# Edge Computing Published articles for Edge Computing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Intern 2 offers an isolated environment and cloud inference for running personal bots DevFeed: [Intern 2 offers an isolated environment and cloud inference for running personal bots]() Original publisher: [Read original article]() Author: Thomas Claburn Published: 2026-09-16T23:28:59Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Bots](), [Mac Mini](), [Inference]() Tags: [ai-agents](), [ai-and-ml](), [automate](), [autonomous-ai](), [bots](), [edge-computing](), [inference](), [intern-2](), [mac-mini](), [open-source](), [personal-tech]() ### AI overview Intern 2 provides an isolated environment and cloud inference for automating tasks with personal bots. ### Source excerpt 'Intern 2' offers an isolated environment and cloudy inference to automate your world ## Cloudflare Tests Cache Transcoding to Reduce Storage Requirements DevFeed: [Cloudflare Tests Cache Transcoding to Reduce Storage Requirements]() Original publisher: [Read original article]() Author: Renato Losio Published: 2026-09-13T10:35:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Caching](), [Transcodings](), [cloud-infrastructure](), [Pingora]() Tags: [architecture-design](), [cache](), [caching](), [cdn](), [cloud](), [cloudflare](), [cloudflare-cache-transcoding](), [compression](), [cpu](), [data-centers](), [development](), [edge-computing](), [news](), [pingora](), [rust](), [storage]() ### AI overview Cloudflare is testing Cache Transcoding, a prototype that Zstandard-compresses eligible uncompressed text before it is stored in cache. The approach aims to increase effective cache capacity and reduce inter-data-center transfer, with configurable CPU and storage trade-offs. ### Source excerpt Cloudflare recently described a prototype called Cache Transcoding that compresses eligible cache content, mainly uncompressed text such as HTML, JSON, CSS, and JavaScript, using Zstandard before storing it on disk. The hyperscaler estimates that the approach could provide petabytes of additional effective cache capacity, although broader testing is still needed. By Renato Losio ## Red Hat edge platforms: Choosing the right one for your use case DevFeed: [Red Hat edge platforms: Choosing the right one for your use case]() Original publisher: [Read original article]() Author: Daniel Froehlich Published: 2026-09-11T13:01:48Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Edge](), [Containers](), [Kubernetes](), [Architecture & Design](), [Hardware](), [Availability](), [Microservice]() Tags: [containers](), [deployment](), [edge](), [edge-computing](), [hardware](), [high-availability](), [kubernetes](), [management-tools](), [microservices](), [red-hat](), [virtual-machines]() ### AI overview This article introduces a series about choosing Red Hat edge platforms for specific deployment needs. It compares Red Hat Enterprise Linux and Red Hat OpenShift across workload types, platform sizes, hardware requirements, Kubernetes use cases, availability, storage, and management needs. ### Source excerpt Choosing the right platform for an edge deployment is one of the most consequential decisions an organization makes--and one of the most confusing. The options range from a single-board computer running a handful of containers to a full Kubernetes cluster with high availability, software-defined storage, and centralized management. Pick too small, and you hit a wall when requirements grow. The post Red Hat edge platforms: Choosing the right one for your use case appeared first on Red Hat Developer. ## Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson DevFeed: [Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-04T16:21:04Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson](), [model-deployment](), [Inference](), [Inference Performance](), [Optimization](), [Mixture of Experts (MoE)]() Tags: [agentic-ai-generative-ai](), [edge](), [edge-computing](), [how-to](), [inference](), [inference-performance](), [jetpack](), [jetson](), [jetson-orin](), [mixture-of-experts-moe](), [nvfp4](), [optimization](), [physical-ai](), [reasoning](), [thor](), [tutorial](), [vllm]() ### AI overview A tutorial on deploying and optimizing compact reasoning and agentic AI models on NVIDIA Jetson. It covers choosing models, improving inference with NVFP4 quantization and speculative decoding, serving example models with vLLM, and validating a configuration for a workload. ### Source excerpt Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run... ## NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network DevFeed: [NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-03T16:00:00Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference](), [Local AI](), [GPU](), [Hardware]() Tags: [ai-agent](), [ai-agents](), [ai-inference](), [compute](), [content-creation-rendering](), [dgx-spark](), [edge-computing](), [gaming](), [geforce](), [gpu](), [hardware](), [inference](), [local](), [nvidia](), [open-source]() ### AI overview NVIDIA PAIR is a beta virtual inference router that distributes independent local inference requests across eligible machines on a home network. It works through compatible Ollama and LM Studio interfaces without requiring changes to an agent harness. ### Source excerpt AI agents are learning to do more by working together. A lead agent can break a complex task into smaller jobs and assign those jobs to specialized subagents.... ## Amlogic A311Y3 Cortex-A78/A55 Edge AI system-on-module delivers up to 8 TOPS DevFeed: [Amlogic A311Y3 Cortex-A78/A55 Edge AI system-on-module delivers up to 8 TOPS]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-03T13:46:18Z Content type: article Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Systems](), [Artificial Intelligence](), [cpu](), [SOC](), [GPU](), [RISC-V](), [SDK](), [Android](), [Linux]() Tags: [2025](), [4g-lte](), [amlogic](), [android](), [arm](), [boardcon](), [camera](), [compiler](), [cortex-a55](), [cortex-a78](), [cpu](), [development-board](), [development-tools](), [display](), [edge-ai](), [edge-computing](), [embedded](), [embedded-systems](), [hardware](), [linux](), [npu](), [opencl](), [risc-v](), [rs485](), [som](), [specifications](), [vulkan]() ### AI overview The article examines Boardcon's CM311Y3 system-on-module, which uses the Amlogic A311Y3 octa-core SoC with Cortex-A78 and Cortex-A55 CPUs, an 8 TOPS NPU, LPDDR5 memory, eMMC storage, and interfaces for embedded applications. It also describes the module's development board, software support, and key specifications. ### Source excerpt Boardcon CM311Y3 system-on-module (SoM) is powered by an Amlogic A311Y3 octa-core Cortex-A78/A55 SoC with an 8 TOPS NPU and targets 4K video surveillance systems, AI edge computing devices, interactive terminals, enterprise thin clients, and autonomous robots. The module comes with up to 16GB LPDDR5 and up to 256 GB eMMC flash, exposes 210 pins through castellated edges, and a development board is also provided for evaluation and early software development. We don't often see new SoMs or SBCs based on Amlogic SoCs these days, so let's have a closer look. Boardcon CM311Y3 System-on-Module CM311Y3 specifications: SoC - Amlogic A311Y3 CPU 2x ARM Cortex-A78 cores @ 2.4GHx 6x ARM Cortex-A55 cores @ 2.0GHz RISC-V core for system control processing RISC-V core for Always-on power management and Sensor Hub GPU - Arm Mali-G625 MC1 with support for OpenGL ES 3.2, Vulkan 1.4, and OpenCL 3.0 VPU Video Decoder - H.264 up to [...] The post Amlogic A311Y3 Cortex-A78/A55 Edge AI system-on-module delivers up to 8 TOPS appeared first on CNX Software - Embedded Systems News. ## Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect DevFeed: [Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-28T17:06:28Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering](), [C++](), [Orchestration]() Tags: [ai](), [ai-inference](), [ai-models](), [api](), [applications](), [c-plus-plus](), [data-center-cloud](), [deployment](), [developer-tools-techniques](), [edge-computing](), [hugging-face](), [inference](), [integration](), [kernel](), [model](), [nvidia](), [open](), [open-source](), [orchestration](), [python](), [pytorch](), [tensorrt]() ### AI overview The article explains NVIDIA TensorRT Model Connect, a collection of modifiable reference implementations for deploying supported open models from a Hugging Face ID or local checkpoint to native C++ inference. It describes a two-phase deployment bundle workflow, semantic and module-level C++ APIs, and custom GPU-kernel integration. ### Source excerpt Open AI models are evolving faster than ever, but bringing them into native applications can still require model-specific conversion, preprocessing,... ## Why fleet management matters for Kubernetes at the edge DevFeed: [Why fleet management matters for Kubernetes at the edge]() Original publisher: [Read original article]() Author: Arvind Bhoj Published: 2026-08-20T13:00:00Z Content type: opinion Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Kubernetes](), [Cloud Native Ecosystem](), [Orchestration]() Tags: [automation](), [cloud-native](), [clusters](), [edge-computing](), [kubernetes](), [management](), [nutanix](), [operations](), [post](), [security](), [sponsor-nutanix](), [sponsored](), [sponsored-post]() ### AI overview The article argues that edge computing has become a mainstream strategic concern and that Kubernetes provides a suitable platform for edge workloads, including generative AI. It identifies dispersed, customized clusters and piecemeal configuration as operational bottlenecks that fleet management can address. ### Source excerpt Not too long ago, edge computing was seen as a niche use case, limited to telcos, manufacturing plants, and large The post Kubernetes at the edge has hit a wall. Fleet management is the way through. appeared first on The New Stack. ## Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents DevFeed: [Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-19T22:22:37Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Holoscan](), [AI-assisted coding](), [Command-line interface](), [Artificial Intelligence](), [real-time](), [Medical imaging](), [Robotics]() Tags: [agentic-ai-generative-ai](), [agents](), [ai-coding](), [clara](), [cli](), [computer-vision-video-analytics](), [edge](), [edge-computing](), [featured](), [healthcare-life-sciences](), [holoscan](), [medical-devices](), [medical-imaging](), [monai](), [nvidia](), [real-time](), [robotics](), [skills](), [video-analytics]() ### AI overview An NVIDIA Holoscan developer article about building real-time edge AI applications with CLI skills and AI coding agents, covering use cases including medical imaging and robotics. ### Source excerpt NVIDIA Holoscan is a platform for building real-time AI applications at the edge, from medical imaging to robotics. HoloHub is its companion repository: a... ## Building Federated Multimodal AI Workflows with NVIDIA FLARE DevFeed: [Building Federated Multimodal AI Workflows with NVIDIA FLARE]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-19T17:50:47Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [vlm](), [SDKs]() Tags: [agentic-ai-generative-ai](), [ai](), [data-science](), [edge-computing](), [featured](), [federated-learning](), [multimodal](), [multimodal-ai](), [nvidia](), [python](), [streaming](), [training](), [training-ai-models](), [updates](), [vlms](), [workflows]() ### AI overview The article explains how NVIDIA FLARE supports federated training for multimodal and vision-language models when data remains distributed across sites. It focuses on deciding which model state to exchange and on efficiently transferring and aggregating large updates through externalization, tensor streaming, and disk-backed aggregation. ### Source excerpt Modern vision-language models (VLMs) can support tasks such as visual question answering, captioning, and image-text reasoning. In practice, however, the data... ## Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control DevFeed: [Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-19T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Cosmos](), [AI, ML & Data Engineering]() Tags: [agentic-ai-generative-ai](), [cosmos](), [deployment](), [edge](), [edge-computing](), [featured](), [foundation-models](), [inference](), [jetson](), [latency](), [nvidia](), [on-device](), [physical-ai](), [post-training](), [robotics](), [robotics-simulation](), [robots](), [simulation-modeling-design](), [thor](), [tutorial]() ### AI overview A tutorial on post-training NVIDIA Cosmos 3 Edge as an on-device robot manipulation policy, serving it on Jetson Thor, running receding-horizon inference, and evaluating it in closed-loop simulation. ### Source excerpt Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for... ## Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer DevFeed: [Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-17T18:12:48Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Nemotron](), [NVFP4](), [quantization](), [Post-training optimization](), [Nvidia](), [post-training](), [Mamba]() Tags: [agentic-ai-generative-ai](), [benchmarks](), [compute](), [developer-tools-techniques](), [developers](), [edge-computing](), [evaluation](), [featured](), [latency](), [mamba](), [megatron](), [memory](), [model](), [model-optimizer](), [models](), [nemotron](), [nvfp4](), [nvidia](), [open](), [post-training](), [quantization](), [speed](), [training](), [training-ai-models]() ### AI overview This tutorial explains how quantization-aware distillation (QAD) creates the Nemotron 3.5 Lightning NVFP4 checkpoint using NVIDIA Model Optimizer. It covers post-training quantization, teacher-student distillation, and evaluation, showing how QAD can recover accuracy while reducing memory usage and increasing throughput. ### Source excerpt Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models, developers can find... ## APNIC 62 keynotes explore automation, trust, and the future of the Internet DevFeed: [APNIC 62 keynotes explore automation, trust, and the future of the Internet]() Original publisher: [Read original article]() Author: Dan Fidler Published: 2026-08-13T05:57:46Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Automation](), [Internet](), [Network](), [networking](), [Cybersecurity](), [Incident response](), [Threat Research](), [Cloud](), [cloud-computing](), [telemetry](), [Resilience](), [Latency]() Tags: [ai](), [apnic-62](), [automation](), [cloud](), [community](), [cybersecurity](), [edge-computing](), [events](), [incident-response](), [internet](), [latency](), [network](), [networking](), [real-time](), [resilience](), [routing](), [security](), [technology](), [telemetry]() ### AI overview APNIC 62 will feature keynotes on autonomous network operations and trusted cybersecurity collaboration. The article describes automation, software-defined infrastructure, real-time telemetry, automated routing, self-healing fibre architectures, and zero-touch operations as ways to improve Internet resilience and performance, alongside the importance of cooperation among cybersecurity incident response teams. ### Source excerpt APNIC 62 will explore two essential foundations of a resilient Internet: Intelligent network automation and trusted cybersecurity collaboration. Keynotes from Amajit Gupta and Yukako Uchida offer complementary perspectives on how technology and human relationships will shape the Internet's future. ## NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation DevFeed: [NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-11T19:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson](), [SDKs](), [CUDA](), [GPU](), [AI Inference](), [Inference Performance](), [Latency](), [Low Latency](), [C++](), [Python](), [Artificial Intelligence](), [Automation]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [automation](), [c-plus-plus](), [cuda](), [edge-computing](), [featured](), [gpu](), [inference](), [jetpack](), [jetson](), [latency](), [low-latency](), [nvidia](), [python](), [robotics](), [robotics-compute](), [sdks](), [video-analytics](), [video-codec-sdk]() ### AI overview NVIDIA JetPack 7.2.1 adds PyNvVideoCodec 2.2 support on Jetson Thor, enabling Python-based hardware video encoding and decoding with GPU-resident frames. It also introduces agentic video skills that turn developer goals into device inspection, configuration, execution, measurement, and evidence-driven codec workflows. ### Source excerpt Video is a core data path across NVIDIA Jetson applications, from robotics and intelligent video analytics to industrial automation, healthcare, media... ## Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA DevFeed: [Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-10T13:27:19Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Local AI](), [Large Language Model](), [long-context](), [Inference](), [Meta](), [Open Source](), [GPU](), [NVIDIA DGX](), [NVLink](), [Jetson](), [Automation]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [ai](), [automation](), [context-window](), [data-center-cloud](), [dgx-spark](), [dgx-station](), [edge-computing](), [featured](), [gpu](), [inference](), [jetson](), [local-ai](), [nemoclaw](), [nvidia](), [nvidia-dgx](), [nvlink](), [open-source](), [top-stories]() ### AI overview Meta's Muse Glimmer is a 30B open-weight dense model designed for local agentic AI workflows. With a 120K+ context window and performance of up to 20K tokens per second on a single GPU, it supports sustained, multi-step tool use and local processing of sensitive data. ### Source excerpt Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI... ## The 2-Second Rule: How to make your website feel like magic in 2026 DevFeed: [The 2-Second Rule: How to make your website feel like magic in 2026]() Original publisher: [Read original article]() Author: Louise North Published: 2026-08-04T11:43:00Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Web](), [Latency](), [browser](), [Workers](), [Compression](), [Artificial Intelligence](), [Edge]() Tags: [architecture](), [avif-images](), [browser](), [compression](), [core-web-vitals](), [developers](), [digital-hospitality](), [edge](), [edge-computing](), [frontend-development](), [guide](), [http3](), [inclusive-design](), [interaction-to-next-paint](), [javascript-optimization](), [mobile-optimization](), [page-speed-optimization](), [performance](), [predictive-ai](), [site-reliability](), [speed](), [technical-seo](), [user-experience](), [ux-design-2026](), [web-development](), [web-development-trends](), [web-performance](), [website-usability](), [workers](), [zero-latency-ux]() ### AI overview A guide to making websites feel faster and more responsive by focusing on interaction performance, Web Workers, AVIF images, and responsive image delivery. It presents speed as an important part of user experience in 2026. ### Source excerpt In 2026, a "pretty" website is no longer enough--if it doesn't feel instant, it's invisible. This guide reveals how the world's top developers are using predictive AI and "Edge" architecture to kill the loading bar for good. ## Maximize Spectral Efficiency with AI-Native RAN and NVIDIA AI Aerial DevFeed: [Maximize Spectral Efficiency with AI-Native RAN and NVIDIA AI Aerial]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-07T17:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [cpu]() Tags: [ai](), [ai-networking](), [algorithms](), [architecture](), [compute](), [cpu](), [developer-tools-techniques](), [edge-computing](), [featured](), [gpu](), [networking-communications](), [nvidia](), [performance]() ### AI overview The article explains how GPU acceleration and an AI-native RAN architecture can improve spectral efficiency in Massive MIMO deployments. It describes using more complex Layer 1 and Layer 2 tracking, scheduling, channel-estimation, and beamforming algorithms to improve throughput and quality of service. ### Source excerpt Spectrum is one of the most valuable assets in wireless communications. Over the last 30 years, telecom operators in the US have spent more than $240B to... ## How Wasmer used Codex to build a Node.js runtime for the edge DevFeed: [How Wasmer used Codex to build a Node.js runtime for the edge]() Original publisher: [Read original article]() Published: 2026-06-03T12:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [codex](), [Node.js](), [JavaScript](), [WebAssembly](), [Artificial Intelligence](), [Cloud]() Tags: [ai](), [cloud](), [codex](), [edge-computing](), [javascript](), [node-js](), [speed](), [webassembly]() ### AI overview Wasmer used Codex to build Edge.js, a JavaScript runtime capable of running Node.js workloads in WebAssembly sandboxes for AI and edge computing. The team reports accelerating development by 10x to 20x and completing the runtime in two weeks instead of an estimated year. ### Source excerpt See how Wasmer used Codex with GPT-5.5 to build a Node.js runtime for the edge, accelerating development 10x to 20x and shipping in weeks instead of months. ## Gesture Recognition Based on TFLite DevFeed: [Gesture Recognition Based on TFLite]() Original publisher: [Read original article]() Author: John Lee Published: 2026-04-10T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [TensorFlow Lite](), [Espressif](), [model-deployment](), [Tensorflow](), [Keras](), [On-device AI]() Tags: [ai](), [blog](), [deep-learning](), [deployment](), [development-board](), [edge-ai](), [edge-computing](), [esp-idf](), [espressif](), [how-to](), [inference](), [keras](), [model-deployment](), [model-training](), [tensorflow-lite]() ### AI overview This tutorial demonstrates gesture recognition on Espressif SoCs using TensorFlow Lite Micro. It covers data collection, model training, conversion for TFLite Micro, and deployment with C++ code for model loading, preprocessing, and inference. ### Source excerpt This article demonstrates how to implement gesture recognition using TensorFlow Lite Micro on Espressif SoCs. It covers the complete workflow from data collection and model training to model deployment, showcasing TensorFlow Lite Micro's applications in edge AI. ## Hono + Cloudflare Workers: How to Process Payments at the Edge DevFeed: [Hono + Cloudflare Workers: How to Process Payments at the Edge]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-27T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Cloudflare Workers](), [Workers](), [Hono](), [Latency](), [Back end](), [Scalability](), [Architecture & Design](), [Software as a service]() Tags: [cloudflare-workers](), [developer-tools](), [edge-computing](), [how-to](), [latency](), [payments](), [performance](), [platform](), [set](), [subscriptions](), [webhooks](), [workers]() ### AI overview This guide explains how to build a globally distributed payment system with Hono and Cloudflare Workers. It covers the benefits of edge-first payment processing, environment setup, and integration with Dodo Payments through a Hono adapter, including checkout, subscriptions, and webhooks. ### Source excerpt Build a high-performance, globally distributed payment system using Hono and Cloudflare Workers. Learn how to integrate Dodo Payments at the edge. ## 5 Tech Predictions for 2026: AI Inference, Open Systems, Kubernetes, Edge, and Specialized Agents DevFeed: [5 Tech Predictions for 2026: AI Inference, Open Systems, Kubernetes, Edge, and Specialized Agents]() Original publisher: [Read original article]() Author: Prachi Kothiyal Published: 2026-01-27T10:11:48Z Content type: opinion Language: en Sources: [Backend Archives | Talent500 blog]() Topics: [AI Inference](), [Kubernetes](), [systems](), [AI Platform](), [Cloud]() Tags: [ai](), [ai-agents](), [ai-inference](), [backend](), [cost](), [edge-computing](), [infrastructure](), [kubernetes](), [newsletters](), [open-infrastructure](), [scale](), [systems]() ### AI overview A forward-looking article identifies five technology themes for 2026: AI inference, open infrastructure, Kubernetes consolidation, edge computing, and specialized AI agents. The supplied text discusses inference efficiency, portable and hybrid infrastructure, open standards, and Kubernetes as a unified control plane. ### Source excerpt As 2026 begins, the technology landscape is shifting from experimental AI pilots to hard questions about cost, scale, and operational [...] The post 5 Tech Predictions for 2026: AI Inference, Open Systems, Kubernetes, Edge, and Specialized Agents appeared first on Talent500 blog. ## The MonkCast: WASM and Edge Compute DevFeed: [The MonkCast: WASM and Edge Compute]() Original publisher: [Read original article]() Author: Fermyon Staff Published: 2025-11-18T12:00:00Z Content type: article Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [wasm](), [Latency](), [Network](), [Serverless]() Tags: [ceo](), [edge-compute](), [edge-computing](), [latency](), [network](), [oss](), [serverless](), [wasm]() ### AI overview Fermyon CEO Matt Butcher joined the Monkcast for an episode discussing WebAssembly, edge computing, serverless compute, network latency, and open-source software. ### Source excerpt Our CEO Matt Butcher recently joined the Monkcast for an episode on Wasm, Edge Computing and beyond - diving deep into topics around serverless compute, network latency and OSS. ## Touchpad Digit Recognition Based on ESP-DL DevFeed: [Touchpad Digit Recognition Based on ESP-DL]() Original publisher: [Read original article]() Author: John Lee Published: 2025-06-18T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32-S3](), [ESP32-P4](), [Embedded Systems](), [Deep learning](), [Jetson](), [Artificial Intelligence](), [quantization](), [Inference](), [Code]() Tags: [ai](), [applications](), [blog](), [c-plus-plus](), [deep-learning](), [devices](), [edge-computing](), [embedded-systems](), [esp-dl](), [esp32](), [esp32-p4](), [esp32-s3](), [inference](), [neural-networks](), [quantization](), [recognition]() ### AI overview This tutorial demonstrates touchpad-based digit recognition on ESP32-S3 and ESP32-P4 using ESP-DL. It covers touch-data collection and preprocessing, lightweight CNN design, model training and evaluation, quantization, and C++ implementation for model loading and inference. ### Source excerpt This article demonstrates how to implement a touchpad-based digit recognition system using ESP-DL on ESP32 series chips. It covers the complete workflow from data collection and preprocessing to model training, quantization, and deployment, showcasing ESP-DL's capabilities in edge AI applications. ## Lightweight MQTT Broker for ESP32: Mosquitto ported to ESP-IDF DevFeed: [Lightweight MQTT Broker for ESP32: Mosquitto ported to ESP-IDF]() Original publisher: [Read original article]() Author: John Lee Published: 2025-05-28T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32](), [MQTT](), [ESP-IDF](), [Internet of things](), [Testing](), [Home Assistant](), [P2P](), [TLS (Transport Layer Security)]() Tags: [blog](), [broker](), [edge-computing](), [esp](), [esp-idf](), [esp32](), [home-assistant](), [iot](), [mqtt](), [p2p](), [security](), [testing](), [tls]() ### AI overview This article introduces a lightweight port of the Eclipse Mosquitto MQTT broker to ESP-IDF for ESP32 devices. It describes support for TLS and TCP transports and outlines uses including private local IoT networks, on-device MQTT testing, and bridged brokers over peer-to-peer connections. ### Source excerpt Mosquitto - the industry-standard MQTT broker - has been ported to ESP-IDF. Its lightweight version retains Mosquitto's core functionality and security features to run on resource-constrained IoT devices. This MQTT broker is ideal for edge computing, testing, and standalone IoT deployments. In this article, we will do an overview and show you how to get started. [Next page]()