470 skills found · Page 6 of 16
danielkrupinski / StringPoolA performant and memory efficient storage for immutable strings with C++17. Supports all standard char types: char, wchar_t, char16_t, char32_t and C++20's char8_t.
thedivergentai / Gd Agentic SkillsThe official "Long-Term Memory" for Godot 4 AI Agents. A high-density library of 82+ expert skills and 26 genre blueprints, providing deterministic, strictly typed GDScript patterns optimized for recursive machine discovery and production-grade game engineering.
pku-liang / MAGISMAGIS: Memory Optimization via Coordinated Graph Transformation and Scheduling for DNN (ASPLOS'24)
dongmanqing / Code For MAMOThe code for paper MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
KeinNiemand / LargePageInjectorModsPerformance booster for Stellaris and Factorio that injects Microsoft's mimalloc allocator with Large Pages support for optimized memory management.
finnchen11 / VLLM PromptCacheOptimize vLLM with persistent system prompt caching and block reuse for faster, memory-efficient inference.
sevenDatabase / SevenDBSevenDB — reactive,scalable , in-memory database designed for real-time systems with deterministic execution, fine-grained backpressure, and modern hardware optimization.
qualcosa / Compact RAM CleanerSimple RAM Cleaner
mahdi-usask / Wind Speed Forecasting For Wind Power Generation Plant. Neural Network ML Based Prediction Algo. For largescale wind power penetration Wind speed prediction is a basic requirement of wind energy generation. There are many artificial neural network (ANN), ARMA, ARIMA approaches proposed in the recent literature in order to tackle this problem. This paper will use the artificial neural network (ANN) approach to get a prediction of wind speed using historical wind speed data. The historical data used here were gathered from NREL website ,as hourly basis from 80 meter hub height. The measurement location is NREL Flatirons Campus (M2). The readings displayed are derived from instruments mounted on or near a 82 meter (270 foot) meteorological tower located at the western edge of the Flatirons Campus (formerly NWTC) and about 11 km (7 miles) west of Broomfield, and approximately 8 km (5 miles) south of Boulder, Colorado. The tower is located at 39o 54' 38.34" N and 105o 14' 5.28" W (datum WGS84) with its base at an elevation of 1855 meters (6085 feet) above mean sea level. Data from year 2014 to 2018, in total 5 years of data has been used here as dataframe. Here the neural network has been implemented by Tensorflow’s Keras API. The used model is “sequential”. Four dense layer has been used in the optimized model. LSTM(Long- short-term memory) architecture has been used here as neural network architecture. Activation function being used in the dense layers are dropout function. The optimizer being used here is Adam. Here various range of Dropout function has been examined and chosen the best fit for this model. Also this paper examined various kinds of optimization method and used the best fitted one. The model performances were evaluated using the mean squared error using adam optimizer. Various kinds of data analytic techniques has been used here for better visualization and in depth understanding of the dataset and its variables. Since it is mostly a time series data so in the analytic part how the data is being changed with time has been shown. From the result of the predicted dataset it can be state that, this wind speed prediction model works best for all kinds of winds speed besides overfitted/ abnormal wind speeds which is a very rare case scenario.
agenticsorg / Lean AgenticA hybrid programming language combining Lean4's formal verification with blazing-fast compilation, actor-based agent orchestration, AI-driven optimization, and vector-backed agent memory.
sirixdb / BrackitQuery processor with proven optimizations, ready to use for your JSON store to query semi-structured data with JSONiq. Can also be used as an ad-hoc in-memory query processor.
HFTHaidra / Deep Reinforcement Learning For Automated Stock Trading StrategyStock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.
koshak9855 / GTA V ModMenu V2FiveM | Kiddions is a high-performance external utility for Grand Theft Auto V, utilizing a non-invasive memory management engine. This framework provides an optimized overlay, telemetry monitoring, and script execution environment based on the industry-standard Modest Menu architecture
mhss1 / KubitMicro-optimized, fast, memory-efficient Kotlin utilities.
radif / ReplayLibReplayLib is a comprehensive utility library for Unity game development, providing core patterns, extensions, and systems used throughout "The Last Word" project. The library emphasizes memory management, performance optimization, and consistent coding patterns.
Erlite / UE4 SimpleAssetStreamingOptimize your memory usage using this simple subsystem to stream assets in and out of memory only when needed.
bowbarrel46 / Process Hacker Pro 2026Process Hacker 2 Extended Edition — Advanced System Monitoring & Memory Management Suite. Enhanced Kernel-Level Access, Process Security Research Tool, and Real-Time Resource Analysis. Optimized for Windows 10/11 with Extended Plugin Support and Unlocked System Privileges.
quentinf00 / Article Memory LogImplémentation of the article **Deep Learning CUDA Memory Usage and Pytorch optimization tricks**
OnlyTerp / TurboquantFirst open-source implementation of Google TurboQuant (ICLR 2026) -- near-optimal KV cache compression for LLM inference. 5x compression with near-zero quality loss.
ssatwik975 / SpeedifySpeeds up the Spotify desktop client by optimizing scrolling, animations and memory usage.
