Ask-search provides a way for AI agents to perform web searches privately and without the need for paid API keys. It works by wrapping SearxNG, which aggregates results from multiple sources such as Google and Bing into a single meta-search engine. This allows local tools like Claude Code or Antigravity to access real-time information while maintaining data privacy.
* Zero cost via self-hosted SearxNG backend
* High compatibility with CLI, MCP servers, and OpenClaw skills
* Flexible search options including language and category filters
* Privacy protection through local query aggregation
* Zero cost via self-hosted SearxNG backend
* High compatibility with CLI, MCP servers, and OpenClaw skills
* Flexible search options including language and category filters
* Privacy protection through local query aggregation
This repository is a curated collection of academic research and industrial materials focused on the application of Large Language Models (LLMs) within Artificial Intelligence for IT Operations (AIOps).
The list covers several primary domains including:
- Incident management, such as root cause analysis, reporting, mitigation, and postmortem analysis.
- Log analysis tasks like parsing, anomaly detection, and logging statement generation.
- Infrastructure management involving benchmarks, vision, infrastructure-as-code, and training platforms.
The list covers several primary domains including:
- Incident management, such as root cause analysis, reporting, mitigation, and postmortem analysis.
- Log analysis tasks like parsing, anomaly detection, and logging statement generation.
- Infrastructure management involving benchmarks, vision, infrastructure-as-code, and training platforms.
Open Code Review is an AI-powered CLI tool designed for automated, high-precision code reviews. Originally developed as Alibaba Group's internal assistant, the project uses a hybrid architecture that combines deterministic engineering with LLM agents to provide stable and accurate feedback. Unlike general-purpose agents, it employs smart file bundling and fine-grained rule matching to maintain context and prevent issues like position drift or incomplete coverage on large changesets.
Key features:
- AI-driven line-level review comments
- Hybrid architecture combining hard constraints with dynamic decision-making
- Support for various LLM endpoints including OpenAI and Anthropic
- Seamless integration with CI/CD pipelines and coding agents like Claude Code
- Customizable rule sets for specific project requirements
Key features:
- AI-driven line-level review comments
- Hybrid architecture combining hard constraints with dynamic decision-making
- Support for various LLM endpoints including OpenAI and Anthropic
- Seamless integration with CI/CD pipelines and coding agents like Claude Code
- Customizable rule sets for specific project requirements
A directory of specialized scripts and capabilities designed for AI agents within the agent-scripts repository. These skills provide automated workflows across various domains including web browsing, software development processes like code review and debugging, system maintenance, and integrations with platforms such as WhatsApp, Discord, and Sonos.
Main topics include:
Browser automation and web interaction
Developer productivity tools for GitHub and coding workflows
Platform-specific automations for messaging and smart home devices
System utility scripts for macOS and developer environments
Main topics include:
Browser automation and web interaction
Developer productivity tools for GitHub and coding workflows
Platform-specific automations for messaging and smart home devices
System utility scripts for macOS and developer environments
Needle is an experimental 26m parameter Simple Attention Network distilled from Gemini 3.1, designed to redefine tiny AI for consumer devices like phones, watches, and glasses. It specializes in single-shot function calling, outperforming larger models such as FunctionGemma-270m and Qwen-0.6B in that specific domain. The project provides fully open weights, dataset generation methods, and tools for local finetuning on consumer hardware.
* 26m parameter architecture optimized for extremely small devices.
* High performance prefill and decode speeds when running on Cactus.
* Pretrained on 200B tokens and post-trained for function calling.
* Support for local finetuning via a web UI playground or CLI.
* 26m parameter architecture optimized for extremely small devices.
* High performance prefill and decode speeds when running on Cactus.
* Pretrained on 200B tokens and post-trained for function calling.
* Support for local finetuning via a web UI playground or CLI.
A post-retrieval temporal layer designed to improve RAG systems by addressing time-blindness in vector searches. This library implements validity filtering, document kind classification, and exponential decay scoring to ensure retrieved information is fresh and accurate. It functions downstream of existing vector search systems without requiring re-indexing or new infrastructure.
gitcrawl is a local-first GitHub triage tool and a drop-in caching shim for the gh CLI. It mirrors repository issues and pull requests into a local SQLite database, enabling semantic clustering and full-text search while preventing API rate limit exhaustion. This setup allows maintainers and AI agents to perform heavy read operations against a local cache rather than live GitHub servers.
Main features:
Local SQLite storage for all issue, PR, and commit metadata.
A gh-compatible shim that handles most read-only calls locally.
Semantic clustering using OpenAI embeddings to group related reports.
An interactive terminal UI for cluster browsing.
JSON support for easy automation with AI agents.
Main features:
Local SQLite storage for all issue, PR, and commit metadata.
A gh-compatible shim that handles most read-only calls locally.
Semantic clustering using OpenAI embeddings to group related reports.
An interactive terminal UI for cluster browsing.
JSON support for easy automation with AI agents.
OpenKB is an open-source command-line system designed to transform raw documents into a structured, interlinked wiki-style knowledge base using Large Language Models. Unlike traditional RAG systems that rediscover information with every query, OpenKB compiles knowledge once into a persistent format where summaries, concept pages, and cross-references are automatically maintained and updated.
Key features and capabilities include:
- Vectorless long document retrieval powered by PageIndex tree indexing.
- Native multi-modality for understanding figures, tables, and images.
- Broad format support including PDF, Word, Markdown, PowerPoint, HTML, and Excel.
- Automated wiki compilation that creates summaries and synthesizes concepts across documents.
- Interactive chat sessions with persisted history and Obsidian compatibility via wikilinks.
- Health check tools (linting) to identify contradictions, gaps, or stale content within the knowledge base.
Key features and capabilities include:
- Vectorless long document retrieval powered by PageIndex tree indexing.
- Native multi-modality for understanding figures, tables, and images.
- Broad format support including PDF, Word, Markdown, PowerPoint, HTML, and Excel.
- Automated wiki compilation that creates summaries and synthesizes concepts across documents.
- Interactive chat sessions with persisted history and Obsidian compatibility via wikilinks.
- Health check tools (linting) to identify contradictions, gaps, or stale content within the knowledge base.
Personal website of Alex L. Zhang, a PhD student at MIT CSAIL focusing on the efficiency and utilization of language models. His research spans ML systems, language model benchmarks, and specialized model development.
Key areas of work include:
- Recursive Language Models (RLMs) and Project Popcorn
- GPU programming competitions via KernelBot and GPU MODE
- Benchmarking capabilities through VideoGameBench and KernelBench
- Development of models like Neo-1 and KernelLLM-8B
Key areas of work include:
- Recursive Language Models (RLMs) and Project Popcorn
- GPU programming competitions via KernelBot and GPU MODE
- Benchmarking capabilities through VideoGameBench and KernelBench
- Development of models like Neo-1 and KernelLLM-8B
This article explores how a team built an AI-powered emoji list generator during a Rubber Duck Thursday live stream. The tool runs in the terminal, takes a list of bullet points, and uses AI to intelligently replace them with relevant emojis before copying the result to the clipboard.
Key highlights include:
- Use of GitHub Copilot CLI for rapid development via plan and autopilot modes.
- Integration of @opentui/core for the terminal user interface.
- Leveraging the GitHub Copilot SDK to provide intelligent emoji selection.
- Implementation of a multi-model workflow using different LLMs for planning and execution.
Key highlights include:
- Use of GitHub Copilot CLI for rapid development via plan and autopilot modes.
- Integration of @opentui/core for the terminal user interface.
- Leveraging the GitHub Copilot SDK to provide intelligent emoji selection.
- Implementation of a multi-model workflow using different LLMs for planning and execution.
