The Lost Art of Understanding: How 'Making Software' Rekindles Technological Literacy

{{IMAGE:1}}

We inhabit a technological landscape where abstraction has become the dominant paradigm. Touchscreens respond to our gestures without revealing their grid of electrodes, fonts render seamlessly without disclosing their Bézier curves, and blurs soften images without exposing their Gaussian distributions. This pervasive abstraction, while enabling unprecedented accessibility, has simultaneously eroded our collective understanding of the mechanisms underpinning our digital world. 'Making Software,' an ongoing book project by an independent creator, emerges as a deliberate counterpoint to this trend—a visual compendium that peels back layers of technological obscurity to reveal the intricate machinery beneath.

The project's ambition becomes evident in its sprawling table of contents, spanning from fundamental concepts like pixel generation and color spaces to advanced topics including GPU architecture, neural networks, and quantum computing. Each chapter functions as an interconnected module: 34 planned sections averaging 3,400 words, accompanied by over 23 illustrations per chapter. This visual approach is intentional—the author explicitly states that diagrams carry the explanatory weight, making complex subjects like rasterization anti-aliasing or Boolean vector operations comprehensible without prerequisite technical knowledge. Such methodology acknowledges that understanding often begins visually before crystallizing into conceptual knowledge.

{{IMAGE:2}}

What distinguishes 'Making Software' from technical manuals is its philosophical orientation. The author positions it not as a practical guide for immediate skill acquisition, but as a 'manual for understanding'—a distinction that reveals deeper concerns about technological literacy. As interfaces grow more intuitive, our comprehension grows more superficial. Where early programmers understood memory allocation when saving to floppy disks, modern developers manipulate cloud storage abstractions. Where designers once hand-tuned CRT displays, we now calibrate monitors through preset profiles. This cognitive distancing from underlying systems creates vulnerability; when tools fail or requirements exceed their capabilities, users lack the foundational knowledge to diagnose or adapt.

The book's structure reflects this pedagogical philosophy. Chapters on touchscreen technology demonstrate how capacitive grids detect finger proximity through electromagnetic field distortions—knowledge that transforms a mundane interaction into comprehensible physics. Sections on Bézier curves reveal how vector graphics software calculates paths between control points, demystifying the pen tools used in applications like Figma. Even seemingly simple operations like Gaussian blurs become gateways to understanding convolution matrices and computational efficiency trade-offs. Each explanation connects discrete technical components to broader systemic understanding.

Critically, the project confronts inherent tensions in technological education. Can a single volume adequately cover domains as divergent as cryptography fundamentals and 3D projection matrices without sacrificing depth? The author addresses this through modular design—readers can traverse chapters based on interest rather than sequence—and through visual scaffolding. With 352 illustrations already completed for the first 15 chapters, the work leverages visual learning to accelerate conceptual bridging between domains. This approach echoes Richard Feynman's pedagogical emphasis on foundational comprehension over procedural knowledge.

When questioned about AI assistance, the author provides a categorical denial—a meaningful stance in an era where synthetic content proliferates. The handmade illustrations (including nostalgic tech artifacts like CRT displays and mechanical keyswitches) reinforce this human-centric approach. The planned evolution from digital to physical coffee-table book further emphasizes the project's tactile philosophy—a deliberate move against the ephemerality of digital-only knowledge.

In a technological ecosystem dominated by opaque APIs and machine learning black boxes, 'Making Software' argues implicitly for the value of mechanistic understanding. It serves not merely as reference material, but as a corrective to the assumption that abstraction layers eliminate the need for fundamental knowledge. As the author notes, this won't make readers better coders tomorrow—but it might prevent them from drowning when the tide of abstraction recedes. The project stands as a reminder that true technological empowerment comes not from seamless interfaces, but from comprehending the seams themselves.

Explore the project