# optimize Optimization is the process of finding the best solution to a computational problem, or transforming code into an equivalent form to improve characteristics such as speed or size. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Size-Specialized Memory Allocation DevFeed: [Size-Specialized Memory Allocation]() Original publisher: [Read original article]() Author: Michael Matloob Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [The Go Blog]() Topics: [Go](), [optimize]() Tags: [collector](), [compiler](), [go](), [performance](), [spans]() ### AI overview Go 1.27 introduces size-specialized allocation functions for allocations smaller than 80 bytes. The change makes those allocations up to 20-30% faster and can improve allocation-heavy programs by up to 1%. ### Source excerpt Go 1.27 improves performance of small allocations using size-specialized allocation functions. ## How to Write to SSDs - Co-Designing DBMS and Flash Storage DevFeed: [How to Write to SSDs - Co-Designing DBMS and Flash Storage]() Original publisher: [Read original article]() Published: 2026-06-22T00:00:00Z Content type: article Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Database](), [Databases](), [systems](), [optimize](), [Parallelism]() Tags: [b-tree](), [checkpoint](), [cycles](), [database](), [databases](), [latency](), [mysql](), [optimize](), [optimizing](), [postgresql](), [ssd](), [storage](), [systems](), [wal](), [write-amplification]() ### AI overview The article explains how database management systems and SSDs jointly amplify writes. It reports that a 4 KiB logical page write can become about 18.85 KiB of flash writes on a Samsung PM9A3, and presents DBMS-SSD co-design, including avoiding in-place updates, as a way to address the combined amplification. ### Source excerpt . [How to Write to SSDs](optimising-ssd-writes-for-dbms-cover... ## Tokenmaxxing, Promomaxxing, and Misaligned Incentives in Tech DevFeed: [Tokenmaxxing, Promomaxxing, and Misaligned Incentives in Tech]() Original publisher: [Read original article]() Author: Engineer's Codex Published: 2026-05-07T17:15:03Z Content type: opinion Language: en Sources: [Engineer's Codex]() Topics: [engineering-culture](), [Development](), [optimize](), [Meta](), [Google]() Tags: [developer](), [engineering](), [infrastructure](), [software-engineering](), [tech]() ### AI overview This opinion article examines how workplace metrics and promotion incentives can become misaligned with productive software engineering. It discusses token consumption leaderboards at Meta, the concept of tokenmaxxing, and promotion-driven complexity at Google. ### Source excerpt When a measure becomes a target, it ceases to be a good measure. ## Fixing SQLite Database Locks in Spring Boot with Connection and Transaction Configuration DevFeed: [Fixing SQLite Database Locks in Spring Boot with Connection and Transaction Configuration]() Original publisher: [Read original article]() Author: Anil Kumar Beesetti Published: 2026-01-30T10:36:55Z Content type: tutorial Language: en Sources: [Rock and Null]() Topics: [Spring Boot](), [SQLite](), [locking](), [jpa](), [optimize]() Tags: [hikaricp](), [jpa](), [locking](), [locks](), [optimize](), [performance](), [spring-boot](), [sqlite]() ### AI overview This tutorial explains how a Spring Boot application using SQLite experienced intermittent database locks and request failures. It attributes the issue to connection pooling and lifecycle settings, then describes configuring HikariCP for a single connection, tightening connection management, enabling leak detection, disabling Open-in-View, and controlling transactions manually. In the reported k6 load test, throughput increased from 1,736 to 11,381 requests in five minutes, success rate rose from 76% to 99%, and failed requests fell from 411 to 1. ### Source excerpt Struggling with intermittent database locks in your Spring Boot and SQLite setup? Learn the specific HikariCP and JPA configurations we used to eliminate production hangs and increase throughput by 6.5x. ## Balatro's Least Erratic Decks DevFeed: [Balatro's Least Erratic Decks]() Original publisher: [Read original article]() Author: Dan Lew Published: 2025-11-03T13:00:52Z Content type: article Language: en Sources: [Dan Lew Blog]() Topics: [Code](), [Rust](), [Programming](), [Lua](), [optimize](), [LÖVE](), [Algorithm](), [bug](), [cpu]() Tags: [algorithm](), [bug](), [code](), [cpu](), [game](), [games](), [jit](), [lua](), [optimize](), [rust]() ### AI overview The author searches for the least erratic Erratic deck in Balatro by examining every possible game seed. After finding Lua too slow for the 2.3 trillion-seed search, they optimize the Lua implementation and then port it to Rust, using all CPU cores to reach about 100 million seeds per minute. The article also explains a bug that can produce decks filled with the ten of spades. ### Source excerpt My most recent "just for fun" project was to find the least erratic of the Erratic decks in Balatro (a poker-ish roguelike deckbuilder game). Before each game, you pick a deck that has a special power. The Erratic deck completely randomizes the ranks/suits of the cards ## How many Activities should I use in my Temporal Workflow? DevFeed: [How many Activities should I use in my Temporal Workflow?]() Original publisher: [Read original article]() Author: Taylor Khan Published: 2025-04-16T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Cloud](), [retry](), [optimize](), [Self-hosted](), [monitor](), [Network]() Tags: [best-practices](), [cloud](), [debug](), [monitor](), [network](), [optimize](), [retry](), [temporal-voices](), [workflow](), [workflows]() ### AI overview This tutorial explains how to decide whether to split work across multiple Temporal Activities. It recommends fine-grained Activities, usually one operation per Activity, while weighing state changes, event history, network requests, infrastructure cost, retry and timeout configuration, debugging, testing, monitoring, and workflow maintainability. ### Source excerpt Learn when to split Temporal Activities for better Workflows. Explore necessity, practicality, and preference-based guidelines to optimize your design. ## Effective Kotlin Item 60: Use appropriate collection types DevFeed: [Effective Kotlin Item 60: Use appropriate collection types]() Original publisher: [Read original article]() Published: 2023-12-04T00:00:00Z Content type: tutorial Language: en Sources: [Kt. Academy]() Topics: [Kotlin](), [Collections](), [Data structures](), [optimize]() Tags: [collections](), [data-structure](), [efficiency](), [kotlin](), [thread](), [time-complexity](), [workshop-learning-programming]() ### AI overview This Kotlin article explains the contracts and implementation considerations of lists, sets, and maps. It focuses on how underlying data structures, mutability, thread safety, and array-based list growth affect collection performance and operation time complexity. ### Source excerpt Using non-standard collection types to improve performance in Kotlin. ## An introduction to Worker tuning DevFeed: [An introduction to Worker tuning]() Original publisher: [Read original article]() Author: Fitz Published: 2023-10-23T07:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Deployment](), [optimize](), [Processes](), [Server]() Tags: [caching](), [client](), [cloud](), [commands](), [deployment](), [how-to](), [metrics](), [performance](), [process](), [queues](), [requirements](), [server](), [streaming](), [temporal-concepts](), [types](), [using](), [worker](), [workers](), [workflow]() ### AI overview This guide explains how to tune Temporal Workers as workloads grow beyond a single Worker. It covers deployment patterns, important configurations, and Worker-side performance considerations for managing a Worker pool. ### Source excerpt Learn how to tune Temporal Workers for peak performance. Discover deployment patterns, key metrics, and configurations to optimize your Worker pool effectively. ## MLIR -- Using Traits DevFeed: [MLIR -- Using Traits]() Original publisher: [Read original article]() Published: 2023-09-07T08:00:00Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Compiler](), [toolchain](), [optimize](), [Code](), [interface](), [interfaces]() Tags: [c-plus-plus](), [compiler](), [heir](), [interface](), [interfaces](), [mathematics](), [mlir](), [optimize](), [programming](), [toolchain]() ### AI overview This tutorial explains how to add predefined MLIR traits to operations in a polynomial-arithmetic dialect. It shows how traits and interfaces enable reuse of existing compiler passes, including loop-invariant code motion, and support operation verification and type inference. ### Source excerpt Table of Contents Last time we defined a new dialect poly for polynomial arithmetic. This time we'll spruce up the dialect by adding some pre-defined MLIR traits, and see how the application of traits enables some general purpose passes to optimize poly programs. The code for this article is in this pull request, and as usual the commits are organized to be read in order. Traits and Loop Invariant Code Motion As a compiler toolchain, MLIR heavily emphasizes code reuse. ## Optimize Your Performance Review In Tech 📊 DevFeed: [Optimize Your Performance Review In Tech 📊]() Original publisher: [Read original article]() Author: Rahul Pandey Published: 2022-07-22T06:00:03Z Content type: article Language: en Sources: [Alex Chiou]() Topics: [optimize](), [Job](), [Software]() Tags: [course](), [covid](), [europe](), [event](), [hiring](), [meta](), [optimize](), [performance](), [software-engineer](), [tech](), [video]() ### AI overview This newsletter discusses how a hiring slowdown and broader economic uncertainty may make performance in an existing tech job more important. It promotes a free live session on performance reviews and introduces a video course for software engineering interns, including material about the Meta internship program. ### Source excerpt Almost all of Big Tech has instituted a hiring freeze, COVID is on the rise again, and Europe is simmering in record high temperatures. ## Beware of the order of operands in some Kotlin Collection operations DevFeed: [Beware of the order of operands in some Kotlin Collection operations]() Original publisher: [Read original article]() Published: 2022-02-01T00:00:00Z Content type: article Language: en Sources: [Arkadiusz Chmura]() Topics: [Kotlin](), [Code](), [optimize](), [Refactoring]() Tags: [collection-operations](), [kotlin](), [optimize](), [performance](), [refactoring]() ### AI overview This article investigates why reversing the operands in some Kotlin collection operations can produce substantially different execution times. It finds that the difference is caused by the type of the second operand, particularly when it is not a Set, and that the issue applies from Kotlin 1.6 onward. ### Source excerpt The order of operands can drastically affect the performance of your code. ## How AWS Customers Can Reduce Unnecessary Database Costs DevFeed: [How AWS Customers Can Reduce Unnecessary Database Costs]() Original publisher: [Read original article]() Author: Andy Pavlo Published: 2021-09-30T04:00:00Z Content type: opinion Language: en Sources: [Andy Pavlo - Carnegie Mellon University]() Topics: [Databases](), [Amazon Web Services (AWS)](), [Cloud](), [optimize](), [MySQL](), [PostgreSQL](), [Benchmark]() Tags: [amazon](), [amazon-web-services](), [aws](), [benchmark](), [blog](), [cloud](), [database](), [databases](), [mysql](), [optimize](), [performance](), [postgresql]() ### AI overview The article discusses how AWS customers may incur unnecessary database costs, particularly through unoptimized RDS instances and blindly increasing instance sizes. It explains the limits of vertical scaling and mentions a PostgreSQL RDS experiment using the TPC-C benchmark. ### Source excerpt Andy discusses why Jeff Bezos's Amazon Web Services makes you overpay for your database management systems. ## SQL: One of the most valuable skills DevFeed: [SQL: One of the most valuable skills]() Original publisher: [Read original article]() Author: Map Published: 2019-02-12T20:55:56Z Content type: opinion Language: en Sources: [Craig Kerstiens]() Topics: [SQL](), [Databases](), [data](), [optimize](), [JavaScript](), [Caching]() Tags: [caching](), [data](), [database](), [javascript](), [optimize](), [postgres](), [postgresql](), [sql]() ### AI overview This opinion article argues that SQL is one of the most valuable technical skills because it applies across roles, helps people work directly with data in relational databases, and remains relatively stable over time. The author describes using SQL for product management, system analysis, query optimization, and performance work. ### Source excerpt I've learned a lot of skills over the course of my career, but no technical skill more useful than SQL. SQL stands out to me as the most valuable skill for a few reasons: It is valuable across different roles and disciplines Learning it once doesn't really require re-learning You seem like a superhero. You seem extra powerful when you know it because of the amount of people that aren't fluent Let me drill into each of these a bit further. SQL a tool you can use everywhere Regardless of what role you are in SQL will find a way to make your life easier. Today as a product manager it's key for me to look at data, analyze how effective we're being on the product front, and shape the product roadmap. If we just shipped a new feature, the data on whether someone has viewed that feature is likely somewhere sitting in a relational database. If I'm working on tracking key business metrics such as month over month growth, that is likely somewhere sitting in a relational database. At the other end of almost anything we do there is likely a system of record that speaks SQL. Knowing how to access it most natively saves me a significant amount of effort without having to go ask someone else the numbers. But even before becoming a product manager I would use SQL to inform me about what was happening within systems. As an engineer it could often allow me to pull information I wanted faster than if I were to script it in say Ruby or Python. When things got slow in my webapp having an understanding of the SQL that was executed and ways to optimize it was indespensible. Yes, this was going a little beyond just a basic understanding of SQL... but adding an index to a query instead of rolling my own homegrown caching well that was well worth the extra time learning. SQL is permanent I recall roughly 20 years ago creating my first webpage. It was magical, and then I introduced some Javascript to make it even more impressive prompting users to click Yes/No or give me some input. Then about ## Optimizing the Django Admin Paginator DevFeed: [Optimizing the Django Admin Paginator]() Original publisher: [Read original article]() Author: Haki Benita Published: 2018-11-05T22:00:00Z Content type: tutorial Language: en Sources: [Haki Benita]() Topics: [Django](), [Scalability](), [optimize]() Tags: [articles](), [count](), [django](), [django-admin](), [optimizing](), [override](), [pages](), [performance](), [query](), [scalability](), [table]() ### AI overview This article examines the Django admin paginator's performance on large tables. It explains that counting all rows can dominate page-load time and discusses optimizing Django admin for consistent performance as datasets grow. ### Source excerpt I often talk about making Django scale but what does it actually mean? It means getting consistent performance regardless of the amount of data. In this article we tackle The last nail in Django admin's scalability coffin - the paginator. ## Django Admin Range-Based Date Hierarchy DevFeed: [Django Admin Range-Based Date Hierarchy]() Original publisher: [Read original article]() Author: Haki Benita Published: 2017-12-10T22:00:00Z Content type: article Language: en Sources: [Haki Benita]() Topics: [Django](), [PostgreSQL](), [optimize](), [Database]() Tags: [articles](), [django](), [django-admin](), [index](), [optimize](), [performance](), [postgresql](), [query](), [regression](), [timeout]() ### AI overview The article investigates a performance regression in a Django Admin transactions list view caused by date hierarchy filtering. It explains that applying database functions to date fields can prevent PostgreSQL from using a range-based index effectively, leading to poor execution plans and slow loading. ### Source excerpt A few weeks ago we encountered a major performance regression in one of our main admin pages. The page took more than 10 seconds to load (at best) and hit the query execution timeout at worst. When we investigated the issue, we found that the date hierarchy was the cause for most of the time spent loading the admin page. In the article we describe how we significantly improved the performance of Django Admin date hierarchy ## 5 things I've learned being a CTO in startups DevFeed: [5 things I've learned being a CTO in startups]() Original publisher: [Read original article]() Author: Jean-Baptiste Escoyez Published: 2015-06-25T13:58:10Z Content type: article Language: en Sources: [eFounders]() Topics: [Development](), [business logic](), [Code](), [optimize](), [App](), [Users]() Tags: [business-logic](), [code](), [cto](), [dev](), [optimize](), [productivity](), [startups](), [users]() ### AI overview A startup CTO shares two rules for making technical decisions: align engineering work with business and customer outcomes, and organize code into small reusable modules to support speed and flexibility while limiting technical debt. ### Source excerpt In the past 5 years, I have been Chief Technical Officer of two startups -- one, GoCar has been acquired and one, Solved, has been discontinued -- and helped many others as a technical advisor. What characterizes startups is that they have to ship a lot of results with limited resources. As CTO, your daily job is to lead the technical team, set the goals and take the right technical decisions. You are in a constant tradeoff of immediate speed VS long-term productivity. In this article, I want to share 5 rules I follow in order to make my choices. So far, they enabled me to keep shipping while avoiding to pile up "technical debt". 1. Dedicate yourself to the business The goal of any startup is to build a solution which will bring value to its customers. There are chances that your business co-founder will spend most of his/her time talking to them and understanding their needs. On this basis, your co-founder will set the priorities and report what customers' problems are. Your role will be to find out and build an outstanding product that provides a solution to them. As a technical person, it is easy to get excited by a new technological challenge or a new service that looks promising. When it occurs to me, I ask myself this simple question: "What is the business outcome of what I am doing". This way, I always know if I am working on the right priority or not. At Solved, we even dedicated a weekly meeting with my business co-founder, Thomas, where we were reviewing the features to ensure they match the business priorities. At any moment, I and any member of my dev team could say why we were working on any feature. In order to ensure I did a good job, I was asking myself: how will it delight our users? how will it help our startup to make money? or help our operations team? If you can also answer these questions for your own project, there are chances you are on the right track. 2. Optimize for speed and flexibility In its early days, your startup's main challenge is to ## Experience Report: Weak Code Ownership DevFeed: [Experience Report: Weak Code Ownership]() Original publisher: [Read original article]() Author: Jay (noreply@blogger.com) Published: 2015-02-23T15:00:00Z Content type: opinion Language: en Sources: [Jay Fields]() Topics: [Code](), [Software](), [optimize]() Tags: [code](), [collective](), [developers](), [experience-report](), [opinions](), [optimize](), [team]() ### AI overview The author reflects on moving from Collective Code Ownership to advocating Weak Code Ownership after experiencing disagreement and reduced productivity on a team of senior developers. The article argues that talented developers often work in incompatible ways, making it difficult to optimize a shared process for everyone. ### Source excerpt In 2006 Martin Fowler wrote about Code Ownership. It's a quick read, I'd recommend checking it out if you've never seen it. At the time I was working at ThoughtWorks; I remember thinking "Clearly Strong makes no sense and I have no idea what scenario would make Weak reasonable". 8 years later, I find myself advocating for Weak Code Ownership within my team. Collective Code Ownership (CCO) served me well between 2005 and 2009. Given the make-up of the teams that I was a part of I found it to be the most effective way to deliver software. Around 2009 I joined a team that eventually grew to around 9 people, all very senior developers. The team practiced Collective Code Ownership. Everyone on the team was very talented, but that didn't translate to constant agreement. In fact, we disagreed far more often than I thought we should. That experience drove me to write about the importance of Compatible Opinions. I still believe in the importance of compatible opinions, but I now wonder if the team wouldn't have been more effective (despite incompatible opinions) if we had adopted Weak Code Ownership. The 2009 project heavily shaped my approach to developing software. I suspect I'm not the only one who (at one time) believed: if we get a team full of massively talented people we can do anything. It turns out, it's not nearly that easy. Too many cooks in the kitchen is the obvious concern, and it does come up. However, the much larger problem is that talented people work in vastly different ways. Some meticulously refactor in small steps, others make wide reaching and large changes. Some prefer one language to rule them all, others are comfortable switching between 12-15 different languages in the same day. Monolithic vs separated codebases. Inherited vs duplicated config. It goes on and on. You try to optimize for everyone, to ensure everyone is maximally effective. Pretty quickly you run into this situation- If you optimize everything, you will always be unhappy. --Donald Kn ## Coding: Increase Your Reading and Writing Speed DevFeed: [Coding: Increase Your Reading and Writing Speed]() Original publisher: [Read original article]() Author: Jay (noreply@blogger.com) Published: 2013-06-11T17:31:00Z Content type: tutorial Language: en Sources: [Jay Fields]() Topics: [coding](), [Snippet](), [Emacs](), [ide](), [optimize](), [Clojure]() Tags: [clojure](), [coding](), [emacs](), [ide](), [optimize](), [productivity](), [snippet]() ### AI overview The article explains how to use code-frequency analysis to choose a small number of editor shortcuts, snippets, and custom font locks. It illustrates the approach with Clojure code, Emacs, and IntelliJ examples. ### Source excerpt A teammate of mine recently expressed a desire for a shortcut for something we type often. I started looking into our shortcut options and came to a common determination: We can do this, but the number of 2 key shortcuts available to us is finite, so we better use them wisely. I wrote the following unix to give me a rough idea of what we type frequently. find . -name "*.clj" | xargs cat | tr -s '[:space:]:#()[]{}\"' '\n' | sort | uniq -c | sort -nnote: If you're not writing clojure you'll want to look for something other than .clj files, and you might also want to tweak what you replace with a new line. The above unix gave me an ordered list of the most typed 'words' across all of my codebases. At this point I had some science for setting up some shortcuts. Writing You'll want to look into whatever editor/ide you use and see if you can find key shortcuts and snippet expansion. My editor is emacs; I assigned some key-chords and some yasnippets. If you're not using emacs you should have something similar in whatever you are using. While I wanted to define some shortcuts, I also didn't want to create so many that I was constantly wasting time looking up what I'd created. Based on that desire I created: 2 shortcuts (key-chords) for two of the most duplicated words. The shortcuts are concise by design, but that makes them a bit harder to remember. You can probably get started with more than 2, but I didn't see much harm in starting there. a dozen snippets for the next most used words. These snippets are descriptive enough to easily remember, thus I felt comfortable defining several of them. e.g. pps expands to (println (pr-str )). Having shortcuts and snippets will obviously make me more productive, and the unix helped me figure out which words were the most important to optimize for. Reading Most editors/ides also give you a summary view for common code patterns. For example, IntelliJ displays lambdas when the actual code is actually an anonymous class. Emacs gives you ## Tips for optimizing CakePHP applications to improve response times and reduce costs DevFeed: [Tips for optimizing CakePHP applications to improve response times and reduce costs]() Original publisher: [Read original article]() Author: Juan Basso (juan.basso@zumba.com) Published: 2012-11-05T00:00:00Z Content type: tutorial Language: en Sources: [Zumba]() Topics: [optimize](), [save]() Tags: [applications](), [better](), [money](), [optimize](), [save](), [times](), [tips]() ### AI overview The article offers tips for optimizing CakePHP applications to improve response times and reduce costs. ### Source excerpt Few tips in how optimize CakePHP applications to get better response times and save money ## Optimize for motivation: Post on Lincoln Loop's blog DevFeed: [Optimize for motivation: Post on Lincoln Loop's blog]() Original publisher: [Read original article]() Author: Graham King Published: 2012-06-15T16:15:05Z Content type: opinion Language: en Sources: [Graham King]() Topics: [optimize]() Tags: [behaviour](), [blog](), [flow](), [intrinsic-motivation](), [lincolnloop](), [motivation](), [optimize](), [post](), [psychology](), [remote]() ### AI overview An opinion article about intrinsic motivation, flow, and the limits of coercion, framed around understanding motivation when working remotely with a client. ### Source excerpt Excited to have my first post on Lincoln Loop's blog, about intrinsic motivation, flow, and why you don't find cats in offices: When your client is hundreds of miles away, but your bed only three feet, it helps to understand motivation. The first thing to understand about motivation is that it's not something you do to someone. That's called coercion. With enough power you can make anyone do almost anything, but you can't make them want to; and typically ... ## Beauty and the Geek Game Theory: Answering the Freakonomics Challenge DevFeed: [Beauty and the Geek Game Theory: Answering the Freakonomics Challenge]() Original publisher: [Read original article]() Published: 2008-08-16T00:00:00Z Content type: article Language: en Sources: [Peter Norvig]() Topics: [Simulation](), [simulator](), [optimize]() Tags: [game](), [game-theory](), [optimize](), [simulation](), [simulator]() ### AI overview The article analyzes strategy in the Beauty and the Geek elimination game described by Alon Nir. It models competing teams with different strengths, enumerates several possible player strategies, and uses simulations of 10,000 games to estimate winning probabilities. ### Source excerpt Answering Alon Nir's Freakonomics Challenge: What strategy to use on the TV show Beauty and the Geek