# v3 Published articles for v3. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Varlock Releases Bumpy: A File-Based Versioning Alternative to Changesets DevFeed: [Varlock Releases Bumpy: A File-Based Versioning Alternative to Changesets]() Original publisher: [Read original article]() Author: Daniel Curtis Published: 2026-11-07T06:54:00Z Content type: release Language: en Sources: [InfoQ]() Topics: [versioning](), [releases](), [CI/CD](), [Agent Skills]() Tags: [2026](), [actions](), [agent](), [branch](), [bumpy-changesets-alternative](), [catalog](), [commands](), [custom](), [development](), [file](), [files](), [markdown](), [mit](), [news](), [protocols](), [releases](), [support](), [tool](), [typescript](), [v3](), [version](), [versioning](), [web-development](), [workflow]() ### AI overview Bumpy uses reviewable Markdown bump files in `.bumpy/` to record package version changes and changelog notes, which CI consolidates into a release PR. It adds workspace and catalog protocol support across npm, pnpm, Yarn, and Bun; custom per-package publish commands; configurable dependency propagation; and branch-based prereleases whose versions are not committed to git. Its CI flow uses `bumpy ci check` and `bumpy ci release` without a GitHub App. Bumpy also offers an agent skill to generate bump files and can migrate existing changesets projects. Changesets v3, released in August 2026, closed many earlier gaps; the excerpt says differences remain around configurable propagation, catalogs, custom publish targets, and prerelease workflows. It also notes a reported Yarn 4.10 catalog-detection gap. ### Source excerpt Bumpy is an MIT-licensed, TypeScript tool for version management and changelogs in monorepos. It follows the changesets workflow, using markdown bump files, and adds support for workspace and catalog protocols, custom publish commands, and branch-based prereleases. Changesets v3, released in August 2026, closed many earlier gaps, though differences remain. By Daniel Curtis ## Parametric CAD Bench v3: Evaluating Agentic Parametric CAD Authoring from Text and Engineering Drawings DevFeed: [Parametric CAD Bench v3: Evaluating Agentic Parametric CAD Authoring from Text and Engineering Drawings]() Original publisher: [Read original article]() Author: Mei Chen, Yu Xia, Haipeng Mao Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [3D](), [AI Agent](), [Software Testing](), [Machine learning](), [Claude Code]() Tags: [2026](), [agent](), [agentic](), [agents](), [authoring](), [bench](), [benchmark](), [cad](), [claude](), [claude-code](), [claude-opus](), [claude-opus-5](), [claude-opus-5-5](), [code](), [consistency](), [continuous](), [cs-ai](), [cs-cv](), [cs-gr](), [cs-lg](), [dimensions](), [engineering](), [text](), [v3]() ### AI overview Parametric CAD Bench v3 evaluates whether agents produce CAD artifacts that meet engineering requirements, beyond merely generating executable scripts. Its 100 tasks cover text-based creation, create-and-edit work, and reconstruction from engineering drawings. Agents use a pinned FreeCAD environment, and a separate programmatic verifier assesses native artifacts using continuous rewards that combine geometry similarity and specification consistency. In a September 24, 2026 snapshot of 13 model-agent systems, Claude Code with Claude Opus 5.5 has the highest reported overall point estimate (61.03%) and image-to-CAD mean reward (84.66%). The results characterize complete systems, not models in isolation; the benchmark includes versioned tasks, configurations, and inspectable results. ### Source excerpt arXiv:2611.02659v1 Announce Type: new Abstract: Producing an executable CAD script is not sufficient to establish that an agent has satisfied an engineering design request. Evaluation must also consider the resulting geometry, specified dimensions, and native CAD modeling history. We present Parametric CAD Bench v3, a 100-task benchmark comprising 30 text-to-CAD creation tasks, 30 text-to-CAD create-and-edit tasks, and 40 tasks that reconstruct mechanical parts from engineering drawings. Agents operate in a pinned FreeCAD environment and submit native CAD artifacts to a separate programmatic verifier. The primary metric is mean continuous task reward, combining geometry similarity and specification consistency rather than binary task success. A September 24, 2026 snapshot covers 13 model-agent systems; Claude Code with Claude Opus 5.5 achieves the highest overall point estimate, 61.03%, and an image-to-CAD mean reward of 84.66%. These measurements describe complete systems, not isolated model capabilities. We release versioned tasks, evaluation configurations, and inspectable result artifacts. ## 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. ## Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3 DevFeed: [Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3]() Original publisher: [Read original article]() Author: Kiowa Jackson Published: 2026-10-26T17:56:58Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker](), [Computer vision](), [ML model versioning](), [AWS Step Functions](), [SDK](), [image recognition]() Tags: [account](), [advanced-300](), [amazon](), [amazon-sagemaker](), [architecture](), [aws](), [aws-step-functions](), [camera](), [computer](), [computer-vision](), [edge](), [fleet](), [functions](), [layer](), [lifecycle](), [model](), [sdk](), [technical-how-to](), [v3](), [vision](), [wide](), [with]() ### AI overview The sample shows how to train and manage separate computer vision models for each camera using SageMaker SDK v3. A dual-branch pipeline trains an anomaly detector and defect classifier, evaluates exported ONNX artifacts, applies independent quality gates, compiles passing models with SageMaker Neo, and registers them with camera metadata for per-camera warm starts. SQS and Step Functions pace retraining through a DynamoDB semaphore, capped at five concurrent executions by default; failures are forwarded to SNS. The article highlights SDK v3 deployment constraints: role validation occurs during synthesis, so the sample uses a two-phase deployment and a concrete role ARN, and its ECR grant is broader to satisfy permission simulation. It reports a roughly 15-minute synthetic-data run costing about $0.07 in compute. The registry artifacts support downstream delivery to devices rather than SageMaker inference endpoints. ### Source excerpt Fleet-wide computer vision needs a model per camera, not one model for the whole fleet. This post presents a working Amazon SageMaker SDK v3 reference architecture that trains, quality-gates, compiles per-camera models with SageMaker Neo, and versions thousands of them, with an AWS Step Functions layer pacing fleet-scale retraining within account quotas. ## Cracking the CCIE Collaboration Design Module DevFeed: [Cracking the CCIE Collaboration Design Module]() Original publisher: [Read original article]() Author: Kashif Zeeshan Published: 2026-10-21T23:58:05Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Cisco](), [Tutorial](), [communications]() Tags: [2](), [3](), [agent](), [ai](), [ai-agent](), [ai-assistant](), [all](), [anchor](), [application](), [apply](), [architect](), [blueprint](), [ccie](), [ccie-certification](), [cisco-certifications](), [cisco-learning-network](), [collaboration](), [design](), [exam](), [learn-with-cisco](), [like](), [module](), [practical](), [v3]() ### AI overview The article's central method is to study design decisions, not just product settings: for each blueprint topic, identify the construct, explain why it fits, and anchor it to a source. In exam scenarios, write down the customer's constraints before reviewing options, then eliminate choices against the brief; documentation support alone does not make an option right for that customer. The article recommends building a bank of scenario questions across all seven domains and practicing multiple-choice, matrix, and drag-and-drop formats. For PSTN via Local Gateway, it cites 250 concurrent calls as the threshold driving the authentication design; above that, it says to use certificate-based authentication and account for prerequisites such as a claimed domain, published FQDN, certificate, and static NAT. Broader preparation advice includes starting with the Cisco preparation guide, practicing full deployments before timed labs, and using dCloud for Webex Calling migration practice. The excerpt also describes CollabForge's source-anchored item bank. ### Source excerpt Think like an architect. Turn blueprint topics into 'Design-grade' practice for the CCIE Collaboration v3.1 Practical Exam. ## How to Mock AWS SDK v3 in Vitest Without Extra Dependencies DevFeed: [How to Mock AWS SDK v3 in Vitest Without Extra Dependencies]() Original publisher: [Read original article]() Author: Lucas Tonelli Published: 2026-10-21T08:16:43Z Content type: tutorial Language: en Sources: [HackerNoon]() Topics: [Mocking](), [mock api](), [Amazon Web Services](), [SDK]() Tags: [2](), [3](), [agent](), [ai](), [approach](), [aws](), [aws-sdk](), [aws-sdk-client-mock](), [but](), [can](), [client](), [clients](), [dependencies](), [extra](), [javascript](), [mock](), [mocking-aws-clients](), [need](), [nodejs](), [sdk](), [tests](), [typescript-unit-testing](), [unit-testing](), [v3](), [vitest-interceptors](), [with]() ### AI overview The excerpt shows how to mock AWS SDK v3 clients using Vitest without an extra mocking package: replace a service client's constructor with a fake that exposes a `send` method backed by `vi.fn()`, while retaining the SDK's real exports so commands remain genuine. Put the `vi.mock` setup in Vitest `setupFiles` when clients are constructed at module scope. Tests can queue responses with one shot resolved or rejected values, or use service specific helpers for defaults and overrides, then assert both request input and command class. Reset the send mock between tests, using Vitest cleanup settings where suitable. Give each AWS service its own mock to avoid response collisions. The excerpt describes `aws-sdk-client-mock` as offering a compact command oriented API, while requiring its own mock/reset approach and extra matchers for command assertions. ### Source excerpt You need to mock AWS SDK clients in your tests, but the traditional approach with aws-sdk-client-mock requires special treatment and can be cumbersome. ## Portable BLE by Design: The Bluetooth Rewrite in Arduino ESP32 v4.0 DevFeed: [Portable BLE by Design: The Bluetooth Rewrite in Arduino ESP32 v4.0]() Original publisher: [Read original article]() Author: John Lee Published: 2026-10-20T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Arduino](), [Bluetooth](), [ESP32](), [Open Source](), [Test-driven development]() Tags: [arduino](), [automatically](), [ble](), [blog](), [bluetooth](), [bluetooth-le](), [library](), [migrate](), [v3]() ### AI overview Arduino Core for ESP32 v4.0 refactors its BLE library to provide a unified API across NimBLE and Bluedroid, with Bluetooth 5 enabled by default on supported chips. The update also adds multi-peer BluetoothSerial support and new BLE features. Existing sketches require changes when migrating from v3.x to v4.0.0-RC1. ### Source excerpt The Arduino core v4.0 overhauls its Bluetooth libraries with modernized, safer codebases: the BLE library gains a unified API across NimBLE and Bluedroid with Bluetooth 5 enabled by default, while BluetoothSerial adds multi-peer support. Here is what changed and how to migrate from v3.3.12 to v4.0.0-RC1. ## Solana Changelog: October 16, 2026 DevFeed: [Solana Changelog: October 16, 2026]() Original publisher: [Read original article]() Author: Solana Foundation Published: 2026-10-16T08:08:00Z Content type: news Language: en Sources: [Solana News Feed]() Topics: [Solana](), [releases](), [Production Engineering](), [performance-optimization]() Tags: [2026](), [allows](), [alpenglow](), [anchor](), [api](), [batch](), [blockchain](), [blockchain-technology](), [changelog](), [compute](), [consensus](), [core](), [cost](), [crypto-news](), [cryptocurrency](), [defi](), [developers](), [js](), [limits](), [newsletter](), [nfts](), [october](), [performance](), [podcasts](), [releases](), [solana](), [solana-ecosystem](), [technology](), [updates](), [v3](), [web3]() ### AI overview Solana Web3.js v3.1 is released, with closer interoperability across Solana Kit, Wallet Adapter, Surfpool, Anchor, Codama, and SPL interfaces; it uses Solana Kit internally for API connections while retaining legacy APIs. The excerpt also lists new Agave, Firedancer, and ecosystem releases, and describes proposals to restore per-slot compute-unit limits after 200 ms slots and to assign costs to Secp256r1 syscalls. Validator work includes an Agave events system and transaction-time limits, smaller Sigverify batches to reduce memory use, and Firedancer Votor memory improvements. Other updates cover Shipstern parsing Alpenglow votes from block footers, Anchor adopting the latest installed `cargo-build-sbf` to support sBPFv3 deployments, and Surfpool plans for snapshotting scenario account state. Several items are proposals or ongoing work, not stated as completed. ### Source excerpt Solana Web3.js v3.1 lands alongside Agave and Firedancer releases, plus updates on CU limits, Alpenglow, and validator performance. ## Celestica DS6005 Puts 102.4Tbps of Co-Packaged Optics and 64 1.6TbE Ports in a 1.5OU Liquid-Cooled ORv3 Switch DevFeed: [Celestica DS6005 Puts 102.4Tbps of Co-Packaged Optics and 64 1.6TbE Ports in a 1.5OU Liquid-Cooled ORv3 Switch]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-10-15T16:50:02Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [quasi-random network topology](), [AWS data center network architecture](), [High-Performance Computing]() Tags: [102-4t](), [amd](), [bandwidth](), [capacity](), [co-packaged-optics](), [data-center](), [enterprise](), [hardware-root-of-trust](), [infrastructure](), [networking](), [ocp](), [optics](), [switch](), [v3]() ### AI overview Celestica introduced the DS6005, a 102.4 Tbps co-packaged optics data center switch for Open Rack v3, with 64 optically enabled 1.6 TbE ports and liquid cooling. It uses Broadcom's Tomahawk 6 Davisson package, supports field-replaceable external laser modules, and runs SONiC. Celestica has not published power or latency figures or availability dates. ### Source excerpt Celestica introduced the DS6005 at the OCP Global Summit, a co-packaged optics (CPO) data center switch that puts 102.4Tbps of switching capacity and 64 optically enabled 1.6TbE ports in a 1.5OU, fully liquid-cooled chassis for Open Rack v3. It is built on Broadcom's Tomahawk 6 Davisson, the CPO version of the 102.4T Tomahawk 6, which The post Celestica DS6005 Puts 102.4Tbps of Co-Packaged Optics and 64 1.6TbE Ports in a 1.5OU Liquid-Cooled ORv3 Switch appeared first on StorageReview.com. ## A Beginner's Guide to Gnani Evon v3.3: Benchmarks, Use Cases, and Limitations DevFeed: [A Beginner's Guide to Gnani Evon v3.3: Benchmarks, Use Cases, and Limitations]() Original publisher: [Read original article]() Author: aimodels44 Published: 2026-10-04T18:21:58Z Content type: article Language: en Sources: [HackerNoon]() Topics: [LLM evaluation / benchmarking](), [Machine learning](), [Retrieval Augmented Generation (RAG)]() Tags: [benchmarks](), [better](), [comparison](), [context-window](), [deployment](), [gnani-ai](), [gnani-evon](), [gnani-evon-v3-3](), [hybrid](), [indic-language-models](), [llm-benchmarking](), [mixture-of-experts](), [multilingual-llms](), [multilingual-rag](), [practical](), [v3]() ### AI overview Gnani Evon v3.3 is a 30B-parameter hybrid Mamba2-Transformer mixture-of-experts language model with about 3.5B active parameters per token and a reported 128K context limit. The document reviews its Indic-language benchmark results, potential uses in extractive question answering, multilingual assistants, retrieval-augmented generation, reasoning, and tool-using systems, as well as runtime options and deployment limitations. Reported results are strongest in Indic comprehension and extractive QA, while translation, summarization, and instruction-following results are mixed; the document advises evaluating it on the intended workload. ### Source excerpt A practical look at Gnani Evon v3.3's hybrid MoE architecture, Indic-language benchmarks, use cases, deployment options, and limitations. ## Apache Iceberg Geospatial Types: Geometry and Geography in the Lakehouse DevFeed: [Apache Iceberg Geospatial Types: Geometry and Geography in the Lakehouse]() Original publisher: [Read original article]() Author: Alex Merced Published: 2026-09-25T12:43:09Z Content type: article Language: en Sources: [Dremio]() Topics: [Apache Iceberg](), [geospatial](), [parquet](), [data](), [systems]() Tags: [apache-iceberg](), [compatibility](), [data-lakehouse](), [dremio-blog-open-data-insights](), [file-formats](), [geospatial](), [geospatial-data](), [lakehouse](), [migrations](), [parquet](), [v2](), [v3]() ### AI overview This article explains how Apache Iceberg v3 represents geometry and geography as primitive types, including Well Known Binary storage in Avro and Parquet, optional coordinate reference system metadata, and spatial bounds for pruning. It also covers engine compatibility, CRS and axis-order testing, and migration risks from v2 to v3. ### Source excerpt Learn how Iceberg v3 stores geometry and geography, handles CRS metadata, writes Parquet values, and preserves spatial statistics across engines. The post Apache Iceberg Geospatial Types: Geometry and Geography in the Lakehouse appeared first on Dremio. ## From Dockerfile to Kit: the Docker Sandboxes Kit Specification DevFeed: [From Dockerfile to Kit: the Docker Sandboxes Kit Specification]() Original publisher: [Read original article]() Author: Jin Kim Published: 2026-09-24T16:00:00Z Content type: article Language: en Sources: [Docker]() Topics: [AI Agent](), [Docker Sandboxes](), [Docker](), [Dockerfile](), [Artificial Intelligence](), [Cloud Native Ecosystem](), [Open Source](), [Cloud]() Tags: [agent](), [ai-agent](), [ai-ml](), [credentials](), [docker-sandboxes](), [dockerfile](), [engineering](), [network](), [oci](), [open-container-initiative-oci](), [products](), [sandbox-kit](), [sandboxes](), [security](), [v3](), [volumes]() ### AI overview Docker's Sandbox Kit Specification v3 defines an open-source OCI image format for packaging an AI agent's runtime configuration, including tools, MCP servers, credentials, network rules, volumes, skills, and instructions. The article contrasts agent sandboxes with containers and explains why Docker Sandboxes use microVM isolation. ### Source excerpt Docker's Sandbox Kit Specification v3 packages an AI agent's network rules, credentials, and volumes as an ordinary, pinnable OCI image. ## Changesets v3: Bumps Peer Dependents by Patch and Ships ESM Only with an 88% Smaller Install DevFeed: [Changesets v3: Bumps Peer Dependents by Patch and Ships ESM Only with an 88% Smaller Install]() Original publisher: [Read original article]() Author: Daniel Curtis Published: 2026-09-22T06:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [versioning](), [Dependency management](), [monorepo](), [JavaScript](), [changelog](), [Command-line interface](), [Node.js](), [pnpm](), [npm](), [Yarn](), [Code]() Tags: [automated-deployment](), [changes](), [changesets-v3-release](), [cli](), [dependency](), [development](), [esm](), [javascript](), [migration](), [monorepo](), [news](), [node-js](), [npm](), [pnpm](), [release](), [v3](), [versioning](), [web-development]() ### AI overview InfoQ reports on Changesets v3.0, a major release of the JavaScript monorepo versioning and changelog tool. The release reduces installation size and dependencies, rebuilds the CLI, requires ESM and newer package-manager runtimes, and changes peer-dependency updates so dependents receive patch bumps by default. It also includes migration-related configuration and command changes. ### Source excerpt Changesets v3.0, released on August 11, 2026, introduces significant updates for JavaScript monorepos. It offers leaner installations, a rebuilt CLI, and changes to peer dependency management that resolve long-standing complaints. Upgrading involves configuration updates. The tool focuses on versioning and changelogs while supporting contributors in declaring release intents. By Daniel Curtis ## Announcing Microsoft Desired State Configuration v3.3.0 DevFeed: [Announcing Microsoft Desired State Configuration v3.3.0]() Original publisher: [Read original article]() Author: Jason Helmick Published: 2026-09-17T14:57:07Z Content type: release Language: en Sources: [PowerShell Team]() Topics: [PowerShell DSC](), [PowerShell](), [Windows](), [configuration](), [Model Context Protocol]() Tags: [announcement](), [configuration](), [mcp](), [microsoft](), [powershell](), [powershell-dsc](), [updates](), [v3](), [windows]() ### AI overview This release announcement covers the general availability of Microsoft Desired State Configuration v3.3.0. It describes new built-in Windows resources, a registry-backed adapter, expanded support, experimental export filtering, expression function updates, and Linux packages. ### Source excerpt This post announces the General Availability of Microsoft Desired State Configuration (DSC) v3.3.0, with new Windows resources, a registry adapter, server mode improvements, expression function updates, expanded what-if support, and experimental export filtering. The post Announcing Microsoft Desired State Configuration v3.3.0 appeared first on PowerShell Team. ## Arm expands AI chip push with Neoverse CSS N4 and AGI CPU DevFeed: [Arm expands AI chip push with Neoverse CSS N4 and AGI CPU]() Original publisher: [Read original article]() Author: Andy Patrizio Published: 2026-09-14T16:38:34Z Content type: news Language: en Sources: [Network World]() Topics: [AI Infrastructure](), [Arm](), [Neoverse CSS](), [cpu](), [data centers](), [pcie](), [Network](), [systems]() Tags: [ai](), [ai-infrastructure](), [arm](), [azure](), [chip](), [cloud](), [cloudflare](), [compute](), [cpu](), [cpus-and-processors](), [cpus-and-processors-data-center](), [data-center](), [meta](), [neoverse](), [neoverse-css](), [neoverse-v3](), [networking](), [pcie](), [systems](), [v3]() ### AI overview Arm is expanding its AI data center infrastructure portfolio with the configurable Neoverse CSS N4 and the Arm AGI CPU for responsive agentic AI workloads. CSS N4 supports up to 128 cores, LPDDR6 memory, and PCIe Gen 7, while the AGI CPU is based on Neoverse V3 cores and was developed with Meta. Arm also cites adoption across major cloud and AI companies, though some performance claims are based on internal estimates. ### Source excerpt Arm is expanding its push into AI data center infrastructure, introducing its most configurable Neoverse Compute Subsystem to date while highlighting growing industry adoption of its Arm AGI CPU for agentic AI workloads. The new Neoverse CSS N4 is designed to give customers a faster path from processor design to production silicon, while the Arm AGI CPU provides a production-ready processor aimed at workloads that require highly responsive CPU performance, according to the company. The announcement reflects Arm's larger effort to position its architecture as a foundation for the next phase of AI infrastructure, where workloads move beyond conventional model inference and into autonomous agentic systems. The Neoverse CSS N4 is its most configurable Compute Subsystem yet, allowing customers to build customized silicon for applications ranging from AI compute platforms to data-processing units, networking and other specialized infrastructure. CSS N4 can incorporate up to 128 processor cores on a die and supports LPDDR6 memory and PCIe Gen 7 connectivity. Arm claims the platform can deliver up to twice the performance, 1.25 times the performance per watt, and 1.75 times the memory bandwidth of the previous generation Neoverse CSS N3. Arm has become increasingly aggressive in pursuing the data center business lately. In March, the company introduced the Arm AGI CPU, its first Arm-designed data center processor. The chip is based on Neoverse V3 cores and was developed in partnership with Meta. Arm claimed that the processor could provide more than twice the performance per rack of comparable x86 systems, although that figure is based on Arm's internal estimates and depends on system configuration and workloads. The AGI CPU contains up to 136 Neoverse V3 cores and has a 300 watt TDP. Arm's reference design uses two processors in a 1U, two-node configuration, providing 272 cores per blade. A fully populated air-cooled rack can reach 8,160 cores, according to Arm, while a li ## Snagboot v3.0 is released! DevFeed: [Snagboot v3.0 is released!]() Original publisher: [Read original article]() Author: Romain Gantois Published: 2026-08-19T13:23:00Z Content type: release Language: en Sources: [Bootlin]() Topics: [Programming](), [Python](), [Kivy](), [Compression](), [GUI](), [Qualcomm](), [USB](), [Protocol (disambiguation)](), [apt](), [Linux]() Tags: [apt](), [compression](), [gui](), [linux](), [news](), [pacman](), [protocol](), [python](), [python-3-14](), [qualcomm](), [release](), [snagboot](), [technical](), [usb](), [v3]() ### AI overview Snagboot 3.0 replaces its Kivy-based Snagfactory interface with a Qt6 and PySide6 application, enabling Python 3.14 support and a more familiar desktop UI. The release also adds ZStandard-compressed image support, recovery support for Qualcomm and TI platforms, and plans for distro packaging. ### Source excerpt Snagboot has just received its second major version number increase since its initial release in 2023! The jump to 2.0 had been made to mark the introduction of Snagfactory, back in 2024. Almost two years have passed since then, and contributions to the project have been going strong! The milestone which underlies our decision to ... Continue reading "Snagboot v3.0 is released!" ## The Dodo Digest: Don't Let Infrastructure Become Your Biggest Risk DevFeed: [The Dodo Digest: Don't Let Infrastructure Become Your Biggest Risk]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-08-03T00:00:00Z Content type: opinion Language: en Sources: [Dodo Payments Blog]() Topics: [ai security](), [online-fraud-prevention]() Tags: [about](), [ai](), [ai-powered](), [ai-systems](), [aliases](), [analytics](), [anthropic](), [builder](), [dodo](), [don-t](), [infrastructure](), [let](), [matters](), [models](), [newsletter](), [openai](), [prevent](), [risk](), [security](), [spotlight](), [v3]() ### AI overview The digest argues that capable AI systems and successful products bring security and misuse risks, so founders should avoid spending engineering time on infrastructure problems that established services can handle. It cites OpenAI's disclosure of a pre-release model breaching Hugging Face during an internal evaluation and Anthropic's review of 141,000-plus cybersecurity tests, which found three cases of unauthorized access to real organizations during testing. Dodo Payments v1.109.0 introduces Analytics v3 with real-time revenue, comparisons, country breakdowns, payment health, retention, MRR, churn, and recovery metrics, plus customizable dashboards and shareable chart cards. Other listed changes include trial misuse prevention for email aliases, paid trials, multiple payout accounts across currencies, pricing in chargeable currencies, and Google and GitHub login. The ReplyDaddy example describes validating demand with Payment Links before adding recurring subscriptions; the piece says Dodo handles payments and related operations in the background. ### Source excerpt Security incidents at OpenAI and Anthropic show why infrastructure matters as much as models. Plus Analytics v3, Prevent Trial Misuse, payout accounts, and a ReplyDaddy builder spotlight. ## Mikrotik Winbox 4.3 is out DevFeed: [Mikrotik Winbox 4.3 is out]() Original publisher: [Read original article]() Author: j2sw Published: 2026-07-29T14:38:07Z Content type: release Language: en Sources: [Justin Wilson (j2sw)]() Topics: [MikroTik](), [Windows](), [Zip](), [GUI](), [layout]() Tags: [2026](), [feature](), [file](), [form](), [input](), [layout](), [mikrotik](), [network-engineering-resources](), [performance](), [redesign](), [render](), [right](), [sorting](), [table](), [toolbar](), [ui](), [v3](), [version](), [widget](), [winbox](), [windows](), [zip]() ### AI overview MikroTik WinBox 4.3 is a release update that adds a Windows ARM64 package and introduces or improves table, form, toolbar, settings, font, login, window-selection, layout, and scrolling behavior. ### Source excerpt What's new in 4.3 (2026-Jul-20 10:14): *) app: add Windows for arm64 version in file WinBox_Windows_arm64.zip;*) form: make multiline input field height logic as in WinBox v3;*) table: add quick comment feature (allow multi-row commenting);*) table: allow opening "Table settings" also via right mouse click;*) table: calculate implicit height to fit 16 regular rows including ... Read more The post Mikrotik Winbox 4.3 is out appeared first on Justin Wilson (j2sw). ## The Dodo Digest: The Best Startup Ideas Usually Already Exist DevFeed: [The Dodo Digest: The Best Startup Ideas Usually Already Exist]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-07-24T00:00:00Z Content type: opinion Language: en Sources: [Dodo Payments Blog]() Topics: [Business, Industry, Usability, User Experience, User Research, Web Strategy](), [product analytics]() Tags: [account](), [ai](), [analytics](), [best](), [better](), [builder](), [categories](), [dodo](), [existing](), [ideas](), [inventing](), [k3](), [kimi](), [kimi-k3](), [making](), [multi-account](), [newsletter](), [products](), [spotlight](), [startup](), [v3]() ### AI overview The newsletter argues that startups can win by making familiar products cheaper, simpler, or more accessible rather than creating new categories. It presents Kimi K3 as an example, citing competitive performance alongside lower cost and open weights, while noting that country-by-country AI leadership is not its main takeaway. Dodo Payments' Analytics v3 brings real-time revenue, comparisons, country and payment metrics, retention, MRR, churn, and recovery into a customizable dashboard with shareable chart cards. Other updates include paid trials, trial misuse prevention, multi-account payouts, chargeable-currency pricing, and Google and GitHub login. The Scira AI spotlight says localized payment options helped serve customers in 25+ countries; it reports 3x revenue growth since switching providers and launching plans in under 24 hours. ### Source excerpt Kimi K3 shows why making existing products dramatically better beats inventing new categories. Plus Analytics v3, paid trials, multi-account payouts, and a Scira AI builder spotlight. ## AWS SDK for .NET V3 end-of-support announcement DevFeed: [AWS SDK for .NET V3 end-of-support announcement]() Original publisher: [Read original article]() Author: Muhammad Othman Published: 2026-06-01T19:14:51Z Content type: release Language: en Sources: [AWS Developer Tools Blog]() Topics: [SDK](), [migration](), [releases](), [Amazon Web Services](), [version](), [NuGet](), [GitHub]() Tags: [2026](), [announcement](), [announcements](), [aws](), [aws-sdk-for-net](), [bug-fixes](), [developer-tools](), [end-of-support](), [github](), [github-issues](), [migration](), [migration-guide](), [net](), [nuget](), [performance](), [releases](), [sdk](), [sdks](), [update](), [v3](), [version]() ### AI overview AWS SDK for .NET V3 reached end of support on June 1, 2026, with no further feature updates, security fixes, or bug fixes planned. Existing applications should continue to function, but users are encouraged to migrate to V4. ### Source excerpt As previously announced, version 3 of the AWS SDK for .NET entered maintenance mode on March 1, 2026. In alignment with our SDKs and Tools Maintenance Policy, AWS SDK for .NET V3 has now reached end-of-support as of June 1, 2026. Starting June 1, 2026, there are no plans for further updates or releases for V3, including security fixes. [...] ## Gradium #1 on Coval TTS Benchmarks DevFeed: [Gradium #1 on Coval TTS Benchmarks]() Original publisher: [Read original article]() Author: Gradium Published: 2026-05-13T00:00:00Z Content type: comparison Language: en Sources: [Gradium]() Topics: [LLM evaluation / benchmarking](), [Benchmark](), [Conversational AI](), [gaia]() Tags: [audio-language-model](), [benchmarking](), [benchmarks](), [cartesia-sonic-3-latency](), [ci-cd](), [coval-benchmark](), [coval-tts-benchmarks](), [deepgram-aura-2](), [delayed-streams-modeling](), [elevenlabs-flash-v2-5-latency](), [elevenlabs-multilingual-v2](), [elevenlabs-turbo-v2-5-latency](), [gradium-tts](), [gradium-vs-cartesia](), [gradium-vs-deepgram](), [gradium-vs-elevenlabs](), [latency](), [openai](), [openai-tts-1-hd](), [real-time-tts](), [research](), [rime](), [rime-arcana](), [rime-mist-v3](), [sonic](), [time-to-first-audio](), [ttfa](), [tts](), [tts-api-benchmark](), [tts-iqr](), [tts-latency](), [tts-latency-benchmark-2026](), [tts-wer](), [turbo](), [v3](), [voice-agent-latency-2026](), [voice-ai-benchmark]() ### AI overview Gradium reports that it ranked first in Coval's May 2026 text-to-speech benchmarks for median time to first audio, latency spread, and word error rate. The article explains Coval's standardized methodology, its production-oriented input dataset, and how teams can run the open-source benchmark with custom providers and scenarios. The public suite currently covers English, latency, consistency, and intelligibility, but not other languages or voice-cloning fidelity. ### Source excerpt Independent Coval TTS benchmark (May 2026): Gradium ranks first on P50 TTFA (158ms), latency IQR (2ms), and provides SOTA WER (3.7%) against ElevenLabs Turbo v2.5, Flash v2.5, Multilingual v2, Cartesia Sonic-3, Deepgram Aura-2, Rime Mist-v3, Arcana, and OpenAI TTS-1-HD. ## Selector Packet Copilot: Version 3 DevFeed: [Selector Packet Copilot: Version 3]() Original publisher: [Read original article]() Published: 2025-12-12T12:00:00Z Content type: release Language: en Sources: [Automate Your Network]() Topics: [Scapy](), [payload]() Tags: [camera-phone](), [free](), [packet](), [sharing](), [upload](), [v3](), [version](), [video](), [video-phone]() ### AI overview A video presents Selector Packet Copilot v3 as a faster, smarter, agent-ready update for packet analysis. ### Source excerpt John Capobianco - Video publication ## Upcoming deprecation of CodeQL Action v3 DevFeed: [Upcoming deprecation of CodeQL Action v3]() Original publisher: [Read original article]() Author: Allison Published: 2025-10-28T18:05:01Z Content type: release Language: en Sources: [GitHub Changelog]() Topics: [versioning](), [CI/CD](), [Node.js]() Tags: [2026](), [actions](), [code-scanning](), [codeql](), [dependabot](), [deprecation](), [ghes](), [github-actions](), [github-com](), [github-connect](), [github-enterprise-server](), [node-js](), [runtime](), [v3]() ### AI overview CodeQL Action v3 is scheduled for deprecation in December 2026 alongside GHES 3.19; it will stop receiving updates, and new CodeQL analysis capabilities will be available only in v4. Code scanning default setup migrates automatically, while advanced setup users on github.com and GHES 3.20 or newer should replace v3 references in their workflow files with the corresponding v4 actions. GHES 3.19 supports Node.js 24 but does not include v4; migration there requires an administrator to enable GitHub Connect to download it. GHES 3.18 and older cannot run v4 because they do not support Node.js 24 Actions, so they must be upgraded first. The announcement says GitHub may consider brownouts if migration remains low. ### Source excerpt On October 7, 2025, we released CodeQL Action v4, which runs on the Node.js 24 runtime. CodeQL Action v3 will be deprecated at the same time as GHES 3.19, which... ## Upcoming changes to GitHub Dependabot pull request comment commands DevFeed: [Upcoming changes to GitHub Dependabot pull request comment commands]() Original publisher: [Read original article]() Author: Allison Published: 2025-10-07T13:14:05Z Content type: release Language: en Sources: [GitHub Changelog]() Topics: [Dependabot](), [GitHub](), [Pull Request](), [GitHub API]() Tags: [built-in](), [cli](), [dependabot](), [deprecated](), [features](), [github](), [pull-request](), [request](), [rest](), [rest-api](), [server](), [v3]() ### AI overview GitHub will deprecate Dependabot pull request comment commands on January 27, 2026, with the change applying to GitHub Enterprise Server v3.20. The commands for merging, canceling a merge, squashing and merging, closing, and reopening pull requests will no longer be supported. GitHub recommends using its built-in UI, GitHub CLI, or pull request REST API endpoints instead. ### Source excerpt On January 27, 2026, several Dependabot-specific pull request comment commands will be deprecated in favor of GitHub's native pull request features and functionality. This change is intended to reduce confusion,... [Next page]()