Search Results for "no knowledge" - Page 7
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Showing 385 open source projects for "no knowledge"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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Data Science Articles from CodeCut
Collection of useful data science topics along with articles
The Data-science repository from CodeCutTech is a curated collection of educational content focused on practical tools and workflows used in modern data science projects. Instead of providing a single software package, the repository aggregates articles, tutorials, and examples covering many topics within the data science ecosystem. The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by... -
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All-in-RAG
Big Model Application Development Practice 1
...The repository provides a structured learning path that covers both theoretical foundations and practical implementation steps for RAG systems. It explains the full development pipeline required to create knowledge-aware AI assistants, including data preparation, document indexing, vector embedding generation, and retrieval strategies. The project also explores advanced topics such as hybrid retrieval methods, query optimization, and evaluation techniques for improving system accuracy. Alongside theoretical explanations, the repository includes hands-on exercises and example projects that demonstrate how to build production-ready RAG systems. ... -
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claude-code-best-practice
Practice made claude perfect
claude-code-best-practice is a structured knowledge repository that documents advanced workflows, architectural patterns, and optimization strategies for developers using Claude Code in agentic development environments. Rather than being a traditional software library, the project functions as a living playbook that demonstrates how to compose skills, agents, memory files, and rules into maintainable AI-assisted coding systems. -
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Linux insides
A book-in-progress about the Linux kernel and its insides
...The repository contains a structured series of chapters that explain low-level topics such as booting, memory management, interrupts, system calls, and synchronization primitives. The project’s stated goal is to share knowledge about Linux kernel internals and related low-level concepts in an accessible narrative format. It is written for readers who already have some familiarity with C and assembly language and want to understand what happens under the hood of Linux. The material is continuously updated as the kernel evolves, reflecting changes in modern kernel versions. ... -
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AI-Researcher
AI-Researcher: Autonomous Scientific Innovation
...It lets users input high-level research goals or questions in natural language and then automatically plans, decomposes, and executes tasks such as literature surveying, summarization, synthesis, experiment design, and draft generation. The system integrates retrieval mechanisms to pull in external knowledge sources, contextually analyze documents and papers, and build structured representations of ideas and arguments that can later be turned into coherent reports or drafts. Rather than simply generating text from prompts, AI-Researcher orchestrates sequences of subtasks — such as extracting definitions, identifying key experiments, and tracking citations — and uses self-refinement loops to iteratively improve outputs. -
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Claude-Flow
The leading agent orchestration platform for Claude
...At its core, Claude-Flow integrates Dynamic Agent Architecture (DAA) for self-organizing agent management, neural pattern recognition accelerated by WebAssembly SIMD, and a SQLite-based memory system for context retention and knowledge persistence across tasks. It automates development workflows via pre- and post-operation hooks, providing seamless coordination, code formatting, validation, and performance optimization. -
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memsearch
A Markdown-first memory system, a standalone library for any AI agent
...Memsearch is designed to be agent-friendly, making it easy to plug into existing AI workflows and enhance reasoning capabilities. Its markdown-first approach ensures transparency and portability of stored knowledge. Overall, it provides a robust foundation for building AI systems with persistent and intelligent memory. -
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TypeAgent Python
Structured RAG: ingest, index, query
TypeAgent Python is an experimental Python implementation of Microsoft’s TypeAgent architecture designed to explore how large language models can interact with structured software systems. The project focuses on implementing structured Retrieval-Augmented Generation workflows that allow agents to ingest information, index it in structured form, and answer queries using language models. Instead of relying solely on free-form prompts, the architecture emphasizes converting natural language... -
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Data Science Interviews
Data science interview questions and answers
Data Science Interviews is an open-source repository that collects common data science interview questions along with community-provided answers and explanations. The project serves as a preparation resource for students, job seekers, and professionals who want to review the technical knowledge required for data science roles. The repository organizes questions into different categories including theoretical machine learning concepts, technical programming questions, and probability or statistics problems. Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. ... -
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MiroThinker
MiroThinker is an open source deep research agent
...Rather than simply generating responses from a single prompt, the agent performs structured multi-step reasoning processes that involve searching for information, analyzing evidence, and synthesizing conclusions. The platform is optimized for research tasks such as financial forecasting, knowledge discovery, and large-scale information synthesis. MiroThinker has been evaluated on several agent benchmarks and has demonstrated strong performance on tests designed to measure deep research capabilities. -
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RecAI
Bridging LLM and Recommender System
RecAI is an open-source research platform developed by Microsoft to explore how large language models can be integrated into modern recommender systems. Traditional recommender systems rely on structured behavioral data such as user interactions and item embeddings, while large language models excel at understanding language and reasoning about user preferences. RecAI aims to bridge these two domains by creating architectures and training methods that allow LLMs to function as intelligent... -
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Llama-Chinese
Llama Chinese community, real-time aggregation
...In addition to model development, the project collects learning resources and open research contributions related to LLM technology in Chinese environments. Overall, Llama-Chinese acts as both a technical ecosystem and knowledge hub dedicated to advancing Chinese-language large model development. -
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ReMe
Memory Management Kit for Agents
...The toolkit provides APIs to offload large, ephemeral outputs to external storage and reload them on demand, which reduces memory bloat and keeps active context concise. By combining embeddings, vector search, and summarization workflows, ReMe lets developers build agent systems that can recall and apply past knowledge in future reasoning tasks. The project fits into the broader agent-oriented programming ecosystem by supplying a standardized memory layer that integrates with agent frameworks. -
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Pathway AI Pipelines
Ready-to-run cloud templates for RAG
...Developers can run the applications locally or deploy them to cloud platforms using Docker with minimal setup. Overall, llm-app functions as a practical accelerator for teams building real-time, production-ready AI knowledge systems. -
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SimpleMem
SimpleMem: Efficient Lifelong Memory for LLM Agents
...It supports customizable embedding models, efficient vector indexes, and relevance weighting, making it practical for building assistants, personal agents, or domain-specific retrieval systems that need persistent knowledge. -
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Anthropic's Original Performance
Anthropic's original performance take-home, now open for you to try
...This take-home includes starter code, tests, and tools to debug performance, aiming to measure how effectively one can apply algorithmic improvements and optimizations. Because it’s framed around beating baseline scores — and even outperforming previous automated systems — it encourages both deep knowledge of Python and creative problem-solving. -
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INS
Inspiration database for Internet practitioners with no ads
INS is described as a kind of “inspiration database for internet workers” — a repository that collects and curates interesting websites, tools, links, or resources that might inspire developers, designers, or any knowledge workers. It aims to operate without ads, focusing purely on the content and resource quality, and leverages automation (e.g. GitHub Actions) to check link validity or site load speed, ensuring that listed resources remain accessible over time. For people in tech who constantly seek new tools, articles, or creative inspiration, ins serves as a living catalogue that can be browsed, contributed to, and relied upon for discovering useful or thought-provoking material without the noise of ads or clickbait. ... -
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VectorVein
No-code AI workflow
Use the power of AI to build your personal knowledge base + automated workflow. No programming, just dragging to create a strong workflow and automate all tasks. Vector vein is affected LangChain as well as langflow The uncode AI workflow software developed by the inspiration aims to combine the powerful capabilities of large language models and allow users to realize the intelligibility and automation of various daily workflows through simple drag. -
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RecBole
A unified, comprehensive and efficient recommendation library
...We have implemented more than 100 recommender system models, covering four common recommender system categories in RecBole and eight toolkits of RecBole2.0, including General Recommendation, Sequential Recommendation, Context-aware Recommendation, and Knowledge-based Recommendation and sub-packages. -
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QASystemOnMedicalKG
A tutorial and implement of disease centered Medical knowledge graph
QASystemOnMedicalKG is a Chinese-language medical knowledge graph and question-answering demonstration built around diseases. It collects structured information from vertical medical websites and imports the resulting entities and relationships into Neo4j. The included graph contains diseases, symptoms, drugs, foods, examinations, departments, and commercial drug products. Python scripts handle data collection, dictionary-based segmentation, graph construction, question classification, parsing, and database queries. ... -
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Frontend Slides
Create beautiful slides on the web using Claude's frontend skills
Frontend Slides is a lightweight tool that enables users to create visually appealing, animation-rich web presentations without requiring knowledge of CSS or JavaScript by leveraging a guided, interactive workflow. It operates on a “show, don’t tell” philosophy, generating visual previews of styles so users can select their preferred design rather than describing it abstractly. The system produces fully self-contained HTML presentations with inline CSS and JavaScript, eliminating the need for external dependencies, build tools, or frameworks. ... -
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MineContext
MineContext is your proactive context-aware AI partner
...The system continuously collects contextual data from sources such as screenshots and user activity, then processes and organizes this information into structured knowledge that can be reused later. Unlike traditional chat-based assistants, MineContext operates in the background and delivers proactive outputs such as daily summaries, task suggestions, and contextual reminders without requiring explicit prompts. It is built around a context engineering framework that manages the full lifecycle of data, including capture, processing, storage, retrieval, and consumption. ... -
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AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
...The project serves as a curated collection of resources, methodologies, and tools covering topics across the entire artificial intelligence development lifecycle. Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real systems that incorporate machine learning, large language models, data pipelines, and AI infrastructure. The curriculum includes a progression of topics such as foundational AI engineering skills, machine learning systems design, large language model usage, retrieval-augmented generation systems, model fine-tuning, and autonomous AI agents. ... -
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Kaggle Solutions
Collection of Kaggle Solutions and Ideas
Kaggle Solutions is an open-source repository that compiles winning solutions, insights, and educational resources from hundreds of Kaggle data science competitions. The repository acts as a knowledge base for competitive machine learning by collecting solution write-ups, discussion threads, code notebooks, and tutorial resources shared by top Kaggle participants. Each competition entry typically includes information about the dataset, evaluation metrics, modeling strategies, and techniques used by high-ranking competitors. ... -
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Transfer Learning Repo
Transfer learning / domain adaptation / domain generalization
Transfer Learning Repo is an open-source repository that compiles resources, code implementations, and academic references related to transfer learning and its related research areas. The project functions as a large knowledge hub that organizes papers, tutorials, datasets, and software implementations across topics such as domain adaptation, domain generalization, multi-task learning, and few-shot learning. The repository includes surveys and theoretical explanations that help readers understand how transfer learning methods allow models trained in one domain to adapt to new tasks or datasets. ...
