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Dark Bears

darkbears

Top 30+ Mobile App Development Companies in USA and India for Developing Customized Apps

Authors:
(1) Howard Zhong, MIT CSAIL (howardzh@mit.edu);
(2) Mark Hamilton, MIT CSAIL, Microsoft (markth@mit.edu). Table of Links Abstract and 1 Introduction 2 Methodology 3 Results 3.1 CryptoPunks 3.2 Aggregate NFT Market 4 Discussion 5. Conclusion/ Acknowledgements/ References A Appendix A.1 Implementation Details A.2 Detailed NFT Information & A.3 Google NFT Searches Map A.1 Implementation Details We elaborate on the methods and the implementation details. This sections follows the structure Section 2. A.1.1 Initial Data Collection Our dataset only includes NFTs transacted on OpenSea, which is the primary marketplace for NFTs on the Ethereum blockchain. We query the OpenSea “v1/collections” endpoint at the end of November 2022 [15] to retrieve collection metadata as well as each individual NFT’s metadata and last sale price. We choose 790 collection names from the Kaggle Ethereum NFTs dataset [11] and NFTs from the top 30-day and all-time OpenSea volume leaderboard around November 2021. Most collections have around 5,000 to 10,000 items, and our queries yield around 70-80% of the data as the other 20-30% do not have transactions. After querying the Application Programming Interface (API) using a script written with the PySpark framework [17] on Databricks, we create a dataframe containing each NFT’s collection name, id, sale information, metadata, and image url. We then loop through this dataframe with multiple workers to download the images in parallel. In the end, we obtain a dataset of about ∼ 2.5 NFTs, each that have been transacted upon. A.1.2 Retrieving Race and Gender Labels To get gender-labelled data, we select collections that have metadata with the words “male” and “female” to find collections where at least 50% of the images have gender labels. We filter out collections that have more than 75% male or female to avoid rarity factors affecting the price. We find a total of 44 such collections with gender labels that satisfy the criteria. These collections with gender labels are It is more challenging to determine the race of an NFT collection because most collections do not directly annotate this information. For the CryptoPunks collection, we label each NFT based on the frequencies of the four different pixel values representing the four different skin tones as displayed in Figure 6. The above process was inspired by [5]. For our analysis, we group darkest and mid as Dark and lighter and lightest as Light. For other collections, we were unable to retrieve race labels from the metadata. We also attempted to find race labels for a subset of the 44 collections with gender labels with the criteria that there were a high proportion of items that could be categorized as either dark or light skinned. We filtered out collections that have more than 75% light-skinned or dark-skinned to avoid rarity factors affecting the price. This required manual review because most collections do not have easy-to-search race labels in the metadata, or even way to categorize race. For Avastar, we categorized “Amber Brown” as dark-skinned and “Pale Pink” as light-skinned in the “skin tone” attribute. For Dynamic Duelers, we categorized “Black” as dark-skinned and “White” as lightskinned in the “Skin color” attribute. A.1.3 Statistical Tools to Analyze Bias in Gender and Race We detail the statistical methods used to analyze the bias in NFT pricing for gender and race. Specifically, we go into more detail about the implementation and underlying assumptions of these tests. Unpaired t-test assumes independence of observations and each point belongs to either male or female


the variances of male and female prices are equal for the population


the prices follows a normal distribution


no significant outliers in each group The unpaired t-test is robust to all but large deviation from these assumptions. Assumption 1 and 2 are satisfied, but 3 and 4 require more careful consideration. Log Transformation: For assumption 3, with rare NFTs worth significantly more than common NFTs, NFT price distributions tend to follow a power law distribution [14]. Because it’s well known that stock prices follow a log-normal distribution [1], we apply the same transformation and found log of price tended to be closer to a normal distribution. We thus decided to also run the t-test on the log of prices, which we refer to as log t-test. Outlier Trimming: For assumption 4, as outliers may occur due to very high selling prices for rare NFTs or very low selling prices due to humans errors while listing, we address this issue by Winsorization [19], or trimming outliers past a certain percentile. Specifically, we report t-test results while trimming both left and right tails of the distribution at 0.1%, 1%, 2.5%, and 5% percentiles. Paired t-test: In addition to the unpaired t-test for independent samples, we also run a paired t-test for two dependent samples in order to better isolate the male versus female price difference while fixing time. Specifically, we calculate t-statistics on the paired difference of male and female prices marked to the daily mean price and the weekly mean price of the NFT. Assumptions 1 and 2 are now (1) male - female price must be continuous and (2) observations are independent of one another. Assumptions 3 and 4 from unpaired test remain the same and. We address assumptions 3 and 4 with the log-transformation and Winsorization, as described earlier. The approach described above is also used to compare the prices of lighter-skinned and darkerskinned CryptoPunks. We utilize the SciPy Python package [21] to implement both types of t-tests. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Howard Zhong, MIT CSAIL (howardzh@mit.edu); (2) Mark Hamilton, MIT CSAIL, Microsoft (markth@mit.edu). Authors: Authors: (1) Howard Zhong, MIT CSAIL (howardzh@mit.edu); (2) Mark Hamilton, MIT CSAIL, Microsoft (markth@mit.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Methodology 2 Methodology 3 Results 3.1 CryptoPunks 3.1 CryptoPunks 3.2 Aggregate NFT Market 3.2 Aggregate NFT Market 4 Discussion 4 Discussion 5. Conclusion/ Acknowledgements/ References Conclusion/ Acknowledgements/ References A Appendix A Appendix A.1 Implementation Details A.1 Implementation Details A.2 Detailed NFT Information & A.3 Google NFT Searches Map A.2 Detailed NFT Information & A.3 Google NFT Searches Map A.1 Implementation Details We elaborate on the methods and the implementation details. This sections follows the structure Section 2. A.1.1 Initial Data Collection Our dataset only includes NFTs transacted on OpenSea, which is the primary marketplace for NFTs on the Ethereum blockchain. We query the OpenSea “v1/collections” endpoint at the end of November 2022 [15] to retrieve collection metadata as well as each individual NFT’s metadata and last sale price. We choose 790 collection names from the Kaggle Ethereum NFTs dataset [11] and NFTs from the top 30-day and all-time OpenSea volume leaderboard around November 2021. Most collections have around 5,000 to 10,000 items, and our queries yield around 70-80% of the data as the other 20-30% do not have transactions. After querying the Application Programming Interface (API) using a script written with the PySpark framework [17] on Databricks, we create a dataframe containing each NFT’s collection name, id, sale information, metadata, and image url. We then loop through this dataframe with multiple workers to download the images in parallel. In the end, we obtain a dataset of about ∼ 2.5 NFTs, each that have been transacted upon. A.1.2 Retrieving Race and Gender Labels To get gender-labelled data, we select collections that have metadata with the words “male” and “female” to find collections where at least 50% of the images have gender labels. We filter out collections that have more than 75% male or female to avoid rarity factors affecting the price. We find a total of 44 such collections with gender labels that satisfy the criteria. These collections with gender labels are It is more challenging to determine the race of an NFT collection because most collections do not directly annotate this information. For the CryptoPunks collection, we label each NFT based on the frequencies of the four different pixel values representing the four different skin tones as displayed in Figure 6. The above process was inspired by [5]. For our analysis, we group darkest and mid as Dark and lighter and lightest as Light. For other collections, we were unable to retrieve race labels from the metadata. We also attempted to find race labels for a subset of the 44 collections with gender labels with the criteria that there were a high proportion of items that could be categorized as either dark or light skinned. We filtered out collections that have more than 75% light-skinned or dark-skinned to avoid rarity factors affecting the price. This required manual review because most collections do not have easy-to-search race labels in the metadata, or even way to categorize race. For Avastar, we categorized “Amber Brown” as dark-skinned and “Pale Pink” as light-skinned in the “skin tone” attribute. For Dynamic Duelers, we categorized “Black” as dark-skinned and “White” as lightskinned in the “Skin color” attribute. A.1.3 Statistical Tools to Analyze Bias in Gender and Race We detail the statistical methods used to analyze the bias in NFT pricing for gender and race. Specifically, we go into more detail about the implementation and underlying assumptions of these tests. Unpaired t-test assumes Unpaired t-test independence of observations and each point belongs to either male or female the variances of male and female prices are equal for the population the prices follows a normal distribution no significant outliers in each group independence of observations and each point belongs to either male or female independence of observations and each point belongs to either male or female the variances of male and female prices are equal for the population the variances of male and female prices are equal for the population the prices follows a normal distribution the prices follows a normal distribution no significant outliers in each group no significant outliers in each group The unpaired t-test is robust to all but large deviation from these assumptions. Assumption 1 and 2 are satisfied, but 3 and 4 require more careful consideration. Log Transformation: For assumption 3, with rare NFTs worth significantly more than common NFTs, NFT price distributions tend to follow a power law distribution [14]. Because it’s well known that stock prices follow a log-normal distribution [1], we apply the same transformation and found log of price tended to be closer to a normal distribution. We thus decided to also run the t-test on the log of prices, which we refer to as log t-test. Log Transformation: Outlier Trimming: For assumption 4, as outliers may occur due to very high selling prices for rare NFTs or very low selling prices due to humans errors while listing, we address this issue by Winsorization [19], or trimming outliers past a certain percentile. Specifically, we report t-test results while trimming both left and right tails of the distribution at 0.1%, 1%, 2.5%, and 5% percentiles. Outlier Trimming: Paired t-test: In addition to the unpaired t-test for independent samples, we also run a paired t-test for two dependent samples in order to better isolate the male versus female price difference while fixing time. Specifically, we calculate t-statistics on the paired difference of male and female prices marked to the daily mean price and the weekly mean price of the NFT. Assumptions 1 and 2 are now (1) male - female price must be continuous and (2) observations are independent of one another. Assumptions 3 and 4 from unpaired test remain the same and. We address assumptions 3 and 4 with the log-transformation and Winsorization, as described earlier. Paired t-test: The approach described above is also used to compare the prices of lighter-skinned and darkerskinned CryptoPunks. We utilize the SciPy Python package [21] to implement both types of t-tests. 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

Behind the Scenes: Data Collection and Statistical Tools for NFT Bias Study

Authors:
(1) Howard Zhong, MIT CSAIL (howardzh@mit.edu);
(2) Mark Hamilton, MIT CSAIL, Microsoft (markth@mit.edu). Table of Links Abstract and 1 Introduction 2 Methodology 3 Results 3.1 CryptoPunks 3.2 Aggregate NFT Market 4 Discussion 5. Conclusion/ Acknowledgements/ References A Appendix A.1 Implementation Details A.2 Detailed NFT Information & A.3 Google NFT Searches Map 3. Results Our goal is to investigate biases in pricing with respect to gender and race for NFT collections. We first analyze CryptoPunks, as it is one of the first NFT collections and is commonly credited for starting the 2021 NFT revolution [20]. The dataset we use is compiled from querying the OpenSea API as described in Section 2.1. 3.1 CryptoPunks While [8] identifies biases in prices for both gender and race for CryptoPunks, we find only darkerskinned CryptoPunks are sold for less than lighter-skinned CryptoPunks. 3.1.1 Gender We found price difference across gender was not significant. In Figure 3a, male and female weekly mean sale price are roughly equal with blue and red lines being roughly level. This conclusion is corroborated by the male and female box plots at similar price levels for different months in Figure 3b. We supplement the visual analysis with statistical anylsis. From 2021-01 to 2022-11, the median selling price of male CryptoPunks (64.9 eth) is greater than median price of female CryptoPunks (64.0 eth), but the mean selling price of male CryptoPunks (63.5 eth) is less than that of female CryptoPunks (63.9 eth). Because the means and medians yield different conclusions, we also analyze the p-value of different one-sided t-tests on whether male prices are greater than female prices. As shown in Table 2, we vary outlier detection percentiles, whether to run unpaired or paired t-test, and whether to apply the log transformation. Only at a 0.1% outlier percentile level do some of the t-tests imply male price is greater than female price. This is reasonable because from Figure 3b, male outliers tend to be higher priced than female outliers. As a precondition for the t-test is the absence of outliers, t-tests at other outlier levels (2.5% and 5%) may be more informative. Thus, there is not enough statistical evidence to conclude there is gender bias in CryptoPunks. 3.1.2 Race Although we do not find gender bias in the pricing of CryptoPunks, we corroborate the visual analysis in [8] with statistical results that lighter-skinned CryptoPunks are valued more than darker-skinned CryptoPunks. We first plot the weekly mean sale prices of Dark and Light CryptoPunks between 2021-01 and 2022-11 in Figure 4a and find that Light CryptoPunks are consistently sold at a higher price than Dark CryptoPunks. Examining the box plot in Figure 4b for CryptoPunks prices across different months, we observe similar results. At a 2.5% trim level, the median sale price for Light CryptoPunks (65.6 eth) is greater than that of Dark CryptoPunks (64.0 eth), and the mean sale price for Light CryptoPunks (66.1 eth) is also greater than that of Dark CryptoPunks (64.5 eth). In addition, from Table 3, every paired t-test across different outlier detection schemes supports this hypothesis with very low p-values, indicating the effect is large. Furthermore, the unpaired t-test results were above 0.05 at 2.5% and 5.0% outlier trim levels may be due to CryptoPunks’ large price variation across time that increases standard deviation and lowers t-stat. Thus, the evidence suggests that Light CryptoPunks are sold for more than Dark CryptoPunks. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Howard Zhong, MIT CSAIL (howardzh@mit.edu); (2) Mark Hamilton, MIT CSAIL, Microsoft (markth@mit.edu). Authors: Authors: (1) Howard Zhong, MIT CSAIL (howardzh@mit.edu); (2) Mark Hamilton, MIT CSAIL, Microsoft (markth@mit.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Methodology 2 Methodology 3 Results 3.1 CryptoPunks 3.1 CryptoPunks 3.2 Aggregate NFT Market 3.2 Aggregate NFT Market 4 Discussion 4 Discussion 5. Conclusion/ Acknowledgements/ References Conclusion/ Acknowledgements/ References A Appendix A Appendix A.1 Implementation Details A.1 Implementation Details A.2 Detailed NFT Information & A.3 Google NFT Searches Map A.2 Detailed NFT Information & A.3 Google NFT Searches Map 3. Results Our goal is to investigate biases in pricing with respect to gender and race for NFT collections. We first analyze CryptoPunks, as it is one of the first NFT collections and is commonly credited for starting the 2021 NFT revolution [20]. The dataset we use is compiled from querying the OpenSea API as described in Section 2.1. 3.1 CryptoPunks While [8] identifies biases in prices for both gender and race for CryptoPunks, we find only darkerskinned CryptoPunks are sold for less than lighter-skinned CryptoPunks. 3.1.1 Gender 3.1.1 Gender We found price difference across gender was not significant. In Figure 3a, male and female weekly mean sale price are roughly equal with blue and red lines being roughly level. This conclusion is corroborated by the male and female box plots at similar price levels for different months in Figure 3b. We supplement the visual analysis with statistical anylsis. From 2021-01 to 2022-11, the median selling price of male CryptoPunks (64.9 eth) is greater than median price of female CryptoPunks (64.0 eth), but the mean selling price of male CryptoPunks (63.5 eth) is less than that of female CryptoPunks (63.9 eth). Because the means and medians yield different conclusions, we also analyze the p-value of different one-sided t-tests on whether male prices are greater than female prices. As shown in Table 2, we vary outlier detection percentiles, whether to run unpaired or paired t-test, and whether to apply the log transformation. Only at a 0.1% outlier percentile level do some of the t-tests imply male price is greater than female price. This is reasonable because from Figure 3b, male outliers tend to be higher priced than female outliers. As a precondition for the t-test is the absence of outliers, t-tests at other outlier levels (2.5% and 5%) may be more informative. Thus, there is not enough statistical evidence to conclude there is gender bias in CryptoPunks. 3.1.2 Race 3.1.2 Race Although we do not find gender bias in the pricing of CryptoPunks, we corroborate the visual analysis in [8] with statistical results that lighter-skinned CryptoPunks are valued more than darker-skinned CryptoPunks. We first plot the weekly mean sale prices of Dark and Light CryptoPunks between 2021-01 and 2022-11 in Figure 4a and find that Light CryptoPunks are consistently sold at a higher price than Dark CryptoPunks. Examining the box plot in Figure 4b for CryptoPunks prices across different months, we observe similar results. At a 2.5% trim level, the median sale price for Light CryptoPunks (65.6 eth) is greater than that of Dark CryptoPunks (64.0 eth), and the mean sale price for Light CryptoPunks (66.1 eth) is also greater than that of Dark CryptoPunks (64.5 eth). In addition, from Table 3, every paired t-test across different outlier detection schemes supports this hypothesis with very low p-values, indicating the effect is large. Furthermore, the unpaired t-test results were above 0.05 at 2.5% and 5.0% outlier trim levels may be due to CryptoPunks’ large price variation across time that increases standard deviation and lowers t-stat. Thus, the evidence suggests that Light CryptoPunks are sold for more than Dark CryptoPunks. 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

Gender-Based NFT Price Differences Found to Be Insignificant

(Edit) I have added some gif to this article. I’m glad you like ! In fact, I am a die hard fan and I’ve been using it for ages (since ST2). I have a few tricks I would like to share them with you. Sublime Text 3 This is not a language oriented article. This article ranges from use of ST3 and we are going to learn and do: basics to advanced Fuzzy searching basics. What the files under folder are. User Some important functionalities as a reminder. Working with words and lines. Selection and deleting techniques. Useful keys and advance techniques. navigation Project Management. Install a few handy plugins. Optimisation by changing a few settings. Add missing to the commands Command Palette. In case you don’t know how to open the Command Palette, hit . The is for files, otherwise known as . ctrl+shift+p ctrl+p go to go to anything Fuzzy searching It works like this: if you are after a term in any palettes, for instance , you don’t need to type in the whole thing. I would do which matches the term. Also there is no space needed. Preferences: Key Bindings kybi Files under User folder There are some under . I’m using GNU/Linux, but the principals are the same in other OSs. The contents should be in valid format, otherwise they don’t work. files ~/.config/sublime-text-3/Packages/User JSON The is for global settings. It can be accessed by command palette ( ). Preferences.sublime-setting Preferences: Settings The is where you can add key shortcuts. It can be accessed in command palette ( ). Default (Linux).sublime-keymap Preferences: Key Bindings The contains the mouse actions. For now you only can open it by browsing to the directory and edit. However in this article I will show you how to add it to the Command Palette with ease. Default (Linux).sublime-mousemap There is another kind of file that doesn’t exist in that folder by default but you can create one, which the functionalities, I will explain it in below. extends Command Palette Basic functionalities and concepts can be invoked in any of these ways: Multi cursor Select multiple lines, then hit ctrl+shift+l on different places ctrl+left-click Find under expand. Put your cursor on a word, then hit it will select that word. Then hit a few more times to find similar words while creating a new cursor. It also works with partially selected words (a few characters or a few words/lines). ctrl+d ctrl+d Find all: and click on ctrl+f Find All shows the current file’s location in the sidebar and highlights it. Right click on the view and choose . We will create a command for it below as it doesn’t exists (yet). Reveal file in sidebar Reveal In Sidebar by hitting . You can create a command for this one too if you want. Toggle Sidebar ctrl+k ctrl+b is an important one. You can invoke its palette by . It shows you a list of available files in your current project (see below). It too obeys the fuzzy searching rules. Here are more things you can do with it: Go To Anything ctrl+p shows a list of symbols (classes/functions/methods) you can navigate to. @ takes you to a line by typing the line number. : shows you a list of token words (they can be set up in files and they usually come with plugins you install) # .tmLanguage If you don’t type in any file names, they work on the current file. Always have a mental image of your project and possible symbol names and navigate with this palette. Example 1: If you want to go to a file called and you know you want to land on a function called , type this in the palette: . This is just an example and the letters don’t have any significance other than matching. You need to make sure the destination file is focused then add to the mix. brew_beer.go Ingredients() bre@ingrd @ Example 2: If you want to go to the line of a file called , you can type in . 18 drunk_snake.py drnksnk:18 extremely useful for using in multiple panes (more about it in the origami section) New View into File while keeping the cursor at its position can be done by . Note that if you don’t have any selection, the cursor will travel by the edge of the view. In case you want to avoid and keep the cursor where it is, select one word and scroll. It retains the cursor position. Scrolling ctrl+up/down is handy when your lines are long and don’t fit into the view. You can invoke it with Command Palette. Wrap lines can be done with . It uses the ruler position to wrap. Wrapping a paragraph alt+q More useful actions will be mentioned in other sections of this article, please read on. Working with Words and Lines Let’s assume you have your cursor on a word/line and you want to: Go to next/previous word: ctrl+right/left Go to matching bracket: . It also works if you are not a bracket. It will navigate you to the closest bracket. ctrl+m Select the : hit . Please do not go to the end/beginning of the word and do , those combinations have a different purpose. It’s not what a would do! current word ctrl+d ctrl+shift+left/right ninja Select to next/previous word as you go: ctrl+shift+right/left Delete to the : end of a word ctrl+delete Delete to the : beginning of a word ctrl+backspace Delete to the : end of the line ctrl+shift+delete lines: Duplicate ctrl+shift+d Delete current line all together: ctrl+x up/down**:** Swap lines ctrl+shift+up/down . It expands the selection in blocks, and expands more if you hit it more times until it selects the whole file. Expand Selection: ctrl+shift+space Delete a block: hit until you covered the block you want to remove, then delete. ctrl+shit+space Indent line(s) left: and Indent line(s) right: ctrl+[ ctrl+] Jump forward and back (settings all the way down on this article): as you are moving around in your project, you can always go back and forth to the place you were. This is handy for inspecting the project. Insert a tab where ST3 insists on a completion: shift+tab (Edit 1) When you copy/cut stuff in ST3, it keeps them in a history. If you do it shows you a pop up of the history and you can choose to paste it. This is super useful! ctrl+k ctrl+v Join Lines: ctrl+j Project Management Projects Projects contain a series of files/folders added into a project. You can add multiple folders to a project if you wish. I have a folder in my folder called where I keep all my ST3 projects. I also put them into their own sub-folder to manage different workspaces. I suggest you do the same as it clutters the whole folder if you work with a lot of projects. You can open the project settings by invoking in command palette. Here are a few tips: Projects Sublime Text Project: Edit "folders": [{"file_exclude_patterns": ["*.test", "*.out"],"folder_exclude_patterns": ["tmp"],],"path": "/path/to/project/folder",}], Workspaces You can have multiple workspaces per project. They share the same settings as their parent project, therefore when you change the project settings it applies to all of its workspaces. Use them if you have different layouts or you have different concerns while working on a project. You can save them in the same folder you keep your ST3 project by choosing menu. You can also create a brand new workspace for the current project from the menu. Project> Save Workspace As.. Project Fast Switching Projects Every time you open/create projects or workspaces, ST3 keeps a history of what you’ve been working on. You can then hit to navigate between projects or workspaces easily. ctrl+alt+p Handy Plugins is useful for aligning cursors. I have changed the key binding to Alignment { "keys": ["super+a"], "command": "alignment" } Make a multi cursor and hit to align them. It will move the text with which is really handy. You can create such sight in your file: super+a shows you the opening and closing pairs of brackets. I use it to identify the block I am working on. Bracket Highlighter can change word/character cases. It has these modes: Case Conversion snake_case camelCase PascalCase dot.case dash-case Separate words Separate with forward slashes Separate with backslashes Toggle between snake_case, camelCase and PascalCase can change the file name. I use this to depend less on my mouse. File Rename shows git changes in the gutter. You also can view the diffs and roll them back, really handy. Git Gutter I use it every all the times. You can land your cursor on an indented area (maybe a bracket or beginning of a line) and hit to jump to the respective indention. Jump Along Indent: alt+up/down is a must have one if you do on github. Just generate a token on your github account (with only gist enabled permission) and add it to the plugin. You never need to browse to gists web page for your gists any more. You also can load a gist, edit and save it. It updates the gist automatically. HaoGist gists can move your tabs like in your browser. MoveTab is useful if you increment numbers. Create a bunch of cursors and type then select all those zeros and hit . It makes a series of incremented numbers. You can seed it with any numbers. Increment Selection 0 ctrl+alt+i is another must have plugin to make todo lists. It also tracks the time spent on each item. It is very straightforward and starts with its own tutorial when you install it, so there is no need to explain it here. PlainTasks will enhance your sidebar. Make sure you install this one. SideBarEnhancements is a better version of sublime’s bookmark system. You can name your bookmarks and navigate them in the command palette. Very handy. Sublime Bookmarks looks really cool, give it a go. Material Theme : this awesome plugin adds some cool functionalities to multi-cursor functionality. For instance you can edit individual cursors while retaining the who multi-cursor state. You also can cycle through the cursors and edit them! Head over to its page to see the full description. (Edit 1) MultiEditUtils : I mention this the last, because I also wanted to tell you a little story. Please read this part as I explain the decisions I have made to set this very plugin. Origami important I can’t live without . The way ST3 handles panes is way primitive in comparison to tmux and my ST3 work flow has been a bit slow before I started using origami. Before using origami, I rarely utilised panes and only relied on goto anything. The very first time I saw origami, it blew my mind, but soon I left it for the following reasons: tmux The shortcuts were different than my tmux set up It made me slower to code because it didn’t feel natural My tmux prefix is and I couldn’t use it for origami because it is also functionality. Until I asked myself these questions: ctrl+a select all How many times am I selecting the whole file’s contents in a day? Which one contributes to my productivity: Selecting all or having a more flexible workspace? The answer to the second question is obvious, duh! However the answer to the first question turned out very interesting: at most 10 times a day! Now the other obstacle: ? Here is what I came up with, which worked amazingly well for me and I’ve been a happier programmer. I changed the select all to: How am I going to set these two up in such a way I can have best of both { "keys": ["ctrl+a", "ctrl+a"], "command": "select_all" } And changed all prefixes of origami to . Now here is what happens: ctrl+b ctrl+a My pane control/navigation is like the tmux set up, and it feels very natural to me. exactly If I want to select all, I do twice, why not?! ctrl+a Problem resolved. I also disabled creating empty panes, which is really annoying. This means if you do a it creates an empty pane with nothing in it, which is not useful unless you want to create a new file. You can achieve that by creating a new file and move it to a new pane by . In origami’s user settings I have: ctrl+a right ctrl+a shift+right "create_new_pane_if_necessary": false, And in the preferences I added this to close empty panes when the last file is closed: "origami_auto_close_empty_panes": true Now here is a few handy tricks for origami. I call it here, the key I have set to which is by default. Lets call arrow keys a direction. For instance this combination means you hit then hit . prefix ctrl+a ctrl+b up/down/left/right prefix, ctrl+direction ctrl+a ctrl+up creates pane on the direction. prefix, ctrl+direction destroys pane on that direction. prefix, ctrl+shift+direction travels to the direction. prefix, direction travels while carries the file to the direction. prefix, shift+direction clones (makes a new view on the file) while bringing the new view to the direction. prefix, alt+direction zooms the pane. prefix, ctrl+z un-zooms the pane back to normal. prefix, ctrl+shift+z shows you an input box to put in the proportion of pane’s horizontal (column) size. I’ll explain in a bit. prefix, ctrl+c shows you an input box to put in the proportion of pane’s vertical (row) size. prefix, ctrl+r The sizes work like this: If you are on a pane that has only one pane of its side (or above/below): 0.3 means the the occupies 30 percent of the space in respect to the whole space. pane you are on If you are on the middle pane: 0.2, 0.9 means the left (or top) most pane gets 20 percent, the right one gets 90 percent and you get the rest. In the first point by whole space I mean if you have three panes and you are on the first one, it accounts the percentage as if there is only two, and it pushes the other panes to make room. : You can make more and put them side by side in order to work on different portion of the file and navigate through the panes as opposed to jumping up and down. A tip Views Into a File Origami has a concept of saving layouts. You also can create a layout and save the workspace. Either one works, but I prefer to save the layout. Now you can get rid of the sidebar and enjoy more space. Changing Some Settings I found these settings to be useful: {"bold_folder_labels": true,"close_windows_when_empty": true,"detect_slow_plugins": false,"enable_tab_scrolling": false,"ensure_newline_at_eof_on_save": true,"fade_fold_buttons": false,"font_face": "Inconsolata-g for Powerline","font_options":["subpixel_antialias","gray_antialias","directwrite"],"font_size": 11,"line_padding_bottom": 3,"line_padding_top": 3,"margin": 0,"caret_extra_bottom": 0,"caret_extra_top": 0,"caret_extra_width": 1,"caret_style": "wide","tab_size": 4,"spell_check": true,"dictionary": "Packages/Language - English/en_GB.dic","file_exclude_patterns":["*.sqlite3","*.exe","*.pyc","*.pyo","*.dia"],"folder_exclude_patterns":[".svn",".git",".gitignore",".hg","CVS","_build","dist","build","site"],"highlight_line": true,"highlight_modified_tabs": false,"indent_guide_options":["draw_active"],"match_brackets_angle": true,"material_theme_accent_orange": true,"material_theme_contrast_mode": true,"material_theme_small_statusbar": true,"material_theme_small_tab": true,"material_theme_tabs_autowidth": true,"origami_auto_close_empty_panes": true,"save_on_focus_lost": false,"shift_tab_unindent": false,"show_encoding": true,"show_line_endings": true,"show_tab_close_buttons": true,"theme": "Material-Theme-Darker.sublime-theme","translate_tabs_to_spaces": true,"trim_trailing_white_space_on_save": true,"use_tab_stops": true,"word_wrap": "false"} Notice the file_exclude_patterns and folder_exclude_patterns are also in project settings, when you set it here it applies it to every projects you have. I have excluded .git because I don’t want ST3 to search in those files when I do to search in everything. ctrl+shift+f Commands Create a file by any name and extension. Like so: and add this to the file: sublime-command arsham.sublime-command [{"caption": "Open Mouse Settings", "command": "open_file", "args": {"file": "${packages}/User/Default (Linux).sublime-mousemap"}},] Then invoke the Command Palette and type in . It will open your mouse map file (I have mentioned this earlier in this article). There is a wide range of commands you can do. In fact, I would like you to do an experiment. Open the by hitting then type this in there: Open Mouse Settings Console Panel ctrl+` sublime.log_commands(True) Without closing the console, do something to sublime. Like toggling the The short key is . You can see in the console it says: SideBar. ctrl+k ctrl+b command: toggle_side_bar Now you know the command name, you can add this to your commands: {"caption": "Toggle My sidebar please", "command": "toggle_side_bar"} And invoke it with command palette. Go ahead and try a few more! Here are some useful commands: {"caption": "Reveal File In SideBar", "command": "reveal_in_side_bar"},{"caption": "Open Mouse Settings", "command": "open_file", "args": {"file": "${packages}/User/Default (Linux).sublime-mousemap"}},{"caption": "Open Project", "command": "prompt_open_project_or_workspace"},{"caption": "Fold Comments", "command": "fold_comments"},{"caption": "Unfold Comments", "command": "unfold_comments"},{"caption": "Fold One", "command": "fold_by_level", "args": {"level": 1}},{"caption": "Fold Two", "command": "fold_by_level", "args": {"level": 2}},{"caption": "Fold Three", "command": "fold_by_level", "args": {"level": 3}},{"caption": "Fold Four", "command": "fold_by_level", "args": {"level": 4}},{"caption": "Fold Five", "command": "fold_by_level", "args": {"level": 5}},{"caption": "Fold Six", "command": "fold_by_level", "args": {"level": 6}},{"caption": "Fold Seven", "command": "fold_by_level", "args": {"level": 7}},{"caption": "Fold Eight", "command": "fold_by_level", "args": {"level": 8}},{"caption": "Fold Nine", "command": "fold_by_level", "args": {"level": 9}}, Here are some useful key bindings: { "keys": ["super+a"], "command": "alignment" },{ "keys": ["alt+left"], "command": "jump_back" },{ "keys": ["alt+right"], "command": "jump_forward" },{ "keys": ["ctrl+0"], "command": "reset_font_size" }, Please let me know in the comments if you have any suggestions how to improve this article, thanks! Conclusion Sublime Text 3 is really fast and comes with a lot of functionalities. When extended with plugins can be the best editor around. Please try to use your keyboard and rely less on mouse navigations. Enjoy!

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