# deploy Published articles for deploy. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Sub-second artifact deployments are now supported in Vercel CLI DevFeed: [Sub-second artifact deployments are now supported in Vercel CLI]() Original publisher: [Read original article]() Author: Janos Szathmary Published: 2026-09-17T22:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Command-line interface](), [deploy](), [file](), [Markdown](), [HTML]() Tags: [cli](), [deploy](), [deployments](), [file](), [html](), [markdown](), [vercel]() ### AI overview Vercel CLI now supports sub-second deployments of eligible static artifacts, including small directories containing HTML or Markdown files. The artifacts skip the build step and return a live URL immediately. ### Source excerpt You and your agents can now deploy static artifacts to Vercel in under one second through Vercel CLI. Run vercel deploy to share a prototype, publish an HTML report, or preview a page created by your coding agent. Vercel automatically detects eligible deployments, and valid artifacts will skip the build step, immediately returning a live URL. Instant deployments support directories with: Up to 10 HTML or Markdown files A total size of 5 MB or less Supported file extension include .html, .htm, and .md. Update to Vercel CLI version 59.16.0 or later to get started, and run vercel deploy --help for all available options. Read more ## Turbo build machines can now be enabled per deployment DevFeed: [Turbo build machines can now be enabled per deployment]() Original publisher: [Read original article]() Author: Mehul Kar Published: 2026-09-17T20:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Deployment](), [Vercel](), [Command-line interface](), [REST API](), [GitHub](), [builds]() Tags: [build](), [cli](), [deploy](), [deployment](), [github](), [rest-api](), [vercel]() ### AI overview Vercel now allows Turbo build machines to be enabled for individual deployments. Developers can opt in through a Git commit message, Vercel CLI 59.20.0 or later, or the REST API without changing project settings. ### Source excerpt You can now opt into Turbo build machines on any individual deployment. This is useful when you need to increase resources temporarily without changing project settings. You can do this in three ways: Include #VERCEL_BUILD_MACHINE=TURBO in your Git commit message before pushing to GitHub Use vc deploy --turbo (Vercel CLI 59.20.0 or later) Set buildMachine to turbo when creating a deployment with the REST API Learn more in the managing builds documentation. Read more ## Neon launches a production-ready backend suite with Postgres, storage, functions, auth, and AI Gateway DevFeed: [Neon launches a production-ready backend suite with Postgres, storage, functions, auth, and AI Gateway]() Original publisher: [Read original article]() Author: Bryan Clark Published: 2026-09-17T12:00:00Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [Back end](), [Databases](), [Database](), [service](), [functions](), [gateway](), [deploy](), [autoscaling](), [Provisioning]() Tags: [ai-models](), [autoscaling](), [backend](), [backends](), [ci-cd](), [company](), [database](), [databases](), [deploy](), [functions](), [gateway](), [github](), [product]() ### AI overview Neon announces that its backend suite is generally available for production. The suite combines Lakebase Postgres, Object Storage, Functions, Managed Better Auth, and AI Gateway around a shared architecture designed for developer and coding-agent workflows. ### Source excerpt Neon is now a complete suite of backend primitives built around the database and rooted on the lakebase architecture: Lakebase Postgres, Object Storage, Functions, Managed Better Auth, and AI Gateway. All tools are GA and ready for production. Tell your agent to deploy them. ## Learn How to Deploy, Secure, and Automate Full-Stack Web Apps DevFeed: [Learn How to Deploy, Secure, and Automate Full-Stack Web Apps]() Original publisher: [Read original article]() Author: Beau Carnes Published: 2026-09-16T15:28:18Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [web applications](), [Deployment](), [CI/CD](), [FastAPI](), [GitHub Actions](), [Vue.js](), [nginx](), [firewalls](), [Python](), [Cloudflare](), [SSL](), [Ubuntu]() Tags: [analysis](), [authentication](), [ci-cd](), [deploy](), [fastapi](), [full-stack](), [github-actions](), [how-to](), [web](), [web-apps](), [youtube]() ### AI overview This article introduces an 11-hour freeCodeCamp course on deploying, securing, and automating a Vue.js and FastAPI web application. It covers Ubuntu server setup, SSH hardening, firewalls, application runtimes, Meilisearch, DNS, SSL, Nginx, Cloudflare, GitHub Actions, security testing, and observability. ### Source excerpt Taking a web application from local development to a live, secure production environment can be daunting if you've never looked under the hood. We just published a comprehensive course on the freeCode ## datasette-publish-fly 1.4 DevFeed: [datasette-publish-fly 1.4]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-11T02:58:52Z Content type: release Language: en Sources: [Simon Willison's Weblog]() Topics: [fly](), [App](), [bug]() Tags: [bug](), [datasette](), [datasette-1-542](), [deploy](), [fly](), [fly-40](), [https](), [release](), [volume]() ### AI overview Release notes for datasette-publish-fly 1.4. The release sets force_https=true in fly.toml, fixes a volume lookup bug, and adds compatibility with app-scoped deploy tokens. ### Source excerpt Release: datasette-publish-fly 1.4 Sets force_https=true in fly.toml. #31 Fix for Volume could not be found bug. #32 Compatible with app-scoped deploy tokens. #34 Tags: datasette, fly ## Why a DevOps Portal Cannot Replace an Operating Model DevFeed: [Why a DevOps Portal Cannot Replace an Operating Model]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-09-11T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [DevOps](), [incident](), [Deployment]() Tags: [deploy](), [deployment](), [devops](), [incident](), [operations](), [platform-engineering](), [production](), [sre](), [startups]() ### AI overview A DevOps portal can reduce operational friction, but it cannot create service ownership, incident discipline, or an operating model. The article recommends defining ownership, health signals, runbooks, escalation paths, and rollback practices before building a small, practical portal. ### Source excerpt A portal can remove friction. It cannot create service ownership, incident discipline, or a working operating model that does not yet exist. ## Creating an AI Platform for classic ML online inference DevFeed: [Creating an AI Platform for classic ML online inference]() Original publisher: [Read original article]() Author: Rohith Leeladharan Published: 2026-09-10T07:26:46Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Platform](), [Inference](), [MLOps](), [Machine learning](), [model-deployment]() Tags: [ai](), [ai-platform](), [ai-platform-engineering](), [deploy](), [experiments](), [feature-store](), [inference](), [machine-learning](), [ml](), [mlops](), [monitoring](), [predictions]() ### AI overview This article describes how American Express Global Business Travel built an AI platform for deploying classic machine-learning systems and supporting online inference. It explains the platform's requirements--simplicity, self-service, experimentation, and continuous improvement--and details the pre-process, predict, post-process pattern used by inference engines. ### Source excerpt Introduction In 2021, we were given the mission to have AI Systems running in production. The team, instead of just following a classical MLOps process, that involves transforming a Jupyter notebook into a product running in production, decided to go further by creating a platform to deploy AI systems in production. The team decided the platform should respect these requirements: Simplicity: The code powering AI systems should be simple, readable, and easy to maintain -- less intricacy means fewer bugs in production and greater reliability. Self-service: Anyone should be able to build and deploy AI systems autonomously, without depending on a central team. Experimentation: The platform should make it easy to run and iterate on experiments. Continuous improvement: Data related to events and interactions within AI systems must be captured, enabling monitoring and continuous improvement over time. In this article, we will walk through the work done to build a platform that fulfills these four requirements. Background At American Express Global Business Travel, we use machine learning (ML) models for a variety of user experiences like ranking hotel and flight search results. Our ML models are wrapped in inference engines that handle both pre-processing of input data before we run a prediction with the model, and post-processing of output data before returning the output to the caller. The overall flow looks something like this: Figure 1: Handling an inference request A client service that would like the ML model's predictions provides necessary context about the request like which user the request is for. Then, optionally, the inference engine fetches any necessary features for inference from our feature store [part 1][part 2]. Finally, it pre-processes the data, runs the predictions using the trained ML model, and does any necessary post-processing of the model output before returning the response to the caller. We call this the pre-process, predict, post-process patter ## Build a support chatbot with Appwrite Functions and VectorsDB DevFeed: [Build a support chatbot with Appwrite Functions and VectorsDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Chat Bot](), [Retrieval-Augmented Generation](), [Embeddings](), [React](), [SDKs](), [Node.js]() Tags: [api](), [build](), [chat](), [deploy](), [embedding](), [functions](), [node-js](), [rag](), [react](), [retrieval](), [support](), [tutorials]() ### AI overview This tutorial builds Harbor Help, a support chatbot for a fictional project-management product. It uses Appwrite VectorsDB to store help-center article embeddings, an Appwrite Function to retrieve relevant excerpts and generate answers through OpenRouter, and Appwrite Sites to host a React interface. The chatbot uses retrieval-augmented generation and can refuse questions outside the help center. ### Source excerpt Seed a help center, retrieve relevant articles with VectorsDB, and answer customer questions through an Appwrite Function. Deploy the chat interface on Appwrite Sites. ## Product Engineering for PMs, Part 1: Build a SaaS App Without Coding DevFeed: [Product Engineering for PMs, Part 1: Build a SaaS App Without Coding]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-08-31T15:35:45Z Content type: tutorial Language: en Sources: [The Product Compass]() Topics: [Software as a service](), [Claude Code](), [Supabase](), [Browser Automation](), [Automation](), [test](), [coding](), [Artificial Intelligence]() Tags: [automation](), [browser](), [build](), [claude](), [claude-code](), [coding](), [deploy](), [how-to](), [monetization](), [saas]() ### AI overview A step-by-step series for PMs and non-coders to design, build, secure, deploy, launch, monetize, analyze, and monitor a commercial SaaS app using Claude Code, Clerk, and Supabase. The case study is AskOne, a live Q&A product, and the article includes prompts, templates, browser automation, and testing workflows. ### Source excerpt Design, build, secure, and deploy a real multi-tenant SaaS app in 3-4 hours: Claude Code, Clerk, Supabase. No coding. All prompts included. ## Why Senior Leaders Should Attend Post-Incident Reviews for Major Cloud Incidents DevFeed: [Why Senior Leaders Should Attend Post-Incident Reviews for Major Cloud Incidents]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-29T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [incident](), [Cloud](), [control-plane](), [Deployment]() Tags: [cloud](), [control-plane](), [customers](), [deploy](), [incident](), [incident-management](), [leadership](), [major](), [operations](), [post](), [postmortems](), [reliability](), [review](), [service]() ### AI overview The article argues that senior leaders should attend Post Incident Reviews for major cloud incidents because failures can affect many customers and teams. Leadership helps approve cross-team changes, resolve trade-offs, and ensure corrective actions are completed. ### Source excerpt For major incidents, a Post Incident Review is not an operations meeting. It is where leaders remove the blockers that keep the service from becoming safer. ## Fine-Grained Access Control Now Available for All Heroku Customers DevFeed: [Fine-Grained Access Control Now Available for All Heroku Customers]() Original publisher: [Read original article]() Author: Alberto Sigismondi Published: 2026-08-21T17:07:29Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [Access Control](), [Security](), [IAM](), [Zero Trust](), [legacy]() Tags: [access-control](), [deploy](), [heroku](), [iam](), [identity-and-access-management](), [least-privilege](), [legacy](), [news](), [permissions](), [pipelines](), [security](), [security-compliance](), [zero-trust]() ### AI overview Heroku announces that Fine-Grained Access Control is available to all customers. The feature replaces fixed legacy roles with capability-based roles and app-specific permissions for roles such as view, deploy, operate, and manage. ### Source excerpt Fine-Grained Access Controls is now available to all Heroku customers. Heroku's legacy system gave you predefined roles like admin, member, or collaborator, each with a fixed bundle of permissions. It replaces that system with fine-grained roles like view, deploy, operate, and manage, with specific capability sets. Access control is managed at an app-specific layer, giving [...] The post Fine-Grained Access Control Now Available for All Heroku Customers appeared first on Heroku. ## Your agent can sign up for Mintlify fully on its own DevFeed: [Your agent can sign up for Mintlify fully on its own]() Original publisher: [Read original article]() Author: Kyan Yang Published: 2026-08-12T00:00:00Z Content type: release Language: en Sources: [Mintlify Blog]() Topics: [Command-line interface](), [Documentation](), [AI-assisted coding](), [Automation](), [Development]() Tags: [agent](), [announcements](), [automation](), [cli](), [coding-agents](), [deploy](), [documentation](), [setup](), [terminal]() ### AI overview Mintlify's mint signup command lets coding agents create an account and set up a documentation project from the CLI without opening a browser. The article describes a terminal-first workflow covering signup, verification, authentication, project scaffolding, preview, validation, custom domains, and deployment. ### Source excerpt Mintlify's mint signup command creates an account from the CLI so coding agents can set up docs without a browser. Every step from signup to deploy works from the terminal. ## Automating Everything That Doesn't Need Me DevFeed: [Automating Everything That Doesn't Need Me]() Original publisher: [Read original article]() Author: Josh Sherman Published: 2026-07-05T00:00:00Z Content type: opinion Language: en Sources: [Josh Sherman]() Topics: [agentic-coding](), [Automation](), [AI Development](), [Code]() Tags: [agentic-coding](), [automation](), [code](), [coding](), [deploy](), [monitoring](), [projects](), [side-projects](), [trust]() ### AI overview The author describes an ongoing experiment in using a personal agent and automation to reduce human involvement across side-project workflows, including design, coding, review, merging, deployment, and monitoring. Trust is built through repeated supervised iterations, with tests and intervention when the agent fails. ### Source excerpt I've been writing a lot about LLMs, AI (or the so-called "Artificial Intelligence") and the like. Part of that is because I haven't been running into many problems that I feel need documenting. Bigger part is because I'm finding that "how-to" content is easy to slop with a robot, so I'd rather write about things that the clankers can't. In a word, I'm just trying to write more human. While I don't think I've achieved full tokenmaxxing AI psychosis levels yet, I do like agentic coding. I'm also a huge proponent for automation. If it comes up regularly, it should be automated. If it can't be fully automated, then it should at least be partially automated. If I don't need to be materially involved, then I shouldn't be involved at all. My latest foray into building my own agent has included a refocus around trying to automate any and everything that I can. I still consider this to be an experiment more than me drinking any sort of Kool-Aid. Similar motives to my push for a dumber home, if I need to be involved, then it doesn't need to be smart. Well if I don't need to be involved, it should be automated. That's led me down a rabbit hole of trying to be less and less involved with certain things. Keep in mind, this is an experiment against my personal agent, and my myriad of smaller side projects. Most of the work is code based, so that includes writing a spec and/or design, slinging code, reviewing it, merging it, and deploying it. Most of that chain doesn't need me at all. Robots can take my half baked idea and turn it into a design spec. They can generate code that's coherent enough. Then they can review the code. I get to play gatekeeper and make them wait for my approval. From there, they can worry about merging it in and monitoring the deploy. I measure success on my agentic game loops by how much trust I have in the output that I'm reviewing. Run the loop enough times with a watchful eye, I can see where it fucks up. We iterate, and repeat the process. Usually wit ## Learning Software Development When AI Can Generate, Debug, and Deploy Code DevFeed: [Learning Software Development When AI Can Generate, Debug, and Deploy Code]() Original publisher: [Read original article]() Author: Kevin Naughton Jr. Published: 2026-06-14T13:45:27Z Content type: opinion Language: en Sources: [The Software Engineer Weekly]() Topics: [Artificial Intelligence](), [Development](), [Code generation](), [debug](), [deploy]() Tags: [ai](), [code-generation](), [debug](), [deploy](), [development]() ### AI overview The document discusses learning software development as code-generation models can scaffold features, debug complex stack traces, and deploy apps without requiring users to understand the underlying mechanics. ### Source excerpt We're currently living through the most radical shift in the history of software development. Code generation models have become so frictionless that you can scaffold entire features, debug complex stack traces, and deploy apps without ever truly understanding the underlying mechanics ## How to Eliminate Static Credentials from Trading Infrastructure DevFeed: [How to Eliminate Static Credentials from Trading Infrastructure]() Original publisher: [Read original article]() Author: info@goteleport.com (Gus Luxton) Published: 2026-05-20T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Security](), [ssh](), [private cloud](), [Server]() Tags: [admin](), [ci](), [credentials](), [deploy](), [how-to](), [infrastructure](), [private-cloud](), [production](), [root](), [security](), [ssh]() ### AI overview This guide explains why static credentials such as SSH keys and hardcoded API tokens create security risks in trading infrastructure. It discusses excessive permissions, credentials that do not expire, and access events that cannot be tied to specific identities, then introduces strategies for reducing or eliminating these risks, including Teleport-based approaches. ### Source excerpt Learn why static credentials like SSH keys are dangerous in trading and financial infrastructure, alongside strategies for mitigating their risk. ## The Cost of Implicitness DevFeed: [The Cost of Implicitness]() Original publisher: [Read original article]() Author: Davide Angelocola Published: 2026-05-09T00:00:00Z Content type: opinion Language: en Sources: [Davide Angelocola]() Topics: [Code](), [Architecture & Design](), [Requirements](), [Testing](), [ci](), [Test coverage](), [deploy](), [Security]() Tags: [architecture](), [code](), [compiler](), [coverage](), [deploy](), [documentation](), [implementation](), [opinion](), [pipeline](), [requirements](), [teams](), [testing]() ### AI overview This self-reflection post argues that implicit assumptions create deferred costs in software teams, code, and service boundaries. It advocates encoding assumptions in documentation, types, versioned contracts, tests, and CI so they become verifiable and reversible. ### Source excerpt You don't pay for implicit assumptions when you write them. You pay when a new joiner interprets them differently, when two teams deploy on different schedules and discover the shared understanding was never shared, when a hotfix swaps two int arguments that meant two different things and the compiler had no opinion. The cost is deferred, invisible, and always larger than expected. ## Introducing Temporal Serverless Workers: Deploy Temporal Workers to AWS Lambda DevFeed: [Introducing Temporal Serverless Workers: Deploy Temporal Workers to AWS Lambda]() Original publisher: [Read original article]() Author: Brandon Chavis Published: 2026-05-06T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [AWS Lambda](), [Serverless](), [autoscaling](), [Deployment]() Tags: [autoscaling](), [aws-lambda](), [deploy](), [deployment](), [lambda](), [product-news](), [serverless]() ### AI overview Temporal announced that Serverless Workers for AWS Lambda has graduated from pre-release to Public Preview for Temporal Cloud. The feature supports Go, Python, Java, .NET, and TypeScript SDKs and provides automatic invocation, autoscaling, and scale-to-zero behavior. ### Source excerpt Deploy Temporal Workers directly to AWS Lambda. Serverless Workers handle invocation, autoscaling, and scale-to-zero, so you can skip the infrastructure setup ## Deploying SSH host keys and certificates with Ansible DevFeed: [Deploying SSH host keys and certificates with Ansible]() Original publisher: [Read original article]() Author: Jan-Piet Mens Published: 2026-04-06T22:00:00Z Content type: tutorial Language: en Sources: [Jan-Piet Mens]() Topics: [Ansible](), [OpenSSH](), [deploy](), [private key](), [public key](), [passwords]() Tags: [ansible](), [certificates](), [configuration](), [deploy](), [jan-piet-mens](), [jpm](), [jpmens](), [openssh](), [private-key](), [public-key]() ### AI overview A practical Ansible playbook deploys SSH host keys and certificates to nodes. It generates host key pairs on each node, delegates local temporary-file handling and certificate signing to the Ansible controller, and uses an SSH agent-held CA key. ### Source excerpt If all you have is a hammer ... It occurs to me that we can deploy SSH host keys and their certificates to nodes using Ansible, as it has two existing modules we can use for the task: community.crypto.openssh_keypair generates OpenSSH private public key pairs community.crypto.openssh_cert generates OpenSSH user or host certificates Assuming we want a host's private key to be generated on a node and reside on the node only, and assuming our SSH CA (certification authority) is on the Ansible controller, we can use delegation to localhost for the bits which should happen on the controller. In the following simple playbook I use a block in which I create a temporary unique directory locally into which the public key from the host is copied and signed, and the always portion of the block ensures the directory is cleared out even on error within the block. The public portion of the SSH key pair is returned in openssh_keypair's metadata. - hosts: d13 gather_facts: yes remote_user: jp vars: dirname: "/etc/ssh" tasks: - name: Generate ED25519 SSH host key on node community.crypto.openssh_keypair: backend: "opensshbin" path: "{{ dirname }}/ssh_host_ed25519_key" comment: "ansible-made™ for host {{ inventory_hostname }}" type: "ed25519" become: true register: keydata - debug: var=keydata - block: - name: Create a local temporary directory ansible.builtin.tempfile: prefix: "jp" suffix: "cert" state: directory register: p delegate_to: localhost - name: Save public host key to local temporary file ansible.builtin.copy: content: "{{ keydata.public_key }}" dest: "{{ p.path }}/{{ keydata.filename | basename }}" delegate_to: localhost - name: Sign SSH certificate on local copy of public host key community.crypto.openssh_cert: identifier: "{{ inventory_hostname }}" public_key: "{{ p.path }}/{{ keydata.filename | basename }}" principals: "{{ [ ansible_fqdn ] + ansible_all_ipv4_addresses }}" path: "{{ p.path }}/{{ keydata.filename | basename }}-cert.pub" serial_number: 10 signing_key: "../ ## Five Lessons from My Journey as a Platform Product Manager at REA DevFeed: [Five Lessons from My Journey as a Platform Product Manager at REA]() Original publisher: [Read original article]() Author: Sree Pradeep Published: 2026-03-20T00:31:53Z Content type: opinion Language: en Sources: [REA Group]() Topics: [internal developer platform](), [Platform Engineering](), [Developer Platform](), [scaling](), [reliability](), [Availability]() Tags: [availability](), [deploy](), [developer](), [infrastructure](), [internal-developer-platform](), [latency](), [life-at-rea](), [platform](), [platform-engineering](), [reliability](), [tech]() ### AI overview A platform product manager at REA shares lessons from working on the company's Internal Developer Platform. The article argues that platform success depends not only on infrastructure expertise but also on understanding engineers, simplifying complexity, building trust, and measuring platform impact despite imperfect metrics. ### Source excerpt Five Lessons from My Journey as a Platform Product Manager at REA When I started my journey in platform engineering, I worked on REA's Internal Developer Platform with the aim of giving engineers a fast, safe and simple way to build, deploy and run applications in production. I thought success meant mastering infrastructure, APIs, scaling and reliability. ## Inside the Tech That Powers Dream Cricket's Multiplayer Experience DevFeed: [Inside the Tech That Powers Dream Cricket's Multiplayer Experience]() Original publisher: [Read original article]() Author: Dream Blog Published: 2025-12-13T07:38:59Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [Kubernetes](), [cloud-infrastructure](), [Open Source](), [Server]() Tags: [architecture](), [cloud](), [cricket](), [deploy](), [game-development](), [game-servers](), [games](), [gaming](), [high-performance](), [infrastructure](), [kubernetes](), [latency](), [multiplayer](), [open-source](), [production](), [scale](), [tech]() ### AI overview This article describes how Dream Cricket scaled its multiplayer infrastructure after a simple queue and fixed server pool could no longer handle growing adoption and large-scale events. It discusses challenges including peak matchmaking delays, unfair pairings, idle capacity, risky rollouts, and limited operational visibility, then outlines a design using Open Match, Agones, Kubernetes, and a health and latency service. ### Source excerpt By Vaibhav Naik Every great game starts with great tech -- and great people behind it. If you're passionate about crafting seamless, high-performance gaming experiences that bring players closer to the sport they love, we'd love to have you on our team. Explore our open roles at Dream Cricket. Why Multiplayer Matters on Dream Cricket We've always believed that the best games are the ones that bring people together. And that's exactly what multiplayer on Dream Cricket does -- it turns gaming from a solo challenge into a shared moment. The banter, the thrill of a close finish, the satisfaction of a fair match -- that's what keeps players coming back. Over the years, as our player base grew and expectations rose, we knew our multiplayer system needed to be scaled. It had to be faster. Fairer. The kind of system that doesn't break under pressure, whether it's lakhs of players logging in during a live event or two friends battling it out over spotty mobile data. This blog is our story of getting there. It's about how we rethought everything, from how we match players to how we run thousands of servers all at once. You'll get a look at the roadblocks we hit, the calls we had to make, and how open-source tech like Open Match and Agones helped us build something truly scalable. Building for the Next Era of MultiplayerThe Challenge Our initial multiplayer setup used a simple queue and a fixed pool of servers, which worked well. As adoption grew and large-scale events became frequent, the system was unable to manage the load. We had to move from basic matchmaking to skill-based fairness, surge handling, safe releases, and cloud portability. And as we scaled, familiar pain points surfaced: High P95 time-to-match at peak; cancellations rose Unfair pairings in upper tiers; noticeable re-matches Idle server waste off-peak; slow warm-up during spikes Risky rollouts without clean canaries; limited on-call visibility The challenge was clear: we needed to decouple matchmaking logic from ## PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility DevFeed: [PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility]() Original publisher: [Read original article]() Author: Gillian Gual Hinkle Published: 2025-12-04T19:50:28Z Content type: article Language: en Sources: [Heroku]() Topics: [Heroku](), [Amazon Web Services](), [Platform as a Service (PaaS)](), [Infrastructure as a Service (IaaS)](), [Cloud](), [Development]() Tags: [applications](), [aws](), [cloud](), [cloud-infrastructure](), [cloud-native](), [deploy](), [development](), [engineering](), [heroku](), [heroku-enterprise](), [infrastructure-as-a-service-iaas](), [maintenance](), [platform-as-a-service-paas](), [providers](), [scale](), [services](), [velocity]() ### AI overview The article explains how enterprises can combine Heroku's managed PaaS with AWS infrastructure to build and operate cloud-native applications. It highlights reduced operational burden, greater developer focus, and a Salesforce example involving applications used by more than 76,000 employees. ### Source excerpt For modern enterprises building cloud-native applications, success hinges on achieving maximum development velocity at every scale. Infrastructure as a Service (IaaS) providers like AWS provide hundreds of services with the unmatched reliability and scale needed for enterprise infrastructure, but they can require significant effort and expertise for organizations to be effective and efficient. To achieve [...] The post PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility appeared first on Heroku. ## Recent Guides on AI Inference and FastAPI Backend Engineering DevFeed: [Recent Guides on AI Inference and FastAPI Backend Engineering]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2025-11-29T14:02:54Z Content type: article Language: en Sources: [Neural Bits]() Topics: [AI Development](), [FastAPI](), [Inference Performance](), [backends](), [model-deployment](), [Pydantic](), [Retrieval Augmented Generation (RAG)]() Tags: [ai-engineer](), [ai-inference](), [deploy](), [fastapi](), [guides](), [inference](), [production]() ### AI overview A curated collection of recent guides for AI engineers. It highlights FastAPI and Pydantic practices for building maintainable AI and ML backends, along with inference engines and serving frameworks for deploying models in production across different scales and infrastructure. ### Source excerpt A curated list of the most actionable guides I've published in the past months. ## Deploy a Spin Application to Send Azure Monitor Alerts to Slack DevFeed: [Deploy a Spin Application to Send Azure Monitor Alerts to Slack]() Original publisher: [Read original article]() Author: Kate Goldenring Published: 2025-09-18T12:00:00Z Content type: tutorial Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Azure](), [Slack](), [notifications](), [monitor](), [App]() Tags: [application](), [azure](), [bridge](), [deploy](), [monitor](), [notifications](), [slack]() ### AI overview A tutorial on deploying a Spin application that transforms Azure Monitor alerts into formatted Slack notifications. ### Source excerpt Deploy a Spin application that transforms Azure Monitor alerts into beautifully formatted Slack notifications ## Preview: Slice Up Bare-Metal with Slicer DevFeed: [Preview: Slice Up Bare-Metal with Slicer]() Original publisher: [Read original article]() Author: Alex Ellis Published: 2025-08-30T08:09:48Z Content type: release Language: en Sources: [Alex Ellis' Blog]() Topics: [Firecracker](), [Kubernetes](), [OpenFaaS](), [systemd](), [Arch Linux](), [Arm](), [migration](), [Ollama]() Tags: [arm](), [bare-metal](), [boot](), [deploy](), [firecracker](), [k3s](), [kubernetes](), [linux](), [network](), [preview](), [self-hosting](), [slicer](), [storage](), [systemd](), [testing](), [work](), [x86-64]() ### AI overview OpenFaaS Ltd is previewing Slicer, an internal tool that partitions bare-metal hosts into Firecracker microVMs. The article describes use cases including Kubernetes cluster scaling, customer simulations, chaos testing, production troubleshooting, Arm and x86_64 testing, persistent workloads, multi-host deployments, and GPU access for Ollama. ### Source excerpt The easiest and best supported way to learn and deploy Firecracker and microVMs. [Next page]()