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IP geolocation technology has been around for a long while. It is the only non-intrusive tool a service provider can use to estimate the geographical location of online visitors. IP geolocation has proved itself as the main driving engine for delivering location-based services such as content localisation, digital rights management, customer targeting and fraud detection. But how accurate is an IP Geolocation lookup? Can we trust it to make critical business decisions? IP Geolocation accuracy has been one of the heavily debated topics for many years across many platforms. We can find many complaints on forums such as IP addresses not showing the correct location or only country-level data being reliable. One of the most popular myths is that the IP Geolocation is inaccurate because it is based on public data and that it usually points to the organisation’s headquarter location rather than the real user’s location. The reality varies widely because not all IP Geolocation services are made the same, and they often utilise different technologies to source their data. The goal of this article is not to compare different IP Geolocation providers. Check out the Daily IP Geolocation Accuracy Report for various service providers. We address the fundamentals of IP Geolocation and then explains the best accuracy and outcomes we can theoretically expect out of ‘perfect’ IP Geolocation. It then outlines conceptual limitations we should be aware of, and to what extent we can trust the data we receive. IPv4 is here to stay When we refer to an IP Geolocation, we are considering the IPv4 address space first because most of our web traffic is still coming from IPv4 addresses. IPv6 was created to solve the global shortage of IPv4 address space by providing a whopping 2128 extension. However, it has not happened even though two decades have passed since it was introduced. There are many reasons why IPv6 is not taking flight. The main reason is probably the most surprising one – there isn’t a real shortage of IPv4 addresses, after all. Well, to some extent there is, as it is almost impossible to get some for free anymore even if it is vital for your business. It has just become a cherished commodity, and it is here to stay. How is it possible, you may wonder? There are only a limited number of 2^32  or roughly 4.3 billion addresses available on IPv4, so how can they accommodate over 7 billion internet hungry people on our planet and allow for enormous expansion into the IoT space? Surprisingly - yes, it can, and it currently does it for even substantially less than that! First, despite the theoretical maximum of 4,294,967,296 IPv4 addresses, not all are allocated for public internet use. And then, out of those allocated, how many do you think are being actively used? Remarkably, there are currently merely 2.9 billion publicly routable IPv4 addresses servicing our entire internet! Visit our online for up-to-date figures. IpV4 address space monitoring And this is whilst there are: more than 4.5 billion home internet users in the world over 300 million active websites over 3.5 billion smartphone users in the world over 7 billion IoT devices and it is growing millions of public IP addresses utilised by the global network infrastructures So how is that even possible? It is because we only need a dedicated, publicly routable IP address when we require two-way communication such as when we need to be able to reach out at will and also to receive incoming connection requests from the outside world. However, most of our internet activities are perfectly suitable for one-way communication only. For instance, when we browse the internet, we initiate the connection ourselves, and we don’t expect websites or other web service providers to stay actively connected to our device. We even hope they don’t, otherwise, it would give rise to significant security challenges. Websites, on the other hand, are not expected to reach out or initiate a connection. And since websites work with hostnames, it is perfectly suitable to hook them up behind a shared IP address. Theoretically, it is possible to put all of our over 300 million worldwide active websites behind a single anycast IP address, or just a few of them for redundancy. It will be even much more effective to protect them from cyber attacks that way. Cloudflare is an excellent example of how this can work. Technically, there are plenty of technologies already available to share an IP address when providing one-way communication. We use Network Address Translation (NAT) at home or in small offices, proxies at larger organisations, and name-based virtual hosting for websites, to name a few. We tend to classify IP addresses as static (yours forever) or dynamically allocated (yours for a limited time). However, we often don’t get an IP address assigned to us exclusively. We only temporarily make use of it, and possibly even share it with other remote peers simultaneously. Cellular operators, for example, often implement Carrier-grade NAT (CGNAT) and make use of small IP address blocks to cater for a large active customer base. Technically, they assign a pool of IP addresses available to them to a section of their network, and mobile users are only making use of an IP address to get access to the internet, the same as it was with a proxy, for example. The connection is strictly one-directional, there is no way an outside world IP address can initiate a link back to the mobile device using that address. This maintains robust security and reduces the number of public IP addresses required. The downside, however, is that the old and widespread assumption that every single IP address has a single device behind it and therefore can be tracked down to an exact geographical location is not valid anymore! Therefore, when we consider IP Geolocation, we must first consider how an IP address is being used. Static IP addresses A static IP address’s geolocation is the easiest to get right. Regardless of the exact method that helps IP Geolocation providers to source their data, there is one common overarching principle. IP Geolocation is always evidence-based. It could be public RIR data, self-published geofeeds, actively sourced measurements or otherwise processed information or an end-user reported or otherwise obtained IP-location pairs. Any way you put it, it is always about a piece of field evidence or at least a clue from data received. Not all evidence data is correct, it could be a way off, just like a faulty report of a GPS device’s location, for example. The quality of an IP geolocation service always relies on what data they have access to and how they handle it. Therefore, when we’ve got a fresh, valid, and highly precise geographic location as a piece of evidence for a static IP address used by a stationary device – the resulting IP Geolocation accuracy can be scarily accurate – often up to a few meters away. This is why BigDataCloud deliberately obscures the provided location coordinates slightly by rounding it up to the nearest kilometre — essentially capping the maximum accuracy down to within one square kilometre. Hence, IP Geolocation is not suitable for identifying the exact location of a user. Furthermore, it should not be as accurate as it can be because we must also respect the privacy of the IP address end-users. Dynamically allocated IP addresses. A dynamic IP address is an IP address that our Internet Service Provider (ISP) assigns to us temporarily. The only noticeable difference between a static allocation and a dynamic one is that with the static one, we are promised that the address would not change as long as we maintain the contract. In case of a dynamic assignment, it may vary, as often as we reboot our router or even more, depending on the ISP’s policies. Some ISPs could enforce address changes as often as every few hours and others let us have the same address for months even if we reboot our router occasionally. The longer the same IP address remains at the same physical location, the better chance that an IP Geolocation service provider can pick it up and locate it as accurately as a static IP address. But what happens if the IP address we’ve just got was spotted previously at another location? Yes, the IP geolocation results will be off. But, by how much? Let’s now turn to how this will affect the accuracy of the IP Geolocation. A dynamic IP address usually comes from a Dynamic Host Configuration Protocol (DHCP) allocation. Just like we’ve got our home computer receiving a local private IP address from our home router using DHCP. There is also a router at our ISP’s network, which is responsible for our section of the system. This router, in turn, has a pool of IP addresses it can allocate to customers. These IP addresses could be sequential, resembling a single network block, or even a list of several blocks. Sometimes these blocks can be as small as a single IP address. This is precisely why the IP geolocation data granularity is so important. The ‘perfect’ IP Geolocation service must support up to a single IP address granulation - just a side note. Noticeably, the network router which is servicing us directly is essentially responsible for our section of the network, and most often this section resembles a very distinct geographical boundary - a service area. Therefore, the maximum IP Geolocation error we should expect when using an IP address which was witnessed and noted somewhere else is the maximum distance from our physical location to the outermost end of the boundary of that area. The service area or confidence area as we call it at BigDataCloud is a critically important piece of information which can tell us where else the IP address of interest can potentially be allocated if it was assigned dynamically. We must consider it if the resulting decision we make upon IP geolocation data is substantial. Screenshot of maps showing the estimated point location and confidence area (service area) of the respective IP addresses generated using BigDataCloud’s IP Geolocation API . You can check your IP address here . For instance, if we grant or ban access to services or decide on a likelihood of e-commerce fraud, we should definitely include the service area in our considerations too. A location point estimation might not be enough as it is only an estimate which is usually based on the latest or the most likely (frequent) location for it. Cellular networks The IP address allocation by a cellular network operator can loosely resemble a fixed network, ISP dynamic allocation. However, there are at least three noticeable differences which can make IP Geolocation much more challenging for mobile networks. The device behind an IP address is not necessarily a stationary one. We can sometimes drive several hundred kilometres and still be using the same cellular network assigned public IP address. We often do not have access to a full IP address, as we are only ‘riding on it’, sharing it with other, often very remote customers elsewhere. Cellular network operators can often consolidate several internal network sections giving them a shared internet access point with the same shared pool of IP addresses available. This can make the service areas for the cellular address blocks significantly bigger. Therefore, what is the outcome we should expect from a ‘perfect’ IP Geolocation service for cellular originated IP addresses? Unfortunately, even a theoretically ‘perfect’ IP Geolocation service can’t always estimate the correct, up to the moment location for every IP address on a cellular network Even the cellular network operators themselves often don’t have this data in real-time. They may be able to tell better which customers are using which IP address, but not exactly where they are. Most likely, they cannot without digging deep into their internal logs. Hence, as a first step, it would be beneficial for businesses if the IP geolocation providers could just indicate whether an IP address of interest belongs to a cellular block or not. A straightforward way of doing this is checking if the enterprise that services this IP address also operates a cellular network like Vodafone, AT&T etc. But this would not always be helpful because some of these companies run mixed services. Hence, we should be able to detect cellular networks regardless of the ASN announcing them. The next critical point for IP geolocation services is the service area of a cellular network even though it can be a substantially large one. Some cellular operators can extend their allocation for the same pool of IP addresses to areas often as big as the whole country. This means that an IP address within the cellular network can be used by a mobile customer practically anywhere within the servicing country. Hence, it is critical to know this if we need to make an important business decision based on IP Geolocation. Hosting Another essential categorisation applicable to an IP address is to distinguish between those that are servicing a human-operated device directly, or just a middlebox server or a bot. At BigDataCloud, we tend to categorise these as and networks. Consumers Hosting Hosting is the overarching term we use to describe all sorts of unattended computers such as those that usually run from data centers and also offices or private properties. Hosting network IP addresses are essentially those which power public websites, mail servers, VPN services, TOR, proxy and many lawful and also malicious applications. Residential proxy or VPN networks, for example, often reside on benevolent IP address blocks announced by ASNs which belong to legitimate ISPs or even cellular network operators. BigDataCloud also detects these and marks them as Hosting too. Most of the Hosting IP addresses are static allocations. Edge cases There are some outliers with all the above cases, and the next sections define how IP Geolocation would be expected to handle these. Roaming cellular When we arrive in a foreign country and turn our roaming data on, we can find that, surprisingly, the websites we visit still recognise us as never leaving our home country. We can often continue using the same IP address even though we are thousands of miles away! How is that possible? Most if not all mobile network operators worldwide tend to 'tunnel' their remote customers' traffic back to their home networks. This way, they can better control the billing of their super costly data services. Technically, this is very similar to a VPN that tunnels our traffic back to our home networks. It makes IP Geolocation extremely challenging for roaming customers. On top of a regular CGNAT where several scattered mobile customers can share the same IP address for their internet use, roaming will add a chance that some of these could be shared anywhere in the world. What can we expect from a perfect IP Geolocation service in this case? BigDataCloud is often able to recognise IP addresses used overseas. However, keeping in mind that there is a strong possibility that the same IP address is simultaneously allocated at the home country as well, we decide to ignore that data and report on the home country user's location instead. VPN, TOR, PROXY and other anonymisers We often see internet users taking extreme measures to mask their real IP addresses. There are zillions of services available out there to help them hide. Usually, these services are offered as a safer option to surf the internet, a very questionable claim, to say the least. We can only wonder why people tend to trust a ‘no street address’ and ‘no phone number’ company listed on some remote island more than their home ISP for handling their private communication and data. These services are vastly overused in the free parts of the world. Anyway, regardless of their motives, we must respect their choices. Even if it was possible to recover the real IP address and the current location of the user, we see IP Geolocation services often reporting on the estimated position of the middlebox IP address instead of the real end-user one. Conclusion IP Geolocation is categorically not suitable for tracking the exact geographical location of a person or device. However, in the majority of cases, it can assuredly give crucial insights into the geographical area the IP address is being used. The various supporting information such as the confidence area, network type and risk factors can make the IP Geolocation based upon business decisions very robust and trustworthy. Also published at https://www.bigdatacloud.com/blog/how-accurate-can-ip-geolocation-get

IP Geolocation Technology: How Exact is a Pinpoint? 

Source Maps to the rescue.! So you’ve set up Sentry for you javascript project & released it to production, excited about the first error that’ll come up & you got this! Uh oh, so “name of undefined”, oh I know, an is all I need. If check where again? — In: chatModule-322816772254.js h.e.uploadFile at line 1:27468. in Wait, where’s that? — Darn! That’s exactly what source maps help you fix, to map this error location to the line of code you typed before it became a mess while shipping to the client. The Approach Sentry provides multiple approaches from where it can read source maps: Host the source maps along with your minified files Push the source maps to sentry Hosting your source maps seems like the easiest task but it also exposes your un-minified code to the public, making it easier  to find security loopholes. One way to tackle this is to set up a VPN so these are not accessible anywhere else other than your company. Since we want the freedom to be able to read those sentry errors from anywhere, we’ll discuss the latter approach here. The articles originate from the problems I faced when trying to upload source maps to sentry & how I made it work in the end. The Steps Generating source maps Pushing them to sentry with a release tag Label the issues to tie up with the same release tag Generating source maps This step will vary based on your build system. If you’re using browserify, gulp, uglify, here’s how you should do it: Add in your browserify options debug:true Import & initialise the same before minifying the code gulp-sourcemaps Final code might look something like: sourcemaps   = ( );

browserify(path, { : [ ], debug: ,
}) .pipe(sourcemaps.init({ : }))
.pipe(uglify())
.pipe(sourcemaps.write( )) .pipe(gulp.dest( )); var require 'gulp-sourcemaps' paths './node_modules' // Enables source maps true // Any babel config may come here // --- // Initialises & writes source maps loadMaps true '.' // Used to export generated minified files & source maps './web/js' Pushing them to sentry with a release tag At this point we have our minified files & source maps generated in the folder like & which are hosted as & respectively. /web/js /web/js/file.min.js /web/js/file.js https://{your_domain}/js/file.min.js https://{your_domain}/js/file.js Here’s a script which uses to upload the javascript files from a folder to sentry & remove the source maps files post that: sentry-cli spawn = ( ).spawnSync; sentryHost, sentryAuthToken, sentryProjectName, sentryOrg, release; sourceMapsPath = ; jsUrlPrefix = ; sentryConfig = ( ) (sentryConfig) {
  sentryHost = sentryConfig[ ];
  sentryProjectName = sentryConfig[ ];
  sentryOrg = sentryConfig[ ];
  sentryAuthToken = sentryConfig[ ];
}
release = process.env.COMMIT_HASH; (!sentryAuthToken || !sentryHost || !sentryProjectName || !sentryOrg) { .log( .log( ));
  process.exit( );
} (!release) { .log( .log( ));
  process.exit( );
}

spawn( , 
  [ ,  sentryAuthToken, , sentryHost, , ,  sentryOrg, ,  sentryProjectName, , release, , sourceMapsPath, , jsUrlPrefix, ],
  { : }
);

spawn( , 
  [sourceMapsPath, , , , , ],
  { : }
); // Credit: https://github.com/supriya-raj var require 'child_process' var /*
  Change based on your requirement:
  sourceMapsPath: relative path to the folder that contains your minified scripts & source maps
  jsUrlPrefix:  ~ (tilde) -> Acts as your domain. Ex: if the source maps are hosted on domain.com/js/file.js.map
    this should be '~/js'
  release: source maps will be pushed under this release name
  sentryConfig: {
    org: 'dummyorg'
    project: 'my-js-project'
    host: 'https://sentry9.dummyorg.com'
    auth_token: ~
  }
  ^ Example configuration for the sentry url: https://sentry9.dummyorg.com/dummyorg/my-js-project
*/ var 'web/js' var '~/js' var require './sentry-config.js' if 'host' 'project' 'org' 'auth_token' if console console '[Error] One or more config parameters required for source map upload are missing!' 1 if console console '[Error] Environment variable COMMIT_HASH does not exist!' 1 './node_modules/.bin/sentry-cli' '--auth-token' '--url' 'releases' '--org' '--project' 'files' 'upload-sourcemaps' '--url-prefix' "--no-sourcemap-reference" stdio "inherit" 'find' '-type' 'f' '-iname' '\*.js.map' '-delete' stdio "inherit" Auth token can be generated by: Sentry Project -> -> Search (Auth Tokens) -> Create new token Settings For the above project, we’re using the latest commit hash to mark release tag so, that needs to be passed as an environment variable before executing the provided script: COMMIT_HASH= node ./upload-source-maps-to-sentry.js ${COMMIT_HASH} Verify the source maps in Sentry should now be available under: P R {C Artifacts -> roject -> eleases } -> ommit_Hash Label the issues to tie up with the same release tag At this point, the source maps are pushed to sentry but for a particular issue, sentry doesn’t know where to find them to un-minify & give you those pretty errors. To achieve that, send a attribute along with the sentry init configuration, Here’s a sample config for release @Sentry/browser 5.7.1 .Sentry.init({ : , : ,
}) window dsn '<sentry_dsn>' release '<COMMIT_HASH>' & Voila! There you have it, the same error but this time identifiable 👁, thanks to source maps. Troubleshooting Here’s a to detect what might have gone wrong with your implementation. good resource

Pinpoint Those Pesky Minified Javascript Errors With Sentry

In business, the single most valuable thing you can have is what we like to call “unfair advantages.” After graduating from Columbia University, our father (the CEO of a large industrial company) gave us some sound advice for starting our first venture: unfair advantages are assets you have that others don’t — and put you into a unique position within the marketplace. They leave competitors saying, “I can’t believe they have X, Y, Z. How can we possibly compete with that?” This is exactly what we lacked. When we were starting VEEV, we were walking into one of the most notoriously difficult industries to break into: the liquor business. There’s a reason so many liquor companies are started by people whose families are in the liquor industry: the space is uniquely complicated from a distribution and legislative standpoint, and learning to navigate it from scratch is challenging (and in many ways, a fool’s errand). Essentially, we knew no distilleries or distributions and had no experience or connections. As we eventually learned, our lack of “unfair advantages” meant we had to do everything from scratch — the hard way. After successfully building and exiting VEEV, and recently opening our venture capital firm, , we’ve become obsessed with finding “unfair advantages” in the companies we invest in — because they are exactly what we lacked in our first company. M13 Here’s how you can find yours: 1. How can you reverse engineer a consumer need? Most entrepreneurs make the mistake of coming up with an idea they feel is original or unique, and then spending an exorbitant amount of time trying to figure out how to make consumers find it valuable. This doesn’t work. Instead, we encourage entrepreneurs to flip conventional thinking on its head: what do consumers find valuable that you have access to? already That’s an “unfair advantage.” 2. What’s different about the way you see the world? In business, vantage point is what allows you to see opportunities others can’t. Chia Company is an incredible example. It started as a family farm in Australia, which at the time was the only place in the world where chia was being grown. Founder John Foss was a farmer and scientist by trade, and he saw the demand for chia increasing at exponential rates. So, he reached out to other farmers to combine forces and corner the market on chia. They used their vantage point as suppliers to see the growing demand — and then they capitalized on it fast enough to lead the chia craze. So, ask yourself: are you in a position to spot trends early? And if so, what might you be able to do to get ahead of those trends? 3. What do you do best? I’m sure you’ve heard the quote, “Do what you do best — and outsource the rest.” Well, when it comes to unfair advantages, this is advice that sounds simple, but isn’t commonly practiced. Many founders continue to insist on building their vision from the ground up, handling all the “important functions” to make sure they get it right. And while that’s certainly the traditional way entrepreneurs have grown businesses, it leaves founders feeling like they’re playing a game of whack-a-mole. It’s time-consuming — and in an increasingly complex and specialized world, unrealistic. Instead, our philosophy for building companies has become centered around this idea of exploiting a business’s unfair advantages and outsourcing everything else. Remember the Chia Company? Well, the founder set up a world-class global chia supply chain, creating economies of scale and a major unfair advantage. Unfortunately, the Chia Company ran into trouble when its directors decided to venture too far up the value chain and become a consumer brand. It spent time and dollars on marketing, rather than being an industrial supplier for consumer brands, leveraging its unfair advantage, and letting other companies fill in the gaps. We believe that the startup game these days is all about creating the minimum amount of new infrastructure necessary to take advantage of your “unfair advantages.” Which means your job, as the founder(s), is to find them, and then exploit them.

3 Questions Every Entrepreneur Should Ask Themselves To Pinpoint Their Company’s Unfair Advantages

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COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 21 of 42. 19. Tominaka Nakamoto: Monumenta Nipponica {ID_004019} / {L2/245/1} 355. The document presents as a printout of a journal article concerning a historical figure called “Nakamoto”, purportedly downloaded on 5 January 2008, and annotated by Dr Wright by hand in his own handwriting in terms associating himself with that historical figure “Nakamoto”. (a) COPA’s Reasons for Alleging Forgery 356. The document has been backdated. [PM6 [55]. The timestamp at the top of the document has been altered, with the digits of the hour and of the year entered in different font to the rest of the timestamp. [PM6 [3]]. 357. The document is a hand-annotated printout of a digital document [PM6 [5]]. The underlying digital document has not been disclosed. 358. The original source of the document was discovered by searching on the internet. [PM6 [27-30]]. 359. The genuine date of the source document is not 2008, but 2015. The document remains available online now. [PM6 [27-32]]. 360. The original document was downloaded from the academic repository website JSTOR. By inspection of the footer against a comparator data set of over 180 other documents downloaded from JSTOR, it is possible to identify 7 epochs during which the design of the footer of JSTOR documents changed in the decade between January 2007 and December 2016. The dates of changes of design can be pinpointed to six specific days in that decade. By comparison of the footer of {ID_004019} to that data set, it is possible to establish that the footer is contemporary to the period between 19 March 2013 and 22 March 2016. [PM6 [33-47]]. 361. Further, by comparison with 10 other independently-downloaded documents dated 5 Jan 2008 within that dataset, it can be established that the footer of the {ID_004019} is notably different from those on documents from 2008, and therefore is not contemporaneous with such documents from 2008. [PM6 [48-51]]. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 362. The underlying digital document has not been disclosed. 363. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (i.e. by suggesting that he had read a paper about Tominaga Nakamoto before he coined the pseudonym Satoshi Nakamoto), contrary to fact. 364. In his evidence in these proceedings, Dr Wright claims that the name “Satoshi Nakamoto” was chosen due to his ‘admiration for the philosopher Tominaga Nakamoto’. It is to be inferred that this forgery was done to support that aspect of his claim. [Wright1 [66]]. 365. Dr Wright has publicly asserted this document to be supportive of his claim. 366. Dr Wright has publicly maintained the authenticity of this document in social media posts. 367. This document was photographed by Dr Wright for transmission via WhatsApp [PM6 [7]]. The photograph was taken around 1 week before this document was scanned into PDF form. This is consistent with other examples within disclosure where Dr Wright has photographed other documents bearing indicia of tampering, for sending via WhatsApp, before they were scanned and disclosed. (c) Dr Wright’s Explanations and COPA’s Rebuttal 368. In Appendix B to Wright11, Dr Wright claimed that the document dating from 2015 which Mr Madden downloaded from the internet and identified as the source document was copied from his document and then edited. He claims that this was done after he had mentioned the document as his inspiration. See {CSW/2/68}. In his oral evidence, Dr Wright added that this had been done by the person operating the gwern.net website, who he claimed had been working together with Ira Kleiman to discredit him: {Day2/21:13} to {Day2/22:16}. Thus, Dr Wright’s claim is that his document, with an “accessed” date from 2008 is genuine and that a false version was produced by his enemies to reflect an “accessed” date from 2015 and uploaded to the internet in order to undermine his claim to be Satoshi Nakamoto. 369. COPA submitted that Dr Wright’s explanation should be rejected as dishonest, for the following reasons: 369.1. It is inherently implausible that Dr Wright’s version should be authentic and the version accessed from the internet should have been doctored, since the numerals in the “accessed” date are misaligned in Dr Wright’s version (suggesting manipulation) but the numerals in that date are perfectly aligned in the version on the internet. 369.2. There is a second powerful reason why Dr Wright’s version is inauthentic, namely that the footer design corresponds to JSTOR documents dating from 2015 and not to such documents dating from 2008. Mr Madden has reviewed a large sample of JSTOR documents and established that the footer design was updated at set points in time, so that he can be confident from the footer design that Dr Wright’s document does not date to 2008. He was unshaken in that position on crossexamination: {Day16/66:7} to {Day16/68:7}. 369.3. Although Dr Wright asserted that different JSTOR repositories had footer designs updating at different times and that he had documents to make good that proposition ({Day2/20:13} to {Day2/21:6}), there is no documentary support for that assertion (which is at odds with Mr Madden’s findings). It is reasonably to be inferred that, if that assertion could be made good, Dr Wright’s well-resourced legal team would have done so. 369.4. Dr Wright’s essential conspiracy theory does not work as a matter of the dates. He gave the interview in which he publicly cited this document as his inspiration for the Nakamoto name in December 2019 (see {L16/86/1}; {L16/47/1}). The document which he now says was doctored and released to discredit him has been available online in precisely the same form since 2016: see PM6, [30]. 369.5. There is not a shred of evidence for Dr Wright’s conspiracy theory. It is also an odd story, even on its own terms, since Ira Kleiman’s claim depended on saying that Dr Wright was part of “Satoshi Nakamoto”. (d) Conclusion 370. I can only agree with COPA’s submissions. The document was plainly forged by Dr Wright. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.

This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 21 of 42. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 21 of 42. HackerNoon’s Legal PDF Series HackerNoon’s Legal PDF Series here here 19. Tominaka Nakamoto: Monumenta Nipponica {ID_004019} / {L2/245/1} 355. The document presents as a printout of a journal article concerning a historical figure called “Nakamoto”, purportedly downloaded on 5 January 2008, and annotated by Dr Wright by hand in his own handwriting in terms associating himself with that historical figure “Nakamoto”. (a) COPA’s Reasons for Alleging Forgery (a) COPA’s Reasons for Alleging Forgery 356. The document has been backdated. [PM6 [55]. The timestamp at the top of the document has been altered, with the digits of the hour and of the year entered in different font to the rest of the timestamp. [PM6 [3]]. 357. The document is a hand-annotated printout of a digital document [PM6 [5]]. The underlying digital document has not been disclosed. 358. The original source of the document was discovered by searching on the internet. [PM6 [27-30]]. 359. The genuine date of the source document is not 2008, but 2015. The document remains available online now. [PM6 [27-32]]. 360. The original document was downloaded from the academic repository website JSTOR. By inspection of the footer against a comparator data set of over 180 other documents downloaded from JSTOR, it is possible to identify 7 epochs during which the design of the footer of JSTOR documents changed in the decade between January 2007 and December 2016. The dates of changes of design can be pinpointed to six specific days in that decade. By comparison of the footer of {ID_004019} to that data set, it is possible to establish that the footer is contemporary to the period between 19 March 2013 and 22 March 2016. [PM6 [33-47]]. 361. Further, by comparison with 10 other independently-downloaded documents dated 5 Jan 2008 within that dataset, it can be established that the footer of the {ID_004019} is notably different from those on documents from 2008, and therefore is not contemporaneous with such documents from 2008. [PM6 [48-51]]. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 362. The underlying digital document has not been disclosed. 363. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (i.e. by suggesting that he had read a paper about Tominaga Nakamoto before he coined the pseudonym Satoshi Nakamoto), contrary to fact. 364. In his evidence in these proceedings, Dr Wright claims that the name “Satoshi Nakamoto” was chosen due to his ‘admiration for the philosopher Tominaga Nakamoto’. It is to be inferred that this forgery was done to support that aspect of his claim. [Wright1 [66]]. 365. Dr Wright has publicly asserted this document to be supportive of his claim. 366. Dr Wright has publicly maintained the authenticity of this document in social media posts. 367. This document was photographed by Dr Wright for transmission via WhatsApp [PM6 [7]]. The photograph was taken around 1 week before this document was scanned into PDF form. This is consistent with other examples within disclosure where Dr Wright has photographed other documents bearing indicia of tampering, for sending via WhatsApp, before they were scanned and disclosed. (c) Dr Wright’s Explanations and COPA’s Rebuttal (c) Dr Wright’s Explanations and COPA’s Rebuttal 368. In Appendix B to Wright11, Dr Wright claimed that the document dating from 2015 which Mr Madden downloaded from the internet and identified as the source document was copied from his document and then edited. He claims that this was done after he had mentioned the document as his inspiration. See {CSW/2/68}. In his oral evidence, Dr Wright added that this had been done by the person operating the gwern.net website, who he claimed had been working together with Ira Kleiman to discredit him: {Day2/21:13} to {Day2/22:16}. Thus, Dr Wright’s claim is that his document, with an “accessed” date from 2008 is genuine and that a false version was produced by his enemies to reflect an “accessed” date from 2015 and uploaded to the internet in order to undermine his claim to be Satoshi Nakamoto. 369. COPA submitted that Dr Wright’s explanation should be rejected as dishonest, for the following reasons: 369.1. It is inherently implausible that Dr Wright’s version should be authentic and the version accessed from the internet should have been doctored, since the numerals in the “accessed” date are misaligned in Dr Wright’s version (suggesting manipulation) but the numerals in that date are perfectly aligned in the version on the internet. 369.2. There is a second powerful reason why Dr Wright’s version is inauthentic, namely that the footer design corresponds to JSTOR documents dating from 2015 and not to such documents dating from 2008. Mr Madden has reviewed a large sample of JSTOR documents and established that the footer design was updated at set points in time, so that he can be confident from the footer design that Dr Wright’s document does not date to 2008. He was unshaken in that position on crossexamination: {Day16/66:7} to {Day16/68:7}. 369.3. Although Dr Wright asserted that different JSTOR repositories had footer designs updating at different times and that he had documents to make good that proposition ({Day2/20:13} to {Day2/21:6}), there is no documentary support for that assertion (which is at odds with Mr Madden’s findings). It is reasonably to be inferred that, if that assertion could be made good, Dr Wright’s well-resourced legal team would have done so. 369.4. Dr Wright’s essential conspiracy theory does not work as a matter of the dates. He gave the interview in which he publicly cited this document as his inspiration for the Nakamoto name in December 2019 (see {L16/86/1}; {L16/47/1}). The document which he now says was doctored and released to discredit him has been available online in precisely the same form since 2016: see PM6, [30]. 369.5. There is not a shred of evidence for Dr Wright’s conspiracy theory. It is also an odd story, even on its own terms, since Ira Kleiman’s claim depended on saying that Dr Wright was part of “Satoshi Nakamoto”. (d) Conclusion (d) Conclusion 370. I can only agree with COPA’s submissions. The document was plainly forged by Dr Wright. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. judiciary.uk judiciary.uk

COPA Challenges Authenticity of Craig Wright’s Tominaga Nakamoto Document

Artificial Intelligence (AI) is transforming the electronics manufacturing sector through higher output, more robust quality control, and greater creativity. Machine learning techniques enhance production schedules while minimizing downtime and maximizing resource utilization. Artificial intelligence (AI)-powered predictive maintenance solutions anticipate equipment issues and address them before they worsen, protecting production cycles. With the use of modern computer vision, AI-powered quality control performs entire product inspections, significantly reducing operational costs and defect rates. Additionally, Generative AI makes adaptive manufacturing processes possible. In these processes, machines freely adapt their operations in response to actual time data on performance, enabling consumer demands for operational flexibility and flexibility. Let's analyze how AI impacts electronics in the future. Overview of AI in Electronics: Artificial intelligence (AI) in electronics is the imitation of human intelligence in machines, enabling them to perform tasks like language comprehension, learning, reasoning, and problem-solving. The core concepts of artificial intelligence (AI) include neural networks, which process information through networked nodes to identify patterns, machine learning, which uses algorithms to teach computers to learn from data and make decisions without the need for explicit programming, and data processing, which is necessary for creating AI models and involves obtaining, cleaning, and analyzing vast amounts of data to increase accuracy and efficiency. Specific Difficulties in the Manufacturing of Electronics The sector faces several distinct challenges. Accuracy must be preserved throughout the production process since even minor errors can have an important effect on the dependability and functionality of electronic products. Rapid advances in technology also require substantial costs for brand-new machinery and instruction, in addition to continuous process alterations in the manufacturing sector. Durability is a major concern as well because there is an increasing desire to embrace sustainable methods even in spite of the consumption of dangerous chemicals and waste. Furthermore, companies need to continuously innovate in the face of intense competition in order to remain economic and of use in the market. AI in the Background of Electronics Timeline Over the years, there have been several phases of transition in artificial intelligence (AI) development on electronics. Machine learning and neural networks are much newer ideas but can be traced back to early AI research, which was first written about in the 1950s-60s. In the 1980s, robotics and automated technology began to make their way into the industry. This is when AI first really started disrupting industries with mini-cars that ran through tubes taking parts from one end of a factory floor to another. Consumer electronics manufacturers integrated a form of artificial intelligence in their products such as smartphones and smart home appliances, starting in the 2000s. The 2010s saw a transformation in AI capabilities, propelled primarily by advancements in deep learning and big data analytics. AI and Digital Transformation Are Expanding Manufacturers in particular have had their regulations evolve because of the digitization across many industries. This move can mostly be attributed to the work of AI which is responsible for increasing efficiency, bringing in quality control levels, and predicting maintenance. This would improve productivity by decreasing wait times and maximizing the use of resources as production schedules and resources go through the Terms of Service. It does a thorough quality check of any particular product using cutting-edge computer vision, which reduces faults and scrap rates. Predictive maintenance uses artificial intelligence (AI) to quickly determine when equipment is likely to be more dependable before it actually fails, keeping your business running smoothly. An AI-driven manufacturing IoT business meets-loop is the key to cutting-edge innovation in a competitive market, allowing firms to stay ahead of the curve without falling into their own success trap. Artificial Intelligence (AI) explained Creating machines and software that can undertake tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and language understanding. AI also involves developing the technologies that will make this possible. It can range from simple rule-based systems to complex machine learning and deep learning algorithms. How Production Can Benefit From AI AI is utilized throughout a large number of the device-making process. Predictive maintenance is improved by using machine data to anticipate and prevent equipment issues before they occur. Artificial intelligence (AI)-based quality control systems precisely manage inspection procedures to ensure that products strictly fulfill quality requirements. Making Gadgets With Artificial Intelligence. AI is being used in a new way in the business of making gadgets. Systems that use artificial intelligence (AI) to find problems before they happen look at data from industry monitors from the past and the present. As a result, maintenance and downtime costs are decreased. When used for inspection jobs, AI's better visual recognition systems can find flaws more accurately than human inspectors. This reduces downsizing, improves the quality of products, satisfies customers, and more efficiency in the production of the goods. Additionally, AI adjusts production schedules by using data analytics to better manage supplies, modify schedules, and respond quickly to changes in demand. A Look at How AI Has Been Used in the Making of Electronics Putting AI to use in the real world shows how it can help make things better and faster. While individuals take longer to create things, AI-powered robots are significantly more useful than human labor since they can accomplish challenging jobs more rapidly and consistently. Machine learning systems employ production line data to identify issues and defects early on. This improves quality control. This ensures that buyers will only get products free of flaws. By utilizing AI's power and intelligence, organizations may pinpoint important issues, establish goals, and get ready for a reliable, scalable solution. AI applications require a robust infrastructure made up of data management systems, clever software, and long-lasting hardware. Training and development for the workforce is also very important because it gives workers the skills they need to use AI systems properly and get the information they need to make smart business choices. AI is a big deal in the business of making gadgets. It helps companies solve long-standing problems, become more efficient, and make better products that they sell. Artificial Intelligence (AI) is transforming the electronics manufacturing sector through higher output, more robust quality control, and greater creativity. Machine learning techniques enhance production schedules while minimizing downtime and maximizing resource utilization. Artificial intelligence (AI)-powered predictive maintenance solutions anticipate equipment issues and address them before they worsen, protecting production cycles. With the use of modern computer vision, AI-powered quality control performs entire product inspections, significantly reducing operational costs and defect rates. Additionally, Generative AI makes adaptive manufacturing processes possible. In these processes, machines freely adapt their operations in response to actual time data on performance, enabling consumer demands for operational flexibility and flexibility. Generative AI Generative AI Let's analyze how AI impacts electronics in the future. Overview of AI in Electronics: Artificial intelligence (AI) in electronics is the imitation of human intelligence in machines, enabling them to perform tasks like language comprehension, learning, reasoning, and problem-solving. The core concepts of artificial intelligence (AI) include neural networks , which process information through networked nodes to identify patterns, machine learning, which uses algorithms to teach computers to learn from data and make decisions without the need for explicit programming, and data processing, which is necessary for creating AI models and involves obtaining, cleaning, and analyzing vast amounts of data to increase accuracy and efficiency. Artificial intelligence (AI) in electronics Artificial intelligence (AI) in electronics artificial intelligence (AI) include neural networks artificial intelligence (AI) include neural networks Specific Difficulties in the Manufacturing of Electronics The sector faces several distinct challenges. Accuracy must be preserved throughout the production process since even minor errors can have an important effect on the dependability and functionality of electronic products. Rapid advances in technology also require substantial costs for brand-new machinery and instruction, in addition to continuous process alterations in the manufacturing sector. Durability is a major concern as well because there is an increasing desire to embrace sustainable methods even in spite of the consumption of dangerous chemicals and waste. Furthermore, companies need to continuously innovate in the face of intense competition in order to remain economic and of use in the market. AI in the Background of Electronics Timeline Over the years, there have been several phases of transition in artificial intelligence (AI) development on electronics. Machine learning and neural networks are much newer ideas but can be traced back to early AI research, which was first written about in the 1950s-60s. In the 1980s, robotics and automated technology began to make their way into the industry. neural networks neural networks This is when AI first really started disrupting industries with mini-cars that ran through tubes taking parts from one end of a factory floor to another. Consumer electronics manufacturers integrated a form of artificial intelligence in their products such as smartphones and smart home appliances, starting in the 2000s. The 2010s saw a transformation in AI capabilities, propelled primarily by advancements in deep learning and big data analytics. AI and Digital Transformation Are Expanding Manufacturers in particular have had their regulations evolve because of the digitization across many industries. This move can mostly be attributed to the work of AI which is responsible for increasing efficiency, bringing in quality control levels, and predicting maintenance. This would improve productivity by decreasing wait times and maximizing the use of resources as production schedules and resources go through the Terms of Service. It does a thorough quality check of any particular product using cutting-edge computer vision, which reduces faults and scrap rates. Predictive maintenance uses artificial intelligence (AI) to quickly determine when equipment is likely to be more dependable before it actually fails, keeping your business running smoothly. An AI-driven manufacturing IoT business meets-loop is the key to cutting-edge innovation in a competitive market, allowing firms to stay ahead of the curve without falling into their own success trap. Artificial Intelligence (AI) explained Creating machines and software that can undertake tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and language understanding. AI also involves developing the technologies that will make this possible. It can range from simple rule-based systems to complex machine learning and deep learning algorithms. How Production Can Benefit From AI AI is utilized throughout a large number of the device-making process. Predictive maintenance is improved by using machine data to anticipate and prevent equipment issues before they occur. Artificial intelligence (AI)-based quality control systems precisely manage inspection procedures to ensure that products strictly fulfill quality requirements. Making Gadgets With Artificial Intelligence. AI is being used in a new way in the business of making gadgets. Systems that use artificial intelligence (AI) to find problems before they happen look at data from industry monitors from the past and the present. As a result, maintenance and downtime costs are decreased. When used for inspection jobs, AI's better visual recognition systems can find flaws more accurately than human inspectors. This reduces downsizing, improves the quality of products, satisfies customers, and more efficiency in the production of the goods. Additionally, AI adjusts production schedules by using data analytics to better manage supplies, modify schedules, and respond quickly to changes in demand. A Look at How AI Has Been Used in the Making of Electronics Putting AI to use in the real world shows how it can help make things better and faster. While individuals take longer to create things, AI-powered robots are significantly more useful than human labor since they can accomplish challenging jobs more rapidly and consistently. Machine learning systems employ production line data to identify issues and defects early on. This improves quality control. This ensures that buyers will only get products free of flaws. AI-powered robots AI-powered robots By utilizing AI's power and intelligence, organizations may pinpoint important issues, establish goals, and get ready for a reliable, scalable solution. AI applications require a robust infrastructure made up of data management systems, clever software, and long-lasting hardware. Training and development for the workforce is also very important because it gives workers the skills they need to use AI systems properly and get the information they need to make smart business choices. AI is a big deal in the business of making gadgets. It helps companies solve long-standing problems, become more efficient, and make better products that they sell.

AI: Powering the Future of Electronics

এআই: ইলেকট্রনিক্সের ভবিষ্যতকে শক্তিশালী করে

KI: Antrieb für die Zukunft der Elektronik

IA: impulsando el futuro de la electrónica

IA : alimenter l’avenir de l’électronique

एआई: इलेक्ट्रॉनिक्स के भविष्य को सशक्त बनाना

AI: エレクトロニクスの未来を支える

AI: 전자제품의 미래를 주도하다

IA: impulsionando o futuro da eletrônica

Искусственный интеллект: будущее электроники

Yapay Zeka: Elektroniğin Geleceğine Güç Vermek

AI: Cung cấp năng lượng cho tương lai của điện tử

人工智能：驱动电子产品的未来

Exploring Graph RAG: Enhancing Data Access and Evaluation Techniques

Google Cloud x Gemini: Accomplish More in the Cloud with Generative AI

If you have done any programming, you will have debugged the code. Without learning to debug, you cannot be a programmer. But have you spent hours looking at a piece of code only to find the problem is elsewhere? Have you pulled your hair searching for the offending line of code? Have you ever cursed the day you decided to learn programming as you cannot find the reason for a particular bug? We have all been there. But it doesn’t always need to happen when you are coding. There are strategies you can use to find the issue quicker. Reproduce the issue This is the first step in any debugging process when you have found a bug in existing code. It becomes very challenging to fix the issue unless you can reproduce it on your laptop.  So find ways to reproduce the issue in your development environment a.k.a your laptop. This is important as then you can leverage the powers of your IDE to do quick debugging. Now, there are scenarios when this is not possible. One example is that your laptop does not have all the integrations required to reproduce the problem. This can happen when you are working in a distributed architecture. You can use remote debugging to overcome this issue. Isolate the problem To effectively debug, whether it's your own code malfunctioning or an existing bug, the key is to isolate the problem. Aim to pinpoint where the issue might lie. One effective strategy is to thoroughly test until you're confident the code operates correctly. There are several methods to achieve this goal. In most programming languages, simply logging into the console will give you an idea as to when the code is working fine. But this process is tedious. So you need to find markers that will tell you when the code is not dodgy. This could be reading the code you are debugging as it will also help you understand the flow. The other option is to take help from the existing log statements. You could also add breakpoints which I mention as the next step. But that will mean a longer debugging process. The idea is to get into the vicinity of the code where the bug might be. Use breakpoints Once you are in the vicinity of the code, use breakpoints. As you have isolated the problem in the last step, you can add a breakpoint just before the point where you think the problem code is present. This is powerful as you don’t need to g through the code one line after the other. Step through the code and inspect the variables Once you reach your breakpoint, start stepping through the code.  Also, add a watch on variables that will change state during the process. This will help you understand the behavior of your code. Experiment by changing code on the fly You might already have a suspicion of where the issue could be. Advanced IDEs can help you change your code on the fly to test the outcomes of those changes. This will give you a better understanding of whether the code you think is dodgy is dodgy. Read Documentation Sometimes, you might know what a method is doing in your code. But it just helps to read the documentation one more time. It helps you re-visit your understanding. Sometimes, a re-read of a line in the docs provides you with the solution to the problem. Rubber-ducking What you do here is imagine a duck and start explaining the code you are troubleshooting to it. Logical flaws show up when you verbalize your understanding and listen to your voice. You can use this technique even to isolate the problem. Call a friend Yes, if nothing seems to work — call a friend. This is the next level of rubber-ducking. In rubber-ducking, you are talking to an inanimate object. When talking to a friend, you can get feedback and get ideas on what other options you can try. Tips to avoid getting into debugging hell-hole Write clean code. Yes, write code the way you want it when you are debugging. Other tips are: Use method names that will help you understand what the method is doing
Put in log statements so that you know what’s happening in the code
Use meaningful variable names
Follow SOLID principles
Follow the DRY principle
Add unit tests Join my substack like other passionate developers where I send weekly nuggets on how to improve your developer craft 1 week at a time If you have done any programming, you will have debugged the code. Without learning to debug, you cannot be a programmer. But have you spent hours looking at a piece of code only to find the problem is elsewhere? Have you pulled your hair searching for the offending line of code? Have you ever cursed the day you decided to learn programming as you cannot find the reason for a particular bug? We have all been there. But it doesn’t always need to happen when you are coding. There are strategies you can use to find the issue quicker. Reproduce the issue This is the first step in any debugging process when you have found a bug in existing code. It becomes very challenging to fix the issue unless you can reproduce it on your laptop.  So find ways to reproduce the issue in your development environment a.k.a your laptop. This is important as then you can leverage the powers of your IDE to do quick debugging. Now, there are scenarios when this is not possible. debugging One example is that your laptop does not have all the integrations required to reproduce the problem. This can happen when you are working in a distributed architecture. You can use remote debugging to overcome this issue. Isolate the problem To effectively debug, whether it's your own code malfunctioning or an existing bug, the key is to isolate the problem. Aim to pinpoint where the issue might lie. One effective strategy is to thoroughly test until you're confident the code operates correctly. There are several methods to achieve this goal. In most programming languages, simply logging into the console will give you an idea as to when the code is working fine. But this process is tedious. So you need to find markers that will tell you when the code is not dodgy. This could be reading the code you are debugging as it will also help you understand the flow. The other option is to take help from the existing log statements. You could also add breakpoints which I mention as the next step. But that will mean a longer debugging process. The idea is to get into the vicinity of the code where the bug might be. Use breakpoints Once you are in the vicinity of the code, use breakpoints. As you have isolated the problem in the last step, you can add a breakpoint just before the point where you think the problem code is present. This is powerful as you don’t need to g through the code one line after the other. Step through the code and inspect the variables Once you reach your breakpoint, start stepping through the code.  Also, add a watch on variables that will change state during the process. This will help you understand the behavior of your code. Experiment by changing code on the fly You might already have a suspicion of where the issue could be. Advanced IDEs can help you change your code on the fly to test the outcomes of those changes. This will give you a better understanding of whether the code you think is dodgy is dodgy. Read Documentation Sometimes, you might know what a method is doing in your code. But it just helps to read the documentation one more time. It helps you re-visit your understanding. Sometimes, a re-read of a line in the docs provides you with the solution to the problem. Rubber-ducking What you do here is imagine a duck and start explaining the code you are troubleshooting to it. Logical flaws show up when you verbalize your understanding and listen to your voice. You can use this technique even to isolate the problem. Call a friend Yes, if nothing seems to work — call a friend. This is the next level of rubber-ducking. In rubber-ducking, you are talking to an inanimate object. When talking to a friend, you can get feedback and get ideas on what other options you can try. Tips to avoid getting into debugging hell-hole Write clean code. Write clean code. Yes, write code the way you want it when you are debugging. Other tips are: Other tips are: Use method names that will help you understand what the method is doing Put in log statements so that you know what’s happening in the code Use meaningful variable names Follow SOLID principles Follow the DRY principle Add unit tests Use method names that will help you understand what the method is doing Put in log statements so that you know what’s happening in the code Use meaningful variable names Follow SOLID principles Follow the DRY principle Add unit tests Join my substack like other passionate developers where I send weekly nuggets on how to improve your developer craft 1 week at a time Join my substack like other passionate developers where I send weekly nuggets on how to improve your developer craft 1 week at a time Join my substack like other passionate developers where I send weekly nuggets on how to improve your developer craft 1 week at a time Join my substack like other passionate developers where I send weekly nuggets on how to improve your developer craft 1 week at a time

Navigate Debugging Challenges with These Proven Techniques

Building Advanced Video Search: Frame Search Versus Multi-Modal Embeddings

উন্নত ভিডিও অনুসন্ধান তৈরি করা: ফ্রেম অনুসন্ধান বনাম মাল্টি-মোডাল এম্বেডিং

Erstellen einer erweiterten Videosuche: Frame-Suche im Vergleich zu multimodalen Einbettungen

Creación de búsqueda de vídeo avanzada: búsqueda de fotogramas frente a incrustaciones multimodales

Création d'une recherche vidéo avancée : recherche de trames et intégrations multimodales

उन्नत वीडियो खोज का निर्माण: फ़्रेम खोज बनाम मल्टी-मोडल एम्बेडिंग

高度なビデオ検索の構築: フレーム検索とマルチモーダル埋め込み

고급 비디오 검색 구축: 프레임 검색과 다중 모드 임베딩 비교

Construindo pesquisa avançada de vídeo: pesquisa de quadros versus incorporações multimodais

Создание расширенного поиска видео: поиск кадров в сравнении с мультимодальными встраиваниями

Gelişmiş Video Arama Oluşturma: Çerçeve Aramaya Karşı Çok Modlu Yerleştirmeler

Xây dựng Tìm kiếm video nâng cao: Tìm kiếm khung so với nhúng đa phương thức

构建高级视频搜索：帧搜索与多模态嵌入

Hi All! Here is my weekly letter discussing mental models, performance, business, and entrepreneurship. If you love this content (please share it), but also… Connect with me on YouTube/Twitter, or subscribe to my daily business newsletter here. What’s in today’s newsletter? Networking is a skill. But it's not one they teach you in school. They don't give you lessons on how to build genuine connections, how to give before you ask, or how to curate a personal board of directors. So, here's the playbook I developed to network better: (1) Become a Talent Scout, (2) Curate Your Personal Board of Directors, and (3) Become a "Reverse Mentor." Your entire career can change the moment you stop seeing networking as a chore and start seeing it as a strategic advantage. Your Network, Your Net Worth Ever heard the saying, "Don't call me unless you need something?" That's the exact opposite of how to build a powerful network. Here's a thought: The strongest relationships are forged in the fires of mutual support, not the desperation of last-minute asks. Let me share a story. Back when I was first starting out, hustling to get my podcast off the ground, I had this mentor, Sarah. She was a powerhouse in the industry, someone I deeply admired. But here's the thing: I didn't just reach out to Sarah when I needed funding advice or a warm intro. I grabbed coffee with her when I didn’t need anything at all. We’d catch up, I'd share my small wins and setbacks, just to get her perspective. Over time, a real friendship blossomed. It turns out, I didn’t need anything at all for about 3 years. And guess what? When the time came that I did need something – a crucial connection, a sounding board for a tough decision – Sarah was there in a heartbeat. Not out of obligation, but because we had built a genuine bond. Think about it. When you reach out to someone only when you're in need, it's like waving a giant flag that says, "I'm using you!" It feels transactional, desperate even. Nobody likes to feel used. But when you reach out genuinely, to offer support, share insights, or just say hello, you're building something far more valuable: trust. You're signaling that you value the relationship for its own sake, not just for what you can get out of it. Trust is worth its weight in gold. It's the currency of a powerful network; one that will have your back when you need it most. Don't be that person who only shows up when they want something. Be the person who invests in relationships before they need them. The Networking Blindside: Think about it. Have you ever been caught off guard by a professional setback? Maybe a dream job fell through, a project got derailed, or a client suddenly went dark. In those moments, it's easy to feel isolated and overwhelmed. That’s when you NEED people. That’s also when you CAN’T reach out to people. It’s happened to the best of us. We wish we had a network of people we could turn to for support, advice, or even a new lead. That's the power of strategic networking. You have to build that network of people who can support you before you need it. So, how do you do that? Here’s the system I developed to network better - build relationships before you need them, so you're never starting from scratch when the unexpected happens. The Networking System: 3 Strategies To Built True Ties 1. Become a Talent Scout: The saying goes, "Bet on the jockey, not the horse." The same goes for your network. Don't just collect business cards – invest in people. Scout out the rising stars; the ones with that fire in their eyes. They're the future CEOs, the disruptors, the game-changers. Spot the talent, the drive, the vision. Be their champion. Mentor them. Share your hard-won wisdom. Open doors they didn't even know existed. As they rise, so do you. Building a network isn't about quantity, it's about quality. Bet on the jockey, and you'll win the race. 2. Become a "Reverse Mentor": You just scouted new talent; now, it’s time to tap into wisdom and experience. Mentorship isn't a one-way street; it's a two-way exchange of knowledge and perspective. Flip the script and become a "reverse mentor." Don't just seek wisdom; offer yours in return (before you need it). Your fresh perspective on emerging tech, digital trends, or innovative solutions is GOLD to those in power. Pinpoint Your Power: What unique skills or insights do you have that a senior leader could benefit from?
Target the Right Players: Look for open-minded leaders who value diverse perspectives.
Make Your Pitch: Show them how your knowledge can solve their problems or drive their goals.
Start the Conversation: Don't be afraid to reach out and offer your expertise. Become a trusted advisor, build cross-generational bridges, and accelerate your own career. Remember: The student can become the teacher. 3. Build Your "Personal Board of Directors": Jim Rohn famously said, "You are the average of the five people you spend the most time with." Elevate your average. Hand-pick your board of directors. Not just mentors or colleagues, but your trusted advisors. Seek diversity – experience, skills, perspectives. Surround yourself with those who've "been there, done that" and those who challenge you to think differently. Invest in these relationships. Be present. Be supportive. Be the kind of friend you wish you had. When failure strikes, this is your A-team. They'll offer guidance, diverse perspectives, and unwavering support. Your board of directors isn't just a network; it's your lifeline. Your Network, Your Net Worth: Stop chasing connections; start cultivating relationships. Ditch the transactional mindset and embrace genuine connection. Invest in people, not just business cards. Remember, the strongest networks aren't built on desperation; they're built on mutual support, trust, and shared ambition. This isn't just about climbing the ladder faster; it's about building a community that will catch you if you fall, push you when you hesitate, and celebrate with you when you win. Your network isn't just a safety net; it's your launchpad. This is how you network, properly. Scott Subscribe to get this newsletter in your inbox, every week. My Links Success Story (Top 10 Business Podcast)
Newsletter (321k Subs)
10 Minute MBA (Daily Podcast)
10 Minute MBA (Daily Newsletter)
Social Media Hi All! Here is my weekly letter discussing mental models, performance, business, and entrepreneurship. If you love this content (please share it), but also… Connect with me on YouTube / Twitter, or subscribe to my daily business newsletter here. YouTube YouTube Twitter , Twitter daily business newsletter here. daily business newsletter here. What’s in today’s newsletter? What’s in today’s newsletter? Networking is a skill. But it's not one they teach you in school. They don't give you lessons on how to build genuine connections, how to give before you ask, or how to curate a personal board of directors. Networking is a skill. But it's not one they teach you in school. They don't give you lessons on how to build genuine connections, how to give before you ask, or how to curate a personal board of directors. So, here's the playbook I developed to network better: (1) Become a Talent Scout, (2) Curate Your Personal Board of Directors, and (3) Become a "Reverse Mentor." So, here's the playbook I developed to network better: (1) Become a Talent Scout, (2) Curate Your Personal Board of Directors, and (3) Become a "Reverse Mentor." Your entire career can change the moment you stop seeing networking as a chore and start seeing it as a strategic advantage. Your entire career can change the moment you stop seeing networking as a chore and start seeing it as a strategic advantage. Your Network, Your Net Worth Ever heard the saying, "Don't call me unless you need something?" That's the exact opposite of how to build a powerful network. exact opposite Here's a thought: The strongest relationships are forged in the fires of mutual support, not the desperation of last-minute asks. The strongest relationships are forged in the fires of mutual support, not the desperation of last-minute asks. Let me share a story. Back when I was first starting out, hustling to get my podcast off the ground, I had this mentor, Sarah. She was a powerhouse in the industry, someone I deeply admired. But here's the thing: I didn't just reach out to Sarah when I needed funding advice or a warm intro. I grabbed coffee with her when I didn’t need anything at all. We’d catch up, I'd share my small wins and setbacks, just to get her perspective. Over time, a real friendship blossomed. It turns out, I didn’t need anything at all for about 3 years. And guess what? When the time came that I did need something – a crucial connection, a sounding board for a tough decision – Sarah was there in a heartbeat. did Not out of obligation, but because we had built a genuine bond. Think about it. When you reach out to someone only when you're in need, it's like waving a giant flag that says, "I'm using you!" It feels transactional, desperate even. Nobody likes to feel used. But when you reach out genuinely, to offer support, share insights, or just say hello, you're building something far more valuable: trust. You're signaling that you value the relationship for its own sake, not just for what you can get out of it. Trust is worth its weight in gold. It's the currency of a powerful network; one that will have your back when you need it most. Don't be that person who only shows up when they want something. Be the person who invests in relationships before they need them. before The Networking Blindside: The Networking Blindside: Think about it. Have you ever been caught off guard by a professional setback? Maybe a dream job fell through, a project got derailed, or a client suddenly went dark. In those moments, it's easy to feel isolated and overwhelmed. That’s when you NEED people. That’s also when you CAN’T reach out to people. It’s happened to the best of us. We wish we had a network of people we could turn to for support, advice, or even a new lead. That's the power of strategic networking. You have to build that network of people who can support you before you need it. So, how do you do that? Here’s the system I developed to network better - build relationships before you need them, so you're never starting from scratch when the unexpected happens. before The Networking System: 3 Strategies To Built True Ties 1. Become a Talent Scout: 1. Become a Talent Scout: The saying goes, "Bet on the jockey, not the horse." The same goes for your network. Don't just collect business cards – invest in people. Scout out the rising stars; the ones with that fire in their eyes. They're the future CEOs, the disruptors, the game-changers. Spot the talent, the drive, the vision. Be their champion. Mentor them. Share your hard-won wisdom. Open doors they didn't even know existed. As they rise, so do you. Building a network isn't about quantity, it's about quality. Bet on the jockey, and you'll win the race. 2. Become a "Reverse Mentor": 2. Become a "Reverse Mentor": You just scouted new talent; now, it’s time to tap into wisdom and experience. Mentorship isn't a one-way street; it's a two-way exchange of knowledge and perspective. Flip the script and become a "reverse mentor." Don't just seek wisdom; offer yours in return (before you need it). Your fresh perspective on emerging tech, digital trends, or innovative solutions is GOLD to those in power. Pinpoint Your Power: What unique skills or insights do you have that a senior leader could benefit from? Target the Right Players: Look for open-minded leaders who value diverse perspectives. Make Your Pitch: Show them how your knowledge can solve their problems or drive their goals. Start the Conversation: Don't be afraid to reach out and offer your expertise. Pinpoint Your Power: What unique skills or insights do you have that a senior leader could benefit from? Pinpoint Your Power: Target the Right Players: Look for open-minded leaders who value diverse perspectives. Target the Right Players: Make Your Pitch: Show them how your knowledge can solve their problems or drive their goals. Make Your Pitch: Start the Conversation: Don't be afraid to reach out and offer your expertise. Start the Conversation: Become a trusted advisor, build cross-generational bridges, and accelerate your own career. Remember: The student can become the teacher. 3. Build Your "Personal Board of Directors": 3. Build Your "Personal Board of Directors": Jim Rohn famously said, "You are the average of the five people you spend the most time with." "You are the average of the five people you spend the most time with." Elevate your average. Hand-pick your board of directors. Not just mentors or colleagues, but your trusted advisors. Seek diversity – experience, skills, perspectives. Surround yourself with those who've "been there, done that" and those who challenge you to think differently. Invest in these relationships. Be present. Be supportive. Be the kind of friend you wish you had. When failure strikes, this is your A-team. They'll offer guidance, diverse perspectives, and unwavering support. Your board of directors isn't just a network; it's your lifeline. Your Network, Your Net Worth: Stop chasing connections; start cultivating relationships. Ditch the transactional mindset and embrace genuine connection. Invest in people, not just business cards. Remember, the strongest networks aren't built on desperation; they're built on mutual support, trust, and shared ambition. This isn't just about climbing the ladder faster; it's about building a community that will catch you if you fall, push you when you hesitate, and celebrate with you when you win. Your network isn't just a safety net; it's your launchpad. This is how you network, properly. Scott Subscribe to get this newsletter in your inbox, every week. Subscribe to get this newsletter in your inbox, every week. Subscribe to get this newsletter in your inbox, every week. My Links My Links Success Story (Top 10 Business Podcast) Newsletter (321k Subs) 10 Minute MBA (Daily Podcast) 10 Minute MBA (Daily Newsletter) Social Media Success Story (Top 10 Business Podcast) Success Story (Top 10 Business Podcast) Success Story (Top 10 Business Podcast) Newsletter (321k Subs) Newsletter (321k Subs) Newsletter (321k Subs) 10 Minute MBA (Daily Podcast) 10 Minute MBA (Daily Podcast) 10 Minute MBA (Daily Podcast) 10 Minute MBA (Daily Newsletter) 10 Minute MBA (Daily Newsletter) 10 Minute MBA (Daily Newsletter) Social Media Social Media Social Media

Your Network, Your Net Worth: 3 Tips to Network Better

How Emotional Loyalty Is Key for Digital Tech Companies' Success?

ডিজিটাল টেক কোম্পানির সাফল্যের জন্য কতটা মানসিক আনুগত্য চাবিকাঠি?

Inwiefern ist emotionale Loyalität der Schlüssel zum Erfolg digitaler Technologieunternehmen?

¿Por qué la lealtad emocional es clave para el éxito de las empresas de tecnología digital?

Dans quelle mesure la fidélité émotionnelle est-elle la clé du succès des entreprises de technologie numérique ?

डिजिटल टेक कंपनियों की सफलता के लिए भावनात्मक वफादारी कितनी महत्वपूर्ण है?

デジタルテクノロジー企業の成功にとって感情的な忠誠心が鍵となる理由とは？

디지털 기술 기업의 성공에 있어 정서적 충성도가 어떻게 중요합니까?

Como a lealdade emocional é fundamental para o sucesso das empresas de tecnologia digital?

Насколько эмоциональная лояльность является ключом к успеху компаний, занимающихся цифровыми технологиями?

Dijital Teknoloji Şirketlerinin Başarısında Duygusal Sadakat Ne Kadar Önemlidir?

Lòng trung thành về mặt cảm xúc là chìa khóa thành công của các công ty công nghệ kỹ thuật số như thế nào?

情感忠诚度如何成为数字科技公司成功的关键？

New AI writing tools such as ChatGPT are changing how people create content. Instead of manually writing out long texts, such as blog posts, essays, and articles, one word at a time, people can now describe what they want in a prompt and let an AI create it for them. However, some tools known as AI detectors, such as ZeroGPT, try to detect if the content is AI-generated and tag it, which can cause issues. The ability to get past ZeroGPT AI detection is vital for many reasons, including for SEO and marketing. Read on to learn how you can bypass ZeroGPT AI detection easily and with no hassle. All You Should Be Aware of About ZeroGPT ZeroGPT is an AI detector and was designed to determine whether a piece of text was written by an AI writer. It launched in 2023, in January, and is based in Hamburg, Germany. It quickly proved to be quite popular and attracted people from all over the world. ZeroGPT is well known for its ease of use and reliability, alleging an accuracy rate of 98%. It has the ability to pinpoint indicators of AI content and identify essays, articles, and other content as being created by AI. Therefore, many editors and other professionals prefer this tool to determine whether a text has been generated by AI or was originally created by a human. Many teachers and professors also turn to ZeroGPT to analyze the work of their students with the goal of ensuring academic integrity. How to Ensure Your Text Beats ZeroGPT AI Detection To avoid having ZeroGPT flag your article as created by AI, you’ll have to understand how you can get past the ZeroGPT AI detector. There are a few ways you can go about this, including resorting to an undetectable AI writer and making changes yourself to the text. Here are some of the better methods you could use. Resorting to an Undetectable AI Writer One of the most effective approaches to gaining a passing score on the ZeroGPT AI detector is to use an undetectable AI writer such as BypassGPT. It employs cutting-edge humanizing tech to make content created by AI sound human. It is effective at turning robotic, AI-generated articles and posts into text that is far more likely to pass as human-created. This way, you can make sure that your content won’t be flagged as AI and penalized by Google. Furthermore, your content will maintain its high quality and authenticity, while also being engaging and informative. Read on for some of BypassGPT’s key features and advantages: Features: Uses advanced humanizing technology to make AI text sound more human-written.
Allows adjusting the reading level, so anyone from students to professionals can understand.
Works with different document types like essays, articles, letters, and more. Benefits: Shown to be able to pass the ZeroGPT AI detector tool as well as other well-known tools.
Uses state-of-the-art content humanization techniques.
Keeps the original meaning while making it less robotic. Does BypassGPT really work? To demonstrate this, we carried out a brief test to assess its effectiveness by bypassing ZeroGPT. We created a piece of content using AI and made it more human-like on BypassGPT. Below are the findings. From there, we scanned the rewritten output on ZeroGPT and as you can see, it passed with flying colors. More Methods to Avoid ZeroGPT AI Detection Besides using a simple undetectable AI writing tool, there are some other tactics you might want to employ to bypass ZeroGPT AI detection, including: Manual Changes It's always a smart idea to look over what an AI writes and make little changes so it sounds more like your voice if you want to get past AI detectors. You can swap out words you wouldn't say or fix anything that sounds awkward. Even small tweaks to tone and structure can help the writing feel more natural. Don’t Use AI for Everything Limit your use of AI for the most critical parts of the piece and create other sections that aren’t quite as important, such as the introduction and the outro. By writing these sections yourself, you reduce the risk of the AI detectors flagging your content as AI-generated. Get Creative with Your Prompts When creating your AI content, use various prompt types, like requiring the AI writing tool to adopt the style of a particular famous blogger or writer or the role of a particular professional in the relevant field. This should help make the AI-generated content more human and less stilted. Modify the Structure Edit the AI text closely, shortening and paraphrasing where possible. Also, remove sections that are repetitive and might sound too much like they were AI-created. Intentional Errors Some might advise that you add intentional mistakes or discrepancies in a bit to bypass ZeroGPT. However, this is an inadvisable strategy because it makes your work appear unprofessional. The general rule of thumb is to ensure you are offering accurate information that is grammatically correct, coherent, and professional. Incorporate Personal Anecdotes To make your AI-generated content more authentic, blend in personal anecdotes or experiences. By sharing your own stories and perspectives, you can add a touch of human emotion and credibility to the writing, making it less likely to be flagged as AI-generated. Vary Sentence Structure and Length AI detectors often look for patterns in sentence structure. By intentionally diversifying your sentence structure and length, you can disrupt these patterns and reduce the chances of being detected. Mix up long and short sentences, incorporate different types of clauses, and experiment with rhetorical devices to create a more natural flow. Use References and Citations To enhance the credibility of your AI-generated content, include references and citations to reliable sources within the text. By incorporating external information to support your claims or ideas, you not only add depth to the content but also reduce suspicion of AI involvement. Employ Interactive Elements Engage your readers by including interactive elements within your content. This could be in the form of embedded polls, surveys, or quizzes that encourage active participation. By incorporating such features, you not only make the content more engaging but also create a unique user experience that is less likely to be considered AI-generated. Why Is It Important to Bypass ZeroGPT? Evade detection Bypassing ZeroGPT means that instructors and any person on the hunt for AI-created text won’t be able to flag your work. This is particularly critical for students to avoid any problems and maintain academic integrity. Boost SEO and avoid Google penalties Google’s algorithms have gotten smarter in recent years and now consider the quality of content and how natural it is. By evading ZeroGPT detection, you turn robotic content created by AI into something that is more human and more engaging, which prevents you from getting penalized by Google and boosts your shot of getting better search engine optimization scores. Improve readability AI-created text can seem somewhat boring and simple for readers. Using an undetectable AI writer to change up your text will result in articles your audience will find more interesting and engaging. Stay ahead of competitors As more people utilize AI writing tools, the need to differentiate your content from others becomes increasingly important. By passing ZeroGPT detection and delivering content that feels more human, you can gain a competitive advantage. Your content will stand out and capture the attention of readers who are seeking a more personalized and engaging experience. Conclusion AI writers are valuable, but they also come with a set of new issues, particularly with the launch of AI detection tools such as ZeroGPT. Those having to create reams of text regularly with AI content creation tools need to know how to bypass ZeroGPT and other AI detectors. As you can see from this article, you have a number of strategies you can use to get past ZeroGPT’s detector. You can try to edit or change your text manually, though it does mean you have to put in some time and work yourself. A more expedient approach is to rely on an undetectable AI writing tool like BypassGPT, which is a great way to get past AI detection without deteriorating your content’s quality. So, test BypassGPT now and make sure your content passes ZeroGPT! This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author New AI writing tools such as ChatGPT are changing how people create content. Instead of manually writing out long texts, such as blog posts, essays, and articles, one word at a time, people can now describe what they want in a prompt and let an AI create it for them. However, some tools known as AI detectors, such as ZeroGPT, try to detect if the content is AI-generated and tag it, which can cause issues. The ability to get past ZeroGPT AI detection is vital for many reasons, including for SEO and marketing. Read on to learn how you can bypass ZeroGPT AI detection easily and with no hassle. All You Should Be Aware of About ZeroGPT ZeroGPT is an AI detector and was designed to determine whether a piece of text was written by an AI writer. It launched in 2023, in January, and is based in Hamburg, Germany. It quickly proved to be quite popular and attracted people from all over the world. ZeroGPT ZeroGPT ZeroGPT is well known for its ease of use and reliability, alleging an accuracy rate of 98%. It has the ability to pinpoint indicators of AI content and identify essays, articles, and other content as being created by AI. Therefore, many editors and other professionals prefer this tool to determine whether a text has been generated by AI or was originally created by a human. Many teachers and professors also turn to ZeroGPT to analyze the work of their students with the goal of ensuring academic integrity. How to Ensure Your Text Beats ZeroGPT AI Detection To avoid having ZeroGPT flag your article as created by AI, you’ll have to understand how you can get past the ZeroGPT AI detector. There are a few ways you can go about this, including resorting to an undetectable AI writer and making changes yourself to the text. Here are some of the better methods you could use. Resorting to an Undetectable AI Writer One of the most effective approaches to gaining a passing score on the ZeroGPT AI detector is to use an undetectable AI writer such as BypassGPT . It employs cutting-edge humanizing tech to make content created by AI sound human. BypassGPT BypassGPT It is effective at turning robotic, AI-generated articles and posts into text that is far more likely to pass as human-created. This way, you can make sure that your content won’t be flagged as AI and penalized by Google. Furthermore, your content will maintain its high quality and authenticity, while also being engaging and informative. Read on for some of BypassGPT’s key features and advantages: Features: Uses advanced humanizing technology to make AI text sound more human-written. Allows adjusting the reading level, so anyone from students to professionals can understand. Works with different document types like essays, articles, letters, and more. Uses advanced humanizing technology to make AI text sound more human-written. Allows adjusting the reading level, so anyone from students to professionals can understand. Works with different document types like essays, articles, letters, and more. Benefits: Shown to be able to pass the ZeroGPT AI detector tool as well as other well-known tools. Uses state-of-the-art content humanization techniques. Keeps the original meaning while making it less robotic. Shown to be able to pass the ZeroGPT AI detector tool as well as other well-known tools. Uses state-of-the-art content humanization techniques. Keeps the original meaning while making it less robotic. Does BypassGPT really work? To demonstrate this, we carried out a brief test to assess its effectiveness by bypassing ZeroGPT. We created a piece of content using AI and made it more human-like on BypassGPT. Below are the findings. From there, we scanned the rewritten output on ZeroGPT and as you can see, it passed with flying colors. More Methods to Avoid ZeroGPT AI Detection Besides using a simple undetectable AI writing tool, there are some other tactics you might want to employ to bypass ZeroGPT AI detection, including: Manual Changes It's always a smart idea to look over what an AI writes and make little changes so it sounds more like your voice if you want to get past AI detectors. You can swap out words you wouldn't say or fix anything that sounds awkward. Even small tweaks to tone and structure can help the writing feel more natural. Don’t Use AI for Everything Limit your use of AI for the most critical parts of the piece and create other sections that aren’t quite as important, such as the introduction and the outro. By writing these sections yourself, you reduce the risk of the AI detectors flagging your content as AI-generated. Get Creative with Your Prompts When creating your AI content, use various prompt types, like requiring the AI writing tool to adopt the style of a particular famous blogger or writer or the role of a particular professional in the relevant field. This should help make the AI-generated content more human and less stilted. Modify the Structure Edit the AI text closely, shortening and paraphrasing where possible. Also, remove sections that are repetitive and might sound too much like they were AI-created. Intentional Errors Some might advise that you add intentional mistakes or discrepancies in a bit to bypass ZeroGPT. However, this is an inadvisable strategy because it makes your work appear unprofessional. The general rule of thumb is to ensure you are offering accurate information that is grammatically correct, coherent, and professional. Incorporate Personal Anecdotes To make your AI-generated content more authentic, blend in personal anecdotes or experiences. By sharing your own stories and perspectives, you can add a touch of human emotion and credibility to the writing, making it less likely to be flagged as AI-generated. Vary Sentence Structure and Length AI detectors often look for patterns in sentence structure. By intentionally diversifying your sentence structure and length, you can disrupt these patterns and reduce the chances of being detected. Mix up long and short sentences, incorporate different types of clauses, and experiment with rhetorical devices to create a more natural flow. Use References and Citations To enhance the credibility of your AI-generated content, include references and citations to reliable sources within the text. By incorporating external information to support your claims or ideas, you not only add depth to the content but also reduce suspicion of AI involvement. Employ Interactive Elements Engage your readers by including interactive elements within your content. This could be in the form of embedded polls, surveys, or quizzes that encourage active participation. By incorporating such features, you not only make the content more engaging but also create a unique user experience that is less likely to be considered AI-generated. Why Is It Important to Bypass ZeroGPT? Evade detection Bypassing ZeroGPT means that instructors and any person on the hunt for AI-created text won’t be able to flag your work. This is particularly critical for students to avoid any problems and maintain academic integrity. Boost SEO and avoid Google penalties Google’s algorithms have gotten smarter in recent years and now consider the quality of content and how natural it is. By evading ZeroGPT detection, you turn robotic content created by AI into something that is more human and more engaging, which prevents you from getting penalized by Google and boosts your shot of getting better search engine optimization scores. Improve readability AI-created text can seem somewhat boring and simple for readers. Using an undetectable AI writer to change up your text will result in articles your audience will find more interesting and engaging. Stay ahead of competitors As more people utilize AI writing tools, the need to differentiate your content from others becomes increasingly important. By passing ZeroGPT detection and delivering content that feels more human, you can gain a competitive advantage. Your content will stand out and capture the attention of readers who are seeking a more personalized and engaging experience. Conclusion AI writers are valuable, but they also come with a set of new issues, particularly with the launch of AI detection tools such as ZeroGPT. Those having to create reams of text regularly with AI content creation tools need to know how to bypass ZeroGPT and other AI detectors. As you can see from this article, you have a number of strategies you can use to get past ZeroGPT’s detector. You can try to edit or change your text manually, though it does mean you have to put in some time and work yourself. A more expedient approach is to rely on an undetectable AI writing tool like BypassGPT, which is a great way to get past AI detection without deteriorating your content’s quality. So, test BypassGPT now and make sure your content passes ZeroGPT! This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Bypass ZeroGPT: 10 Secrets to Beat ZeroGPT AI Detection

জিরোজিপিটি বাইপাস করুন: জিরোজিপিটি এআই সনাক্তকরণকে হারানোর 10টি গোপনীয়তা

ZeroGPT umgehen: 10 Geheimnisse, um die ZeroGPT-KI-Erkennung zu umgehen

Evite ZeroGPT: 10 secretos para vencer la detección de IA ZeroGPT

Contourner ZeroGPT : 10 secrets pour vaincre la détection de l'IA ZeroGPT

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