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Mikhail Shilkov
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Mikhail Shilkov
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| Propiedad | Contenido |
|---|---|
| title | Mikhail Shilkov |
| description | Serverless, Cloud, Azure, AWS, F#, Functional Programming, and more |
| type | website |
| url | https://mikhail.io/ |
| image | https://mikhail.io//2019/02/from-yaml-to-typescript-developers-view-on-cloud-automation/teaser.jpg |
Titulos
| H1 | H2 | H3 | H4 | H5 | H6 |
| 0 | 77 | 0 | 78 | 0 | 0 |
- [H2] Leaving Pulumi
- [H2] Inside Claude Code Skills: Structure, prompts, invocation
- [H2] Inside Claude Code's Web Tools: WebFetch vs WebSearch
- [H2] Claude Code 2.0 System Prompt Changes
- [H2] AI-Assisted Infrastructure as Code with Pulumi's Model Context Protocol Server
- [H2] Introducing Customizable Resource Auto-naming in Pulumi
- [H2] Pulumi + Azure Deployment Environments: Better Together for Enterprise Developers
- [H2] Infrastructure as Code with Java and Pulumi
- [H2] Get Up and Running with Azure Synapse and Pulumi
- [H2] Deploying new Azure Container Apps with familiar languages
- [H2] How To Deploy Temporal to Azure Kubernetes Service (AKS)
- [H2] How To Deploy Temporal to Azure Container Instances
- [H2] Eliminate Cold Starts by Predicting Invocations of Serverless Functions
- [H2] Choosing the Number of Shards in Temporal History Service
- [H2] Maru: Load Testing Tool for Temporal Workflows
- [H2] Cold Starts in Serverless Functions
- [H2] Farmer or Pulumi? Why not both!
- [H2] Running Container Images in AWS Lambda
- [H2] How To Deploy Temporal to Azure Kubernetes Service (AKS)
- [H2] How To Deploy Temporal to Azure Container Instances
- [H2] A Practical Approach to Temporal Architecture
- [H2] Temporal: Open Source Workflows as Code
- [H2] Announcing Next Generation Pulumi Azure Provider
- [H2] How to Drain a List of .NET Tasks to Completion
- [H2] The Emerging Landscape of Edge-Computing
- [H2] The Best Interview is No Interview: How I Get Jobs Without Applying
- [H2] Eliminate Cold Starts by Predicting Invocations of Serverless Functions
- [H2] Serverless in the Wild: Azure Functions Production Usage Statistics
- [H2] InfiniCache: Distributed Cache on Top of AWS Lambda (paper review)
- [H2] Hosting Azure Functions in Google Cloud Run
- [H2] Serverless Containers with Google Cloud Run
- [H2] Provisioned Concurrency: Avoiding Cold Starts in AWS Lambda
- [H2] Santa Brings Cloud to Every Developer
- [H2] Choosing the Best Deployment Tool for Your Serverless Applications
- [H2] AWS Lambda vs. Azure Functions: 10 Major Differences
- [H2] How To Deploy a Function App with KEDA (Kubernetes-based Event-Driven Autoscaling)
- [H2] How To Build Globally Distributed Applications with Azure Cosmos DB and Pulumi
- [H2] How to Avoid Cost Pitfalls by Monitoring APIs in AWS Lambda
- [H2] Ten Pearls With Azure Functions in Pulumi
- [H2] How to Measure the Cost of Azure Functions
- [H2] 7 Ways to Deal with Application Secrets in Azure
- [H2] Load-Testing Azure Functions with Loader.io
- [H2] How Azure CLI Manages Your Access Tokens
- [H2] Globally-distributed Serverless Application in 100 Lines of Code. Infrastructure Included!
- [H2] Concurrency and Isolation in Serverless Functions
- [H2] Reducing Cold Start Duration in Azure Functions
- [H2] Visualizing Cold Starts
- [H2] From YAML to TypeScript: Developer's View on Cloud Automation
- [H2] Serverless at Scale: Serving StackOverflow-like Traffic
- [H2] A Fairy Tale of F# and Durable Functions
- [H2] Making Sense of Azure Durable Functions
- [H2] From 0 to 1000 Instances: How Serverless Providers Scale Queue Processing
- [H2] AWS Lambda Warmer as Pulumi Component
- [H2] Monads explained in C# (again)
- [H2] Cold Starts Beyond First Request in Azure Functions
- [H2] Load Testing Azure SQL Database by Copying Traffic from Production SQL Server
- [H2] Tic-Tac-Toe with F#, Azure Functions, HATEOAS and Property-Based Testing
- [H2] Azure Functions Get More Scalable and Elastic
- [H2] Precompiled Azure Functions in F#
- [H2] Authoring a Custom Binding for Azure Functions
- [H2] Custom Autoscaling with Durable Functions
- [H2] Custom Autoscaling of Azure App Service with a Function App
- [H2] Sending Large Batches to Azure Service Bus
- [H2] Finding Lost Events in Azure Application Insights
- [H2] Reliable Consumer of Azure Event Hubs
- [H2] Azure Functions as a Facade for Azure Monitoring
- [H2] Coding Puzzle in F#: Find the Number of Islands
- [H2] Event Sourcing: Optimizing NEventStore SQL read performance
- [H2] Introducing Stream Processing in F#
- [H2] Event Sourcing and IO Complexity
- [H2] Azure SQL Databases: Backups, Disaster Recovery, Import and Export
- [H2] Comparing Scala to F#
- [H2] Building a Poker Bot: Functional Fold as Decision Tree Pattern
- [H2] Building a Poker Bot with Akka.NET Actors
- [H2] Functional Actor Patterns with Akka.NET and F#
- [H2] Building a Poker Bot: Mouse Movements
- [H2] How we do message processing
- [H4] Reflections on six years at Pulumi: the product, the work, the growth, and the people.
- [H4] Under the hood of Claude Code skills: folder layout, tool definition, and runtime flow.
- [H4] How Claude Code uses web tools under the hood: schemas, prompts, execution, and design trade-offs
- [H4] Analyzing the system prompt changes between Claude Code 1.x and 2.0, powered by Sonnet 4.5
- [H4] Learn how AI assistants like Cursor with Pulumi's MCP server accelerate IaC workflows and improve developer experience.
- [H4] Discover how to customize Pulumi's resource naming to align with your organization's standards and naming conventions.
- [H4] Author Azure Deployment Environments definitions with Pulumi using your favorite programming language.
- [H4] Learn about Pulumi's support for Java and JVM languages, which enable you to use Infrastructure As Code on any Cloud with the JVM ecosystem.
- [H4] Use infrastructure as code to automate deployment of an Azure Synapse workspace
- [H4] Learn how to deploy Docker containers to Azure Container Apps using Pulumi. A step-by-step guide for building scalable serverless apps in any language.
- [H4] Get up and running with Temporal workflows in Azure and Kubernetes in several CLI commands
- [H4] Get up and running with Temporal workflows in Azure in several CLI commands
- [H4] Azure Functions introduce a data-driven strategy to pre-warm serverless applications right before the next request comes in
- [H4] Tuning the sharding configuration for the optimal cluster performance with the numHistoryShards config.
- [H4] Benchmarking Temporal deployments with a simple load simulator tool
- [H4] Exploring the phenomenon of increased latency while instances of cloud functions are dynamically allocated.
- [H4] Azure Infrastucture as Code using F#: combining Pulumi and Farmer
- [H4] AWS Lambda launches support for packaging and deploying functions as container images
- [H4] Get up and running with Temporal workflows in Azure and Kubernetes in several CLI commands
- [H4] Get up and running with Temporal workflows in Azure in several CLI commands
- [H4] What it takes to get Temporal workflows up and running
- [H4] Temporal reimagines state-dependent service-orchestrated application development
- [H4] Next Generation Pulumi Azure Provider with 100% API Coverage and Same-Day Feature Support is now available in beta
- [H4] Custom await logic for a dynamic list of .NET tasks, fast and on-time
- [H4] What is edge computing, and what are the primary use cases in the world today? (a paper review)
- [H4] My humble story of getting (or not) a job at Amazon, Qualcomm, Jet.com, Pulumi, and more
- [H4] Azure Functions introduce a data-driven strategy to pre-warm serverless applications right before the next request comes in
- [H4] Insightful statistics about the actual production usage of Azure Functions, based on the data from Microsoft's paper
- [H4] My review of the paper "InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache"
- [H4] Running Azure Functions Docker container inside Google Cloud Run managed service
- [H4] Google Cloud Run is the latest addition to the serverless compute family. While it may look similar to existing services of public cloud, the feature set makes Cloud Run unique.
- [H4] AWS recently announced the launch of Provisioned Concurrency, a new feature of AWS Lambda that intends to solve the problem of cold starts.
- [H4] How Santa Cloud uses F# and Pulumi to bring cloud resources to the homes of software engineers.
- [H4] Factors to consider while deploying cloud infrastructure for serverless apps.
- [H4] A comparison AWS Lambda with Azure Functions, focusing on their unique features and limitations.
- [H4] Hosting Azure Functions in Kubernetes: how it works and the simplest way to get started.
- [H4] A reusable component to build highly-available, low-latency applications on Azure
- [H4] How to monitor your APIs using serverless technologies and an Epsagon dashboard.
- [H4] Ten bite-sized code snippets that use Pulumi to build serverless applications with Azure Functions and infrastructure as code.
- [H4] Azure pricing can be complicated—to get the most value out of your cloud platform, you need to know how to track spend and measure the costs incurred by Azure Functions.
- [H4] From config files to Key Vault and role-based access, learn how infrastructure as code helps manage application secrets in Azure.
- [H4] Verifying your Function App as a valid target for the cloud load testing.
- [H4] Azure CLI is a powerful tool to manage your cloud resources. Where does it store the sensitive information and why might you want to care?
- [H4] Building a serverless application on Azure with both the data store and the HTTP endpoint located close to end users for fast response time.
- [H4] Exploring approaches to sharing or isolating resources between multiple executions of the same cloud function and the associated trade-offs.
- [H4] The influence of the deployment method, application insights, and more on Azure Functions cold starts.
- [H4] Serverless cold starts illustrated with animated GIFs.
- [H4] An expressive and powerful way to design cloud-native and serverless infrastructure
- [H4] Scalability test for HTTP-triggered serverless functions across AWS, Azure and GCP
- [H4] How F# and Azure Durable Functions make children happy (most developers are still kids at heart)
- [H4] Why and How of Stateful Workflows on top of serverless functions
- [H4] Comparison of queue processing scalability for FaaS across AWS, Azure and GCP
- [H4] Preventing cold stats of AWS Lambda during longer periods of inactivity, implemented as a reusable Pulumo component
- [H4] Yet another Monad tutorial, this time for C# OOP developers
- [H4] Can we avoid cold starts by keeping Functions warm, and will cold starts occur on scale out? Let's try!
- [H4] Azure SQL Database is a managed service that provides low-maintenance SQL Server instances in the cloud. You don’t have to run and update VMs, or even take backups and setup failover clusters. Microsoft will do administration for you, you just pay an hourly fee.
- [H4] A toy application built with F# and Azure Functions: a simple end-to-end implementation from domain design to property-based tests.
- [H4] Back in August this year, I’ve posted Azure Functions: Are They Really Infinitely Scalable and Elastic? with two experiments about Azure Function App auto scaling. I ran a simple CPU-bound function based on Bcrypt hashing, and measured how well Azure was running my Function under load.
- [H4] This post is giving a start to F# Advent Calendar in English 2017. Please follow the calendar for all the great posts to come. Azure Functions is a “serverless” cloud offering from Microsoft. It allows you to run your custom code as response to events in the cloud. Functions are very easy to start with; and you only pay per execution - with free allowance sufficient for any proof-of-concept, hobby project or even low-usage production loads. And when you need more, Azure will scale your project up automatically.
- [H4] The process of creating a custom binding for Azure Functions.
- [H4] Leverage Azure Durable Functions to scale-out and scale-in App Service based on a custom metric
- [H4] How to scale-out and scale-in App Service based on a custom metric
- [H4] Azure Service Bus client supports sending messages in batches. However, the size of a single batch must stay below 256k bytes, otherwise the whole batch will get rejected.
- [H4] One of the ways we use Azure Application Insights is tracking custom application-specific events. For instance, every time a data point from an IoT device comes in, we log an AppInsights event. Then we are able to aggregate the data and plot charts to derive trends and detect possible anomalies.
- [H4] Azure Event Hubs is a log-based messaging system-as-a-service in Azure cloud. It’s designed to be able to handle huge amount of data, and naturally supports multiple consumers.
- [H4] Azure Functions are the Function-as-a-Service offering from Microsoft Azure cloud. Basically, an Azure Function is a piece of code which gets executed by Azure every time an event of some kind happens. The environment manages deployment, event triggers and scaling for you. This approach is often reffered as Serverless.
- [H4] Here’s a programming puzzle. Given 2D matrix of 0’s and 1’s, find the number of islands. A group of connected 1’s forms an island. For example, the below matrix contains 5 islands
- [H4] In my previous post about Event Store read complexity I described how the growth of reads from the event database might be quadratic in respect to amount of events per aggregate.
- [H4] The post was published for F# Advent Calendar 2016, thus the examples are themed around the Christmas gifts. This article is my naive introduction to the data processing discipline called Stream Processing.
- [H4] Event Sourcing is an approach, when an append-only store is used to record the full series of events that describe actions taken on a particular domain entity. This event store becomes the main source of truth to reconstruct the current state of the entity and its complete history.
- [H4] Azure SQL Database is a managed cloud database-as-a-service. It provides application developers with SQL Server databases which are hosted in the cloud and fully managed by Microsoft.
- [H4] F# and Scala are quite similar languages from 10.000 feet view. Both are functional-first languages developed for the virtual machines where imperative languages dominate. C# for .NET and Java for JVM are still lingua franca, but alternatives are getting stronger.
- [H4] This is the fifth part of Building a Poker Bot series where I describe my experience developing bot software to play in online poker rooms. I’m building the bot with .NET framework and F# language which makes the task relatively easy and very enjoyable. Here are the previous parts:
- [H4] This post lays out the most exciting part of the bot. I'll compose the recognition, flow, decision and mouse clicking parts together into the bot application. The application is a console executable interacting with multiple windows of poker room software.
- [H4] My exploration of Actor model started with Akka.NET framework - a .NET port of JVM-based Akka. Actor programming model made a lot of sense to me, but once I started playing with it, some questions arose. Most of those questions were related to the following definition:
- [H4] The last step of the poker bot flow: clicking the buttons. The screen is already recognized, the hand is understood, the decisions are made and now the bot needs to execute the actions. This means clicking the right button at the poker table.
- [H4] Our team develops a back-end system that processes messages from mobile devices. The devices collect information from complex machines and send messages to our data center. In this article I want to share our approaches to building such processing software. The ideas are quite general and can be applied to any system of the following architecture...
- [H4] See more posts in Archives
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