Results for “perpetual-futures”

Entry-point skill for Phoenix perpetuals through Vulcan/Rise SDK inside solana-clawd. Use before answering or acting on Vulcan, Phoenix DEX, Solana perps, paper trading, live trading, margin, TP/SL, TWAP, grid, TA strategies, or perps agent setup.

Implements a continual learning loop for AI coding agents using hooks, two-tier memory (global and local), and automatic pattern detection to persist and apply learnings across sessions.

Trade perpetuals on Arbitrum's Nexus DEX with autonomous agent support, including position management, thesis publishing, and leaderboard tracking.

How to read, resolve, and reason about commitments — durable records of future obligations (your own promises and the operator's requests). Use whenever the `## Upcoming commitments` prompt block surfaces something you might act on, or when you want to inspect what's pending beyond what the block shows.

Forecast any univariate time series (sales, sensors, energy, vitals, weather) zero-shot using Google's TimesFM foundation model, with point forecasts and prediction intervals from CSV, DataFrame, or array inputs.

Write idiomatic Elixir code with OTP patterns, supervision trees, and Phoenix LiveView. Masters concurrency, fault tolerance, and distributed systems. Use PROACTIVELY for Elixir refactoring, OTP design, or complex BEAM optimizations.

Provides a persistent, queryable virtual filesystem backed by SurrealDB for AI agents, with a Rust core and a Python agent interface.

Orchestrates multi-agent project builds with persistent state, parallel execution, and atomic git commits.

Evaluates computational throughput and real-time efficiency of embedded CPU and GPU platforms by measuring peak FLOPS via a matrix rotation kernel and assessing inference latency and power consumption on a robotic vision pipeline.

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

Bypasses safety filters on API-served LLMs using jailbreak templates, input obfuscation, and multi-model racing.

PyTorch deep learning development with transformers, diffusion models, and GPU optimization.