mhtirogla
Home
Systems
API
Pricing
About
Log in
← Back
Home
›
Systems
›
Agentic AI
coding-agent
autonomous coding agent
Claude Code, Codex
System
Techniques · 30
Primitives · 94
coding-agent
Edit application
agentic-ai · medium: Apply model-proposed code edits robustly: search/replace blocks, unified diffs, fuzzy anchor matching and conflict reporting
Prompt injection defense
agentic-ai · medium: Treat every untrusted string as data, track where values came from, and make tool calls and rendered output depend on provenance, not on what the model says.
›
Incremental parsing
compilers · medium: Reparse only the region an edit damaged and splice the new subtrees into the old tree, sharing everything else; O(edit) instead of O(file).
›
Language server protocol
compilers · medium: One JSON-RPC conversation over a pipe lets any editor drive any language's analysis server; the cost is keeping a live, edited document in sync and answering before it changes.
›
Myers diff
search-sort · medium: Greedy O(ND) shortest edit script between two sequences, the default of git and GNU diff; fast when the inputs are similar.
›
Linear-time regular expression matc…
search-sort · medium: Match a regular expression in O(pattern x text) worst case by tracking all live parse positions at once, then cache the state sets as a lazily built DFA for speed.
›
Process sandboxing
security · medium: Run risky work in a separate, deliberately powerless process and let a small privileged broker do the few things it needs, over a narrow pipe.
›
SIMD substring search
search-sort · low: Find bytes or substrings by comparing 16-64 haystack positions per instruction against the needle's first and last byte, then verifying the rare candidates.
›
Agent evaluation
agentic-ai · unknown:
Agent loop
agentic-ai · unknown:
Agent memory
agentic-ai · unknown:
Agent sandboxing
agentic-ai · unknown:
Code context retrieval
agentic-ai · unknown:
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.
›
Subagent orchestration
agentic-ai · unknown:
Task planning
agentic-ai · unknown:
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.
›
File system watching
hardware-os · low: Treat OS change notices as hints, debounce them, and rescan and diff a snapshot whenever notices overflow or may be lost.
›
Pseudo terminals
hardware-os · low: Pseudo-terminals, job control and terminal escape handling
Git object model
storage · low: Content-addressed blobs, trees, commits and refs; packfiles with delta compression; worktrees
Edit distance
search-sort · low: Levenshtein/Damerau distance by DP, bit-parallel Myers variant
Fuzzy string matching
search-sort · low: Edit distance, Jaro-Winkler and trigram indexes for approximate name and address matching
+ 23 more techniques
+ 78 more primitives
System
Techniques · 30
Primitives · 94
coding-agent
Edit application
agentic-ai · medium: Apply model-proposed code edits robustly: search/replace blocks, unified diffs, fuzzy anchor matching and conflict reporting
Prompt injection defense
agentic-ai · medium: Treat every untrusted string as data, track where values came from, and make tool calls and rendered output depend on provenance, not on what the model says.
›
Incremental parsing
compilers · medium: Reparse only the region an edit damaged and splice the new subtrees into the old tree, sharing everything else; O(edit) instead of O(file).
›
Language server protocol
compilers · medium: One JSON-RPC conversation over a pipe lets any editor drive any language's analysis server; the cost is keeping a live, edited document in sync and answering before it changes.
›
Myers diff
search-sort · medium: Greedy O(ND) shortest edit script between two sequences, the default of git and GNU diff; fast when the inputs are similar.
›
Linear-time regular expression matc…
search-sort · medium: Match a regular expression in O(pattern x text) worst case by tracking all live parse positions at once, then cache the state sets as a lazily built DFA for speed.
›
Process sandboxing
security · medium: Run risky work in a separate, deliberately powerless process and let a small privileged broker do the few things it needs, over a narrow pipe.
›
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.
›
User simulation
agentic-ai · medium: LLM-driven simulated users following scripted goals to test conversational agents
Code symbol indexing
compilers · medium: Extract definitions and references per file, give each symbol a stable string id, and store symbol to occurrences, re-indexing only files whose content hash changed.
›
Lossless syntax trees
compilers · medium: Concrete syntax trees with trivia, green/red node split (Roslyn, rowan) for tools that rewrite code
Parser error recovery
compilers · medium: Recover from syntax errors via synchronization tokens and error nodes to keep a full tree
Three-way merge
distributed · medium: Merge two descendants against their common ancestor: take whichever side changed, report a conflict only where both changed the same spot differently.
›
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.
›
Text classification
machine-learning · medium: Assign one or more labels to a text; climb a ladder from regexes to linear models on n-grams, embeddings, fine-tuned encoders and LLM prompts, only as far as the data and budget demand.
›
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.
›
Trigram index
search-sort · medium: Trigram posting lists accelerating regex and substring search
Egress proxy filtering
security · medium: Forcing sandbox network traffic through a proxy with domain allowlists, TLS SNI checks and request logging
Microvm isolation
security · medium: Minimal KVM-based VMs (Firecracker-style) with tiny device model, fast boot and snapshot restore
Taint tracking
security · medium: Track untrusted sources to sensitive sinks through dataflow
Unicode confusables
security · medium: UTS #39 maps every string to a "skeleton" so look-alikes collide, and restricts script mixing so most spoofs never get registered.
›
Hermetic execution
systems · medium: Run actions with only declared inputs in a sandbox and fixed environment
Dataflow analysis
compilers · medium: Compute a fact per program point (live variables, reaching definitions, available expressions) by iterating transfer functions over the CFG to a fixed point.
›
Dialogue state tracking
machine-learning · medium: Keep a structured belief of the user's goal (slot → value, plus requests) and update it every turn from NLU output, the old state and the last system action.
›
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.
›
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.
›
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.
›
Intent classification and slot fill…
machine-learning · medium: Turn one utterance into an intent label and typed slot values with a joint model: a sentence-level classifier plus a token-level BIO tagger on a shared encoder.
›
Record linkage
search-sort · medium: Probabilistic record matching (Fellegi-Sunter) with blocking for patient or entity indexes
Named entity recognition
machine-learning · medium: Find the character spans of entities in text and give each a type, by labelling every token and decoding a valid label sequence.
›
SIMD substring search
search-sort · low: Find bytes or substrings by comparing 16-64 haystack positions per instruction against the needle's first and last byte, then verifying the rare candidates.
›
Agent evaluation
agentic-ai · unknown:
Agent loop
agentic-ai · unknown:
Agent memory
agentic-ai · unknown:
Agent sandboxing
agentic-ai · unknown:
Code context retrieval
agentic-ai · unknown:
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.
›
Subagent orchestration
agentic-ai · unknown:
Task planning
agentic-ai · unknown:
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.
›
File system watching
hardware-os · low: Treat OS change notices as hints, debounce them, and rescan and diff a snapshot whenever notices overflow or may be lost.
›
Pseudo terminals
hardware-os · low: Pseudo-terminals, job control and terminal escape handling
Git object model
storage · low: Content-addressed blobs, trees, commits and refs; packfiles with delta compression; worktrees
Edit distance
search-sort · low: Levenshtein/Damerau distance by DP, bit-parallel Myers variant
Fuzzy string matching
search-sort · low: Edit distance, Jaro-Winkler and trigram indexes for approximate name and address matching
Html sanitization
security · low: Allowlist-based HTML sanitizing of untrusted markup
Least privilege and isolation
security · low: Assume every component will be compromised and limit what it can then reach: narrow identities, separate accounts, sandboxed processes.
›
Recursive-descent parsing
compilers · low: Hand-written top-down parser with one function per grammar rule; simple, fast, with full control over error messages and recovery.
›
Rope
graphics · low: A balanced tree of small string chunks with cached byte and newline counts, so seek, insert, delete and snapshot cost O(log n) instead of O(n).
›
Rolling hash
data-structures · low: Rabin-Karp polynomial rolling hashes and winnowing for substring fingerprints and document matching
Aho-Corasick multi-pattern matching
search-sort · low: Find all occurrences of many fixed patterns in one pass over the text, in time independent of the pattern count, using a trie with failure links.
›
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.
›
Cgroups and namespaces
hardware-os · low: Linux namespaces limit what a process can see; cgroup v2 limits and accounts what it may use. Containers, sandboxes and job schedulers are built from the two.
›
Overlay filesystem
hardware-os · low: Union mounts (overlayfs) stacking read-only layers under a writable layer
Seccomp bpf
security · low: Syscall filtering with seccomp BPF programs
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.
›
Time decay scoring
search-sort · low: Scoring items by recency with exponential decay or half-life, combined with relevance
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.
›
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.
›
Name resolution
compilers · low: Bind every use of a name to its declaration with a scope stack, handling shadowing, hoisting and closure capture in one AST walk.
›
Protobuf wire format
networking · low: Protocol Buffers wire format: tags, varints, length-delimited fields and schema evolution
Text position encoding
compilers · low: Store byte offsets, convert to (line, column) on demand with a line-start table and binary search, and count columns in the unit the peer negotiated.
›
Hash consing
data-structures · low: Intern structurally equal immutable terms so equality is pointer comparison and sharing is maximal
String interning
data-structures · low: Store each distinct string once and refer to it by id
Lowest common ancestor
graphs · low: LCA/merge-base computation in trees and DAGs
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
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.
›
Calibration
machine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.
›
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.
›
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.
›
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.
›
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.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
Scalar quantization
search-sort · low: Per-dimension int8/binary quantization of vectors with rescoring
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.
›
DNS message format and client looku…
networking · low: Binary query/response format of DNS (RFC 1035) and how a stub client uses it: record types, name compression, UDP/TCP/EDNS sizes, TTL caching.
›
Http connect tunneling
networking · low: HTTP CONNECT tunnels for proxying TLS and arbitrary TCP
Public Suffix List
networking · low: Find the registrable domain (eTLD+1) of a host name by matching the longest rule in a community-maintained list of shared suffixes.
›
Glob matching
search-sort · low: Glob/gitignore pattern matching without backtracking blowup
Hardware virtualization kvm
hardware-os · low: VT-x/AMD-V via KVM: vCPU run loop, VM exits, MMIO emulation
Nested paging ept
hardware-os · low: EPT/NPT two-level address translation for guests
Virtio devices
hardware-os · low: Virtqueue rings for paravirtual block, net and vsock devices
Sanitizers
security · low: ASan/UBSan/TSan shadow memory and instrumentation to catch memory bugs
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.
›
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.
›
Cryptographic hashes (SHA-2, BLAKE3)
security · low: Collision-resistant fingerprints of data; SHA-256 for interoperability, BLAKE3 for speed, HMAC/HKDF for keyed uses.
›
Content-addressed storage
storage · low: Store each blob under the hash of its bytes: immutable, deduplicated by construction, self-verifying on every read, and composable into Merkle trees.
›
Control-flow graph
compilers · low: Split a linear instruction list into basic blocks and connect them by jumps; the graph every optimizer, SSA builder and dataflow pass walks.
›
Topological sort
graphs · low: Order the vertices of a DAG so every edge points forward; the core of build systems, schedulers and dependency resolution, with cycle detection for free.
›
Statecharts
graphics · low: State machines with nesting, parallel regions, history and entry/exit actions, so UI and animation logic stays linear in size instead of exploding.
›
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.
›
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.
›
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.
›
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.
›
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.
›
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.
›
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.
›
Convolutional codes and the Viterbi…
signal-processing · low: Streaming error-correcting code: each output bit is a parity of the last K input bits; the Viterbi decoder finds the most likely path through a 2^(K-1)-state trellis.
›
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.
›
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.
›
Merkle dag
data-structures · low: Content-addressed DAG of hashed nodes (git trees/commits) for dedupe and verifiable history
Radix trie
data-structures · low: Compressed prefix tree for path routing, domain matching and prefix lookups
Record replay
distributed · low: Record every nondeterministic input (time, randomness, I/O, model and tool outputs, per-tick player input) and re-execute deterministically for debugging, replays and state rebuild
BFS and DFS
graphs · low: The two basic graph traversals; BFS gives hop distances and levels, DFS gives discovery/finish order for cycles, components and topological order.
›
Atomic file replacement
hardware-os · low: Write temp file, fsync, rename over target, fsync directory: crash-safe whole-file updates
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.
›
Pass at k
machine-learning · low: Unbiased pass@k estimator from n samples with c correct, for code and agent benchmarks
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.
›
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.
›
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.
›
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.
›
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.
›
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).
›
Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
›
Finite state transducer
search-sort · low: Minimal acyclic FST mapping sorted keys to values; compact term dictionaries with prefix/fuzzy search
Hybrid search and rank fusion
search-sort · low: Merge a keyword (BM25) result list and a vector (ANN) list into one ranking, by ranks (RRF) or by normalized scores (convex combination).
›
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.
›
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.
›
Bootstrap confidence intervals
statistics · low: Resampling-based confidence intervals for arbitrary statistics
Exponential backoff with jitter
systems · low: Retry after delays that grow geometrically and are randomized, so failed clients spread out instead of hammering a recovering service in synchronized waves.
›
Hierarchical state machine
systems · low: Nest states so a substate inherits its parent's reactions and only overrides what differs, with ordered entry and exit actions along the path.
›
Copy for AI
Similar systems
Share the most building blocks with this one.
automated-ai-research
27 %
agent-runtime
24 %
codebase-indexing
18 %
multi-agent-orchestrator
18 %
deep-research
18 %