Results for “foundationstereo”

Trains, evaluates, exports, and runs inference for FastFoundationStereo (FFS) stereo depth estimation models, a distilled variant of FoundationStereo with lower latency.

Deploy and verify smart contracts on Base with Foundry, including testnet faucet access, encrypted keystore management, and BaseScan verification.

PHPStan static analysis in Sage/Acorn projects — lando composer phpstan, phpstan.neon configuration, PHPStan level 0-9, WordPress PHP stubs, szepeviktor/phpstan-wordpress, baseline generation, CI phpstan, suppress errors, type errors, return type mismatch, nullable types, WordPress function stubs, WP_Post WP_Query WP_User types

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA, including picking the right base pipeline, designing a tailored UI, and publishing the Space.

Deploys a secure, enterprise-grade Google Cloud landing zone foundation with organization policies, resource hierarchy, billing association, and centralized logging and monitoring.

Trains, evaluates, exports, quantizes, and runs inference for a Grounding DINO model that detects objects described by text prompts without a fixed class vocabulary.

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.

Build, review, or restructure a fundraising deck for pre-seed through Series A, with a clear narrative arc, investor-grade slide logic, and explicit asks.

Zero-shot univariate time-series forecasting with Google's TimesFM foundation model, producing point forecasts and prediction intervals from CSV/DataFrame/array inputs, with a preflight system checker for RAM/GPU. Use to forecast any univariate series (sales, sensors, energy, vitals, weather) without training a custom model. Part of the AlterLab Academic Skills suite.

Build and maintain a model book for Stockbee-style Momentum Burst setups by ingesting screener candidates, updating 3-day and 5-day forward outcomes with MFE/MAE and stop-hit status, and summarizing cohort statistics to improve setup recognition.

Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

Perform coverage-guided fuzzing of compiled binaries using AFL++ to discover memory corruption, crashes, and security vulnerabilities.

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.

HeartMuLa: Suno-like song generation from lyrics + tags.

Creates or updates a canonical founder context document capturing company, product, market, team, and fundraising facts so downstream workflows operate on concrete data.

Generates full songs from lyrics and tags using the open-source HeartMuLa music models, with multilingual support and local GPU or CPU inference.

Foundational Elixir patterns covering modules, pattern matching, processes, OTP behaviors (GenServer, Supervisor), Phoenix framework basics, and functional programming idioms. Use when writing Elixir code, building concurrent systems, working with Phoenix, or needing guidance on Elixir development patterns. This is the entry point for Elixir development.

Foundational Swift development patterns covering modern Swift syntax, SwiftUI, protocol-oriented programming, and Cocoa Touch frameworks. Use when writing Swift code, building iOS/macOS/watchOS/tvOS applications, working with SwiftUI or UIKit, understanding Swift concurrency, or needing guidance on Swift project structure.

RF Toolbox and RF Blockset in MATLAB -- S-parameter I/O, network conversions (S/Z/Y/ABCD/T/H/G, mixed-mode), cascade/de-embedding, rfbudget analysis, circuit composition, matching networks, amplifier stability, mixer spurs, rational fitting, SI channels, baseband processing, Circuit Envelope simulation. Trigger: sparameters, Touchstone, .s2p, .s4p, rfplot, smithplot, rfparam, rfwrite, zparameters, yparameters, abcdparameters, s2sdd, cascadesparams, deembedsparams, rfbudget, noise figure, OIP3, IIP3, amplifier, modulator, nport, rffilter, attenuator, seriesRLC, shuntRLC, lcladder, txline, circuit, setports, clone, matchingnetwork, stabilityk, stabilitymu, powergain, gammams, gammaml, mixerIMT, OpenIF, rational, rationalfit, stepresp, txlineWRLGC, rf.Amplifier, rf.Mixer, rf.Filter, rf.Sparameter, rfsystem, RF Blockset.

Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.

Build modern PHP applications with strict typing, PHPStan level 9, Laravel or Symfony, and enterprise patterns including DTOs, DI, and comprehensive testing.

Record buy rationale and structured sell conditions for stock positions, with automatic monitoring to detect when exit triggers fire.

Track stocks, ETFs, indices, crypto (where available), and FX pairs with caching + provider fallbacks.

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

HeartMuLa: Suno-like song generation from lyrics + tags.

Foundational skills for Zephyr RTOS development. Covers essential Embedded C patterns (BIT, CONTAINER_OF), real-time concurrency primitives (mutexes, semaphores, spinlocks), hardware literacy (datasheet-to-DTS mapping), and defensive programming. Trigger when writing core application logic, drivers, or troubleshooting foundational behavior.

Set up and run HeartMuLa, the open-source music generation model family (Suno-like). Generates full songs from lyrics + tags with multilingual support.

Design secure, scalable Firebase backends with Firestore, Auth, Functions, and Storage, following best practices for security rules, data modeling, and real-time listeners.

v5.6.0 joint multi-TF model: single model per symbol with broadcast 1Hour context replaces dual 15Min/1Hour models. Trigger: (1) replacing weighted-voting model aggregation, (2) adding broadcast features to vectorized env, (3) limited training data + worried about overfitting from doubling obs_dim, (4) backtest builder mismatch with newer feature counts.

AI music generation with ACE-Step 1.5 — background music, vocal tracks, covers, stem extraction for video production. Use when generating music, soundtracks, jingles, or working with audio stems. Triggers include background music, soundtrack, jingle, music generation, stem extraction, cover, style transfer, or musical composition tasks.

HeartMuLa: Suno-like song generation from lyrics + tags.

Foundry Testing & Toolchain

Computes phylogenetic distance matrices and trees from genomic VCF or FASTA data using the fastreeR hybrid Java/Python toolkit.

Inspects metadata, hidden channels, and appended payloads in media files to recover concealed data in steganography challenges.

Fine-tune Cosmos Reason video QA models using supervised fine-tuning with FSDP parallelism, including dataset preparation, spec construction, and AutoML support.