v1 Announce Type: new Abstract: Achieving performance portability is challenging for irregular scientific applications built as repeated multi-kernel GPU pipelines, because throughput depends on out-of-kernel decisions as much as on kernel efficiency. Using molecular docking as a representative case study, we propose an out-of-kernel tuning methodology that treats batching policy, worker concurrency, and per-worker memory allocation as explicit optimization parameters, replacing backend-dependent occupancy-driven choices with a more portable execution strategy based on explicit resource bounds. We implement this methodology in the muDock mini-app under a manager/worker execution model where multiple host workers share a GPU through independent streams. We then evaluate the methodology on NVIDIA, AMD, and Intel GPUs across CUDA, HIP, and SYCL implementations. Across all evaluated platform/backend pairs, the memory-bounded formulation exceeds the peak throughput of the occupancy-guided baseline. The fully tuned methodology improves the end-to-end throughput on all tested configurations, achieving an average speedup of 1.53x over the baseline.

## Let each customer bring their own OAuth app with WorkOS Pipes

DevFeed: [Let each customer bring their own OAuth app with WorkOS Pipes](<https://devfeed.tech/articles/let-each-customer-bring-their-own-oauth-app-with-workos-pipes-121081.md>)

Original publisher: [Read original article](<https://workos.com/blog/pipes-bring-your-own-oauth-app>)

Author: WorkOS

Published: 2026-11-05T00:00:00Z

Content type: article

Language: en

Sources: [WorkOS](<https://devfeed.tech/sources/workos-blog.md>)

Topics: [OAuth](<https://devfeed.tech/topics/oauth.md>), [OAuth 2.0](<https://devfeed.tech/topics/oauth2.md>)

Tags: [admin](<https://devfeed.tech/tags/admin.md>), [api](<https://devfeed.tech/tags/api.md>), [app](<https://devfeed.tech/tags/app.md>), [application](<https://devfeed.tech/tags/application.md>), [backend](<https://devfeed.tech/tags/backend.md>), [credentials](<https://devfeed.tech/tags/credentials.md>), [custom](<https://devfeed.tech/tags/custom.md>), [customer](<https://devfeed.tech/tags/customer.md>), [data](<https://devfeed.tech/tags/data.md>), [data-integration](<https://devfeed.tech/tags/data-integration.md>), [docs](<https://devfeed.tech/tags/docs.md>), [oauth](<https://devfeed.tech/tags/oauth.md>), [secret](<https://devfeed.tech/tags/secret.md>), [widget](<https://devfeed.tech/tags/widget.md>), [workos](<https://devfeed.tech/tags/workos.md>)

### AI overview

WorkOS Pipes organization credentials let each customer register and control its own OAuth app. This is configured per provider for the whole environment: once enabled, every organization must supply a client ID and secret before its members can connect. Admins can configure credentials through the permission-gated PipesAdmin widget or API; members use the standard Pipes widget, and each member still authorizes a personal connection scoped to their organization. Setup requires creating the OAuth app, registering Pipes' redirect URI, saving the credential pair, choosing scopes, and enabling the provider. Rotation replaces the stored secret; retirement happens in the provider's console, while disabling a provider only hides it from that organization's members. The excerpt notes there is no documented remove-credentials control and no Node SDK method for the configuration API.

### Source excerpt

When a provider needs each customer's own OAuth app, the Pipes Admin widget lets their admin register it, rotate the secret and retire it without your help.

## Caches, Certificates, Streaming Uploads and a Simulator That Types

DevFeed: [Caches, Certificates, Streaming Uploads and a Simulator That Types](<https://devfeed.tech/articles/caches-certificates-streaming-uploads-and-a-simulator-that-types-118815.md>)

Original publisher: [Read original article](<https://www.codenameone.com/blog/backend-caches-certificates-and-a-simulator-that-types/>)

Author: Shai Almog

Published: 2026-11-05T00:00:00Z

Content type: article

Language: en

Sources: [CodeName One](<https://devfeed.tech/sources/codename-one.md>)

Topics: [backend frameworks](<https://devfeed.tech/topics/backend-frameworks.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [keyboard](<https://devfeed.tech/topics/keyboard.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [certificates](<https://devfeed.tech/topics/certificates.md>), [GUI](<https://devfeed.tech/topics/gui.md>), [cache-invalidation](<https://devfeed.tech/topics/cache-invalidation.md>), [infrastructure monitoring](<https://devfeed.tech/topics/infrastructure-monitoring.md>)

Tags: [backend](<https://devfeed.tech/tags/backend.md>), [cache](<https://devfeed.tech/tags/cache.md>), [certificates](<https://devfeed.tech/tags/certificates.md>), [keyboard](<https://devfeed.tech/tags/keyboard.md>), [simulator](<https://devfeed.tech/tags/simulator.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [test](<https://devfeed.tech/tags/test.md>), [uploads](<https://devfeed.tech/tags/uploads.md>), [validation](<https://devfeed.tech/tags/validation.md>), [video](<https://devfeed.tech/tags/video.md>)

### AI overview

The excerpt describes Codename One backend additions and simulator features. The backend now includes a bounded, expiring cache with eviction callbacks and metrics; cached OAuth access tokens with pre-expiry refresh; PEM certificate-chain validation against private anchors; centralized exception handling, interceptors and validation; streaming request bodies that spool large uploads to disk; and a proxy restricted to its target and allowed hosts. JSON mapping adds custom codecs and broader type support, while ORM maps selected java.time values to numeric epoch units. Controller test slices start only listed components and use MockMvc without a socket. The simulator can emulate iOS keyboard behavior and Android resize or pan modes, and record video or GIF with captions and touch indicators. Its keyboard behavior is an emulation, so the excerpt recommends a final device check. The backend remains a tech preview.

### Source excerpt

Eight backend upgrades, from a bounded cache to certificate validation and test slices, plus a simulator that slides up a virtual keyboard and records video.

## Spring Cloud 2026.0.0-RC2 (aka Paddington) Has Been Released

DevFeed: [Spring Cloud 2026.0.0-RC2 (aka Paddington) Has Been Released](<https://devfeed.tech/articles/spring-cloud-2026-0-0-rc2-aka-paddington-has-been-released-121034.md>)

Original publisher: [Read original article](<https://spring.io/blog/2026/11/05/spring-cloud-2026-0-0-RC2-has-been-released>)

Author: ryanjbaxter

Published: 2026-11-05T00:00:00Z

Content type: release

Language: en

Sources: [Spring](<https://devfeed.tech/sources/spring.md>)

Topics: [Spring Framework](<https://devfeed.tech/topics/spring-framework.md>), [project release](<https://devfeed.tech/topics/project-release.md>), [Spring Boot](<https://devfeed.tech/topics/spring-boot.md>), [Maven](<https://devfeed.tech/topics/maven.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [backend](<https://devfeed.tech/tags/backend.md>), [batch](<https://devfeed.tech/tags/batch.md>), [boot](<https://devfeed.tech/tags/boot.md>), [candidate](<https://devfeed.tech/tags/candidate.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-storage](<https://devfeed.tech/tags/cloud-storage.md>), [config](<https://devfeed.tech/tags/config.md>), [event-driven](<https://devfeed.tech/tags/event-driven.md>), [files](<https://devfeed.tech/tags/files.md>), [filter](<https://devfeed.tech/tags/filter.md>), [gateway](<https://devfeed.tech/tags/gateway.md>), [google](<https://devfeed.tech/tags/google.md>), [google-cloud-storage](<https://devfeed.tech/tags/google-cloud-storage.md>), [java](<https://devfeed.tech/tags/java.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [reactive](<https://devfeed.tech/tags/reactive.md>), [release](<https://devfeed.tech/tags/release.md>), [resource](<https://devfeed.tech/tags/resource.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [spring](<https://devfeed.tech/tags/spring.md>), [spring-boot](<https://devfeed.tech/tags/spring-boot.md>), [spring-boot-4](<https://devfeed.tech/tags/spring-boot-4.md>), [spring-cloud](<https://devfeed.tech/tags/spring-cloud.md>), [text](<https://devfeed.tech/tags/text.md>), [train](<https://devfeed.tech/tags/train.md>), [web-applications](<https://devfeed.tech/tags/web-applications.md>)

### AI overview

Spring Cloud 2026.0.0-RC2 (Paddington) is available from the Spring milestone repository and is based on Spring Boot 4.2.0-RC2. Notable changes include Google Cloud Storage support as a Spring Cloud Config Server backend, serving plain text and binary files as a Resource, a LocalResponseCache filter for Spring Cloud Gateway Server Web MVC, and nested query parameters for nested objects in OpenFeign's @SpringQueryMap. The announcement shows how to import the Spring Cloud BOM with Maven or Gradle and use starters such as Config and Netflix Eureka Client. This is an RC2 release; the supplied text points to release notes and a GitHub project board for full details.

### Source excerpt

On behalf of the community, I am pleased to announce that the Release Candidate 2 (RC2) of the Spring Cloud 2026.0.0-RC2 Release Train is available today. The release can be found in the Spring milestone repository. You can check out the 2026.0.0-RC2 release notes for more information. Notable Changes in the 2026.0.0-RC2 Release Train This release is based on Spring Boot 4.2.0-RC2. A complete list of changes can be found in the project board on GitHub. Spring Cloud Config Google Cloud Storage now supported as a backend of the Config Server (#3343) Plain text and binary files can now be fetched from the Config Server as a Resource (#3355) Spring Cloud Gateway Add LocalResponseCache filter to Spring Cloud Gateway Server Web MVC (#4249) Spring Cloud Openfeign Send the nested objects of @SpringQueryMap as nested query parameters (#1448) The following modules were updated as part of 2026.0.0-RC2: Module Version Issues Spring Cloud Build 5.1.0-RC2 (issues) Spring Cloud Circuitbreaker 5.1.0-RC2 (issues) Spring Cloud Commons 5.1.0-RC2 (issues) Spring Cloud Config 5.1.0-RC2 (issues) Spring Cloud Consul 5.1.0-RC2 (issues) Spring Cloud Function 5.1.0-RC2 (issues) Spring Cloud Gateway 5.1.0-RC2 (issues) Spring Cloud Kubernetes 5.1.0-RC2 (issues) Spring Cloud Netflix 5.1.0-RC2 (issues) Spring Cloud Openfeign 5.1.0-RC2 (issues) Spring Cloud Starter Build 2026.0.0-RC2 (issues) Spring Cloud Stream 5.1.0-RC2 (issues) Spring Cloud Task 5.1.0-RC2 (issues) Spring Cloud Vault 5.1.0-RC2 (issues) Spring Cloud Zookeeper 5.1.0-RC2 (issues) As always, we welcome feedback on GitHub, on Gitter, on Stack Overflow, or on Twitter. To get started with Maven with a BOM (dependency management only): <properties> <spring-cloud.version>2026.0.0-RC2</spring-cloud.version> </properties> <dependencyManagement> <dependencies> <dependency> <groupId>org.springframework.cloud</groupId> <artifactId>spring-cloud-dependencies</artifactId> <version>${spring-cloud.version}</version> <type>pom</type> <scope>import

## NVIDIA CUDA 13.5 Brings NVIDIA Vera Rubin GA Support

DevFeed: [NVIDIA CUDA 13.5 Brings NVIDIA Vera Rubin GA Support](<https://devfeed.tech/articles/nvidia-cuda-13-5-brings-nvidia-vera-rubin-ga-support-117642.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-cuda-13-5-brings-nvidia-vera-rubin-ga-support/>)

Author: Shara Tibken

Published: 2026-11-04T18:46:07Z

Content type: release

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [CUDA](<https://devfeed.tech/topics/cuda.md>), [CUDA Tile](<https://devfeed.tech/topics/cuda-tile.md>), [NVIDIA Vera Rubin](<https://devfeed.tech/topics/nvidia-vera-rubin.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [CUDA Graphs](<https://devfeed.tech/topics/cuda-graphs.md>), [Tool](<https://devfeed.tech/topics/tool.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [64-bit](<https://devfeed.tech/tags/64-bit.md>), [accelerated-computing](<https://devfeed.tech/tags/accelerated-computing.md>), [acceleration](<https://devfeed.tech/tags/acceleration.md>), [accounting](<https://devfeed.tech/tags/accounting.md>), [agent-skill](<https://devfeed.tech/tags/agent-skill.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-coding-agents](<https://devfeed.tech/tags/ai-coding-agents.md>), [ai-workloads](<https://devfeed.tech/tags/ai-workloads.md>), [arm](<https://devfeed.tech/tags/arm.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [attention](<https://devfeed.tech/tags/attention.md>), [automatic](<https://devfeed.tech/tags/automatic.md>), [backend](<https://devfeed.tech/tags/backend.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-tile](<https://devfeed.tech/tags/cuda-tile.md>), [cutile](<https://devfeed.tech/tags/cutile.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [machine-learning-artificial-intelligence](<https://devfeed.tech/tags/machine-learning-artificial-intelligence.md>), [nsight-tools-compute](<https://devfeed.tech/tags/nsight-tools-compute.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera-rubin](<https://devfeed.tech/tags/nvidia-vera-rubin.md>), [python](<https://devfeed.tech/tags/python.md>), [release](<https://devfeed.tech/tags/release.md>), [rubin](<https://devfeed.tech/tags/rubin.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

CUDA Toolkit 13.5 makes NVIDIA Vera Rubin support generally available and adds platform and workload controls across CUDA. CUDA 13.5 can partition all SMs across locality domains, with discovery mode reporting potentially unequal partition sizes. MPS v3 adds weighted timeslicing, borrowable SM capacity, cgroup routing, namespace assignments, and polling as a memory-accounting fallback; polling adds work during eviction decisions. CUDA Graphs can capture batched 1D copies, and CUDA Tile adds multi-GPU communication plus preview support for calling selected SIMT library functions; custom device-function calls remain planned. CUDA Python and CCCL add batch copies, graph updates, public CUB tuning, batched Top-K, reproducible scans, and reductions that can consume GPU-resident counts. The release also updates profiling and debugging tools, math libraries, compiler controls, PTX, and binary inspection. On Rubin, WDDM and MCDM are supported; TCC is not. The excerpt describes many enhancements but directs readers to release notes for full compatibility details.

### Source excerpt

As artificial intelligence and accelerated computing continue to push the boundaries of what's possible, the latest NVIDIA CUDA release arrives to unlock...

## Render just removed its AI sandbox time limit -- and Vercel still has a 24-hour cap

DevFeed: [Render just removed its AI sandbox time limit -- and Vercel still has a 24-hour cap](<https://devfeed.tech/articles/render-just-removed-its-ai-sandbox-time-limit-and-vercel-still-has-a-24-hour-cap-117679.md>)

Original publisher: [Read original article](<https://thenewstack.io/render-vercel-ai-sandboxes/>)

Author: Amanda Caswell

Published: 2026-11-04T18:04:17Z

Content type: news

Language: en

Sources: [The New Stack](<https://devfeed.tech/sources/the-new-stack.md>)

Topics: [AI Sandbox](<https://devfeed.tech/topics/ai-sandbox.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Vercel](<https://devfeed.tech/topics/vercel.md>), [JavaScript](<https://devfeed.tech/topics/javascript.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agent-workflows](<https://devfeed.tech/tags/agent-workflows.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [ai-sandbox](<https://devfeed.tech/tags/ai-sandbox.md>), [ai-sandboxes](<https://devfeed.tech/tags/ai-sandboxes.md>), [api](<https://devfeed.tech/tags/api.md>), [applications](<https://devfeed.tech/tags/applications.md>), [aws](<https://devfeed.tech/tags/aws.md>), [backend](<https://devfeed.tech/tags/backend.md>), [background](<https://devfeed.tech/tags/background.md>), [background-work](<https://devfeed.tech/tags/background-work.md>), [cloud-services](<https://devfeed.tech/tags/cloud-services.md>), [long-running](<https://devfeed.tech/tags/long-running.md>), [render](<https://devfeed.tech/tags/render.md>), [run](<https://devfeed.tech/tags/run.md>), [sandbox](<https://devfeed.tech/tags/sandbox.md>), [sandboxes](<https://devfeed.tech/tags/sandboxes.md>), [vercel](<https://devfeed.tech/tags/vercel.md>)

### AI overview

Render removed its 30-day continuous runtime limit on paid workspace plans, offering AI sandboxes that can suspend when idle and resume with files, memory, and running processes intact. Vercel caps individual sandbox sessions at 24 hours; the article says persistent Vercel sandboxes can restart, but their processes start over. Render's Durable Workflows, written in Python or TypeScript, save each completed step and can resume after a crash on another machine; idempotency keys may be needed if a payment succeeds before its result is saved. Shared Filesystems let sandboxes and workflow tasks access persistent files, but concurrent writes to the same file remain unresolved. Credential brokering attaches secrets to outbound requests so they never enter the sandbox; it is rolling out first to selected customers, while beta developers get controls to block all outbound traffic or allow listed domains. Private sandbox networking is still in development.

### Source excerpt

Render is taking on Vercel with AI sandboxes that can run indefinitely, along with new tools for managing long-running agent The post Render just removed its AI sandbox time limit -- and Vercel still has a 24-hour cap appeared first on The New Stack.

## Inside a Kylon room, where people and agents build apps on Neon

DevFeed: [Inside a Kylon room, where people and agents build apps on Neon](<https://devfeed.tech/articles/inside-a-kylon-room-where-people-and-agents-build-apps-on-neon-118977.md>)

Original publisher: [Read original article](<https://neon.com/blog/inside-a-kylon-room-where-people-and-agents-build-apps-on-neon>)

Author: Carlota Soto

Published: 2026-11-04T12:00:00Z

Content type: article

Language: en

Sources: [Neon Docs](<https://devfeed.tech/sources/blog-neon-docs.md>)

Topics: [agentic-coding](<https://devfeed.tech/topics/agentic-coding.md>), [Multi Agent Systems](<https://devfeed.tech/topics/multi-agent-systems.md>), [Slack](<https://devfeed.tech/topics/slack.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [app](<https://devfeed.tech/tags/app.md>), [apps](<https://devfeed.tech/tags/apps.md>), [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [backend](<https://devfeed.tech/tags/backend.md>), [branch](<https://devfeed.tech/tags/branch.md>), [build](<https://devfeed.tech/tags/build.md>), [builds](<https://devfeed.tech/tags/builds.md>), [case-studies](<https://devfeed.tech/tags/case-studies.md>), [company](<https://devfeed.tech/tags/company.md>), [harness](<https://devfeed.tech/tags/harness.md>), [inside](<https://devfeed.tech/tags/inside.md>), [neon](<https://devfeed.tech/tags/neon.md>), [people](<https://devfeed.tech/tags/people.md>), [room](<https://devfeed.tech/tags/room.md>), [team](<https://devfeed.tech/tags/team.md>), [tools](<https://devfeed.tech/tags/tools.md>), [work](<https://devfeed.tech/tags/work.md>)

### AI overview

Kylon brings teammates and their agents into shared rooms where work, approvals, and connected tools are visible to the team. Apps built in a workspace use Neon: each workspace gets a project, each app an isolated database, and each draft a branch copied from app data. Agents can test schema or code changes on that branch; a person reviews the preview, and changes reach production when published. Kylon cites Neon's fast branching, scale-to-zero and autoscaling for mostly idle apps, programmatic API, point-in-time restore, and Neon MCP connections for customers' own accounts as useful features. People and agents use the same app data and API, and record changes can trigger scheduled or event-driven automations. The account describes Kylon's architecture and workflow but does not provide comparative benchmarks.

### Source excerpt

Kylon is your AI company harness - a workspace where your team, their agents, and their tools can work together in shared rooms.

## StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents

DevFeed: [StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents](<https://devfeed.tech/articles/storebench-a-live-commerce-environment-for-evaluating-and-training-autonomous-operator-agents-116771.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.10942>)

Author: Daksh Raghuvanshi, Ved Vedere, Yifan Wang

Published: 2026-11-04T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [llm-reasoning](<https://devfeed.tech/topics/llm-reasoning.md>), [post-training](<https://devfeed.tech/topics/post-training.md>)

Tags: [abstract](<https://devfeed.tech/tags/abstract.md>), [actions](<https://devfeed.tech/tags/actions.md>), [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [anchor](<https://devfeed.tech/tags/anchor.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [backend](<https://devfeed.tech/tags/backend.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [best](<https://devfeed.tech/tags/best.md>), [budget](<https://devfeed.tech/tags/budget.md>), [capabilities](<https://devfeed.tech/tags/capabilities.md>), [cells](<https://devfeed.tech/tags/cells.md>), [commerce](<https://devfeed.tech/tags/commerce.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-cl](<https://devfeed.tech/tags/cs-cl.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [environment](<https://devfeed.tech/tags/environment.md>), [live](<https://devfeed.tech/tags/live.md>), [operator](<https://devfeed.tech/tags/operator.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

StoreBench evaluates autonomous agents managing an online apparel store amid continuous customer orders, supplier repricing and failures, and market shocks. Agents use 29 merchant tools with an action-based operation budget; calibrated pass thresholds, reward-hack defenses, and deterministic episode replay support evaluation. Across seven models, 11 scenarios, and three seeds, the best model, DeepSeek-V4-Pro, passed 49% of task-seed cells versus 97% for a scripted smart-triage policy; human experts also scored slightly above models on the mean composite (0.708 vs. 0.700). The abstract reports improved performance over a simulated year and a GRPO run in which Qwen3.5-27B rose from 0.136 to 0.373 on held-out tasks after training on five disjoint tasks. Some sample tasks, trajectories, and scoring tools are released; the full environment and evaluation suite are withheld.

### Source excerpt

arXiv:2610.10942v2 Announce Type: replace-cross Abstract: Reinforcement learning environments are now a primary lever for improving large language model (LLM) capabilities in post-training, yet most agentic benchmarks remain static: the world moves only when the agent acts, the reward is a terminal verdict, and the pass bar is set arbitrarily. We introduce StoreBench, a live-commerce environment in which an agent runs a mid-size online apparel store on a production-grade commerce backend, testing long-horizon planning and economic judgment under uncertainty. Customers order around the clock, suppliers reprice and fail, and market shocks arrive with partial or no warning. The agent acts through the same 29 merchant tools a human operator would use, under a windowed operation budget that makes simulated time a function of actions taken, so model latency cannot influence simulated time. Pass thresholds are calibrated against scripted anchor policies, the reward is hardened against a catalogue of reward hacks, and every episode replays identically given a sequence of actions. We evaluate seven frontier LLMs on 11 scenarios of 30 to 45 days and a full simulated year, over three world seeds at matched reasoning effort. No model matches the scripted smart-triage policy on average: the best, DeepSeek-V4-Pro, passes 49% of task-seed cells against the heuristic's 97%. Human experts working through the same tools and budgets outscore every model (mean composite 0.708 vs. 0.700). Over a full simulated year under the Claude Code harness, most models show dramatic performance improvement. In a GRPO post-training run, Qwen3.5-27B trained on only five disjoint tasks raises its mean composite on the held-out evaluation tasks from 0.136 to 0.373. We release five example training-split tasks, ten sample trajectories, and the scoring and verification tooling; the full environment and evaluation suite are withheld to keep the benchmark uncontaminated.

[Next page](<https://devfeed.tech/tags/backend.md?cursor=WyIyMDI2LTExLTA0VDA1OjAwOjAwKzAwOjAwIiwgImY5NDE4ZjFjLWYwMGQtNGVjMS1hMjlmLTZlYmU4NzZmMjM2YiJd>)