TOTP and Authenticator Apps for 2FA in Go

Back in 2019, I wrote “Using Time-Based One-Time Passwords for Two-Factor Authentication“, explaining how to allow a server written in C# to generate and verify codes in sync with authenticator apps. In this article, I’ll show how to do the same thing in Go.

How It Works

Logins that use Two-Factor Authentication (2FA) typically rely on the user’s password as the first factor, as well as a second factor that could be anything from an email/SMS to something biometric. Among all these options, the Time-Based One-Time Password (TOTP) protocol is a simple way to provide the benefits of 2FA without the disadvantages of other options (such as SMS gateways being unreliable and expensive, and biometrics being complex to implement).

The way TOTP works is that the client and server each generate a code, and these have to match. The codes are generated based on a shared secret and the current time, so as long as the client and server are using the same secret and generate the codes at roughly the same time, they will match.

The client usually takes the form of an authenticator app – such as Google Authenticator, Microsoft Authenticator, or Authy – on a mobile device. Once it acquires the shared secret from the server – typically by scanning a QR code – it then generates codes every 30 seconds. Usually the server will accept that code during these 30 seconds and also for an additional 30 seconds while the next code is displayed.

This might all sound a little abstract, but it will become clear shortly once we go through implementing TOTP on the server side and testing it out.

TOTP in Go – Getting Started

Before looking at TOTP, let’s first do the usual steps to create a simple program in Go:

  • Create a new folder, e.g. go-otp
  • Create a new file in it, e.g. main.go
  • Run go mod init main
  • Add a basic program stub:
package main

func main() {


In this article, we’ll be using the excellent otp library by Paul Querna. Install it as follows:

go get

Now we can start adding code in that main() function.

Generating a Shared Secret (Server Side)

To generate a shared secret, you call totp.Generate(), passing in a totp.GenerateOpts object. The Issuer and AccountName are required, and you can set other options if you need to.

	// generate shared secret

	options := totp.GenerateOpts{
		Issuer:      "My Web Application",
		AccountName: "",

	key, err := totp.Generate(options)
	if err != nil {
		fmt.Println("Failed to generate shared secret: ", err)

In order to do something with that key object, you have to convert it to base32, and you do that by simply calling its Secret() method:

	secret := key.Secret()

The output will change every time you run this, but the following is an example of what it looks like. In a real implementation, this would be saved in a database for each user.


Generating QR Codes (Server Side)

The next step is to transmit the shared secret to the user. Normally, at some point during the registration process, the server generates the shared secret for the user, and sends it to the frontend, which displays it as a QR code. The user then scans the QR code with their authenticator app.

Although the server has no need to generate QR codes, it’s useful to do this for testing purposes, and it’s certainly faster than typing in the shared secret manually (which apps usually allow as a fallback option).

The otp library’s example code shows how to generate a QR code from the shared secret. Adapting this example a little, we get:

	// generate QR code

	var buf bytes.Buffer
	img, err := key.Image(200, 200)
	if err != nil {
		fmt.Println("Failed to save QR code: ", err)
	png.Encode(&buf, img)
	os.WriteFile("qr-code.png", buf.Bytes(), 0644)

Running this code generates a neat little QR code:

QR code for the shared secret.

Acquiring the Shared Secret (Client Side)

We can now pass the QR-encoded secret to the client. For this, we’ll need an authenticator app. Google Authenticator, Microsoft Authenticator and Authy should all work, but I personally prefer the latter because of its cloud backup feature, which is handy if your phone dies or goes missing.

Once the app is installed, locate the option to add a new account, after which you’ll be able to add a shared secret either by scanning a QR code or typing it manually. Excuse the quality of the next two pictures; none of these authenticator apps allow screenshots to be taken, so the closest thing I could do was take a photo with another phone.

Adding an account in Authy.

Generating Authenticator Codes (Client Side)

Once your app has acquired the shared secret, it will automatically start generating 2FA codes, typically six digits long at 30-second intervals:

Authy generating 2FA codes.

Alternatively, for testing purposes or if you need to develop a client with similar functionality, you can generate similar 2FA codes using the otp library itself. To do this, simply call totp.GenerateCode(), passing in the shared secret you generated earlier, and the current time:

	// generate 2FA code

	secondFactorCode, err := totp.GenerateCode(secret, time.Now())
	if err != nil {
		fmt.Println("Failed to generate 2FA code: ", err)

The output is the same kind of six-digit 2FA code that you’d get with an authenticator app, for instance:


In fact, if you run this while you also have the authenticator app set up, you’ll see the exact same codes.

Validating 2FA Codes (Server Side)

To complete the second factor part of the login process, a user grabs a 2FA code from their authenticator app and enters it in the prompt in the web application’s frontend, essentially sending it over to the server. The server’s job is to ensure that the code is valid. The otp library allows us to do this validation using the totp.Validate() function, which takes the input 2FA code, and the shared secret from earlier.

In order to test how this behaves over a period of time, we can do a little infinite loop that requests 2FA codes from the user and then validates them:

	for {
		fmt.Print("Enter 2FA code: ")

		var input2faCode string

		codeIsValid := totp.Validate(input2faCode, secret)
		fmt.Println("Code is valid: ", codeIsValid)

While playing with this, you’ll notice that:

  • Entering a code shown by the authenticator app returns true
  • Entering the same code twice returns true
  • Entering the same code still returns true within 30 seconds after it has stopped displaying in the authenticator app
  • Entering the same code after that returns false
  • Entering anything else (other than the current or previous code) returns false

In theory, you should prevent the application from accepting the same code twice, because they’re supposed to be one-time-passwords. To do this, you’ll need to add additional state and logic to your application. Or, you can accept the security tradeoff knowing that the 2FA codes are only valid for a minute, giving little opportunity for an attacker to exploit them.


My earlier article showed how easy it is to work with 2FA codes using a TOTP library in C#, and in Go it’s no different. After generating a secret from the otp library, you can either share it with your authenticator app (e.g. by generating a QR code) to generate 2FA codes, or use it to generate 2FA codes directly. The library also allows you to validate the generated 2FA codes, assuming they are valid for two 30-second windows.

In the interest of simplicity, I’ve only shown basic usage of the library. If you have more complex requirements, you can customise the generation of secrets, generation of 2FA codes, and also the validation of 2FA codes.

Finally, one thing to keep in mind is that 2FA becomes a major headache when one of the factors goes missing (e.g. phone is lost or stolen). So you’ll need to consider how to handle this situation (e.g. recovery codes, reset shared secret, etc.), and how to handle normal password resets (you may want to reset the shared secret too).

Migrating Cartography to Memgraph

I’ve recently written about how to use Cartography to collect infrastructural data from both AWS and Okta into a Neo4j graph database for security analysis.

Neo4j is a long-standing player in the graph database market, with a robust product, great documentation, and a massive following. However, its long legacy is in a way also a disadvantage, as it can be costly, slow, and resource-hungry (due in no small part to its reliance on the JVM). Sometimes people would like to use an alternative for any of these reasons.

Memgraph, on the other hand, is a relatively young graph database, and certainly not as fully-featured as Neo4j. A key difference is that it is written in C++, meaning it’s designed to be faster and more lightweight than Neo4j (whether it lives up to this is something you’ll need to evaluate for your own use cases). Memgraph also made a very wise decision to support the Bolt protocol and the Cypher language – both of which Neo4j uses – meaning that it’s compatible with existing Neo4j clients and queries. Although there are variations in Cypher dialect, the incompatibilities are few, and moving from Neo4j to Memgraph is significantly less painful than, say, transitioning to a graph database that uses Gremlin as its query language.

At the time of writing this article, Cartography requires Neo4j 4.x, and does not work with Memgraph. However, I’m going to show you how to make at least part of it (the Okta intel module) work with minor alterations to the Cartography codebase. This serves as a demonstration of how to get started migrating an existing application from Neo4j to Memgraph.

Running Memgraph

Before we start looking at Cartography, let’s run an instance of Memgraph. To do this, we’ll take a tip from my earlier article, “Using the Neo4j Bolt Driver for Python with Memgraph“, and run it under Docker as follows (drop the sudo if you’re on Mac or Windows):

sudo docker run --rm -it -p 7687:7687 -p 3000:3000 -e MEMGRAPH="--bolt-server-name-for-init=Neo4j/" memgraph/memgraph-platform

That --bolt-server-name-for-init=Neo4j/ is a first critical step in Neo4j compatibility. As explained in that same article, the Neo4j Bolt Driver (i.e. client) for Python (which Cartography uses) checks whether the server sends an “agent” value that starts with “Neo4j/”. By setting this, Memgraph is effectively posing as a Neo4j server, and the Neo4j Bolt Driver for Python can’t tell the difference.

Update 19th September 2023: as of Memgraph v2.11, --bolt-server-name-for-init has a default value compatible with the Neo4j Bolt Driver, and therefore no longer needs to be provided.

If it’s successful, you should see output such as the following:

Memgraph is running. You can also execute queries directly from here.

Cloning the Cartography Repo

The next thing to do is grab a copy of the Cartography source code from the Cartography GitHub repo:

git clone

Next, run the following command to install the necessary dependencies:

pip3 install -e .

Note: in the past, I’ve usually had to upgrade the Neo4j Bolt Driver for Python to 5.2.1 to get anything working, but as I try this again, it seems to work even with the default 4.4.x that Cartography uses. If you have problems, try changing to require neo4j>=5.2.1 and run the above command again.

Creating a Launch Configuration in Visual Studio Code

In order to run Cartography from its source code, you could run it directly from the terminal, for instance:

cd cartography/cartography

However, as I’ve recently been using Visual Studio Code for all my polyglot software development needs, I find it much more convenient to set up a launch configuration that allows me to easily debug Cartography and pass whatever command-line arguments and environment variables I want.

The following launch.json is handy to run Cartography with an Okta configuration as described in “Getting Started with Cartography for Okta“:

    "version": "0.2.0",
    "configurations": [
            "name": "Run Cartography",
            "type": "python",
            "request": "launch",
            "program": "cartography/",
            "console": "integratedTerminal",
            "justMyCode": true,
            "args": [
            "env": {
                "NEO4J_PASS": "ignore",
                "OKTA_API_TOKEN": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

You might notice we’re telling Cartography to connect to Neo4j (Memgraph actually) with username and password both set to “ignore”. The reason for this is that while the Community Edition of Memgraph does not require (or support) authentication, the Neo4j Bolt Driver for Python (i.e. Neo4j client) does require a username and password to be provided. So, as a second critical compatibility step, we pass any arbitrary value for the Neo4j username and password so long as they are not left empty.

As for the Okta configuration, remember to replace the Organisation ID and API Token with real ones.

Incompatible Index Creation Cypher

Pressing F5, we can now run Cartography from inside Visual Studio Code, and we immediately run into the first problem:

The error says: “no viable alternative at input ‘CREATEINDEXIF'”.

Memgraph is choking on the index creation step in indexes.cypher (in VS Code, use Ctrl+P / Command+P to quickly locate the file) because the index creation syntax is one aspect of Memgraph’s Cypher implementation that is not compatible with that of Neo4j. If we take the first line in the file, the Neo4j-compatible syntax is:

CREATE INDEX IF NOT EXISTS FOR (n:AWSConfigurationRecorder) ON (;

…whereas the equivalent on Memgraph would be:

CREATE INDEX ON :AWSConfigurationRecorder(id);

Note: in Memgraph, the “IF NOT EXISTS” bit is implicit: an index is created if it doesn’t exist; if it does, the operation is a no-op that does not cause any error.

Fortunately, this syntactic difference is easily resolved by replacing (using VS Code search & replace syntax, with regex enabled) this:

CREATE INDEX IF NOT EXISTS FOR \(n:(.*?)\) ON \(n.(.*?)\);

…with this:


Tip: although not in scope here, you’ll need to make a similar change also in and if you also want to get other intel modules (e.g. AWS) working.

Neo4j Result Consumption

After fixing the index creation syntax and rerunning Cartography, we run into another problem:

The error says: “The result is out of scope. The associated transaction has been closed. Results can only be used while the transaction is open.”

I’m told that consume() is used to fix a problem in which Neo4j connections hang in situations where internal buffers fill up, although the Cartography team is re-evaluating whether this is necessary. In practice, I have seen that removing this doesn’t seem to cause problems with datasets I’ve tested with, although your mileage may vary. Let’s fix this problem by removing usage of consume() in

First, we drop the .consume() at the end of line 76 inside the run() function:

    def run(self, session: neo4j.Session) -> None:
        Run the statement. This will execute the query against the graph.
        if self.iterative:
            session.write_transaction(self._run_noniterative)"Completed {self.parent_job_name} statement #{self.parent_job_sequence_num}")

Then, in the _run_iterative() function, we remove the entire while loop (lines 120-128) except for line 121, which we de-indent:

        # while True:
        result: neo4j.Result = session.write_transaction(self._run_noniterative)

            # Exit if we have finished processing all items
            # if not result.consume().counters.contains_updates:
            #     # Ensure network buffers are cleared
            #     result.consume()
            #     break
            # result.consume()

When we run it again, it should finish the run without problems and return control of the terminal with the prompt showing:

INFO:cartography.sync:Finishing sync stage 'duo'
INFO:cartography.sync:Starting sync stage 'analysis' analysis because no job path was provided.
INFO:cartography.sync:Finishing sync stage 'analysis'
INFO:cartography.sync:Finishing sync with update tag '1689401212'

Querying the Graph

The terminal we’re using to run Memgraph has the mgconsole client running (that’s the memgraph> prompt you see in the earlier screenshot), meaning we can try running queries directly there. For starters, we can try the ubiquitous “get everything” Cypher query:

memgraph> match (n) return n;

Note: if you get a “mg_raw_transport_send: Broken pipe”, just run the query again and it should reconnect.

This gives us some data back:

Querying Memgraph using mgconsole.

As you can see, this is not great to visualise results. Fortunately, Memgraph has its own web client (similar to Neo4j Browser) called Memgraph Lab, that you can access on http://localhost:3000/:

Memgraph Lab: Quick Connect page.

On the Quick Connect page, click the “Connect now” button. Then, switch to the “Query Execution” page using the left navigation sidebar, and you can run queries and view results more comfortably:

Seeing some nodes in Memgraph Lab.

Unlike Neo4j Browser, Memgraph Lab does not return relationships by default when you run this query. If you want to see them as well, you can run this instead:

match (a)-[r]->(b)
return a, r, b
Nodes and relationships in Memgraph Lab.

If the graph looks too cluttered, just drag the nodes around to rearrange them in a way that is more pleasant.

More Cartography with Memgraph

Cartography is a huge project that gathers data from a variety of data sources including AWS, Azure, GitHub, Okta, and others.

I’ve intentionally only covered the Okta intel module in this article because it’s small in scope and easy to digest. To use Cartography with other data sources, additional effort is required to address other problems with incompatible Cypher queries. For instance, at the time of writing this article, there are at least 9 outstanding issues that need to be fixed before Cartography can be used with Memgraph for AWS (that’s quite impressive considering that the AWS intel module is the biggest). Other intel modules may have other problems that need solving; nobody has explored them with Memgraph yet.


In this article, I’ve shown how one could go about taking an existing application that depends on Neo4j and migrating it to Memgraph. I’ve used Cartography with its Okta intel module to keep things relatively straightforward. The steps involved include:

  1. Running Memgraph with --bolt-server-name-for-init=Neo4j/
  2. Using the same Bolt-compatible Neo4j client, providing arbitrary Neo4j username and password values
  3. Fixing any incompatible Neo4j client code (in this case, consume()), if applicable
  4. Adjusting any incompatible Cypher queries

Getting Started with Cartography for Okta

Cartography is a great security tool that gathers infrastructure and security data from various sources for subsequent analysis. Last year, I wrote an article about Getting Started with Cartography for AWS. Although Cartography focuses mostly on AWS, it also gathers data from several other sources including major cloud and SaaS providers.

In this article, we’ll use Cartography to ingest Okta data. For the unfamiliar, Okta is an enterprise identity management tool that is great for its Single Sign On (SSO) capability. From a single dashboard, it provides seamless access to many different services (e.g. AWS, Gmail, and many others), without having to login every time. See also: What is Okta and What Does Okta Do?

It’s worth noting before we start this journey that Cartography’s support for Okta isn’t great. It only supports a handful of types, and it uses a retired version of the Okta SDK for Python. Nonetheless, it retrieves the most important types, and they enable analysis of some more interesting attack paths (e.g. an Okta user gaining unauthorised access to resources in AWS).

Creating an Okta Developer Account

We’ll first need an Okta account. There are a few different options including a trial, but for development, the best is to sign up for an Okta Developer account as follows.

Click on the Sign up button in the top-right.
In this confusing selection screen, go for the Developer Edition on the right.
Fill the sign-up form and proceed.

Once you get to the sign-up form, fill in the four required fields, and then either sign-up via email or use your GitHub or Google account. Note that Okta demands a “business email”, so you can’t use a Gmail account for this.

After signing up, you’ll get an email to activate your account. Follow its instructions to choose a password, and then you will be logged in and redirected to your Okta dashboard.

The Okta dashboard.

Creating an Okta API Token

Cartography’s Okta Configuration documentation says it’s necessary to set up an Okta API token, so let’s do that. From the Okta Dashboard:

  1. Go to Security -> API via the left navigation menu.
  2. Switch to the “Tokens” tab.
  3. Click the “Create token” button.
Security -> API, Tokens tab, Create token button.

You will then be prompted to enter a name for the API token, and subsequently given the token itself. Copy the token and keep it handy. Take note also of your organisation ID, which you can find either in the URL, or in the top-right under your name (but remove the “okta-” prefix). The organisation ID for a developer account looks like “dev-12345678”.

Running Neo4j

Before we run Cartography, we need a running instance of the Neo4j graph database, because that’s where the data gets stored after being retrieved from the configured data sources (in this case Okta). When I wrote “Getting Started with Cartography for AWS“, Cartography only supported up to Neo4j 3.5. Thankfully, that has changed. The Cartography Installation documentation specifically asks for Neo4j 4.x, further remarking that “Neo4j 5.x will probably work but Cartography does not explicitly support it yet.” The latest Neo4j Docker image at the time of writing this article seems to be 5.9, and I’m feeling adventurous, so let’s give it a try.

I did explain in “Getting Started with Cartography for AWS” how to run Neo4j under Docker, but we’ll do it a little better this time. Use the following command:

sudo docker run --rm -p 7474:7474 -p 7473:7473 -p 7687:7687 -e NEO4J_AUTH=neo4j/password neo4j:5.9

Here’s a brief explanation of what all this means:

  • sudo: I’m on Linux, so I need to run Docker with elevated privileges. If you’re on Windows or Mac, omit this.
  • docker run: runs a new Docker container with the image specified at the end.
  • --rm: destroys the container after you shut it down. This is because we’re just doing a quick test and don’t want to keep containers around. If you want to keep the container, remove this.
  • -p 7474:7474 -p 7473:7473 -p 7687:7687: maps ports 7473, 7474 and 7687 from the Docker container to the host, so that we can access Neo4j from the host machine. 7474 in particular lets us access the Neo4j Browser, which we’ll see in a moment.
  • -e NEO4J_AUTH=neo4j/password: sets up the initial username and password to “neo4j” and “password” respectively. This bypasses the need to reset the password from the Neo4j Browser as I did in the earlier article. Remember it’s just a quick test, so excuse the silly “password” and choose a better one in production.
  • neo4j:5.9: This is the image we’re going to run – neo4j with tag 5.9.
  • Note that any data will be lost when you stop the container, regardless of the --rm argument. You’ll need to use Docker volumes if you want to retain the data.

Once the container has started, you can access the Neo4j Browser at http://localhost:7474/, and login using the username “neo4j” and password “password”. We’ll use this later to run Cypher queries, but for now it is a sign that Neo4j is running properly.

The Neo4j Browser’s login screen.

Running Cartography

Following the Cartography Installation documentation, run the following to install Cartography:

pip3 install cartography

As per Cartography’s Okta Configuration documentation, assign the Okta API token you created earlier to an environment variable (the following will set it only for your current terminal session):

export OKTA_API_TOKEN=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Then, run Cartography with the following command:

cartography --neo4j-uri bolt://localhost:7687 --neo4j-password-prompt --neo4j-user neo4j --okta-org-id dev-xxxxxxxx --okta-api-key-env-var OKTA_API_TOKEN

Here’s a brief summary of the parameters:

  • --neo4j-uri bolt://localhost:7687: specifies the Neo4j URI to connect to
  • --neo4j-user neo4j: will login with the username “neo4j”
  • --neo4j-password-prompt: means that you will be prompted for the Neo4j password and will have to type it in
  • --okta-org-id dev-xxxxxxxx: will connect to Okta using the organisation ID “dev-xxxxxxxx” (replace this with yours)
  • --okta-api-key-env-var OKTA_API_TOKEN: will use the value of the OKTA_API_TOKEN environment variable as the API token when connecting to Okta

If you see “cartography: command not found” when you run this (especially on Linux), there’s a very good Stack Overflow answer that explains why this happens and offers a simple solution:

export PATH="$HOME/.local/bin:$PATH"

When you manage to run Cartography with the earlier command, enter the Neo4j password (it’s “password” in this example). It will take some time to collect the data from Okta and will write to the terminal periodically as it makes progress. You’ll know it’s done because you’ll see your terminal’s prompt again, and hopefully won’t see any errors.

Querying the Graph

You should now have data in Neo4j, so open your Neo4j Browser at http://localhost:7474/ and run some queries to look at the data. The easiest to start with is the typical “get everything” query:

match (n) return n

On a fresh new account, this gives you back a handful of nodes and the relationships between them:

Okta data in the Neo4j Browser.

Although this is not great for analysis, it’s all you need to get started using Cartography for Okta. You can get more data to play with by either building out your directory (users, groups, etc) via the Okta Dashboard, or else connecting to a real production account with real data.

If you want to analyse attack paths from Okta to AWS, then do the necessary AWS setup (see my earlier article, “Getting Started with Cartography for AWS“), and follow Cartography’s Okta Configuration documentation to set up the bridge between Okta and AWS.


To get Cartography to collect your Okta data:

  1. Sign up for an Okta account if you don’t have one already.
  2. Create an Okta API Token, and take note of your Okta Organisation ID
  3. Run Neo4j
  4. Run Cartography, providing settings to access Neo4j and Okta

Once the data is in Neo4j, you can analyse it and visualise how the nodes are connected. This can help you understand the paths that an attacker could take to breach the critical parts of your infrastructure. In the case of Okta, this is particularly useful when considering how an attacker could exploit the privileges of an Okta user to access resources in other cloud or SaaS providers.

Unity3D: Dungeon Crawler Movement with Collisions

Strolling through the 11th level of Eye of the Beholder (1991).

It’s been almost two years since I wrote “First Person Grid Movement with Unity3D“, in which I showed how to use Unity3D to replicate the grid-based movement typical of “blobbers” like Eye of the Beholder. The most common question I’ve had about that article is how to implement collisions. So that’s what I’m going to show you in this article, using Unity Editor 2021.3.25f1 LTS.

Creating a Project

Creating a new Unity3D project.

Open the Unity Hub and create a new project. Make sure you select the 3D template, and call it whatever you want.

Adding the Game Script

Attach the new Game script to the Main Camera object.
  1. In the Assets pane, right-click and select Create -> C# Script. You can call it whatever you want, but I’ll call it Game.
  2. Drag it onto your Main Camera object.
  3. Double-click the script to open it in your IDE (e.g. Visual Studio Code).
  4. In your Update() function, paste the following code which is where we left off in “First Person Grid Movement with Unity3D“:
void Update()
    if (Input.GetKeyDown(KeyCode.W))
        this.transform.position += this.transform.rotation * Vector3.forward;
    else if (Input.GetKeyDown(KeyCode.S))
        this.transform.position += this.transform.rotation * Vector3.back;
    else if (Input.GetKeyDown(KeyCode.A))
        this.transform.position += this.transform.rotation * Vector3.left;
    else if (Input.GetKeyDown(KeyCode.D))
        this.transform.position += this.transform.rotation * Vector3.right;
    else if (Input.GetKeyDown(KeyCode.Q))
        this.transform.rotation *= Quaternion.Euler(0, -90, 0);
    else if (Input.GetKeyDown(KeyCode.E))
        this.transform.rotation *= Quaternion.Euler(0, 90, 0);

Adding a Map

So now we can move the camera, but we don’t have collisions yet. In fact, we don’t even have anything to collide with yet.

Unity3D comes with its own set of colliders you can use, but since we’re assuming movement in a 2D grid, then it’s much easier to just store a simple map in a 2D array and check what’s in the destination cell before moving into it. We’ll use this same 2D array to generate walls in the scene rather than putting cubes manually into the scene as we did in the earlier article. The following should do:

    private string[] map = new[] {
        "X          X",
        "XX XX  XX XX",
        "XX XX XXX XX",
        "XX  XXX    X",
        "XXX X X XX X",
        "X     X  X X",
        "X XXXXXXXX X",
        "X      X   X",
        "XXX XX    XX",
        "X    X XX  X",

Since a string behaves like an array of characters, it’s good enough to use an array of strings instead of a 2D array of characters.

Generating Walls

After the map, let’s also add a field that we can use to set the prefab we’ll use as a wall:

    GameObject wallPrefab;
Use the Wall prefab as the input GameObject to the Game script.

Back in the Unity Editor:

  1. Create a cube (GameObject menu -> 3D Object -> Cube) in the scene.
  2. Drag it from the Hierarchy pane into your Assets pane to create a prefab.
  3. Rename the Cube prefab in your Assets pane to Wall.
  4. Delete the cube in the scene.
  5. Select the Main Camera, then drag the Wall prefab into the Wall Prefab slot of the Game script as shown above.

Now that we have the Wall prefab set up, let’s go back into the script and add some code that will create the walls on startup:

    void Start()
        for (int z = 0; z < map.Length; z++)
            for (int x = 0; x < map[z].Length; x++)
                if (map[z][x] == 'X')
                    Vector3 position = new Vector3(x, 0, z);
                    Instantiate(wallPrefab, position, Quaternion.identity);

Because the Y-axis points upwards and we’re dealing with a flat 2D grid, it’s useful to note that Y is always zero and we’re dealing with the X- and Z-axes when moving.

Positioning the Camera

Press Play and we can get a first peek at the generated walls:

An initial view of the generated walls.

It’s clear that the camera is a little off, but we can already see that we’ve successfully generated some sort of maze. Thanks to the movement script we copied earlier, you can move around (WASD for forward, backward, left and right movement, Q to turn left, and E to turn right) and find a good starting point for the camera, e.g. (X, Y, Z) = (5, 0, 1):

The view from (X, Y, Z) = (5, 0, 1).

Implementing Collision Detection

So now we come to the original problem: we’re able to walk through the walls. Now that we have a map, all we need to do to detect collisions is to check what’s in the square we’re moving to, before we move into it. For this, we’ll add a little helper function:

    private void updatePositionIfNoCollision(Vector3 newPosition)
        var x = System.Convert.ToInt32(newPosition.x);
        var z = System.Convert.ToInt32(newPosition.z);

        if (map[z][x] == ' ')
            this.transform.position = newPosition;

This function takes the position of the square we’re about to move into as an input, then if that square is clear on the map, it updates the camera’s position. Conversely, if that square contains an ‘X’, then nothing will happen.

So in the Update() function, instead of updating the position directly when we handle the movement keys, we instead call this helper function to move only on condition that the target square is clear:

    void Update()
        if (Input.GetKeyDown(KeyCode.W))
            updatePositionIfNoCollision(this.transform.position + this.transform.rotation * Vector3.forward);
        else if (Input.GetKeyDown(KeyCode.S))
            updatePositionIfNoCollision(this.transform.position + this.transform.rotation * Vector3.back);
        else if (Input.GetKeyDown(KeyCode.A))
            updatePositionIfNoCollision(this.transform.position + this.transform.rotation * Vector3.left);
        else if (Input.GetKeyDown(KeyCode.D))
            updatePositionIfNoCollision(this.transform.position + this.transform.rotation * Vector3.right);
        else if (Input.GetKeyDown(KeyCode.Q))
            this.transform.rotation *= Quaternion.Euler(0, -90, 0);
        else if (Input.GetKeyDown(KeyCode.E))
            this.transform.rotation *= Quaternion.Euler(0, 90, 0);

That’s all. If you press Play now, you’ll find that you can roam freely around the map, but you can’t move into walls.


Collision detection on a 2D grid is pretty easy. You just need to keep track of where you are and what’s in the square you’re moving into.

10 Years of Programmer’s Ranch

Today marks the 10th anniversary since the launch of my earlier tech blog, Programmer’s Ranch, with its first article. To celebrate this occasion, I’d like to reflect on the impact of this blog.


Programmer’s Ranch today.

Having launched my first website back in 2002, I was writing language tutorials as early as 2003. These helped me consolidate the web technology (and, later, programming languages) I was learning, while also sharing that knowledge with others. I enjoyed this, but it’s also true that lengthy technical writing is tedious, time-consuming, and gets obsolete relatively quickly. I soon grew weary of formal language tutorials and decided to shift towards more casual articles in which I could write about any topics I found interesting at the time, without having to adhere to any particular structure.

Programmer’s Ranch was launched on 2nd May 2013, after a series of websites and blogs that preceded it, and during some very unfortunate life circumstances. Still early in my career at the time, I observed that many programming topics aren’t really as hard as they seem; it would be much easier to learn them if they were explained in a simple and clear manner. I sought to develop this idea with Programmer’s Ranch by regularly writing fun and concise articles that explained various programming and other tech concepts.

Between 2nd May 2013 and 4th October 2014 – less than a year and a half – I published 91 articles at Programmer’s Ranch. By then, it was clear that the Blogger platform wasn’t great for writing technical articles, and so I launched Gigi Labs on 24th October 2014 to continue writing on a better platform. With over 300 articles published here since then, my writing style, focus and frequency have changed over the years, but they continue to build upon the foundations and values that Programmer’s Ranch established ten years ago.

Writing Style

As I mentioned earlier, by the time I launched Programmer’s Ranch, I felt that programming didn’t need to be so hard for beginners. I was frustrated by unnecessary complexity and poor communication that led to so many obstacles to learning, even in a time when internet adoption was widespread. Today, an additional decade of IT and life experience has only served to reinforce this idea. Over the years, I’ve observed that poor communication, incompetence, bureaucracy and even corruption have not only brought many IT companies down to their knees, but also adversely affect various aspects of everyday life.

After some time trying to find my voice with the blog, I wrote the Programmer’s Ranch Writing Style Guide, hoping to keep my own writing of consistent quality and also inspire others. It’s nothing more than a few tips defining the general writing style that I felt worked for Programmer’s Ranch. The writing style has simplicity and clarity at its core, and is also reflected in the blog’s tagline: “More beef. Less bull.” It’s a radical departure from formal scientific papers, lengthy books, and various kinds of documentation which actually make learning harder.

Documentation is, in my opinion, one of the biggest failures of the software industry. Many companies and individuals seem to think of documentation as reference material, like an encyclopaedia. For instance, they publish a list of classes or endpoints exposed by their API and expect users of their software to make sense of them. In reality, what users usually need when working with a new library or API for the first time is basic usage examples. Given that (in my experience) most developers don’t like to write, the proliferation of open source projects hasn’t quite improved the situation.

Poor writing is, in reality, a specific case of poor communication. I can think of many examples outside of technical writing where overcomplication and lack of clarity cause problems. For instance, mystery meat navigation shifting to household furniture and appliances with modern/minimal designs, agile development approaches exacerbating the problems they were designed to solve, and the automation of customer service channels leaving customers struggling to ask a basic question about a service they’re paying for.

As a result, I feel it’s a breath of fresh air to read a technical article that is clear and concise once in a while. Even though there are countless tutorials about basic topics like HTML and CSS, it’s still nice (and helpful for newcomers) whenever someone writes about them in an accessible manner. Tania Rascia‘s website is the closest example of the Programmer’s Ranch writing style that I’ve found in the wild, and her focus on quality content and distancing from “ads, trackers, social media, affiliates, and sponsored posts” is quite likely behind its success.

Why I Write

There are many reasons to write on the web. The more altruistic of these is to share knowledge. Writing is a medium that endures, and although technical topics you write about may not have as long-lasting an impact as the works of Shakespeare, it is still very common for an article to help people for many years to come. Also, writing is easy to search and skim through, unlike other media such as audio or video.

There are also more personal and individual reasons to write, including:

  • Teaching others helps consolidate one’s own knowledge.
  • It’s therapeutic, sometimes requiring a level of focus that enables flow.
  • It can help demonstrate expertise and build one’s own reputation.
  • It helps remember topics and solutions from several years earlier.
  • It’s useful to save time arguing about the same topics over and over again.

Writing on the web does also have some disadvantages that the aspiring tech blogger would do well to be aware of:

  • It takes a lot of time to write good quality articles.
  • You won’t necessarily get any tangible benefit from it.
  • More specialised and unique articles will likely get less attention.
  • There are lots of rude and ungrateful people on the internet.


When I launched Programmer’s Ranch ten years ago, it was the beginning of my own journey towards maturity in technical writing. Although I haven’t always written good quality articles, I believe that many of them have been useful to a large audience and continue to be so. Their success lies not only in their content but also in the way it is communicated.

The web, the IT industry, and society in general are filled with content that is mediocre at best, making it hard for us to find the information we need even in an age where information is abundant and easy to obtain. There’s a lot we can improve in society by raising the bar, communicating better, and focusing on quality in the things that are important to us.

"You don't learn to walk by following rules. You learn by doing, and by falling over." — Richard Branson