Results for “parma”

Prisma ORM expert for schema design, migrations, query optimization, relations modeling, and database operations. Use PROACTIVELY for Prisma schema issues, migration problems, query performance, relation design, or database connection issues.

Provides production patterns and non-obvious traps for Prisma ORM in TypeScript backends, covering schema design, query optimization, transactions, pagination, and common pitfalls.

Expert in Prisma - the TypeScript ORM for type-safe database access. Covers schema design, migrations, Prisma Client queries, relations, edge deployment, and performance optimization. Essential for building reliable database layers in TypeScript applications. Use when "prisma, orm, database typescript, prisma client, prisma migrate, type-safe database, prisma, orm, database, typescript, postgresql, migrations, type-safe" mentioned.

Prisma ORM expert for schema design, migrations, query optimization, relations modeling, and database operations. Use PROACTIVELY for Prisma schema issues, migration problems, query performance, relation design, or database connection issues.

Decompose an unknown quantity into 3-5 estimable factors and produce a defensible order-of-magnitude answer without needing precise data. Load when the user needs to size something without data — market size, resource requirements, effort estimates, user numbers, costs — or when a decision is blocked by "we don't know the numbers". Also triggers on "ballpark this", "rough estimate", "how big is this market", "how long would this take", "how many users", or when deep-thinking diagnoses a sizing/estimation frame. The goal is not precision — it is a defensible answer that enables a decision to be made. Based on Enrico Fermi's estimation method.

Set up worker environment and per-worker state for parallel pools. Use when code needs paths, environment variables, database connections, loaded libraries, or expensive objects available on workers before parfor/parfeval runs. Teaches parallel.pool.Constant, parfevalOnAll, and parpool name-value pairs. Also use when refactoring existing code that uses spmd for side-effect setup (an anti-pattern). Triggers: worker setup, pool constant, per-worker state, non-serializable, loadlibrary on workers, database connection parfor, addpath workers, spmd before parfor, worker environment, reduce parfor overhead, parfor setup, resource creation in parallel loop, cannot serialize error, undefined function or variable on workers error, load data per worker, reduce data transfer, parallelize setup, improve parallel code.

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

TypeScript 后端的 Prisma ORM 模式——Schema 设计、查询优化、事务、分页、迁移与测试

Production patterns and non-obvious traps for Prisma ORM in TypeScript backends, covering schema design, query optimization, transactions, pagination, and serverless connection handling.

Structured verbal communication framework for Senior PM and TPM interviews. Use when preparing for interviews, rehearsing answers, practicing English (or Spanish) delivery under pressure, or when any interview question needs to be answered in a crisp, structured way. Covers SCQA/STAR/C-F-I scaffolding, answer templates by question type, pushback handling, English- and Spanish-under-pressure tactics, and post-interview debrief methodology. Trigger on any variant of "how do I answer this?", "rehearse with me", "how would you say this?", or "practice interview question".

Screen US equities for parabolic exhaustion patterns, generate conditional pre-market short plans, and monitor intraday trigger signals using 5-min bars.

Runs a relentless interview that sharpens a plan or design. Use when the user wants to be grilled on an idea, pressure-test a plan, or refine a design through questioning.

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.

Use for writing, reviewing, debugging, testing, or optimizing Python SymPy symbolic mathematics. Trigger on Symbol, assumptions, Expr, Eq, solve/solveset, simplify, factor, expand, calculus, matrices, exact arithmetic, lambdify, code generation, or symbolic-to-numeric conversion. Do not use for NumPy-only arrays, mpmath-only arbitrary-precision numerics, CVXPY optimization models, or parsing untrusted mathematical text.

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Solve single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including Pareto front analysis, constraint handling, and benchmarking on standard test problems.

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

Analyze proteomics and metabolomics mass spectrometry data with PyOpenMS: read/write MS file formats, process spectra, detect and quantify features, identify peptides and proteins, and run end-to-end LC-MS/MS pipelines using ready-to-run scripts.

Runs a relentless interview that asks every frontier question at once, round by round. Use when the user wants to be grilled on a design or map a design tree.

Prisma ORM for TypeScript - Type-safe database toolkit with schema-first development, auto-generated client, migrations, relations, and Prisma Studio

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Provides expert guidance for fine-tuning LLMs with LLaMA-Factory, covering WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, and multimodal support.

Manim CE animations: 3Blue1Brown math/algo videos.

Creates executable Polars dataframe contracts using Pandera's Polars backend for runtime validation of schemas, columns, and checks.

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support