# Blog Styling Update

It’s been over 6 years since I started this blog, and today I felt like it was time for a styling update. It’s not that anything is broken, rather, the internet is constantly evolving, so that a even a good website will inevitably become obsolete in 6 years.

To illustrate, here’s the old style, using the Contempt theme:

The problem is over half of the screen space is taken up by the gray bars at the side. The content has a fixed width of 800px or so, which is great for the 1280×1024 displays that were common in 2010, but looks pretty terrible on the larger displays today.

At first, I tried to tweak the CSS for the theme, but you need to get a premium subscription on WordPress to change the CSS (and it didn’t look that good anyway). So instead, I browsed through a bunch of themes and switched everything to the Hew theme:

This is more in line with the trend for blogs nowadays, with minimal clutter and text in large font in the center of the page. The widgets, which once occupied a third of the vertical space, are now hidden behind an options menu.

It’s a cosmetic change, but hopefully it will motivate me to blog more.

# How to succeed in your first tech internship

Congratulations, you’ve just landed your first software engineer internship! You’ve passed a round or two of interviews, signed an offer letter, and you’re slated to start next month. What now? You might be a bit excited, a bit apprehensive, wondering what the startup life is like, are you even smart enough to do the work they give you…

I felt all these things when I started my first internship three years ago. Now, I’ve completed four internships and I’m halfway through my fifth one; I’m sort of a veteran intern by now. In these five internships, I’ve learned a good deal about what it takes to succeed in an internship, things that are not obvious to those just starting out. Hopefully by sharing this, others can avoid some of the mistakes I made.

### Your first week at [startup]

Chances are that you’ve coded in assignments for schoolwork, and maybe you’ve coded a few side projects for fun. Work is a bit different: you’re working with a massive codebase that you didn’t write, and probably no single engineer in the company understands it all. Facing a codebase of this complexity, you might feel overwhelmed, struggling to find the right file to start. You feel uneasy that a small change is taking you hours, afraid that your boss thinks you’re underperforming.

Relax, you’re doing fine. If you got the job, it means they have faith in your abilities to learn and to succeed. I’ve talked to hundreds of Waterloo interns, and I’ve never heard of anyone getting dismissed for underperforming. The first few weeks will be rough as you come to terms with the codebase and technology stack, but trust me, it gets much, much easier afterwards.

As an intern, you’re not expected to know everything, and often you will be asking for help from more experienced, full-time engineers.

Before asking for help, you should spend a minute or so searching Google, or Stack Overflow, or the company wiki. Most general questions (not relating to company specific code) can be answered with Google, and you save everyone’s time this way.

When you do ask for help, be aware that they might be working on a completely different project, so they don’t have the same mental context as you. Rather than jump straight into the intricate technical details of your problem, you should describe at a high level what you’re trying to accomplish, and what you tried, and only then delve into the exact technical details.

An example of a poorly phrased question would be: “hey, how do I invalidate a FooBarWindow object if its parent is not visible?” You’re likely to get some confused stares — this might make perfect sense to you, but they’re wondering what is FooBarWindow and why are you trying to invalidate it at all.

A better way to phrase it would be something like: “hey, I’m working on X feature, and I’m encountering a problem where the buttons stop working after you press the back button. After looking a bit, I discovered my component should have been invalidated when its parent is no longer visible, but that’s not happening…” This time, you’ve done a much better job of describing your problem.

It’s always helpful to take notes, so you never ask the same question again. How do you commit your code to Git? How do you deploy the app to stage? If you don’t write it down, you’re going to forget.

At the start, you’re going to be asking 5 questions an hour, which is okay. Soon you will find yourself needing to ask less and less, and eventually you’ll only ask a handful per day.

### Taking charge of your own learning

Like it or not, software engineering is a rapidly shifting field, where a new Javascript framework comes out every six months. You have to be continuously learning things, or your skills will become obsolete. Learning is even more important when you’re an intern, still learning the ropes. Fortunately, a tech internship is a great opportunity to learn quickly.

Not all software engineers are equal — at some point, you get to choose what you want to do: frontend, backend, or full stack? Web, iOS, or Android? Become an expert in Django or Ruby on Rails? Depending on the company, you often get considerable say on what team you’re on, and what project you work on within your team. Use this as an opportunity to get paid to learn new, interesting stuff!

Good technologies to learn should satisfy two criteria: it should be something you’re interested in, and it should also be widely used in the industry. That is to say, it’s more useful to know a popular web framework than an internal company-specific framework that does the same thing.

When you get to pick what project to take next, it might be tempting to pick something familiar, where you already know how to do everything. But you learn a lot more by working on something new; in my experience, employers have always been accommodating to my desire to work on a variety of different things.

You will overhear people talk passionately, with phrases like, “oh, it’s running Nginx inside Docker and fetches the data from a Cassandra cluster…” If you’ve never heard of these technologies, this sentence would be nonsensical to you. It’s well worth the time to spend 10 minutes reading about each technology that you hear mentioned, not to become an expert, but just to have a passing understanding of what each of these things do. With a few minutes of research, you’d be able to answer: “when should you use Cassandra over MySQL?

Learning is valuable even when it’s not immediately relevant to you. Occasionally, you’ll find yourself in meetings where you don’t have a clue what’s going on, say with business managers or projects you’re not involved in. Rather than zone out and browse Reddit for the duration of the meeting, listen in and learn as much as you can, and take notes if you begin to fall asleep! The human brain has near infinite capacity for learning new things, and at no point will it reach “capacity” like a hard drive.

### Take responsibility and deliver results

A common misconception is programmers are paid to write code. Wrong: as a programmer, your job is to deliver results and provide value to your company; part of this job involves writing code, but a lot of the work is communicating with managers, designers, and other engineers to figure out what code to write.

When you’re assigned a project, you own it and you’re in charge of any tasks required to push it through to completion. What if something is broken in an API owned by another team? You might be tempted to hand in your code and proclaim, “my code works fine, so my job here is done, I can show you that their API is broken, so it’s their fault.” No, if your feature is broken then you need to fix it one way or another. So go and ping the engineer responsible, schedule a meeting with him, anything to get your project completed.

Sometimes you run into problems that seem insurmountable, so complex that you feel compelled to put down your sword and give up, and tell yourself, “this is too hard for an intern“. This is a bad idea, you should never expect a full-time engineer to come in, take over, and bail you out of the situation. Your mentors are not superhuman — it’s not like they can instantly conjure a solution, no, they have to work through the problem one piece at a time, just like you. There’s no reason you can’t do the same.

The product you deliver is what ultimately matters, so don’t worry about secondary measures of productivity, like how many lines of code you commit, or how many story points you rack up on Jira. There’s an apocryphal tale of a programmer who disagreed with management measuring productivity by lines of code, and writing “-2000” because he made the code simpler. Likewise, you aren’t being judged if you come in 30 minutes after your manager does, or if you leave 30 minutes before he does, or if you just feel like taking a mid-day stroll in the park, as long as you’re consistently delivering quality features.

Many interns suffer from “intern mentality” and consider themselves fundamentally different from full-times in some way. This is an irrational belief — your skills are probably on par with those of a junior engineer (or will be in a few weeks). This means you should behave like any other full-time engineer (albeit minus interview and on-call duties); the only difference is you’re leaving in a few months. Don’t be afraid to contribute your insights and ideas and consider them less valuable because you’re “just an intern”.

### Other tips

What should you learn to prepare for an internship if you have spare time? Learn Git! Git is a version control system used in most companies, and is both non-trivial to pick up, and used more or less the same way everywhere. Other stuff is less useful to pre-learn because they’re either easy to pick up, or can be used in lots of different ways so it’s more efficient to learn on the job.

Internships are a great way to travel places, if that interests you. I picked 5 internships in 4 different cities for this reason. Unlike school, you don’t have to think about work during weekends, which leaves you lots of time to travel to nearby destinations.

I’ve only talked about what happens during work. If your internship is in the USA, the Unofficial Waterloo USA Intern Guide was super helpful in answering all my logistical questions. Also, some of my friends have written about crafting a resume, and how to ace the coding interview.

# I have a Youtube channel!

Here’s something I’ve been working on recently: a Youtube channel of my guitar covers. I’ve been playing guitar for a few years now (I started in first year university) and I thought it would be fun to record myself playing my favorite songs.

At time of writing, I have 11 videos. Here’s a few of them:

# A Brief Introduction to DNA Computing

DNA computing is the idea of using chemical reactions on biological molecules to perform computation, rather than silicon and electricity. We often hear about quantum computers, and there is a lot of discussion about whether it will actually work, or crack RSA, stuff like that. In the domain of alternative computers, DNA computers are often overlooked, but they’re easy to understand (none of the quantum weirdness), and have potential to do massively parallel computations efficiently.

I first heard about DNA computers when doing my undergrad research project this term. I won’t bore you with the details, but it has to do with the theoretical aspects of DNA self-assembly which we will see is related to DNA computing.

The study of DNA computing is relatively new: the field was started by Leonard Adleman who published a breakthrough paper in Science in 1994. In this paper, he solved the directed Hamiltonian Path problem on 7 vertices using DNA reactions. This was the first time anything like this had been done. In this article, I will summarize this paper.

### Operations on DNA

DNA is a complicated molecule with a lot of interesting properties, but we can view it as a string over a 4 letter alphabet (A, C, G, T). Each string has a Watson-Crick complement, where A is complement to T, and C is complement to G.

Without delving too deep into chemistry, I’ll describe some of the operations we can do with DNA.

1. Synthesis. We can use a machine to create a bunch of single DNA strands of any string we like. The technical term for these is oligonucleotides, but they’re just short DNA pieces. One limitation is we can only make strands of 20-25 nucleotides with current lab techniques.

2. Amplify. Given a test tube with only a few strands of DNA, we can amplify them into millions of strands using a process called polymerase chain reaction (PCR).

3. Annealing. Given a test tube with a lot of single stranded DNA, cooling it will cause complementary strands to attach to each other to form double strands.

4. Sort by length. By passing an electrical field through a solution, we can cause longer DNA strands to move to one side of the solution, a technique called gel electrophoresis. If desired, we can extract only strands of a certain length.

5. Extract pattern. Given a test tube of DNA, we can extract only those that contain a given pattern as a substring. To do this, put the complement of the pattern string into the solution and cause it to anneal. Only strands that contain the pattern will anneal, and the rest can be washed away.

This list is by no means exhaustive, but gives a sample of what operations are possible.

### Solving Directed Hamiltonian Path with DNA

The Directed Hamiltonian Path problem asks, given a directed graph, does there exist a path from s to that goes through all the vertices?

For example, in this graph, if s=1 and t=3, then 1->4->2->3 is a directed Hamiltonian path.

This problem is related to the Travelling Salesman Problem, and is particularly interesting because it is NP-complete, so conventional computers can’t solve it efficiently. It would be really nice if DNA could solve it better than normal computers.

Here I’ll describe the process Leonard Adleman performed in 1994. He solved an instance of Directed Hamiltonian Path on 7 vertices, which is obviously trivial, and yet this took him 7 days of laboratory time. Early prototypes often tend to be laughably impractical.

Main Idea: we represent each vertex as a random string of 20 nucleotides, divided into two parts, each of 10 nucleotides. We represent a directed uv-edge by taking the second half of u and the first half of v, and taking the complement of all that.

The idea is that now, a directed path consists of vertex strands interleaved with edge strands, in a brick wall pattern, like this:

When we put all the vertex and edge strands into a test tube, very quickly the solution will anneal (and not just one, but millions of copies of it). However, the test tube also contains all kinds of strands that don’t represent Hamiltonian paths at all. We have to do a tricky sequence of chemical reactions to filter out only the DNA strands representing valid solutions.

Step 1. Keep only paths that start on s and end on t. This is done by filtering only strands that start and end with a given sequence, and this is possible with a variation of PCR using primers.

Step 2. Sort the DNA by length, and only keep the ones that visit exactly n vertices. Since each vertex is encoded by a string of length 20, in our example we would filter for strands of length 80.

Step 3. For each vertex, perform an extract operation to filter only paths that visit this vertex. After doing this n times, we are left with paths that visit every vertex. This is the most time consuming step in the whole process.

Step 4. Any strands remaining at this point correspond to Hamiltonian paths, so we just amplify them with PCR, and detect if any DNA remain in the test tube. If yes, there exists a directed Hamiltonian path from s to t in the graph.

That’s it for the algorithm. Adleman went on to describe the incredible potential of DNA computers. A computer today can do about $10^9$ operations a second, but you can easily have $10^{20}$ DNA molecules in a test tube.

### DNA Computing since 1994

Shortly after Adleman’s paper, researchers applied similar ideas to solve difficult problems, like 3-SAT, the maximal clique problem, the shortest common superstring problem, even breaking DES. Usually it was difficult to implement these papers in the lab, for example, Richard Lipton proposed a procedure to solve 3-SAT in 1995, but only in 2002 did Adleman solve an instance of 3-SAT with 20 variables in the lab.

On the theoretical side, there was much progress in formalizing rules and trying to construct “universal” DNA computers. Several different models of DNA computing were proven Turing complete (actually my research adviser Lila Kari came up with one of them). It has been difficult to build these computers, because some of the enzymes required for some operations don’t exist yet.

There has been progress on the practical side as well. Since Adleman, researchers have looked into other models of using biological molecules for computation, like solving 3-SAT with hairpin formation or solving the knight’s tour problem with RNA instead of DNA.

In 2006, a simplified DNA computer was built that had the ability to detect if a combination of enzymes were present, and only release medicine if they are all present (indicating that the patient had a disease). In 2013, researchers built the “transcriptor”: DNA versions of logic gates. One reason these are important because transcriptors are reusable, whereas previously all reagents have to be thrown away after each operation.

### Current Limitations of DNA Computing

Clearly, the method I described is very time consuming and labor intensive. Each operation takes hours of lab work. This is not really a fundamental problem, in the future we might use robots to automate these lab operations.

The biggest barrier to solving large instances is that right now, we can’t synthesize arbitrary long strands of DNA (oligonucleotides). We can synthesize strands of 20-25 nucleotides with no problem, but as this number increases, the yield quickly becomes too low to be practical. The longest we can synthesize with current technology is a strand of length about 60. (Edit: Technology has improved since the papers I was looking at were written. According to my adviser, we can do 100 to a few thousand base pairs now in 2016).

Why do we need to synthesize long oligonucleotides? To represent larger problem instances, each vertex needs a unique encoding. If the encoding is too short, there will be a high probability of random sections overlapping by accident when they’re not supposed to, thereby ruining the experiment.

One promising direction of research is DNA self-assembly, so instead of painstakingly building oligonucleotides one base at a time, we put short strands in a test tube and let them self-assemble into the structures we want. My URA project this term deals with what kind of patterns can be constructed with self-assembly.

Today, if you need to solve a Hamiltonian path problem, like finding the optimal way to play Pokemon Go, you would still use a conventional computer. But don’t forget that within 100 years, computers have turned from impractical contraptions into devices that everyone carries in their pockets. I’ll bet that DNA computers will do the same.

### References

1. Adleman, Leonard. “Molecular computation of solutions to combinatorial problems“. Science, volume 266, 1994.
2. Lipton, Richard. “DNA solution of hard computational problems“. Science, volume 268, 1995.

# CS488 Final Project: OpenGL Boat Game

Here’s something I’ve been working on for the past few weeks for one of my courses, CS488 – Intro to Computer Graphics. For the final project, you’re allowed to do any OpenGL or raytracing project, as long as it has 10 reasonable graphics related objectives. Here’s a video of mine:

A screenshot:

It’s a simple game where you control a boat and go around a lake collecting coins. When you collect a coin, there’s a bomb that spawns and follows you around. You die when you hit a bomb. Also if two bombs collide then they both explode (although you can’t see that in the video).

Everything is implemented in bare-metal OpenGL, so none of those modern game engines or physics engines. It’s around 1000-ish lines of C++ (difficult to count because there’s a lot of donated code).

Edit (8/10/2016) – I received an Honorable Mention for this project!

### CS488 – Introduction to Computer Graphics

For those that haven’t heard about CS488, it’s one of the “big three” — fourth year CS courses with the heaviest workload and with large projects (the other two being Real-time and Compilers). It’s one of the hardest courses at Waterloo, but also probably the most rewarding and satisfying course I’ve taken.

There are four assignments, each walking you step by step through graphics techniques, like drawing a cube with OpenGL, or building a puppet with hierarchical modelling, or writing a simple ray tracer. Then there’s the final project, where you can choose to make something with OpenGL or extend your ray tracer. The class is split 50/50, about half the class did OpenGL and the other half did a ray tracer. I personally feel that OpenGL gives you more room to be creative and create something unique whereas ray tracing projects end up implementing a mix of different algorithms.

The first two assignments weren’t too bad (I estimate it took me about 10 hours each), but some time during assignment 3 I realized I was spending a lot of time in the lab, so I got an hours tracking app on my phone to track exactly how much time I was spending working on this course. Assignments 3 and 4 each took me 15 hours. I spent 35 hours on my final project, over a period of 3 weeks. I chose relatively easy objectives that I was confident I could do well, which left time to polish the game and do a few extra objectives. I’m not sure what the average is for time spent on the final project, but it’s common to spend 50-100 hours. Bottom line: you can put in potentially unbounded amounts of time to try to get the gold medal, but the effort actually required to get a good grade is quite reasonable.

Now the bad part about this course (obviously not the instructor’s fault) is OpenGL is so incredibly difficult to work with. Even to draw a line on the screen, you have to deal with a lot of low level concepts like vertex array objects, vertex buffer objects, uniform attributes to pass to shaders, stuff like that. It doesn’t help that when something goes wrong in a shader (which runs on the GPU), there’s no way to pass an error message back to the CPU so you can print out variables and debug it. It also doesn’t help that there’s a lot of incompatible OpenGL versions, and code you find in an online tutorial could be subtly broken for the version you’re using. On the other hand, working with OpenGL really makes you appreciate modern game engines like Unity which takes care of all the low level stuff for you.

I’d like to share a side project I’ve been working on for the past few weeks. Roboroast is an app that automatically generates humorous insults for you or a friend based on how you look. It was written in collaboration with my friend Andrei Danciulescu.

The basic operation is as follows. There’s a subreddit called /r/RoastMe where random people post a picture of themselves, and other people proceed to “roast” the person with funny comments making fun of his appearance.

Our app takes your photo and uses a face recognition algorithm to find a poster in /r/RoastMe who looks like you. Then we display the comments for your closest matches.

You can try it at roboroast.tk.

### Sample Results

Here’s some roasts for myself:

Here’s some for Andrei:

### High Level Overview

The project comprises of roughly 3 parts:

Part 1 is the Reddit scraper. We use the PRAW API to go through all posts on the /r/RoastMe subreddit, saving comments to MongoDB and saving images to the filesystem.

Part 2 is the Face++ uploader. Face++ is a cloud service with a REST API that handles our face matching. To use it, we upload all the images from part 1 into a “faceset” which we can query later.

The first two components only need to be run periodically, maybe once a month to update the faceset with new posts from Reddit. Part 3 is the webapp, which is the use facing component. It accepts user uploads, searches for matches using the Face++ API, and renders a list of insults to the user.

### Technology Stack

As mentioned before, we used a number of third party APIs; PRAW for scraping Reddit posts, and Face++ for face recognition.

All the backend code is written in Python. The web app uses the Flask web framework, and is wrapped with NGINX and Gunicorn to handle connections and serve static files. We use MongoDB for the database.

The frontend is built with Bootstrap. We also use javascript libraries jQuery and handlebars.js.

The whole thing is hosted on a single AWS EC2 instance.

### How good is the face matching?

The face matching is actually decent. Face++ produces reasonable matches most of the time.

To see the matching results for yourself, you can append ?r=1 to the end of the URL (on the results page). This is hidden by default.

### Do the insults make sense?

Although the face matching does a decent job, we found that the quality of results were somewhat hit-or-miss.

When we envisioned the concept for this app, we assumed that most insults were going to make fun of the subject’s face. However, many insults refer to their non-facial appearance, or clothing, or objects in the background. Since we only do face matching, these comments will make no sense.

Other times, comments will refer to the title of the post — in other words, an insult depends on both the submission title and the picture. Again, these make no sense with only the picture.

We attempt to mitigate this with heuristics that analyze the comment, in order to exclude roasts which refer to the title or articles of clothing. This approach had limited success because natural language processing is hard.

### Conclusion

When Andrei initially proposed this idea for an app, I thought the concept was pretty cool and unique. In a month or so we had a prototype, and I spent a few more weeks polishing the project for release. The quality of results you get is still highly variable, but we’re working on improving our algorithms.

In any case, it’s my first time with a lot of these technologies, and I had fun and learned a lot building it.

# Teaching Myself Electronics: Zero to Arduino in 5 Weeks

I’m about to graduate with a degree in computer science, but I can’t describe how a computer works. Okay, maybe that’s an exaggeration. I can tell you all about assembly language and operating system kernels, and I have a good idea of how to build a CPU out of basic logic gates.

That’s where my knowledge ends. I have no idea how to build an AND gate, or how to coerce my 120V power supply to gently power these gates without frying them.

Learning is good, and this is a pretty big knowledge gap. I’m going to teach myself electronics. My plan is to learn by building things. There’s a lot of mathematical theory to learn, much of it is not that useful, and it’s easy to get bogged down in random details. Much better is to just experiment and go back and learn the theory when needed.

### Week 1: Electronic Playground

The first problem was getting components. Unlike computer programming, where everything you need is on the internet, for hardware I’ll actually need to buy things. This is difficult when you don’t know exactly what you need. I also didn’t want a million different parts littering my bedroom haphazardly.

Eventually I settled on this all-in-one kit (cost \$30).

It has a lot of components: LEDs, resistors, capacitors, even antenna and speakers. All the components are fixed to a board, and to connect them together, you use wires that clip to springs protruding from the board.

The kit comes with an instruction booklet that describes all kinds of things you can wire with it. For example, here’s a “harp” — it makes different tones when you hover your hand over the photoresistor:

This schematic is a bit too advanced for me at this stage — unfortunately the booklet doesn’t attempt to explain how it works.

That’s fine, the following books do an excellent job of starting from the basics:

After playing with this for a while, I learned a lot of basic things like how current / voltage / resistance works, how to read common schematic symbols, and how to decode a resistor.

### Week 2: Multimeter

Electricity is invisible, and debugging circuits is difficult without being able to see what’s going on. I went ahead and got a multimeter (cost \$20):

It was easy enough to measure resistance and voltage (both AC and DC). The current measurement was not very sensitive though and I could barely register any reading.

Around this time I attended a workshop in Manhattan that taught how to read schematics and build it on a breadboard. We made a 555 timer circuit which made a LED blink on and off:

I can’t understand how it works right now, but breadboards are pretty neat. Much easier than sticking wires into springs on my electronic kit at home.

### Week 3: Baby steps with Arduino

By now I was reaching the limits of what my electronic kit could offer, and I needed to graduate to something more serious.

So I went to the nearest electronics shop and got an Arduino Uno kit (cost \$90). The Arduino is a microcontroller and lets you prototype circuits easily with a breadboard. The Arduino Uno is only \$25, but my kit comes with an assortment of components and sensors.

Before long I had the Arduino up and running. It runs a dialect of C, so I felt at home in the programming environment.

Here’s a program that blinks the onboard LED on and off in a loop (kind of equivalent of hello world):

```// the setup function runs once when you press reset or power the board
void setup() {
// initialize digital pin 13 as an output.
pinMode(13, OUTPUT);
}

// the loop function runs over and over again forever
void loop() {
digitalWrite(13, HIGH);   // turn the LED on (HIGH is the voltage level)
delay(1000);              // wait for a second
digitalWrite(13, LOW);    // turn the LED off by making the voltage LOW
delay(1000);              // wait for a second
}
```

### Week 4: Transistor Switching

I didn’t really know what a transistor did, but it’s what logic gates are made of and the backbone of all computers, so it can’t hurt to learn about them, right?

I started off building logic gates from transistors, but couldn’t get it work. It turned out that I misunderstood how a transistor operates (it’s not the most intuitive at first glance). Luckily, I had a friend in electrical engineering and she patiently cleared up my misconceptions.

Transistors are used for logic gates, but I didn’t know that transistors can also amplify a current. I also learned about the many different types of transistors.

Here’s a circuit that I built (from the Arduino kit manual). It uses a transistor as a switch to control a motor:

By the way, here’s how you make an AND gate with two transistors:

While wiring things up, I accidentally burned a LED and a BJT transistor. Apparently 5 volts without a resistor is fatal to many components. In software, if you mess up, you get a segmentation fault in your console or something — never the smell of burnt plastic in your room.

### Week 5: Arduino Controlled Desk Lamp

Here’s an idea. Wouldn’t it be nice if your lamp can turn itself on when it gets dark? Useful or not, let’s build it!

This is actually a major milestone for me. Up until now, I’ve been mostly following existing schematics, using parts carefully selected by people who wrote the schematics. But for this project, we improvise everything from scratch. Oh yea, also it’s the first time I’m working with 120V alternating current.

First I got a \$15 desk lamp. It’s the kind that plugs into the wall AC socket. I start by using a wire stripper to expose copper wires that I can plug into the breadboard:

I’m a bit nervous working with 120V current, obviously it’s a lot more powerful than the 5V of Arduino. Generally, 120V won’t kill or seriously injure you, but it does deliver an unpleasant shock.

After stripping the cord, the lamp can be plugged into the breadboard. But the voltage is too high for the Arduino to handle directly; instead, I need a relay which acts as a buffer and a switch. A transistor can act as a switch too, but relays can handle more current.

To detect light, I have a separate circuit that uses a photoresistor (changes resistance depending on amount of light shining on it). The Arduino can read the photoresistor by measuring analog voltage.

Here’s the schematic of my design:

Now we do software. It does a loop every 500ms, detects the amount of light, and decides whether the lamp should be on or off. It’s slightly complicated by the fact that when the lamp is on, it produces light and that affects the photoresistor readings. I need to compensate for that but it’s not too bad.

Here’s the code I came up with:

```int photoPin = 5;
int lampPin = 3;

bool isLampOn = false;

void setup(){
pinMode(lampPin, OUTPUT);
Serial.begin(9600);
}

bool shouldTurnLampOn(){
if(isLampOn){
return lightLevel < 70;
}
else{
return lightLevel < 50;
}
}

void loop(){
if(shouldTurnLampOn()){
digitalWrite(lampPin, true);
isLampOn = true;
}
else{
digitalWrite(lampPin, false);
isLampOn = false;
}
delay(500);
}
```

Here’s a video demo:

### Onwards

Actually my lamp idea probably isn’t that useful. Nevertheless, I made a lot of progress in just a few weeks and I’m proud of myself for that.

I’ve barely scratched the surface of all the cool things you can do with electronics, but it’s a good start. Now I have all kinds of ideas on what to build next. I’d better get to it!