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ai-tutor
AI tutor / game coach
Duolingo Max, Khanmigo
System
Techniques · 12
Primitives · 54
ai-tutor
Alpha-beta search
game-ai · medium: Minimax that skips branches which cannot change the result; with iterative deepening, PVS and aspiration windows it is the core of classical adversarial search.
›
Decision quality scoring
game-ai · medium: Measuring move quality vs an engine: centipawn loss, win-probability accuracy, top-move matching rates
Monte Carlo tree search
game-ai · medium: Grows a search tree one simulation at a time, picking visits by a bandit rule (UCT or PUCT) and backing up averages; the search behind AlphaZero.
›
Knowledge tracing
machine-learning · medium: Estimating a learner's skill mastery from answer history (BKT, deep knowledge tracing)
LLM-as-judge
machine-learning · medium: Grade open-ended outputs with a language model under a rubric, then correct its known biases by order swapping, voting and calibration against human labels.
›
Multi-armed bandits
machine-learning · medium: Choose among K options repeatedly while learning their payoffs: epsilon-greedy, UCB1 and Thompson sampling keep total regret low where a fixed split or a fixed exploration rate pays forever.
›
HNSW
search-sort · medium: Hierarchical Navigable Small World: a layered proximity graph searched greedily from a sparse top layer; the default in-memory approximate nearest-neighbour index.
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Agent memory
agentic-ai · unknown:
Constrained decoding
agentic-ai · low: Force LLM output to match a regex, JSON schema or grammar by masking, at every step, the tokens that would leave the language.
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Context window management
agentic-ai · low: Keep an agent's growing history inside the model's window by truncating, clearing and summarizing old turns in a cheap-to-lossy ladder, at turn boundaries.
›
Prompt caching
agentic-ai · low: Reuse the attention keys/values of a repeated prompt prefix, so an agent's growing context is prefilled once and later turns pay only for the new tokens.
›
Tool calling
agentic-ai · low: The model emits a structured call (id, name, JSON arguments) instead of prose; your code validates, runs it and returns a result tagged with the same id.
›
Elo rating
game-networking · low: One number per competitor, moved by K × (actual − expected score) after each pairwise result; expected score is logistic in the rating difference.
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Spaced repetition
machine-learning · low: Review scheduling by memory models (SM-2, FSRS) predicting recall probability
Template-based text generation
machine-learning · low: Realise facts as sentences by filling slots in alternative templates, with agreement, referring expressions and seeded variation; every word traces to data or a template.
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Text chunking
machine-learning · low: Cut documents into token-bounded pieces along structure boundaries, keeping offsets and section paths, so each piece embeds well and can be cited.
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Item response theory
statistics · low: Answer probability is a logistic curve of ability minus item difficulty (1PL), with item slope (2PL) and a guessing floor (3PL); fit items by marginal ML, score people, measure precision by information.
›
Candidate ordering and pruning
game-ai · low: The heuristics that make alpha-beta and branch-and-bound fast: try likely-best candidates first (TT, captures, killers, history) and skip or reduce the rest (null move, LMR, futility).
›
Search time management
game-ai · low: Allocate thinking time per move from clock, increment and search instability
Transposition table
game-ai · low: A fixed-size, lossy hash table of search results keyed by a state hash; gives ordering and cutoffs for repeated states, shared lock-free between threads.
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Zobrist hashing
game-ai · low: A state's hash is the XOR of one random 64-bit key per (item, slot) fact that holds in it, updated in a few XORs per change.
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Linear and logistic regression
machine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.
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PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
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+ 5 more techniques
+ 38 more primitives
System
Techniques · 12
Primitives · 54
ai-tutor
Alpha-beta search
game-ai · medium: Minimax that skips branches which cannot change the result; with iterative deepening, PVS and aspiration windows it is the core of classical adversarial search.
›
Decision quality scoring
game-ai · medium: Measuring move quality vs an engine: centipawn loss, win-probability accuracy, top-move matching rates
Monte Carlo tree search
game-ai · medium: Grows a search tree one simulation at a time, picking visits by a bandit rule (UCT or PUCT) and backing up averages; the search behind AlphaZero.
›
Knowledge tracing
machine-learning · medium: Estimating a learner's skill mastery from answer history (BKT, deep knowledge tracing)
LLM-as-judge
machine-learning · medium: Grade open-ended outputs with a language model under a rubric, then correct its known biases by order swapping, voting and calibration against human labels.
›
Multi-armed bandits
machine-learning · medium: Choose among K options repeatedly while learning their payoffs: epsilon-greedy, UCB1 and Thompson sampling keep total regret low where a fixed split or a fixed exploration rate pays forever.
›
HNSW
search-sort · medium: Hierarchical Navigable Small World: a layered proximity graph searched greedily from a sparse top layer; the default in-memory approximate nearest-neighbour index.
›
Memory consolidation
agentic-ai · medium: Periodically merge near-duplicate memories, resolve contradictions, decay the unused and cap the store, so an agent's long-term memory stays small, current and cheap to search.
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Near-duplicate detection with MinHa…
machine-learning · medium: Estimate set Jaccard similarity from k min-hashes and find similar pairs with banded LSH buckets; the standard dedup for web crawls, training corpora and sequence collections.
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Entity resolution
storage · medium: Decide which records describe the same real-world thing: block to cut the pair space, score pairs with a probabilistic model, cluster the matches transitively.
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Record linkage
search-sort · medium: Probabilistic record matching (Fellegi-Sunter) with blocking for patient or entity indexes
Inverted index
storage · medium: Map each term to a sorted, compressed list of the documents containing it; the core of full-text search and other fast set queries.
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Agent memory
agentic-ai · unknown:
Constrained decoding
agentic-ai · low: Force LLM output to match a regex, JSON schema or grammar by masking, at every step, the tokens that would leave the language.
›
Context window management
agentic-ai · low: Keep an agent's growing history inside the model's window by truncating, clearing and summarizing old turns in a cheap-to-lossy ladder, at turn boundaries.
›
Prompt caching
agentic-ai · low: Reuse the attention keys/values of a repeated prompt prefix, so an agent's growing context is prefilled once and later turns pay only for the new tokens.
›
Tool calling
agentic-ai · low: The model emits a structured call (id, name, JSON arguments) instead of prose; your code validates, runs it and returns a result tagged with the same id.
›
Elo rating
game-networking · low: One number per competitor, moved by K × (actual − expected score) after each pairwise result; expected score is logistic in the rating difference.
›
Spaced repetition
machine-learning · low: Review scheduling by memory models (SM-2, FSRS) predicting recall probability
Template-based text generation
machine-learning · low: Realise facts as sentences by filling slots in alternative templates, with agreement, referring expressions and seeded variation; every word traces to data or a template.
›
Text chunking
machine-learning · low: Cut documents into token-bounded pieces along structure boundaries, keeping offsets and section paths, so each piece embeds well and can be cited.
›
Item response theory
statistics · low: Answer probability is a logistic curve of ability minus item difficulty (1PL), with item slope (2PL) and a guessing floor (3PL); fit items by marginal ML, score people, measure precision by information.
›
Candidate ordering and pruning
game-ai · low: The heuristics that make alpha-beta and branch-and-bound fast: try likely-best candidates first (TT, captures, killers, history) and skip or reduce the rest (null move, LMR, futility).
›
Search time management
game-ai · low: Allocate thinking time per move from clock, increment and search instability
Transposition table
game-ai · low: A fixed-size, lossy hash table of search results keyed by a state hash; gives ordering and cutoffs for repeated states, shared lock-free between threads.
›
Zobrist hashing
game-ai · low: A state's hash is the XOR of one random 64-bit key per (item, slot) fact that holds in it, updated in a few XORs per change.
›
Linear and logistic regression
machine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
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Recurrent networks
machine-learning · low: GRU and LSTM cells for streaming sequence models
Token sampling
ml-training · low: Turn a vector of scores into one chosen item with temperature, top-k, top-p, min-p, repetition penalties and a seed.
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Annotator agreement
statistics · low: Inter-annotator agreement measures: Cohen and Fleiss kappa, Krippendorff alpha, gold-question accuracy
Bradley terry model
statistics · low: Pairwise comparison model P(i beats j)=s_i/(s_i+s_j); MLE fit, link to Elo and reward models
Bayesian rate estimation
statistics · low: Estimate a conversion or event rate from few observations with a Beta-Binomial or Gamma-Poisson update: two counters, an honest interval, and P(B beats A) directly.
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d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
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SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
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Scalar quantization
search-sort · low: Per-dimension int8/binary quantization of vectors with rescoring
Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
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Time decay scoring
search-sort · low: Scoring items by recency with exponential decay or half-life, combined with relevance
Fast hash functions
data-structures · low: Non-cryptographic hashes built from 64×64→128-bit multiplies and xor-shifts; pick by key size, trust level and whether the hash is persisted.
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Rolling hash
data-structures · low: Rabin-Karp polynomial rolling hashes and winnowing for substring fingerprints and document matching
Swiss table
data-structures · low: Open-addressing hash table that filters 8-16 slots at once with 1-byte hash tags and SIMD, giving fast lookups at 87.5 % load.
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Union-find
graphs · low: Disjoint-set forest with union by size and path compression; merges sets and answers same-set queries in effectively constant time.
›
Fuzzy string matching
search-sort · low: Edit distance, Jaro-Winkler and trigram indexes for approximate name and address matching
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
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Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
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Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
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BM25 ranking
search-sort · low: The standard lexical relevance score: IDF-weighted term frequency with saturation (k1) and length normalization (b); default in Lucene, Elasticsearch and SQLite FTS5.
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Sorted-list intersection
search-sort · low: Intersect sorted id lists by merging, galloping from the short list into the long one, and skipping whole compressed blocks, shortest list first.
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Reference counting
compilers · low: Keep a count of owners in each object, free it when the count hits zero; deterministic and local, but pays on every copy and leaks cycles.
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Memory ordering
concurrency · low: What Relaxed, Acquire, Release and SeqCst atomics actually guarantee, what they cost on x86 and ARM, and which one each pattern needs.
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Radix trie
data-structures · low: Compressed prefix tree for path routing, domain matching and prefix lookups
Byte-pair encoding (BPE) tokenizati…
machine-learning · low: Learn a subword vocabulary by repeatedly merging the most frequent adjacent pair; byte-level BPE encodes any input losslessly with 32k-200k tokens.
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Text normalization
machine-learning · low: Rewrite messy text into one canonical form for matchers and models, or into spoken words for speech output (and back), keeping a map to the original offsets.
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AdamW
ml-training · low: Adam's per-parameter adaptive steps with weight decay applied directly to the weights; the default optimizer for transformers and LLM pretraining.
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Paged KV cache
ml-training · low: Store each sequence's attention keys/values in fixed-size blocks mapped through a block table, like virtual memory pages; near-zero fragmentation and copy-on-write sharing.
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Reverse-mode automatic differentiat…
ml-training · low: Record the forward computation on a tape, then sweep it backwards once to get the gradient of a scalar loss for all inputs.
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Transformer block
ml-training · low: The decoder-only pre-norm block (attention plus MLP on a residual stream) behind every modern LLM, with exact shapes, parameter, FLOP and KV-cache counts.
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Json rpc
networking · low: JSON-RPC 2.0 requests, notifications and batching
L-BFGS
numerics · low: Quasi-Newton minimizer that approximates the inverse Hessian from the last m gradient differences via the two-loop recursion; O(m·n) memory and work per step.
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LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
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Finite state transducer
search-sort · low: Minimal acyclic FST mapping sorted keys to values; compact term dictionaries with prefix/fuzzy search
Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
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Unicode text tokenization for search
search-sort · low: Turn multilingual text into index terms: segment words (UAX 29 style, CJK bigrams), normalize, case-fold and accent-fold, identically at index and query time.
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JSON Schema validation
security · low: Compile a JSON Schema once, then check every untrusted document against it, within hard size and depth limits, and report failures as JSON Pointers.
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LRU and CLOCK
systems · low: LRU evicts the least recently used entry via hash map plus linked list; CLOCK approximates it with a reference bit, so hits take no lock.
›
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