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- Gradient Descent: The DescentDon't read about gradient descent — play it. Roll a ball into the valley of a loss landscape by hand and waste steps, then let the algorithm read the slope and step downhill on its own. Crank the learning rate until it overshoots and explodes, dial it down until it crawls, then switch on momentum to escape a local minimum. Four acts — descend it, follow the gradient, tune the learning rate, escape the trap.Read →
- The TokenizerA language model can't read words — it reads tokens. Watch your text shatter into sub-word chunks, race character vs word vs sub-word tokenizers head-to-head, then build Byte-Pair Encoding by hand: merge the most frequent pair over and over to grow a vocabulary and shrink the sequence. Five acts — shatter it, race three tokenizers, build BPE, tune the vocab size, and feel why 'strawberry' trips up an LLM.Read →
- The Dice Loader: SamplingDon't read about temperature, top-k, and top-p — drive them. A language model turns logits into a probability over the next token, then rolls; three dials load those dice. Slide temperature to sharpen or flatten the odds, clamp the tail with top-k or top-p, and watch the bar chart renormalize live — then hit Sample and roll. See exactly why an LLM sounds robotic at low temperature and unhinged at high.Read →
- The Gravity Wells: K-MeansDon't read about k-means — watch the clusters form. Drop three centroids into a cloud of thirty unlabeled points, then run two moves on repeat: assign every point to its nearest centroid, then slide each centroid to the mean of its crowd. Watch the mess snap into clean groups and the inertia drop each round. Unsupervised clustering, made playable, with theory, a runnable challenge and a quiz.Read →
- The Meaning Map: EmbeddingsDon't read about embeddings — explore the map. Every word becomes a point in space, placed so that closeness means similar meaning. Click a word to rank its nearest neighbours by cosine similarity, then run the famous vector arithmetic — king − man + woman lands right on queen, paris − france + japan lands on tokyo. Word embeddings and cosine similarity, made playable, with theory and a quiz.Read →
- The Sliding Stencil: ConvolutionDon't read about convolution — slide the stencil yourself. A 3×3 kernel glides over an image and, at each spot, multiplies the pixels underneath by its weights and sums them into one output pixel. Swap kernels — identity, edge-detect, blur, sharpen, emboss, Sobel — and watch the same image become edges, or blur, or sharpened. The single operation inside every CNN, made playable, with theory, a runnable challenge and a quiz.Read →
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APA
Singh, S. (2026). The Deployment Target Chooser. Vibe Engines. https://vibeengines.com/lab/deployment-target-chooserMLA
Singh, Saurabh. “The Deployment Target Chooser.” Vibe Engines, 2026, vibeengines.com/lab/deployment-target-chooser.BibTeX
@online{vibeengines-deployment-target-chooser,
author = {Singh, Saurabh},
title = {The Deployment Target Chooser},
year = {2026},
organization = {Vibe Engines},
url = {https://vibeengines.com/lab/deployment-target-chooser}
}