# profiling Published articles for profiling. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Profiling App Engine Memcached DevFeed: [Profiling App Engine Memcached]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-05-01T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Latency](), [Logging](), [Data structures]() Tags: [engineering](), [infrastructure](), [latency](), [logging](), [memcached](), [news](), [patterns](), [profiling](), [tests]() ### AI overview Khan Academy engineers profile App Engine Memcached to estimate access latency and evaluate whether on-demand content fetching could support separate content trees for each language. Most requests showed 1-4 ms latency, usually below 10 ms, while a single multi-get performed similarly to a single get. ### Source excerpt By Ben Kraft Last year, Willow wrote about how we optimized our in-memory content data to take up ... Read more ## Memcached-Backed Content Infrastructure DevFeed: [Memcached-Backed Content Infrastructure]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-05-15T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Caching](), [Back end](), [data](), [Front end]() Tags: [backend](), [cache](), [engineering](), [infrastructure](), [memcached](), [news](), [profiling]() ### AI overview Khan Academy describes testing and rolling out a Memcached-backed content-serving backend. The article explains the existing content versioning and storage model, the performance limitations of fetching many individual items, and the prior approach of loading a compressed site-wide content bundle at server startup. ### Source excerpt By Ben Kraft Last post, I wrote about how we did profiling on App Engine's Memcached service to ... Read more ## Running Agent Harnesses with Local Models DevFeed: [Running Agent Harnesses with Local Models]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-15T21:59:31Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Large Language Model](), [coding](), [Hardware](), [Open Source](), [Claude Code](), [codex]() Tags: [agent](), [coding](), [hardware](), [local](), [models](), [open-source](), [opentelemetry](), [profiling](), [recommendations](), [run-agent](), [video]() ### AI overview A video walkthrough explains how Magnitude profiles computer hardware, benchmarks local models, recommends practical candidates, and connects a selected model to coding agent harnesses such as Claude Code, Codex, OpenCode, and Pi. The article also presents a Dynatrace reference application for tracing LLM pipelines with OpenTelemetry. ### Source excerpt ...explained with a full video walkthrough. ## Faster Starts, Less JavaScript Overhead DevFeed: [Faster Starts, Less JavaScript Overhead]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [JavaScript](), [Compiler](), [Instrumentation]() Tags: [benchmarks](), [compiler](), [instrumentation](), [javascript](), [profiling]() ### AI overview The article describes startup-performance work in Codename One. It identifies launch delays caused by repeated screen-scale queries, theme scans, unnecessary synchronous dispatches, premature GC-park handshakes, and JavaScript suspension preparation. The fixes publish screen state atomically, avoid waiting when operations can proceed immediately, index theme keys, and improve instrumentation so profiling reveals hidden stalls. ### Source excerpt Codename One removes native startup waits, repeated style scans, and unnecessary JavaScript suspension. Profiles and compiler benchmarks expose costs that bundle size and frame rates miss. ## The Real Python Podcast - Episode #310: Performance Engineering: Profiling and Making Apps Fast by Default DevFeed: [The Real Python Podcast - Episode #310: Performance Engineering: Profiling and Making Apps Fast by Default]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-04T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [Developer experience](), [Frameworks](), [Microservice](), [Large Language Model]() Tags: [engineering](), [frameworks](), [llms](), [microservices](), [performance](), [podcast](), [profiling](), [python]() ### AI overview This Real Python Podcast episode discusses performance engineering for Python applications with Den Odell, author of "Fast by Default: Practical Performance Engineering." It covers performance budgets, profiling, data-driven decisions, planning for performance, and maintaining speed as systems and user bases grow. ### Source excerpt How do you plan for the performance of your Python applications? What does a performance budget entail, and where should you spend your resources? This week on the show, we speak with Den Odell about his new book "Fast by Default: Practical Performance Engineering." ## Value Classes Still Need Compiler Sympathy DevFeed: [Value Classes Still Need Compiler Sympathy]() Original publisher: [Read original article]() Author: Johan Sjölén Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Inside Java]() Topics: [JDK 28](), [Optimization](), [Compiler]() Tags: [compiler](), [immutability](), [jdk-28](), [jep-401](), [jvm](), [openjdk](), [optimization](), [payload](), [profiling](), [project-valhalla](), [valhalla]() ### AI overview This article examines value classes as a preview feature in JDK 28 and explains how giving up object identity can enable JVM optimizations such as flattening and scalarization. It also describes limitations caused by representation changes, abstraction boundaries, mutability, and generic virtual calls. ### Source excerpt Declaring a value class is first and foremost a semantic decision. It tells our fellow programmers that its instances are defined entirely by their state and do not need identity. That clearer model is valuable in itself! The JVM's additional freedom to optimize how those values are represented is a welcome bonus. C2 can do amazing things with that freedom, but it cannot always recover information hidden behind abstraction boundaries. Profiling and inspecting the generated code remain the best ways to understand what is happening. To get the best results, we may still need to have a little sympathy for the compiler. ## From slow tests to slow production: Debugging with Stackprof DevFeed: [From slow tests to slow production: Debugging with Stackprof]() Original publisher: [Read original article]() Published: 2026-08-13T00:00:00Z Content type: tutorial Language: en Sources: [Aha! Engineering Blog]() Topics: [debugging](), [Tooling](), [RSpec](), [Databases]() Tags: [ci](), [cpu-time](), [database](), [debugging](), [logs](), [overhead](), [performance](), [production-debugging](), [profiling](), [rspec](), [snapshots](), [tests](), [timing]() ### AI overview An investigation into tests that became six times slower traces the stall to code generating example data and Active Record callbacks. The article explains using Stackprof with RSpec to profile the test, including I/O time, and reports that the test spent 71% of its time sleeping. ### Source excerpt One morning, a developer announced in our Slack channel that some tests were failing on our main branch. The cause of the failures was not obvious. No recent PRs seemed related, and neither the code nor the tests had changed in a long time. The test ## Profile-guided optimization in Go DevFeed: [Profile-guided optimization in Go]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-08-09T23:17:36Z Content type: article Language: en Sources: [Daniel Lemire]() Topics: [Go Language](), [Optimization](), [Compiler](), [inlining](), [JSON]() Tags: [compiler](), [cpu](), [go](), [inlining](), [interface](), [json](), [measurement](), [optimization](), [profile](), [profiling](), [speed](), [standard-library](), [systems]() ### AI overview This article explains how profile-guided optimization in Go uses runtime measurements to guide compiler decisions such as inlining hot call sites and devirtualizing interface calls. It also describes a benchmark parsing three JSON documents with separately trained PGO builds and comparing their speed against a profile-free baseline. ### Source excerpt When a compiler optimizes your program, it has to guess. Which functions are worth inlining? Which side of a branch is the common one? Which method does this interface call actually reach? At compile time it cannot know, so it uses heuristics. Profile-guided optimization (PGO) replaces the guessing with measurement: you run your program, record ... Continue reading Profile-guided optimization in Go ## Mobile App Security Without Sacrificing UX | Guardsquare DevFeed: [Mobile App Security Without Sacrificing UX | Guardsquare]() Original publisher: [Read original article]() Author: Ryan Lloyd - Chief Product Officer Published: 2026-07-21T13:02:06Z Content type: article Language: en Sources: [Guardsquare Blog]() Topics: [Mobile Security](), [Application Security](), [Security](), [obfuscation](), [User experience (UX)](), [Reverse Engineering](), [Jailbreak](), [debug]() Tags: [application-security](), [jailbreak](), [mobile](), [obfuscation](), [performance](), [profiling](), [reverse-engineering](), [security](), [technical](), [time](), [ux]() ### AI overview The article explains how profiling instrumented mobile applications helps teams apply obfuscation and runtime security controls at appropriate levels while limiting effects on stability, performance, and user experience. It also discusses automating application profiling at scale through AI-driven and agentic testing. ### Source excerpt Mobile application security has evolved significantly over the past decade. Modern applications routinely employ code obfuscation, runtime application self-protection (RASP), anti-tampering controls, jailbreak and root detection, debugger detection, certificate pinning, and a variety of other runtime defenses designed to protect intellectual property and sensitive user data. ## A Maturity Matrix for Game Development DevFeed: [A Maturity Matrix for Game Development]() Original publisher: [Read original article]() Author: Dean Hume Published: 2026-07-21T09:21:57Z Content type: article Language: en Sources: [Dean Hume]() Topics: [Game Development](), [Software Engineering](), [Development]() Tags: [best-practices](), [build](), [engineering](), [game-development](), [performance](), [profiling](), [software-engineering]() ### AI overview The article presents a maturity matrix for game development teams, adapting the author's earlier software-engineering framework. It uses Red, Amber, and Green ratings to assess practices and identify priorities, including build pipeline health and performance profiling culture. ### Source excerpt A few years ago, I wrote about using a Maturity Matrix to assess software engineering teams. The idea was simple - what does a great team look like, and how honestly can you measure yourself against that picture? Since then, I've moved into the gaming industry. Anyone who has ## Android Studio Quail 2 is Stable: Multi-task with the Android Studio AI agent DevFeed: [Android Studio Quail 2 is Stable: Multi-task with the Android Studio AI agent]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-07-14T11:00:00Z Content type: release Language: en Sources: [Android Developers Blog]() Topics: [Android Studio](), [AI Agent](), [Memory Leaks](), [debug]() Tags: [agentic](), [ai-agent](), [android-studio](), [memory-leak](), [profiling]() ### AI overview Android Studio Quail 2 is presented as a stable release with parallel Agent Mode chats, concurrent agentic workflows, native LeakCanary integration in the Profiler, and Agent Mode integration with App Quality Insights for crash analysis. ### Source excerpt Posted by Amman Asfaw, Product Manager, Android Studio Android Studio Quail 2 is now stable and ready for you to use in production, bringing a shift to your IDE with concurrent agentic workflows, natively integrated memory leak profiling, and context-aware crash remediation. Whether you are performing a sweeping architectural overhaul, tracing a memory leak, or resolving a critical production crash, Android Studio keeps you anchored in your workspace by reducing manual friction. Here's a deep dive into what's new: Multi-tasking with parallel chats In Android Studio Quail 2, we've been hard at work redesigning Agent Mode from the ground up. This new architecture provides better performance, offers more flexibility for decomposing complex tasks, and improves the suite of internal tools the agent uses to do its work. In addition to these behind-the-scenes improvements, these changes also allow you to converse across multiple agent chats simultaneously. Waiting for the Android Studio agent to finish a task before you can ask another question or initiate a separate task in Agent Mode is a bottleneck of the past. You can multi-task seamlessly: kick off a UI refactor in one tab, fix a ProGuard rule in a second, and generate documentation in a third. You can also change which models the agent uses from chat to chat based on the requests you have. Take a look at Android Bench for an analysis of how LLMs perform Android development tasks. How to use: Click the "+" icon to start a new parallel conversation, and use the History icon to navigate between active tasks. Alternatively, select File > New > New Agent Tab to open a conversation in a dedicated tab. Note: Worktree support is currently unavailable. Exercise caution when running concurrent chats that modify the same project files, which can potentially lead to editor conflicts. Run multiple agent tasks in parallel with different models of your choice. Use the History icon to navigate between active tasks. Memory leak detec ## Profiling Performance Bottlenecks in Production DevFeed: [Profiling Performance Bottlenecks in Production]() Original publisher: [Read original article]() Author: Doximity Published: 2026-06-18T12:53:00Z Content type: tutorial Language: en Sources: [Doximity]() Topics: [Ruby](), [Code](), [User Experience]() Tags: [how-to](), [latency](), [performance](), [production](), [profiling](), [ruby](), [time]() ### AI overview This article explains how to use profiling to identify performance bottlenecks in production Ruby applications. It contrasts instrumenting and sampling profilers, emphasizing that sampling has lower overhead and is suitable for production. The article reports that profiling reduced a background job's runtime by about 80%. ### Source excerpt One of our background jobs was so slow that users assumed it was broken. They would kick it off, wander away, knowing it would take ages to complete. Turns out the job was fine. It was just taking its sweet time. We eventually cut its runtime by about 80%, and the fix was so small it was almost insulting. You could have stared at the code for hours and missed it. A profiler found it in minutes. This post is about how to think about profiling so you reach for it at the right moment, not as a first reflex. Performance is a KPI, Not a Vibe It's easy to treat performance as something you tune when someone complains. But latency is a feature, and a slow background job has real downstream costs: a worse user experience, more compute burned, queues backing up, and a quietly growing tolerance for "well, that's just how long it takes." The mindset shift that helped us was treating performance like any other key metric we hold ourselves accountable to. That means it has to be measured, it has to have a number, and that number has to move in a direction we choose on purpose. "It feels faster" is not a result. "p95 went from 214 seconds to 69 seconds on a representative input" is a result. Which raises the obvious question: Once you've decided to take a number seriously, how do you find out where the time actually goes? Profilers, And How They Work A profiler answers one question very well: Inside a single execution of code, where is the time being spent? We can get that answer using one of two broad strategies: Instrumenting (tracing) profilers wrap every method call to record when it starts and stops. You get extremely precise, complete call counts and timings. But the overhead is high, and that overhead can distort the very timings you're trying to measure. Wrapping millions of tiny method calls makes the cheap ones look expensive. Sampling profilers take the opposite approach. Instead of instrumenting every call, they periodically interrupt the program -- say, hundreds of ti ## New AMD Radeon Developer Tool Suite update brings shader source code, Extended PIX Markers, and command-line capture DevFeed: [New AMD Radeon Developer Tool Suite update brings shader source code, Extended PIX Markers, and command-line capture]() Original publisher: [Read original article]() Author: Chris Hesik Published: 2026-06-11T16:26:00Z Content type: release Language: en Sources: [AMD GPUOpen]() Topics: [GPU](), [shaders](), [Ray Tracing](), [Command-line interface](), [Software](), [Hardware]() Tags: [agility-sdk](), [amd](), [announcement](), [command-line](), [developer](), [directx](), [driver](), [gpu](), [gpu-open-tools](), [gpuopen-other-amd](), [gpuopen-tools](), [graphics-apis](), [hardware](), [memory](), [microsoft](), [microsoft-agility-sdk](), [microsoft-directx](), [news](), [optimization](), [other-amd](), [performance](), [product-blogs](), [product-release](), [profiling](), [radeon-developer-panel](), [radeon-developer-tool-suite](), [radeon-gpu-analyzer](), [radeon-gpu-detective](), [radeon-gpu-profiler](), [radeon-memory-visualizer](), [radeon-raytracing-analyzer](), [ray-tracing](), [raytracing](), [rdp](), [rdts](), [release](), [rga](), [rgd](), [rgp](), [rmv](), [rra](), [shaders](), [source](), [technical-articles](), [tool](), [tools]() ### AI overview AMD's Radeon Developer Tool Suite update adds shader source code viewing, instruction-level divergence metrics, and Extended PIX Marker support in RGP 2.7. It also expands hardware compatibility and updates other tools in the suite. ### Source excerpt The new AMD Radeon Developer Tool Suite release delivers RGP 2.7 with shader source code viewing, instruction-level divergence metrics, and Extended PIX Marker support, expanded hardware compatibility, and updates across RGD, RRA, RMV, RGA, and RDP. ## Unlocking TPU performance: Deep kernel profiling with XProf DevFeed: [Unlocking TPU performance: Deep kernel profiling with XProf]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-06-08T18:30:00Z Content type: release Language: en Sources: [Google Open Source Blog]() Topics: [AI Development](), [telemetry](), [debugging](), [Compiler](), [Google]() Tags: [compiler](), [debugging](), [google](), [ml-dev-tools](), [performance](), [profiling](), [telemetry](), [tpu](), [tpu-optimization](), [tpu-performance]() ### AI overview Google introduces the Kernel Profiling suite in XProf for debugging and optimizing custom Pallas kernels on Google TPUs. It combines static compilation tracking with dynamic, sub-microsecond hardware telemetry and exposes compilation details through HLO graphs and lowered MLIR text. ### Source excerpt by Yogesh SY, AI Infra Google Unlocking TPU performance: Deep kernel profiling with XProf As machine learning workloads scale to unprecedented heights, developers are increasingly writing highly specialized Tensor Processing Unit (TPU) kernels using frameworks like Pallas, Mosaic, and Triton to maximize hardware performance. However, customizing high-performance kernels has historically introduced a major engineering challenge: optimization blind spots. To legacy performance profilers, custom compilation paths appear as opaque execution paths. Developers are left with single, massive execution blocks in their trace captures, lacking granular visibility into what is actually occurring inside the chip's internal components. Did a vector processing instruction stall? Was matrix math idle due to data loading bottlenecks? Traditional profiling relies heavily on compile-time static cost models to estimate kernel efficiency. While helpful for standard operations, these models cannot capture dynamic runtime realities like instruction execution stalls, memory subsystem congestion, or hardware scheduling conflicts. To open this opaque execution path, we are excited to introduce the Kernel Profiling suite in XProf--a low-level hardware debugging suite engineered specifically for Pallas kernel authoring and optimization on Google TPUs. By combining static compilation tracking with dynamic, sub-microsecond hardware telemetry, XProf Kernel provides the deep transparency required to optimize high-scale ML workloads. Deep visibility: HLO Graphs & MLIR Inspection The first step in debugging any custom kernel is understanding how your high-level code is translated by the compiler. When compiling a JAX or PyTorch model, the compiler generates a High-Level Optimizer (HLO) graph. Previously, custom calls inside these graphs remained completely obscured. XProf's updated Graph Viewer resolves this by exposing the internal compilation logic of these custom regions directly. To unlock this d ## A guide to measuring Python performance DevFeed: [A guide to measuring Python performance]() Original publisher: [Read original article]() Author: Aneesh Appukuttan Published: 2026-05-04T04:01:01Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Python](), [performance-optimization](), [Monitoring](), [Refactoring]() Tags: [data-science](), [guide](), [optimization](), [performance](), [profiling](), [python](), [software-engineering]() ### AI overview A tutorial on evaluating Python performance with timing and profiling methods. It explains how to establish baselines, identify bottlenecks, and make informed optimization decisions. ### Source excerpt Imagine this: Your elegant data processing script works beautifully with 1,000 customer records, completing in under a second. But when your startup grows to 100,000 customers, that same script now takes 45 minutes to run. Your AWS bill jumps from $X to $12X while customers are complaining about slow dashboards. This isn't hypothetical. It happens to Python developers every day. In this guide, we'll tackle this exact scenario step-by-step and show you the tools and techniques to identify and fix performance bottlenecks before they impact your users. Table of Contents Why Performance Matters Quick Decision Guide Step-by-Step Performance Evaluation Real Performance Evaluation Session Best Practices Why performance matters more than you think As Python developers, we often get caught up in the beauty of readable, pythonic code. But what happens when your elegant solution takes minutes instead of seconds to process data? Performance evaluation isn't just about optimization -- it's about understanding your code's behavior, identifying bottlenecks, and making informed decisions about performance optimization. The real impact: Cost efficiency -- Poor performance directly translates to higher infrastructure costs. A function that runs 10x slower means 10x more compute time and resources. User experience -- Nobody waits for slow applications. Performance directly impacts user satisfaction and retention. Performance planning -- Understanding your code's performance characteristics helps predict how it will behave under load. Technical debt management -- Regular performance monitoring helps identify code that needs refactoring before it becomes a critical bottleneck. In the following sections, we'll walk you through the essential tools and methods to establish effective performance monitoring in your Python projects. Quick decision guide Figure 1: Interactive decision tree to help you select the right Python profiling tool based on your specific performance evaluation needs. Choose ## Who's on call? How we improved schedule rendering performance 2,500x DevFeed: [Who's on call? How we improved schedule rendering performance 2,500x]() Original publisher: [Read original article]() Author: Rory Bain Published: 2026-04-28T14:43:46Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [Claude](), [Algorithms](), [Code](), [Go Language](), [real-time]() Tags: [algorithms](), [claude](), [code](), [cpu](), [go](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [outage](), [post-mortem](), [profiling](), [real-time](), [slack-incident]() ### AI overview The article explains how incident.io improved on-call schedule rendering performance by 2,500x. The system combines historical database entries with future schedule entries calculated in real time, and the improvement came from profiling, algorithmic changes, and Claude Opus 4.6. ### Source excerpt A look at how on-call schedules work, and how we made rendering them 2,500x faster -- through profiling, smarter algorithms, and some Claude. ## Supercharging Redpanda Streaming with profile-guided optimization DevFeed: [Supercharging Redpanda Streaming with profile-guided optimization]() Original publisher: [Read original article]() Author: Stephan Dollberg Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Optimization](), [Streaming](), [Latency](), [Compiler](), [LLVM](), [Instrumentation](), [build performance](), [Benchmark]() Tags: [benchmark](), [bugs](), [compilation](), [engineering](), [instrumentation](), [latency](), [llvm](), [meta](), [optimization](), [performance](), [profile](), [profiling]() ### AI overview This article describes how Redpanda optimized its binary for Redpanda Streaming 26.1 using compiler-based profile-guided optimization (PGO) and LLVM BOLT. It explains how profiling data guides optimization decisions, compares the two approaches, and reports lower latency and CPU usage for intensive workloads. ### Source excerpt A behind-the-scenes look into how we optimized performance and cut latency in Redpanda 26.1 with profile-guided optimization. ## Continuous profiling at Mercado Libre: Turning flamegraphs into fixes DevFeed: [Continuous profiling at Mercado Libre: Turning flamegraphs into fixes]() Original publisher: [Read original article]() Author: Elton Hoffmann Published: 2026-03-11T01:27:33Z Content type: article Language: en Sources: [Mercado Libre Tech]() Topics: [observability](), [Tooling](), [Microservices](), [Latency](), [systems]() Tags: [ai](), [article](), [efficiency](), [end-user-experience](), [flamegraph](), [garbage-collection](), [latency](), [memory](), [microservices](), [observability](), [performance](), [performance-engineering](), [platform-engineering](), [profiling](), [uptime]() ### AI overview Mercado Libre describes building a continuous profiling platform for about 35,000 microservices. The platform treats profiling as an observability signal alongside metrics, logs, and traces, with the goal of helping teams detect performance issues and automate fixes for certain issue classes. ### Source excerpt We continue our series on Performance Engineering at Mercado Libre. In previous articles, we discussed how we crafted an observability culture across the company, how performance relates to other observability signals, and how the Performance Engineering team partners with Business Units to enhance platform efficiency. Today, we'll show how we built a continuous profiling platform for 35,000 microservices and how we started turning profiles into automated fixes. Context Our goal in the Performance Engineering team is threefold: to ensure uptime; to improve the end-user experience by reducing latency; to reduce computing costs. As a cross-functional team, we usually work with application owners to achieve these goals. In our two years of existence, our joint efforts have taken us through countless incidents and bottlenecks. Each problem is unique, but over time, we've seen some common patterns: high memory allocation, lack of garbage collection (GC) tuning, thread pool saturation, blocking I/O, and heavy workloads processing repeated tasks. Image 1: Typical flamegraph of high CPU usage replacing string patterns Trained eyes spot these signals quickly. But two eyeballs won't scale to thousands of services. Mercado Libre is an ever-growing company, with about 35,000 microservices, 30,000 deploys per day, and more than 16,000 people in IT roles. What are the odds that a performance issue found on one microservice doesn't exist in another? Or at least a similar issue? We often encounter systems with issues that are easy to fix but hard to detect (unless you have the right tools). It became clear we had to scale. We needed a platform-level solution that would let teams self-diagnose and self-tune. By the end of this article, you'll see how we built that and how we now automate both detection and fixes for certain classes of issues. Continuous profiling After setting our goal to improve performance tooling, we developed our own continuous profiling solution. Why profiling, ## DroidCon: Debugging App Performance at Scale DevFeed: [DroidCon: Debugging App Performance at Scale]() Original publisher: [Read original article]() Author: Shreyas Patil Published: 2026-01-19T04:58:14Z Content type: article Language: en Sources: [Shreyas Patil's Blog]() Topics: [Android](), [debug](), [Tooling]() Tags: [2025](), [android](), [android-app-development](), [app-development](), [app-performance](), [community](), [conference](), [droidcon](), [droidconindia](), [performance](), [profiling](), [public-speaking](), [recap](), [talk](), [tech-talk]() ### AI overview A recap of the author's DroidCon India 2025 session on debugging Android app performance at scale. The article links to the session recording and slides and discusses the event, audience response, and developer community. ### Source excerpt Recap of my DroidCon India 2025 talk on debugging Android app performance at scale. Watch the session and learn about performance profiling tools. ## Python 3.15.0 alpha 3 DevFeed: [Python 3.15.0 alpha 3]() Original publisher: [Read original article]() Author: Hugo van Kemenade Published: 2025-12-16T00:00:00Z Content type: release Language: en Sources: [Python Insider]() Topics: [Python](), [releases]() Tags: [c](), [core](), [developer](), [pre-release](), [profiling](), [release](), [release-schedule](), [releases]() ### AI overview Python 3.15.0 alpha 3 is an early developer preview and the third of seven planned alpha releases. It introduces a statistical sampling profiler, UTF-8 as the default encoding, a new C API for creating Python bytes objects, and improved error messages. ### Source excerpt This is an early developer preview of Python 3.15 www.python.org/downloads/release/python-3150a3/ Major new features of the 3.15 series, compared to 3.14 Pyt... ## The Hidden Cost of Convenience: Rethinking Old ORM Patterns for Scale DevFeed: [The Hidden Cost of Convenience: Rethinking Old ORM Patterns for Scale]() Original publisher: [Read original article]() Author: Andrew Houts Published: 2025-12-08T22:06:50Z Content type: article Language: en Sources: [Wealthfront]() Topics: [Object-relational mapping](), [Code](), [data](), [Tooling](), [dashboards](), [Database](), [Concurrency]() Tags: [backend](), [best-practices](), [code](), [data](), [database](), [flame-graph](), [orm](), [parallelism](), [performance](), [profiling](), [sql](), [tooling](), [wealthfront-engineering]() ### AI overview Wealthfront examines performance problems in an aging portfolio balance calculation system whose overnight batch processing scaled poorly as client growth and assets under management increased. The article describes profiling the job with command-line and sampling-profiler tools, using collected data and flame graphs to investigate where runtime was spent. ### Source excerpt Ever been here before? Stuck with a job that needs to be continually revisited because its performance gets worse with every passing day, and each attempt at improving said performance yields diminishing returns? This is the situation we found ourselves in with the portfolio balance calculation system--the code responsible for aggregating data from multiple sources... Read more ## React Conf 2025 Recap DevFeed: [React Conf 2025 Recap]() Original publisher: [Read original article]() Author: Matt Carroll and Ricky Hanlon Published: 2025-10-16T00:00:00Z Content type: article Language: en Sources: [React Blog]() Topics: [React](), [React Native](), [Compiler](), [developer tooling](), [Document Object Model (DOM)](), [Open Source](), [Artificial Intelligence](), [migration](), [Expo](), [Next.js](), [Vite]() Tags: [2025](), [ai](), [announcements](), [compiler](), [event](), [migration](), [next-js](), [open-source](), [performance](), [profiling](), [react-native](), [recap](), [vite](), [web]() ### AI overview A recap of React Conf 2025 covering React 19.2, Canary features, React Compiler v1.0, the React Foundation, and React Native announcements. It also highlights performance tooling, web compatibility, application migrations, and the growth of React Native. ### Source excerpt Last week we hosted React Conf 2025, in this post, we summarize the talks and announcements from the event... ## Python 3.15.0 alpha 1 DevFeed: [Python 3.15.0 alpha 1]() Original publisher: [Read original article]() Author: Hugo van Kemenade Published: 2025-10-15T00:00:00Z Content type: release Language: en Sources: [Python Insider]() Topics: [Python](), [releases](), [Development](), [C]() Tags: [2025](), [c](), [developer](), [development](), [pre-release](), [profiling](), [python](), [release](), [releases]() ### AI overview Python 3.15.0a1 is the first alpha release in the planned Python 3.15 series. It introduces early changes including a dedicated profiling package, UTF-8 as the default encoding, a new C API for creating Python bytes objects, and improved error messages. The preview is intended for testing and is not recommended for production use. ### Source excerpt This is an early developer preview of Python 3.15 https://www.python.org/downloads/release/python-3150a1/ Major new features of the 3.15 series, compared to ... ## Remarks on SFrame DevFeed: [Remarks on SFrame]() Original publisher: [Read original article]() Published: 2025-09-28T07:00:00Z Content type: article Language: en Sources: [MaskRay]() Topics: [Linux](), [Compiler](), [Optimization](), [x86]() Tags: [compiler](), [linker](), [linux](), [llvm](), [optimization](), [profiling](), [sframe](), [x86-64]() ### AI overview An analysis of SFrame, a stack-walking format for userspace profiling inspired by Linux's ORC unwind format. It examines SFrame's trade-offs against .eh_frame and Windows ARM64 unwind codes, its section layout, versioning, and opportunities to reduce data-structure overhead. ### Source excerpt SFrame is a new stack walking format for userspace profiling, inspired by Linux's in-kernel ORC unwind format. While SFrame eliminates some .eh_frame CIE/FDE overhead, it sacrifices functionality (e.g., personality, LSDA, callee-saved registers) and flexibility, and its stack offsets are less compact than .eh_frame's bytecode-style CFI instructions. In llvm-project executables I've tested on x86-64, .sframe section is 20% larger than .eh_frame. It also remains significantly larger than highly compact schemes like Windows ARM64 unwind codes. SFrame describes three elements for each function: Canonical Frame Address (CFA): The base address for stack frame calculations Return address Frame pointer An .sframe section follows a straightforward layout: Header: Contains metadata and offset information Auxiliary header (optional): Reserved for future extensions Function Descriptor Entries (FDEs): Array describing each function Frame Row Entries (FREs): Arrays of unwinding information per function [Next page]()