Results for “pnl”

Programmatically access Google NotebookLM via reverse-engineered RPC calls, managing notebooks, adding sources, querying, and generating or downloading artifacts like audio, video, quizzes, and slide decks.

Train quantum circuits with automatic differentiation and build hybrid quantum-classical models using PennyLane, including VQE, QAOA, and integration with PyTorch, JAX, and TensorFlow.

Use to convert references to PNAS's numbered citation style — cited in order of appearance, a numbered reference list, full author lists, abbreviated journal titles. Provides formats for article/book/chapter/dataset/preprint and an author-date → PNAS conversion table. Late-stage style pass.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.

Model a Phase-Locked Loop (PLL) IC from its datasheet or system specs using Mixed-Signal Blockset. Without this skill, agents universally select the wrong solver and produce non-functional PLL models — 100% of unguided attempts fail. Covers Integer-N, Fractional-N, Dual Modulus architectures, loop filter design, lock time optimization, VCO phase noise configuration, and msbPllArchitectures/msbPllFoundation block assembly. Use when: PLL modeling, frequency synthesizer design, phase noise simulation, lock time analysis, charge pump design, loop filter tuning, datasheet-to-model, Mixed-Signal Blockset PLL, msbPllArchitectures.

Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), and distributed training (DDP, FSDP, DeepSpeed) for scalable neural network training.

Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.

Provides guidance on using PyTorch for deep learning, covering tensors, autograd, nn.Module, DataLoaders, and best practices.

Interpreted crypto wallet data for AI agents. Use when an agent needs portfolio values, token positions, DeFi positions, NFT holdings, transaction history, PnL data, token prices, charts, gas prices, swap quotes, or DApp information across 41+ chains. Zerion transforms raw blockchain data into agent-ready JSON with USD values, protocol labels, and enriched metadata. Supports x402 pay-per-request ($0.01 USDC on Base) and API key access. Triggers on mentions of portfolio, wallet analysis, positions, transactions, PnL, profit/loss, DeFi, token balances, NFTs, swap quotes, gas prices, or Zerion.