Search Results for "code::blocks" - Page 9
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Showing 2586 open source projects for "code::blocks"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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magentic
Seamlessly integrate LLMs as Python functions
Easily integrate Large Language Models into your Python code. Simply use the @prompt and @chatprompt decorators to create functions that return structured output from the LLM. Mix LLM queries and function calling with regular Python code to create complex logic. -
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AICGSecEval
A.S.E (AICGSecEval) is a repository-level AI-generated code security
AICGSecEval is an open-source benchmark framework designed to evaluate the security of code generated by artificial intelligence systems. The project was developed to address concerns that AI-assisted programming tools may produce insecure code containing vulnerabilities such as injection flaws or unsafe logic. The framework constructs evaluation tasks based on real-world software repositories and known vulnerability cases derived from CVE records. -
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Trail of Bits Skills Marketplace
Trail of Bits Claude Code skills for security research, vulnerability
Trail of Bits Skills Marketplace is a specialized Claude Code skills marketplace built by the security research firm Trail of Bits that focuses on enhancing AI-assisted workflows for vulnerability discovery, testing, and secure development. The repository groups a set of plug-in skills tailored toward static analysis, code auditing, secure defaults detection, and other practices that matter in software security. -
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Quark Agent
Quark Agent - Your AI-powered Android APK Analyst
With Quark Agent, you can perform analyses using only natural language. It creates Quark Script code following your ideas and adjusts the code promptly as you provide feedback. -
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ChatDBG
ChatDBG - AI-assisted debugging. Uses AI to answer 'why'
ChatDBG is an AI-assisted debugging tool that integrates large language models into standard debuggers like pdb, lldb, and gdb. It allows developers to engage in a dialog with the debugger, asking open-ended questions about their program's behavior, and provides error diagnoses and suggested fixes. -
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gpt-oss
gpt-oss-120b and gpt-oss-20b are two open-weight language models
...Both models use a native MXFP4 quantization for efficient memory use and support OpenAI’s Harmony response format, enabling transparent full chain-of-thought reasoning and advanced tool integrations such as function calling, browsing, and Python code execution. The repository provides multiple reference implementations—including PyTorch, Triton, and Metal—for educational and experimental use, as well as example clients and tools like a terminal chat app and a Responses API server. -
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Qwen
The official repo of Qwen chat & pretrained large language model
...These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a versatile tool for developers looking to integrate advanced AI functionalities into their applications. -
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Unsloth Studio
Unified web UI for training and running open models locally
...The platform supports fine-tuning, pretraining, and reinforcement learning workflows, making it suitable for both experimentation and production use. Users can interact with models through chat, upload files like PDFs or images, and execute code within the environment to improve outputs. By combining powerful optimization techniques with an intuitive UI, Unsloth Studio simplifies the process of building and customizing AI models locally. -
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xLSTM
Neural Network architecture based on ideas of the original LSTM
xLSTM is an open-source machine learning architecture that reimagines the classic Long Short-Term Memory (LSTM) network for modern large-scale language modeling and sequence processing tasks. The project introduces a new recurrent neural network design that incorporates exponential gating mechanisms and enhanced memory structures to overcome limitations of traditional LSTM models. By introducing innovations such as matrix-based memory and improved normalization techniques, xLSTM improves the... -
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Zeta
Build high-performance AI models with modular building blocks
zeta is a deep learning library focused on providing cutting-edge AI and neural network models with a strong emphasis on research-grade architectures. It includes state-of-the-art implementations for rapid experimentation and model building. -
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Serena
Agent toolkit providing semantic retrieval and editing capabilities
Serena is a coding-focused agent toolkit that turns an LLM into a practical software-engineering agent with semantic retrieval and editing over real repositories. It operates as an MCP server (and other integrations), exposing IDE-like tools so agents can locate symbols, reason about code structure, make targeted edits, and validate changes. The toolkit is LLM-agnostic and framework-agnostic, positioning itself as a drop-in capability for different chat UIs, orchestrators, or custom agent stacks. It emphasizes symbol-level understanding rather than naive file-wide diffs, enabling more precise refactors and additions. ... -
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sparkmagic
Jupyter magics and kernels for working with remote Spark clusters
...Sparkmagic interacts with remote Spark clusters through a REST server. Automatic visualization of SQL queries in the PySpark, Spark and SparkR kernels; use an easy visual interface to interactively construct visualizations, no code required. Ability to capture the output of SQL queries as Pandas dataframes to interact with other Python libraries (e.g. matplotlib). Send local files or dataframes to a remote cluster (e.g. sending pretrained local ML model straight to the Spark cluster) Authenticate to Livy via Basic Access authentication or via Kerberos. -
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Devicon
Set of icons representing programming languages, design & development
Devicon is a set of icons representing programming languages, designing, and development tools. You can use it as a font or directly copy/paste the SVG code into your project. Devicon aims to gather all logos representing development languages and tools. Each icon comes in several versions: font/SVG, original/plain/line, colored/not colored, wordmark/no wordmark. Devicon has 150+ icons. And it's growing! Open icomoon.io and import icomoon.json. Choose yes when being asked if you would like to restore the settings stored in the configuration file. ... -
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GPT All Star
AI-powered code generation tool for scratch development of web apps
AI-powered code generation tool for scratch development of web applications with a team collaboration of autonomous AI agents. This is a research project, and its primary value is to explore the possibility of autonomous AI agents. -
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DSPy
DSPy: The framework for programming—not prompting—language models
Developed by the Stanford NLP Group, DSPy (Declarative Self-improving Python) is a framework that enables developers to program language models through compositional Python code rather than relying solely on prompt engineering. It facilitates the construction of modular AI systems and provides algorithms for optimizing prompts and weights, enhancing the quality and reliability of language model outputs. -
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Thinc
A refreshing functional take on deep learning
...Develop faster and catch bugs sooner with sophisticated type checking. Trying to pass a 1-dimensional array into a model that expects two dimensions? That’s a type error. Your editor can pick it up as the code leaves your fingers. -
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Nexent
Zero-code platform for building AI agents from natural language input
Nexent is an open source platform designed to enable users to create intelligent agents using natural language instead of traditional programming or visual orchestration tools. It focuses on a zero-code approach, allowing users to define workflows and agent behavior purely through language prompts, significantly lowering the barrier to entry for AI development. Built on the MCP ecosystem, Nexent integrates a wide range of tools, models, and data sources into a unified environment for agent creation and execution. Nexent supports multi-agent collaboration, enabling multiple intelligent agents to interact and coordinate tasks within complex workflows. ... -
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Python API for JMComic
Python crawler and API for downloading JMComic albums and images
...It includes command-line functionality and configuration files so users can customize download behavior, directory structures, and performance settings without modifying code. It also supports plugin-based extensions that allow additional processing. -
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DocETL
A system for agentic LLM-powered data processing and ETL
...Instead of relying on single prompts or ad-hoc scripts, DocETL provides a declarative pipeline framework that breaks complex document analysis tasks into manageable operations that can be optimized and orchestrated automatically. Pipelines are typically defined using a low-code YAML interface, giving users full control over prompts and processing steps while still simplifying workflow creation. -
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UpTrain
Your open-source LLM evaluation toolkit
...By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users. Unleash unparalleled power with a single line of code and tailor every detail as per as your use-case. -
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Claude Cognitive
Persistent context and multi-instance coordination
Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant code. -
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AlphaGenome
Programmatic access to the AlphaGenome model
The AlphaGenome API provides access to AlphaGenome, Google DeepMind’s unifying model for deciphering the regulatory code within DNA sequences. This repository contains client-side code, examples, and documentation to help you use the AlphaGenome API. AlphaGenome offers multimodal predictions, encompassing diverse functional outputs such as gene expression, splicing patterns, chromatin features, and contact maps. The model analyzes DNA sequences of up to 1 million base pairs in length and can deliver predictions at single-base-pair resolution for most outputs. ... -
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Mercury
Convert Python notebook to web app and share with non-technical users
...You define interactive widgets for your notebook with the YAML header. Your users can change the widgets values, execute the notebook and save result (as PDF or html file). You can hide your code to not scare your (non-coding) collaborators. Easily deploy to any server. Mercury is dual-licensed. Looking for dedicated support, a commercial-friendly license, and more features? The Mercury Pro is for you. -
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TinyDB
Document oriented database optimized for you
TinyDB is a lightweight document oriented database optimized for your happiness :) It's written in pure Python and has no external dependencies. The target are small apps that would be blown away by a SQL-DB or an external database server. The current source code has 1800 lines of code (with about 40% documentation) and 1600 lines tests. Like MongoDB, you can store any document (represented as dict) in TinyDB. TinyDB is designed to be simple and fun to use by providing a simple and clean API. TinyDB neither needs an external server (as e.g. PyMongo) nor any dependencies from PyPI. TinyDB works on all modern versions of Python and PyPy. ... -
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Typer
Typer, build great CLIs, based on Python type hints
...Designed to be easy to use and learn. Less time reading docs. It's easy to use for the final users. Automatic help, and automatic completion for all shells. Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs. The simplest example adds only 2 lines of code to your app: 1 import, 1 function call. Grow in complexity as much as you want, create arbitrarily complex trees of commands and groups of subcommands, with options and arguments. And it's intended to be the FastAPI of CLIs. ...
