# limits Published articles for limits. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Marathon Incidents Expose Organizational Fragility and Require Structured Incident Response DevFeed: [How Marathon Incidents Expose Organizational Fragility and Require Structured Incident Response]() Original publisher: [Read original article]() Author: Vanessa Huerta Granda Published: 2026-09-17T09:30:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [incident](), [Incident response]() Tags: [devops](), [incident](), [incident-response](), [infoq](), [limits](), [organizational](), [outages](), [presentation](), [qcon-san-francisco-2026](), [qcon-software-development-conference](), [real-world](), [stream-incidents](), [structured](), [system](), [transcripts]() ### AI overview Vanessa Huerta Granda discusses how prolonged incidents reveal gaps between planned work and work as performed. Drawing on real-world scenarios, the presentation examines organizational fragility, human limits, system interdependencies, and the need for structured, humane, cross-functional incident response. ### Source excerpt Vanessa Huerta Granda explains how marathon incidents expose the gap between work as imagined and work as done. Drawing from real-world scenarios, she shares how complex outages reveal organizational fragility, human limits, and system interdependencies--and why incident response requires structured endurance, humane rotations, and holistic cross-functional coordination. By Vanessa Huerta Granda ## Rate limits on GitLab.com are changing DevFeed: [Rate limits on GitLab.com are changing]() Original publisher: [Read original article]() Author: Sam Wiskow Published: 2026-09-17T00:00:00Z Content type: release Language: en Sources: [GitLab]() Topics: [GitLab](), [Automation](), [Users](), [account]() Tags: [2026](), [automation](), [credentials](), [gitlab](), [limits](), [platform](), [product](), [user](), [users]() ### AI overview GitLab.com will align rate limits with subscription tiers beginning October 19, 2026. Free accounts and unauthenticated requests change first, while Premium and Ultimate limits change in January 2027. Unauthenticated requests are limited to 60 requests per hour per IP address, with preview windows scheduled for October 7 and 14. ### Source excerpt GitLab.com hosts millions of projects for teams of every size that need a platform they can rely on. Demand is climbing quickly, and we expect platform load to grow several times over this year. Predictable limits are what keep GitLab.com fast for everyone on it, including the automation and agent workloads teams are building on the platform. To hold that as we scale, we're updating how rate limits work. Starting October 19, 2026, rate limits on GitLab.com will align with your subscription tier. Free accounts and unauthenticated requests happen first, on October 19. Premium and Ultimate move in January 2027. What is changing Limits align with your subscription. Free, Premium, and Ultimate subscription plans get their own limits, applied per user and per top-level group. Free takes effect October 19; Premium and Ultimate in January 2027. Signing in gets you the full limit. An authenticated request is governed by your subscription plan below. A request that arrives with no credentials gets 60 requests per hour per IP address. The per-plan limits are published in the rate limits documentation. What happens on October 19 There will be two preview windows for Free and unauthenticated traffic, on October 7 and October 14 from 15:00 to 19:00 UTC. Signed-in Premium and Ultimate requests are not affected, since those limits do not change until January. Unauthenticated requests are capped no matter where they come from, including automation running against a paid account without credentials. A preview window (engineers call these brownouts) is a short, planned window where we switch the new limits on and then switch them back off. Nothing else about the service changes while it runs. The point is to give you a real look at how your own workloads behave under the new limits, weeks before they apply for good. On October 19 the new limits take effect. We set these limits by looking at how GitLab.com is actually used. Almost all users are already inside the new limits and won't n ## Making Python respect Docker memory limits DevFeed: [Making Python respect Docker memory limits]() Original publisher: [Read original article]() Author: Carlos Alexandro Becker Published: 2020-11-20T00:00:00Z Content type: tutorial Language: en Sources: [Carlos Becker]() Topics: [Docker](), [Python](), [Linux](), [Containers](), [Processes]() Tags: [containers](), [docker](), [limits](), [linux](), [memory](), [process](), [python]() ### AI overview This article explains that Python running in Docker containers can see the host's available resources instead of the container's memory limit, potentially causing Linux's OOM killer to terminate the process. It presents a workaround that reads the limit and sets Python's maximum address space, then discusses testing, limitations, and Java's container support as a possible model. ### Source excerpt If you run Python inside containers, chances are you have seen Linux's OOMKiller working at least a couple of times. ## 70% cheaper Kubernetes cluster on AWS DevFeed: [70% cheaper Kubernetes cluster on AWS]() Original publisher: [Read original article]() Author: Carlos Alexandro Becker Published: 2017-04-28T00:00:00Z Content type: tutorial Language: en Sources: [Carlos Becker]() Topics: [Kubernetes](), [Amazon Web Services](), [nginx](), [Bash]() Tags: [aws](), [bash](), [clusters](), [deployments](), [ingress](), [kubernetes](), [limits](), [nginx](), [production](), [sandbox]() ### AI overview This tutorial describes strategies for reducing the cost of a sandbox Kubernetes cluster on AWS by about 70%. It covers tuning pod resource requests and limits, routing services through one nginx Ingress load balancer, and scaling down idle deployments with a Bash script. ### Source excerpt We are running Kubernetes on both sandbox and production for some months now. Our production cluster is still small, with few services running on it, but, most of our sandbox environment is running on a Kubernetes cluster on AWS. ## There are Infinitely Many Primes (Erdős) DevFeed: [There are Infinitely Many Primes (Erdős)]() Original publisher: [Read original article]() Published: 2012-11-10T13:48:52Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [primes]() Tags: [limits](), [lower-bounds](), [number](), [primes](), [product](), [square]() ### AI overview A proof by Paul Erdős establishes that infinitely many primes exist by bounding the number of factorizations of integers as a square-free number times a square. This yields π(n) >= 1/2 log(n), which grows without bound. ### Source excerpt Problem: Prove there are infinitely many primes Solution: Denote by $ \pi(n)$ the number of primes less than or equal to $ n$. We will give a lower bound on $ \pi(n)$ which increases without bound as $ n \to \infty$. Note that every number $ n$ can be factored as the product of a square free number $ r$ (a number which no square divides) and a square $ s^2$. ## Big-O Notation--A Primer DevFeed: [Big-O Notation--A Primer]() Original publisher: [Read original article]() Published: 2011-06-14T16:26:27Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithms, Complexity](), [Algorithms](), [Mathematics]() Tags: [algorithm](), [algorithms](), [analysis](), [big-o-notation](), [big-theta-notation](), [complexity](), [computational-complexity](), [example](), [limits](), [mathematics](), [measurement](), [primer](), [real-analysis](), [time]() ### AI overview This primer introduces Big-O notation as a way to describe an algorithm's runtime growth. It defines the notation mathematically and illustrates it with a list-summing algorithm characterized as O(n), while also distinguishing Big-Theta notation. ### Source excerpt The Quest to Capture Speed Companies and researchers spend hundreds of millions of dollars for the fruits of their algorithms. Whether one is indexing websites on the internet for search, folding proteins, or figuring out which warehouse is the most cost-effective to ship a product from, improvements in algorithm speed save immense amounts of money. It's no surprise then, that a similarly immense amount of money has gone into the mathematics behind algorithm analysis. ## Why businesses need a longer-term focus than quarterly goals DevFeed: [Why businesses need a longer-term focus than quarterly goals]() Original publisher: [Read original article]() Author: Map Published: 2008-06-06T01:26:27Z Content type: opinion Language: en Sources: [Craig Kerstiens]() Topics: [Risk](), [ibm]() Tags: [business](), [ceo](), [company](), [employees](), [focus](), [growth](), [hiring](), [ibm](), [limits](), [risk]() ### AI overview The article argues that businesses often prioritize quarterly or yearly targets over long-term goals, leading to poorly timed hiring freezes and rushed recruitment. It contrasts this with smaller companies' more constrained spending decisions and questions whether private ownership enables a longer-term focus. ### Source excerpt Adobe's former CEO, Bruce Chizen, when asked 'What advice do you have for new/young public companies?', gave a response of 'Go private'. While partially a joke he went on to elaborate something that many businesses seem to miss on. The main idea is that businesses are very nearsighted in their focus, they look at quarterly goals and in some cases yearly, but not where they want to be in 10 years. When companies become worried that head count is high they simply freeze hiring across the board without thinking of its ramifications. Good people, well great people are truly hard to find, and when a company enforces a blanket hiring freeze they miss out on those few great people that they truly need to grow. Meanwhile when they decide they have bandwidth for 1000 new employees they open the flood gates and let the first 1000 that can spell their name correctly in, because they can. This short term focus in the long run greatly limits the ability of what a company can achieve. In contrast a smaller company that is actually much more at risk of dying seems to have a better understanding of what their approach should be. While they may be understaffed and overworked, they firmly understand that they only have so many funds and therefore make wiser decisions when using them. The characteristics of the larger business and their short sighted focus leads to the cyclical performance that many experience over a few years, rather than a very steady growth they would like to maintain, and often leads to their end. A prime example would be IBM that laid off so many of their more talented people to lower their numbers years ago. As a result they lost their best workers and had many unexperienced individuals in there when they began hiring again. They are still working to catch back up to where they once were in the IT industry... In part I wonder if being a private entity is really the only way to have the long term focus and not worry about quarterly earnings. Personally I have never