Search Results for "can" - Page 62
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Showing 5017 open source projects for "can"
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Computer Science Flash Cards
Mini website for testing both general CS knowledge and enforce coding
...The cards distill topics like time and space complexity, classic data structures, algorithmic paradigms, operating systems, networking, and databases into short, testable prompts. They are designed for spaced-repetition style study so you can cycle frequently through fundamentals until recall feels automatic. Many cards point at canonical definitions or contrasts (e.g., stack vs. queue, BFS vs. DFS) to strengthen conceptual boundaries. The material favors clarity and breadth over exhaustive proofs, making it ideal for quick refreshers during a study plan. It complements longer resources by giving you a lightweight way to keep key concepts top of mind. -
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ZeusDB Vector Database
Blazing-fast vector DB with similarity search and metadata filtering
ZeusDB is a vector database built for fast, scalable similarity search with strong production ergonomics. It combines high-performance approximate nearest neighbor indexes with clean APIs and metadata filtering so applications can retrieve semantically relevant items at low latency. The storage layer is designed for durability and growth, supporting sharding, replication, and background compaction while keeping query tails predictable. Developers get multiple ingestion paths—batch, streaming, and upsert—making it easy to keep embeddings synchronized as content changes. ... -
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Atheris
A Coverage-Guided, Native Python Fuzzer
...Because many Python libraries are thin wrappers over C/C++ code, Atheris is equally adept at surfacing memory safety issues in extension modules compiled with sanitizers. The tool integrates smoothly with Python’s packaging and unit-test ecosystems, so you can wrap existing tests as fuzz targets and keep results understandable. It supports structured input strategies and custom mutators, which is especially helpful for text and data formats common in Python workloads. In practice, Atheris compresses weeks of edge-case brainstorming into hours of automated exploration with actionable, minimized reproductions. -
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Purple Llama
Set of tools to assess and improve LLM security
Purple Llama is an umbrella safety initiative that aggregates tools, benchmarks, and mitigations to help developers build responsibly with open generative AI. Its scope spans input and output safeguards, cybersecurity-focused evaluations, and reference shields that can be inserted at inference time. The project evolves as a hub for safety research artifacts like Llama Guard and Code Shield, along with dataset specs and how-to guides for integrating checks into applications. CyberSecEval, one of its flagship components, provides repeatable evaluations for security risk, including agent-oriented tasks such as automated patching benchmarks. ... -
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JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
...A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. This makes learning focus on semantics and structure, yielding features that transfer well with simple linear probes and minimal fine-tuning. The repository provides training recipes, data pipelines, and evaluation utilities for image JEPA variants and often includes ablations that illuminate which masking and architectural choices matter. ... -
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Flow Matching
A PyTorch library for implementing flow matching algorithms
...The underlying idea is to parameterize a flow (a time-dependent vector field) that transports samples from a simple base distribution to a target distribution, and train via matching of flows without requiring score estimation or noisy corruption—this can lead to more efficient or stable generative training. The library supports both continuous-time flows (via differential equations) and discrete-time analogues, giving flexibility in design and tradeoffs. It provides examples across modalities (images, toy 2D distributions) to help users understand how to apply flow matching in practice. ... -
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Smallpond
A lightweight data processing framework built on DuckDB and 3FS
...Users write Python-like code (via DataFrame APIs or SQL strings) to express their transformations; behind the scenes, tasks are scheduled (often via Ray) and pushed into DuckDB instances operating on partitioned data. Because the storage layer (3FS) is optimized for random access and high throughput, smallpond can shuffle data, repartition, and manage intermediate results across nodes. -
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DeepEP
DeepEP: an efficient expert-parallel communication library
...Its core role is to implement high-throughput, low-latency all-to-all GPU communication kernels, which handle the dispatching of tokens to different experts (or shards) and then combining expert outputs back into the main data flow. Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP addresses that by providing optimized GPU kernels and efficient dispatch/combining logic. The library also supports low-precision operations (such as FP8) to reduce memory and bandwidth usage during communication. DeepEP is aimed at large-scale model inference or training systems where expert parallelism is used to scale model capacity without replicating entire networks. -
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Kubeflow pipelines
Machine Learning Pipelines for Kubeflow
...The pipeline includes the definition of the inputs (parameters) required to run the pipeline and the inputs and outputs of each component. A pipeline component is a self-contained set of user code, packaged as a Docker image, that performs one step in the pipeline. For example, a component can be responsible for data preprocessing, data transformation, model training, and so on. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Jupyter Enterprise Gateway
Enables Jupyter Notebooks to share resources across clusters
Jupyter Enterprise Gateway is a headless web server with a pluggable framework for anyone supporting multiple notebook users in a managed-cluster environment. Some of the core functionality it provides is better optimization of compute resources, improved multi-user support, and more granular security for your Jupyter notebook environment - making it suitable for enterprise, scientific, and academic implementations. From a technical perspective, Jupyter Enterprise Gateway is a web server... -
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requests-cache
Persistent HTTP cache for python requests
requests-cache is a persistent HTTP cache that provides an easy way to get better performance with the Python requests library. Keep using the requests library you’re already familiar with. Add caching with a drop-in replacement for requests. The session, or install globally to add transparent caching to all request functions. Get sub-millisecond response times for cached responses. When they expire, you still save time with conditional requests. Works with several storage backends including... -
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Cobbler
Cobbler is a versatile Linux deployment server
...It glues together and automates many associated Linux tasks so you do not have to hop between many various commands and applications when deploying new systems, and, in some cases, changing existing ones. Cobbler can help with provisioning, managing DNS and DHCP, package updates, power management, configuration management orchestration, and much more. Automation is the key to speed, consistency and repeatability. These properties are critical to managing infrastructure, whether it is comprised of a few servers or a few thousand servers. Cobbler helps by automating the process of provisioning servers from bare metal, or when deploying virtual machines onto various hypervisors. ... -
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DeepVariant
DeepVariant is an analysis pipeline that uses a deep neural networks
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file. DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. DeepTrio... -
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IVY
The Unified Machine Learning Framework
Take any code that you'd like to include. For example, an existing TensorFlow model, and some useful functions from both PyTorch and NumPy libraries. Choose any framework for writing your higher-level pipeline, including data loading, distributed training, analytics, logging, visualization etc. Choose any backend framework which should be used under the hood, for running this entire pipeline. Choose the most appropriate device or combination of devices for your needs. DeepMind releases an... -
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Selenium-python Helium
Selenium-python but lighter: Helium is the best Python library
...Unlike Selenium, Helium lets you interact with elements inside nested iFrames, without having to first "switch to" the iFrame. Helium notices when popups open or close and focuses / defocuses them like a user would. You can also easily switch to a window by (parts of) its title. -
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Yahoo! Finance market data downloader
Yahoo! Finance market data downloader
Ever since Yahoo! finance decommissioned their historical data API, many programs that relied on it to stop working. yfinance aims to solve this problem by offering a reliable, threaded, and Pythonic way to download historical market data from Yahoo! finance. yfinance aimed to offer a temporary fix to the problem by scraping the data from Yahoo! Finance and returning a the data in the same format as pandas_datareader's get_data_yahoo(), thus keeping the code changes in existing software to a... -
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diskover-community
Open source file indexing & storage analytics powered by Elasticsearch
...By indexing file metadata from sources such as local file systems, network shares like NFS and SMB, and cloud storage, the tool provides a centralized way to analyze heterogeneous storage environments. Diskover also helps identify outdated or unused files, duplicate data, and inefficient storage usage that can waste resources or increase operational costs. A Python-based indexing engine performs the scanning and indexing tasks. -
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promptmap2
A security scanner for custom LLM applications
promptmap is an automated security scanner for custom LLM applications that focuses on prompt injection and related attack classes. The project supports both white-box and black-box testing, which means it can either run tests directly against a known model and system prompt configuration or attack an external HTTP endpoint without internal access. Its scanning workflow uses a dual-LLM architecture in which one model acts as the target being tested and another acts as a controller that evaluates whether an attack succeeded. The repository emphasizes broad coverage, including test rules for prompt stealing, jailbreaks, harmful content generation, hate-related outputs, social bias, and distraction attacks. ... -
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SiteDorks
Automate search engine dorking across hundreds of websites
...A built-in dataset contains hundreds of websites grouped into categories such as cloud services, developer platforms, documentation sites, social platforms, and communication tools. Users can also supply custom domain lists or CSV files to tailor searches for tasks like penetration testing, bug bounty research, or OSINT investigations. -
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handy-ollama
Implement CPU from scratch and play with large model deployments
...The project includes step-by-step guides that walk learners through tasks such as installing Ollama, managing local models, calling model APIs, and building simple AI applications on top of locally hosted models. Through hands-on exercises and practical examples, the tutorial demonstrates how developers can create applications like chat assistants or retrieval systems using locally deployed models. -
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AutoAgent
AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework
...It is structured around modes that cover both “use” and “build” scenarios: a user mode for running a ready-made multi-agent research assistant, plus editors for creating individual agents or multi-agent workflows from conversational requirements. The framework emphasizes self-managing workflow generation, where it can infer steps, refine them, and adapt plans even when users cannot fully specify implementation details up front. It also describes resource orchestration and iterative self-improvement behaviors, including controlled code generation for building tools and agent capabilities when needed. The project is designed to work with multiple LLM providers and model endpoints, allowing users to choose different backends by setting environment variables and model identifiers. -
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Continuous Claude v3
Context management for Claude Code. Hooks maintain state via ledgers
...Rather than relying on a single session’s context, Continuous Claude uses mechanisms like ledgers, YAML handoffs, and a memory system to preserve and recall state across multiple sessions, ensuring that learned insights and plans are not lost when context compaction occurs. The project orchestrates many specialized agents and skills—109 skills and 32 agents—so that complex coding tasks can be broken down, analyzed, and executed collaboratively by different components. It also includes a layered code analysis pipeline to reduce token usage and maintain relevant context efficiently. This continuous learning environment enables workflows such as bug fixing, refactoring, planning, and exploratory investigation while minimizing the need to re-explain context manually. -
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GLM-TTS
Controllable & emotion-expressive zero-shot TTS
...The system introduces a multi-reward reinforcement learning framework that jointly optimizes for voice similarity, emotional expressiveness, pronunciation, and intelligibility, yielding output that can rival commercial options in naturalness and expressiveness. GLM-TTS also supports phoneme-level control and hybrid text + phoneme input, giving developers precise control over pronunciation critical for multilingual or polyphone-rich languages. -
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Acontext
Context data platform for building observable, self-learning AI agents
...The platform observes agent tasks and user feedback in real time, offering robust observability into workflows and helping teams understand how agents perform over time. Acontext also supports agent self-learning by distilling structured skills and experiences from previously completed tasks, which can later be reused or searched to improve future performance. It includes tools to interact with session data, background agents that monitor progress, and a dashboard that visualizes success rates, artifacts, and learned skills. By combining persistent storage, observability, and learning capabilities, Acontext aims to make AI agents more scalable, reliable, and capable. -
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rLLM
Democratizing Reinforcement Learning for LLMs
rLLM is an open-source framework for building and training post-training language agents via reinforcement learning — that is, using reinforcement signals to fine-tune or adapt language models (LLMs) into customizable agents for real-world tasks. With rLLM, developers can define custom “agents” and “environments,” and then train those agents via reinforcement learning workflows, possibly surpassing what vanilla fine-tuning or supervised learning might provide. The project is designed to support large-scale language models (including support for big models via integrated training backends), making it relevant for state-of-the-art research and production use. ...
