Results for “fsdp2”

Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.

Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.

PHOENIX v2.0 — Contribution Velocity Scanner (Hardened). Same battle-tested signal. Fixed trade counter. DSL plugin exits.

Use when designing a Digital Down Converter (DDC) using dsphdl System objects. Triggers on requests involving DDC design, frequency down-conversion for FPGA/ASIC, NCO + mixer + decimation filter chains, fractional/non-integer sample rate conversion, or HDL-optimized receiver front-end signal processing.

FOX v2.0 — Dual-mode emerging movers scanner. All live trading lessons from Fox v1.0 applied, plus one experimental tweak: Stalker minReasons = 3. Stalker entries must have at least 3 distinct scoring reasons, not just pass the score threshold. This forces breadth of confirmation beyond the auto-awarded base climb score + one bonus. Stalker minScore 7, minTotalClimb 8, tighter Phase 1 for low-score entries, consecutive-loss streak gate. XYZ banned. Leverage 7-10x. DSL exit managed by plugin runtime via runtime.yaml.

Flash FPGA firmware on HSB Lattice boards and Leopard Imaging VB1940 cameras connected to NVIDIA devkits, with safety checks and multi-step upgrade/downgrade procedures.

Development with Fpga: tools and best practices

Imported skill reference from anthropic

Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially expressed genes between conditions from raw bulk RNA-seq counts. Part of the AlterLab Academic Skills suite.

Generate cinematic videos with Seedance 2.0 using director-level prompts, camera grammar, and multi-mode generation across Chinese, Global, and VIP tiers.

ORCA v2.0 — Gen-2 Striker with momentum event quality confirmation. FIRST_JUMP detection enhanced with Tier 2 momentum events and TCS trader quality tags. Stalker permanently removed. DSL exit managed by plugin runtime via runtime.yaml.

This skill recommends monitoring desmopressin (DDAVP) doses and adjusting them as needed in patients who have recently started antiepileptic drugs (AEDs). It is triggered when a patient initiates AED therapy and requires DDAVP management for diabetes insipidus.

VIPER v2.1 — Range-bound liquidity sniper. Enters at support/resistance when Bollinger Bands are tight, ATR is low, RSI is at extremes within the range, and volume is declining. Works when nothing is trending — the chop predator. USD-weighted OI filtering (min $5M). DSL High Water Mode (mandatory). 5-minute scan interval. Second-best performer in the Senpi zoo at +6.2% ROI.

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Build type-safe, testable Node.js/Deno backends using fp-ts with ReaderTaskEither, functional dependency injection, and service composition patterns.

Foundational F# patterns covering functional-first programming, type providers, computation expressions, and domain modeling. Use when writing F# code, understanding functional patterns, working with type providers, or building .NET applications with F#. This is the entry point for F# development.

HYDRA v2.0 — Squeeze Detector. Finds crowded trades about to unwind. Funding extreme + SM positioned against the crowd + price starting to move. Goes opposite to the funding crowd. Only liquid assets ($20M+ volume). DSL exit managed by plugin runtime via runtime.yaml.

Read BEFORE writing any code that builds or simulates OFDM systems. The default MATLAB patterns for OFDM (e.g., direct ifft/fft, awgn with 0 dBW power, missing symOffset) produce subtly incorrect results — always use ofdmmod/ofdmdemod instead of direct IFFT/FFT. This skill specifies the correct calling conventions for ofdmmod, ofdmdemod, ofdmChannelResponse, ofdmEqualize, and critical anti-patterns that must be avoided. Use when building OFDM transmitters or receivers, allocating subcarriers and guard bands, inserting pilots, computing SNR for OFDM, configuring fading channels (Rayleigh/Rician), estimating and equalizing channels, implementing timing and frequency synchronization, adding LDPC coding, designing resource grids, or setting up OFDM link simulations.

Patterns for FPGA development including RTL design (Verilog/VHDL), timing closure, clock domain crossing, high-level synthesis, and verification. Covers both traditional HDL and modern HLS approaches. Use when ", " mentioned.

Use when implementing php functionality with production-grade patterns and safeguards.