# v1 Published articles for v1. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Structure is in the zoom: probing neural symmetry through dimensionality scaling DevFeed: [Structure is in the zoom: probing neural symmetry through dimensionality scaling]() Original publisher: [Read original article]() Author: Adil Amin Published: 2026-11-09T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Neural Network](), [Deep neural networks](), [hugging face]() Tags: [cond-mat-dis-nn](), [cs-cl](), [cs-lg](), [language](), [neural](), [q-bio-nc](), [scaling](), [symmetry](), [v1]() ### AI overview The authors argue that participation-ratio scaling depends on how neural activity is sampled, so they separate a sampling floor from shifts measured along a declared axis. In mouse V1, three axes yield slopes of 0.25, 0.31, and 0.35; only drift direction carries structure. That direction-aligned shift exceeds a label-permutation null in eight of eight grating recordings and is positive in 32 of 32 animals from a second lab. The analysis attributes distinct roles to even and odd sectors of the code's O(2) symmetry. Applied to five language-model architectures, the split distinguishes token-inherited content from depth-built structure; a nuisance-matched null attributes a reported grammaticality signal of 20 standard deviations to carrier composition. The abstract describes the method as requiring no trained probe and applicable layer by layer to Hugging Face models. ### Source excerpt arXiv:2611.08506v1 Announce Type: cross Abstract: How much of a neural population's measured dimensionality is the code, and how much is the act of measuring it? The slope of the participation ratio against subsample size is reported for cortical populations and for the layers of language models as a number that belongs to the system. It does not. One patch of eleven thousand mouse V1 neurons returns three slopes, 0.25 along random stimulus subsets, 0.31 along drift direction, 0.35 along neuron count, and only the middle one carries structure; the other two are the sampling floor. We split every exponent into that floor and a shift earned along a declared axis, and read the shift against two nulls: permuting the labels, and permuting them only among samples that share a nuisance. On V1 the direction-aligned shift clears the label null in eight of eight grating recordings, is positive in 32 of 32 animals in a second laboratory, and reads both sectors of the code's O(2) symmetry. The even sector sets how the ladder starts: it decides which order of adding classes climbs faster, predicts the opposite order in an equivariant network, and flows under coarse-graining by a closed-form factor that says what orientation columns do to mesoscale signals. The odd sector, carried by the direction-selective neurons, keeps the ladder climbing past half the classes. On language models the same split separates content inherited from the tokens from construction built with depth, on five architectures, and the second null shows that a grammaticality signal of twenty standard deviations is entirely carrier composition. With a floor and a declared axis, dimensionality scaling is an axis-resolved measurement, and where the axis follows a known symmetry it resolves the sector content of the code. The instrument, rung, trains no probe and reads any array or any Hugging Face model layer by layer (https://github.com/adilamin89/structure-in-the-zoom). ## CVE-2026-71495 - Wanderer: SSRF Vulnerability in /api/v1/trail/download - Unvalidated URL Leads to Internal Network Access DevFeed: [CVE-2026-71495 - Wanderer: SSRF Vulnerability in /api/v1/trail/download - Unvalidated URL Leads to Internal Network Access]() Original publisher: [Read original article]() Author: CVEFEED.io; github.com Published: 2026-11-06T19:17:56Z Content type: news Language: en Sources: [Latest High/Critical Vulnerabilitiy Feed]() Topics: [fetch]() Tags: [api](), [cloud](), [cloud-metadata](), [cve](), [cve-2026-71495](), [data](), [database](), [denial-of-service](), [download](), [fetch](), [handler](), [json](), [metadata](), [network](), [post](), [self-hosted](), [ssrf](), [url](), [v1](), [vulnerability]() ### AI overview Before version 0.20.0, Wanderer's unauthenticated POST /api/v1/trail/download handler is vulnerable when PUBLIC_PRIVATE_INSTANCE is not set to true: it passes the JSON url value directly to event.fetch() and returns the response. An attacker can target internal services, cloud metadata addresses, and other network destinations, potentially exposing returned data, enabling internal network reconnaissance, or causing denial of service. The issue is fixed in version 0.20.0. ### Source excerpt CVE ID :CVE-2026-71495 Published : Nov. 6, 2026, 7:17 p.m. | 41 minutes ago Description :Wanderer is a self-hosted trail database for saving and sharing adventures. Prior to 0.20.0, when PUBLIC_PRIVATE_INSTANCE is not set to true, the unauthenticated POST /api/v1/trail/download handler passes the attacker-controlled JSON url field directly to event.fetch() and returns the fetched response. This allows requests to internal services such as db:8090 and search:7700, cloud metadata addresses, and other network destinations, exposing returned data and permitting internal network reconnaissance or denial of service. This issue is fixed in version 0.20.0. Severity: 10.0 | CRITICAL Visit the link for more details, such as CVSS details, affected products, timeline, and more... ## kpt v1.0.0 is released DevFeed: [kpt v1.0.0 is released]() Original publisher: [Read original article]() Author: epower Published: 2026-11-05T12:00:00Z Content type: release Language: en Sources: [Cloud Native Computing Foundation]() Topics: [project release](), [configuration-management](), [Cloud](), [CI/CD]() Tags: [adoption](), [apis](), [applications](), [blog](), [cloud-native-applications](), [community](), [configuration](), [gitops](), [kubernetes](), [lifecycle](), [modular](), [package-management](), [release](), [stable](), [toolchain](), [v1]() ### AI overview The kpt community released version 1.0.0 after a year focused on stabilizing APIs, deprecating unused features, and bringing Porch back under common governance. The release marks the toolchain's readiness for wider adoption. Standalone kpt manages Kubernetes configuration packages using KRM and "config as data" principles: packages describe repeatable resource manipulation and validation, turning generic KRM into deployable models without templating. Porch adds orchestration for managing kpt packages at scale in zero-touch GitOps workflows. Together, the project aims to provide a modular framework for managing Kubernetes resource lifecycles. ### Source excerpt After many years and much development, the kpt community has released v1.0.0 of the kpt toolchain. This is a big step in maturing the kpt project after having it in pre-release status for several years. In... ## CVE-2026-122224 - WebVirtCloud through commit bb920b0 Missing Authorization in REST API Migrate and Create Endpoints DevFeed: [CVE-2026-122224 - WebVirtCloud through commit bb920b0 Missing Authorization in REST API Migrate and Create Endpoints]() Original publisher: [Read original article]() Author: CVEFEED.io; vulncheck.com Published: 2026-11-04T21:18:10Z Content type: news Language: en Sources: [Latest High/Critical Vulnerabilitiy Feed]() Topics: [Authorization](), [REST API]() Tags: [2026](), [api](), [authorization](), [commit](), [compute](), [cve](), [cve-2026-122224](), [django](), [django-rest-framework](), [domains](), [migrate](), [permission](), [rest](), [rest-api](), [v1](), [virtual-machines](), [volumes](), [vulnerability]() ### AI overview WebVirtCloud through commit bb920b0 has a missing authorization vulnerability in its REST API: MigrateViewSet and CreateInstanceViewSet declare no permission_classes, so Django REST Framework defaults to AllowAny. The excerpt says low-privileged authenticated users can use /api/v1/migrate/ or the compute instance creation endpoint to migrate or undefine other users' virtual machines, and create volumes and domains on any compute host. The listed severity is 8.1 (High). No affected product versions are recorded in the supplied text. ### Source excerpt CVE ID :CVE-2026-122224 Published : Nov. 4, 2026, 9:18 p.m. | 1 hour, 4 minutes ago Description :WebVirtCloud through commit bb920b0 contains a missing authorization vulnerability that allows low-privileged authenticated users to reach MigrateViewSet and CreateInstanceViewSet because no permission_classes are declared, so Django REST Framework defaults to AllowAny. Attackers can send requests to /api/v1/migrate/ or the compute instance create endpoint to migrate or undefine other users' virtual machines and create volumes and domains on any compute host. Severity: 8.1 | HIGH Visit the link for more details, such as CVSS details, affected products, timeline, and more... ## FlagRot: Selective Rotation for Low-Bit KV Caches DevFeed: [FlagRot: Selective Rotation for Low-Bit KV Caches]() Original publisher: [Read original article]() Author: Kunming Shao, Xijie Huang, Jierun Chen, Jiaqi Shao, Yanli Wang, Jiangnan Yu, Qiwei Li, Wei Zhang, Kwang-Ting Cheng, Chi Ying Tsui Published: 2026-11-04T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Machine Learning]() Topics: [quantization](), [vllm]() Tags: [attention](), [bits](), [cache](), [cs-ai](), [cs-cl](), [cs-lg](), [long-context](), [quantization](), [rotation](), [ruler](), [v1](), [vllm]() ### AI overview FlagRot selectively rotates each 32-channel key piece only when calibration predicts lower attention error, then rotates the query at read time. Its decision frequency follows the cache quantization axis: per-channel caches such as KIVI make one choice per head without added write cost; per-token caches choose per key and store the choice in existing sign bits. At 2.5 bits per value, the abstract reports lowering KIVI's twelve-model KL divergence from 18.7 to 12.4 x 10⁻³ and outperforming RotateKV on 9 of 12 models. As a vLLM plugin, it raises RULER at 128k tokens by 2.9 points and LongBench v1 mean by 0.4-1.9 points on three of four models where 2-bit KIVI loses accuracy. The abstract also reports reduced error for 4-bit integer and FP4 caches. ### Source excerpt arXiv:2611.03992v1 Announce Type: cross Abstract: Long-context inference is bound by the KV cache, which 2-bit codes could shrink about sixfold if their error stays in check. The usual remedy rotates keys before quantizing, assuming rotation always helps. However, we observe that it does not: rotation flattens a spiky piece of a key but can give a flat one a new peak, and both coexist within one key. We therefore propose FlagRot, which stores each 32-channel piece rotated or as is, whichever has the smaller calibrated attention error, and rotates the query instead when reading. We further show that how often to decide follows the quantization axis: per-channel caches such as KIVI need one decision per head, at no write cost, while per-token caches decide per key, with the choice stored in existing sign bits. At 2.5 bits per value, FlagRot lowers the twelve-model KL divergence of KIVI from 18.7 to 12.4 x 10^-3, below RotateKV on 9 of 12 models. Served as a vLLM plugin, it raises RULER at 128k tokens by +2.9 points and the LongBench v1 mean by +0.4 to +1.9 points on the three of four models where 2-bit KIVI loses accuracy. It also lowers the error of 4-bit integer and FP4 caches. ## vLLM Hook v1: A Plug-in for Programming Model Internals on vLLM DevFeed: [vLLM Hook v1: A Plug-in for Programming Model Internals on vLLM]() Original publisher: [Read original article]() Author: Ching-Yun Ko, Yunhua Fang, Pin-Yu Chen Published: 2026-11-04T05:00:00Z Content type: release Language: en Sources: [arXiv: Machine Learning]() Topics: [vllm](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [Hallucination detection]() Tags: [activation](), [activation-steering](), [adversarial](), [alignment](), [analysis](), [artificial-intelligence](), [attention](), [capture](), [cases](), [community](), [configuration](), [contribution](), [core](), [cs-cl](), [cs-lg](), [cs-pl](), [cuda](), [internals](), [model](), [programming](), [v1](), [vllm](), [vllm-hook]() ### AI overview vLLM Hook adds configurable access to model internals that vLLM otherwise limits, supporting passive probing that leaves generation intact and active interventions that alter selected states. Version 1, called vLLM Hook MIA, runs on vLLM's V2 model runner and preserves CUDA graphs; the authors report only small decoding overhead for capturing and steering states. It supports offline use and serving with vllm serve. Demonstrated applications include prompt injection and hallucination detection, attention-based reranking for retrieval-augmented generation, activation steering, hidden-state probing, text-to-SQL schema linking, and tool-call risk assessment. ### Source excerpt arXiv:2603.06588v2 Announce Type: replace Abstract: Modern artificial intelligence models are deployed on inference engines to optimize runtime efficiency and resource allocation, particularly for transformer-based large language models. The vLLM project is a major open-source library to support model serving and inference. However, the current implementation of vLLM limits programmability of the internal states of deployed models. This prevents the use of popular test-time model alignment and enhancement methods. For example, it prevents the detection of adversarial prompts based on attention patterns or the adjustment of model responses based on activation steering. To bridge this critical gap, we present vLLM Hook, an opensource plug-in to enable the programming of internal states for vLLM models. Based on a configuration file specifying which internal states to capture, vLLM Hook provides seamless integration to vLLM and supports two essential features: passive programming and active programming. For passive programming, vLLM Hook probes the selected internal states for subsequent analysis, while keeping the model generation intact. For active programming, vLLM Hook enables efficient intervention of model generation by altering the selected internal states. In version 1 (named vLLM Hook MIA), the plugin runs on vLLM's V2 model runner and preserves the engine's CUDA graphs, so that capturing and steering internal states incurs only a small decoding overhead. It supports both offline use and serving through vllm serve. In addition to presenting the core functions of vLLM Hook, we demonstrate use cases including prompt injection detection, attention-based re-ranking for retrieval-augmented generation, activation steering, hidden-state probing, hallucination detection, schema linking for text-to-SQL, and tool-call risk assessment. Finally, we welcome the community's contribution to improve vLLM Hook via https://github.com/IBM/vLLM-Hook. ## frank-dino: a RP2350B helper chip for a real Zilog Z80 DevFeed: [frank-dino: a RP2350B helper chip for a real Zilog Z80]() Original publisher: [Read original article]() Author: Anne Barela Published: 2026-11-02T21:07:49Z Content type: release Language: en Sources: [Adafruit Industries]() Topics: [Raspberry Pi RP2350](), [Microcontroller]() Tags: [atm](), [beta](), [board](), [covers](), [cpu](), [dino](), [firmware](), [first](), [gaming](), [github](), [machine](), [memory](), [microcontroller](), [pico-2](), [raspberry-pi](), [rp2350](), [sound](), [spectrum](), [storage](), [v1](), [video](), [vintage-computing](), [z80](), [zilog](), [zx-spectrum]() ### AI overview frank-dino v1.00 uses an RP2350B to provide memory, video, sound, and storage around a real Zilog Z84C00 CPU on the dino_z80 board. It supports several ZX Spectrum models and about thirty related machines, with HDMI video modes, common Spectrum sound chips, snapshots, disk interfaces, USB input devices, and cassette loading either in real time or instantly. A real cassette deck can connect to J8. To install firmware, hold S3, press S4, then copy the .uf2 file to the drive that appears; games and disk images go on a FAT32 microSD card, and ROMs are built in. The excerpt identifies the license as GPL-3.0-or-later. ### Source excerpt rh1tech (Mikhail Matveev) has released frank-dino v1.00 on GitHub, the first firmware for the dino_z80 board. An RP2350B microcontroller acts as the whole machine around a real Zilog Z84C00 CPU, so the Z80 does the work and the RP2350B handles everything else: memory, video, sound and storage. It covers the ZX Spectrum 16K, 48K, 128K, [...] ## Nace AI Open-Sources Drex Max v1: A 27B Decision Model That Runs Locally on Apple Silicon DevFeed: [Nace AI Open-Sources Drex Max v1: A 27B Decision Model That Runs Locally on Apple Silicon]() Original publisher: [Read original article]() Author: Asif Razzaq Published: 2026-11-01T08:10:07Z Content type: article Language: en Sources: [MarkTechPost]() Topics: [Artificial Intelligence](), [On-device AI](), [llama.cpp](), [Ollama](), [MLX]() Tags: [2](), [3](), [about](), [agent](), [agentic-ai](), [ai](), [ai-shorts](), [all](), [analysis](), [api](), [apple](), [apple-m5](), [apple-silicon](), [applications](), [area](), [article](), [artificial-analysis](), [artificial-intelligence](), [context-engineering](), [decision-model](), [decision-models](), [editors-pick](), [for-devs](), [harness](), [language-model](), [large-language-model](), [max](), [model](), [new-releases](), [open](), [open-source](), [promote](), [silicon](), [software-engineering](), [sponsored](), [staff](), [tech-news](), [technology](), [v1]() ### AI overview Nace AI has released open weights for Drex Max v1, a dense 27B decision model that returns probabilities for named choices, yes/no questions, or ordinal scores from text, JSON, or images. The excerpt describes uses such as routing, triage, and policy checks, with results available in one forward pass. It reports a self-reported 67.52 on Decision Index 0.4, versus 61.25 for Drex v2.0, and 73.92 on a public vision index. Local options include an affine Q4 build for Apple Silicon Macs with at least 24 GB and Q8_0 GGUF for 48 GB; CPU execution is also described. In Nace's tests, Q8_0 on a 48 GB M5 Pro stayed within 0.02 per probability of H100 bf16, while affine Q4 stayed within 0.0329 of Q8_0 with no choice flips. The excerpt says local Mac and CPU paths are text only for now; the hosted model supports images. ### Source excerpt Nace AI open-sources Drex Max v1, a 27B multimodal decision model scoring 67.52 that runs on Apple Silicon. The post Nace AI Open-Sources Drex Max v1: A 27B Decision Model That Runs Locally on Apple Silicon appeared first on MarkTechPost. ## OpenTelemetry eBPF Instrumentation reaches v1.0.0 DevFeed: [OpenTelemetry eBPF Instrumentation reaches v1.0.0]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-10-30T15:42:24Z Content type: release Language: en Sources: [OpenTelemetry]() Topics: [OpenTelemetry](), [eBPF](), [telemetry](), [tracing]() Tags: [2026](), [ai](), [ai-applications](), [alerts](), [announce](), [applications](), [compatibility](), [coverage](), [dependencies](), [ebpf](), [instrumentation](), [linux](), [metrics](), [migration](), [observability](), [opentelemetry](), [release](), [telemetry](), [traces](), [v1]() ### AI overview OBI 1.0 provides a stable foundation for collecting OpenTelemetry metrics and traces from running Linux applications without source changes, using eBPF and runtime-specific instrumentation. It documents compatibility commitments for Config v2, deployment interfaces, and supported environments, plus a telemetry migration path using Weaver-compatible schemas, release notes, and guidance. Config v1 remains supported for backward compatibility but is frozen; existing users are advised to migrate, review environment-based configuration, and compare telemetry in a canary before broad rollout. The release expands protocol, framework, runtime, and AI instrumentation, though coverage and capabilities depend on documented support and configuration. Runtime metrics remain experimental, and detailed AI payload capture requires explicit configuration. OBI can complement language agents and manual instrumentation when application-specific context is needed. ### Source excerpt We're excited to announce v1.0.0 of OpenTelemetry eBPF Instrumentation--OBI! OBI brings OpenTelemetry observability to running Linux applications without requiring changes to their source code. It uses eBPF, together with runtime-specific instrumentation, to capture application metrics and traces across services, languages, and frameworks. You can start with request rates, errors, latency, and service dependencies, then build deeper visibility as your needs grow. This release fulfills the flagship goal we set for 2026: establishing a stable foundation for OBI. It represents a year of expanding instrumentation coverage, improving correctness, strengthening release tooling, and building the compatibility and telemetry migration practices that users need to operate OBI over time. ## CTE-Bench: Counterfactual Trace Evaluation for Stateful Software Simulators DevFeed: [CTE-Bench: Counterfactual Trace Evaluation for Stateful Software Simulators]() Original publisher: [Read original article]() Author: Xinran Zhang Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Software Engineering]() Topics: [Stateful](), [Software](), [AI Chat]() Tags: [api](), [bench](), [benchmarks](), [claude](), [coding-agents](), [cs-se](), [deepseek](), [evaluation](), [kimi](), [oracle](), [predictions](), [python](), [qwen3-6](), [score](), [scripts](), [service](), [software](), [state](), [stateful](), [trace](), [v1]() ### AI overview CTE-Bench evaluates whether models can predict how code edits or state overwrites change a deterministic service's future responses. In each scenario, a model receives Python service code, the pre-intervention call history, an intervention, and 40 fixed future calls; an executable oracle checks its predictions. Three protocols vary whether correct prior responses, no prior responses, or the model's own predictions are available. Across 255 scenarios and six services, four API-hosted models achieved 54.3%-61.7% exact-match accuracy on intervention-affected calls when given correct feedback. Accuracy fell to 23.2%-29.0% without that feedback and was 24.8%-33.2% when models relied on their own predictions; at most 1.2% of scenarios were predicted exactly end to end. The results suggest errors compound across a rollout. The release includes the benchmark, executable oracle, evaluation scripts, and an evaluation card. ### Source excerpt arXiv:2609.36647v2 Announce Type: replace Abstract: Coding agents change running software: they patch a service's code or overwrite its stored state, and then act on their own expectation of how the service will respond afterwards. A wrong expectation may surface only several calls later. Function-level code-execution benchmarks omit persistent service state, and agent benchmarks score the actions an agent takes or the final state it reaches. We introduce CTE-Bench, which measures whether a model can predict how an intervention changes a stateful service's future behavior, without asking it to choose actions. Each scenario gives the model Python service code, the calls and responses observed before the intervention, the intervention itself (a source edit or a state overwrite), and 40 fixed future calls; the model predicts every future response, and predictions are checked by executing the service. Three memory protocols control whether the model sees the correct earlier responses, none of them, or its own earlier predictions. CTE-Bench-Core-v1 contains 255 scenarios over six deterministic Python services, giving 10,200 predictions per model. The main score is effect-step value match (VM): exact response equality on the 2,476 future calls whose response the intervention changes. With correct earlier responses revealed, four API-hosted models (DeepSeek V4-Flash, Kimi K2.5, Qwen3.6-35B-A3B, and Claude Sonnet 4.6) reach 54.3%-61.7% effect-step VM. Hiding those responses lowers effect-step VM to 23.2%-29.0%; conditioning on self-generated predictions gives 24.8%-33.2%, and at most 1.2% of scenarios are predicted exactly end to end. Current models thus track intervention effects mainly when correct feedback is supplied, and their errors compound over a rollout. We release CTE-Bench-Core-v1 with its executable oracle, evaluation scripts, and an evaluation card mapping each claim to its protocol. ## Humans and agents rejoice: Mux Robots is now GA DevFeed: [Humans and agents rejoice: Mux Robots is now GA]() Original publisher: [Read original article]() Author: Phil Cluff Published: 2026-10-27T16:41:27Z Content type: release Language: en Sources: [Mux Blog - Video technology and more]() Topics: [generative-ai-tools](), [agentic workflows](), [X (Twitter)]() Tags: [agents](), [ai](), [ai-workflows](), [analyze](), [api](), [consistency](), [improvements](), [product](), [robots](), [stable](), [structure](), [v1](), [video](), [workflows]() ### AI overview Mux Robots is generally available with a stable v1 API at /robots/v1, production-ready workflows, and six months of v0 support. v1 standardizes timestamps and naming, removes deprecated preview parameters, and uses additive webhook events delivered to existing endpoints; v0 jobs and directives retain their original contracts during support. New analyze-structure jobs return titles, summaries, tags, chapters, scenes, and key moments in one pass; standalone find-scenes is deprecated in v1 but remains on v0 for six months. generate-contextual-text creates requested written artifacts and variants from a shared editorial brief grounded in the transcript, with an option to use video frames as evidence. summarize remains suited to titles, descriptions, and tags. The article also describes a Playground for trying workflows and generating integration prompts, and reports Robots is included on all Mux plans with 100,000 monthly units. GA signals stability, while the product remains under development. ### Source excerpt Mux Robots is generally available (GA), with a stable v1 API, two new AI workflows for video structure and written content, and six months of improvements. ## OpenTelemetry Transformation Language reaches v1 DevFeed: [OpenTelemetry Transformation Language reaches v1]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-10-27T15:00:47Z Content type: release Language: en Sources: [OpenTelemetry]() Topics: [OpenTelemetry](), [telemetry](), [observability pipelines](), [data transformation]() Tags: [api](), [compatibility](), [contrib](), [functions](), [language](), [logs](), [metrics](), [opentelemetry](), [opentelemetry-collector](), [stable](), [syntax](), [telemetry](), [traces](), [transformation](), [v1]() ### AI overview Starting with opentelemetry-collector-contrib v0.164.0, OTTL is stable for traces, metrics, and logs, with 1.x compatibility for documented syntax, standard functions, and the Go API used by custom components. Existing statements are promised to parse and behave the same in later 1.x releases. Because standard functions become permanent, new ones begin as experimental in the 0.x `xottl` module and require the `pkg.ottl.functions.enableExperimental` feature gate; promotion requires at least two minor releases, tests and documentation, no open change proposals, and user demand. Lambda functions remain experimental behind a separate gate, and profiles support remains in separately versioned `xprofile`. Before upgrading, remove obsolete feature gates, including `ottl.set.allowNil` for v0.164.0, and consult intervening `pkg/ottl` changelog entries when skipping releases. The transform processor is being prepared for stability but is not yet described as stable. ### Source excerpt The OpenTelemetry Transformation Language (OTTL) is now v1. Starting with opentelemetry-collector-contrib v0.164.0, OTTL is stable for traces, metrics, and logs. The release of v1 means OTTL meets the Collector's 'stable' stability criteria for testing, benchmarking, documentation, and telemetry. It also comes with a compatibility promise for all of 1.x. If you write OTTL statements, the syntax and the standard functions stay backward compatible. If you build your own components on top of OTTL, the Go API will also remain compatible. ## Depth Anything: Models, Comparisons, and How to Use It DevFeed: [Depth Anything: Models, Comparisons, and How to Use It]() Original publisher: [Read original article]() Author: Dikshant Shah Published: 2026-10-26T19:18:06Z Content type: tutorial Language: en Sources: [Roboflow]() Topics: [Machine learning](), [3D](), [Tool](), [Documentation]() Tags: [comparisons](), [computer-vision](), [deploy](), [how-to](), [images](), [models](), [run](), [streams](), [v1](), [v2](), [v3](), [video](), [workflows]() ### AI overview Depth Anything V1 and V2 estimate relative depth from a single image; V2 uses a teacher trained on synthetic images with precise depth labels, then pseudo-labels real-world images, improving fine detail and boundaries. Metric-depth variants exist, but accuracy may vary outside their training domains. V3 extends the task to spatially consistent 3D geometry using depth, per-pixel rays, and camera outputs, combining multiple views or video and supporting camera-pose estimation and reconstruction. Roboflow Workflows offers V2 and V3 blocks that can be assembled manually or generated with Roboflow Agent, then tested and published. Deployment options include a credit-billed Serverless Cloud API, which avoids managing a dedicated GPU, and running on users' own hardware; generated code supports images, video, webcam, and RTSP inputs. ### Source excerpt Compare Depth Anything V1, V2, and V3, then run Depth Anything V2 or V3 in Roboflow Workflows and deploy it to images, video, or RTSP streams. ## Adafruit New Products this Week DevFeed: [Adafruit New Products this Week]() Original publisher: [Read original article]() Author: Kelly Published: 2026-10-24T00:59:12Z Content type: release Language: en Sources: [Adafruit Industries]() Topics: [ESP32-P4](), [developer-relations]() Tags: [2](), [adafruit](), [board](), [clock](), [esp32](), [esp32-p4](), [hydra](), [metro](), [metro-esp32-p4-hydra](), [new-products](), [products](), [psram](), [real-time](), [rv](), [stemma-qt](), [time](), [v1]() ### AI overview This week's two new products are the Adafruit RV-8803 High Accuracy Real Time Clock Breakout Board with STEMMA QT / Qwiic, and the Adafruit Metro ESP32-P4 Hydra with 16 MB PSRAM and a v1 chipset. The excerpt provides no further product specifications. ### Source excerpt We've got the New nEw NEW for you right here This week we debut 2 New Products. Visit www.adafruit.com/new for more info. Adafruit RV-8803 High Accuracy Real Time Clock Breakout Board - STEMMA QT / Qwiic Adafruit Metro ESP32-P4 Hydra - with 16 MB PSRAM - v1 Chipset ## Solana Changelog: October 22, 2026 DevFeed: [Solana Changelog: October 22, 2026]() Original publisher: [Read original article]() Author: Solana Foundation Published: 2026-10-23T01:09:00Z Content type: release Language: en Sources: [Solana News Feed]() Topics: [Solana]() Tags: [2026](), [22](), [alpenglow](), [blockchain](), [blockchain-technology](), [changelog](), [crypto-news](), [cryptocurrency](), [defi](), [developers](), [i-o](), [network](), [newsletter](), [nfts](), [october](), [podcasts](), [program](), [sdks](), [solana](), [solana-ecosystem](), [technology](), [transaction](), [v1](), [web3](), [work]() ### AI overview The excerpt reports Solana client and SDK releases alongside proposed and ongoing infrastructure changes. Agave is increasing file I/O buffer sizes to reduce page faults, preparing the CLI to reject unsupported sBPF versions before users pay to upload them, and proposing stake-weighted gossip to manage congestion. It is also reducing snapshot loading's reliance on Agave's stake delegation list to improve cross-client startup compatibility. Firedancer, after participating in Alpenglow consensus, is removing its TowerBFT implementation. Other work prepares Web3.js for a possible change to cluster labels and discusses supporting only Transaction V1 if a proposed SIMD phases out legacy and V0 formats. Pinocchio plans to use SVM register r2 for its lazy entrypoint, avoiding a custom implementation before program startup. These are described as proposals or work in progress where indicated; the excerpt does not establish their completion. ### Source excerpt Agave v4.3.2 and Firedancer v26.10.4 arrive with new program SDKs and work on Alpenglow, validator I/O, gossip, and Transaction V1. ## Bun v1.4.4 DevFeed: [Bun v1.4.4]() Original publisher: [Read original article]() Author: Jarred Sumner Published: 2026-10-22T00:58:24Z Content type: release Language: en Sources: [Bun]() Topics: [Bun](), [Prettier](), [npm packages](), [Yarn](), [Query (disambiguation)](), [Cache](), [Test automation](), [npm](), [pnpm](), [CI/CD](), [Svelte](), [User interface design]() Tags: [blog](), [bugfixes](), [bun](), [compatibility](), [copy](), [cpu](), [exponential-backoff](), [fetch](), [fmt](), [hooks](), [install](), [javascriptcore](), [node](), [node-js](), [overlay](), [query](), [resume](), [retries](), [sql](), [sqlite](), [v1]() ### AI overview Bun v1.4.4 adds a built-in Prettier-compatible formatter, bun fmt, and reports 3-6x faster formatting than oxfmt on tested projects; results depend on the project and formatter configuration, and unsupported plugins cause affected files to be skipped with an error. Package-management changes include migrating Yarn Berry lockfiles while preserving pinned versions when possible, retries with exponential backoff, Linux libc-aware dependency selection, peer dependency resolution changes, and commands for cache fetching and recursive publishing. Bun.SQL reports up to 48% lower CPU per query, adds PostgreSQL COPY streams and query cancellation, while noting that already-running MySQL or MariaDB queries continue after cancellation. The release also adds SQLite functions and update hooks, improves npm ws compatibility and WebSocket backpressure controls, and upgrades JavaScriptCore with runtime and Node.js compatibility fixes. The excerpt additionally describes glob, HTTP header iteration, response stream, and dev-server improvements, along with security fixes across listed APIs and commands. ### Source excerpt Fixes 271 issues, addressing 544 👍. bun fmt, a Prettier-compatible formatter built into Bun that is 3x to 6x faster than oxfmt, bun check is 1.4x to 7.8x faster on large projects, bun install migrates Yarn Berry lockfiles, retries with exponential backoff and closes 133 issues, up to 3.2x faster hoisted installs from a warm cache on Linux, bun pm fetch, bun publish --recursive, packageExtensions, Bun.SQL uses up to 48% less CPU per query, query.cancel(), COPY streams, column name transforms, user-defined functions and update hooks in bun:sqlite, ws rewritten to behave like npm ws on Node.js, ws.pause() and ws.resume() in Bun.serve, a JavaScriptCore upgrade with 54x faster toLocaleString, up to 2.8x faster Headers iteration, up to 2.6x faster response.body streams, extglob and brace ranges in Bun.Glob, a redesigned error overlay in the dev server, and many bugfixes and Node.js compatibility improvements. ## API Versioning Strategy When AI Agents Are Your Consumers DevFeed: [API Versioning Strategy When AI Agents Are Your Consumers]() Original publisher: [Read original article]() Author: Swytchcode Team Published: 2026-10-21T09:53:12.592027Z Content type: article Language: en Sources: [Swytchcode]() Topics: [versioning](), [API](), [schema-evolution]() Tags: [agent](), [agent-integration](), [agents](), [ai](), [ai-agents](), [api](), [changelog](), [consumer](), [engineering](), [strategy](), [v1](), [versioning]() ### AI overview Agent callers may keep using the API shape they were built against, so deprecation notices alone may not prevent production breakage. The excerpt highlights schema drift within a still-current version--such as a field becoming required, a default changing, or tighter rate limits--as a key risk; some mismatches may silently drop expected data. It recommends validating each call against the live schema, failing with a specific error on mismatch, and monitoring agent traffic during deprecation windows. It describes Swytchcode's execution pipeline as checking the real provider schema before a call runs. Detecting deprecated-version use also requires logging versions and endpoints and reviewing that traffic. The excerpt does not compare versioning approaches in depth. ### Source excerpt A human reads the changelog. An agent keeps calling v1 until it 404s. What a versioning strategy needs to look like when the consumer never checks release notes. ## COSMIC Epoch 1.11 Released With Wayland xdg-dialog-v1, Better Multi-GPU Handling DevFeed: [COSMIC Epoch 1.11 Released With Wayland xdg-dialog-v1, Better Multi-GPU Handling]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-10-20T22:32:12Z Content type: release Language: en Sources: [Phoronix]() Topics: [Wayland](), [Multi-GPU]() Tags: [compositor](), [desktop](), [desktop-linux](), [dialog](), [editor](), [ensuring](), [games](), [gpu](), [hybrid](), [json](), [library](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [mime](), [modal](), [multi-gpu](), [open-source](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [protocol](), [release](), [support](), [ubuntu-benchmarks](), [ubuntu-hardware](), [v1](), [wayland]() ### AI overview COSMIC Epoch 1.11 adds xdg-dialog-v1 support to its Wayland compositor to improve modal dialog presentation, along with compositor fixes and translation updates. The release also fixes scrollbar padding in libcosmic and adds JSON MIME association in the COSMIC text editor. For hybrid graphics, the COSMIC app library and pop-launcher are updated to ensure video games use the correct GPU. The excerpt gives no further detail on the fixes or their scope. ### Source excerpt System76 developers just released COSMIC Epoch 1.11 as the newest point release to their Rust-written open-source desktop environment catering to their in-house Pop!_OS Linux distribution... ## Versioned metrics: Shipping the first Release Candidate metrics in hostmetrics DevFeed: [Versioned metrics: Shipping the first Release Candidate metrics in hostmetrics]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-10-13T23:35:21Z Content type: article Language: en Sources: [OpenTelemetry]() Topics: [OpenTelemetry](), [version](), [Continuous Delivery (CD)](), [telemetry]() Tags: [covering](), [opentelemetry-collector](), [receiver](), [scraper](), [stage](), [v1]() ### AI overview OpenTelemetry Collector Contrib v0.160.0 introduces release candidate process semantic convention metrics in the hostmetrics receiver's process scraper. The article describes a versioned-metrics migration that lets users opt into new metrics with feature gates while retaining their existing configuration schema. It also explains how generated code handles conflicts when legacy and release candidate metrics share a name, and invites users to test the release candidate before the scraper advances toward stable status. ### Source excerpt OpenTelemetry Collector Contrib v0.160.0 adds the ability to emit release candidate (RC) process semantic conventions metrics to the hostmetrics receiver's process scraper, the last stage before a stable v1. The System SIG has worked on this for the better part of a year, and we think that the process metrics are in a good shape - but RC exists so the community can prove that. If you collect process metrics, we'd like you to try the new versioned output and tell us what breaks. ## How to Get a 'Transformative' AI Fluency Rating as a PM, with Wade Foster | CEO of Zapier DevFeed: [How to Get a 'Transformative' AI Fluency Rating as a PM, with Wade Foster | CEO of Zapier]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-09-24T22:05:37Z Content type: article Language: en Sources: [Product Growth]() Topics: [Artificial Intelligence](), [Shopify](), [Meta](), [Microsoft]() Tags: [ai](), [ceo](), [company](), [employee](), [evaluations](), [me](), [performance](), [v1]() ### AI overview A podcast episode examines Zapier's AI fluency rubric for product managers. Wade Foster explains how the rubric evolved from measuring AI usage to evaluating judgment, work products, and effective application of AI. ### Source excerpt I got the CEO of Zapier to pull up the rubric his company uses to grade every employee on AI. ## Introducing Laravel AI SDK v1.0 DevFeed: [Introducing Laravel AI SDK v1.0]() Original publisher: [Read original article]() Author: Pushpak Chhajed Published: 2026-09-23T10:53:00Z Content type: release Language: en Sources: [Laravel Blog]() Topics: [Laravel](), [SDK](), [Artificial Intelligence](), [API](), [Streaming](), [Front end](), [middleware](), [Large Language Model](), [Database]() Tags: [agents](), [ai](), [api](), [chat](), [classification](), [laravel](), [sdk](), [streaming](), [tool](), [v1]() ### AI overview Laravel AI SDK v1.0 provides a Laravel-native API for multiple AI providers and adds classification, support for text, image, audio, and embedding workflows, protocol integrations for streaming chat interfaces, and customizable agent middleware. ### Source excerpt Laravel AI SDK v1.0 is here: one Laravel-native API for every provider, with conversation storage, streaming chat, agents, tool approvals, and Jev classification. ## tokenizers v1: encode, decode and scaling, measured DevFeed: [tokenizers v1: encode, decode and scaling, measured]() Original publisher: [Read original article]() Author: Arthur Zucker; Simon Brandeis; Luc Georges; Lysandre Published: 2026-09-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face]() Topics: [tokenizers](), [tokenization](), [Open Source](), [Library](), [Hardware]() Tags: [benchmarks](), [cpu](), [decoding](), [hardware](), [latency](), [libraries](), [open-source](), [performance](), [repository](), [rust](), [scaling](), [tokenization](), [tokenizers](), [v1]() ### AI overview This article examines the performance-focused tokenizers v1 release candidate. It describes why faster tokenization matters for large datasets and concurrent model serving, compares v1 with v0.23 and other tokenizers, and discusses benchmarks, scaling, latency, decoding throughput, memory use, compatibility, and platform support. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Kubernetes v1.37: Pod-Level Resource Managers graduated to Beta DevFeed: [Kubernetes v1.37: Pod-Level Resource Managers graduated to Beta]() Original publisher: [Read original article]() Author: Kevin Torres Martinez Published: 2026-09-15T18:30:00Z Content type: release Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [cpu](), [Hardware](), [Latency](), [API](), [gRPC](), [Monitoring]() Tags: [api](), [cpu](), [hardware](), [kubernetes](), [latency](), [monitoring](), [numa](), [release](), [v1]() ### AI overview Kubernetes v1.37 graduates Pod-Level Resource Managers to Beta, disabled by default. The feature lets Kubelet resource managers use pod-level declarations for hardware placement, enabling NUMA-aligned exclusive resources for primary containers while placing sidecars in a shared pod-isolated pool. The release also adds pod-level reporting to the PodResources gRPC API. ### Source excerpt With the release of Kubernetes v1.37, the Pod-Level Resource Managers feature has graduated to Beta status (disabled by default)! First introduced as an Alpha feature in Kubernetes v1.36, this enhancement builds on Pod-Level Resources by equipping Kubelet's Topology Manager, CPU Manager, and Memory Manager to use Pod-level resource declarations (.spec.resources) directly when making hardware placement decisions. Bringing pod-level resources to node managers Before this feature, obtaining exclusive NUMA-aligned CPU cores or memory for latency-critical applications forced cluster operators into an all-or-nothing choice: assign integer resource requests to every container in the Pod, or forfeit exclusive NUMA alignment entirely. For modern workloads running lightweight sidecars (such as logging agents or telemetry exporters), allocating dedicated physical cores to auxiliary containers was wasteful. Pod-Level Resource Managers solves this challenge by enabling hybrid allocation models. The Kubelet can reserve exclusive NUMA-aligned resources for primary application containers while placing non-Guaranteed sidecars into a pod-isolated shared pool. This ensures primary workloads get unthrottled, NUMA-local performance while sidecars benefit from running in a pod-isolated shared pool, enjoying local NUMA alignment and protection from external node interference without consuming dedicated physical cores. What's new in Beta Graduating to Beta brings key operational and API enhancements: Graduation to Beta: Controlled by the PodLevelResourceManagers feature gate, available to opt in (disabled by default) in Kubernetes v1.37. PodResources API Reporting: The v1 PodResources gRPC service (PodResourcesLister) introduces top-level cpu_ids and memory fields on PodResources responses. Monitoring tools and device plugins can query pod-level exclusive assignments directly without double-counting container allocations. Getting started and providing feedback For a deep dive into the tech ## Kubernetes v1.37: Memory QoS Graduates to Beta DevFeed: [Kubernetes v1.37: Memory QoS Graduates to Beta]() Original publisher: [Read original article]() Author: Qi Wang; Sohan Kunkerkar Published: 2026-09-14T18:30:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [releases](), [configuration](), [Linux](), [Kernel]() Tags: [clusters](), [configuration](), [container](), [kernel](), [kubernetes](), [linux](), [memory](), [qos](), [releases](), [upgrade](), [v1]() ### AI overview Kubernetes v1.37 promotes Memory QoS to Beta and enables it by default on Linux nodes using cgroup v2. The article explains the updated defaults, configuration options for memory throttling and tiered memory protection, and upgrade behavior intended to preserve existing runtime behavior. ### Source excerpt Memory QoS has graduated to Beta in Kubernetes v1.37 and is now enabled by default. On Linux nodes running cgroup v2, the feature uses the memory controller to give the kernel better guidance on how to treat container memory. It was first introduced as Alpha in v1.22, and expanded in v1.36 with tiered memory reservation. This post covers what changed in v1.37, what the Beta promotion means for cluster operators, and how to configure the feature. What changed in v1.37Memory QoS is Beta and enabled by default The MemoryQoS feature gate is now Beta in v1.37. This means every v1.37 kubelet has the feature gate turned on without any configuration change. Turning on the feature by default is safe because the default kubelet configuration does not enable memory throttling or memory reservation. No memory.high, memory.min, or memory.low values are written to cgroups unless you explicitly configure them. You can opt into specific behaviors through kubelet configuration fields: Set memoryThrottlingFactor (for example, 0.9) to enable memory.high throttling on Burstable and BestEffort containers. The default is null, which means no throttling. Set memoryReservationPolicy to TieredReservation to enable tiered memory protection via memory.min and memory.low. The default is None, which means no memory reservation. Default memoryThrottlingFactor changed to null In earlier Alpha releases, memoryThrottlingFactor defaulted to 0.9, which meant enabling the feature gate caused the kubelet to set memory.high on containers. In v1.37, the default is null, so the kubelet does not set memory.high unless you configure a value. This change was made because, with the feature gate now on by default, an automatic memory.high could throttle workloads that were previously running without throttling. Making it null ensures that upgrading to v1.37 does not change runtime behavior for existing clusters. If your kubelet configuration file already contains an explicit memoryThrottlingFactor value, that [Next page]()