# flux Published articles for flux. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Rebuilding AUTOMATIC1111 with Gradio Workflow DevFeed: [Rebuilding AUTOMATIC1111 with Gradio Workflow]() Original publisher: [Read original article]() Author: yuvraj sharma; Abubakar Abid Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI Development](), [Graphs](), [stable-diffusion](), [Inference](), [Large Language Model](), [hugging face](), [vlm]() Tags: [automatic1111](), [comfyui](), [diffusion](), [flux](), [gradio](), [hugging-face](), [image-to-image](), [image-to-video](), [inference](), [inference-providers](), [llm](), [models](), [python](), [space](), [spaces](), [text-to-image](), [vlm](), [workflow](), [workflows]() ### AI overview A walkthrough of Workflow1111, a Gradio graph that recreates AUTOMATIC1111-style media pipelines with connected operator nodes for image generation, editing, prompting, and related tasks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Integrate Deepgram Flux with Twilio's Conversation Relay DevFeed: [Integrate Deepgram Flux with Twilio's Conversation Relay]() Original publisher: [Read original article]() Author: Dhruv Patel Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [flux](), [asr](), [Tutorial](), [Latency](), [Node.js](), [WebSocket](), [Streaming]() Tags: [developer-insights](), [flux](), [latency](), [node](), [speech](), [streaming](), [transcription](), [tutorial](), [websocket]() ### AI overview This tutorial shows how to integrate Deepgram Flux with Twilio's Conversation Relay in Node.js. Flux combines speech transcription and turn detection, with configurable end-of-turn confidence, and is described as reducing response latency and false interruptions. ### Source excerpt Integrate Deepgram Flux with Twilio ConversationRelay in Node.js for faster turn detectio, and tunable end-of-turn control. ## Introducing the Remediator Agent: Turning Kyverno Policy Violations into Pull Requests, Automatically. DevFeed: [Introducing the Remediator Agent: Turning Kyverno Policy Violations into Pull Requests, Automatically.]() Original publisher: [Read original article]() Author: Akhil Chandran Published: 2026-08-14T11:29:37Z Content type: article Language: en Sources: [Nirmata]() Topics: [Kyverno](), [GitOps](), [Kubernetes](), [pull-requests](), [argocd](), [flux]() Tags: [agent](), [ai](), [argocd](), [flux](), [gitops](), [kubernetes](), [kyverno](), [other](), [pull-requests]() ### AI overview Nirmata's Remediator Agent is designed to automate remediation of Kyverno policy violations within an existing GitOps workflow. It traces violations to their Git sources, proposes deterministic or LLM-generated fixes, commits changes, opens pull requests for review, and relies on ArgoCD or Flux CD to sync approved changes back to Kubernetes. ### Source excerpt Kyverno detects Kubernetes policy violations. Nirmata's Remediator Agent turns them into reviewed pull requests within your existing GitOps workflow. ## Best Kubernetes Infrastructure as Code Tools in 2026 DevFeed: [Best Kubernetes Infrastructure as Code Tools in 2026]() Original publisher: [Read original article]() Author: Pulumi Content Team Published: 2026-08-14T00:00:00Z Content type: comparison Language: en Sources: [Pulumi]() Topics: [Infrastructure as code](), [Kubernetes](), [Terraform](), [argo-cd](), [Helm charts](), [GitOps](), [flux](), [opentofu](), [Amazon EKS](), [AWS CloudFormation](), [VPC](), [AWS IAM]() Tags: [argo-cd](), [cloudformation](), [code](), [comparisons](), [devops](), [flux](), [general](), [gitops](), [helm](), [helm-charts](), [iam](), [infrastructure](), [infrastructure-as-code](), [kubernetes](), [opentofu](), [platform-engineering](), [terraform](), [vpc]() ### AI overview This comparison explains that Kubernetes infrastructure as code spans cluster and cloud provisioning, in-cluster workload definition, and continuous delivery and reconciliation. It presents Pulumi and Terraform or OpenTofu as general-purpose provisioning options, Helm and Kustomize for workload templating, and Argo CD and Flux for GitOps reconciliation. ### Source excerpt There is no single best Kubernetes infrastructure as code tool, because "Kubernetes IaC" actually spans three different jobs. For provisioning the cluster and its cloud dependencies, Pulumi and Terraform (or OpenTofu) are the strongest general-purpose options. For templating and packaging workloads, Helm and Kustomize dominate. For continuous reconciliation once things are running, Argo CD and Flux lead the GitOps category. The right stack usually combines one tool from each layer, not a single tool that claims to do all three. What counts as infrastructure as code for Kubernetes? Kubernetes infrastructure as code work splits into three layers that get conflated constantly, and the confusion is where most tool comparisons go wrong. The cluster and cloud layer provisions the things Kubernetes itself sits on top of: the managed control plane (EKS, GKE, AKS), node pools, the VPC and subnets, IAM roles, load balancers, and cluster add-ons. Terraform, Pulumi, and cloud-native tools like CloudFormation operate here. The in-cluster workload layer defines what runs on the cluster once it exists: Deployments, Services, ConfigMaps, CustomResourceDefinitions, and the Helm charts or Kustomize overlays that template them. This is where Helm, Kustomize, and Crossplane's custom resources live. The delivery and reconciliation layer keeps what's declared in Git in sync with what's actually running on the cluster, continuously, rather than as a one-shot apply. Argo CD and Flux own this layer, and they consume the output of the other two rather than replacing them. Most real Kubernetes platforms use tools from at least two of these layers together. A team might provision EKS with Terraform, package its application with Helm, and let Argo CD reconcile it continuously. Knowing which layer a tool actually addresses, rather than treating "Kubernetes IaC" as one shopping list, is the first decision that matters. Pulumi provisions the cluster and the workloads on it in the same language Pul ## Announcing new course: GitOps for Platform Engineering DevFeed: [Announcing new course: GitOps for Platform Engineering]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [GitOps](), [Platform Engineering](), [Infrastructure as code](), [Kubernetes](), [CI/CD](), [Orchestration]() Tags: [argo-cd](), [flux](), [infrastructure-as-code](), [kubernetes](), [orchestration](), [platform-engineering](), [training](), [tutorials]() ### AI overview This article announces a course on GitOps for Platform Engineering that addresses enterprise-scale challenges such as managing multi-cluster environments, applying policy-driven workflows, scaling to large fleets, and driving organizational adoption. It covers GitOps fundamentals, Argo CD, Flux, Infrastructure as Code, agent-based reconciliation, configuration sprawl, and platform requirements for AI workloads. ### Source excerpt Bridge the gap between GitOps 101 and enterprise reality. Learn multi-cluster, policy-driven GitOps for Platform Engineering from expert Artem Lajko ## Why installing Argo CD didn't fix your deployments DevFeed: [Why installing Argo CD didn't fix your deployments]() Original publisher: [Read original article]() Author: Koray Oksay Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [GitOps](), [Deployment](), [Git](), [Kubernetes]() Tags: [argo-cd](), [deployment](), [flux](), [git](), [kubernetes](), [organizational](), [production]() ### AI overview The article argues that installing Argo CD or Flux does not by itself make deployments safer, auditable, or self-service. GitOps exposes unresolved organizational questions about production ownership, approval authority, accountability, repository access, and operational responsibility. Successful GitOps adoption therefore depends on organizational design and clearly defined trust boundaries, not only on tooling. ### Source excerpt GitOps tools like Argo CD won't solve underlying organizational issues. This article argues that GitOps is an organizational decision that exposes pre-existing ambiguity around ownership, production responsibility, and trust boundaries. Learn why clear organizational design is the true foundation for GitOps success. ## Kubeflow Trainer v2.2: JAX & XGBoost Runtimes, Flux for HPC Support, and TrainJob progress and metrics observability DevFeed: [Kubeflow Trainer v2.2: JAX & XGBoost Runtimes, Flux for HPC Support, and TrainJob progress and metrics observability]() Original publisher: [Read original article]() Author: Kubeflow Trainer Team Published: 2026-03-20T05:00:00Z Content type: release Language: en Sources: [Kubeflow]() Topics: [Kubernetes](), [distributed-training](), [observability](), [flux](), [Fine-tuning]() Tags: [distributed-training](), [fine-tuning](), [flux](), [gpu](), [hugging-face](), [kubernetes](), [observability](), [release](), [trainer]() ### AI overview Kubeflow Trainer v2.2 adds native JAX and XGBoost training runtimes, Flux runtime support for HPC workloads, enhanced training-job observability, timeout policies, and more flexible runtime configuration through new APIs. It also unifies supported training workloads under the TrainJob abstraction and enables distributed JAX workloads on Kubernetes. ### Source excerpt Just a little over one week ahead of KubeCon + CloudNativeCon EU 2026, the Kubeflow team is excited to ship Trainer v2.2. The v2.2 release reinforces our commitment to expanding the Kubeflow Trainer ecosystem - meeting developers where they are by adding native support for JAX, XGBoost, and Flux, while also delivering deeper observability into training jobs. ## My Home Lab DevFeed: [My Home Lab]() Original publisher: [Read original article]() Author: EduuhMuraya Published: 2026-01-13T00:00:00Z Content type: article Language: en Sources: [EduuhMuraya]() Topics: [Homelab](), [Kubernetes](), [GitOps](), [flux](), [Linux](), [networking](), [YAML](), [Firewall](), [Pi-hole]() Tags: [dns](), [firewall](), [flux](), [home-lab](), [kubernetes](), [linux](), [observability](), [opnsense](), [orchestration](), [os](), [proxmox](), [secrets](), [self-hosted](), [servers](), [storage]() ### AI overview A tour of a Kubernetes homelab built on Talos Linux and managed with Flux GitOps. It covers compact mini-PC hardware, Proxmox, OPNsense, Pi-hole, flat networking, resource usage, YAML-based configuration, automated upgrades, and encrypted secrets. The author explains that the original cluster was later torn down and replaced with a smaller Proxmox-based setup. ### Source excerpt A tour of my Kubernetes homelab on Talos Linux with GitOps and full observability, and why I tore the whole thing down in August 2026. ## Diffusers welcomes FLUX-2 DevFeed: [Diffusers welcomes FLUX-2]() Original publisher: [Read original article]() Author: YiYi Xu; Daniel Gu; Sayak Paul; Alvaro Somoza; Dhruv Nair; Aritra Roy Gosthipaty; Linoy Tsaban; Apolinário from multimodal AI art Published: 2025-11-25T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering]() Tags: [architecture](), [artificial-intelligence](), [black-forest-labs](), [diffusers](), [diffusion](), [embedding](), [flux](), [generation](), [images](), [lora](), [mlp](), [model](), [open-source](), [quantization](), [streams]() ### AI overview The article introduces FLUX.2, an image-generation and editing model that supports text-guided and image-guided generation with multiple reference images. It outlines changes to its text encoder and diffusion-transformer architecture relative to FLUX.1. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Replicate Integration with Encore.ts DevFeed: [Replicate Integration with Encore.ts]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2025-11-17T00:00:00Z Content type: tutorial Language: en Sources: [Encore Updates]() Topics: [AI Development](), [text-to-image](), [API](), [flux](), [stable-diffusion](), [Node.js](), [Secrets Management]() Tags: [ai](), [api](), [diffusion](), [flux](), [integration](), [node-js](), [secrets-management](), [stable-diffusion](), [tutorial]() ### AI overview This tutorial explains how to build a type-safe backend with Encore.ts and Replicate to run AI model predictions and generate images. It covers asynchronous results, image storage, API authentication, secret management, and models such as FLUX and Stable Diffusion. ### Source excerpt Running AI models with Replicate and Encore ## Image and audio models from fal now available on DigitalOcean DevFeed: [Image and audio models from fal now available on DigitalOcean]() Original publisher: [Read original article]() Author: Grace Morgan Published: 2025-10-23T12:30:00Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [AI Platform](), [Digital Ocean](), [multimodal-ai](), [API](), [stable-diffusion](), [flux](), [Serverless](), [voice ai]() Tags: [ai-platform](), [api](), [audio](), [digitalocean](), [flux](), [multimodal](), [multimodal-ai](), [product-updates](), [serverless](), [stable-diffusion]() ### AI overview DigitalOcean announces four multimodal AI models from fal in public preview on the Gradient AI Platform through Serverless Inference. The models support image generation, audio generation, and multilingual text-to-speech through an API. ### Source excerpt We're excited to announce the launch of four multimodal AI models from fal on the DigitalOcean Gradient™ AI Platform, now available in public preview through Serverless Inference. These models allow you to generate images and audio directly via API, without worrying about infrastructure, scaling, or vendor management. With this release, building AI-powered applications that include visual and audio content is easier than ever. Explore the new models The fal models, now in public preview, cover a variety of modalities, enabling you to experiment, prototype, and deploy multimodal AI features quickly: Image generation: Stable Diffusion XL fast (fal-ai/fast-sdxl) - High-resolution image generation FLUX.1 (schnell) (fal-ai/flux/schnell) - Fast image generation for quick prototyping Audio generation: Stable Audio (fal-ai/stable-audio-25/text-to-audio) - Convert text into natural-sounding audio ElevenLabs TTS Multilingual v2 9 (fal-ai/elevenlabs/tts/multilingual-v2) - Multilingual text-to-speech These models are available via Serverless Inference, letting you generate images and audio through the same simple API-driven workflow you already use on Gradient AI Platform. Try it out You can start using these models through the Serverless Inference API (https://inference.do-ai.run) after opting in to the public preview in the DigitalOcean console. Here's a quick look at how to interact with them: First, opt in to the public preview to access the fal models on the Gradient AI Platform. Once opting in, it should take about 10 to 15 minutes for your access to be granted. Example: Generate an Image export MODEL_ACCESS_KEY="YOUR_KEY" curl -sS -X POST 'https://inference.do-ai.run/v1/async-invoke' \ -H "Authorization: Bearer $MODEL_ACCESS_KEY" \ -H "Content-Type: application/json" \ -d '{ "model_id": "fal-ai/flux/schnell", "input": { "prompt": "A high-quality photo of a futuristic city at sunset" } }' Example: Generate an Image with Customized Parameters export MODEL_ACCESS_KEY="YOUR_ ## Generate Images with Claude and Hugging Face DevFeed: [Generate Images with Claude and Hugging Face]() Original publisher: [Read original article]() Author: shaun smith Published: 2025-08-19T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Claude](), [hugging face](), [Model Context Protocol](), [MCP Server](), [Artificial Intelligence](), [gradio](), [flux](), [Image](), [qwen](), [spaces]() Tags: [anthropic](), [applications](), [article](), [artificial-intelligence](), [blog](), [claude](), [flux](), [generation](), [gradio](), [how-to](), [hugging-face](), [image](), [images](), [llm](), [mcp](), [mcp-server](), [models](), [qwen](), [spaces](), [text-to-image](), [tools]() ### AI overview This tutorial explains how to connect Claude to Hugging Face Spaces through the Hugging Face MCP Server to generate detailed images with AI models. It highlights prompt assistance, visual iteration, model selection, and examples including FLUX.1 Krea [dev] and Qwen-Image. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Fast LoRA inference for Flux with Diffusers and PEFT DevFeed: [Fast LoRA inference for Flux with Diffusers and PEFT]() Original publisher: [Read original article]() Author: Sayak Paul; Benjamin Bossan Published: 2025-07-23T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [flux](), [lora](), [Inference Performance](), [Hacktoberfest](), [diffusers](), [Optimization](), [peft](), [quantization](), [model architecture](), [text-to-image](), [GPU]() Tags: [amd](), [diffusers](), [diffusion](), [flux](), [gpu](), [guide](), [inference](), [latency](), [lora](), [model-architecture](), [optimization](), [peft](), [quantization](), [text-to-image]() ### AI overview This article presents an optimization recipe for faster LoRA inference with the Flux.1-Dev text-to-image model using Diffusers and PEFT. The approach addresses LoRA hotswapping and recompilation issues with Flash Attention 3, FP8 quantization from TorchAO, and hotswapping-ready compilation, achieving about a 2.3x speedup while balancing inference speed and memory use. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Upskill your LLMs With Gradio MCP Servers DevFeed: [Upskill your LLMs With Gradio MCP Servers]() Original publisher: [Read original article]() Author: Freddy Boulton Published: 2025-07-09T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Model Context Protocol (MCP)](), [MCP Server](), [gradio](), [LLMs](), [spaces](), [hugging face](), [flux](), [Python](), [cursor]() Tags: [artificial-intelligence](), [claude](), [cursor](), [flux](), [follow](), [gradio](), [hugging-face](), [llm](), [llms](), [mcp](), [open-source](), [python](), [spaces]() ### AI overview This article explains how Gradio applications hosted on Hugging Face Spaces can expose tools through the Model Context Protocol (MCP), allowing LLM clients such as Cursor and Claude Code to use new capabilities. It demonstrates connecting Flux.1 Kontext[dev] as an MCP server for text-guided image editing. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware DevFeed: [(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware]() Original publisher: [Read original article]() Author: Derek Liu; Marc Sun; Sayak Paul; merve; Linoy Tsaban Published: 2025-06-19T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Fine-tuning](), [lora](), [flux](), [quantization](), [Training AI Models](), [GPU]() Tags: [artificial-intelligence](), [dev](), [diffusers](), [fine-tuning](), [flux](), [gpu](), [guide](), [hardware](), [inference](), [lora](), [model-training](), [nvidia-rtx](), [quantization](), [training]() ### AI overview This tutorial explains how to fine-tune the FLUX.1-dev diffusion model efficiently with QLoRA on a single consumer GPU using less than about 10 GB of VRAM. It describes the model components, focuses training on the transformer while keeping the text encoders and VAE frozen, and discusses LoRA, quantization, and FP8 training for memory and speed improvements. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Exploring Quantization Backends in Diffusers DevFeed: [Exploring Quantization Backends in Diffusers]() Original publisher: [Read original article]() Author: Derek Liu; Marc Sun; Sayak Paul Published: 2025-05-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering]() Tags: [artificial-intelligence](), [backends](), [benchmarks](), [black-forest-labs](), [diffusers](), [diffusion](), [diffusion-transformers](), [embeddings](), [flux](), [gpu](), [guide](), [hugging-face](), [images](), [memory](), [quantization]() ### AI overview The article examines quantization backends in Hugging Face Diffusers using the FLUX.1-dev image-generation pipeline. It compares high-precision and 4-bit or 8-bit variants, explains pipeline components and memory use, and discusses backends including bitsandbytes, GGUF, torchao, Quanto, and FP8. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Open Preference Dataset for Text-to-Image Generation by the 🤗 Community DevFeed: [Open Preference Dataset for Text-to-Image Generation by the 🤗 Community]() Original publisher: [Read original article]() Author: David Berenstein; ben burtenshaw; Daniel Vila; Daniel van Strien; Sayak Paul; Ame Vi; Linoy Tsaban Published: 2024-12-09T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Open Source Models & Datasets](), [datasets](), [Synthetic Data Generation](), [flux](), [stable-diffusion](), [argilla](), [distilabel](), [data observability](), [GitHub](), [hugging face]() Tags: [argilla](), [artificial-intelligence](), [data](), [data-is-better-together](), [data-quality](), [datasets](), [distilabel](), [flux](), [generation](), [github](), [hugging-face](), [image](), [open](), [open-source](), [stable-diffusion](), [synthetic-data](), [synthetic-data-generation](), [text-to-image]() ### AI overview The article describes an open community effort to create an image-preference dataset for text-to-image generation. It covers prompt preparation with distilabel, synthetic data generation, image generation with Flux and Stable Diffusion, and filtering with text- and image-based classifiers plus manual review. The resulting dataset and related code are available through the Hugging Face Hub and GitHub. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Mistral has entered the chat DevFeed: [Mistral has entered the chat]() Original publisher: [Read original article]() Published: 2024-11-18T12:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [AI Chat](), [Generative AI](), [LLMs](), [multimodal](), [Canvas](), [flux]() Tags: [ai](), [ai-assistants](), [canvas](), [chat](), [flux](), [multimodal](), [search]() ### AI overview Mistral AI announces beta updates to le Chat, its free generative AI work assistant. The updates add web search with citations, Canvas for ideation and in-line editing, document and image understanding powered by Pixtral Large, image generation powered by Black Forest Labs Flux Pro, integrated workflows from models to outputs, and faster responses through speculative editing. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## SpinKube and Flux automated, on Kubernetes 1.30 DevFeed: [SpinKube and Flux automated, on Kubernetes 1.30]() Original publisher: [Read original article]() Author: Sohan Maheshwar Published: 2024-06-14T12:00:00Z Content type: article Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [flux](), [Kubernetes]() Tags: [automated](), [flux](), [kubernetes](), [livestream](), [recap]() ### AI overview A recap of a livestream with Kingdon Barett, maintainer of the Flux project, covering SpinKube and Flux automation on Kubernetes 1.30. ### Source excerpt A recap of our livestream with Kingdon Barett, maintainer of the Flux project ## A Pattern for Composing Flow Operations DevFeed: [A Pattern for Composing Flow Operations]() Original publisher: [Read original article]() Published: 2024-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Kt. Academy]() Topics: [Kotlin](), [kotlin-coroutines](), [Streams](), [async](), [backpressure](), [flux](), [RxJava]() Tags: [async](), [backpressure](), [flux](), [jpa](), [kotlin](), [kotlin-coroutines](), [rxjava](), [streams](), [workshop-learning-programming]() ### AI overview This tutorial explains a pattern for composing Kotlin Flow operations by collecting one cold flow within another flow definition. It discusses sequential processing, asynchronous operations, and the limitations of using internal flow types when composing streams. ### Source excerpt Extending and operating on top of your existing flows ## Rotating your Encryption Keys and Updating your Secrets with SOPS DevFeed: [Rotating your Encryption Keys and Updating your Secrets with SOPS]() Original publisher: [Read original article]() Author: Techno Tim Published: 2023-03-05T15:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [Encryption](), [GitOps](), [flux](), [Kubernetes](), [Security](), [Script]() Tags: [code](), [encryption](), [flux](), [git](), [gitops](), [how-to](), [infrastructure](), [kubernetes](), [script](), [secrets](), [security](), [utilities]() ### AI overview A tutorial on rotating SOPS encryption keys and re-encrypting secret files. It describes generating a new key, using a script to decrypt files with the old key and encrypt them with the new key, testing decryption, and replacing the corresponding Kubernetes secret. ### Source excerpt If you've been encrypting your secrets with SOPS and Age you know how useful it is to keep your secrets safe from prying eyes. If you're not familiar with encrypting your secrets with SOPS and Age, I highly recommend checking out a post I did a while back that shows you how easy it is to encrypt your secrets and even hide them in plain sight in a Git repo.I am happy (and relieved) that I starte... ## Updating Flux Installation Using the Latest Binary from CLI DevFeed: [Updating Flux Installation Using the Latest Binary from CLI]() Original publisher: [Read original article]() Author: Techno Tim Published: 2023-02-04T01:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [flux](), [Command-line interface](), [Kubernetes](), [GitOps](), [Open Source]() Tags: [cli](), [clusters](), [download](), [flux](), [gitops](), [kubernetes](), [open-source](), [running]() ### AI overview A tutorial on updating a Flux installation by downloading the latest binary from the command line, locating Flux configuration in a Git repository, patching the relevant file, verifying the change, and committing and pushing the update for GitOps to apply. ### Source excerpt What is Flux? Flux is a tool for keeping Kubernetes clusters in sync with sources of configuration (like Git repositories), and automating updates to configuration when there is new code to deploy. It's open source and you can read more about it on the GitHub repo. Looking for a tutorial on how use this? Check out this video on how to use SOPS and Age for your Git Repos! Updating Flux We're... ## Deploying Applications to Kubernetes with FluxCD and GitOps DevFeed: [Deploying Applications to Kubernetes with FluxCD and GitOps]() Original publisher: [Read original article]() Author: Techno Tim Published: 2022-05-13T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [flux](), [GitOps](), [Kubernetes](), [Automation](), [DevOps](), [App]() Tags: [automation](), [deployment](), [devops](), [flux](), [git](), [gitops](), [kubernetes]() ### AI overview This tutorial demonstrates how to use FluxCD with Kubernetes for GitOps-based deployment of applications and infrastructure. It covers monitoring repositories, installing workloads with Kustomize, Helm, and Kubernetes manifests, applying Git changes, and configuring notifications. ### Source excerpt I think I found the perfect GitOps and DevOps toolkit with FluxCD and Kubernetes.Flux is an open source GitOps solution that helps your deploy app and infrastructure with automation.It can monitor git repositories, source control, image container repositories, helm repositories, and more.It can install apps using Kustomize, Helm, Kubernetes manifests so it's designed to fit into your existing ... ## Embedding Teamwork Chat DevFeed: [Embedding Teamwork Chat]() Original publisher: [Read original article]() Author: Dawid Myslak Published: 2017-10-23T15:52:56Z Content type: article Language: en Sources: [Teamwork]() Topics: [Vue.js](), [Front end](), [JavaScript](), [Web Development](), [Webpack](), [Development](), [Framework](), [Library](), [flux](), [ESLint](), [Node.js]() Tags: [chat](), [development](), [eslint](), [flux](), [implementation](), [javascript](), [vuejs](), [vuex](), [webpack]() ### AI overview The article describes how Teamwork.com embedded Teamwork Chat Project Rooms directly in Teamwork Projects. The team chose a new component-based implementation and used Vue.js, Vuex, Webpack, modern JavaScript, ESLint, and reusable Node.js modules while considering performance. ### Source excerpt A couple of weeks ago we introduced a new feature in Teamwork Chat called Project Rooms. Project Rooms are a great way to create dedicated real-time communication channels for specific projects. Our next priority was to take this feature even further. Project Rooms in Teamwork Chat.Embedded Teamwork Chat Currently Project Rooms can be used inside Teamwork Chat, but our goal was to make them accessible directly in Teamwork Projects. To achieve this, we considered the following two options: Decouple only the important bits and pieces from the existing codebase of Teamwork Chat and deliver them as a standalone module. Create a brand new solution that follows component based architecture which could potentially be the future core codebase for the next generation of Teamwork Chat. After looking into this for a while, we decided that the second option will let us deliver a better quality solution. Our main concern with decoupling the existing codebase was the fact that we didn't use a component based architecture in the existing client. Unfortunately at the time of the initial implementation of the app, Knockout.js components weren't a common choice. New tech stack Earlier this year at Teamwork.com we made the decision that all our development teams will start using Vue.js as the main front-end framework. We also started working on creating a new internal build system based on Webpack, which allows us to use the latest front-end tools and makes the development process significantly more productive. Together with Vue.js we started using Vuex, which is a state management library inspired by Flux architecture. Another big change for us was dropping CoffeeScript and replacing it with the next generation of JavaScript. JavaScript ES2015+ comes with great new features and syntax, but also offers incredible editor support and great ESLint that makes our code more consistent across the team. New technology stack for embedded Teamwork Chat. Luckily, during the development of an ex [Next page]()