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Unsplash

unsplash

Introduction I often want to add visually interesting elements to my projects and struggle with content. Let’s say you have a subpage that shows all the posts/any other information related to a city. It would be great to have a photo of said city in the header, wouldn’t it? You may think that the way to go is to generate something with AI. It may work, but there are many issues with this approach. There are great free stock photo sites like Pexels or Unsplash. But how to find the right picture? I stumbled upon this problem while working on one of my side projects. It turns out Unsplash has a great API. In Python, we need to call it manually but there are client packages for JS, Ruby, PHP, mobile, etc. Unsplash API The API basically allows you to find photos matching your search query. A few caveats: Make sure you use direct links from Unsplash. Downloading and re-hosting images is not allowed.


You must attribute Unsplash and the author.


The API is rate limited to 50 requests per hour for unverified apps, 5k after approval and more if you need it. More on usage guidelines: Unsplash. Getting started (in-depth guide): Sign up for an account: https://unsplash.com/oauth/applications


Go to your apps: https://unsplash.com/oauth/applications


Create a “New application.”


Go to your app and save Access key; we’ll need it in the following steps Fetching an Image Have a look at this sample function; you can adapt it to your needs. import requests
import os

# Load Unsplash API key from environment variables
UNSPLASH_ACCESS_KEY = os.getenv('UNSPLASH_ACCESS_KEY')

def fetch_image_info(query):
    url = "<https://api.unsplash.com/search/photos>"
    params = {
        "query": query,
        "client_id": UNSPLASH_ACCESS_KEY,
        "per_page": 1,
        "orientation": "landscape",
    }
    response = requests.get(url, params=params)

    if response.status_code == 200:
        data = response.json()
        if data['results']:
            return data['results'][0]
    elif response.status_code == 403:
		    raise ConnectionError("Rate limited or not authorized")

    return None When you call this function for a “New York” query, you should receive an object like that: {
   "id": "string",
   "slug": "string",
   "created_at": "string",
   "width": "integer",
   "height": "integer",
   "color": "string",
   "blur_hash": "string",
   "alt_description": "string",
   "breadcrumbs": "array",
   "urls": "object",
   "links": "object",
   "likes": "integer",
   "user": "object",
   "tags": "array",
   "...": "..."
} You should pay attention to three of them really. First, urls - this is what you will use in your app. "urls": {
    "raw": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3>",
    "full": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=srgb&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=85>",
    "regular": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=1080>",
    "small": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=400>",
    "thumb": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=200>",
    "small_s3": "<https://s3.us-west-2.amazonaws.com/images.unsplash.com/small/photo-1496442226666-8d4d0e62e6e9>",
 }, Second, user - you will use this info for proper attribution, namely: user.name : Author name


user.links.html : Link to their profile, you must use. Make sure to append utm_source=your_app_name&utm_medium=referral to it, per guidelines. Third, alt_description , make sure to add it to your final IMG tags for accessibility and SEO. This piece of code is really everything you need to get going. With one caveat. Watching for Rate Limit When you’re in initial demo mode, you will only have 50 requests per hour. When trying to fetch more images, hitting rate limit is a given. What to do? Simply wait for a bit if the function above throws ConnectionError. I wait for around 70 seconds. Also mind that if no results are found, you will receive None. In this case, I am using a simplified query (country instead of city). Once the website is online, you can request higher quota in the dashboard. Summary We've covered how to use Python and the Unsplash API to automatically fetch photos for your apps and websites. I hope you’ll build something cool with it. Send your projects my way in the comments or on Twitter. Subscribe to my profile by filling in your email address on the left, and be up-to-date with my articles! I will soon be releasing a crazy interesting project that uses this solution! Don't forget to follow me on Twitter @horosin, and subscribe to my blog’s newsletter for more tips and insights! Introduction I often want to add visually interesting elements to my projects and struggle with content. Let’s say you have a subpage that shows all the posts/any other information related to a city. It would be great to have a photo of said city in the header, wouldn’t it? You may think that the way to go is to generate something with AI. It may work, but there are many issues with this approach. There are great free stock photo sites like Pexels or Unsplash. But how to find the right picture? I stumbled upon this problem while working on one of my side projects. It turns out Unsplash has a great API. In Python, we need to call it manually but there are client packages for JS, Ruby, PHP, mobile, etc. Unsplash API The API basically allows you to find photos matching your search query. A few caveats: Make sure you use direct links from Unsplash. Downloading and re-hosting images is not allowed. You must attribute Unsplash and the author. The API is rate limited to 50 requests per hour for unverified apps, 5k after approval and more if you need it. Make sure you use direct links from Unsplash. Downloading and re-hosting images is not allowed. Make sure you use direct links from Unsplash. Downloading and re-hosting images is not allowed. You must attribute Unsplash and the author. You must attribute Unsplash and the author. The API is rate limited to 50 requests per hour for unverified apps, 5k after approval and more if you need it. The API is rate limited to 50 requests per hour for unverified apps, 5k after approval and more if you need it. More on usage guidelines: Unsplash . Unsplash Getting started ( in-depth guide ): in-depth guide Sign up for an account: https://unsplash.com/oauth/applications Go to your apps: https://unsplash.com/oauth/applications Create a “New application.” Go to your app and save Access key; we’ll need it in the following steps Sign up for an account: https://unsplash.com/oauth/applications Sign up for an account: https://unsplash.com/oauth/applications https://unsplash.com/oauth/applications Go to your apps: https://unsplash.com/oauth/applications Go to your apps: https://unsplash.com/oauth/applications https://unsplash.com/oauth/applications Create a “New application.” Create a “New application.” Go to your app and save Access key; we’ll need it in the following steps Go to your app and save Access key; we’ll need it in the following steps Fetching an Image Have a look at this sample function; you can adapt it to your needs. import requests
import os

# Load Unsplash API key from environment variables
UNSPLASH_ACCESS_KEY = os.getenv('UNSPLASH_ACCESS_KEY')

def fetch_image_info(query):
    url = "<https://api.unsplash.com/search/photos>"
    params = {
        "query": query,
        "client_id": UNSPLASH_ACCESS_KEY,
        "per_page": 1,
        "orientation": "landscape",
    }
    response = requests.get(url, params=params)

    if response.status_code == 200:
        data = response.json()
        if data['results']:
            return data['results'][0]
    elif response.status_code == 403:
		    raise ConnectionError("Rate limited or not authorized")

    return None import requests
import os

# Load Unsplash API key from environment variables
UNSPLASH_ACCESS_KEY = os.getenv('UNSPLASH_ACCESS_KEY')

def fetch_image_info(query):
    url = "<https://api.unsplash.com/search/photos>"
    params = {
        "query": query,
        "client_id": UNSPLASH_ACCESS_KEY,
        "per_page": 1,
        "orientation": "landscape",
    }
    response = requests.get(url, params=params)

    if response.status_code == 200:
        data = response.json()
        if data['results']:
            return data['results'][0]
    elif response.status_code == 403:
		    raise ConnectionError("Rate limited or not authorized")

    return None When you call this function for a “New York” query, you should receive an object like that: {
   "id": "string",
   "slug": "string",
   "created_at": "string",
   "width": "integer",
   "height": "integer",
   "color": "string",
   "blur_hash": "string",
   "alt_description": "string",
   "breadcrumbs": "array",
   "urls": "object",
   "links": "object",
   "likes": "integer",
   "user": "object",
   "tags": "array",
   "...": "..."
} {
   "id": "string",
   "slug": "string",
   "created_at": "string",
   "width": "integer",
   "height": "integer",
   "color": "string",
   "blur_hash": "string",
   "alt_description": "string",
   "breadcrumbs": "array",
   "urls": "object",
   "links": "object",
   "likes": "integer",
   "user": "object",
   "tags": "array",
   "...": "..."
} You should pay attention to three of them really. First, urls - this is what you will use in your app. urls "urls": {
    "raw": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3>",
    "full": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=srgb&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=85>",
    "regular": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=1080>",
    "small": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=400>",
    "thumb": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=200>",
    "small_s3": "<https://s3.us-west-2.amazonaws.com/images.unsplash.com/small/photo-1496442226666-8d4d0e62e6e9>",
 }, "urls": {
    "raw": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3>",
    "full": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=srgb&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=85>",
    "regular": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=1080>",
    "small": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=400>",
    "thumb": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=200>",
    "small_s3": "<https://s3.us-west-2.amazonaws.com/images.unsplash.com/small/photo-1496442226666-8d4d0e62e6e9>",
 }, Second, user - you will use this info for proper attribution, namely: user user.name : Author name user.links.html : Link to their profile, you must use. Make sure to append utm_source=your_app_name&utm_medium=referral to it, per guidelines. user.name : Author name user.name : Author name user.name user.links.html : Link to their profile, you must use. Make sure to append utm_source=your_app_name&utm_medium=referral to it, per guidelines. user.links.html : Link to their profile, you must use. Make sure to append utm_source=your_app_name&utm_medium=referral to it, per guidelines. user.links.html utm_source=your_app_name&utm_medium=referral Third, alt_description , make sure to add it to your final IMG tags for accessibility and SEO. alt_description This piece of code is really everything you need to get going. With one caveat. Watching for Rate Limit When you’re in initial demo mode, you will only have 50 requests per hour. When trying to fetch more images, hitting rate limit is a given. What to do? Simply wait for a bit if the function above throws ConnectionError. I wait for around 70 seconds. Also mind that if no results are found, you will receive None. In this case, I am using a simplified query (country instead of city). Once the website is online, you can request higher quota in the dashboard. Summary We've covered how to use Python and the Unsplash API to automatically fetch photos for your apps and websites. I hope you’ll build something cool with it. Send your projects my way in the comments or on Twitter. Subscribe to my profile by filling in your email address on the left, and be up-to-date with my articles! I will soon be releasing a crazy interesting project that uses this solution! Don't forget to follow me on Twitter @horosin , and subscribe to my blog’s newsletter for more tips and insights! @horosin newsletter

How to Programatically Get Photos Matching Content in Your Apps With Python and Unsplash API

1.4+ million freely usable photos now available in your Hacker Noon editor Unsplash started as a side project. Hacker Noon started as a side project. Unsplash was first funded by the revenue of the soon to be previous business (Crew). Hacker Noon was first funded by the revenue of the soon to be previous business (ArtMap Inc.).  Results - and not intentions - are what makes the world go round. Both companies saw early results, and identified them as an opportunity greater than their existing business. Both companies had the perseverance to run with it. I’m very excited to work with a game changer in the photo distribution space, Unsplash. With this API integration, we are giving our 10k+ contributing writers access to 1.4M+ quality freely usable images within the Hacker Noon editor. The Unsplash library is leveling up the blogging experience on Hacker Noon. To try it out, , start , click on the Unplash logo within the flyout menu and search whatever your curious heart desires. create an account writing a new story Unsplash is now the most visited site in the world. Hacker Noon is now the most visited site in the world. So yeah, they’re a good bit more well known, but we’re headed in the right direction. Since we , we’ve moved up 2,200+ spots, ahead of sites I read and respect tremendously, such as , , , and . 308th 3,048th removed our Medium content management system and replaced it with our own ProductHunt Futurism Dev.to Coindesk To date, thousands of Unsplash photos have already appeared on Hacker Noon stories. This integration builds on existing behaviors to better the workflow for contributing writers. Building a digital product is always a trade off between inventing the wheel, re-inventing the wheel and figuring out a way to plug in an existing solution — all while making the thing feel like just one quality thing. The reality is that the entire internet industry is built atop the keystrokes of nerds. Did the world need another content management system? Who’s to say. No software on the market could do what I wanted the software to do, and more importantly, if it’s not your own software your rate of digital iteration will never reach its potential. With how the internet has evolved, imagery often serves as the story’s second headline. However you slice curation, the accompaniment of quality images serves the distribution of quality words. This integration will save time for our contributing writers, improving the process of finding great imagery to embody their technology stories. P.S. Check out Hacker Noon photos on Unsplash!

📷 📸 Unsplash Integration 📷 📸 

TL;DR We ended the year with our highest quarterly revenue in company history while building some important product integrations (with Google Analytics , Algolia , Unsplash & GUN ), and dark mode . Our Alexa ranking is at 3.5k worldwide, up from a 5.2k ranking before removing Medium, proving the overall success of the move into our own infrastructure. We also had a company wide year end review where 100% of the team answered “yes” to the question “are you happy?” Traffic In 2019, Hacker Noon had 38M total site visitors, an increase of 35% from 2018 year over year. From Nov 2016 through early Dec 2019, Hacker Noon totaled ≈77M visitors. Redacted We closed the year as a top , which is up from worldwide Alexa ranking of 5.2k at the time of from our site. From mid July (launch of our software) through December 2019, we published 4,523 stories, which is an average of 27 stories per day, and our library created . 3.5k site in the world removing the Medium infrastructure 48+ years of time reading Internally, we have promoted from Technology Editor to Managing Editor, and expanded the editorial team with the part-time hiring of top community commenter as Software Editor. Natasha’s voice and data driven approach will be important to scaling our brand voice and rate of publishing. Natasha Nel Arthur Tkachenko Over the course of the year, Hacker Noon stories have been featured in , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and elsewhere around the internet. Forbes ReadWrite VICE Coin tele graph Coindesk The Next Web CSO Tech Startups MarTechSeries TheNews.Asia Real Vail Decrypt Entrepreneur.com Code Opinion NewsBTC Tokenist Network World Robotics Online Silicon Republic Salon ProductHunt Stories GiveBat Reddit Crypto Daily The Block Seeking Alpha JJ’s Javascript Blog Connecticut Post Inverse Sociable Metafilter Inside Blogging JB Supra Crypto Briefing Business.com PR Daily Daily Hodl, Yahoo Yahoo Finance Value Walk Security Boulevard Merca 2.0 Tech Republic CNN CCN Newsmax Windows Club Aithority Packt Wright State University KingsCrowd Wikipedia Everipedia Dice CBS University World News IAB Tech Lab News Click Common Dreams Detailed CryptoNewsZ Tech Story Bitcoinist Interesting Engineering Study International Tech Advocate Blockonomi Hackaday, Singapore Fintech News Torrent Freak SiteJabber Business2Community TechDirt KeenGamer, Popular Mechanics GitHub G2 Jaxenter MarTech Today Irish Times Digital Journal, StackOverflow Blog Thrive Global Thomas ZDnet @Jack @Naval Tech Domains a number of Books Revenue Our 2019 revenue (over ) increased by 73% a year from 2018 year over year. accounted for the majority of our revenue at 55%, even though we could only start serving sponsors mid July after launched. The rest of our yearly revenue was made up of podcast sponsorships at 14% (which ends up breaking-even after expenses), program at 12%, at 6%, at 5%, and events/ /interest/ accounting for 8%. redacted The Top Navigation Billboard sponsorship Hacker Noon 2.0 brand as author newsletter sponsorships The Noonies shirts other In the less than five months of running the brand as author program and newsletter sponsorships, we totaled over 100 paying customers. For 2019 Q4 (pictured below), the ordered revenue by program breakdown was top navigation (64%), brand as author (18%), newsletters (14%), followed by interest and schwag sales for the rest (4%). Redacted Our largest two customers for the year are and . We have a number of six digit sponsorship deals in our pipeline, and will be investing in scaling our sales programs in 2020. In 2019, some of our more notable customers also include , , , , , , , , , , , , , , , , and . Seen by Indeed Heroku by Salesforce Google Cloud Codacy Passbase Stream Kloudless Nodle Polyient Labs Vettery PubNub Mabl Radix LogDNA ByBit SurrveySparrow Snyk Digital Ocean Kin We’ve done a lot of optimization work with our top navigation ad and expect to increase the revenue per day from this. Additionally, we plan to explore sponsorship packages by (i.e. placement by content relevancy) in 2020. tags Product Q4 2019, we integrated with , , , and the . We also . The GUN integration is our first use of blockchain technology in the Hacker Noon publishing platform. Starting in Q1 2020, this open source decentralized database will power our annotations and inline commenting. For the first time, we will be putting some content on the blockchain. With this integration, users browsers can contribute storage and anyone can also run their own “miner” on their computer or a cloud, distributing some of our hosting costs to our readership. Unsplash Algolia Google Analytics GUN blockchain launched dark mode For the full view of our year in product development, please read our CPO Dane Lyons’ . In summary, we now have the initial experience live for our core users: writers, readers, editors, sponsors and admins; and integrated with many great technologies like: Google Cloud, SlateJS, Algolia, Filestack, Unsplash, Google Analytics, Discourse, GUN, Twitter, and more. Product Update 2019-2020 Building a digital product is always a trade-off between inventing the wheel, re-inventing the wheel and figuring out a way to plug in an existing solution — all while making the thing feel like just one quality thing. We're getting there. Very excited to build more this year. We will continue investing heavily in improving and iterating on the core product experience, reading and writing, while also launching more curation tools, such as annotations and collections, to drive account creation and micro contributions. Profit & Loss Redacted Business Development Redacted Outlook and how you can help Redacted

State Of The Noonion Q4 2019: On Dark Mode, Integration With Unsplash and GUN

At , we’re all about building apps faster on blended teams of developers and content editors. In the past, editors using third-party stock photography sites have been encouraged to utilize to save these third-party tools to their Dashboard. Now editors can use the new to find and add stock photography to their media library, all from the comfort of their Bucket Dashboard without having to interface with a third-party website like Unsplash. Cosmic JS Cosmic JS Quick Links Cosmic JS Unsplash Photos Extension TL;DR: Unsplash Photos Extension Unsplash Photos Extension Codebase Unsplash Photos Extension on Product Hunt You can now Search and Add Unsplash Photos to your Bucket, all from your Bucket Dashboard. Simply navigate to the and click to install the . Cosmic JS Cosmic JS Extensions Page Photos Extension Once installed, you will see the “Photos” tab on your left hand navigation bar within your Dashboard. Click the “Photos” tab and start typing in your query to find and add photos to your Media Library for immediate or later use. Cosmic JS I hope that you’ve found this tutorial helpful. If you have questions or comments about Content Editor Features in the system, I’d love to hear about them. You can refer to my for additional content on how to intuitively navigate the Cosmic JS Dashboard. Cosmic JS Cosmic JS Profile

How to Search and Add Unsplash Photos to your Cosmic JS Bucket

Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. Browse the archive here. Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. Browse the archive here. Browse the archive here Hi there, It’s me, Tara García Mathewson, back in the Hello World rotation to get you thinking about civil rights in education. Earlier this week, the Center for Democracy & Technology (CDT) put out a report arguing that decades-old civil rights laws should be protecting students from discrimination based on their race—but they’re not. Reporting from The Markup was at the heart of one of its examples. but they’re not Did you see the story Todd Feathers wrote in April about an early warning system in Wisconsin designed to identify students at risk of dropping out? Well, the point of systems like these is to help schools figure out where to focus limited resources. They use past data about individual students, their schools, and districts to make predictions about current students. In Wisconsin, the system tends to be used by high schools considering how to serve incoming freshmen. But Todd cited some research out of the University of California, Berkeley in his story that looked at 10 years’ worth of data from Wisconsin’s system and found being labeled “high risk”—and therefore targeted for additional services—may not have made a difference. an early warning system in Wisconsin Kristin Woelfel, policy counsel at the Center for Democracy & Technology and lead author of this week’s report, said those findings prompted alarm bells. Government entities have to be careful about where they take race into account when treating people differently. Woelfel told me there’s a basic standard in civil rights law: Use of race must be “narrowly tailored to achieve a compelling government interest.” The Wisconsin system ostensibly used race to help prevent dropouts. That sounds like a compelling government interest! But not if it didn’t work. The UC Berkeley researchers looked at students just above and below the cutoff for being labeled high risk, and expected to see that students labeled high risk got relatively more services and support, and therefore graduated at relatively higher rates—but they didn’t find that. “The “high risk” label may not have helped students at all: the researchers couldn’t rule out a 0 percent increase in graduation rates as a result of the label. I discussed this with Cody Venzke, a senior policy counsel at the ACLU, which co-signed a letter the CDT sent to the U.S. Department of Education and the White House this week. The coalition urged the federal government to follow up its 2022 Blueprint for an AI Bill of Rights with more emphasis on civil rights. to the U.S. Department of Education and the White House Blueprint for an AI Bill of Rights Venzke said it’s always worth being suspicious when race is used to make decisions about how to treat individuals, given historical discrimination. In Wisconsin, The Markup’s reporting made clear educators were not properly trained to understand the dropout risk labels and intervene accordingly. The investigation features students who said they found the labels stigmatizing. And if that stigma isn’t offset by demonstrable benefits for students—in higher graduation rates, for example—Venzke sees the potential for a civil rights violation. But maybe it doesn’t have to be that way. Venzke points to schools’ obligation to protect students’ civil rights, ensure algorithmic systems are fair, and protect student privacy. “One of the ways you can achieve all three of those is by using aggregate data where aggregate data will fulfill the purpose,” Venzke said. As it turns out, the UC Berkeley researchers found it would. In their paper, currently under peer review, they recommend Wisconsin scrap its use of individual factors like a student’s race or even test scores in determining that student’s dropout risk. Juan Perdomo, lead author on the paper and now a postdoctoral fellow at Harvard, told me that in highly segregated Wisconsin, building an early warning system with data from entire schools or districts offers equally reliable predictions about which students are likely to drop out. currently under peer review “It also provides a much more actionable way of improving graduation outcomes,” Perdomo told me. And isn’t that the point? Early warning systems aren’t supposed to just be statistical exercises in identifying students at risk. They’re supposed to help better serve those students. If an early warning system identifies entire schools as high risk—because it doesn’t factor in individual characteristics—maybe it would drive statewide spending or districtwide training toward those schools and create a clearer path toward making a difference. For its part, the Wisconsin Department of Public Instruction is studying its early warning systems and considering changes. Communications Officer Chris Bucher told me the department has heard about concerns for some time now. So, we’ll see. In the meantime, we at The Markup will continue to report on algorithms that fuel discrimination and what that means for people’s lives. Thanks for reading! Tara García Mathewson Reporter The Markup Tara García Mathewson, Investigative Reporter Tara García Mathewson, Investigative Reporter Tara García Mathewson, Investigative Reporter Tara García Mathewson Graphics Graphics Gabriel Hongsdusit Gabriel Hongsdusit Gabriel Hongsdusit Gabriel Hongsdusit Engagement Engagement Maria Puertas Maria Puertas Maria Puertas Maria Puertas Copy Editing and Production Copy Editing and Production Sabrina Toppa Sabrina Toppa Sabrina Toppa Sabrina Toppa Editing Editing Monica Riese
Ko Bragg Monica Riese Monica Riese Monica Riese Ko Bragg Ko Bragg Ko Bragg Also published here Also published here here Photo by Artyom Kabajev on Unsplash Photo by Artyom Kabajev on Unsplash Artyom Kabajev Unsplash

The Dropout Risk System Is Facing Criticism After Our Report

Happy birthday, Go! On Sunday, we celebrated the 15th anniversary of the Go open source release! the Go open source release So much has changed since Go’s 10 year anniversary, both in Go and in the world. In other ways, so much has stayed the same: Go remains committed to stability, safety, and supporting software engineering and production at scale. Go’s 10 year anniversary And Go is going strong! Go’s user base has more than tripled in the past five years, making it one of the fastest growing languages. From its beginnings just fifteen years ago, Go has become a top 10 language and the language of the modern cloud. With the releases of Go 1.22 in February and Go 1.23 in August, it’s been the year of for loops. Go 1.22 made variables introduced by for loops scoped per iteration, rather than per loop, addressing a long-standing language “gotcha”. Over ten years ago, leading up to the release of Go 1, the Go team made decisions about several language details; among them whether for loops should create a new loop variable on each iteration. Amusingly, the discussion was quite brief and distinctly unopinionated. Go 1.22 in February Go 1.23 in August for for scoped per iteration for Rob Pike closed it out in true Rob Pike fashion with a single word: “stet” (leave it be). And so it was. While seemingly insignificant at the time, years of production experience highlighted the implications of this decision. But in that time, we also built robust tools for understanding the effects of changes to Go—notably, ecosystem-wide analysis and testing across the entire Google codebase—and established processes for working with the community and getting feedback. Following extensive testing, analysis, and community discussion, we rolled out the change, accompanied by a hash bisection tool to assist developers in pinpointing code affected by the change at scale. hash bisection tool The change to for loops was part of a five year trajectory of measured changes. It would not have been possible without forward language compatibility introduced in Go 1.21. This, in turn, built upon the foundation laid by Go modules, which were introduced in Go 1.14 four and a half years ago. for forward language compatibility Go 1.23 further built on this change to introduce iterators and user-defined for-range loops. Combined with generics—introduced in Go 1.18, just two and a half years ago!—this creates a powerful and ergonomic foundation for custom collections and many other programming patterns. user-defined for-range loops These releases have also brought many improvements in production readiness, including much-anticipated enhancements to the standard library’s HTTP router, a total overhaul of execution traces, and stronger randomness for all Go applications. Additionally, the introduction of our first v2 standard library package establishes a template for future library evolution and modernization. much-anticipated enhancements to the standard library’s HTTP router total overhaul of execution traces stronger randomness first v2 standard library package Over the past year we’ve also been cautiously rolling out opt-in telemetry for Go tools. This system will give Go’s developers data to make better decisions, while remaining completely open and anonymous. Go telemetry first appeared in gopls, the Go language server, where it has already led to a litany of improvements. This effort paves the way to make programming in Go an even better experience for everyone. opt-in telemetry open gopls litany of improvements Looking forward, we’re evolving Go to better leverage the capabilities of current and future hardware. Hardware has changed a lot in the past 15 years. In order to ensure Go continues to support high-performance, large-scale production workloads for the next 15 years, we need to adapt to large multicores, advanced instruction sets, and the growing importance of locality in increasingly non-uniform memory hierarchies. Some of these improvements will be transparent. Go 1.24 will have a totally new map implementation under the hood that’s more efficient on modern CPUs. And we’re prototyping new garbage collection algorithms designed around the capabilities and constraints of modern hardware. next map Some improvements will be in the form of new APIs and tools so Go developers can better leverage modern hardware. We’re looking at how to support the latest vector and matrix hardware instructions, and multiple ways that applications can build in CPU and memory locality. A core principle guiding our efforts is composable optimization: the impact of an optimization on a codebase should be as localized as possible, ensuring that the ease of development across the rest of the codebase is not compromised. composable optimization We’re continuing to ensure Go’s standard library is safe by default and safe by design. This includes ongoing efforts to incorporate built-in, native support for FIPS-certified cryptography, so that FIPS crypto will be just a flag flip away for applications that need it. Furthermore, we’re evolving Go’s standard library packages where we can and, following the example of math/rand/v2, considering where new APIs can significantly enhance the ease of writing safe and secure Go code. math/rand/v2 We’re working on making Go better for AI—and AI better for Go—by enhancing Go’s capabilities in AI infrastructure, applications, and developer assistance. Go is a great language for building production systems, and we want it to be a great language for building production AI systems, too. Go’s dependability as a language for Cloud infrastructure has made it a natural choice for LLM infrastructure as well. For AI applications, we will continue building out first-class support for Go in popular AI SDKs, including LangChainGo and Genkit. And from its very beginning, Go aimed to improve the end-to-end software engineering process, so naturally we’re looking at bringing the latest tools and techniques from AI to bear on reducing developer toil, leaving more time for the fun stuff—like actually programming! building production AI AI systems LLM infrastructure as well LangChainGo Genkit Thank you All of this is only possible because of Go’s incredible contributors and thriving community. Fifteen years ago we could only dream of the success that Go has become and the community that has developed around Go. Thank you to everyone who has played a part, large and small. We wish you all the best in the coming year. Credits: Austin Clements, for the Go team Austin Clements, for the Go team Photo by Jason Leung on Unsplash Photo by Jason Leung on Unsplash Jason Leung Unsplash This article is available on The Go Blog under a CC BY 4.0 DEED license. This article is available on The Go Blog under a CC BY 4.0 DEED license. The Go Blog The Go Blog

Happy 15th Birthday Go!

The Federal Trade Commission (FTC) has warned five tax preparation companies that they may face civil penalties for sharing confidential data from taxpayers through tracking pixels and other means. warned five tax preparation companies The move from federal regulators follows an investigation by The Markup last year that found major tax prep firms sent sensitive personal and financial information to Facebook as Americans filed their taxes. The data that tax prep companies shared included information on income, filing status, refund amounts, and dependents’ college scholarship amounts. follows an investigation by The Markup “Consumers trust tax preparers with sensitive information about their finances, marital status, children, and health,” Samuel Levine, director of the FTC’s Bureau of Consumer Protection, said in a statement announcing the action. “Companies that violate Americans’ privacy by seeking to monetize personal data without consent can face significant financial consequences.” said in a statement The Markup’s investigation was the first to reveal publicly how major tax prep companies shared private data with Facebook. TaxAct, TaxSlayer, and H&R Block were among the companies sending the most sensitive information, The Markup found. The FTC sent notices to those companies as well as Intuit, parent company of TurboTax, and the company Ramsey Tax Solutions. Rick Heineman, a spokesperson for Intuit, which The Markup initially found shared data like usernames and email addresses with Facebook, said in a statement that the company complies with Internal Revenue Service rules and “does not share tax return information with social media platforms for marketing or any other purpose.” None of the other tax prep companies the received notices immediately responded to a request for comment. A spokesperson for Facebook’s parent company, Meta, declined to comment. The data was sent to Facebook and Meta through a tool called the Meta Pixel. The pixel is a widely used snippet of code that gathers and transmits information as people browse the internet. Businesses that include the pixel on websites can track visitors for advertising purposes, and Meta can use the gathered data to power its algorithms. [ The Markup’s investigation led to a congressional report that found the companies had acted recklessly in their sharing of data and also to calls from federal lawmakers for regulators to take action. The FTC’s notice is the first public move from federal regulators on the issue since the article’s publication. The commission unanimously voted 3–0 to use its authority to send the notices. a congressional report calls from federal lawmakers A spokesperson for the agency did not respond to questions about what specifically spurred the action. But letters to the companies, copies of which the FTC posted publicly, specifically note that “tracking technologies such as pixels” and other means of gathering digital information could lead to financial penalties if consumers reasonably believe their information should have been kept confidential. copies of which the FTC posted publicly Those penalties, the letters go on to say, could cost companies more than $50,000 per violation. The tax prep companies highlighted by The Markup’s investigation, which are used by millions of customers every year, employed the pixel for an unknown period of time. “We are aware of information suggesting that you have engaged in or are engaging in deceptive or unfair conduct,” the letters to the companies state. “You should take prompt action, including by reviewing all your practices, to ensure any deceptive or unlawful claims cease and are removed or corrected, as appropriate, and any other required disclosures are made.” Correction Sept 19th, 2023: an earlier version of this article said the FTC did not name the companies who received notices. Credits: Colin Lecher, Investigative Reporter Colin Lecher Engagement Maria Puertas Maria Puertas Maria Puertas Copy Editing and Production Sabrina Toppa Sabrina Toppa Sabrina Toppa Graphics Gabriel Hongsdusit Gabriel Hongsdusit Gabriel Hongsdusit Editing Ryan Tate
Michael Reilly Ryan Tate Ryan Tate Michael Reilly Michael Reilly Also published here Also published here here Photo by Kelly Sikkema on Unsplash Photo by Kelly Sikkema on Unsplash Kelly Sikkema Unsplash

FTC Send Warning to 5 Tax Prep Companies for Their Invasive Online Tracking

Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. Browse the archive here. Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. Browse the archive here. Browse the archive here Hello, world! I’m Soo (rhymes with “zoo”), an editor here at The Markup and one of the new hires seen in recent newsletters. one of the new hires This week, visual designer Gabriel Hongsdusit published a comic zine about the difficulties of illustrating technology stories and tricks for how to overcome them based on his experience of making, commissioning, selecting, and art-directing visuals for our site. The zine is available in multiple formats: online, print, PDF, and in plain text to be accessible to readers with vision impairments. Gabriel Hongsdusit online PDF plain text The plain-text zine seamlessly inserts text descriptions of the comic panels between Gabriel’s narration, much in the same way audio descriptions for movies and TV shows describe what’s happening on screen between character dialogue. (I loved reading this behind-the-scenes story on the spicy audio descriptions of Netflix’s “Bridgerton” series.) behind-the-scenes story I talked with Gabriel about his approach to visual accessibility, his insights into the zine, and The Markup’s workflow below. How did you plan for the plain-text zine? How did you plan for the plain-text zine? I like to think that here at The Markup, we’re always thinking about accessibility when it comes to our visual storytelling. We try to ensure that every photo, illustration, and graphic that’s published on our site has alt text. There are a lot of reminders baked into our workflow to consider screen reader accessibility. For example, when we publish a graphic, we have a field for alt text that asks us to “describe the graphic to somebody over the phone.” There’s also a toggle for graphics whose HTML can be natively read by screen readers. If we try to publish a visual without alt text on our site, we get a warning message. Credit:The Markup When we came up with the idea of publishing a comic on our site, we knew we had to think about accessibility for screen reader users from the get-go. I read this really helpful blog post about comics accessibility that introduced me to the idea of writing a detailed text transcript. I liked that approach because it’s a really simple and elegant way to present information to a screen reader user. blog post In a comic or manga, there are two things to describe: text and imagery. A detailed text transcript presents both things panel-by-panel, much like how a visual reader would consume an image-based comic. What else is important to know about your approach to visual accessibility? What else is important to know about your approach to visual accessibility? I think it’s important to be very intentional when you include race and gender in alt text. There’s a great guide on writing image descriptions that recommends describing race and gender only if it is relevant, known, and consistent. I try to follow those guidelines. For example, our zine references this fantastic illustration by Brian Britigan, which we describe as: image descriptions fantastic illustration [ Race is relevant in the image because it accompanies a story about a racially-biased scoring system that favors White people over Black people. It’s known because I asked Brian to draw the two people in the piece as Black and White. And it’s consistent because both people’s races are being described. If those three criteria are not present, I try not to include race or gender in image descriptions, because I don’t want to assume those things based on perception alone. Thinking about accessibility with visual storytelling is one way we can make our work more inclusive and participatory. Be sure to check out the visual zine and plain-text zine, and let us know your thoughts. If you’re a screen reader user and you’d like to share your experience reading the plain-text zine, you can reach Gabriel at gabriel@themarkup.org. visual zine plain-text zine gabriel@themarkup.org And a special shoutout to the illustrators whose work was used in our zine: Brian Britigan, Anson Chan, Joi Fulton, Ryan Raphael, Adrián Astorgano, Jarred Briggs, Ariel Davis, Chris McNally, Chanelle Nibbelink, Victor Bizar Gómez, Jaimie Shelton, Eva Redamonti, and Allison Vu. Brian Britigan Anson Chan Joi Fulton Ryan Raphael Adrián Astorgano Jarred Briggs Ariel Davis Chris McNally Chanelle Nibbelink Victor Bizar Gómez Jaimie Shelton Eva Redamonti Allison Vu Thanks for viewing, reading, and/or listening. Soo Oh Editor The Markup Credits: Gabriel Hongsdusit, Visual Designer Gabriel Hongsdusit Soo Oh, Investigative Editor Soo Oh, Investigative Editor Soo Oh Design and Graphics Gabriel Hongsdusit Gabriel Hongsdusit Gabriel Hongsdusit Engagement Maria Puertas Maria Puertas Maria Puertas Copy Editing and Production Sabrina Toppa Sabrina Toppa Sabrina Toppa Editing Monica Riese Monica Riese Monica Riese Also published here Also published here here Photo by Rain Bennett on Unsplash Photo by Rain Bennett on Unsplash Rain Bennett Unsplash

Visual Storytelling Accessibility: This Is How We Approach It At The Markup 

The Future of Private Student Loans in the Age of AI

Die toekoms van private studentlenings in die era van AI

Budoucnost soukromých studentských půjček ve věku AI

El futuro de los préstamos estudiantiles privados en la era de la IA

Հիմնական հոդված՝ Հիմնական հոդված՝ Հիմնական հոդված՝ Հիմնական հոդված՝ The Future of Private Student Loans in the Age of AI

AIの時代における私立学生融資の未来

Студенттер еңбекті талдау арқылы христиан теологиясы және оның ерекшелігімен танысты.

AI-ийн эрт нь хувийн оюутнуудын адил

Budúcnosť súkromných študentských pôžičiek vo veku AI

E ardhmja e huave private studentore në epokën e AI

Ang Buhay ng Private Student Loans sa Era ng AI

Tương lai của các khoản vay sinh viên tư nhân trong thời đại AI

I-Future ye-Student Loans ye-AI

Go developers who have written benchmarks using the testing package might have encountered some of its various pitfalls. Go 1.24 introduces a new way to write benchmarks that’s just as easy to use, but at the same time far more robust: testing.B.Loop. Traditionally, Go benchmarks are written using a loop from 0 to b.N: func Benchmark(b *testing.B) {
  for range b.N {
    ... code to measure ...
  }
} Using b.Loop instead is a trivial change: func Benchmark(b *testing.B) {
  for b.Loop() {
    ... code to measure ...
  }
} testing.B.Loop has many benefits: It prevents unwanted compiler optimizations within the benchmark loop.
It automatically excludes setup and cleanup code from benchmark timing.
Code can’t accidentally depend on the total number of iterations or the current iteration. These were all easy mistakes to make with b.N-style benchmarks that would silently result in bogus benchmark results. As an added bonus, b.Loop-style benchmarks even complete in less time! Let’s explore the advantages of testing.B.Loop and how to effectively utilize it. Old benchmark loop problems Before Go 1.24, while the basic structure of a benchmark was simple, more sophisticated benchmarks required more care: func Benchmark(b *testing.B) {
  ... setup ...
  b.ResetTimer() // if setup may be expensive
  for range b.N {
    ... code to measure ...
    ... use sinks or accumulation to prevent dead-code elimination ...
  }
  b.StopTimer() // if cleanup or reporting may be expensive
  ... cleanup ...
  ... report ...
} If setup or cleanup are non-trivial, the developer needs to surround the benchmark loop with ResetTimer and/or StopTimer calls. These are easy to forget, and even if the developer remembers they may be necessary, it can be difficult to judge whether setup or cleanup are “expensive enough” to require them. Without these, the testing package can only time the entire benchmark function. If a benchmark function omits them, the setup and cleanup code will be included in the overall time measurement, silently skewing the final benchmark result. There is another, more subtle pitfall that requires deeper understanding: (Example source) func isCond(b byte) bool {
  if b%3 == 1 && b%7 == 2 && b%17 == 11 && b%31 == 9 {
    return true
  }
  return false
}

func BenchmarkIsCondWrong(b *testing.B) {
  for range b.N {
    isCond(201)
  }
} In this example, the user might observe isCond executing in sub-nanosecond time. CPUs are fast, but not that fast! This seemingly anomalous result stems from the fact that isCond is inlined, and since its result is never used, the compiler eliminates it as dead code. As a result, this benchmark doesn’t measure isCond at all; it measures how long it takes to do nothing. In this case, the sub-nanosecond result is a clear red flag, but in more complex benchmarks, partial dead-code elimination can lead to results that look reasonable but still aren’t measuring what was intended. How testing.B.Loop helps Unlike a b.N-style benchmark, testing.B.Loop is able to track when it is first called in a benchmark when the final iteration ends. The b.ResetTimer at the loop’s start and b.StopTimer at its end are integrated into testing.B.Loop, eliminating the need to manually manage the benchmark timer for setup and cleanup code. Furthermore, the Go compiler now detects loops where the condition is just a call to testing.B.Loop and prevents dead code elimination within the loop. In Go 1.24, this is implemented by disallowing inlining into the body of such a loop, but we plan to improve this in the future. Another nice feature of testing.B.Loop is its one-shot ramp-up approach. With a b.N-style benchmark, the testing package must call the benchmark function several times with different values of b.N, ramping up until the measured time reached a threshold. In contrast, b.Loop can simply run the benchmark loop until it reaches the time threshold, and only needs to call the benchmark function once. Internally, b.Loop still uses a ramp-up process to amortize measurement overhead, but this is hidden from the caller and can be more efficient. Certain constraints of the b.N-style loop still apply to the b.Loop-style loop. It remains the user’s responsibility to manage the timer within the benchmark loop, when necessary: (Example source) func BenchmarkSortInts(b *testing.B) {
  ints := make([]int, N)
  for b.Loop() {
    b.StopTimer()
    fillRandomInts(ints)
    b.StartTimer()
    slices.Sort(ints)
  }
} In this example, to benchmark the in-place sorting performance of slices.Sort,a randomly initialized array is required for each iteration. The user must still manually manage the timer in such cases. Furthermore, there still needs to be exactly one such loop in the benchmark function body (a b.N-style loop cannot coexist with a b.Loop-style loop), and every iteration of the loop should do the same thing. When to use The testing.B.Loop method is now the preferred way to write benchmarks: func Benchmark(b *testing.B) {
  ... setup ...
  for b.Loop() {
    // optional timer control for in-loop setup/cleanup
    ... code to measure ...
  }
  ... cleanup ...
} testing.B.Loop offers faster, more accurate, and more intuitive benchmarking. Acknowledgements A huge thank you to everyone in the community who provided feedback on the proposal issue and reported bugs as this feature was released! I’m also grateful to Eli Bendersky for his helpful blog summaries. And finally a big thank you to Austin Clements, Cherry Mui and Michael Pratt for their review, thoughtful work on the design options and documentation improvements. Thank you all for your contributions! Credits: Junyang Shao Photo by Will Paterson on Unsplash This article is available on The Go Blog under a CC BY 4.0 DEED license. Go developers who have written benchmarks using the testing package might have encountered some of its various pitfalls. Go 1.24 introduces a new way to write benchmarks that’s just as easy to use, but at the same time far more robust: testing.B.Loop . testing testing.B.Loop Traditionally, Go benchmarks are written using a loop from 0 to b.N : b.N func Benchmark(b *testing.B) {
  for range b.N {
    ... code to measure ...
  }
} func Benchmark(b *testing.B) {
  for range b.N {
    ... code to measure ...
  }
} Using b.Loop instead is a trivial change: b.Loop func Benchmark(b *testing.B) {
  for b.Loop() {
    ... code to measure ...
  }
} func Benchmark(b *testing.B) {
  for b.Loop() {
    ... code to measure ...
  }
} testing.B.Loop has many benefits: testing.B.Loop It prevents unwanted compiler optimizations within the benchmark loop. It automatically excludes setup and cleanup code from benchmark timing. Code can’t accidentally depend on the total number of iterations or the current iteration. It prevents unwanted compiler optimizations within the benchmark loop. It automatically excludes setup and cleanup code from benchmark timing. Code can’t accidentally depend on the total number of iterations or the current iteration. These were all easy mistakes to make with b.N -style benchmarks that would silently result in bogus benchmark results. As an added bonus, b.Loop -style benchmarks even complete in less time! b.N b.Loop Let’s explore the advantages of testing.B.Loop and how to effectively utilize it. testing.B.Loop Old benchmark loop problems Before Go 1.24, while the basic structure of a benchmark was simple, more sophisticated benchmarks required more care: func Benchmark(b *testing.B) {
  ... setup ...
  b.ResetTimer() // if setup may be expensive
  for range b.N {
    ... code to measure ...
    ... use sinks or accumulation to prevent dead-code elimination ...
  }
  b.StopTimer() // if cleanup or reporting may be expensive
  ... cleanup ...
  ... report ...
} func Benchmark(b *testing.B) {
  ... setup ...
  b.ResetTimer() // if setup may be expensive
  for range b.N {
    ... code to measure ...
    ... use sinks or accumulation to prevent dead-code elimination ...
  }
  b.StopTimer() // if cleanup or reporting may be expensive
  ... cleanup ...
  ... report ...
} If setup or cleanup are non-trivial, the developer needs to surround the benchmark loop with ResetTimer and/or StopTimer calls. These are easy to forget, and even if the developer remembers they may be necessary, it can be difficult to judge whether setup or cleanup are “expensive enough” to require them. ResetTimer StopTimer Without these, the testing package can only time the entire benchmark function. If a benchmark function omits them, the setup and cleanup code will be included in the overall time measurement, silently skewing the final benchmark result. testing There is another, more subtle pitfall that requires deeper understanding: ( Example source ) Example source func isCond(b byte) bool {
  if b%3 == 1 && b%7 == 2 && b%17 == 11 && b%31 == 9 {
    return true
  }
  return false
}

func BenchmarkIsCondWrong(b *testing.B) {
  for range b.N {
    isCond(201)
  }
} func isCond(b byte) bool {
  if b%3 == 1 && b%7 == 2 && b%17 == 11 && b%31 == 9 {
    return true
  }
  return false
}

func BenchmarkIsCondWrong(b *testing.B) {
  for range b.N {
    isCond(201)
  }
} In this example, the user might observe isCond executing in sub-nanosecond time. CPUs are fast, but not that fast! This seemingly anomalous result stems from the fact that isCond is inlined, and since its result is never used, the compiler eliminates it as dead code. As a result, this benchmark doesn’t measure isCond at all; it measures how long it takes to do nothing. isCond isCond isCond In this case, the sub-nanosecond result is a clear red flag, but in more complex benchmarks, partial dead-code elimination can lead to results that look reasonable but still aren’t measuring what was intended. How testing.B.Loop helps testing.B.Loop Unlike a b.N -style benchmark, testing.B.Loop is able to track when it is first called in a benchmark when the final iteration ends. The b.ResetTimer at the loop’s start and b.StopTimer at its end are integrated into testing.B.Loop , eliminating the need to manually manage the benchmark timer for setup and cleanup code. b.N testing.B.Loop b.ResetTimer b.StopTimer testing.B.Loop Furthermore, the Go compiler now detects loops where the condition is just a call to testing.B.Loop and prevents dead code elimination within the loop. In Go 1.24, this is implemented by disallowing inlining into the body of such a loop, but we plan to improve this in the future. testing.B.Loop improve Another nice feature of testing.B.Loop is its one-shot ramp-up approach. With a b.N -style benchmark, the testing package must call the benchmark function several times with different values of b.N , ramping up until the measured time reached a threshold. testing.B.Loop b.N b.N In contrast, b.Loop can simply run the benchmark loop until it reaches the time threshold, and only needs to call the benchmark function once. Internally, b.Loop still uses a ramp-up process to amortize measurement overhead, but this is hidden from the caller and can be more efficient. b.Loop b.Loop Certain constraints of the b.N -style loop still apply to the b.Loop -style loop. It remains the user’s responsibility to manage the timer within the benchmark loop, when necessary: ( Example source ) b.N b.Loop Example source func BenchmarkSortInts(b *testing.B) {
  ints := make([]int, N)
  for b.Loop() {
    b.StopTimer()
    fillRandomInts(ints)
    b.StartTimer()
    slices.Sort(ints)
  }
} func BenchmarkSortInts(b *testing.B) {
  ints := make([]int, N)
  for b.Loop() {
    b.StopTimer()
    fillRandomInts(ints)
    b.StartTimer()
    slices.Sort(ints)
  }
} In this example, to benchmark the in-place sorting performance of slices.Sort ,a randomly initialized array is required for each iteration. The user must still manually manage the timer in such cases. slices.Sort Furthermore, there still needs to be exactly one such loop in the benchmark function body (a b.N -style loop cannot coexist with a b.Loop -style loop), and every iteration of the loop should do the same thing. b.N b.Loop When to use The testing.B.Loop method is now the preferred way to write benchmarks: testing.B.Loop func Benchmark(b *testing.B) {
  ... setup ...
  for b.Loop() {
    // optional timer control for in-loop setup/cleanup
    ... code to measure ...
  }
  ... cleanup ...
} func Benchmark(b *testing.B) {
  ... setup ...
  for b.Loop() {
    // optional timer control for in-loop setup/cleanup
    ... code to measure ...
  }
  ... cleanup ...
} testing.B.Loop offers faster, more accurate, and more intuitive benchmarking. testing.B.Loop Acknowledgements A huge thank you to everyone in the community who provided feedback on the proposal issue and reported bugs as this feature was released! I’m also grateful to Eli Bendersky for his helpful blog summaries. And finally a big thank you to Austin Clements, Cherry Mui and Michael Pratt for their review, thoughtful work on the design options and documentation improvements. Thank you all for your contributions! Credits: Junyang Shao Credits: Junyang Shao Photo by Will Paterson on Unsplash Photo by Will Paterson on Unsplash Will Paterson Unsplash This article is available on The Go Blog under a CC BY 4.0 DEED license. This article is available on The Go Blog under a CC BY 4.0 DEED license. The Go Blog The Go Blog

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