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customer-feedback

in-app feedback, NPS and feature voting

Canny, Delighted

SystemTechniques · 25Primitives · 90 customer-feedback Deterministic simulation testingdistributed · medium: Run the whole system in one thread under a seeded scheduler with simulated network, disk and clock, so every failure replays from its seed.›Primary-backup replicationdistributed · medium: One node orders all writes and ships them to standbys; the acknowledgement rule (none, one, all) sets what a crash loses, and an external arbiter decides who takes over.›Density-based clustering (DBSCAN)machine-learning · medium: Group points that are packed densely, label the rest as noise: no k, arbitrary shapes, two parameters (eps, minPts), cost set by the neighbour index.›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.›Text classificationmachine-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.›Email delivery smtpnetworking · medium: Outbound SMTP: queues, MX lookup, retries, bounces, feedback loops and IP warm-upWebhook deliverynetworking · medium: Reliable server-to-server HTTP callbacks: signed POSTs, 2xx-means-done, retries over days with jitter, endpoint disabling, SSRF guards and idempotent receivers.›Hmac request signingsecurity · low: HMAC canonical-request signatures and presigned URLs (SigV4 style) with expiryBM25 rankingsearch-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.›Embeddingsmachine-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.›Hdr histogramdata-structures · low: Log-linear bucketed histograms with bounded relative error for latency percentilesProcess supervisionhardware-os · low: A parent process starts services, reaps their exits and restarts them under a delay and burst-limit policy, then stops them with SIGTERM before SIGKILL.›Open and click trackingnetworking · low: Tracking pixels and signed redirect links for engagement eventsTime decay scoringsearch-sort · low: Scoring items by recency with exponential decay or half-life, combined with relevanceJSON Web Token (JWT)security · low: A signed, URL-safe token carrying claims; the verifier's job is the checklist (pinned algorithm, key by kid, exp, aud, iss), not the format.›Mutual TLSsecurity · low: TLS where the client also proves a certificate key, so every connection carries a verified, rotatable machine identity.›Bayesian average ratingstatistics · low: Score = (C·m + sum of ratings)/(C + n): the posterior mean under a prior worth C ratings, so items with few ratings sit near the global mean.›Bootstrap confidence intervalsstatistics · low: Resampling-based confidence intervals for arbitrary statisticsWilson score intervalstatistics · low: Wilson lower bound for ranking by up/down votes and proportions with few samplesDeterministic bucketingsystems · low: Hash a stable unit id with a per-purpose salt into a bucket; "in" means bucket below the percentage, so assignment is stateless, repeatable and only grows on ramp-up.›Graceful drainingsystems · low: On a termination signal, stop taking new work, finish in-flight work until a deadline, tell clients to reconnect elsewhere, then exit; clients resume from a token.›Cooperative schedulingconcurrency · low: Green threads/coroutines yielding at await points on a user-space schedulerRecord replaydistributed · 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+ 18 more techniques+ 74 more primitives

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