Results for “vector”

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Guides vertical scaling decisions for Qdrant vector databases, covering when to scale up, how to resize nodes in Qdrant Cloud or self-hosted deployments, RAM sizing formulas, and when to switch to horizontal scaling.

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.

Develop interactive PaperJS applications for visual drawing, animation, and vector graphics. Create canvas-based experiences with JavaScript and PaperJS framework.

Use when performing pairwise structural alignments of RNA sequences that incorporate both sequence and structure information through base pair propensity vectors.

Upgrade Qdrant version without interrupting application availability and ensuring data integrity.

Guides scaling Qdrant for multi-tenant workloads using payload partitioning, custom sharding, and tiered multitenancy.

Guides optimization of Qdrant query latency by tuning segments, memory, quantization, and search parameters.

Enforces complexity analysis, correct data structure selection, vectorization, and algorithm implementation discipline at principal-engineer level.

Build production-ready LLM applications, RAG systems, and intelligent agents with architecture design, model selection, and cost controls.

Build LLM-powered applications with agents, chains, and RAG using a framework that supports multiple providers and 500+ integrations.

Augments nearest-neighbour search with graph Laplacian features to retrieve items based on both semantic similarity and structural role.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Builds a Streamlit and FastAPI RAG application that lets users upload documents and query them with natural language through LM Studio.

Diagnoses and resolves slow Qdrant indexing and data ingestion by optimizing batching, sharding, HNSW parameters, and payload indexing strategies.

KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph).

Guides scaling Qdrant query throughput (QPS) through performance tuning, horizontal scaling with read replicas, and disk I/O optimization.

Responds to malware infections across enterprise endpoints by identifying the malware family, determining infection vectors, assessing spread, and executing eradication procedures.

Identify, collect, and analyze ransomware attack artifacts to determine the variant, initial access vector, encryption scope, and recovery options.

Diagnoses and reduces Qdrant memory usage by analyzing resident memory, page cache, and providing optimization techniques like quantization, on-disk storage, and async_scorer.

Sovereign-grade safety OS for AI coding agents. 63 hooks, 2,025 skills, L1 memory, circuit breakers, and cross-engine enforcement — blocks rm -rf, force push, pipe-to-shell, and 40+ attack vectors before they reach your repo.

Train reinforcement learning agents using PPO, SAC, DQN, TD3, DDPG, and A2C algorithms with a scikit-learn-like API. Supports custom Gymnasium environments, vectorized environments, callbacks, and model persistence.

Investigate supply chain attack artifacts including trojanized software updates, compromised build pipelines, and sideloaded dependencies to identify intrusion vectors and scope of compromise.

Use when symbolic mathematics must cross into NumPy vector evaluation or SciPy numerical algorithms: lambdify contracts, domains, dtypes, parameters, residuals, tolerances, and symbolic-versus-numeric verification. Do not use for work confined entirely to one of those libraries.

Create publication-ready scientific figures with multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and journal-specific formatting using matplotlib, seaborn, and plotly.

Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Use for writing, reviewing, debugging, testing, or validating Python mpmath arbitrary-precision numerical code. Trigger on mpf, mpc, mp.dps, workdps, interval arithmetic, high-precision quadrature, root finding, special functions, matrices, inverse transforms, or precision/convergence failures. Do not use for ordinary NumPy vectorization, SymPy symbolic manipulation, decimal currency arithmetic, or machine-float code with no precision requirement.

Failure diagnosis, DAG mutation, and escalation engine for the WinDAGs meta-DAG. Receives failure information and quality vectors from the Evaluator. Classifies failures on four dimensions. Follows a five-level escalation ladder. Applies seven mutation types with saga compensation. Enforces BC-EXEC-002, BC-EXEC-003, BC-FAIL-002, BC-FAIL-005. Activate when operating as the Mutator role in the meta-DAG, when diagnosing node failures, when restructuring a DAG at runtime, or when deciding escalation level.

Perform aerospace unit conversions, time conversions, coordinate frame transformations, and rotation representations using Aerospace Toolbox. Use when converting units (length, velocity, angle, acceleration, angular velocity, force, mass, pressure, temperature, density), computing Julian dates or decimal years, transforming between coordinate frames (ECEF, ECI, LLA, flat Earth, geodetic/geocentric, NED, body, wind, stability), or working with rotation representations (Euler angles, DCM, quaternion, Rodrigues vector). Also use when the user asks about aerospace coordinate systems, reference frames, or rotation conventions.

Compute aerospace environment properties including atmosphere (ISA, COESA, NRLMSISE-00, non-standard, CIRA), gravity (spherical harmonic, WGS84, zonal, centrifugal), horizontal wind (HWM), magnetic field (WMM, IGRF), geoid height, geocentric radius, space weather data, planetary ephemeris, Earth orientation (polar motion, nutation, delta-UT1, CIP). Use when computing atmospheric density, temperature, pressure, gravity vectors, wind profiles, magnetic field components, geoid undulation, solar flux indices, planet positions, or Earth orientation parameters for aerospace vehicle analysis, spacecraft environment modeling, or navigation corrections.

Speed up local parfor, parfeval, or spmd by switching to a thread-based parallel pool. Trigger when a user describes slow or disappointing local parallel performance, even if they don't mention threads. Symptoms: parfor on a laptop/workstation is slower than expected or "only slightly faster than for"; parfor scales poorly with the number of workers; ticBytes/tocBytes, the Parallel Pool dashboard, mpiprofile, or system tools show large per-worker data transfer; large broadcast variables or sliced inputs make iterations slow; opening a process pool dominates a short workload; user mentions serialisation or data transfer overhead. Also trigger on any question about whether code or a function works on a thread pool. For non-pool MATLAB performance work (vectorisation, preallocation, profiling), defer to matlab-optimize-performance.