Search Results for "code::blocks" - Page 6
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Showing 2580 open source projects for "code::blocks"
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LLMStack
No-code multi-agent framework to build LLM Agents, workflows
LLMStack is a no-code platform for building generative AI agents, workflows and chatbots, connecting them to your data and business processes. Build tailor-made generative AI agents, applications and chatbots that cater to your unique needs by chaining multiple LLMs. Seamlessly integrate your own data, internal tools and GPT-powered models without any coding experience using LLMStack's no-code builder. -
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/last30days
Claude Code skill that researches any topic across Reddit + X
/last30days is a specialized Claude Code skill designed to research current trends and practices across Reddit, X, and the wider web from the last 30 days, synthesize that data, and produce copy-paste-ready prompts or summaries that reflect what the community is actually talking about now. Rather than returning generic model responses, it intelligently analyzes social media and community discussions to identify what’s genuinely trending or working in practice across topics ranging from prompt techniques to tool usage or cultural trends. ... -
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Omnara
Talk to Your AI Agents from Anywhere
...The primary way to use CLI coding agents (Claude Code, Codex CLI) with Omnara. -
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Checkov
Prevent cloud misconfigurations during build-time for Terraform
...Scan cloud resources in build-time for misconfigured attributes with a simple Python policy-as-code framework. Analyze relationships between cloud resources using Checkov’s graph-based YAML policies. Execute, test, and modify runner parameters in the context of a subject repository CI/CD and version control integrations. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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Super-Linter
Combination of multiple linters to install as a GitHub Action
This repository is for the GitHub Action to run a Super-Linter. It is a simple combination of various linters, written in bash, to help validate your source code. The super-linter finds issues and reports them to the console output. Fixes are suggested in the console output but not automatically fixed, and a status check will show up as failed on the pull request. The design of the Super-Linter is currently to allow linting to occur in GitHub Actions as a part of continuous integration occurring on pull requests as the commits get pushed. ... -
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fastai
Deep learning library
...These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai is organized around two main design goals: to be approachable and rapidly productive, while also being deeply hackable and configurable. It is built on top of a hierarchy of lower-level APIs which provide composable building blocks. -
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Sentry
Cross-platform application monitoring and error tracking software
Sentry is a cross-platform, self-hosted error monitoring solution that helps software teams discover, monitor and fix errors in real-time. The most users and logs will have to provide are the clues, and Sentry provides the answers. Sentry offers enhanced application performance monitoring through information-laden stack traces. It lets you build better software faster and more efficiently by showing you all issues in one place and providing the trail of events that lead to errors. It also... -
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GLM-4.7
Advanced language and coding AI model
...It delivers significant gains over GLM-4.6 in multilingual agentic coding, terminal-based workflows, and real-world developer benchmarks such as SWE-bench and Terminal Bench 2.0. The model introduces stronger “thinking before acting” behavior, improving stability and accuracy in complex agent frameworks like Claude Code, Cline, and Roo Code. GLM-4.7 also advances “vibe coding,” producing cleaner, more modern UIs, better-structured webpages, and visually improved slide layouts. Its tool-use capabilities are substantially enhanced, with notable improvements in browsing, search, and tool-integrated reasoning tasks. Overall, GLM-4.7 shows broad performance upgrades across coding, reasoning, chat, creative writing, and role-play scenarios. -
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Google Research
This repository contains code released by Google Research
...Because of its breadth, users typically clone only the subdirectories relevant to their specific research interests. Overall, google-research functions as a living archive of state-of-the-art research code supporting both academic and industrial AI innovation. -
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srsly
Modern high-performance serialization utilities for Python
This package bundles some of the best Python serialization libraries into one standalone package, with a high-level API that makes it easy to write code that's correct across platforms and Pythons. This allows us to provide all the serialization utilities we need in a single binary wheel. Currently supports JSON, JSONL, MessagePack, Pickle, and YAML. Serialization is hard, especially across Python versions and multiple platforms. After dealing with many subtle bugs over the years (encodings, locales, large files) our libraries like spaCy and Prodigy have steadily grown a number of utility functions to wrap the multiple serialization formats we need to support (especially json, msgpack and pickle). ... -
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DeepCode
DeepCode: Open Agentic Coding
DeepCode is an agentic coding platform built around a multi-agent architecture that turns high-level inputs, including research papers, documents, and natural-language requirements, into working software artifacts. It positions itself as an “open agentic coding” system that can handle tasks like paper-to-code reproduction, frontend generation, and backend implementation by decomposing problems into structured steps and coordinating specialized agents. The system description highlights an orchestration layer that plans, assigns subtasks, and adapts strategies as complexity changes, rather than relying on a single monolithic prompt. It also describes document parsing capabilities aimed at extracting algorithmic and mathematical details from technical materials, translating them into implementable specifications and code. -
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DVC
Data Version Control | Git for Data & Models
...DVC connects them with code and uses Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network-attached storage, or disc to store file contents. Harness the full power of Git branches to try different ideas instead of sloppy file suffixes and comments in code. Use automatic metric tracking to navigate instead of paper and pencil. -
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NVIDIA Warp
A Python framework for accelerated simulation, data generation
NVIDIA Warp is a high-performance Python framework developed by NVIDIA for building and accelerating simulation, graphics, and physics-based workloads using GPU computing. It enables developers to write kernel-level code in Python that is automatically compiled into efficient CUDA kernels, combining ease of use with near-native performance. The framework is designed for applications such as robotics, reinforcement learning, physical simulation, and differentiable computing, where performance and flexibility are critical. Warp provides a set of primitives for working with arrays, geometry, and physics operations, allowing users to implement complex simulations without writing low-level CUDA code directly. ... -
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Gigi
Framework for rapid prototyping and development of real-time rendering
...You may hit bugs or missing features. Please report these so we can improve Gigi and push forward in the most useful directions. Pull requests are also appreciated. Currently, only dx12 code generation is available in this public version of Gigi, but we are hoping to support other APIs, and engines as well, in the future. -
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Locust
Scalable open source load testing tool
...The idea behind Locust is to swarm your web site or other systems with attacks from simulated users during a test, with each user behavior defined by you using Python code. This swarming process is then monitored from a web UI in real-time, and will help identify any bottlenecks in your code before real users can come in. As it is completely event-based, Locust can have thousands or even millions of simultaneous users distributed over multiple machines swarming your system. Unlike other event-based apps, it doesn’t use callbacks but uses lightweight processes instead, so you can write very expressive scenarios in Python without complicating it with callbacks. -
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Genv
GPU environment management and cluster orchestration
...Genv lets you easily control, configure, monitor and enforce the GPU resources that you are using in a GPU machine or cluster. It is intended to ease up the process of GPU allocation for data scientists without code changes. -
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Open Interpreter
A natural language interface for computers
...It prompts you to approve code before executing, and supports both online LLM models and local inference servers. It seeks to combine convenience (like ChatGPT’s code interpreter) with control and flexibility by running on your own machine. -
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The Hundred-Page Machine Learning Book
The Python code to reproduce illustrations from Machine Learning Book
...The repository complements these explanations by offering practical implementations that demonstrate how various algorithms behave when applied to data. Readers can explore the scripts to reproduce diagrams and observe how mathematical concepts translate into working code. -
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py2many
Transpiler of Python to many other languages
...However, there is no one solution that works well for lower level libraries where there is desire to share code between platforms. Kotlin Mobile Multiplatform (KMM) is a player in this place, but it hasn't really caught on. py2many provides an alternative. -
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Lepton AI
A Pythonic framework to simplify AI service building
A Pythonic framework to simplify AI service building. Cutting-edge AI inference and training, unmatched cloud-native experience, and top-tier GPU infrastructure. Ensure 99.9% uptime with comprehensive health checks and automatic repairs. -
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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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PyMC3
Probabilistic programming in Python
...Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets, or using Gaussian processes to build Bayesian nonparametric models. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. Sometimes an unknown parameter or variable in a model is not a scalar value or a fixed-length vector, but a function. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of continuous functions. PyMC3 provides rich support for defining and using GPs. Variational inference saves computational cost by turning a problem of integration into one of optimization. ... -
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Loki Mode
Multi-agent autonomous startup system for Claude Code
...It orchestrates dozens of agent types across swarms that handle designated roles — such as architecture, coding, QA, deployment, and business workflows — running in parallel to cover both engineering and operational tasks without continuous human intervention. By supporting multiple AI providers (like Claude Code, OpenAI Codex CLI, and Google Gemini CLI), loki-mode dynamically selects and spawns only the needed agents for a given project, optimizing computational resources and task throughput. Its Reason-Act-Reflect-Verify (RARV) cycle with self-verification loops emphasizes quality and resilience, automating end-to-end development lifecycles. -
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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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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.
