# iteration

Iteration is the repeated execution or application of operations or processes, including programming loops such as for, foreach, do, and while statements.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## A Model for Understanding AI Success in Organizations

DevFeed: [A Model for Understanding AI Success in Organizations](<https://devfeed.tech/articles/tbm-431-the-denominator-that-matters-40057.md>)

Original publisher: [Read original article](<https://cutlefish.substack.com/p/tbm-431-the-denominator-that-matters>)

Author: John Cutler

Published: 2026-07-19T15:21:27Z

Content type: opinion

Language: en

Sources: [The Beautiful Mess](<https://devfeed.tech/sources/the-beautiful-mess.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [context](<https://devfeed.tech/topics/context.md>), [domain](<https://devfeed.tech/topics/domain.md>), [iteration](<https://devfeed.tech/topics/iteration.md>), [Risk](<https://devfeed.tech/topics/risk.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [context](<https://devfeed.tech/tags/context.md>), [domain](<https://devfeed.tech/tags/domain.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [risk](<https://devfeed.tech/tags/risk.md>)

### AI overview

The article presents a multiplicative model for AI success based on technology understanding, problem understanding, practice evolution, and an organizational social contract. It argues that missing any essential factor can undermine the outcome, while resistance to AI mandates may reflect rational self-preservation and insufficient evidence.

### Source excerpt

Here's a simple model for thinking about AI success in organizations.

## A series of tricks and techniques I learned doing tiny GLSL demos

DevFeed: [A series of tricks and techniques I learned doing tiny GLSL demos](<https://devfeed.tech/articles/a-series-of-tricks-and-techniques-i-learned-doing-tiny-glsl-demos-26122.md>)

Original publisher: [Read original article](<http://blog.pkh.me/p/48-a-series-of-tricks-and-techniques-i-learned-doing-tiny-glsl-demos.html>)

Published: 2025-12-07T17:48:26Z

Content type: tutorial

Language: en

Sources: [The Last Static Blog RSS](<https://devfeed.tech/sources/the-last-static-blog-rss.md>)

Topics: [glsl](<https://devfeed.tech/topics/glsl.md>), [Demo](<https://devfeed.tech/topics/demo.md>), [Code](<https://devfeed.tech/topics/code.md>), [iteration](<https://devfeed.tech/topics/iteration.md>), [color](<https://devfeed.tech/topics/color.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [demo](<https://devfeed.tech/tags/demo.md>), [fun](<https://devfeed.tech/tags/fun.md>), [glsl](<https://devfeed.tech/tags/glsl.md>), [graphics](<https://devfeed.tech/tags/graphics.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [math](<https://devfeed.tech/tags/math.md>), [prog](<https://devfeed.tech/tags/prog.md>), [techniques](<https://devfeed.tech/tags/techniques.md>), [tricks](<https://devfeed.tech/tags/tricks.md>)

### AI overview

This tutorial examines four tiny GLSL demos--Moonlight, Entrance 3, Archipelago, and Cutie--and explains selected techniques learned while creating them. It introduces an alternative raymarching approach using inverse-density color contributions and relates it to photon-density integration.

### Source excerpt

In the past two months or so, I spent some time making tiny GLSL demos. I wrote an article about the first one, Red Alp. There, I went into details about the whole process, so I recommend to check it out first if you're not familiar with the field. We will look at 4 demos: Moonlight, Entrance 3, Archipelago, and Cutie. But this time, for each demo, we're going to cover one or two things I learned from it. It won't be a deep dive into every aspect because it would be extremely redundant. Instead, I'll take you along a journey of learning experiences. Moonlight Moonlight demo in 460 characters // Moonlight [460] by bµg // License: CC BY-NC-SA 4.0 void main(){vec3 o,p,u=vec3((P+P-R)/R.y,1),Q;Q++;for(float d,a,m,i,t;i++<1e2;p=t<7.2?Q:vec3(2,1,0),d=abs(d)*.15+.1,o+=p/m+(t>9.?d=9.,Q:p/d),t+=min(m,d))for(p=normalize(u)*t,p.z-=5e1,m=max(length(p)-1e1,.01),p.z+=T,d=5.-length(p.xy*=mat2(cos(t*.2+vec4(0,33,11,0)))),a=.01;a<1.;a+=a)p.xz*=mat2(8,6,-6,8)*.1,d-=abs(dot(sin(p/a*.6-T*.3),p-p+a)),m+=abs(dot(sin(p/a/5.),p-p+a/5.));o/=4e2;O=vec4(tanh(mix(vec3(-35,-15,8),vec3(118,95,60),o-o*length(u.xy*.5))*.01),1);} Note See it on its official page, or play with the code on its Shadertoy portage. In Red Alp, I used volumetric raymarching to go through the clouds and fog, and it took quite a significant part of the code to make the absorption and emission convincing. But there is an alternative technique that is surprisingly simpler. In the raymarching loop, the color contribution at each iteration becomes 1/d or c/d where d is the density of the material at the current ray position, and c an optional color tint if you don't want to work in grayscale level. Some variants exist, for example 1/d^2, but we'll focus on 1/d. 1/d explanation Let's see how it looks in practice with a simple cube raymarch where we use this peculiar contribution: One glowing and rotating cube void main() { float d, t; vec3 o, p, u = normalize(vec3(P+P-R,R.y)); // screen to world coordinate for (int i = 0; i < 30

## Three Levels of Writing: Thinking, Communication, and Innovation

DevFeed: [Three Levels of Writing: Thinking, Communication, and Innovation](<https://devfeed.tech/articles/3-levels-of-writing-29070.md>)

Original publisher: [Read original article](<https://blog.alexewerlof.com/p/3-levels-of-writing>)

Author: Alex Ewerlöf

Published: 2025-11-21T18:09:39Z

Content type: opinion

Language: en

Sources: [Alex Ewerlof Notes](<https://devfeed.tech/sources/alex-ewerlof-notes.md>)

Topics: [iteration](<https://devfeed.tech/topics/iteration.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [communication](<https://devfeed.tech/tags/communication.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [innovation](<https://devfeed.tech/tags/innovation.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [writing](<https://devfeed.tech/tags/writing.md>)

### AI overview

The article presents three levels of writing: organizing thoughts and reaching clarity, communicating ideas, and pushing the boundaries of the unknown. It also discusses how generative AI has changed the author's writing process and argues that writing remains relevant in the age of generative AI.

### Source excerpt

A leveling model to use writing for thinking, communication, and innovation

## Minimizing NixOS images

DevFeed: [Minimizing NixOS images](<https://devfeed.tech/articles/minimizing-nixos-images-32439.md>)

Original publisher: [Read original article](<https://nixcademy.com/posts/minimizing-nixos-images/>)

Author: Jacek Galowicz

Published: 2025-10-21T00:00:00Z

Content type: tutorial

Language: en

Sources: [Nixcademy Blog](<https://devfeed.tech/sources/nixcademy-blog.md>)

Topics: [Nix](<https://devfeed.tech/topics/nix.md>), [gnu linux](<https://devfeed.tech/topics/gnu-linux.md>), [build performance](<https://devfeed.tech/topics/build-performance.md>), [iteration](<https://devfeed.tech/topics/iteration.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [build](<https://devfeed.tech/tags/build.md>), [capabilities](<https://devfeed.tech/tags/capabilities.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [iteration](<https://devfeed.tech/tags/iteration.md>)

### AI overview

Part two of a four-part series that explains how to reduce a NixOS appliance image from 1.5 GB to around 360 MB. It covers using Nix tools to inspect dependencies and applying configuration techniques to create smaller system images.

### Source excerpt

Part 2 of our NixOS appliance image series - Learn how to use native Nix tools to shrink the size of your NixOS system images with quick iteration cycles!

## Using Viability and Feasibility to Guide Ambiguous Design Projects

DevFeed: [Using Viability and Feasibility to Guide Ambiguous Design Projects](<https://devfeed.tech/articles/turning-ambiguity-into-opportunity-how-8th-light-designers-innovate-from-the-unknown-33273.md>)

Original publisher: [Read original article](<https://8thlight.com/insights/human-centered-design-clarity>)

Author: Agustin Sanchez

Published: 2025-07-25T05:00:00Z

Content type: article

Language: en

Sources: [8th Light Insights](<https://devfeed.tech/sources/8th-light-insights.md>)

Topics: [iteration](<https://devfeed.tech/topics/iteration.md>), [Testing](<https://devfeed.tech/topics/testing.md>), [Users](<https://devfeed.tech/topics/users.md>)

Tags: [experiential-design](<https://devfeed.tech/tags/experiential-design.md>), [experiment](<https://devfeed.tech/tags/experiment.md>), [human-centered-design](<https://devfeed.tech/tags/human-centered-design.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [onboarding](<https://devfeed.tech/tags/onboarding.md>), [value](<https://devfeed.tech/tags/value.md>)

### AI overview

This article presents human-centered design as a way to navigate ambiguous projects. It explains how defining business objectives and measurable outcomes establishes viability, while testing ideas early and iterating helps assess feasibility and reduce risk.

### Source excerpt

Updated 7/25/2025. Originally published 12/10/2024 Ambiguity in design isn't a roadblock -- it's an opportunity. Design ingenuity thrives in the gray area where rules haven't been written, and solutions are waiting to be discovered. So how do you turn "I have no idea where to start" into breakthrough ideas that actually work? At 8th Light, our approach is anchored in the three pillars of human-centered design: viability, feasibility, and desirability. These are the tools that transform chaos into clarity and make ambiguity your ally, not your enemy. Viability: Define Your Destination Before Picking the Route The truth is, you don't need to know the "how" right away. However, you absolutely need to know the "why." Viability ensures your team stays aligned with business objectives, reducing costly detours and keeping your efforts focused on delivering measurable value. Picture this: You're tasked with fixing an outdated customer onboarding process. The details are vague, but the goal is crystal clear -- reduce the time it takes for new users to find value. Once you've mastered the "why," the rest becomes an experiment. Early setbacks are simply data points, guiding you toward smarter decisions. Viability keeps you aligned with business objectives, ensuring that every test and rapid iteration thereafter, serves a purpose. Pro tip: Start every ambiguous project with these two questions: What are we trying to achieve? Why does this matter? -> Takeaway: Before starting a project, define clear business objectives and measurable outcomes. Use these as your north star to guide every decision. Feasibility: Stop Guessing, Start Testing With your goals in place, it's time to embrace the unknown and start experimenting. Feasibility minimizes risk by validating ideas early, reducing the chance of expensive missteps, and accelerating your path to market-ready solutions, encouraging you to iterate effectively to reach the solution. It answers the question: Can we actually build this?

## Unlocking Safer, Faster Experimentation for a Global Tech Leader

DevFeed: [Unlocking Safer, Faster Experimentation for a Global Tech Leader](<https://devfeed.tech/articles/unlocking-safer-faster-experimentation-for-a-global-tech-leader-33283.md>)

Original publisher: [Read original article](<https://8thlight.com/insights/unlocking-safer-faster-experimentation-for-a-global-tech-leader>)

Author: Kristin Kaeding

Published: 2025-07-18T22:18:00Z

Content type: article

Language: en

Sources: [8th Light Insights](<https://devfeed.tech/sources/8th-light-insights.md>)

Topics: [Development](<https://devfeed.tech/topics/development.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [iteration](<https://devfeed.tech/topics/iteration.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [cross-functional-teams](<https://devfeed.tech/tags/cross-functional-teams.md>), [delivery-and-practice](<https://devfeed.tech/tags/delivery-and-practice.md>), [development](<https://devfeed.tech/tags/development.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [operational-risk](<https://devfeed.tech/tags/operational-risk.md>), [platform-innovation](<https://devfeed.tech/tags/platform-innovation.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

A case study describes 8th Light's seven-week assessment for a Fortune 100 consumer technology company whose growing experimentation pipeline was slowed by manual approvals, fragmented workflows, and unclear ownership. The work combined human-centered design, technical architecture, organizational strategy, and the Double-Diamond process to define a more scalable, self-service experimentation platform.

### Source excerpt

Why It Matters At 8th Light, we help enterprise teams remove friction, foster alignment, and build tools that scale with confidence. This project exemplifies what happens when human-driven design meets technical rigor: major time and cost savings. By focusing on systems thinking, user needs, and resilient architecture, the organization created a path forward that supports both immediate impact and long-term innovation. This story is one of many showing how that approach delivers lasting results. The Challenge: A Strong System Slowed by Scale A Fortune 100 consumer technology company built a powerful experimentation pipeline -- one that fueled product innovation across its digital ecosystem. As the platform grew, so did its complexity; manual approvals, fragmented workflows, and inconsistent tools created friction that slowed development cycles and added risk. Rather than patch over the pain points, the company sought a bold transformation to reduce this risk: to turn a solid foundation into a modern, scalable, and self-service experimentation platform. The Goals Accelerate the end-to-end experimentation pipeline Reduce manual bottlenecks and operational risk Align cross-functional teams around shared metrics and ownership Unlock safe, scalable innovation at speed Our Approach: Human-Centered Meets Technically Grounded 8th Light was brought in to lead a seven-week high-level assessment, blending human-centered design with technical architecture and organizational strategy. We followed the Double-Diamond Process to move from discovery to delivery: Problem Discovery Problem Synthesis Solution Discovery Solution Synthesis The TimelineWeeks 1-2: Solving the Friction We kicked off with deep interviews across engineering and product leadership. These conversations uncovered systemic blockers -- ranging from delays in approvals to lack of experiment ownership. Weekly playback sessions helped align stakeholders and refine the focus on the end-to-end experimentation lifecycle.

## Mildly overengineering my Glance configuration

DevFeed: [Mildly overengineering my Glance configuration](<https://devfeed.tech/articles/mildly-overengineering-my-glance-configuration-38551.md>)

Original publisher: [Read original article](<https://msfjarvis.dev/posts/mildly-overengineering-my-glance-configuration/>)

Author: Harsh Shandilya

Published: 2025-05-07T17:35:00Z

Content type: article

Language: en

Sources: [Posts on Harsh Shandilya](<https://devfeed.tech/sources/posts-on-harsh-shandilya.md>)

Topics: [configuration](<https://devfeed.tech/topics/configuration.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [Nix](<https://devfeed.tech/topics/nix.md>), [YAML](<https://devfeed.tech/topics/yaml.md>), [Bash](<https://devfeed.tech/topics/bash.md>), [iteration](<https://devfeed.tech/topics/iteration.md>), [browser](<https://devfeed.tech/topics/browser.md>)

Tags: [bash](<https://devfeed.tech/tags/bash.md>), [configuration](<https://devfeed.tech/tags/configuration.md>), [dashboard](<https://devfeed.tech/tags/dashboard.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [nix](<https://devfeed.tech/tags/nix.md>), [script](<https://devfeed.tech/tags/script.md>), [terminal](<https://devfeed.tech/tags/terminal.md>)

### AI overview

A personal account of configuring a Glance dashboard with NixOS. The author converts Nix configuration into YAML, uses automatic regeneration and reloads, and combines watchexec with a small Bash script to streamline iteration in an editor and browser.

### Source excerpt

The story of setting up a live environment for configuring my Glance dashboard

## How Senior Engineers Take a Feature from Start to Delivery

DevFeed: [How Senior Engineers Take a Feature from Start to Delivery](<https://devfeed.tech/articles/how-to-ship-39996.md>)

Original publisher: [Read original article](<https://www.saiyangrowthletter.com/p/how-to-ship>)

Author: Tiger Abrodi

Published: 2024-03-28T17:06:09Z

Content type: tutorial

Language: en

Sources: [Saiyan Growth Letter](<https://devfeed.tech/sources/saiyan-growth-letter.md>)

Topics: [Development](<https://devfeed.tech/topics/development.md>), [Users](<https://devfeed.tech/topics/users.md>), [Requirements](<https://devfeed.tech/topics/requirements.md>), [data](<https://devfeed.tech/topics/data.md>), [iteration](<https://devfeed.tech/topics/iteration.md>), [pull-requests](<https://devfeed.tech/topics/pull-requests.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [delivery](<https://devfeed.tech/tags/delivery.md>), [developers](<https://devfeed.tech/tags/developers.md>), [development](<https://devfeed.tech/tags/development.md>), [feature-flagging](<https://devfeed.tech/tags/feature-flagging.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [pull-requests](<https://devfeed.tech/tags/pull-requests.md>), [requirements](<https://devfeed.tech/tags/requirements.md>), [simplicity](<https://devfeed.tech/tags/simplicity.md>), [users](<https://devfeed.tech/tags/users.md>)

### AI overview

This tutorial presents an approach for taking a software feature from initial justification through delivery. It recommends validating why the feature is needed, defining a minimum viable product, choosing a simple technical solution, breaking the work into small tasks, and using feature flagging and rollout practices.

### Source excerpt

How Senior engineers take a feature from start to delivery

## 5 ways Temporal Cloud can help startups

DevFeed: [5 ways Temporal Cloud can help startups](<https://devfeed.tech/articles/5-ways-temporal-cloud-can-help-startups-35695.md>)

Original publisher: [Read original article](<https://temporal.io/blog/5-ways-temporal-cloud-can-help-startups>)

Author: Meagan Speare

Published: 2023-11-28T05:00:00Z

Content type: article

Language: en

Sources: [Temporal Blog](<https://devfeed.tech/sources/temporal-blog.md>)

Topics: [Development](<https://devfeed.tech/topics/development.md>), [reliability](<https://devfeed.tech/topics/reliability.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [business logic](<https://devfeed.tech/topics/business-logic.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [iteration](<https://devfeed.tech/topics/iteration.md>), [retry](<https://devfeed.tech/topics/retry.md>)

Tags: [business-logic](<https://devfeed.tech/tags/business-logic.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [developers](<https://devfeed.tech/tags/developers.md>), [development](<https://devfeed.tech/tags/development.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [reliability](<https://devfeed.tech/tags/reliability.md>), [retry](<https://devfeed.tech/tags/retry.md>), [startups](<https://devfeed.tech/tags/startups.md>)

### AI overview

This article explains how Temporal Cloud may help startup teams by accelerating feature development, abstracting state management and retry handling, improving reliability through durable execution, and making distributed-systems development more accessible. It also describes Temporal Cloud for Startups, which offers free credits and support.

### Source excerpt

We've frequently heard from developers at startups that Temporal Cloud would provide substantial benefits for their projects, but remains just out of reach of their limited budgets. To help these developers, we recently launched Temporal Cloud for Startups

## Improving Developer Experience, Level 3: The Triad of Clarity, Focus, and Flow

DevFeed: [Improving Developer Experience, Level 3: The Triad of Clarity, Focus, and Flow](<https://devfeed.tech/articles/improving-developer-experience-level-3-the-triad-of-clarity-focus-and-flow-39917.md>)

Original publisher: [Read original article](<https://mende.io/blog/improving-developer-experience-level-3-the-triad-of-clarity-focus-and-flow/>)

Author: tobi@techunicorn.builders (Tobias Mende)

Published: 2023-08-26T04:00:00Z

Content type: opinion

Language: en

Sources: [Tobias Mende](<https://devfeed.tech/sources/tobias-mende.md>)

Topics: [devex](<https://devfeed.tech/topics/devex.md>), [Developer experience](<https://devfeed.tech/topics/developer-experience.md>), [Development](<https://devfeed.tech/topics/development.md>), [iteration](<https://devfeed.tech/topics/iteration.md>)

Tags: [developer-experience](<https://devfeed.tech/tags/developer-experience.md>), [developer-experience-developer-productivity-engineering-excellence](<https://devfeed.tech/tags/developer-experience-developer-productivity-engineering-excellence.md>), [engineering-excellence](<https://devfeed.tech/tags/engineering-excellence.md>), [flow](<https://devfeed.tech/tags/flow.md>), [focus](<https://devfeed.tech/tags/focus.md>), [loops](<https://devfeed.tech/tags/loops.md>), [sync](<https://devfeed.tech/tags/sync.md>)

### AI overview

This article examines clarity, focus, and flow as connected dimensions of developer experience. It discusses purpose-driven development, defined outcomes, iterative feedback, and autonomy as ways to improve engineering work.

### Source excerpt

Improving Developer Experience, Level 3: The Triad of Clarity, Focus, and Flow Last week, we delved deep into "Belonging, Collaboration, and Communication". Further up in the Maslow Pyramid of DevEx, we have the factors that affect focus, clarity, and flow. This article will explore how we can improve those factors.

## Space News

DevFeed: [Space News](<https://devfeed.tech/articles/space-news-36979.md>)

Original publisher: [Read original article](<https://shostack.org/blog/spacenews/>)

Author: Adam

Published: 2022-12-18T00:00:00Z

Content type: news

Language: en

Sources: [Shostack & Friends Blog](<https://devfeed.tech/sources/shostack-friends-blog.md>)

Topics: [3D](<https://devfeed.tech/topics/3d.md>), [Rocket](<https://devfeed.tech/topics/rocket.md>), [Software](<https://devfeed.tech/topics/software.md>), [iteration](<https://devfeed.tech/topics/iteration.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [3d-printed](<https://devfeed.tech/tags/3d-printed.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [model](<https://devfeed.tech/tags/model.md>), [nasa](<https://devfeed.tech/tags/nasa.md>), [news](<https://devfeed.tech/tags/news.md>), [software](<https://devfeed.tech/tags/software.md>), [space](<https://devfeed.tech/tags/space.md>)

### AI overview

A commentary post discusses spacecraft shown in a NASA schematic and a 3D-printed aerospike rocket engine from Hyperganic. It notes that the engine's claimed efficiency has not been demonstrated by firing it and describes the company's software-driven approach to rapid iteration and analysis.

### Source excerpt

Interesting space news, including ISS and 3d printed engines

## A HashiConf art collab

DevFeed: [A HashiConf art collab](<https://devfeed.tech/articles/a-hashiconf-art-collab-35522.md>)

Original publisher: [Read original article](<https://meowni.ca/posts/hashicollab/>)

Author: Monica Dinculescu

Published: 2021-10-15T00:00:00Z

Content type: opinion

Language: en

Sources: [Monica Dinculescu](<https://devfeed.tech/sources/monica-dinculescu.md>)

Topics: [color](<https://devfeed.tech/topics/color.md>), [Requirements](<https://devfeed.tech/topics/requirements.md>), [iteration](<https://devfeed.tech/topics/iteration.md>)

Tags: [art](<https://devfeed.tech/tags/art.md>), [conference](<https://devfeed.tech/tags/conference.md>), [custom](<https://devfeed.tech/tags/custom.md>), [design](<https://devfeed.tech/tags/design.md>), [generative](<https://devfeed.tech/tags/generative.md>), [hashicorp](<https://devfeed.tech/tags/hashicorp.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [printer](<https://devfeed.tech/tags/printer.md>), [printing](<https://devfeed.tech/tags/printing.md>), [requirements](<https://devfeed.tech/tags/requirements.md>), [things](<https://devfeed.tech/tags/things.md>)

### AI overview

The article describes creating custom generative-art prints for HashiConf Europe speakers. Each print combines the speaker's name, talk title, and the relevant HashiCorp product color in a randomly generated geometric grid, following conference design guidelines.

### Source excerpt

A couple of months ago my friend Jana helped organize HashiConf Europe, and asked me to work on generating custom artwork for each of their speakers. This was my first experience with creating art for someone else, and especially art that had to match someone else's artistic guidelines. I've said this before, but I live and nap by the idea that rules (and editing) are at the core of the artistic process and not having to do that myself was brilliant. I wanted to write a bit about it because a) I did a bunch of work that I'm really proud of and I never got to talk about it and b) I am vain and I want to talk about it. This was the final poster: And this is what we wrote about it on the card that accompanied it: Enclosed you will find an art print made for you by generative artist and engineer Monica Dinculescu. This individual print is unique to you and you alone, as Monica has incorporated your name, the title of your talk, and the color of the HashiCorp product that you are talking about at this year's conference. Each cell in the grid has been randomly generated to contain either a letter from your name, or a pattern in the product color. Cells located towards the middle of the grid are more likely to contain a letter rather than a pattern, and this likelihood decreases in the cells towards the edges. This print is the only iteration of its kind. Were it to be generated again, it would look slightly different each time. I print everything in my art store myself, because I sell very limited editions and third-party printing companies only make sense financially when you're printing in the several dozen. This does mean that sometimes I'll fight with my printer thinking that its colour profile is whack, only to discover that it was Doing Just Fine™ and my MacBook oversaturates things like it's an Instagram filter in 2011. Every day is a school day. Here are all the prints drying on their totes profesh drying rack that is definitely not just a string I hung around in

## ICFPC 2019

DevFeed: [ICFPC 2019](<https://devfeed.tech/articles/icfpc-2019-34254.md>)

Original publisher: [Read original article](<https://tonsky.livejournal.com/322258.html>)

Published: 2019-06-27T11:03:26Z

Content type: opinion

Language: ru

Sources: [Tonsky Стой под стрелой](<https://devfeed.tech/sources/tonsky.md>)

Topics: [Clojure](<https://devfeed.tech/topics/clojure.md>), [iteration](<https://devfeed.tech/topics/iteration.md>)

Tags: [clojure](<https://devfeed.tech/tags/clojure.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [livejournal](<https://devfeed.tech/tags/livejournal.md>), [tag-5e1c061eda96](<https://devfeed.tech/tags/tag-5e1c061eda96.md>), [tag-c40b78a8c8a6](<https://devfeed.tech/tags/tag-c40b78a8c8a6.md>), [tag-d16a2c26554f](<https://devfeed.tech/tags/tag-d16a2c26554f.md>), [tag-e3028c5e0ab2](<https://devfeed.tech/tags/tag-e3028c5e0ab2.md>)

### AI overview

A personal report on participating in ICFPC 2019, a three-day programming competition involving maze-solving with additional equipment. The author describes the team's 29th-place finish among 142 teams, team size considerations, and the perceived performance limitations of Clojure for this challenge.

### Source excerpt

В этот понедельник закончился трехдневный марафон под названием ICFPC. Это такое соревнование, где команды программистов со всего мира пытаются на время как можно лучше решить некую задачу. В этот раз - обход лабиринтов с разным доп. инвентарем. Условия можно прочитать здесь. Это как бы отчет, но на самом деле памятка самому себе на случай, если буду играть еще через год. Мне очень понравилось. То есть я конечно устал как собака, но есть что-то приятное в том что этот опыт а) имеет конечную продолжительность, а не тянется годами, как основная работа. И б) можно полностью отдаться задаче, не думая о том зачем это все и что ты делаешь со своей жизнью. Такой вот повод упоенно фигачить на полной скорости какое-то время, чтобы ветер свистел в ушах. Ну и просто весело. Очень любопытно посмотреть, чего ты стоишь. В голове-то ты мог много себе про себя нафантазировать, а тут вот объективная реальность, ладдер, и ты либо можешь компьютер заставить делать что ты хочешь, либо не можешь. Никаких "если бы", никаких "возможно, наверное, мне кажется". Мы довольно посредственно выступили (на момент закрытия 29 место из 142 участвовавших, в лучший свой момент были на пятом). Исторический скриншот. Дальше мы сильно сдали Участвовали втроем, я в первый раз. Как я понял, средний размер команды ~5 человек, не редкость и восемь встретить. Втроем у нас довольно хорошо делились области ответственности, было бы больше появился бы организационный оверхед (как мне кажется). Восемь человек я бы вообще офигел менеджить и вообще ничего бы не написал, наверное. С другой стороны, больше рук - можно попробовать больше подходов. Можно вложиться в инфрастуктуру. Наверное. Задача достаточно нетривиальная, чтобы решить ее до конца было в принципе невозможно. Но и не супер-сложная, чтобы как-то ее решить можно было бы даже иногда и руками (ну, самые простые примеры). Как правило это значит перебор вариантов в каком-то NP-полном поле, соревнование эвристик. Собери бонусы, закрась лабиринт Clojure, несмот

## Bicycling and Threat Modeling

DevFeed: [Bicycling and Threat Modeling](<https://devfeed.tech/articles/bicycling-and-threat-modeling-36701.md>)

Original publisher: [Read original article](<https://shostack.org/blog/bicycling-and-threat-modeling/>)

Author: adam

Published: 2017-06-06T00:00:00Z

Content type: opinion

Language: en

Sources: [Shostack & Friends Blog](<https://devfeed.tech/sources/shostack-friends-blog.md>)

Topics: [Security](<https://devfeed.tech/topics/security.md>), [iteration](<https://devfeed.tech/topics/iteration.md>), [Requirements](<https://devfeed.tech/topics/requirements.md>), [consistency](<https://devfeed.tech/topics/consistency.md>)

Tags: [iteration](<https://devfeed.tech/tags/iteration.md>), [requirements](<https://devfeed.tech/tags/requirements.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

This commentary uses hacked dockless bike-sharing systems in China to illustrate threat-modeling issues. It argues that teams should identify obvious misuse cases early, iterate through threats, requirements, and mitigations, and consider incentives that encourage proper bike return.

### Source excerpt

[no description provided]

## Clojure: Combining Calls To Doseq And Let

DevFeed: [Clojure: Combining Calls To Doseq And Let](<https://devfeed.tech/articles/clojure-combining-calls-to-doseq-and-let-31898.md>)

Original publisher: [Read original article](<http://blog.jayfields.com/2013/05/clojure-combining-calls-to-doseq-and-let.html>)

Author: Jay (noreply@blogger.com)

Published: 2013-05-16T12:00:00Z

Content type: tutorial

Language: en

Sources: [Jay Fields](<https://devfeed.tech/sources/jay-fields.md>)

Topics: [Clojure](<https://devfeed.tech/topics/clojure.md>), [function](<https://devfeed.tech/topics/function.md>), [iteration](<https://devfeed.tech/topics/iteration.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [clojure](<https://devfeed.tech/tags/clojure.md>), [clojure-functions](<https://devfeed.tech/tags/clojure-functions.md>), [code](<https://devfeed.tech/tags/code.md>), [function](<https://devfeed.tech/tags/function.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [modifiers](<https://devfeed.tech/tags/modifiers.md>)

### AI overview

This Clojure tutorial explains that doseq supports the :let, :when, and :while modifiers also available in the for macro. It compares multiple doseqs with multiple bindings, noting that binding choices can change how often functions are evaluated and that :let can control evaluation frequency.

### Source excerpt

I've you've ever looked at the docs for clojure's for macro, then you probably know about the :let, :when, and :while modifiers. What you may not know is that those same modifiers are available in doseq. I was recently working with some code that had the following form. Upon seeing this code, John Hume asked if I preferred it to a single doseq with multiple bindings. He sent over an example that looked similar to the following example. That was actually the first time that I'd seen multiple bindings in a doseq, and my immediate reaction was that I preferred the explicit simplicity of having multiple doseqs. However, I always have a preference for concise code, and I forced myself to starting using multiple bindings instead of multiple doseqs - and, unsurprisingly, I now prefer multiple bindings to multiple doseqs. You might have noticed that the second version of the code slightly changes what's actually being done. In the original version the 'name' function is called once per 'id', and in the second version the 'name' function is called once per 'sub-id'. Calling name significantly more often isn't likely to have much impact on your program; however, if you were calling a more expensive function this change could have a negative impact. Luckily, (as I previously mentioned) doseq also provides support for :let. The second example can be evolved to the following code - which also demonstrates that the let is only evaluated once per iteration. That's really the final version of the original code, but you can alter it slightly for experimentation purposes if you'd like. Let's assume we have another function we're calling in an additional let and it's expensive, it would be nice if that only occurred when an iteration was going to happen. It turns out, that's exactly what happens. Whether you prefer multiple bindings or multiple doseqs, it's probably a good idea to get comfortable reading both. © Jay Fields - www.jayfields.com

## Streaming Median

DevFeed: [Streaming Median](<https://devfeed.tech/articles/streaming-median-40277.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2012/06/14/streaming-median/>)

Published: 2012-06-14T22:03:55Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Streaming](<https://devfeed.tech/topics/streaming.md>), [Python](<https://devfeed.tech/topics/python.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [generators](<https://devfeed.tech/topics/generators.md>), [iteration](<https://devfeed.tech/topics/iteration.md>), [Programming](<https://devfeed.tech/topics/programming.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [approximation](<https://devfeed.tech/tags/approximation.md>), [big-data](<https://devfeed.tech/tags/big-data.md>), [data-analysis](<https://devfeed.tech/tags/data-analysis.md>), [element](<https://devfeed.tech/tags/element.md>), [generators](<https://devfeed.tech/tags/generators.md>), [implementation](<https://devfeed.tech/tags/implementation.md>), [infinite](<https://devfeed.tech/tags/infinite.md>), [input](<https://devfeed.tech/tags/input.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [median](<https://devfeed.tech/tags/median.md>), [programming](<https://devfeed.tech/tags/programming.md>), [python](<https://devfeed.tech/tags/python.md>), [sequence](<https://devfeed.tech/tags/sequence.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [streaming-data](<https://devfeed.tech/tags/streaming-data.md>), [yield](<https://devfeed.tech/tags/yield.md>)

### AI overview

This tutorial presents a Python generator that approximates the median of a potentially infinite integer sequence using constant space. The algorithm adjusts its current estimate by one for each input value that is above or below the estimate, and the article discusses how changing stream distributions affect the result.

### Source excerpt

Problem: Compute a reasonable approximation to a "streaming median" of a potentially infinite sequence of integers. Solution: (in Python) def streamingMedian(seq): seq = iter(seq) m = 0 for nextElt in seq: if m > nextElt: m -= 1 elif m < nextElt: m += 1 yield m Discussion: Before we discuss the details of the Python implementation above, we should note a few things. First, because the input sequence is potentially infinite, we can't store any amount of information that is increasing in the length of the sequence.

## Languages in Computer Science

DevFeed: [Languages in Computer Science](<https://devfeed.tech/articles/languages-in-computer-science-40706.md>)

Original publisher: [Read original article](<https://radek.io/posts/language-in-computer-science/>)

Published: 2011-10-21T00:00:00Z

Content type: tutorial

Language: en

Sources: [Radek Pazdera](<https://devfeed.tech/sources/radek-pazdera.md>)

Topics: [Computer science](<https://devfeed.tech/topics/computer-science.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [abstraction](<https://devfeed.tech/topics/abstraction.md>), [iteration](<https://devfeed.tech/topics/iteration.md>)

Tags: [abstraction](<https://devfeed.tech/tags/abstraction.md>), [computer](<https://devfeed.tech/tags/computer.md>), [computer-science](<https://devfeed.tech/tags/computer-science.md>), [concatenation](<https://devfeed.tech/tags/concatenation.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [language](<https://devfeed.tech/tags/language.md>)

### AI overview

This tutorial introduces formal languages as sets of strings over an alphabet. It explains how set operations and string operations extend to languages, then defines language concatenation, powers, and positive and zero-inclusive iteration.

### Source excerpt

Certainly not the typical ones.

## Strings in Computer Science

DevFeed: [Strings in Computer Science](<https://devfeed.tech/articles/strings-in-computer-science-40745.md>)

Original publisher: [Read original article](<https://radek.io/posts/strings-in-computer-science/>)

Published: 2011-09-23T00:00:00Z

Content type: tutorial

Language: en

Sources: [Radek Pazdera](<https://devfeed.tech/sources/radek-pazdera.md>)

Topics: [Computer science](<https://devfeed.tech/topics/computer-science.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [data type](<https://devfeed.tech/topics/data-type.md>), [iteration](<https://devfeed.tech/topics/iteration.md>)

Tags: [computer-science](<https://devfeed.tech/tags/computer-science.md>), [data-type](<https://devfeed.tech/tags/data-type.md>), [iteration](<https://devfeed.tech/tags/iteration.md>), [programming](<https://devfeed.tech/tags/programming.md>)

### AI overview

This tutorial explains strings as finite ordered sequences of characters over an alphabet. It introduces the empty string and defines concatenation, prefixes, suffixes, substrings, and reversal, while distinguishing theoretical strings from programming string literals.

### Source excerpt

The concept of a string in Computer Science and what can you do with it.