Hypertext Transfer Protocol (HTTP) Status Code Registry

Hypertext Transfer Protocol (HTTP) Status Code Registry

Registry included below

HTTP Status Codes

Registration Procedure(s)
IETF Review
1xx: Informational - Request received, continuing process
2xx: Success - The action was successfully received, understood, and accepted
3xx: Redirection - Further action must be taken in order to complete the request
4xx: Client Error - The request contains bad syntax or cannot be fulfilled
5xx: Server Error - The server failed to fulfill an apparently valid request
Available Formats

Value Description Reference
100 Continue [RFC7231, Section 6.2.1]
101 Switching Protocols [RFC7231, Section 6.2.2]
102 Processing [RFC2518]
103-199 Unassigned
200 OK [RFC7231, Section 6.3.1]
201 Created [RFC7231, Section 6.3.2]
202 Accepted [RFC7231, Section 6.3.3]
203 Non-Authoritative Information [RFC7231, Section 6.3.4]
204 No Content [RFC7231, Section 6.3.5]
205 Reset Content [RFC7231, Section 6.3.6]
206 Partial Content [RFC7233, Section 4.1]
207 Multi-Status [RFC4918]
208 Already Reported [RFC5842]
209-225 Unassigned
226 IM Used [RFC3229]
227-299 Unassigned
300 Multiple Choices [RFC7231, Section 6.4.1]
301 Moved Permanently [RFC7231, Section 6.4.2]
302 Found [RFC7231, Section 6.4.3]
303 See Other [RFC7231, Section 6.4.4]
304 Not Modified [RFC7232, Section 4.1]
305 Use Proxy [RFC7231, Section 6.4.5]
306 (Unused) [RFC7231, Section 6.4.6]
307 Temporary Redirect [RFC7231, Section 6.4.7]
308 Permanent Redirect [RFC7538]
309-399 Unassigned
400 Bad Request [RFC7231, Section 6.5.1]
401 Unauthorized [RFC7235, Section 3.1]
402 Payment Required [RFC7231, Section 6.5.2]
403 Forbidden [RFC7231, Section 6.5.3]
404 Not Found [RFC7231, Section 6.5.4]
405 Method Not Allowed [RFC7231, Section 6.5.5]
406 Not Acceptable [RFC7231, Section 6.5.6]
407 Proxy Authentication Required [RFC7235, Section 3.2]
408 Request Timeout [RFC7231, Section 6.5.7]
409 Conflict [RFC7231, Section 6.5.8]
410 Gone [RFC7231, Section 6.5.9]
411 Length Required [RFC7231, Section 6.5.10]
412 Precondition Failed [RFC7232, Section 4.2]
413 Payload Too Large [RFC7231, Section 6.5.11]
414 URI Too Long [RFC7231, Section 6.5.12]
415 Unsupported Media Type [RFC7231, Section 6.5.13][RFC7694, Section 3]
416 Range Not Satisfiable [RFC7233, Section 4.4]
417 Expectation Failed [RFC7231, Section 6.5.14]
418-420 Unassigned
421 Misdirected Request [RFC7540, Section 9.1.2]
422 Unprocessable Entity [RFC4918]
423 Locked [RFC4918]
424 Failed Dependency [RFC4918]
425 Unassigned
426 Upgrade Required [RFC7231, Section 6.5.15]
427 Unassigned
428 Precondition Required [RFC6585]
429 Too Many Requests [RFC6585]
430 Unassigned
431 Request Header Fields Too Large [RFC6585]
432-450 Unassigned
451 Unavailable For Legal Reasons [RFC7725]
452-499 Unassigned
500 Internal Server Error [RFC7231, Section 6.6.1]
501 Not Implemented [RFC7231, Section 6.6.2]
502 Bad Gateway [RFC7231, Section 6.6.3]
503 Service Unavailable [RFC7231, Section 6.6.4]
504 Gateway Timeout [RFC7231, Section 6.6.5]
505 HTTP Version Not Supported [RFC7231, Section 6.6.6]
506 Variant Also Negotiates [RFC2295]
507 Insufficient Storage [RFC4918]
508 Loop Detected [RFC5842]
509 Unassigned
510 Not Extended [RFC2774]
511 Network Authentication Required [RFC6585]
512-599 Unassigned

The Shape of Things — Welcome to Thington — Medium

The Shape of Things — Welcome to Thington — Medium

In particular I want to talk about the relationship we’re starting to build between physical network-connected objects and some kind of software or service layer that sits alongside them, normally interacted with via a mobile phone.

I think we all forget how quickly things can change, but I think it’s fair to say that the era of the modern smart-phone starts with the iPhone, and it’s really important to remember that only launched a little under nine years ago. This by the way, is the very first advert for the iPhone which essentially replaced single use telephones with general purpose computers connected to the phone network.

Three years after the iPhone launched — so about six years ago now — in addition to all of the desktop and laptop computers we were buying, we were also buying 150 million smart phones a year.

Five years later — 2016 — and it’s projected that 1.6 billion smartphones will be sold. In one single year, one smart phone will be bought for every five people on the planet.

But what happens next? A world of connected objects.


Internet Archive on Twitter: “It seems like we should mention that there are no fines when you borrow an ebook:”

“It seems like we should mention that there are no fines when you borrow an ebook.

When the Cat’s Away, Digital Artists Will Play | The Creators Project

When the Cat’s Away, Digital Artists Will Play | The Creators Project

New show uses an abstract visual language to depict the intersection of URL with IRL.

A new group exhibition generates both creative and art-focused perspectives towards the neverending back and forth between physical and virtual spheres. From curator Tina Sauerländer, who previously brought us PORN TO PIZZA—Domestic Clichés, an investigation into how porn, pets, plants, and pizza took over the internet, WHEN THE CAT’S AWAY, ABSTRACTION continues this dig into how the web is shaping new behaviors and contemporary senses of well-being.

Wait! The Web Isn’t Dead After All. Google Made Sure of It | WIRED

Wait! The Web Isn’t Dead After All. Google Made Sure of It | WIRED

IN 2010, THE web died. Or so said the publication you’re reading right now.

In a WIRED cover story that summer, then-editor-in-chief Chris Anderson proclaimed the demise of the World Wide Web—that vast, interconnected, wonderfully egalitarian universe of internet pages and services we can visit through browser software running on computers of all kinds. We had, he said, departed the web for apps—those specialized, largely unconnected, wonderfully powerful tools we download onto particular types of phones and tablets. “As much as we love the open, unfettered Web,” he wrote, “we’re abandoning it for simpler, sleeker services that just work.”

At about the same time, Rahul Roy-Chowdhury took charge of the Google team that oversees Chrome, the company’s web browser. “I remember the ‘Web is Dead’ article very clearly,” he remembers. “I thought: ‘Oh My God. I’ve made a huge mistake.’” Needless to say, he didn’t really believe that. But there’s some truth in there somewhere. Though the web was hardly dead, it was certainly struggling in the face of apps. Six years later, however, Roy-Chowdhury believes the web is on the verge of a major resurgence, even as the world moves more and more of its Internet activities away from the desktop and onto phones.

As evidence, he points to the growing popularity of the mobile version of Chrome. This morning, as Google releases the latest incarnation of its browser, the company has revealed that a billion people now use Chrome on mobile devices each month—about the same number that use it on desktops and laptops.

But Roy-Chowdhury goes further still. After another six years of work, he says, Google and others have significantly improved the web’s underlying technologies to the point where services built for browsers can now match the performance of apps in some cases—and exceed it in others. “The web needed to adapt to mobile. And it was a rocky process. But it has happened,” he proclaims from a room inside the Google building that houses the Chrome and Android teams. “We’ve figured out.”

Unicode Consortium

Unicode Consortium

The Unicode Consortium enables people around the world to use computers in any language. Our freely-available specifications and data form the foundation for software internationalization in all major operating systems, search engines, applications, and the World Wide Web.

Fundamentally, computers just deal with numbers. They store letters and other characters by assigning a number for each one. Before Unicode was invented, there were hundreds of different encoding systems for assigning these numbers. No single encoding could contain enough characters: for example, the European Union alone requires several different encodings to cover all its languages. Even for a single language like English no single encoding was adequate for all the letters, punctuation, and technical symbols in common use.

These encoding systems also conflict with one another. That is, two encodings can use the same number for two different characters, or use different numbers for the same character. Any given computer (especially servers) needs to support many different encodings; yet whenever data is passed between different encodings or platforms, that data always runs the risk of corruption.

Unicode is changing all that!

Unicode provides a unique number for every character, no matter what the platform, no matter what the program, no matter what the language.

The Unicode Standard has been adopted by such industry leaders as Apple, HP, IBM, JustSystems, Microsoft, Oracle, SAP, Sun, Sybase, Unisys and many others. Unicode is required by modern standards such as XML, Java, ECMAScript (JavaScript), LDAP, CORBA 3.0, WML, etc., and is the official way to implement ISO/IEC 10646. It is supported in many operating systems, all modern browsers, and many other products. The emergence of the Unicode Standard, and the availability of tools supporting it, are among the most significant recent global software technology trends.

Incorporating Unicode into client-server or multi-tiered applications and websites offers significant cost savings over the use of legacy character sets. Unicode enables a single software product or a single website to be targeted across multiple platforms, languages and countries without re-engineering. It allows data to be transported through many different systems without corruption.

About the Unicode Consortium

The Unicode Consortium was founded to develop, extend and promote use of the Unicode Standard, which specifies the representation of text in modern software products and standards. The Consortium is a non-profit, 501(c)(3)charitable organization. The membership of the Consortium represents a broad spectrum of corporations and organizations in the computer and information processing industry. The Consortium is supported financially through membership dues and donations. Membership in the Unicode Consortium is open to organizations and individuals anywhere in the world who support the Unicode Standard and wish to assist in its extension and implementation. All are invited to contribute to the support of the Consortium’s important work by making a donation.

For more information, see the Glossary, Technical Introduction and Useful Resources.

Investigating the algorithms that govern our lives – Columbia Journalism Review

Investigating the algorithms that govern our lives – Columbia Journalism Review

Just an old-school style investigative look into technology, data, algorithms and humanity.

As online users, we’ve become accustomed to the giant, invisible hands of Google, Facebook, and Amazon feeding our screens. We’re surrounded by proprietary code like Twitter Trends, Google’s autocomplete, Netflix recommendations, and OKCupid matches. It’s how the internet churns. So when Instagram or Twitter, or the Silicon Valley titan of the moment, chooses to mess with what we consider our personal lives, we’re reminded where the power actually lies. And it rankles.

While internet users may be resigned to these algorithmic overlords, journalists can’t be. Algorithms have everything journalists are hardwired to question: They’re powerful, secret, and governing essential parts of society. Algorithms decide how fast Uber gets to you, whether you’re approved for a loan, whether a prisoner gets parole, who the police should monitor, and who the TSA should frisk.

Algorithms are built to approximate the world in a way that accommodates the purposes of their architect, and “embed a series of assumptions about how the world works and how the world should work,” says Hansen.

It’s up to journalists to investigate those assumptions, and their consequences, especially where they intersect with policy. The first step is extending classic journalism skills into a nascent domain: questioning systems of power, and employing experts to unpack what we don’t know. But when it comes to algorithms that can compute what the human mind can’t, that won’t be enough. Journalists who want to report on algorithms must expand their literacy into the areas of computing and data, in order to be equipped to deal with the ever-more-complex algorithms governing our lives.

The reporting so far

Few newsrooms consider algorithms a beat of their own, but some have already begun this type of reporting.

Algorithms can generally be broken down into three parts: the data that goes in; the “black box,” or the actual algorithmic process; and the outcome, or the value that gets spit out, be it a prediction or score or price. Reporting on algorithms can be done at any of the three stages, by analyzing the data that goes in, evaluating the data that comes out, or reviewing the architecture of the algorithm itself to see how it reaches its judgements.

Currently, the majority of reporting on algorithms is done by looking at the outcomes and attempting to reverse-engineer the algorithm, applying similar techniques as are used in data journalism. The Wall Street Journal used this technique to find that Staples’ online prices were determined by the customer’s distance from a competitor’s store, leaving prices higher in rural areas. And FiveThirtyEight used the method to skewer Fandango’s movie ratings—which skewed abnormally high, rarely dipping below 3 stars—while a ProPublica analysis suggested that Uber’s surge pricing increases cost but not the supply of drivers.



Can an algorithm be racist?

“Algorithms are like a very small child,” says Suresh Venkatasubramanian. “They learn from their environment.”

Venkatasubramanian is a computer science professor at the University of Utah. He’s someone who thinks about algorithmic fairness, ever since he read a short story by Cory Doctorow published in 2006, called “Human Readable.” The story takes place in a future world, similar to ours, but in which all national infrastructure (traffic, email, the media, etc.) is run by “centralized emergent networks,” modeled after ant colonies. Or in other words: a network of algorithms. The plot revolves around two lovers: a network engineer who is certain the system is incorruptible, and a lawyer who knows it’s already been corrupted.

“It got me thinking,” says Venkatasubramanian. “What happens if we live in a world that is totally driven by algorithms?”

He’s not the only one asking that question. Algorithmic accountability is a growing discipline across a number of fields. Computer scientists, legal scholars, and policy wonks are all grappling with ways to identify or prevent bias in algorithms, along with the best ways to establish standards for accountability in business and government. A big part of the concern is whether (and how) algorithms reinforce or amplify bias against minority groups.

Algorithmic accountability builds on the existing body of law and policy aimed at combatting discrimination in housing, employment, admissions, and the like, and applies the notion of disparate impact, which looks at the impact of a policy on protected classes rather than itsintention. What that means for algorithms is that it doesn’t have to be intentionally racist to have racist consequences.

Algorithms can be especially susceptible to perpetuating bias for two reasons. First, algorithms can encode human bias, whether intentionally or otherwise. This happens by using historical data or classifiers that reflect bias (such as labeling gay households separately, etc.). This is especially true for machine-learning algorithms that learn from users’ input. For example, researchers at Carnegie Mellon University found that women were receiving ads for lower-paying jobson Google’s ad network but weren’t sure why. It was possible, they wrote, that if more women tended to click on lower-paying ads, the algorithm would learn from that behavior, continuing the pattern.

Second, algorithms have some inherently unfair design tics—many of which are laid out in a Medium post, “How big data is unfair.” The author points out that since algorithms look for patterns, and minorities by definition don’t fit the same patterns as the majority, the results will be different for members of the minority group. And if the overall success rate of the algorithm is pretty high, it might not be noticeable that the people it isn’t working for all belong to a similar group.

To rectify this, Venkatasubramanian, along with several colleagues, wrote a paper on how computer scientists can test for bias mathematically while designing algorithms, the same way they’d check for accuracy or error rates in other data projects. He’s also building a tool for non-computer scientists, based on the same statistical principles, which scores uploaded data with a “fairness measure.” Although the tool can’t check if an algorithm itself is fair, it can at least make sure the data you’re feeding it is. Most algorithms learn from input data, Venkatasubramanian explains, so that’s the first place to check for bias.

Much of the reporting on algorithms thus far has focused on their impact on marginalized groups. ProPublica’s story on The Princeton Review, called “The Tiger-Mom Tax,” found that Asian families were almost twice as likely to be quoted the highest of three possible prices for an SAT tutoring course, and that income alone didn’t account for the pricing scheme. A team of journalism students at the University of Maryland, meanwhile, found that Uber wait times were longer in non-white areas in DC.

Bias is also the one of the biggest concerns with predictive policing software like PredPol, which helps police allocate resources by identifying patterns in past crime data and predicting where a crime is likely to happen. The major question, says Maurice Chammah, a journalist at The Marshall Project who reported on predictive policing, is whether it will just lead to more policing for minorities. “There was a worry that if you just took the data on arrests and put it into an algorithm,” he says, “the algorithm would keep sending you back to minority communities.”