10 weird algorithms every developer should know

A handful of “weird” algorithms end up doing serious work across medicine, graphics, machine learning, security, and distributed computing—often by turning counterintuitive ideas into practical speedups or robust guarantees. The through-line is that clever constraints and precomputed structure can transform an otherwise brute-force problem into something workable at scale.

One of the clearest examples is marching cubes, created in the late 1980s at General Electric. It takes a 3D scalar field—like the intensity values produced by CT or MRI scans—and converts it into a polygonal mesh. The method samples each point’s eight neighbors, treats the resulting values as an 8-bit pattern (256 possibilities), and maps each pattern to a precomputed set of polygons. Marching through the volume yields a surface that can be rendered in 3D software, making medical slices usable as tangible geometry.

Several other algorithms lean on “structure first, randomness later.” Wave function collapse starts from a set of tiles that represent possible states in a procedural world. Instead of generating a map by brute force, it treats the initial layout as a superposition of possibilities and then “collapses” it into a single consistent outcome when observed—choosing random tiles but enforcing rules like connected roads. Diffusion, originally developed at OpenAI and behind image generators such as DALL·E and Stable Diffusion, works in the opposite direction: it adds noise step-by-step to an image until it becomes random, then learns to reverse that process. After training on millions of labeled images, the learned weights can generate new images by starting from noise and iteratively refining it into coherent structure.

Optimization appears in simulated annealing, which tackles landscapes full of local maxima and minima. It begins with a high “temperature” that allows exploration, then gradually cools so the algorithm becomes less willing to accept worse solutions—balancing exploration versus exploitation. Sorting gets a darkly comic detour with sleep sort and BOGO sort: one delegates ordering to the CPU scheduler by sleeping proportional to values; the other repeatedly guesses randomly until the array happens to be sorted. The transcript pushes the idea further with a hypothetical “quantum BOGO sort,” where parallel universes might contain a sorted version.

Security and reliability bring the stakes back up. RSA is presented as a public-key system whose security depends on the difficulty of factoring large numbers. Quantum computing threatens that foundation via Shor’s algorithm, which can factor integers exponentially faster than classical methods by using cubits, superposition, and entanglement—though the transcript notes practical limits and cites a recent Chinese factoring result using a different approach. For distributed systems, the Byzantine generals problem motivates PBFT (Practical Byzantine Fault Tolerance), designed to keep a network consistent even when up to one-third of nodes behave maliciously or fail.

Finally, the transcript highlights algorithms that mirror nature or exploit text structure. Boids (from 1986) uses three simple flocking rules—avoid crowding, align headings, and move toward local center of mass—to produce emergent bird-like patterns. Boyer–Moore string search (and its “bad character” and “good suffix” style skipping) explains why tools like grep can be fast: it scans from right to left and uses preprocessed tables to skip large chunks of text, with performance improving as more text becomes available.