# running Published articles for running. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The Autonomous Dumbledore DevFeed: [The Autonomous Dumbledore]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-04-25T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [API](), [Job](), [test](), [maintenance](), [Code]() Tags: [api](), [bugs](), [engineering](), [feature](), [fetch](), [job](), [maintenance](), [news](), [running](), [scheduled](), [test](), [tests](), [web-frontend]() ### AI overview An intern at Khan Academy describes the Autonomous Dumbledore, a system that monitors PSAT score-linking endpoints through recurring tests and automatically enables or disables the related feature based on recent results. ### Source excerpt By Evy Kassirer When I arrived at Khan Academy to start my internship on the Official SAT Practice ... Read more ## Quiz: How to Get Started With Ollama DevFeed: [Quiz: How to Get Started With Ollama]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-15T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Ollama](), [Python](), [Code](), [Hardware]() Tags: [chat](), [generate](), [hardware](), [how-to](), [install](), [internet](), [local](), [models](), [ollama](), [python](), [running]() ### AI overview An interactive 10-question quiz tests understanding of installing Ollama, pulling local models, and calling chat and generate functions from Python. It also covers multi-turn conversations, local hardware, and data privacy. ### Source excerpt Check your understanding of installing Ollama, pulling local models, and calling the chat and generate functions from your Python code. ## Lessons Learned from Fixing Flaky Tests with Claude DevFeed: [Lessons Learned from Fixing Flaky Tests with Claude]() Original publisher: [Read original article]() Author: Henrik Warne Published: 2026-08-15T15:50:33Z Content type: article Language: en Sources: [Henrik Warne]() Topics: [Claude](), [Testing]() Tags: [claude](), [flaky-tests](), [integration](), [learning](), [llm](), [net-9](), [parallel](), [programming](), [running](), [testing](), [tests]() ### AI overview The article describes lessons learned from using Claude to diagnose and fix flaky integration tests after parallelizing a test suite. It explains that blindly accepting plausible fixes was ineffective, while understanding and reviewing each change was important. ### Source excerpt We recently sped up our integration tests by a factor of ten by running many of them in parallel. There were several flaky tests in the new test suite. Claude was really useful in getting rid of almost all flakyness, ... Continue reading -> ## Zeta2.1: 3x Fewer Tokens, 50ms Faster DevFeed: [Zeta2.1: 3x Fewer Tokens, 50ms Faster]() Original publisher: [Read original article]() Author: Ben Kunkle, Oleksiy Syvokon Published: 2026-05-08T00:00:00Z Content type: release Language: en Sources: [Zed Industries - Blog]() Topics: [model-deployment](), [hugging face](), [Open Source](), [Code](), [D]() Tags: [efficiency](), [hugging-face](), [model](), [open-source](), [predictions](), [production](), [pypi](), [release](), [running](), [rust](), [tokens]() ### AI overview Zed announces Zeta2.1, an open-weight edit prediction model that uses a new Multi-Region prompt format. Compared with Zeta2, it emits fewer output tokens, delivers faster predictions, and requires fewer servers for the same traffic. The release also supports local use and includes Rust bindings published to PyPI. ### Source excerpt Zeta2.1 is out and it's more efficient than Zeta2. ## How does the Kubernetes controller manager work? DevFeed: [How does the Kubernetes controller manager work?]() Original publisher: [Read original article]() Author: Daniele Polencic Published: 2026-04-13T00:00:00Z Content type: tutorial Language: en Sources: [Learnk8s]() Topics: [Kubernetes](), [Deployment]() Tags: [controllers](), [kubernetes](), [running](), [self-healing](), [state](), [yaml]() ### AI overview A tutorial explaining how the Kubernetes controller manager runs reconciliation loops that watch resources and move the cluster toward its desired state. It demonstrates how a Deployment and ReplicaSet replace a deleted Pod and contrasts declarative control with imperative scripting. ### Source excerpt The controller manager runs the control loops that make Kubernetes self-healing. Learn how controllers watch resources, reconcile desired state, and drive the chain reaction from Deployment to running Pods. ## DigitalOcean's Inference Optimized Image improves Llama 3.3 70B inference efficiency on GPU Droplets DevFeed: [DigitalOcean's Inference Optimized Image improves Llama 3.3 70B inference efficiency on GPU Droplets]() Original publisher: [Read original article]() Author: Hemasumanth Rasineni Published: 2026-02-19T14:42:18Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Inference](), [Digital Ocean](), [Optimization](), [GPU](), [benchmarking](), [Large Language Model](), [llama](), [model-deployment](), [quantization]() Tags: [ai](), [benchmark](), [benchmarking](), [cost](), [digitalocean](), [droplets](), [engineering](), [gpu](), [hardware](), [inference](), [llama](), [optimization](), [quantization](), [running](), [tokens]() ### AI overview This article describes DigitalOcean's Inference Optimized Image, a pre-configured OS image for GPU Droplets that combines several inference optimizations. In the reported test, Llama 3.3 70B achieved higher throughput, lower time to first token, and lower cost per million tokens while running on two H100 GPUs instead of four. ### Source excerpt Production-grade LLM inference demands more than just access to GPUs; it requires deep optimization across the entire serving stack, from quantization and attention kernels to memory management and parallelism strategies. Most teams deploying models like Llama 3.3 70B on vanilla configurations are leaving the majority of their hardware's capability on the table: underutilized FLOPs, wasted memory bandwidth, and GPU hours spent waiting instead of computing. To solve this, we built the Inference Optimized Image a fully pre-configured OS image available on DigitalOcean's GPU Droplets -- that layers speculative decoding, FP8 quantization, FlashAttention-3, paged attention, concurrent optimization, and prompt caching into a single deployable image. The result of our particular test: 143% higher throughput (2,000 vs. 823 tokens/second), 40.7% lower TTFT (187.9ms vs. 316.83ms), and a 75% reduction in cost per million tokens ($1.472 vs. $5.80) -- all while running Llama 3.3 70B on 2 H100 GPUs instead of 4. In this post, we walk through the optimization stack, the engineering reasoning behind each layer, and the benchmark methodology and our test results showing these gains. Prefill, Decode, and Why Optimization is Multiplicative As we covered in our LLM Inference Benchmarking post, inference works in two distinct phases with fundamentally different computation characteristics. The prefill phase processes the entire input prompt through the model's forward pass self-attention, layer norms, feed-forward networks and is compute-bound, with high arithmetic intensity (FLOPs per byte transferred). The decode phase generates tokens one at a time, loading the full weight matrix and KV cache from HBM for each token, making it strictly memory-bandwidth-bound. This distinction matters because each optimization in our stack targets a specific bottleneck. Speculative decoding attacks the sequential nature of decode. FP8 quantization reduces memory footprint and accelerates compute via hig ## Running Nix Apps Outside a Sandbox DevFeed: [Running Nix Apps Outside a Sandbox]() Original publisher: [Read original article]() Published: 2025-11-25T00:00:00Z Content type: tutorial Language: en Sources: [Garnix Blog]() Topics: [Nix]() Tags: [apps](), [running](), [sandbox]() ### AI overview This article discusses running Nix apps outside a sandbox. ### Source excerpt Running Nix apps outside a sandbox ## Running Cloudflare Workers Inside Spin Apps DevFeed: [Running Cloudflare Workers Inside Spin Apps]() Original publisher: [Read original article]() Author: Matt Butcher Published: 2025-08-04T12:00:00Z Content type: article Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Cloudflare Workers](), [Cloudflare](), [function](), [Serverless]() Tags: [apps](), [can](), [cloudflare](), [cloudflare-workers](), [function](), [run](), [running](), [serverless](), [workers]() ### AI overview The article states that Cloudflare Workers are a type of serverless function and can run within Spin apps. ### Source excerpt Cloudflare Workers are another flavor of serverless function. You can run them from within Spin apps. ## Fermyon at Google Cloud Next 2025 DevFeed: [Fermyon at Google Cloud Next 2025]() Original publisher: [Read original article]() Author: Jasmine Mae Alindayu Published: 2025-04-02T12:00:00Z Content type: article Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Gke](), [Google]() Tags: [2025](), [cloud-next](), [generation](), [gke](), [google](), [running](), [team]() ### AI overview Fermyon will attend Google Cloud Next 2025 and showcase next-generation scaling and density running on GKE. ### Source excerpt The Fermyon team will be at Google Cloud Next, where we will be showcasing next generation scaling and density running on GKE. ## Best Pastebin Alternatives for Secure Text (or Code) Sharing in 2025 DevFeed: [Best Pastebin Alternatives for Secure Text (or Code) Sharing in 2025]() Original publisher: [Read original article]() Published: 2025-03-25T20:57:16Z Content type: comparison Language: en Sources: [Learn on Tailscale]() Topics: [Security](), [Encryption](), [End-to-End Encryption](), [passwords](), [web applications](), [tailscale]() Tags: [alternatives](), [article](), [code-sharing](), [encryption](), [running](), [security](), [syntax-highlighting]() ### AI overview This comparison examines Pastebin's use for sharing text and code, along with concerns about privacy, spam, malicious content, and feature limitations. It presents more secure alternatives that may offer encryption, password protection, or private sharing. ### Source excerpt Pastebin is an online platform for sharing, storing and managing text-based content, but has grown into a general-purpose tool or sharing plain text securely. The article addresses some concerns and alternative options to this service. ## Introducing Fermyon Platform for Kubernetes for Enterprise WebAssembly Applications DevFeed: [Introducing Fermyon Platform for Kubernetes for Enterprise WebAssembly Applications]() Original publisher: [Read original article]() Author: MacKenzie Adam Published: 2024-04-03T12:00:00Z Content type: release Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Kubernetes](), [WebAssembly]() Tags: [applications](), [enterprise](), [kubernetes](), [platform](), [running](), [webassembly]() ### AI overview The article introduces Fermyon Platform for Kubernetes, a platform for running enterprise-grade WebAssembly applications on Kubernetes. ### Source excerpt Introducing Fermyon Platform for Kubernetes - the hyper-efficient platform for running enterprise-grade WebAssembly applications on Kubernetes ## Lessons Learned From Running Web Experiments DevFeed: [Lessons Learned From Running Web Experiments]() Original publisher: [Read original article]() Author: Sneha Vasanth Published: 2023-07-05T05:00:00Z Content type: opinion Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [experiments](), [Web](), [data](), [Framework]() Tags: [data](), [data-science](), [experiment](), [experimentation](), [experiments](), [frameworks](), [insights](), [lead-generation](), [metric](), [metrics](), [monitoring](), [products](), [running](), [software](), [teams](), [web]() ### AI overview A Product Data Scientist at Square shares lessons from running web experiments on high-traffic pages. The article explains how experimentation supports risk assessment and data-driven decisions, and how teams can distinguish primary, secondary, and guardrail metrics when evaluating tests. ### Source excerpt Unveiling key strategies & frameworks ## Spin 1.0 -- The Developer Tool for Serverless WebAssembly DevFeed: [Spin 1.0 -- The Developer Tool for Serverless WebAssembly]() Original publisher: [Read original article]() Author: Radu Matei Published: 2023-03-22T16:00:00Z Content type: release Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Serverless](), [WebAssembly](), [Open Source](), [Tool]() Tags: [applications](), [building](), [developer](), [open](), [open-source](), [release](), [running](), [serverless](), [source](), [tool](), [webassembly]() ### AI overview Spin 1.0 is introduced as the first stable release of an open source developer tool for building, distributing, and running serverless applications with WebAssembly. ### Source excerpt Introducing the first stable release for Spin, the open source developer tool for building, distributing, and running serverless applications built with WebAssembly. ## 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... ## Personal Heatmaps DevFeed: [Personal Heatmaps]() Original publisher: [Read original article]() Author: J Evans Published: 2022-10-28T21:15:46Z Content type: tutorial Language: en Sources: [Strava Engineering]() Topics: [data](), [Algorithm](), [navigation](), [pixel](), [real-time](), [Google Maps](), [Website]() Tags: [algorithm](), [cycling](), [data](), [engineering](), [gis](), [google-maps](), [gps](), [heat](), [map](), [mapping](), [navigation](), [pixel](), [real-time](), [recursion](), [running](), [strava](), [zoom]() ### AI overview This article explains the algorithm behind Strava's Personal Heatmap, which renders an athlete's GPS activity as a frequency-based heatmap on an interactive world map. It focuses on Mercator projection and recursive map tiling, where 256x256 pixel tiles are served at different zoom levels for real-time navigation. ### Source excerpt This post discusses the algorithm behind Personal Heatmap, one of Strava's most acclaimed subscriber features. Product Overview The Personal Heatmap feature has been around in some form since 2015, but we gave it a major overhaul in 2020. For those unfamiliar, the heatmap is an aggregated view of an athlete's GPS-enabled activities on Strava. Activity data is rendered onto a map according to path traversal frequency; the more an athlete runs on a road, the brighter -- i.e. "hotter" -- that road shows up on the map. The result is a beautiful color gradient displaying the intricate web of past activities, unique to each athlete. The Personal Heatmap is displayed on an interactive world map, also known as a slippy map. Athletes can seamlessly zoom in and out or pan around to arbitrary views of the map, and their activity heat stays displayed the entire time. To enable this, we must provide a scheme for rendering and serving subsections of the world map on the fly. Enter map tiling. Mercator Projection & Map Tiling Map tiling is the practice of subdividing a map into many discrete images that can be stitched together and rendered as a single image in real-time during map navigation. The first step in choosing a tiling scheme is picking what world map to use. Enter, the Mercator Projection, the most commonly used projection of Earth as a flat, rectangular surface. We use this projection in all of our mapping products, including heatmaps. https://medium.com/media/d9e120d13950dd0f1bb4ee14108aa9a9/href Now, imagine a grid overlaying the Mercator projection. Each grid section is known as a tile, a pixel image with resolution 256x256. Every tile has an associated zoom level, and tiles are defined recursively according to this zoom level. A tile at a given zoom level can be subdivided into four equal sized tiles at a next zoom level. For example, Zoom level 0 displays the entire planet in a single 256x256 pixel image. Zoom level 1 displays the world in four 256x256 pixel images. ## LtU is now running in a new, more stable environment DevFeed: [LtU is now running in a new, more stable environment]() Original publisher: [Read original article]() Published: 2022-08-31T06:37:45Z Content type: news Language: en Sources: [Lambda the Ultimate]() Topics: [Software](), [coding]() Tags: [admin](), [downtime](), [infrastructure](), [running](), [software]() ### AI overview LtU experienced an extended period of downtime because its software infrastructure had become outdated and difficult to maintain. It has since migrated to a new environment expected to improve stability. ### Source excerpt LtU has experienced a long period of downtime recently. Its software infrastructure was outdated enough that it became difficult to maintain when problems arose. It has now been migrated to a brand new environment. It should be much more stable from now on. ## Build Your Own Content Management System (CMS) From a Template DevFeed: [Build Your Own Content Management System (CMS) From a Template]() Original publisher: [Read original article]() Author: Tim McCallum Published: 2022-08-26T19:00:00Z Content type: tutorial Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Content Management System](), [Template]() Tags: [blog](), [blog-post](), [build](), [cms](), [content](), [files](), [generate](), [how-to](), [management](), [needs](), [repositories](), [repository](), [running](), [structure](), [templates]() ### AI overview This tutorial explains how repository templates can be used to generate a content management system with a consistent directory structure, branches, and files, which can then be customized. ### Source excerpt Repository templates allow you and others to generate new repositories with the same directory structure, branches, and files. This blog post shows you how to build your own CMS from a template. The process is very quick and once up and running you can go ahead and customize your CMS to meet your needs. ## Introducing Spin DevFeed: [Introducing Spin]() Original publisher: [Read original article]() Author: Matt Butcher Published: 2022-03-31T16:45:08Z Content type: release Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Framework](), [WebAssembly](), [Open Source]() Tags: [apps](), [building](), [framework](), [open-source](), [running](), [webassembly]() ### AI overview Spin is an open source WebAssembly framework for building, distributing, and running WebAssembly apps. ### Source excerpt Spin is our new, open source WebAssembly framework - for building, distributing, and running WebAssembly apps. ## Running Python in WebAssembly DevFeed: [Running Python in WebAssembly]() Original publisher: [Read original article]() Author: Adam Reese and Matt Butcher Published: 2022-03-23T20:06:45Z Content type: tutorial Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Python](), [WebAssembly](), [Cloud]() Tags: [cloud](), [how-to](), [python](), [running](), [webassembly]() ### AI overview This tutorial explains how to compile Python to WebAssembly and run cloud-side Python in a WebAssembly runtime. ### Source excerpt Python can now be compiled to Wasm. In this post, we show how to run cloud-side Python in a WebAssembly runtime. ## Setting Trace Flags and Configuring SQL Server in Kubernetes DevFeed: [Setting Trace Flags and Configuring SQL Server in Kubernetes]() Original publisher: [Read original article]() Author: Anthony Nocentino Published: 2021-09-12T12:24:45Z Content type: tutorial Language: en Sources: [Kubernetes on Anthony Nocentino's Blog]() Topics: [Kubernetes](), [sql-server](), [configuration](), [Deployment]() Tags: [blog](), [blog-post](), [code](), [configuration](), [containers](), [deployment](), [kubernetes](), [manifest](), [running](), [sql-server](), [sqlserver](), [trace]() ### AI overview A tutorial showing how to configure SQL Server in Kubernetes. It demonstrates overriding a container command to set a Database Engine Service Startup Option and using a ConfigMap to inject an mssql.conf configuration file. ### Source excerpt In this blog post, we will walk through a few examples of configuring SQL Server in Kubernetes. First, we will create a Deployment for SQL Server, override the container's command, and specify a Database Engine Service Startup Option. Second, we will create a Deployment for SQL Server using a ConfigMap to inject an mssql.conf configuration file. Creating a SQL Server Deployment and Overriding the Container's Command and Arguments First up, let's create a Deployment for SQL Server and override the container's command specify a Database Engine Service Startup Option. ## TestRunnerComponent Provides a Visual UI for Running Unit Tests Inside an App DevFeed: [TestRunnerComponent Provides a Visual UI for Running Unit Tests Inside an App]() Original publisher: [Read original article]() Author: Steve Hannah Published: 2020-10-23T00:00:00Z Content type: tutorial Language: en Sources: [CodeName One]() Topics: [App](), [debug](), [ide]() Tags: [debug](), [ide](), [project](), [running](), [screen](), [test](), [tests](), [ui](), [unit-tests]() ### AI overview The article introduces TestRunnerComponent, which lets developers run unit tests inside a regular app project through a visual interface. It also makes the tests easier to debug in an IDE, and displays results on screen after execution. ### Source excerpt A while back we added a new TestRunnerComponent that provides a visual UI for running unit tests inside an app. Sometimes, while I'm developing unit tests, I find it easier to write them inside a regular app project instead of in the "tests" directory. This allows me to debug the unit tests in the IDE more easily, just like I debug regular apps. The TestRunner component makes it simple to do this. ## Spadina Testnet Postmortem and Zinken Testnet Launch Update DevFeed: [Spadina Testnet Postmortem and Zinken Testnet Launch Update]() Original publisher: [Read original article]() Author: Danny Ryan Published: 2020-10-01T00:00:00Z Content type: news Language: en Sources: [Ethereum Foundation Blog]() Topics: [Network](), [client](), [configuration]() Tags: [issue](), [launch](), [network](), [node](), [peering](), [research-development](), [running](), [update](), [upgrade]() ### AI overview The article reviews the Spadina testnet launch, including low initial validator participation, client and configuration errors, and a peering issue affecting Prysm. It announces the Zinken testnet as another dress rehearsal before mainnet and provides launchpad and genesis-deposit details. ### Source excerpt Oh you, Spadina, Finally, finality Hello and farewell tl;dr A quick Spadina postmortem New testnet: Zinken. Launchpad is live; Genesis deposits due in one week!... ## Running Node.js in Docker for local development DevFeed: [Running Node.js in Docker for local development]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2020-09-14T04:00:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [Docker](), [Node.js](), [Containers](), [Development](), [Front end]() Tags: [apps](), [benefit](), [buildchains](), [containers](), [dependency](), [development](), [docker](), [don-t](), [easy](), [frontend](), [hassle](), [inside](), [insights](), [know](), [local](), [need](), [node-js](), [onboarding](), [running]() ### AI overview This tutorial explains how to run Node.js buildchains and applications in Docker containers during local development. It presents containerization as a way to isolate application dependencies, simplify onboarding, and reduce conflicts between projects. ### Source excerpt You don't need to know Docker to benefit from running local dev Node.js buildchains & apps inside of Docker containers. You get easy onboarding, and less hassle. ## Homelab Services Tour -- What am I running on my Homelab? DevFeed: [Homelab Services Tour -- What am I running on my Homelab?]() Original publisher: [Read original article]() Author: Techno Tim Published: 2020-07-18T14:00:00Z Content type: article Language: en Sources: [Techno Tim]() Topics: [Homelab](), [Self-hosted](), [Hardware](), [Network](), [hosting]() Tags: [hardware](), [homelab](), [hosting](), [infrastructure](), [network](), [running](), [self-hosted](), [services]() ### AI overview A tour of the self-hosted services running in a homelab, following an earlier overview of the hardware and network setup that supports the home infrastructure. ### Source excerpt In my homelab tour, I showed you my hardware and network setup that powers all the infrastructure at home.Then, many of you asked which services I am hosting on this hardware.Well, here it is.This is a tour of all the self hosted services I have running in my Homelab. 📺 Watch Video Links 🛍 Check out the new Merch Shop at https://l.technotim.com/shop ⚙ See all the hardware I recommend a... 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