Let’s take a closer look at nine Democratic cities where the fabric of urban life appears to be unraveling. Photo Credit: Unsplash. The Windy City, despite its rich cultural history and architectural grandeur, is plagued by some of the nation’s highest ...

Liberal Losers – 9 Democratic Cities That Are Falling Apart

Photo Credit: Unsplash. Nestled in the Pacific’s “Ring of Fire,” Tokyo’s ground is familiar with the earth’s trembles. Beyond the frequent earthquakes, Tokyo is at the mercy of tsunamis, whose massive waves have historically washed away both ...

Wildfire Haven: 8 Cities Facing More Natural Disasters Than Southern California

Combining The Classic Tastes Of Jersey With Japanese Flare, This NJ Fusion Restaurant Is A Can't Miss For Foodies Everywhere!

This New Jersey Fusion Restaurant Is Being Called One Of The Best In The State

Photo by Adam Hornyak on Unsplash Photo by Adam Hornyak on Unsplash While driving, you may experience slightly rough stops, intermittent radio and accessory shutdowns, as well as dimming lights and accessories when pressing the accelerator. To jump-start ...

A Quick Guide to Jump-Starting a Dead Battery as Bangor’s Chill Sets In

This definitely sounds like Bluegrass state blasphemy.

It Turns Out the Most Popular Alcoholic Beverage in Kentucky Isn’t Bourbon

A woman shot her boyfriend to death in north Fort Worth and telephoned authorities on Sunday evening to report the killing, according to a police record. The woman called 911 about 6:45 p.m. and said she had shot the 24-year-old man, whose name the Tarrant County Medical Examiner’s Office has not released.

Woman shot boyfriend to death in north Fort Worth apartment bedroom, authorities say

Fake Spirit Halloween Costumes: On this day four years ago, the Facebook meme page Guy Fieri's Post-Ironic Meme Vault uploaded an edited picture of a Guy Fieri costume being sold at a Spirit Halloween. pic.twitter.com/OceLHJMmq9 34. We enjoy a good two-for-one!

60 of the Best Halloween Memes for a Frightfully Fun Laugh

When you think about Halloween, you think about trick or treating and candy and costumes. But what you should really be thinking about is money.

New Jersey Halloween Lovers Won’t Believe These Incredible Holiday Numbers

A group of women spent hours hiding under bedsheets after a hidden bat infestation suddenly awakened, turning their Airbnb into a biting, screeching “house of horror,” according to their attorney. A lawsuit filed Monday, Oct. 30 in Alpena County, Michigan, claims Airbnb and others are responsible for the traumatic night the women suffered through.

Swarming bats attack women, turning Airbnb into ‘house of horror’, Michigan lawsuit says

Get the most recent info and news about Unsplash on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

In 2013, Unsplash launched as a modest Tumblr blog. Since then, Unsplash has evolved into one of the most impactful online communities. Today, over 10 billion Unsplash images are seen every month. A photo featured on Unsplash will now be viewed more than the front page of The New York Times. The Unsplash community is fueled by contributors who offer their images freely for anyone to use, inspiring hundreds of millions of creators from 13-year-old graphic designers to multi-platinum recording artists. Unsplash is a special community, where the powerful principles of sharing and openness have taken the place of strict copyright and legal red tape. The web was meant to connect, inform, and inspire us in unprecedented ways. The Unsplash community embodies this philosophy fully. Unsplash has become a place where the impact of our connected world is on full display. Instead of images being hoarded and shut down, images on Unsplash are fuel for creativity. At Unsplash, our mission is to enable everyone to create. You don’t need to know someone, or have an agent, or have a name to be great on Unsplash. We're a place where creators meet their audience. A place where individuals become a community. And a source of inspiration for millions of people from every country in the world. We’ve seen how the willingness to give a single image unlocks the creative potential that exists in us all.

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 Hello! I’m Ramsey Isler, and I’m the director of product here at The Markup. I manage the Product & Technology team, which maintains our website and helps develop the many awesome tools The Markup is known for. We accomplish all that without a single tracker on the site, and that’s not easy! We live in a world where our digital products are packed with algorithms trying to suss out our needs and desires by looking at every action we take. We don’t do that at The Markup. We try to collect as little data as possible from our visitors because we respect their privacy. Still, as a company that primarily works on the web, we need to know how well we’re serving our audience and how we might do better. We believe that one of the best ways to get to know your users is old-fashioned and simple: Just talk to them. So this summer we’ve been interviewing some of the many researchers who have used one of our premier tools, the Blacklight Privacy Inspector. Blacklight scans websites to reveal the specific user-tracking technologies they use and who they share your data with. We launched Blacklight in autumn 2020, and since then various journalists, academics, and researchers have used it to perform more than 10 million scans. The tool has been a critical resource for dozens of research projects as well. We reached out to the people behind those projects (and some of them reached out to us!) to open a dialogue about how Blacklight can better serve them in the future. Blacklight Privacy Inspector research projects Here’s what we’ve learned. Blacklight users aren’t as interested in scanning specific pages as we thought they would be When a user requests a Blacklight scan of a website, the current version of the code (as of publication) scans the homepage and one random page it can find within the site’s structure. Internally, one of the biggest items on our Blacklight feature wish list was the ability to scan specific pages within a domain. We thought that would be an impactful and highly desired capability. Surprisingly, most of the users I spoke with didn’t see that as a top priority. Some of them were definitely interested, but that interest didn’t spark the level of excitement we were expecting, and some users flatly said the feature wouldn’t make a difference to their work. We’re still going to build the feature, but this insight did change our prioritization process. Lesson learned: Your users don’t always see the world the way you do. They often have very specific jobs to do, and they may not care as much about the things that you think are useful. Lesson learned: People are REALLY interested in TikTok trackers A recurring topic in the interviews was how TikTok is starting to become a top area of interest for researchers who are trying to understand the state of tracking on the internet. Some researchers who use Blacklight work with state or federal government officials, and the political issue of banning TikTok in the U.S. is driving the surge of interest here to some extent. But it’s also about the rapidly expanding popularity of the TikTok platform, especially with young people who may be particularly vulnerable to harmful data-sharing practices. Lesson learned: The internet doesn’t operate in a vacuum, and neither do the people researching it. Hot topics change the conversation and affect what research projects get attention and funding. The more our tools can adapt to cover emerging trends, the better we can serve our audience. Lesson learned Researchers want to see what’s going on with trackers in Europe We’re an American company, and many of the researchers who use Blacklight are based in the U.S. and doing research on sites run by American organizations. For years, Blacklight’s behind-the-scenes systems have performed scans from a U.S. IP address by default. We haven’t seen many requests to change that, so it didn’t seem likely that there would be a massive unmet demand for information on the tracking situation from the perspective of a European web visitor. It turns out that we underestimated the level of interest here. Even though these researchers are mostly based in the U.S., they’re very curious about how Europe’s General Data Protection Regulation (GDPR) and other EU regulations are affecting the state of internet tracking in other countries—and whether companies are truly following all the rules. Whenever I brought up the possibility of adding a Blacklight feature to allow users to “spoof” scans from the EU, people’s faces lit up. The overwhelming response was, “Yes, please!” We are now working on that feature 🙂 Lesson learned: At The Markup we like to dig deep and expose what’s hidden, but we don’t often venture outside our national borders when we do that. But the web is international, and our tools are at their best when they can shine light in dark places near and far. Lesson learned There’s intense interest in monitoring trackers over time Researchers who use Blacklight are looking at many different spaces on the internet. Some are investigating how highly sensitive personal data is being leaked by websites for opioid clinics, some are looking at government websites, and some are monitoring tracking on extremist and hate group sites. But a unifying theme from the interviews was that researchers wanted a more quantitative view of how the tracking situation on sites has changed over time. As one researcher put it, “Sites are getting better, but they were nasty in the past.” People who are invested in creating and enforcing policies and laws that protect user data want an objective and measurable idea of how things have gotten better (or worse). Blacklight provides a snapshot of a website for only a single point in time; it doesn’t give you a sense of how trackers have evolved for that site. This appears to be a need that our users have been cobbling together their own solutions for with varying degrees of success. Lesson learned: Researchers who use Blacklight care deeply about protecting user data, and they’re in it for the long haul. Tools that can give them historical perspective allow them to discover new avenues for research and determine whether regulations and pressure campaigns are working. We don’t have a solid way to address this with Blacklight features yet, but we’re working on it. Lesson learned Our takeaway: Blacklight is helpful, and our users want to see what’s next Each of the researchers interviewed started the conversation with abundant praise for Blacklight. Its ease of use and feature set have made it something they use often and recommend to others. Some people even use it as a teaching tool. But with that praise came a lot of expectation that we’ll keep building out Blacklight to address new tracking techniques and empower research at a larger scale. This is a daunting challenge for a small team like ours, but we’re definitely encouraged by the impact we’ve had so far. If you’ve used Blacklight on a project or have other feedback for our team, we’d love to hear about it! Please reach out at blacklight@themarkup.org. blacklight@themarkup.org Thanks for reading. Ramsey Isler Director of Product The Markup Credits: Ramsey Isler Credits: Ramsey Isler Ramsey Isler Also published here Also published here here Photo by Pietro Jeng on Unsplash Photo by Pietro Jeng on Unsplash Pietro Jeng Unsplash

How Do Researchers Use Our Blacklight Privacy Inspector Tool?

Update, November 1: Federal Trade Commission Chair Lina Khan responded in writing to the report led by Senator Elizabeth Warren and triggered by The Markup’s investigation into tax preparation companies’ use of the Meta Pixel. Khan’s letter acknowledged Warren’s request for FTC action and said that, “in response to public reporting on this issue,” the FTC recently warned the companies involved that they could face financial penalties for mishandling customers’ tax data. The letter also highlighted related FTC work on web tracking, including of sensitive medical information—an issue that The Markup has also investigated. “We continue to look closely at this important area,” Khan wrote. Update, November 1: Update, November 1: Federal Trade Commission Chair Lina Khan responded in writing responded in writing to the report led by Senator Elizabeth Warren and triggered by The Markup’s investigation into tax preparation companies’ use of the Meta Pixel. Khan’s letter acknowledged Warren’s request for FTC action and said that, “in response to public reporting on this issue,” the FTC recently warned the FTC recently warned the companies involved that they could face financial penalties for mishandling customers’ tax data. The letter also highlighted related FTC work on web tracking, including of sensitive medical information—an issue that The Markup has also investigated. “We continue to look closely at this important area,” Khan wrote. an issue that The Markup has also investigated Meta and major tax preparation companies inappropriately shared millions of taxpayers’ financial data for years, according to a congressional report released today that was spurred by a Markup article. a congressional report a Markup article Our investigation, which was published in November, revealed how tax filing services including H&R Block, TaxAct, and TaxSlayer were transmitting data to Facebook’s parent company, Meta, through a tool called the Meta Pixel. The data was sent as taxpayers filed their taxes and included personal information like first and last names, income, filing status, and refund amounts. Some data was also sent to Google through its analytics tools, and Google was also a subject of the congressional investigation. Today’s report from lawmakers was informed by interviews with representatives of Meta, Google, and major tax prep services. It cited and confirmed The Markup’s report and chided the tax companies for being ​​”shockingly careless with their treatment of taxpayer data” and the tech firms for acting “with stunning disregard for taxpayer privacy.” The report determined that the tax prep companies installed tracking tools from Meta and Google on their services without a full understanding of how tax data might be collected and used, and that the companies were “still not fully aware of the current status of millions of taxpayers’ data.” Tax data is tightly regulated, with penalties for improper sharing including fines and jail time. The report found the companies involved likely didn’t receive proper consent to share the data and could face criminal penalties. The lawmakers provided the report today to various federal enforcement agencies and asked them in a letter to investigate and prosecute any company or individual who broke the law. The agencies include the Internal Revenue Service, the Treasury Inspector General for Tax Administration, the Department of Justice, and the Federal Trade Commission. Meta, H&R Block, and TaxSlayer didn’t immediately respond to requests for comment. Angela Krieger, a spokesperson for Google, said the company has “strict policies and technical features” to stop Google Analytics users from collecting sensitive data and that it is site owners’ responsibility to police the information they collect. Dermot Halpin, executive chair of TaxAct, said in a statement emailed after publication that the company cooperated with Warren’s staff and had disabled the “standard analytics tools” to evaluate concerns. “Protecting the rights and privacy of our customers is our top priority, and we are committed to engaging with stakeholders to address any concerns and to help advance public policy,” he said. The government enforcement agencies the letter was sent to did not respond to a request for comment, except the FTC, which declined to comment. The investigation was led by Massachusetts senator Elizabeth Warren and signed on to by Sens. Ron Wyden of Oregon, Richard Blumenthal of Connecticut, Tammy Duckworth of Illinois, Bernie Sanders of Vermont, and Sheldon Whitehouse of Rhode Island, as well as California representative Katie Porter. All are Democrats except Sanders, who is an independent. The Meta Pixel is a widely used piece of tracking code that businesses and organizations can place on their websites to better target ads on Facebook. When web users visit a website with a pixel, the tool can record information about the visit and send it back to Facebook, and companies can then use that information to tailor ads. A business may put a pixel on their checkout page to advertise new products to past customers on Facebook, for example. But while the code is used on millions of sites around the web, Meta cautions against using it to collect potentially sensitive information, like financial and health data. Nonetheless, as part of a project called Pixel Hunt, The Markup has found several cases in which sensitive data has been repeatedly sent to Meta, including from major hospitals, telehealth companies, and the U.S. Department of Education. Pixel Hunt major hospitals telehealth companies the U.S. Department of Education Meta claims to have automated mechanisms to filter out sensitive data, but the lawmakers’ report claims those safeguards are “woefully inadequate” and appear to exist only to provide “a modicum of deniability.” According to the report, Meta told congressional staff that it sent notifications to tax prep companies about the data after The Markup requested comment ahead of publication, but the tax prep companies said they never received those notifications. The tax prep companies also contacted Meta multiple times after The Markup’s article was published to learn about the final status of their customers’ data, according to the report, but walked away without a satisfying answer. In one instance, according to comments from TaxSlayer provided to the lawmakers, a Meta advertising representative specifically advised using a feature to collect data on pages viewed by web visitors. TaxSlayer told lawmakers’ staff that it was unaware how much data would be collected. Unlike many other countries, the United States does not have a widely available, free tax-filing option run by the government itself, essentially forcing many taxpayers to go through tax preparation companies. The report urges the IRS to change that, concluding that the “investigation raises serious doubts about the ability of the tax prep industry to safeguard taxpayer information and highlights the urgent need for the IRS to develop its own online tax filing system—to protect taxpayer privacy and provide a better alternative for taxpayers to file their returns.” Update July 13, 2023 This article has been updated to add a response from TaxAct that was received after publication. Credits: Colin Lecher Credits: Colin Lecher Colin Lecher Also published here Also published here here Photo by Olga DeLawrence on Unsplash Photo by Olga DeLawrence on Unsplash Olga DeLawrence Unsplash

Meta and Tax Prep Companies “Recklessly” Shared Taxpayers’ Data, According to Congressional Report

Photo by Ben Sweet on Unsplash Ben Sweet Unsplash About seven years ago, I worked with digital identity on blockchain. It was an interesting challenge—using smart contracts with federated Ethereum to create a self-sovereign digital identity for citizens. Later, I moved on to another field, focusing on privacy and data protection. Today, as I reflect on those experiences, I felt inspired to write about the concept of self-sovereignty from a privacy perspective. Self-sovereignty in the digital context refers to an individual’s autonomous authority over their personal data and identity. In practice, this concept is exemplified by Self-Sovereign Identity (SSI) systems, wherein users generate and manage their own digital identifiers and credentials without reliance on a central identity provider. Under an SSI model, individuals have complete control, ownership, and autonomy over their identity data​. Self-Sovereign Identity (SSI) This means a person can create and possess verifiable digital credentials (such as proofs of age, citizenship, or qualifications) and present them to others for validation, all without needing a government registry or corporate database to intermediate each transaction. By eliminating the dependence on a single authority or Identity Provider (IdP), self-sovereign systems ensure that identity is not conferred by an outside entity but established and controlled by the individual themselves. Self-sovereign identity represents an evolutionary step in identity management. Traditional models (siloed or federated identity systems) required users to trust third-party identity providers to manage credentials and mediate authentication. In contrast, self-sovereignty is decentralized and user-centric. Key elements of self-sovereign identity include: User Control and Ownership: Individuals hold the keys to their identity data (such as cryptographic keys and credentials) and decide how this data is used or shared. No central authority can arbitrarily alter or revoke their digital identity.


Decentralization of Trust: Trust is distributed across a network rather than vested in a single provider. Technologies like decentralized identifiers (DIDs) and blockchain or distributed ledgers are often leveraged so that no single organization monopolizes identity information. This decentralization helps prevent any external entity from asserting control over personal data, aligning with the principle that no private infrastructure controlled by a single party can fully satisfy user sovereignty.


Portability and Interoperability: A self-sovereign identity is not tied to one platform. Individuals can use their credentials across different services and domains, much as one would use a government-issued ID in various contexts. This ensures that control remains with the user across the digital ecosystem.


Privacy by Design: Self-sovereign systems are built with privacy preservation as a core tenet. They minimize the data shared in any interaction, reducing exposure of personal information. This aspect is closely linked to the concept of selective disclosure, discussed below. User Control and Ownership: Individuals hold the keys to their identity data (such as cryptographic keys and credentials) and decide how this data is used or shared. No central authority can arbitrarily alter or revoke their digital identity. User Control and Ownership: Individuals hold the keys to their identity data (such as cryptographic keys and credentials) and decide how this data is used or shared. No central authority can arbitrarily alter or revoke their digital identity. User Control and Ownership: Decentralization of Trust: Trust is distributed across a network rather than vested in a single provider. Technologies like decentralized identifiers (DIDs) and blockchain or distributed ledgers are often leveraged so that no single organization monopolizes identity information. This decentralization helps prevent any external entity from asserting control over personal data, aligning with the principle that no private infrastructure controlled by a single party can fully satisfy user sovereignty. Decentralization of Trust: Trust is distributed across a network rather than vested in a single provider. Technologies like decentralized identifiers (DIDs) and blockchain or distributed ledgers are often leveraged so that no single organization monopolizes identity information. This decentralization helps prevent any external entity from asserting control over personal data, aligning with the principle that no private infrastructure controlled by a single party can fully satisfy user sovereignty. Decentralization of Trust: Portability and Interoperability: A self-sovereign identity is not tied to one platform. Individuals can use their credentials across different services and domains, much as one would use a government-issued ID in various contexts. This ensures that control remains with the user across the digital ecosystem. Portability and Interoperability: A self-sovereign identity is not tied to one platform. Individuals can use their credentials across different services and domains, much as one would use a government-issued ID in various contexts. This ensures that control remains with the user across the digital ecosystem. Portability and Interoperability: Privacy by Design: Self-sovereign systems are built with privacy preservation as a core tenet. They minimize the data shared in any interaction, reducing exposure of personal information. This aspect is closely linked to the concept of selective disclosure, discussed below. Privacy by Design: Self-sovereign systems are built with privacy preservation as a core tenet. They minimize the data shared in any interaction, reducing exposure of personal information. This aspect is closely linked to the concept of selective disclosure, discussed below. Privacy by Design: Together, these aspects define self-sovereignty as a model where the individual, and not an external institution, is the principal authority over personal digital identity. By empowering users in this way, self-sovereignty promises greater autonomy and security in managing one's digital life. Importance of Self-Sovereignty for Privacy and Data Control Placing individuals in charge of their own data has profound implications for privacy and personal data protection. In a self-sovereign model, because users control when and with whom their information is shared, there is inherently stronger protection against unwarranted data exposure. This stands in contrast to centralized identity systems where a breach or misuse by the provider can compromise the private data of millions. Self-sovereignty ensures that personal data is released only on the user's terms, aligning with the principle of data minimization (only the necessary data should be exchanged for a given purpose). privacy Self-sovereignty data minimization One of the driving motivations for self-sovereign identity is to prevent the emergence of pervasive digital surveillance and data monopolies. As societies transition legal identities and credentials into digital forms, there is a risk that, without proper safeguards, these systems could enable a new era of enhanced surveillance and erosion of privacy​. Self-sovereign identity offers a privacy-preserving alternative: since there is no central honeypot of identity data, it becomes much harder for malicious actors or even authorities to track individuals across all their activities. The autonomy granted by self-sovereignty also means users can opt out of sharing certain details, reducing the amount of personal information circulating in cyberspace. In effect, self-sovereignty operationalizes the individual's right to privacy by giving them the technical means to guard their personal data. opt out Furthermore, self-sovereignty aligns with legal and ethical standards for data protection. Regulations such as the European GDPR emphasize individuals’ rights to access, correct, and delete their personal data. These rights reflect the notion that people own their personal information and should have agency over it. Self-sovereign systems inherently support such rights by design: the user, as the data owner, can decide to revoke a credential, refuse a data request, or selectively share attributes as needed. This design minimizes the chances of data being harvested or repurposed without consent. Overall, the importance of self-sovereignty lies in restoring control to the individual and building trust into digital interactions. When people know they have command over their data, they can engage online with greater confidence that their privacy and autonomy will be respected. own control trust Selective Disclosure and Privacy Preservation A cornerstone of privacy in self-sovereign identity is the principle of selective disclosure. Selective disclosure allows an individual to reveal only the specific pieces of information required for a transaction, and nothing more. In practical terms, rather than handing over a full identity document (which might contain a wealth of sensitive data), one can disclose just the necessary attribute. For example, a person can prove they are an adult without exposing their full date of birth or address. This concept is enabled by advanced cryptographic techniques. In many SSI implementations, zero-knowledge proofs (ZKPs) or similarly privacy-preserving credentials allow the holder to demonstrate the truth of a statement about their data (such as “I am over 18” or “I have a valid driver's license”) without revealing the underlying personal details​. By using cryptographic proofs, the verifier can be convinced of the claim’s validity, yet remains ignorant of any extraneous information. selective disclosure without zero-knowledge proofs (ZKPs) Selective disclosure embodies the maxim "less is more" in data sharing. By minimizing data exposure, it greatly reduces opportunities for misuse, profiling, or identity theft. It also serves to limit correlation of personal data across different services. If each service only sees the attributes relevant to its need, they cannot easily collude to build a full profile of the individual. This fragmentation of disclosed data enhances privacy. Self-sovereign systems typically incorporate selective disclosure as a fundamental feature, for instance through verifiable credentials that support revealing only partial data or through protocols that generate one-time proofs. As a result, the individual retains fine-grained control over personal information flow. limit correlation verifiable credentials fine-grained control To illustrate, consider an online age-restricted service: under a conventional system, one might upload a scan of a government ID, inadvertently revealing name, birth date, ID number, and more. With self-sovereign identity, the user could instead present a cryptographic proof that they are over the required age threshold, without divulging any other personal details. The verifier gets the answer needed (“age verified”) and nothing else. Such capabilities are not just theoretical; they are increasingly implemented in modern digital identity frameworks. By enabling selective disclosure, self-sovereign identity demonstrates how individual-centric design can uphold privacy while still meeting information requirements of various transactions. The Necessity of "Self-" in Sovereignty The prefix "self-" in self-sovereignty is more than a linguistic detail—it signifies a fundamental shift in the locus of authority and rights over personal data. Traditionally, sovereignty in identity has resided with external entities: nation-states issue passports and ID cards, corporations provide login identities and manage user accounts. In those paradigms, individuals are largely subjects of another entity's sovereignty when it comes to identity; one must rely on the state or a provider to assert who they are. By contrast, self-sovereignty posits that individuals themselves are the sovereign entities over their identity. The prefix "self-" underscores that this sovereignty emanates from the individual rather than being granted by an outside authority. self-sovereignty sovereignty self-sovereignty Emphasizing "self-" is necessary to distinguish this personal empowerment from other forms of sovereignty. If one were to speak simply of "digital sovereignty" or "identity sovereignty" without qualification, it might be unclear who holds the sovereign power. It could imply state sovereignty in digital matters (as the term "digital sovereignty" is sometimes used for national control over internet infrastructure) or corporate control over a digital platform. The term self-sovereignty explicitly centers the individual, making clear that the person is the one possessing ultimate control. In practical terms, this means that the individual has the right to control their personal data—deciding when to share it, with whom, and under what conditions—just as a nation has the right to govern itself without outside interference. who self-sovereignty right to control their personal data Crucially, the "self-" prefix also reflects principles of personal autonomy and consent. It asserts that identity is something inherently personal, and that each person should have the freedom to manage their identity on their own terms. Under self-sovereign models, others (governments, companies, institutions) can recognize or verify your credentials, but they do not own or control them. This flips the traditional dynamic: for instance, while a government might issue a credential like a driver's license, in a self-sovereign system the citizen could hold a verifiable digital version of that license in their own wallet and decide how it is used. The government’s role shifts to attesting to a fact (e.g. that one is licensed to drive), but the custody and control of that credential rest with the individual. In this way, self-sovereignty emphasizes that rights over personal data are innate to the individual, not derived from the good graces of a governing body or service provider. recognize verify issue The necessity of highlighting "self" becomes evident when considering the power imbalances in today’s digital landscape. Without self-sovereignty, tech companies and institutions often end up as de-facto sovereigns over user data, dictating terms through lengthy privacy policies and sometimes sharing or exploiting data without clear consent. By reclaiming the term sovereignty for the individual, the self-sovereign identity movement reinforces the idea that consent and agency must be front and center. It aligns with human rights perspectives that each person has dominion over their personal information as an extension of their dignity and freedom. In summary, the prefix "self-" is indispensable because it marks the difference between a world where identity is administered by external powers and one where identity is an expression of personal freedom and control. consent agency These developments indicate that the essence of self-sovereignty—granting individuals greater power over their digital selves—is resonating as a guiding ideal. In conclusion, self-sovereignty in the digital age represents both a response to contemporary privacy challenges and a proactive step toward a more equitable digital future, one in which each person can truly be the ruler of their own digital identity. self-sovereignty in the digital age References Naghmouchi, M., & Laurent, M. Privacy by Design for Self-Sovereign Identity Systems: An In-Depth Component Analysis. arXiv preprint arXiv:2502.02520 (2025) Privacy by Design for Self-Sovereign Identity Systems: An In-Depth Component Analysis

Reclaiming Control Over Your Digital Identity and Privacy

Photo by Carla Quario on Unsplash Responding to cybersecurity threats means a company must be five steps ahead of the hackers. Clone traps, a vanguard in deception technology, automate threat response by deceiving the deceivers and fighting AI with AI. By the end of 2024, there were 240,830 live cybersecurity vulnerabilities and exposures(CVE): These are only the publicly disclosed vulnerabilities, and there may be many more. In the first half of 2024  new common CVEs identification represented a 30% increase, potentially leaving doors wide open for cybercriminal exploitation. The advent of AI technologies is only adding to this list, with cybercriminals using Generative AI to empower their attack chains. As a result of security gaps and advanced attacks, a staggering 95% of bot attacks go undetected. There is also a 19% increase in manual/human attacks; manual attacks are often complicated and multi-part, making detection challenging. Organizations around the world stand at a precipice. Dealing with this unparalleled complex cyber-attack level has led to an intelligence gap. As such, the tsunami of zero-day and complex exploits requires a sophisticated approach. Here, we look at how a vanguard in AI-powered deception technology, clone traps, will help firms of all sizes persistently protect and strengthen their systems and reduce the risk of a successful cyber-attack. Clone traps, fighting AI with AI A new security kid is on the block: Clone traps are next-gen honeypots that are about to turn the table on cybercriminals. This is less a new type of honeypot, and more a quantum leap in deception technology to catch even the most persistent and evasive attackers. Clone traps are deeply integrated with a firewall and provide AI-driven intelligence to super-target protection, fighting AI with AI. Clone traps also provide crucial data to the entire cybersecurity system and enhance a customer’s cyber resilience. One of the most powerful features of a clone trap is the dynamic and real time use of data. This dynamism allows firewall data to be put to immediate use, the clone trap’s AI engine learning from firewall data to identify an attack instantly, and protect the firewall – stopping an attack before it becomes an incident. Future clone trap developments include AI-driven "modelling,” used to generate attacks to identify weaknesses in firewalls and to train defensive AI. Unification of data, AI, and firewall integration – the secret sauce of clone traps The continuous innovation in cyberattack methods requires a similar innovative approach to detection and prevention. The central pivot upon which the digital world turns is data. Therefore, the next generation of deception technologies must be able to optimize the use of data. This is precisely what clone traps do. Clone traps are part of a broad cybersecurity ecosystem: the traps, firewalls, data, and the cybersecurity/ SOC (Security Operational Center) team. This ecosystem approach provides exceptional results, rates of detection improved by up to ten times the market average. Clone traps serve as the entry gates for valuable data, creating up-to-date feeds of malicious attack sources, strange URL patterns, abnormal frequency request signatures, client geo triggers, and behavioural changes in a system. All this information is delivered to the core cybersecurity platform, which matches the data from honeypots with the usual requests and intelligence from over 100 sources: open source data, proprietary sources such as known attacker databases, and even the darknet, which can provide vital attack intelligence. With all this data collated, the core system is able to decide what constitutes an attack and what does not, and packages these decisions into a threat feed for firewalls. Data from hundreds, even thousands of traps is used to form a complex mesh of intelligent insights that are used to identify emerging threats, zero-days, and complex multi-part attacks. The integration of clone traps with a firewall is designed to provide an automated response to all types of threats by leveraging the power of AI and real-time data. Data unification is core to the clone trap's success in attack detection. However, the security team is another essential part of the success mix. Once the decoy is set using the most enticing data and the system hardened, the security team can wait for the attack to begin. Once detected, the cybersecurity platform shares the data with the firewall and the rest of the company's infrastructure, and the firewall automatically blocks the hacker. Your internal security team or SOC uses these alerts to respond to the attack, closing down the pathways that can lead to ransomware infection, data breaches, and other cybersecurity events. Meanwhile, the trap lets the hacker in, revealing all its depths so that you can study his behavior. Ongoing threat intelligence generated by clone traps provides the data needed to create a robust cyber security strategy and to update and adapt the policies based on clone trap feedback. Clone traps take the decoy technique to new levels of response, handling the aftermath of the attack through auto-remediation and auto-healing. Using automation, detection and resolution of cyber threats require no direct human intervention, which removes human error and reduces the time to threat resolution. Also, the intelligence generated by clone traps provides the documentation auditors need to demonstrate that a company is using robust security measures. What if the clone trap is successfully revealed? No one should be able to figure out the trap; however, clone traps must be discoverable, as an undetectable honeypot may lead to hackers finding out that it's a trap.  On the contrary, they invite the hacker in. Of course, clone traps have to be close to reality and sufficiently complex, so that the whole concept works by providing information about the hacker's techniques, rather than just distracting them. In a scenario where a hacker has managed to penetrate the clone system, the internal security team or SOC will receive an alert. The report provides complete details on the attack, providing the team with insights into the attack method to allow reverse engineering of the attack. The intelligence gathered will be used to further harden the system against future attacks. The clone trap itself, after being hacked, may either remain unchanged - waiting for the next "victim" - or be protected by a firewall if desired. A question that may come to mind is, “What if a legitimate user, like an employee, falls for a clone trap?” Employees and legitimate users are almost unable to interact with a clone trap. In other words, even though clone traps camouflage themselves as standard services, their unique positioning means that usual clients will rarely stumble upon them without prior knowledge. Hackers, however, are likely to encounter them while searching for vulnerabilities. Clone traps and false positives False positive alerts are a serious problem for the security team. False positives don’t just waste time, but block regular users and create false alert fatigue, which can cause real attack signals to be missed. The following impact can also result in lowered employee morale. Clone traps prevent false positive alerts as legitimate users cannot normally navigate to the cloned instance – the net result is that the intelligence derived from the clone trap is from genuine attackers engaging the clone traps; this data is therefore rich with threat actor IoCs (Indicators of Compromise). As a result, by combining clone trap intelligence with known threat data sources, false positives are effectively eliminated. How AI-powered security bolsters human security professionals Clone traps provide the security team with a powerful tool to automate the detection and resolution of cyber threats. They, however, are augmented by a security professional. Administrators of clone traps provide configuration guidance, and each time a clone trap generates an incident report, they interpret the results, log into systems, set stricter policies, use different configuration approaches, and ensure that config scripts reflect the current challenges. The automated response to cyber threats by clone traps allows security teams to use their industry knowledge to work on strategic system protection. Empowering detection and response using clone traps Clone traps are a result of ongoing research and development. Cybersecurity scientists use their deep knowledge of the threat surface to mimic hackers and understand their deviant tactics. This threat intelligence has allowed researchers to create clone traps perfectly designed to entrap their hacker prey and extract their tactics. By using a mix of AI and human experience, clone traps can stop the most persistent and complex cyber-attacks and stay five steps ahead of cybercriminals. Photo by Carla Quario on Unsplash Carla Quario Unsplash Responding to cybersecurity threats means a company must be five steps ahead of the hackers. Clone traps, a vanguard in deception technology, automate threat response by deceiving the deceivers and fighting AI with AI. Responding to cybersecurity threats means a company must be five steps ahead of the hackers. Clone traps, a vanguard in deception technology, automate threat response by deceiving the deceivers and fighting AI with AI. Responding to cybersecurity threats means a company must be five steps ahead of the hackers. Clone traps, a vanguard in deception technology, automate threat response by deceiving the deceivers and fighting AI with AI. By the end of 2024, there were 240,830 live cybersecurity vulnerabilities and exposures (CVE): These are only the publicly disclosed vulnerabilities, and there may be many more. In the first half of 2024  new common CVEs identification represented a 30% increase , potentially leaving doors wide open for cybercriminal exploitation. The advent of AI technologies is only adding to this list, with cybercriminals using Generative AI to empower their attack chains. As a result of security gaps and advanced attacks, a staggering 95% of bot attacks go undetected. There is also a 19% increase in manual/human attacks; manual attacks are often complicated and multi-part, making detection challenging. 240,830 live cybersecurity vulnerabilities and exposures 240,830 live cybersecurity vulnerabilities and exposures represented a 30% increase represented a 30% increase 95% of bot attacks 95% of bot attacks 19% increase in manual/human 19% increase in manual/human Organizations around the world stand at a precipice. Dealing with this unparalleled complex cyber-attack level has led to an intelligence gap. As such, the tsunami of zero-day and complex exploits requires a sophisticated approach. Here, we look at how a vanguard in AI-powered deception technology, clone traps, will help firms of all sizes persistently protect and strengthen their systems and reduce the risk of a successful cyber-attack. Clone traps, fighting AI with AI A new security kid is on the block: Clone traps are next-gen honeypots that are about to turn the table on cybercriminals. This is less a new type of honeypot, and more a quantum leap in deception technology to catch even the most persistent and evasive attackers. Clone traps are deeply integrated with a firewall and provide AI-driven intelligence to super-target protection, fighting AI with AI. Clone traps also provide crucial data to the entire cybersecurity system and enhance a customer’s cyber resilience. One of the most powerful features of a clone trap is the dynamic and real time use of data. This dynamism allows firewall data to be put to immediate use, the clone trap’s AI engine learning from firewall data to identify an attack instantly, and protect the firewall – stopping an attack before it becomes an incident. Future clone trap developments include AI-driven "modelling,” used to generate attacks to identify weaknesses in firewalls and to train defensive AI. Unification of data, AI, and firewall integration – the secret sauce of clone traps The continuous innovation in cyberattack methods requires a similar innovative approach to detection and prevention. The central pivot upon which the digital world turns is data. Therefore, the next generation of deception technologies must be able to optimize the use of data. This is precisely what clone traps do. Clone traps are part of a broad cybersecurity ecosystem: the traps, firewalls, data, and the cybersecurity/ SOC (Security Operational Center) team. This ecosystem approach provides exceptional results, rates of detection improved by up to ten times the market average. Clone traps serve as the entry gates for valuable data, creating up-to-date feeds of malicious attack sources, strange URL patterns, abnormal frequency request signatures, client geo triggers, and behavioural changes in a system. All this information is delivered to the core cybersecurity platform, which matches the data from honeypots with the usual requests and intelligence from over 100 sources: open source data, proprietary sources such as known attacker databases, and even the darknet, which can provide vital attack intelligence. With all this data collated, the core system is able to decide what constitutes an attack and what does not, and packages these decisions into a threat feed for firewalls. Data from hundreds, even thousands of traps is used to form a complex mesh of intelligent insights that are used to identify emerging threats, zero-days, and complex multi-part attacks. The integration of clone traps with a firewall is designed to provide an automated response to all types of threats by leveraging the power of AI and real-time data. Data unification is core to the clone trap's success in attack detection. However, the security team is another essential part of the success mix. Once the decoy is set using the most enticing data and the system hardened, the security team can wait for the attack to begin. Once detected, the cybersecurity platform shares the data with the firewall and the rest of the company's infrastructure, and the firewall automatically blocks the hacker. Your internal security team or SOC uses these alerts to respond to the attack, closing down the pathways that can lead to ransomware infection, data breaches, and other cybersecurity events. Meanwhile, the trap lets the hacker in, revealing all its depths so that you can study his behavior. Ongoing threat intelligence generated by clone traps provides the data needed to create a robust cyber security strategy and to update and adapt the policies based on clone trap feedback. Clone traps take the decoy technique to new levels of response, handling the aftermath of the attack through auto-remediation and auto-healing. Using automation, detection and resolution of cyber threats require no direct human intervention, which removes human error and reduces the time to threat resolution. Also, the intelligence generated by clone traps provides the documentation auditors need to demonstrate that a company is using robust security measures. What if the clone trap is successfully revealed? No one should be able to figure out the trap; however, clone traps must be discoverable, as an undetectable honeypot may lead to hackers finding out that it's a trap.  On the contrary, they invite the hacker in. Of course, clone traps have to be close to reality and sufficiently complex, so that the whole concept works by providing information about the hacker's techniques, rather than just distracting them. In a scenario where a hacker has managed to penetrate the clone system, the internal security team or SOC will receive an alert. The report provides complete details on the attack, providing the team with insights into the attack method to allow reverse engineering of the attack. The intelligence gathered will be used to further harden the system against future attacks. The clone trap itself, after being hacked, may either remain unchanged - waiting for the next "victim" - or be protected by a firewall if desired. A question that may come to mind is, “What if a legitimate user, like an employee, falls for a clone trap?” Employees and legitimate users are almost unable to interact with a clone trap. In other words, even though clone traps camouflage themselves as standard services, their unique positioning means that usual clients will rarely stumble upon them without prior knowledge. Hackers, however, are likely to encounter them while searching for vulnerabilities. Clone traps and false positives False positive alerts are a serious problem for the security team. False positives don’t just waste time, but block regular users and create false alert fatigue, which can cause real attack signals to be missed. The following impact can also result in lowered employee morale. Clone traps prevent false positive alerts as legitimate users cannot normally navigate to the cloned instance – the net result is that the intelligence derived from the clone trap is from genuine attackers engaging the clone traps; this data is therefore rich with threat actor IoCs (Indicators of Compromise). As a result, by combining clone trap intelligence with known threat data sources, false positives are effectively eliminated. How AI-powered security bolsters human security professionals Clone traps provide the security team with a powerful tool to automate the detection and resolution of cyber threats. They, however, are augmented by a security professional. Administrators of clone traps provide configuration guidance, and each time a clone trap generates an incident report, they interpret the results, log into systems, set stricter policies, use different configuration approaches, and ensure that config scripts reflect the current challenges. The automated response to cyber threats by clone traps allows security teams to use their industry knowledge to work on strategic system protection. Empowering detection and response using clone traps Clone traps are a result of ongoing research and development. Cybersecurity scientists use their deep knowledge of the threat surface to mimic hackers and understand their deviant tactics. This threat intelligence has allowed researchers to create clone traps perfectly designed to entrap their hacker prey and extract their tactics. By using a mix of AI and human experience, clone traps can stop the most persistent and complex cyber-attacks and stay five steps ahead of cybercriminals.

UNSPLASH

unsplash.com

51-200 employees

Since 2013

COMPANY RANKING

UNSPLASH

EVERGREEN INDEX #553

Representing this company? Click here to claim and customize this page!

Unsplash WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

UNSPLASH

unsplash.com

51-200 employees

Since 2013

COMPANY RANKING

UNSPLASH

EVERGREEN INDEX #553

RELATED COMPANIESRANK

STORIES ABOUT UNSPLASH

Representing this company? Click here to claim and customize this page!

Unsplash WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!