Search Results for "ai code writer" - Page 2
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Showing 37 open source projects for "ai code writer"
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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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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Sail
A drop-in Apache Spark replacement written in Rust
Sail is an open-source distributed computation framework designed to unify batch processing, stream processing, and AI workloads into a single, high-performance engine. It is built entirely in Rust, eliminating JVM overhead and enabling predictable performance, fast startup times, and improved memory safety compared to traditional big data frameworks. Sail is compatible with the Spark Connect protocol, which means existing Spark SQL and DataFrame workloads can run without code changes, making adoption seamless for teams already using Spark-based pipelines. ... -
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Daft
Distributed DataFrame for Python designed for the cloud
Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python Dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend. -
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gptcommit
A git prepare-commit-msg hook for authoring commit messages with GPT-3
A git prepare-commit-msg hook for authoring commit messages with GPT-3. With this tool, you can easily generate clear, comprehensive and descriptive commit messages letting you focus on writing code. To use gptcommit, simply run git commit as you normally would. The hook will automatically generate a commit message for you using a large language model like GPT. If you're not satisfied with the generated message, you can always edit it before committing. By default, gptcommit uses the GPT-3... -
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shimmy
Python-free Rust inference server
...Written primarily in Rust, the tool provides a small standalone binary that exposes an API compatible with the OpenAI interface, allowing existing applications to interact with local models without significant code changes. This compatibility enables developers to replace remote AI services with locally hosted models while keeping their existing software architecture intact. Shimmy focuses on performance and simplicity, using efficient runtime components to minimize memory usage and startup time compared to heavier inference frameworks. It supports modern model formats such as GGUF and SafeTensors and can automatically discover models stored locally or in common directories used by other AI tools. ... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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DeepReasoning
High-performance API combining reasoning and creative AI models
DeepReasoning is a high-performance large language model inference API designed to unify advanced reasoning and creative generation capabilities into a single system. It combines DeepSeek R1’s chain-of-thought reasoning with Claude’s strengths in code generation and conversational output, enabling more capable and balanced responses. DeepReasoning provides both an API and a chat interface, allowing developers and users to interact with the combined models in a streamlined way. It emphasizes... -
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yek
Serialize repositories into LLM-ready context w/ smart prioritization
...It can stream output when piped or save results to a temporary file, depending on usage. Configuration is handled through a yek.yaml file, allowing users to define ignore rules and priority settings. By consolidating code and documents into a single, ordered format, Yek simplifies preparing repositories for AI-driven analysis, debugging, or automation tasks. -
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Code2Prompt
Convert codebases into structured prompts optimized for LLM analysis
code2prompt is an open source command line tool designed to convert an entire codebase into a structured prompt that can be easily used with large language models. It analyzes a project directory, gathers relevant source files, and formats them into a single prompt that includes the source tree and code content. This approach helps developers quickly provide full project context to AI models without manually copying files or assembling prompts. code2prompt is built in Rust and focuses on performance, enabling fast traversal of large repositories while maintaining low resource usage. It also respects common project conventions such as .gitignore, ensuring that unnecessary files are automatically excluded from the generated prompt. ... -
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AppFlowy
Bring projects, wikis, and teams together with AI.
AppFlowy is an AI collaborative workspace where you can achieve more without losing control of your data. It is the best open source alternative to Notion, offering a 100% offline mode and self-hosting with a cloud service of your choice. Build a centralized workspace for your wiki, projects, and notes with AppFlowy. It allows you to organize and visualize your data in tables, Kanban boards, calendars, and more. You can filter and sort your data in any way you want. AppFlowy comes... -
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Shinkai: Local AI Agents
Shinkai allows you to create advanced AI (local) agents effortlessly
Shinkai is a free, open-source AI platform that lets anyone create powerful AI agents without coding. These agents can collaborate with each other, handle complex tasks, and operate in decentralized crypto environments. Key Features: - No-Code Agent Creation - Build specialized agents (trading bots, sentiment trackers, etc.) with simple descriptions - Multi-Agent Collaboration - Agents work together to solve complex problems - Crypto Integration - Built-in support for decentralized payments and transactions - Flexible AI Models - Choose from cloud models (GPT-4, Claude) or run locally - Universal Compatibility - Works with Model Context Protocol (MCP) for cross-platform integration - Local Security - Crypto keys and computations stay on your device Shinkai transforms AI from single-task tools into collaborative, autonomous systems that can operate in decentralized networks while maintaining privacy and security. -
Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Flow Teleprompter
A Windows first teleprompter with voice tracking and AI drafting.
Flow is a high performance desktop teleprompter built with Tauri and Rust. It is designed for creators who need a clean reading surface without sacrificing advanced features like native speech tracking and app wide voice commands. It features five playback styles including scroll and voice sync, local first privacy, and built in script editing. Version 1.6.1 introduces a rebuilt Vosk engine for faster recognition and significantly lower RAM usage. Whether you are using the Groq powered AI... -
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Chidori
A reactive runtime for building durable AI agents
A reactive runtime for building durable AI agents. Chidori is an open-source orchestrator, runtime, and IDE for building software in symbiosis with modern AI tools. When using Chidori, you author code with python or javascript, we provide a layer for interfacing with the complexities of AI models in long-running workflows. We have avoided the need for declaring a new language or SDK in order to provide these capabilities so that you can leverage software patterns that you are already familiar with. -
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aigc
An e-book about the real-world application of LLM
"Building Large Language Model Applications: Application Development and Architecture Design" is an open source e-book about the real-world application of LLM. It introduces the basics and applications of large language models, as well as how to build your own models. These include writing, developing, and managing prompts, exploring what the best large language models can bring, and pattern and architecture design for LLM application development.
