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Infowijs

infowijs

It’s been almost 2 years , a Dutch EdTech company, as a Lead Developer. With the number of learnings and changes we’ve made to our platform thus far, I think it’s time to unfold our progress and share some of those learnings. since I joined Infowijs Back in the days The history of started when our two founders and , who met in university, were working as freelancers. Thom is a designer with a background in Communication Science and Tobias is a marketeer/business developer with a background in (Growth) Marketing and Political Science. At some point, Thom was asked by some Dutch schools to renew their websites. our company (Infowijs) Thom Tobias A recurring part of those projects was creating or updating the school guides, containing all sorts of important information for parents. At a certain point, Thom and Tobias saw an opportunity and came up with a plan to actually digitise those school guides. Up to that moment schools created and printed their guides before the start of a new school year (and to be fair, many schools still do). Any changes would only be updated in the next cycle. And there’s a mismatch with user expectations; we are so used to continuously being updated with new and changing information, that an online/digital solution seems to be the logical next step. After some initial tryouts, which were co-created together with a handful of Dutch secondary schools, the first digital school guides were created with WordPress. The product was (and still is) called . For the first couple of years, this worked well enough, however as with every MVP, a next step was needed to be made. Plans grew bigger and schools requested more and more features over time. One of those requests was for a more real-time communication tool. Schoolwiki Simultaneously a bigger question grew in the minds of our founders, what if we could make a private chat app where teachers and parents could talk, send requests and even fill out inquiries digitally? And so was born. Hoy We continue As more and more schools started using , the backlog grew and the founders started selling the product more seriously as a company instead of freelancers. This led to their search for a developer to join their founding team. Someone who could help them to develop more robust features and preferably someone with some knowledge of the internals of Dutch school administrations systems. The companies building this administration software tend to have a closed system and are making it very difficult to connect with. Schoolwiki That’s when they met . A tech guy and a pragmatic developer who hacked, while in school, onto one of those administration systems. After joining the founding team of Infowijs he grew the tech side of our company to a point where both and were running on a PHP framework he’d created over the years. Thomas Schoolwiki Hoy 🧑‍💻 His PHP framework was a Laravel-inspired, project, using Twig and/or React for rendering its web applications while being deployed on AWS EC2’s and being connected to a RDS-powered MySQL database. When I joined the company 2 years ago, Thomas had the opportunity to work full-time in another startup he founded ( ) and so Infowijs was looking for a new Lead Developer. The mission was to scale up the development team and transition the existing applications to a more scalable approach to support the further growth of the company. NearSt Now This brings us to today. Currently, we have a monthly load of approximately 2 million requests from our users. This number is growing exponentially as our sales grow fast. Every month, we add a dozen new schools. But apart from scaling our AWS tech stack and burning through our credit cards, there wasn’t much to scale further with the existing platform. There were limits on the database processing side, and our PHP framework has a limited number of requests it can handle per minute. Therefore I’ve decided 2 years ago to create an entirely new architecture to support our applications and the company’s growth. We chose to create this new platform with written in and planned to run it on instances. This way we can run fast applications that scale both horizontally and vertically at minimum costs. Together with our new codebase, we’ve decided to switch the main database of the new platform to Postgres instead of MySQL. This gives us lots of benefits in speed as well as the ability to have both a relational database and proper support for JSON storage (like a NoSQL database). Vert.x Kotlin AWS ECS with Fargate You set it up as a microservice-based architecture where each domain lives in its own isolated services. Services only talk with their own set of tables and databases and cross-communication is only done over the . Vert.x is based on the principle of verticles. A verticle is basically a lightweight actor executing an isolated unit of work, that can scale independently. It’s a simple as your microservice architecture down to the level of your codebase. Where your domains are separated on service level, your units of work are separated in verticals. EventBus Vert.x offers us flexible and easy asynchronous programming especially in combination with Kotlin Coroutines. In some sense, it’s somewhat comparable with Node.js, for those unfamiliar with Vert.x and Kotlin (Coroutines). However, since it runs on the JVM it empowers all its great performance benefits. This way, even in the cloud, we can handle tenfold the requests per minute, more than we ever could do with any other stack, and especially our previous PHP stack. At the moment of writing, we are in the middle of migrating our platform and even though we aren’t even halfway, we’ve already seen some interesting results that prove we made the right decision going forward. Right now, we process 2.5 million daily requests while all the services are running on the cheapest Fargate and Postgres instances. We haven’t seen any spikes in our system even on “busy” days. While our old platform, with a massively upgraded MySQL RDS database, is having a really hard time with 99% CPU usage. At those times, processing basically the same data in different domains of our platform, our new Postgres database barely hits 20% CPU peaks while running on the cheapest tier. The of Vert.x with Postgres are actually quite accurate in a production environment. TechEmpower benchmark tests Our Future In the coming year or so we will continue to move more domains from our old platform to the new one. So far we’ve moved 2 main domains of our systems to the new architecture, while also developing new (web) clients to support them with both new designs and more performant APIs. We have high hopes to scale up our small, distributed, Netherlands-based development team in the coming years and be the best in our field. Providing the best digital communication tools for Dutch schools.

EdTech: How Dutch Secondary Schools Are Transforming With Tech

Worst. Blog. Post. Title. Ever. Shit. Did you just click on an article titled “How to pass complex data to custom InfoWindows on a GoogleMap”? I’m impressed by your thirst for knowledge. An InfoWindow is the thing that pops up when you tap a marker on a GoogleMap object on Android. The InfoWindow contains (unsurprisingly) info, which is usually relevant to the location the marker is placed at. Here is how a default InfoWindow looks (screenshot taken from my app, ) Looxie See that “Level 4 user: 2059 points collected” thing? That’s how the default InfoWindow looks. Sometimes, that won’t work. For example, you may want to place an image on the InfoWindow. Or perhaps set a custom typeface on it so that it’s consistent with the look of the rest of your app. For example, this is what I wanted the InfoWindow to look like in my latest experimental app See that at work? And the cute “Play” button? That required the use of a custom InfoWindow. Custom InfoWindows are not really the point of this article (but rather the info that’s passed to the InfoWindow is) but here’s are some steps, for the uninitiated sweet Bariol font First, add the Google Play Services and Google Maps SDK by . You’ll also have to get an API key. following this tutorial After you’ve done that, you’ll have to add a <fragment> in your layout with the attributes you want it to have and add the bolded line to it <fragment android:id=”@+id/map” android:layout_width=”match_parent”android:layout_height=”match_parent” /> android:name=”com.google.android.gms.maps.SupportMapFragment” This will allow you to cast the <fragment> to a in your Java code, like this SupportMapFragment SupportMapFragment mapFragment = (SupportMapFragment) getSupportFragmentManager() .findFragmentById(R.id.map); Finally, show the map in the fragment by calling getMapAsync() on the fragment and passing in the current Activity context mapFragment.getMapAsync(this); If your setup went OK, you should now see a Google Map in the fragment. To start placing and manipulating markers on the Google Map, it’s always a good idea to have the Activity that’s hosting the map implement the interface. This will allow you (force you, actually) to implement a callback method called in your Activity, which will execute as soon as the map is loaded and ready. OnMapReadyCallback onMapReady() public void onMapReady(GoogleMap map) { } This method passes a GoogleMap instance to you, which you can then use to perform various operations on the map. You can now place a marker on the map (in ) by doing the following onMapReady() Marker marker = map.addMarker(new MarkerOptions().position( ) .icon(BitmapDescriptorFactory.fromResource( )) .title( ).snippet( )); latLng iconResource title snippet The bolded variables are, respectively, a object that contains your latitude and longitude LatLng an icon resource, eg. R.drawable.my_icon a String variable that contains the marker title another String variable that holds extra info about the marker, called a snippet. Remember this one, it’s important. This will place a Marker with a default InfoWindow on the map. However, as everybody knows Default InfoWindows are ugly. Default InfoWindows are bo-ring. When default InfoWindows show up at InfoWindow School they get bullied by cooler, more customized InfoWindows. Do you want your InfoWindows to get bullied, you savage? That’s a straight quote from the “What to expect when you are placing Markers on a Google Map” book, so shut up and follow my lead. To create your own customized InfoWindow, you create your own class that implements and then override two methods: and GoogleMap.InfoWindowAdapter getInfoContents() getInfoWindow() The difference between these two methods is that provides a default “bubble”, shadow and caret that points towards the marker (see my first screenshot above) while provides , like that stingy uncle of yours at a family vacation in 1998, when you were at the pool and wanted a freaking ice cream but he was too cheap to get you one. getInfoContents() getInfoWindow() nothing [deep breaths] When you override getInfoWindow() you provide the entire layout, background and all by inflating an XML layout. Here is what an implementation of InfoWindowAdapter looks like public class InfoWindowCustom implements GoogleMap.InfoWindowAdapter { Context context;   
LayoutInflater inflater;

public InfoWindowCustom(Context context) {  
       this.context = context;  
}

@Override      
public View **getInfoContents(Marker marker)** {          
    return null;      
}

@Override  
public View **getInfoWindow(Marker marker)** {

inflater = (LayoutInflater) context.getSystemService(Context.LAYOUT_INFLATER_SERVICE); // is a layout in my// . You can provide your own R.layout.echo_info_window res/layout folder View v = inflater.inflate(_R.layout.echo\_info\_window_, null);     
       
TextView title = (TextView) v.findViewById(R.id.info\_window\_title); TextView subtitle = (TextView) v.findViewById(R.id.info_window_subtitle); title.setText(marker.getTitle());  
subtitle.setText(marker.getSnippet());

return v;

} } Meanwhile, back in the magical land of in the Activity where the map is shown, you have to set the InfoWindowAdapter implementation on the map, like this onMapReady() map.setInfoWindowAdapter(new InfoWindowCustom(this)); From now on, whenever a marker is tapped, all the marker info will be beautifully displayed, framed by your delightful custom InfoWindow. A Promise Fulfilled on Russian Hill BUT WAIT! That’s not what I promised! What I promised was the ability to pass complex data to the InfoWindow adapter, which sort of seems difficult right now, since the marker can only carry two pieces of data: a and a . title snippet When I first came upon this problem, my solution was to concatenate everything I wanted to display into the snippet, with line breaks (“\n”) to seperate different lines that would represent different data. That meant, however, that I would have to mess around with Spannables and String lengths if I wanted to style the various pieces of data differently. Have you ever worked with Spannables on concatenated String data? I have and I’m pretty sure that anal violation by Satan is a more exciting prospect. Nope. I needed something simpler. And then suddenly my eyes lit up and I felt like freakin’ Werner Einsten when he discovered Marxism. Why couldn’t I simply pass a JSON representation of an object to the snippet and then retrieve that snippet in the InfoWindowAdapter and turn it back into an object? Turns out I could. And I did. Let’s say you have this class called MarkerInfo public class MarkerInfo { private String title;  
private String subtitle;  
private String soundUrl;  
private int numberOfPlays; public MarkerInfo() { } // getters and setters omitted because I'm not writing this in// an IDE. But assume that they are there } A super-easy way to turn an object of this class into a JSON representation and back into an object is to use a library like . Gson After importing it into your project, you can just turn a MarkerInfo object into a String (JSON) representation simply by doing this MarkerInfo markerInfo = new MarkerInfo();markerInfo.setTitle("Shit went down");markerInfo.setSubtitle("In Hell's Kitchen");markerInfo.setSoundUrl("https://www.luke.com/matthew/jessica/dumbass.mp3");markerInfo.setNumberOfPlays(66); Gson gson = new Gson(); String markerInfoString = gson.toJson(markerInfo); So when you’re setting the snippet property on your Marker, you pass the resulting String to it and then in your custom InfoWindowAdapter class, you retrieve the String representation of the object and turn it back into an object. Gson gson = new Gson(); MarkerInfo aMarkerInfo = gson.fromJson(marker.getSnippet(), MarkerInfo.class); Now, you can access any object field individually and set it on your views as you see fit. Cool, huh? Of course, after some research on the Internet using the correct keywords, I found out that other people had used this trick prior to my “discovery” but fuck those guys because they are not me.

How to pass complex data to custom InfoWindows on a GoogleMap

Cybercrooks Are Using Fake Job Listings to Steal Crypto

Cybercrooks gebruik vals poslys om Crypto te steel

Cyberskurke bruger falske jobannoncer til at stjæle krypto

Los cibercriminales están usando anuncios de empleo falsos para robar criptomonedas

Cybercrook-ek lan-zerrenda faltsuak erabiltzen ari dira Cryptoa lapurtzeko

Kyberrikolliset käyttävät väärennettyjä työpaikkailmoituksia varastaakseen krypton

נוכלי סייבר משתמשים ברשימות עבודה מזויפות כדי לגנוב קריפטו

サイバー犯罪者は偽の求人情報を利用して仮想通貨を盗んでいる

Mampiasa lisitra asa sandoka ny Cybercrooks mba hangalarana Crypto

साइबर अपराधीहरूले क्रिप्टो चोरी गर्न नक्कली जागिर सूचीहरू प्रयोग गरिरहेका छन्

Mashtruesit kibernetikë po përdorin listime të rreme pune për të vjedhur kripto

Ti Cybercrooks Ti Tirhisa Ti Fake Job Listings Ku Yiva Crypto

AmaCybercrooks Asebenzisa Uhlu Lwemisebenzi Okungelona Iqiniso Ukweba i-Crypto

Authors:
(1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab;
(2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Table of Links Abstract and Introduction Why work on QAnon? Specificities and social impact Who is Q? The theories put to test Authorship attribution Results Discussion Corpus constitution Quotes of authors outside of the corpus have been Definition of two subcorpus: dealing with generic difference and an imbalanced dataset The genre of “Q drops”: a methodological challenge Detecting style changes: rolling stylometry Ethical statement, Acknowledgements, and References Corpus constitution Because of their contested contents, many accounts have been deleted by the social media platforms. Other accounts were simply deleted by the user themselves. This raised many challenges for the corpus constitution, which had to rely on data collection realized before the deletion, or on difficulty searchable web archives. Moreover, all sources do not have the same time-span, and were created at different dates, discussing potentially different news, a source of heterogeneity in training material that creates challenge in the attribution procedure. We list here the sources we used for each candidate: Roger S We collected Roger S.’s posts on Gab (https://gab.com/RogerJStoneJr), from June 14th to August 2nd, 2021. Michael F Michael F. wrote a series of 10 articles for the Western Journal, from June 29, 2020 to July 31, 2021. (https://www.westernjournal.com/author/mflynn/). He also wrote a letter to ask for support to Roger S.’s wife, published on FrankReport (https://frankreport.com/2021/06/11/bannedby-twitter-gen-michael-flynn-is-published-on-frankreport-concerning-roger-stone/). His article for Fox News about ISIS has also been analyzed. (https://web.archive.org/web- /20161215042531/http://www.foxnews.com/opinion/2016/11/02/gen-michaelflynn-after-mosul-is-liberated-isis-could-attack-usnext.html). Finally, we collected posts on his Twitter account from October 19th, 2016 to September 18th, 2017. Paul F We collected Paul F.’s personal writing on his website (https://paulfurber.net/). We retrieved archives from his twitter on account of a threadreader (https://threadreaderapp.- com/thread/1158540523008905216.html). We also captured archives from the CBTS boards on 8chan, where he wrote as “The Board Owner”. A few posts he left on Discord were transcribed from pictures found online. Finally, Paul F. wrote a book: Q: Inside The Greatest Intelligence Drop In History, included in our large corpus. Jim W Archives of Jim W.’s Twitter account were found on archive.today (https://archive.is- /https://twitter.com/xerxeswatkins), which preserves 9 screenshots (22 Dec 2014, 6 Feb 2016, 15 Mar 2016, 30 Mar 2016, 4 Apr 2016, 5 May 2016, 28 May 2016, 25 Mar 2017, 8 Apr 2017) taken prior to the account suspension. A large amount of these tweets were only citing article titles from his own media The Goldwater. As these titles were not necessarily (and probably not) written by him, we chose to exclude them. A small text was written by Jim W. on 5ch about a service problem on 8ch that he blames on a government attack. (https://fox.5ch.net/test/read.cgi/poverty/1418- 027836/826) Finally, posts from his Parler account were added to the corpus. Ron W A sample of 3130 tweets by Ron W. have been collected through the Twitter API. We also collected his posts on Telegram from November 30th, 2021 to December, 20th, 2021. Coleman R Under the pseudonym PamphletAnon, Coleman R. wrote a vast number of posts on 8chan, especially on the board “the Storm” (https://8ch.net/thestorm/catalog.html) of which he was the owner. We collected his posts on the Wayback Machine, which seems to provide a complete archive of the board. A few messages he posted on Discord (Q Central, 2017) have been archived by DDOS (https://ddosecrets.com/wiki/Distributed Denial of Secrets), at this address https://whispers.ddosecrets.com/discord/user/376607495470448643. 347 messages by PamphletAnon on Discord (Patriots’ Soapbox, 2018) are also available at: https://discordleaks.unicornriot.ninja/discord/user/41936. We also found a small text on Reddit, where Coleman R. announces a future talk on Infowars with Rob Dew. On September 11, 2020, Pamphlet Anon wrote a text on “Patriots’ Soapbox”, the media he curates with Christina U. (https://patriotssoapbox.com/opinion/memories-of9-11-surreal-and-terrifying/). Courtney T Collaborating with Ron and Jim W., Courtney T. publicly announced that she knew the truth about the Q drops, and that it would be highly disappointing to their public (Huback, 2021). We collected her posts on Twitter, under her account IWillRedPillYou, archived here (http://web.archive.org/web/20180113162029if /-https://twitter.com/IWillRedPillYou). Tracy D Under the pseudonym Tracy Beanz, Tracy D. published a large number of tweets, on an account now suspended, but of which archives.today retains 92 captures (https://archive.vn/lDRyR), from December 2016 to January 2021. She also published a long post explaining herself about quarrels over Q related publications on Steemit (https://steemit.com/drama/@tracybeanz/shestood-in-the-storm) Christina U Christina U. wrote a number of articles on PatriotsSoapbox (https://patriotssoapbox.com/), of which the 5 most recent on July 7h, 2021 were collected. We also found an online conversation she had on Muckrock with Homeland Security on the 7th and 22nd of July 2021. (https://www.muckrock.com/foi/united-statesof-america-10/patriots-soapbox-department-ofhomeland-security-115196/#file-956999) Archives of her GAB profile were also analyzed. Donald T Former president of the United States tweets were collected during the month of december 2020. We removed tweets suspected to have been written by his staff by the site https://factba.se/. Melania T We collected a sample of tweets by former FLOTUS thanks to the Twitter API, from January 21st, 2017 to January 19th, 2021. Eric T We collected a sample of 3000 tweets by the son of former POTUS thanks to the Twitter API from September 10th 2016, to June 24th 2021. Dan S We collected a sample of tweets by the former POTUS’ deputy chief of staff thanks to the Twitter API, from October 25th, 2018 to January 20th, 2021. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab; (2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Authors: Authors: (1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab; (2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Table of Links Abstract and Introduction Abstract and Introduction Why work on QAnon? Specificities and social impact Why work on QAnon? Specificities and social impact Who is Q? The theories put to test Who is Q? The theories put to test Authorship attribution Authorship attribution Results Results Discussion Discussion Corpus constitution Corpus constitution Quotes of authors outside of the corpus have been Quotes of authors outside of the corpus have been Definition of two subcorpus: dealing with generic difference and an imbalanced dataset Definition of two subcorpus: dealing with generic difference and an imbalanced dataset The genre of “Q drops”: a methodological challenge The genre of “Q drops”: a methodological challenge Detecting style changes: rolling stylometry Detecting style changes: rolling stylometry Ethical statement, Acknowledgements, and References Ethical statement, Acknowledgements, and References Corpus constitution Because of their contested contents, many accounts have been deleted by the social media platforms. Other accounts were simply deleted by the user themselves. This raised many challenges for the corpus constitution, which had to rely on data collection realized before the deletion, or on difficulty searchable web archives. Moreover, all sources do not have the same time-span, and were created at different dates, discussing potentially different news, a source of heterogeneity in training material that creates challenge in the attribution procedure. We list here the sources we used for each candidate: Roger S We collected Roger S.’s posts on Gab (https://gab.com/RogerJStoneJr), from June 14th to August 2nd, 2021. Roger S Michael F Michael F. wrote a series of 10 articles for the Western Journal, from June 29, 2020 to July 31, 2021. (https://www.westernjournal.com/author/mflynn/). He also wrote a letter to ask for support to Roger S.’s wife, published on FrankReport (https://frankreport.com/2021/06/11/bannedby-twitter-gen-michael-flynn-is-published-on-frankreport-concerning-roger-stone/). Michael F His article for Fox News about ISIS has also been analyzed. (https://web.archive.org/web- /20161215042531/http://www.foxnews.com/opinion/2016/11/02/gen-michaelflynn-after-mosul-is-liberated-isis-could-attack-usnext.html). Finally, we collected posts on his Twitter account from October 19th, 2016 to September 18th, 2017. Paul F We collected Paul F.’s personal writing on his website (https://paulfurber.net/). Paul F We retrieved archives from his twitter on account of a threadreader (https://threadreaderapp.- com/thread/1158540523008905216.html). We also captured archives from the CBTS boards on 8chan, where he wrote as “The Board Owner”. A few posts he left on Discord were transcribed from pictures found online. Finally, Paul F. wrote a book: Q: Inside The Greatest Intelligence Drop In History, included in our large corpus. Jim W Archives of Jim W.’s Twitter account were found on archive.today (https://archive.is- /https://twitter.com/xerxeswatkins), which preserves 9 screenshots (22 Dec 2014, 6 Feb 2016, 15 Mar 2016, 30 Mar 2016, 4 Apr 2016, 5 May 2016, 28 May 2016, 25 Mar 2017, 8 Apr 2017) taken prior to the account suspension. A large amount of these tweets were only citing article titles from his own media The Goldwater. As these titles were not necessarily (and probably not) written by him, we chose to exclude them. Jim W A small text was written by Jim W. on 5ch about a service problem on 8ch that he blames on a government attack. (https://fox.5ch.net/test/read.cgi/poverty/1418- 027836/826) Finally, posts from his Parler account were added to the corpus. Ron W A sample of 3130 tweets by Ron W. have been collected through the Twitter API. Ron W We also collected his posts on Telegram from November 30th, 2021 to December, 20th, 2021. Coleman R Under the pseudonym PamphletAnon, Coleman R. wrote a vast number of posts on 8chan, especially on the board “the Storm” (https://8ch.net/thestorm/catalog.html) of which he was the owner. We collected his posts on the Wayback Machine, which seems to provide a complete archive of the board. Coleman R A few messages he posted on Discord (Q Central, 2017) have been archived by DDOS (https://ddosecrets.com/wiki/Distributed Denial of Secrets), at this address https://whispers.ddosecrets.com/discord/user/376607495470448643. 347 messages by PamphletAnon on Discord (Patriots’ Soapbox, 2018) are also available at: https://discordleaks.unicornriot.ninja/discord/user/41936. We also found a small text on Reddit, where Coleman R. announces a future talk on Infowars with Rob Dew. On September 11, 2020, Pamphlet Anon wrote a text on “Patriots’ Soapbox”, the media he curates with Christina U. (https://patriotssoapbox.com/opinion/memories-of9-11-surreal-and-terrifying/). Courtney T Collaborating with Ron and Jim W., Courtney T. publicly announced that she knew the truth about the Q drops, and that it would be highly disappointing to their public (Huback, 2021). We collected her posts on Twitter, under her account IWillRedPillYou, archived here (http://web.archive.org/web/20180113162029if /- https://twitter.com/IWillRedPillYou). Courtney T https://twitter.com/IWillRedPillYou). Tracy D Under the pseudonym Tracy Beanz, Tracy D. published a large number of tweets, on an account now suspended, but of which archives.today retains 92 captures (https://archive.vn/lDRyR), from December 2016 to January 2021. Tracy D She also published a long post explaining herself about quarrels over Q related publications on Steemit (https://steemit.com/drama/@tracybeanz/shestood-in-the-storm) Christina U Christina U. wrote a number of articles on PatriotsSoapbox (https://patriotssoapbox.com/), of which the 5 most recent on July 7h, 2021 were collected. Christina U We also found an online conversation she had on Muckrock with Homeland Security on the 7th and 22nd of July 2021. (https://www.muckrock.com/foi/united-statesof-america-10/patriots-soapbox-department-ofhomeland-security-115196/#file-956999) Archives of her GAB profile were also analyzed. Donald T Former president of the United States tweets were collected during the month of december 2020. We removed tweets suspected to have been written by his staff by the site https://factba.se/. Donald T Melania T We collected a sample of tweets by former FLOTUS thanks to the Twitter API, from January 21st, 2017 to January 19th, 2021. Melania T Eric T We collected a sample of 3000 tweets by the son of former POTUS thanks to the Twitter API from September 10th 2016, to June 24th 2021. Eric T Dan S We collected a sample of tweets by the former POTUS’ deputy chief of staff thanks to the Twitter API, from October 25th, 2018 to January 20th, 2021. Dan S This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Challenges in Building a QAnon Authorship Corpus

When writing code, the order of condition checks can significantly impact the correctness and efficiency of your program. Let's explore this through a recent update I made to Skaffold's source code, which led to a 50% performance increase, but before diving into the details, which option would you prefer? Option 1: if util.IsEmptyDir(path) || !info.IsDir() Option 2: if !info.IsDir() || util.IsEmptyDir(path) Check the functions: util.IsEmptyDir(path): This function checks if a directory at the specified path is empty. func IsEmptyDir(path string) bool {
    d, err := os.Open(path)
    if err != nil {
        return false
    }
    defer d.Close()
    if _, err := d.Readdirnames(1); err == io.EOF {
        return true
    }
    return false
} info.IsDir(): This method checks if the current instance of the struct is a directory. func (x X) IsDir() bool {
    return x.modeType & os.ModeDir != 0
} Let's remember that in Go (as in many other programming languages), the order of condition checks in an if statement is evaluated from left to right. This means that the first condition is evaluated before the second. If the first condition returns true, the second condition will not be executed and it’s important. For example: if !info.IsDir() || util.IsEmptyDir(path) {} Here, !info.IsDir() is evaluated first. If it returns true, the util.IsEmptyDir(path) is not evaluated. But why is that so important? info.IsDir() method makes only static checks, and this operation is pretty fast while the util.IsEmptyDir() makes I/O operations; it opens the given path and reads directory names. When Go performs I/O operations like os.Open(path) or Readdirnames, several things happen: File Descriptor Management:
When you open a file with os.Open(path), Go uses system calls like open (on Unix-like systems) to obtain a file descriptor, which represents an open file in the operating system.


System Calls:
Performing I/O operations involves making system calls to the underlying operating system. For example, os.Open(path) will ultimately call the open system call to open the file at the specified path.


Blocking Nature: Many I/O operations in Go are blocked by default, meaning the program waits until the operation completes before proceeding. Taking into consideration all the information above, the right answer here is the second option, for checking !info.IsDir() first, unnecessary calls to IsEmptyDir on non-directory paths are avoided. This reduces the number of costly I/O operations, in my case: First build: from 102,958 to 20,764 calls


On file change: from 411,832 to 103,820 calls Based on our experience, here are some practical guidelines for optimizing condition order in your code: Prioritize Fast Checks: Place conditions that involve static checks or computations before those requiring I/O operations or more resource-intensive tasks. Profile and Benchmark: Always profile your code to identify performance bottlenecks. Use benchmarks to validate the impact of condition order changes on overall performance. When writing code, the order of condition checks can significantly impact the correctness and efficiency of your program. Let's explore this through a recent update I made to Skaffold's source code , which led to a 50% performance increase , but before diving into the details, which option would you prefer? Skaffold's source code 50% performance increase Option 1: Option 1: if util.IsEmptyDir(path) || !info.IsDir() if util.IsEmptyDir(path) || !info.IsDir() Option 2: Option 2: if !info.IsDir() || util.IsEmptyDir(path) if !info.IsDir() || util.IsEmptyDir(path) Check the functions: util.IsEmptyDir(path): This function checks if a directory at the specified path is empty. util.IsEmptyDir(path) : This function checks if a directory at the specified path is empty. util.IsEmptyDir(path) func IsEmptyDir(path string) bool {
    d, err := os.Open(path)
    if err != nil {
        return false
    }
    defer d.Close()
    if _, err := d.Readdirnames(1); err == io.EOF {
        return true
    }
    return false
} func IsEmptyDir(path string) bool {
    d, err := os.Open(path)
    if err != nil {
        return false
    }
    defer d.Close()
    if _, err := d.Readdirnames(1); err == io.EOF {
        return true
    }
    return false
} info.IsDir(): This method checks if the current instance of the struct is a directory. info.IsDir() : This method checks if the current instance of the struct is a directory. info.IsDir() func (x X) IsDir() bool {
    return x.modeType & os.ModeDir != 0
} func (x X) IsDir() bool {
    return x.modeType & os.ModeDir != 0
} Let's remember that in Go (as in many other programming languages), the order of condition checks in an if statement is evaluated from left to right . This means that the first condition is evaluated before the second. If the first condition returns true, the second condition will not be executed and it’s important . Let's remember that in Go (as in many other programming languages), the order of condition checks in an if statement is evaluated from left to right and it’s important For example: if !info.IsDir() || util.IsEmptyDir(path) {} if !info.IsDir() || util.IsEmptyDir(path) {} Here, !info.IsDir() is evaluated first. If it returns true , the util.IsEmptyDir(path) is not evaluated. But why is that so important? !info.IsDir() true util.IsEmptyDir(path) info.IsDir() method makes only static checks, and this operation is pretty fast while the util.IsEmptyDir() makes I/O operations; it opens the given path and reads directory names. info.IsDir() util.IsEmptyDir() When Go performs I/O operations like os.Open(path) or Readdirnames , several things happen: os.Open(path) Readdirnames File Descriptor Management:
When you open a file with os.Open(path), Go uses system calls like open (on Unix-like systems) to obtain a file descriptor, which represents an open file in the operating system. System Calls:
Performing I/O operations involves making system calls to the underlying operating system. For example, os.Open(path) will ultimately call the open system call to open the file at the specified path. Blocking Nature: Many I/O operations in Go are blocked by default, meaning the program waits until the operation completes before proceeding. File Descriptor Management:
When you open a file with os.Open(path), Go uses system calls like open (on Unix-like systems) to obtain a file descriptor, which represents an open file in the operating system. File Descriptor Management :
When you open a file with os.Open(path) , Go uses system calls like open (on Unix-like systems) to obtain a file descriptor, which represents an open file in the operating system. File Descriptor Management os.Open(path) open System Calls:
Performing I/O operations involves making system calls to the underlying operating system. For example, os.Open(path) will ultimately call the open system call to open the file at the specified path. System Calls :
Performing I/O operations involves making system calls to the underlying operating system. For example, os.Open(path) will ultimately call the open system call to open the file at the specified path. System Calls os.Open(path) Blocking Nature: Many I/O operations in Go are blocked by default, meaning the program waits until the operation completes before proceeding. Blocking Nature : Many I/O operations in Go are blocked by default, meaning the program waits until the operation completes before proceeding. Blocking Nature Taking into consideration all the information above, the right answer here is the second option, for checking !info.IsDir() first, unnecessary calls to IsEmptyDir on non-directory paths are avoided. This reduces the number of costly I/O operations, in my case: !info.IsDir() IsEmptyDir First build: from 102,958 to 20,764 calls On file change: from 411,832 to 103,820 calls First build: from 102,958 to 20,764 calls First build: from 102,958 to 20,764 calls On file change: from 411,832 to 103,820 calls On file change: from 411,832 to 103,820 calls Based on our experience, here are some practical guidelines for optimizing condition order in your code: Prioritize Fast Checks: Place conditions that involve static checks or computations before those requiring I/O operations or more resource-intensive tasks. Prioritize Fast Checks : Place conditions that involve static checks or computations before those requiring I/O operations or more resource-intensive tasks. Prioritize Fast Checks Profile and Benchmark: Always profile your code to identify performance bottlenecks. Use benchmarks to validate the impact of condition order changes on overall performance. Profile and Benchmark : Always profile your code to identify performance bottlenecks. Use benchmarks to validate the impact of condition order changes on overall performance. Profile and Benchmark

How Order in iF Statement Impacts on Performance

Launching a new product or application comes with many exciting moments, not just for the owner but for the larger audience to be aware of a new service. A waitlist application is used by agencies, companies, marketing campaign websites, and many more to collect user data from a website when a user fills in their email address. This strategy serves the purpose of remarketing or reminders before the launch. In this post, you’ll learn how to use Xata to store data by integrating it into a Next.js app, a React full-stack application framework for building user interfaces. Also, this guide will show you how to create a new database (DB) in your Xata admin workspace. What is Xata? From the official documentation, Xata is a serverless database platform powered by PostgreSQL packed with features such as the fuzzy search, the ability to import CSV (comma-separated value), file attachments, Python and TypeScript SDK integration, and so on. Xata Prerequisites To complete this tutorial, you need an Xata account. If you don’t have one, . sign up for a free developer account now This tutorial assumes you’re familiar with running a app locally. Other requirements include the following: Next Understanding of writing CSS with Tailwind CSS Basic JavaScript knowledge Demo Try the demo application in this . URL Installation and setup Download the setup for the demo waitlist application from the . main branch of this repository Open up your terminal and into the cloned directory, . cd xata-with-nextjs cd xata-with-nextjs Install the project dependencies using the package manager, : npm npm install Now, let’s start the development server to preview the landing page with the command: npm run dev The boilerplate code runs on and should look something like this: port 3000 Configuring Xata instance To initialize the waitlist project, install the Xata CLI globally. Run this command: npm install -g @xata.io/cli Next, you need to authenticate the Xata CLI with your Xata account, as this will ensure your system is configured correctly for your profile. You do not need to repeat this process once the configuration is complete with this command: xata auth login This command will open a new window for first-time users to authenticate and create a new API key for your user account. Creating a new database In your Xata account workspace dashboard, create with a name. a new database Navigate into the DB and create a with a desired name. The defined columns in the hold all the data information. waitlist table table Click on the icon to add a new column. Select the data type and give the column the name, . plus Email email To see how the data looks with the inferred data types, click on the left navigation pane of your dashboard. Schema : The are unique for each new record and auto-generated. Note IDs The other way to create a database is by outlined in the Next.js starter guide. Make sure you run the xata init in the project directory. following the process Inserting a new record Within the directory, create a new folder, pages/api/post-info.js. Copy-paste the following code: src pages/api/post-info.js import { getXataClient } from "@/xata"

    const handler = async (request, response) => {
      const xata = await getXataClient()
      const { email } = request.body
      await xata.db.details.create({
        email
      })
      response.end()
    }

    export default handler Let’s break down the code snippets above. First, import the function from , which is present in the app upon initialization with xata init. Then, in the asynchronous function, pass the parameters, request, and response, and within the function, initialize getXataClient to a xata variable. getXataClient src/xata.js handler The will handle the value email (the exact name in Xata DB) from the data submitted in the request body in the form input. request.body Next, pass in the data, email as an object in the function. create The method will cause the webserver to stop once the data is created in the DB, meaning a successful status code, 200. response.end() Integrating Xata into the app In this section, if you experience this error when running the server, it is because the code needs to run on the server, not the client. That is within the file. app/page.js Uncaught Error: You are trying to use Xata from the browser, which is potentially a non-secure environment. If you understand the security concerns, such as leaking your credentials, pass `enableBrowser: true` to the client options to remove this error. The guide shows you the proper process to follow. Get started with Next.js and Xata Copy-paste the following code in the to update the file: src/app/page.js "use client"
    import { useState } from "react"
    import Footer from "@/components/Footer"
    import Services from "@/components/Services"
    import Image from "next/image"
    import { bigShoe1 } from "@/assets/images"

    export default function Home() {
      const [email, setEmail] = useState('')
      const [isSubmitted, setIsSubmitted] = useState(false)
      const [error, setError] = useState(null)
      const resetFormField = () => {
        setEmail('')
      }
       const submit = () => {
        fetch('/api/post-info', {
          method: 'POST',
          headers: {
            'Content-Type': 'application/json'
          },
          body: JSON.stringify({
            email
          }),
        })
      };
      const handleSubmit = (e) => {
        e.preventDefault()
        if (!email) {
          setError('Email is invalid')
          return
        }
        submit()
        resetFormField()
        setIsSubmitted(true)
      }
      const isValidEmail = (email) => {
        return /\S+@\S+\.\S+/.test(email);
      }
      const handleChange = (e) => {
        if (!isValidEmail(e.target.value)) {
          setError('Email is invalid');
        } else {
          setError(null)
        }
        setEmail(e.target.value)
      }
      return (
        <main>
          <section className="w-full py-8 z-10 sm:px-16 px-8 flex xl:flex-row flex-col justify-center gap-10">
          <div className="xl:w-2/5 flex flex-col justify-center items-start w-full">
            <p className="text-xl font-montserrat text-coral-red">Our summer collection</p>
            <h1 className='mt-10 font-palanquin text-8xl max-sm:text-[72px] font-bold'>
              <span className='xl:bg-white xl:whitespace-nowrap z-10 pr-10'>
                The New Arrival
              </span>
              <br />
              <span className='text-coral-red inline-block mt-3'>Nike </span> Shoes
            </h1>
            <p className='font-montserrat text-slate-gray text-lg leading-8 mt-6 mb-14 sm:max-w-sm'>
              Discover stylish Nike arrivals, quality comfort, and innovation for
              your active life.
            </p>
            {isSubmitted ? (
              <div>
                <p className="font-bold text-2xl">Well received! We will keep you updated.</p>
              </div>) : (
              <form className="w-full flex items-center max-sm:flex-col gap-5 p-2.5 sm:border sm:border-slate-gray rounded-full" onSubmit={handleSubmit} id="joinwaitlist">
              <input type="email" name="email" id="email" placeholder="Enter your email address" className="input" value={email} onChange={handleChange} />
                <div className="flex max-sm:justify-end items-center max-sm:w-full">
                <button className="w-full bg-coral-red rounded-full text-white border-coral-red px-7 py-4" type="submit">Join waitlist</button>
                </div>
              </form>
            )}
            {error && <p className="text-rose-700 mt-5 ml-3">{error}</p>}
          </div>
          <div className='flex-1 flex justify-center items-center bg-center bg-cover'>
            <Image
              src={bigShoe1}
              alt='shoe collection'
              width={610}
              height={500}
              className='object-contain'
            />
          </div>
          </section>
          <section className="sm:px-16 px-8 sm:py-24 py-12">
            <Services />
          </section>
          <Footer />
        </main>
      )
    } Without the in the code above, you may encounter this error, as shown. "use client" To learn more about this error, check on using client components in Next.js. this documentation The final demo should look like this: showing some validation error if no email is typed or clicking the button on an empty input field. Conclusion Xata integration with Next.js is excellent for building your ideas and capturing user data on the backend without so much effort in setting up the architecture yourself from scratch. Check out the if you want to learn about Xata and its use case with other frameworks. documentation

Building an Efficient Waitlist App with Next.js and Xata

In this blog post, we'll learn how to create Apple Music's background blur effect using Blurhash. I often wonder how Apple adds this blur effect in their app while playing the music. It's super cool if you open the playing music on the Apple Music app. The background blur color changes based on the playing music thumbnail image. This feature intrigued me to find the approach to how to do it. In this blog post, we'll learn how to create this effect. Getting Started I assume that everyone knows how to create a Next.js project. If you need help, follow this to get started. Now we need to install the following packages into our project. link # yarn 
yarn add blurhash react-blurhash sharp

# npm
npm install --save blurhash react-blurhash sharp Let's start coding. First, we will create an API that takes a URL as a parameter and returns the blurhash string. I will use the Next.js v13 router to implement this feature. If you are unfamiliar with this App folder approach, use the earlier router approach to achieve this behavior. App Page // app/api/gethash/route.ts

import * as blurhash from 'blurhash'

import { NextResponse } from 'next/server' import sharp from 'sharp';

export async function GET(req: Request) {

    const { searchParams } = new URL(req.url) const url = searchParams.get('url')


if (!url) return NextResponse.json({
    message: 'Required fields is empty'
}, {
    status: 400
})

try {
    const image = await fetch(url);
    const buffer = Buffer.from(await image.arrayBuffer())

    const { data, info } = await sharp(buffer)
        .raw()
        .ensureAlpha()
        .resize(32, 32)
        .toBuffer({
            resolveWithObject: true
        });

    const hash = blurhash.encode(
        new Uint8ClampedArray(data),
        info.width,
        info.height,
        4,
        4
    )

    return NextResponse.json({
        hash
    })
} catch (err) {
    return NextResponse.json({
        error: err
    }, {
        status: 404
    })
}
} We created a new route handler file inside the folder. The route handler file name should be . While working with route handler files, you must be very conscious of the page.js file, and the route.js should be at a different level. Check this for further details. app/api/gethash route.js|ts link Let's break the code line by line to understand what is happening. As mentioned above, our route takes the URL as a parameter. We need to get the the URL passed in the query params and check whether the value of the URL is empty or not. const { searchParams } = new URL(req.url) const url = searchParams.get('url')

if (!url) return NextResponse.json({
    message: 'Required fields is empty'
}, {
    status: 400
}) We need to convert our image URL to an array buffer. To achieve this, we need to fetch the image from the source URL and convert it into an array buffer using the method. const image = await fetch(url); const buffer = Buffer.from(await image.arrayBuffer()) Now, pass the buffer into the sharp function. is an image processing package. We use this package to resize our images to smaller ones because it helps us to generate the hash string faster. Sharp const { data, info } = await sharp(buffer) .raw() .ensureAlpha() .resize(32, 32) .toBuffer({ resolveWithObject: true }); Finally, pass the buffer returned from sharp into encode function of the blurhash package. It produces a hash string. const hash = blurhash.encode( new Uint8ClampedArray(data), info.width, info.height, 4, 4 ) Return the hash string from the API. Now we have the API to generate a hash string based on the image URL. Let's start rendering it on the UI. We are going to use the react-hash package. We need to pass the hash string to this package. It then generates the blurred background based on the input URL. // page.tsx

import Image from 'next/image'
import BlurHash from './components/BlurHash'

async function getBlurHash() {
  const response = await fetch('http://localhost:3000/api/gethash?url=https://plus.unsplash.com/premium_photo-1685077715983-772598c45360?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=200')

  return await response.json()
}

export default async function Home() {
  const { hash } = await getBlurHash()

  return (
    <main className='main'>
      <div className='hash'>
        <BlurHash hash={hash} />
      </div>
      <Image src='https://plus.unsplash.com/premium_photo-1685077715983-772598c45360?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=986&q=80' alt='' width={500} height={500} />
    </main>
  )
} // components/BlurHash.tsx

'use client';

import React from 'react'
import { Blurhash } from 'react-blurhash';

const BlurHash = ({ hash }: { hash: string }) => {
    return (
        <Blurhash
            hash={hash}
            width={"100%"}
            height={"100%"}
            resolutionX={32}
            resolutionY={32}
            punch={1} 
            />
    )
}

export default BlurHash Here is the link to my . If you have any questions or comments, let me know on Twitter at or via email at . But until next time, happy coding! repo @jana__sundar mailtojana23[at]gmail.com Also published . here

How to Create Apple Music's Background Blur Effect Using Blurhash

ChatGPT was released in June 2020 and it is developed by OpenAI. It has led to revolutionary developments in many areas. One of these areas is the creation of database queries.The database queries can be generated from plain text via . It helps you with databases that you have little knowledge about. ChatGPT Artificial intelligence, which has progressively integrated into our daily lives, has gained significant momentum with the rise of ChatGPT. Before moving into the project, let’s briefly explore ChatGPT. To fully comprehend the capabilities of ChatGPT, it is essential to have a solid grasp of its underlying architecture. ChatGPT has been developed based on the architecture. So let’s take a look at transformers first. GPT For direct access to the project, click . here Transformers utilize seq2seq frameworks, enabling the conversion of one sequence into another. The sequences have defined ordering. As an illustration, sentences can be viewed as a succession of words. The Transformers can also be used to solve NLP such as text translation. We need a lot of labeled data for training this architecture. This would be difficult for transformers to learn. We can use transfer learning to solve this problem. Transformers consist of two components (Fig 1): an encoder and a decoder, both adept at acquiring a proficient language representation. This proficiency allows us to construct language models from each component. By stacking the encoders, we obtain a idirectional ncoder epresentation of ransformers, commonly known as . Similarly, by stacking the decoder units, we can achieve enerative re- raining that is GPT. In the our case we are focusing on GPT. Let’s think about transfer learning in the context of GPT. When we train a model from scratch, it typically demands a substantial amount of data as the parameters are initially set randomly. However, imagine a scenario where the parameters fortuitously align with the values we require. In such cases, we don’t necessitate an extensive dataset to attain our desired outcome. As will be understood, BERT and GPT are used in transfer learning concept. B E R T BERT G P T The GPT training is thus divided into two parts. We have pre-training part where we train the GPT architecture to understand what language is and then fine tuning part where we use transfer learning to further train the GPT architecture to perform well on specific language tasks GPT has a structure that takes random word sequences as input and predicts the next most suitable word. Sample prediction can be seen at Fig 2. Language modeling is selected as an ideal foundation for understanding the fundamental aspects of language and can be readily fine-tuned. It is commonly known as a self-supervised task since the sentences themselves serve as both input and output labels. Let’s move on to ChatGPT. As it can be seen in Fig 3., the entire ChatGPT process can be divided into three major steps. In the first step, GPT model is used that has been pre-trained on understanding language itself. The next step involves fine-tuning the model to effectively process user prompts and generate appropriate responses based on those prompts. To facilitate this process, labeled data is obtained by engaging labelers who not only provide prompts but also specify the desired response for each prompt. This allows for engaging in supervised fine-tuning of the GPT model, as there are both input prompts and corresponding output responses. In the next step, the supervised fine-tuned model is used that it was obtained from first step. A single prompt is passed through the model, and multiple responses are generated. The labeler then evaluates and ranks the quality of these responses. Each generated response is assigned a corresponding reward, which is utilized to train another GPT model known as the rewards model. Being a model itself, the rewards model functions by taking an initial prompt and one of the responses as input and generating a reward as output. This reward quantifies the quality or goodness of the response. In the third step, an unseen prompt will be taken and passed through a replica of the supervised fine-tuned model. This will generate a response, which will then be evaluated using a rewards model to determine its rank or quality. The rank obtained will be used to further refine our already fine-tuned model. This is achieved by incorporating the rank into the loss function of the PPO model, allowing updates to the model’s parameters through backpropagation. What’s particularly fascinating is that this process not only helps the model exhibit non-toxic behavior but also enables it to generate factual responses. This is because the reward itself was generated based on the quality of the response. ChatSQL Now let’s make a project with ChatGPT using python. LangChain framework is used for this project. Our objective is to retrieve information from our database using plain text. In our work, we utilized MySQL as the target database. Nevertheless, this approach can also be used to generate queries on other databases. The sample database is shown below. ( All codes can be found here https://github.com/ademakdogan/ChatSQL ) +-----+--------------------------------------------------------+------------------------+-------------------+--------+------------------+
| ID  | Title                                                  | Author                 | Genre             | Height | Publisher        |
+-----+--------------------------------------------------------+------------------------+-------------------+--------+------------------+
|   1 | Fundamentals of Wavelets                               | Goswami, Jaideva       | signal_processing |    228 | Wiley            |
|   2 | Data Smart                                             | Foreman, John          | data_science      |    235 | Wiley            |
|   3 | God Created the Integers                               | Hawking, Stephen       | mathematics       |    197 | Penguin          |
|   4 | Superfreakonomics                                      | Dubner, Stephen        | economics         |    179 | HarperCollins    |
|   5 | Orientalism                                            | Said, Edward           | history           |    197 | Penguin          |
|   6 | Nature of Statistical Learning Theory, The             | Vapnik, Vladimir       | data_science      |    230 | Springer         |
|   7 | Integration of the Indian States                       | Menon, V P             | history           |    217 | Orient Blackswan |
|   8 | Drunkard's Walk, The                                   | Mlodinow, Leonard      | science           |    197 | Penguin          |
|   9 | Image Processing & Mathematical Morphology             | Shih, Frank            | signal_processing |    241 | CRC              |
|  10 | How to Think Like Sherlock Holmes                      | Konnikova, Maria       | psychology        |    240 | Penguin          |
|  11 | Data Scientists at Work                                | Sebastian Gutierrez    | data_science      |    230 | Apress           |
|  12 | Slaughterhouse Five                                    | Vonnegut, Kurt         | fiction           |    198 | Random House     |
|  13 | Birth of a Theorem                                     | Villani, Cedric        | mathematics       |    234 | Bodley Head      |
|  14 | Structure & Interpretation of Computer Programs        | Sussman, Gerald        | computer_science  |    240 | MIT Press        |
|  15 | Age of Wrath, The                                      | Eraly, Abraham         | history           |    238 | Penguin          |
|  16 | Trial, The                                             | Kafka, Frank           | fiction           |    198 | Random House     |
|  17 | Statistical Decision Theory'                           | Pratt, John            | data_science      |    236 | MIT Press        |
|  18 | Data Mining Handbook                                   | Nisbet, Robert         | data_science      |    242 | Apress           |
|  19 | New Machiavelli, The                                   | Wells, H. G.           | fiction           |    180 | Penguin          |
|  20 | Physics & Philosophy                                   | Heisenberg, Werner     | science           |    197 | Penguin          |
|  21 | Making Software                                        | Oram, Andy             | computer_science  |    232 | O'Reilly         |
|  .  | .......                                                | .......                | ....              |    ... | ....             |
|  .  | .......                                                | .......                | ....              |    ... | ....             | The ChatSQL consists of two main parts. First of all, mysql queries are generated from the given plain text via ChatGPT. These generated queries are executed. In the second step, the results returned in the database are evaluated. At this stage, the results can either be presented directly to the user or sent back to ChatGPT for further analysis and interpretation. So you can chat about your database with ChatGPT. Suppose the user wants “Show me the book type fiction which they height bigger than 175 and smaller than 178. The author shouldn’t be ‘Doyle, Arthur Conan’.”. So the following command can be used in this case. python3 chatsql.py -p 'Show me the book type fiction which they height bigger than 175 and smaller than 178. The author shouldn't be 'Doyle, Arthur Conan'. ' As a result, the following information is obtained. CHATGPT QUERY------------------:
SELECT * FROM bt WHERE Genre = 'Fiction' AND Height > 175 AND Height < 178 AND Author != 'Doyle, Arthur Conan'
RAW RESULT------------------:
[(32, 'Pillars of the Earth, The', 'Follett, Ken', 'fiction', 176, 'Random House'), (37, 'Veteran, The', 'Forsyth, Frederick', 'fiction', 177, 'Transworld'), (38, 'False Impressions', 'Archer, Jeffery', 'fiction', 177, 'Pan'), (72, 'Prisoner of Birth, A', 'Archer, Jeffery', 'fiction', 176, 'Pan'), (87, 'City of Joy, The', 'Lapierre, Dominique', 'fiction', 177, 'vikas'), (128, 'Rosy is My Relative', 'Durrell, Gerald', 'fiction', 176, 'nan')]
PROCESSED RESULT------------------ :
The books 'Pillars of the Earth, The' by Ken Follett, 'Veteran, The' by Frederick Forsyth, 'False Impressions' by Jeffery Archer, 'Prisoner of Birth, A' by Jeffery Archer, 'City of Joy, The' by Dominique Lapierre, and 'Rosy is My Relative' by Gerald Durrell are all fiction books with 176 or 177 pages published by Random House, Transworld, Pan, Vikas, and Nan, respectively. If your database columns are named appropriately, chatgpt will understand these names and respond queries accordingly. However, there may be instances where the column names in the database are not meaningful or ChatGPT may not fully figure out their context. Hence, in order to ensure the system functions correctly, It is necessary to provide ChatGPT with prior information about the database. Info.json file can be used to adding information. +-----+--------------------------------------------------------+------------------------+-------------------+------+------------------+
| ID  | aa                                                     | bb                     | cc                | dd   | ee               |
+-----+--------------------------------------------------------+------------------------+-------------------+------+------------------+
|   1 | Fundamentals of Wavelets                               | Goswami, Jaideva       | signal_processing |  228 | Wiley            |
|   2 | Data Smart                                             | Foreman, John          | data_science      |  235 | Wiley            |
|   3 | God Created the Integers                               | Hawking, Stephen       | mathematics       |  197 | Penguin          |
|   4 | Superfreakonomics                                      | Dubner, Stephen        | economics         |  179 | HarperCollins    |
|   5 | Orientalism                                            | Said, Edward           | history           |  197 | Penguin          |
|  .  | .......                                                | .......                | ....              |    ... | ....           |
|  .  | .......                                                | .......                | ....              |    ... | ....           | As an example, let’s have a badly named database as below. In this case, the required information about the database is inputted into the info.json file. {“bt”: “Table Name”, “aa”: “Title of the book”, “bb”: “Author of the book”, “cc”: “Type of book”, “dd”: “Height of the book”, “ee”: “Book Publisher”} and then use the same command: python3 chatsql.py -p 'Show me the book type fiction which they height bigger than 175 and smaller than 178. The author shouldn't be 'Doyle, Arthur Conan'. ' Even if the column names in the database are poorly selected, chatgpt generates the correct query because we provide the correct information {'query': "SELECT aa, bb, cc, dd FROM bt WHERE cc = 'fiction' AND dd > 175 AND dd < 178 AND bb != 'Doyle, Arthur Conan'", 'raw_result': "[('Pillars of the Earth, The', 'Follett, Ken', 'fiction', 176), ('Veteran, The', 'Forsyth, Frederick', 'fiction', 177), ('False Impressions', 'Archer, Jeffery', 'fiction', 177), ('Prisoner of Birth, A', 'Archer, Jeffery', 'fiction', 176), ('City of Joy, The', 'Lapierre, Dominique', 'fiction', 177), ('Rosy is My Relative', 'Durrell, Gerald', 'fiction', 176)]", 'processed_result': '\nThe books "Pillars of the Earth, The" by Ken Follett, "Veteran, The" by Frederick Forsyth, "False Impressions" by Jeffery Archer, "Prisoner of Birth, A" by Jeffery Archer, "City of Joy, The" by Dominique Lapierre and "Rosy is My Relative" by Gerald Durrell are all fiction and have page lengths of 176 or 177.'} The next project could be on generating queries (mongo, sql) from prompts with free models (Llama). Project Repo : https://github.com/ademakdogan/ChatSQL Github : https://github.com/ademakdogan Linkedin : https://www.linkedin.com/in/adem-akdoğan-948334177/ References [1] Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P. and Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. [2] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog. [3] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. and Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998–6008). [4] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 4171–4186). [5] Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., & Zettlemoyer, L. (2019). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. [6] Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., … & Liu, P. J. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683. [7] Vaswani, A., & et al. (2017). Transformer-XL: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860.

ChatSQL: Enabling ChatGPT to Generate SQL Queries from Plain Text

চ্যাটএসকিউএল: প্লেইন টেক্সট থেকে এসকিউএল কোয়েরি তৈরি করতে ChatGPT সক্ষম করা

ChatSQL: ChatGPT aktivieren, um SQL-Abfragen aus reinem Text zu generieren

ChatSQL: habilitar ChatGPT para generar consultas SQL a partir de texto sin formato

ChatSQL – Activation de ChatGPT pour générer des requêtes SQL à partir de texte brut

ChatSQL: प्लेन टेक्स्ट से SQL क्वेरी जनरेट करने के लिए ChatGPT को सक्षम करना

ChatSQL: ChatGPT を有効にしてプレーン テキストから SQL クエリを生成する

ChatSQL: ChatGPT를 활성화하여 일반 텍스트에서 SQL 쿼리 생성

ChatSQL : habilitando o ChatGPT para gerar consultas SQL a partir de texto simples

ChatSQL: ChatGPT'nin Düz Metinden SQL Sorguları Oluşturmasını Etkinleştirme

ChatSQL: Bật ChatGPT để tạo truy vấn SQL từ văn bản thuần túy

ChatSQL：启用 ChatGPT 从纯文本生成 SQL 查询

Editor’s Note: Soon after publishing, an astute reader pointed out that Musk cannot run for the U.S. Presidency because he’s not a natural-born U.S. citizen. So please bear that in mind as you read the rest of the article. Ooh boy, looks like the rumors were true. Despite decrying the dangers of AI and leading a call to pause the training of AI systems, Elon Musk has decided that he too wants a piece of the AI pie and made a not-so-surprising announcement that he'll be launching a competitor to Microsoft-backed OpenAI's ChatGPT and Google's Bard (lol). Called 'TruthGPT', Musk's spin on generative AI will be a "maximum truth-seeking AI that tries to understand the nature of the universe." Presumably, this will prevent TruthGPT from absolutely annihilating humans. While the announcement itself might not be a complete surprise (we reported just weeks ago that Musk was quietly recruiting people to form his own AI company), what caught our attention was where Musk made that announcement: Fox News. Yes, you read that right. Elon Musk was on Fox News, giving an interview to Tucker Carlson, and peddling his doomsday scenarios to the largely conservative base that tunes into the network for their dose of news. Now, in case you're not.. Uhm, aware of the intricacies of the media landscape, we'll just come out and spell it out for you: Fox News is just so heavily tilted towards the Republican party that you'd be forgiven for thinking that Republicans actually run the network. Even though Fox News keeps throwing shade at other media outlets, everyone knows that it pushes the Republican agenda hard, most recently during the Trump era when it amplified false claims that voting machines were manipulated to favor Democrat Joe Biden. Those claims landed Fox News in trouble, and the media network has agreed to pay over three quarters of a billion ($787.5 million) to the voting machine company that supposedly colluded with Democrats to make Biden win. But that's beside the point. Why was Musk even there? One possible explanation (and this might be a stretch for many a reader) is that he's ultimately going to run for president and wants to appeal to Fox News' core audience, who, as many know, are tired of "career politicians" and want an outsider to shake things up in Washington, D.C., as was the case with Donald Trump. We know we've said this in the past, but Musk's constant news coverage eerily resembles that of Donald Trump, particularly in the run-up to Trump's election as President of the United States. There is never a day when Musk isn't in the news, either as an individual or via the companies he owns: Twitter, SpaceX, Tesla. So, it's wholly possible that Musk is following the Trump playbook by being in the news all the damn time so he could ultimately run for president. What separates Musk from Trump, though, is that Musk's companies are successful by comparison and his wealth is verifiably in the billions. That said, Musk's announcement of launching his own AI raises even more questions about the timing of the open letter he signed just a while ago asking for an immediate pause of at least 6 months in the training of AI systems more powerful than GPT-4. Specifically, did Musk want a pause in AI development so he could work on building his own AI in the meantime? While six months might not necessarily be a long period of time, it could have been sufficient enough for Musk to finetune his vision of what his take on AI should look like (in this case 'TruthGPT'). But what even is "truth"? Well, for the purposes of AI, it's whatever data it has been trained on. If the data is biased, so will be the AI. If Elon Musk thinks he can train his AI model on threads from 4chan or news from Infowars, for example, and present them as truth because they're so unfiltered and conform to a certain world view, the result is going to be more disinformation peddling as "truth" in an attempt to manipulate public opinion. In all fairness, Musk appears to be terrified of the idea of AI__manipulating__ public opinion, but like all things Musk, will probably go and do the opposite of what he says. Ironically, when Trump was kicked off of major social media websites, the social media app he ended up creating for his followers also had the word "truth" in it: Truth Social. So the similarities between Musk and Trump are more than one. Nonetheless, only time will tell what Musk is aiming for. For now, he's interested in upending "Woke AI" and TruthGPT will be a step in that direction. It could also be a sign of what's to come, with more and more billionaires trying to cash in on the AI gold rush. Elon Musk's Tesla ranked #31 this week on HackerNoon's Tech Company Rankings, while Twitter was not far behind at #33. Microsoft, which has heavily betted on OpenAI, was on the #62 spot, while Google surpassed them all on the #25 spot. 👋 You’re reading part 1 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 2 goes live tomorrow. Hate waiting? No problemo! Just subscribe here to receive the complete newsletter a day early in your inbox. In Other News.. 📰 HODL GANG ASSEMBLE! Britain's Standard Chartered believes bitcoin could touch a peak of $100,000 by the end of 2024. Though of course, they also expected the cryptocurrency to fall below $5,000 in 2023. 😆
John Oliver has some thoughts on cryptocurrencies. 🤣
Elon Musk's SpaceX had a failed rocket launch. Here's why that was a success. 🚀
Twitter removed the verified "blue tick" from legacy users, only to restore it later, free of charge, to celebrity users of the social network. 📳 And that's a wrap! Don't forget to subscribe to my profile and share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ Featured image generated using Kadinsky 2 with the following prompt: “Elon Musk sitting in the oval office.” Editor’s Note: Soon after publishing, an astute reader pointed out that Musk cannot run for the U.S. Presidency because he’s not a natural-born U.S. citizen. So please bear that in mind as you read the rest of the article. Editor’s Note: Soon after publishing, an astute reader pointed out that Musk cannot run for the U.S. Presidency because he’s not a natural-born U.S. citizen. So please bear that in mind as you read the rest of the article. Editor’s Note: Soon after publishing, an astute reader pointed out that Musk cannot run for the U.S. Presidency because he’s not a natural-born U.S. citizen. So please bear that in mind as you read the rest of the article. Ooh boy, looks like the rumors were true. rumors rumors Despite decrying the dangers of AI and leading a call to pause the training of AI systems, Elon Musk has decided that he too wants a piece of the AI pie and made a not-so-surprising announcement that he'll be launching a competitor to Microsoft -backed OpenAI's ChatGPT and Google 's Bard ( lol ). leading a call leading a call Microsoft Microsoft Google Google lol lol Called 'TruthGPT', Musk's spin on generative AI will be a "maximum truth-seeking AI that tries to understand the nature of the universe." Presumably, this will prevent TruthGPT from absolutely annihilating humans. spin spin While the announcement itself might not be a complete surprise (we reported just weeks ago that Musk was quietly recruiting people to form his own AI company), what caught our attention was where Musk made that announcement: Fox News. reported reported our Yes, you read that right. Elon Musk was on Fox News, giving an interview to Tucker Carlson, and peddling his doomsday scenarios to the largely conservative base that tunes into the network for their dose of news. Now, in case you're not.. Uhm, aware of the intricacies of the media landscape, we'll just come out and spell it out for you: Fox News is just so heavily tilted towards the Republican party that you'd be forgiven for thinking that Republicans actually run the network. largely conservative base largely conservative base Even though Fox News keeps throwing shade at other media outlets, everyone knows that it pushes the Republican agenda hard, most recently during the Trump era when it amplified false claims that voting machines were manipulated to favor Democrat Joe Biden. Those claims landed Fox News in trouble, and the media network has agreed to pay over three quarters of a billion ($787.5 million) to the voting machine company that supposedly colluded with Democrats to make Biden win. pay pay But that's beside the point. Why was Musk even there? One possible explanation (and this might be a stretch for many a reader) is that he's ultimately going to run for president and wants to appeal to Fox News' core audience, who, as many know, are tired of "career politicians" and want an outsider to shake things up in Washington, D.C., as was the case with Donald Trump. We know we've said this in the past, but Musk's constant news coverage eerily resembles that of Donald Trump, particularly in the run-up to Trump's election as President of the United States. There is never a day when Musk isn't in the news, either as an individual or via the companies he owns: Twitter , SpaceX, Tesla . So, it's wholly possible that Musk is following the Trump playbook by being in the news all the damn time so he could ultimately run for president. we've said this we've said this Twitter Twitter Tesla Tesla all the damn time What separates Musk from Trump, though, is that Musk's companies are successful by comparison and his wealth is verifiably in the billions. That said, Musk's announcement of launching his own AI raises even more questions about the timing of the open letter he signed just a while ago asking for an immediate pause of at least 6 months in the training of AI systems more powerful than GPT-4. Specifically, did Musk want a pause in AI development so he could work on building his own AI in the meantime? While six months might not necessarily be a long period of time, it could have been sufficient enough for Musk to finetune his vision of what his take on AI should look like (in this case 'TruthGPT'). signed just a while ago signed just a while ago But what even is "truth"? Well, for the purposes of AI, it's whatever data it has been trained on. If the data is biased, so will be the AI . so will be the AI so will be the AI If Elon Musk thinks he can train his AI model on threads from 4chan or news from Infowars, for example, and present them as truth because they're so unfiltered and conform to a certain world view, the result is going to be more disinformation peddling as "truth" in an attempt to manipulate public opinion. In all fairness, Musk appears to be terrified of the idea of AI__ manipulating __ public opinion, but like all things Musk, will probably go and do the opposite of what he says. manipulating Ironically, when Trump was kicked off of major social media websites, the social media app he ended up creating for his followers also had the word "truth" in it: Truth Social. So the similarities between Musk and Trump are more than one. Nonetheless, only time will tell what Musk is aiming for. For now, he's interested in upending "Woke AI" and TruthGPT will be a step in that direction. It could also be a sign of what's to come, with more and more billionaires trying to cash in on the AI gold rush . cash in on the AI gold rush cash in on the AI gold rush Elon Musk's Tesla ranked #31 this week on HackerNoon's Tech Company Rankings , while Twitter was not far behind at #33. Microsoft, which has heavily betted on OpenAI, was on the #62 spot, while Google surpassed them all on the #25 spot. Tech Company Rankings Tech Company Rankings 👋 You’re reading part 1 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 2 goes live tomorrow. Hate waiting? No problemo! Just subscribe here to receive the complete newsletter a day early in your inbox. 👋 You’re reading part 1 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 2 goes live tomorrow. Hate waiting? No problemo! Just subscribe here to receive the complete newsletter a day early in your inbox. You’re reading part 1 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 2 goes live tomorrow. Hate waiting? No problemo! Just subscribe here to receive the complete newsletter a day early in your inbox. part 1 Part 2 here complete In Other News.. 📰 In Other News.. 📰 HODL GANG ASSEMBLE! Britain's Standard Chartered believes bitcoin could touch a peak of $100,000 by the end of 2024. Though of course, they also expected the cryptocurrency to fall below $5,000 in 2023. 😆 John Oliver has some thoughts on cryptocurrencies. 🤣 Elon Musk's SpaceX had a failed rocket launch. Here's why that was a success. 🚀 Twitter removed the verified "blue tick" from legacy users, only to restore it later, free of charge, to celebrity users of the social network. 📳 HODL GANG ASSEMBLE! Britain's Standard Chartered believes bitcoin could touch a peak of $100,000 by the end of 2024. Though of course, they also expected the cryptocurrency to fall below $5,000 in 2023. 😆 $100,000 $100,000 expected expected John Oliver has some thoughts on cryptocurrencies. 🤣 thoughts thoughts Elon Musk's SpaceX had a failed rocket launch. Here's why that was a success. 🚀 why why Twitter removed the verified "blue tick" from legacy users, only to restore it later, free of charge, to celebrity users of the social network. 📳 removed removed And that's a wrap! Don't forget to subscribe to my profile and share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ subscribe to my profile Featured image generated using Kadinsky 2 with the following prompt: “Elon Musk sitting in the oval office.” Featured image generated using Kadinsky 2 with the following prompt: “Elon Musk sitting in the oval office.”

Elon Musk to Launch His Own AI — Will He Run For President Next? 🏛️

এলন মাস্ক তার নিজস্ব এআই লঞ্চ করবেন - তিনি কি পরবর্তী রাষ্ট্রপতি পদে লড়বেন? 🏛️

Elon Musk startet seine eigene KI – wird er als nächstes für das Präsidentenamt kandidieren? 🏛️

Elon Musk, 자신의 AI 출시 — 그는 다음 대통령에 출마할까요? 🏛️

Elon Musk Kendi Yapay Zekasını Başlatacak - Bundan Sonra Başkanlığa Aday Olacak mı? 🏛️

In this article, we’ll dive into bytecode, and examine the bytecode generation and constant pool in detail. 1. Bytecode Generation The GenerateUnoptimizedCodeForToplevel is responsible for transforming an AST tree into bytecodes. 1.  MaybeHandle<SharedFunctionInfo> GenerateUnoptimizedCodeForToplevel(
2.      Isolate* isolate, ParseInfo* parse_info, AccountingAllocator* allocator,
3.      IsCompiledScope* is_compiled_scope) {
4.  //omit............
5.     while (!functions_to_compile.empty()) {
6.  //omit............
7.       if (job->ExecuteJob() == CompilationJob::FAILED ||
8.           FinalizeUnoptimizedCompilationJob(job.get(), shared_info, isolate) ==
9.               CompilationJob::FAILED) {
10.           return MaybeHandle<SharedFunctionInfo>();
11.         }
12.         if (FLAG_stress_lazy_source_positions) {
13.           SharedFunctionInfo::EnsureSourcePositionsAvailable(isolate, shared_info);
14.         }
15.         if (shared_info.is_identical_to(top_level)) {
16.           *is_compiled_scope = shared_info->is_compiled_scope();
17.           DCHECK(is_compiled_scope->is_compiled());
18.         }
19.       }
20.       parse_info->ResetCharacterStream();
21.       return top_level;
22.     } Line 7 reads an AST tree from parse_info and generates bytecode. By debugging it, we step into the following function which analyses every AST node and generates a corresponding bytecode. job->ExecuteJob() 1.  void BytecodeGenerator::GenerateBytecodeBody() {
2.    VisitArgumentsObject(closure_scope()->arguments());
3.    VisitNewTargetVariable(closure_scope()->new_target_var());
4.    FunctionLiteral* literal = info()->literal();
5.    if (IsResumableFunction(literal->kind())) {
6.      BuildGeneratorObjectVariableInitialization();
7.    }
8.    if (FLAG_trace) builder()->CallRuntime(Runtime::kTraceEnter);
9.  //省略.........................
10.    BuildIncrementBlockCoverageCounterIfEnabled(literal, SourceRangeKind::kBody);
11.    VisitDeclarations(closure_scope()->declarations());
12.    VisitModuleNamespaceImports();
13.    builder()->StackCheck(literal->start_position());
14.    if (IsBaseConstructor(function_kind())) {
15.      if (literal->requires_brand_initialization()) {
16.        BuildPrivateBrandInitialization(builder()->Receiver());
17.      }
18.      if (literal->requires_instance_members_initializer()) {
19.        BuildInstanceMemberInitialization(Register::function_closure(),
20.                                          builder()->Receiver());
21.      }
22.    }
23.    VisitStatements(literal->body());
24.    if (!builder()->RemainderOfBlockIsDead()) {
25.      builder()->LoadUndefined();
26.      BuildReturn();
27.    }
28.  } In the above function, lines 11, 12, and 23 first analyze the declaration, import modules, and statements respectively, and then generate corresponding bytecodes. In our case, we will examine line 11 VisitDeclarations() in more depth. In VisitDeclarations(), the following function is called. 1.  BytecodeArrayBuilder& BytecodeArrayBuilder::LoadConstantPoolEntry(
2.      size_t entry) {
3.    OutputLdaConstant(entry);
4.    return *this;
5.  }
6.  //============================
7.  #define DEFINE_BYTECODE_OUTPUT(name, ...) In line 3, OutputLdaConstant stores one global constant into the constant-pool and generates bytecode LdaGlobal for loading this constant, so that we can get out the constant through the LdaGlobal during execution. About the constant pool, I will talk later. By debugging OutputLdaConstant(), we step into the EmitBytecode() which generates one bytecode for a corresponding statement and also is the function that generates the LdaGlobal for our global constant. 1.  void BytecodeArrayWriter::EmitBytecode(const BytecodeNode* const node) {
2.  //omit..........
3.     for (int i = 0; i < operand_count; ++i) {
4.       switch (operand_sizes[i]) {
5.         case OperandSize::kNone:
6.           UNREACHABLE();
7.           break;
8.         case OperandSize::kByte:
9.           bytecodes()->push_back(static_cast<uint8_t>(operands[i]));
10.           break;
11.         case OperandSize::kShort: {
12.           uint16_t operand = static_cast<uint16_t>(operands[i]);
13.           const uint8_t* raw_operand = reinterpret_cast<const uint8_t*>(&operand);
14.           bytecodes()->push_back(raw_operand[0]);
15.           bytecodes()->push_back(raw_operand[1]);
16.           break;
17.         }
18.         case OperandSize::kQuad: {
19.           const uint8_t* raw_operand =
20.               reinterpret_cast<const uint8_t*>(&operands[i]);
21.           bytecodes()->push_back(raw_operand[0]);
22.           bytecodes()->push_back(raw_operand[1]);
23.           bytecodes()->push_back(raw_operand[2]);
24.           bytecodes()->push_back(raw_operand[3]);
25.           break;
26.         }
27.       }
28.     }
29.   } From lines 4 to 25, calculate the operand size for our LdaGlobal, because in execution, a bytecode needs the operand size to operate the operand correctly. Back to GenerateBytecodeBody(), in line 23, the VisitStatements() reads one AST node that represents a corresponding JavaScript statement, and generate one bytecode. If one AST node represents a JavaScript statement block, the VisitStatements() will recursively call itself to generate many bytecodes. Note: void BytecodeGenerator::VisitStatements(
    const ZonePtrList<Statement>* statements) {
  for (int i = 0; i < statements->length(); i++) {
    // Allocate an outer register allocations scope for the statement.
    RegisterAllocationScope allocation_scope(this);
    Statement* stmt = statements->at(i);
    Visit(stmt);
    if (builder()->RemainderOfBlockIsDead()) break;
  }
} Figure 1 shows the VisitStatements’s call stack. 2. Constant Pool function test(x)
{
 if(x>0)
 add3(x);//x=x+3
 dosomething........
} When compiling the function test(), the compiler infers that the add3 will be called, and the add3 address should be pushed into the constant pool. But, there’s a question here. We have no address if the add3() is not compiled yet. In this case, the compiler reserves a slot for the add3 in the constant pool and fills it once the add3() is compiled, namely DeferredConstants. Let’s give a glance at FinalizeBytecode(). 1.  Handle<BytecodeArray> BytecodeGenerator::FinalizeBytecode(
2.      Isolate* isolate, Handle<Script> script) {
3.    DCHECK_EQ(ThreadId::Current(), isolate->thread_id());
4.    AllocateDeferredConstants(isolate, script);
5.    if (block_coverage_builder_) {
6.      info()->set_coverage_info(
7.          isolate->factory()->NewCoverageInfo(block_coverage_builder_->slots()));
8.      if (FLAG_trace_block_coverage) {
9.        info()->coverage_info()->Print(info()->literal()->GetDebugName());
10.      }
11.    }
12.    if (HasStackOverflow()) return Handle<BytecodeArray>();
13.    Handle<BytecodeArray> bytecode_array = builder()->ToBytecodeArray(isolate);
14.    if (incoming_new_target_or_generator_.is_valid()) {
15.      bytecode_array->set_incoming_new_target_or_generator_register(
16.          incoming_new_target_or_generator_);
17.    }
18.    return bytecode_array;
19.  } In line 4, AllocateDeferredConstants creates the DeferredConstants. 1.  void BytecodeGenerator::AllocateDeferredConstants(Isolate* isolate,
2.                                                    Handle<Script> script) {
3.    // Build global declaration pair arrays.
4.    for (GlobalDeclarationsBuilder* globals_builder : global_declarations_) {
5.      Handle<FixedArray> declarations =
6.          globals_builder->AllocateDeclarations(info(), script, isolate);
7.      if (declarations.is_null()) return SetStackOverflow();
8.      builder()->SetDeferredConstantPoolEntry(
9.          globals_builder->constant_pool_entry(), declarations);
10.     }
11.     // Find or build shared function infos.
12.     for (std::pair<FunctionLiteral*, size_t> literal : function_literals_) {
13.    //omit..................
14.     }
15.     // Find or build shared function infos for the native function templates.
16.     for (std::pair<NativeFunctionLiteral*, size_t> literal :
17.          native_function_literals_) {
18.   //omit..................
19.     }
20.     // Build object literal constant properties
21.     for (std::pair<ObjectLiteral*, size_t> literal : object_literals_) {
22.       ObjectLiteral* object_literal = literal.first;
23.   //omit..................
24.       }
25.     }
26.     // Build array literal constant elements
27.     for (std::pair<ArrayLiteral*, size_t> literal : array_literals_) {
28.   //omit..................
29.     }
30.     // Build class literal boilerplates.
31.     for (std::pair<ClassLiteral*, size_t> literal : class_literals_) {
32.   //omit..................
33.     }
34.     // Build template literals.
35.     for (std::pair<GetTemplateObject*, size_t> literal : template_objects_) {
36.   //omit..................
37.     }
38.   } From lines 4 to 10, the loop-for store global variables in the constant pool. From lines 11 to 35 store the sharedfunction, constant property, and other stuff in the constant pool. 3. FinalizeBytecode The FinalizeBytecode() is the last step for generating bytecode. Let’s give a glance at line 13 of the FinalizeBytecode, the reorganizes the bytecodes and returns a bytecode array that holds a bytecode array representing your JavaScript code. What is “reorganize”? It is that just put bytecode, constant pool, etc. together because all the stuff is already done well before ToBytecodeArray. builder()->ToBytecodeArray 1.  Handle<BytecodeArray> BytecodeArrayBuilder::ToBytecodeArray(Isolate* isolate) {
2.    DCHECK(RemainderOfBlockIsDead());
3.    DCHECK(!bytecode_generated_);
4.    bytecode_generated_ = true;
5.    int register_count = total_register_count();
6.    if (register_optimizer_) {
7.  //omit
8.    }
9.    Handle<ByteArray> handler_table =
10.        handler_table_builder()->ToHandlerTable(isolate);
11.    return bytecode_array_writer_.ToBytecodeArray(
12.        isolate, register_count, parameter_count(), handler_table);
13.  } Line 9 gets out the handler_table, namely the dispatch_table that I have talked about. Line 11 stores the handler_table address into a bytecode array. Remember that each bytecode has only one handler that is the actual worker and is held by the dispatch_table. Let’s give a glance at line 11 bytecode_array_writer_.ToBytecodeArray. 1.  Handle<BytecodeArray> BytecodeArrayWriter::ToBytecodeArray(
2.      Isolate* isolate, int register_count, int parameter_count,
3.      Handle<ByteArray> handler_table) {
4.    DCHECK_EQ(0, unbound_jumps_);
5.    int bytecode_size = static_cast<int>(bytecodes()->size());
6.    int frame_size = register_count * kSystemPointerSize;
7.    Handle<FixedArray> constant_pool =
8.        constant_array_builder()->ToFixedArray(isolate);
9.    Handle<BytecodeArray> bytecode_array = isolate->factory()->NewBytecodeArray(
10.        bytecode_size, &bytecodes()->front(), frame_size, parameter_count,
11.        constant_pool);
12.    bytecode_array->set_handler_table(*handler_table);
13.    return bytecode_array;
14.  } Line 7 generates constant_pool, and line 9 generates a bytecode array that the source code is below: 1.  Handle<BytecodeArray> Factory::NewBytecodeArray(
2.      int length, const byte* raw_bytecodes, int frame_size, int parameter_count,
3.      Handle<FixedArray> constant_pool) {
4.    DCHECK_LE(0, length);
5.    if (length > BytecodeArray::kMaxLength) {
6.      isolate()->heap()->FatalProcessOutOfMemory("invalid array length");
7.    }
8.    // Bytecode array is AllocationType::kOld, so constant pool array should be
9.    // too.
10.    DCHECK(!Heap::InYoungGeneration(*constant_pool));
11.    int size = BytecodeArray::SizeFor(length);
12.    HeapObject result = AllocateRawWithImmortalMap(size, AllocationType::kOld,
13.                                                   *bytecode_array_map());
14.    Handle<BytecodeArray> instance(BytecodeArray::cast(result), isolate());
15.    instance->set_length(length);
16.    instance->set_frame_size(frame_size);
17.    instance->set_parameter_count(parameter_count);
18.    instance->set_incoming_new_target_or_generator_register(
19.        interpreter::Register::invalid_value());
20.    instance->set_osr_loop_nesting_level(0);
21.    instance->set_bytecode_age(BytecodeArray::kNoAgeBytecodeAge);
22.    instance->set_constant_pool(*constant_pool);
23.    instance->set_handler_table(*empty_byte_array());
24.    instance->set_source_position_table(*undefined_value());
25.    CopyBytes(reinterpret_cast<byte*>(instance->GetFirstBytecodeAddress()),
26.              raw_bytecodes, length);
27.    instance->clear_padding();
28.    return instance;
29.  } Line 12 allocates memory from the V8 heap, and then lines 15–25 copy the bytecode array into the memory, and line 28 returns the bytecode finally. Takeaways Constant pool holds constants, function addresses, etc. If a variable name is known, it as a constant is stored in the constant pool; Note: Constant pool always is at bytecode array[0]. In other words, given a bytecode array, the first element of the array is always the constant pool; A bytecode array is a heap object and its type is FixedArray. Okay, that wraps it up for this share. I’ll see you guys next time, take care! Please reach out to me if you have any issues. : qq9123013 : WeChat Email v8blink@outlook.com Also Published here

Understanding Chrome V8 - Chapter 22: Compiler Workflow Bytecode, Constant Pool

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