When we think of Computer Science education, Home we often picture a linear progression: learn the syntax of Python or Java, master data structures, grapple with algorithms, and finally, build projects. However, the true bottleneck for most students isn’t understanding a for loop or a binary tree; it is the operational layer—the messy reality of managing code, automating tasks, and understanding how different parts of a system communicate.
For me, that bottleneck was resolved in a surprising way: by studying the English language within the context of the make build system, applied to the esoteric yet fascinating dialect of PicoLisp. This unlikely combination didn’t just help me pass my assignments; it fundamentally changed how I approach problem-solving in computer science.
The Tower of Babel in Code
My junior year was a gauntlet. I was taking three upper-division courses simultaneously: Operating Systems, Programming Languages, and Distributed Systems. Each required a different toolchain. One assignment required C with a complex GCC compilation that had to link against specific libraries. Another required a Lisp dialect for a meta-circular evaluator, while the third demanded I orchestrate Docker containers and Python scripts.
My workflow was chaos. I had a text file filled with shell commands that I copy-pasted into the terminal, hoping I ran them in the right order. If a compilation failed halfway through, I would have to manually clean up artifacts before restarting. I spent more time acting as a human compiler and orchestrator than I did actually solving the algorithmic problems the assignments were meant to test.
I knew about make. Every CS student does. It’s that ancient utility (dating back to 1976) that we use when a professor hands us a Makefile and we type make all without really understanding what happens next. To me, make was just a relic—a tool with a syntax that looked like a strange hybrid of bash and a config file.
The turning point came when I took a course on Programming Language Paradigms. The professor introduced us to PicoLisp, a minimalistic dialect of Lisp that runs on a virtual machine. PicoLisp is unique. It is not just a language; it is an operating system philosophy. It treats the application and the database as a single unit, and it heavily favors a functional, symbolic style.
My assignment was to build a simple REST API proxy using PicoLisp. The project required starting the PicoLisp interpreter with a specific initialization file, ensuring a database directory existed with correct permissions, and running a background process that needed to be killed and restarted gracefully during testing.
I realized that manually managing these steps for every test iteration was impossible. I needed automation. I needed make.
The English of Automation
What I discovered when I finally sat down to write my own Makefile from scratch—without copying from Stack Overflow—was that make is not a programming language in the traditional sense. It is a declarative language of dependency. It reads like English.
Consider the basic structure:
makefile
target: dependencies recipe
In English, this reads: “To create the target, you must first have the dependencies, and then you run the recipe.”
This grammatical structure—subject (target), predicate (dependencies), action (recipe)—mirrors how we naturally describe processes. When I stopped fighting the syntax and started treating make as a way to translate my mental workflow into a machine-readable format, everything clicked.
For my PicoLisp project, informative post I started writing rules that described my project in plain, logical terms.
I wrote:
makefile
# To run the server, the database directory must exist. run: db_dir sudo picolisp my-server.l -wait -go # The database directory is created if it doesn't exist. db_dir: mkdir -p /var/lib/myapp/db sudo chown $(USER):$(USER) /var/lib/myapp/db
Suddenly, I wasn’t typing commands anymore. I was defining the state of my project. If the database directory was missing, make would create it before starting the server. If it already existed, make would skip that step. This idempotence—the property that running a command multiple times has the same effect as running it once—was revolutionary for my workflow.
The Symbiosis: PicoLisp and Make
The real magic happened when I realized that PicoLisp and make share a fundamental philosophy: simplicity through minimalism.
PicoLisp is often criticized for its lack of syntactic sugar. Everything is a function call. There is no for loop; you use recursion or map. Similarly, make has no arrays, no complex data structures, and no native package management. Both tools force you to think in terms of processes and symbols.
While working on my Distributed Systems assignment (which was in C, not PicoLisp), I applied the lessons learned from this pairing. I used make to manage the build, but I started using PicoLisp as the scripting language inside the Makefile.
Instead of writing complex shell scripts to generate configuration files for different distributed nodes, I wrote tiny PicoLisp scripts that generated the configuration data. Because PicoLisp excels at symbolic manipulation, I could define a list of server names and have it generate three different .conf files, ensuring they were all consistent.
My Makefile looked like this:
makefile
configs: $(NODES:%=node-%.conf) node-%.conf: generate_conf.l picolisp generate_conf.l -node $* -o $@
In English: “To generate any node configuration file, use the generate_conf.l script. Pass the node number as an argument and output the file to the target name.”
This abstraction allowed me to scale my project from 3 nodes to 10 nodes with a single variable change. While my classmates were manually editing 10 different configuration files and recompiling their C code by hand, I would simply type make clean all and watch as the system cleaned old artifacts, regenerated all configurations, and compiled the entire distributed cluster in under 10 seconds.
Acing the Assignments
The impact on my grades was immediate, but not for the reason I expected. I didn’t ace my assignments because I wrote better C or faster PicoLisp. I aced them because my process was flawless.
- Reproducibility: With a
Makefile, I could guarantee that the code I submitted, when extracted to the professor’s test environment, would build exactly as it did on my machine. I never lost points for “environment issues” or “it works on my machine” excuses. - Automated Testing: I wrote
make testtargets that would build the project, run the PicoLisp server in the background, execute a suite of curl commands, and then tear everything down. I could run 20 tests in the time it took my peers to manually run 2. - Mental Clarity: By externalizing the build logic into
make, I freed up cognitive load. I stopped juggling terminal commands in my head and focused entirely on the logic of the assignment. When my C code for the Operating Systems assignment had a memory leak, I didn’t have to remember the five commands to recompile it; I just typedmakeand the debugger.
Conclusion: The Hidden Curriculum
Computer Science education teaches you algorithms, data structures, and theory. But the hidden curriculum—the skills that separate the overwhelmed student from the efficient developer—revolves around tooling.
Learning make by treating it as an extension of natural language was the gateway. It taught me that the best tools are not always the flashy new frameworks, but the stable, Unix-philosophy utilities that do one thing well. PicoLisp, with its stark minimalism, reinforced this lesson. Together, they forced me to adopt a rigorous, declarative approach to managing complexity.
If you are struggling with your computer science assignments, stop trying to memorize syntax or brute-force your way through compilation errors. Instead, take a step back. Look at your process. Write a Makefile. Describe your project to the computer in simple English sentences: “To get an A, I need to finish the assignment. To finish the assignment, I need to pass the tests. To pass the tests, I need to compile the code correctly.”
Once you define those dependencies, the machine will handle the rest. It turns out that the secret to acing computer science isn’t always writing more code; sometimes, click for more info it’s writing a little bit of English to help the computer help you.
