Search Results for "self learning" - Page 3
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Showing 337 open source projects for "self learning"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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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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OpenSpace
OpenSpace: Make Your Agents: Smarter, Low-Cost, Self-Evolving
OpenSpace is a self-evolving agent framework designed to improve the performance, efficiency, and collaboration of AI agents through continuous learning and shared knowledge. It introduces a system where agents develop reusable “skills” based on real task execution, allowing them to improve over time without retraining underlying models. The platform emphasizes collective intelligence, enabling multiple agents to share learned behaviors and benefit from each other’s experiences. ... -
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handson-ml2
Jupyter notebooks that walk you through the fundamentals of ML
This repository contains the Jupyter notebooks and code for the second edition of a popular hands-on machine learning book that teaches both classical ML and deep learning using modern tooling. The notebooks emphasize end-to-end workflows: data preparation, model selection, tuning, and reliable evaluation. Deep learning sections use the contemporary Keras/TensorFlow 2 ecosystem, highlighting clean APIs and eager execution to make experiments easier to reason about. ... -
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Acontext
Context data platform for building observable, self-learning AI agents
...The platform observes agent tasks and user feedback in real time, offering robust observability into workflows and helping teams understand how agents perform over time. Acontext also supports agent self-learning by distilling structured skills and experiences from previously completed tasks, which can later be reused or searched to improve future performance. It includes tools to interact with session data, background agents that monitor progress, and a dashboard that visualizes success rates, artifacts, and learned skills. By combining persistent storage, observability, and learning capabilities, Acontext aims to make AI agents more scalable, reliable, and capable. -
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Bittensor
Internet-scale Neural Networks
Bittensor is a decentralized machine learning protocol that allows AI models to collaborate, learn, and earn tokens within a global network. It introduces a blockchain-based economy for neural networks, where participants are incentivized to contribute valuable knowledge and compute power. Bittensor combines peer-to-peer learning with on-chain rewards, creating a self-governing, scalable AI system that evolves without centralized control. -
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Spice.ai OSS
A self-hostable CDN for databases
Spice is a portable runtime offering developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake. Spice connects, fuses, and delivers data to applications, machine-learning models, and AI backends, functioning as an application-specific, tier-optimized Database CDN. The Spice runtime, written in Rust, is built-with industry-leading technologies such as Apache DataFusion, Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB.... -
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SentencePiece
Unsupervised text tokenizer for Neural Network-based text generation
SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece implements subword units (e.g., byte-pair-encoding (BPE) [Sennrich et al.]) and unigram language model [Kudo.]) with the extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific... -
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OpenOutreach
Linkedin Automation Tool
...According to the repository, it combines large language model classification with a Bayesian machine learning layer based on profile embeddings, which helps it shift from broad exploration to more confident qualification as it gathers more decisions. It is designed to automate personalized outreach as well, including connection requests and follow-up messaging, while keeping deployment under the user’s control through a local or self-hosted setup. -
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Ditto
The simplest self-building coding agent
...Its modular structure separates generated Flask components into cleaner directories for routes, templates, and static files. It is best suited for prototyping, learning, and exploring how natural-language app generation can work in a small local project. -
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claude-reflect
A self-learning system for Claude Code that captures corrections
claude-reflect is a self-learning enhancement system for Claude Code that captures user corrections, positive feedback, and preferences during interactive coding sessions and turns them into persistent knowledge that improves future responses. It watches what you correct Claude about — such as preferring a particular model, style, or workflow — and automatically queues those learnings, then lets you review and sync them back into configuration files like CLAUDE.md and agent definitions so Claude remembers them across sessions. ... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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PML
The easiest way to use deep metric learning in your application
This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you... -
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Python-programming-exercises
100+ Python challenging programming exercises
Python-programming-exercises is an educational repository containing more than 100 Python programming challenges. It is designed to help learners practice problem solving through short exercises rather than full applications. The exercises cover fundamentals such as strings, numbers, loops, lists, dictionaries, functions, regular expressions, file handling, classes, generators, and algorithmic thinking. Many problems include both a prompt and a suggested solution, making the repository... -
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MediaCrawler
Self-media platform crawler
MediaCrawler is an open-source tool for collecting public content from major Chinese social media platforms. It supports Xiaohongshu, Douyin, Kuaishou, Bilibili, Weibo, Tieba, Zhihu, and other services. The project uses Playwright to automate browser login and preserve authenticated sessions. It obtains required request signatures from the active browser context, avoiding complex JavaScript reverse engineering. Users can search by keyword, crawl specific posts, collect nested comments, and... -
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Maths, CS & AI Compendium
Become a cracked AI/ML Research Engineer
...The repository is useful for self-study, interview preparation, and deeper AI research engineering training. -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
...The repository includes quizzes, solutions, and instructor materials to make the content usable in classrooms or self-study. It emphasizes ethical considerations and model evaluation—accuracy is not the only metric—so students learn to validate and communicate results responsibly. By the end, participants can build end-to-end ML experiments, interpret outputs, and iterate with confidence rather than just copying code. -
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Dockerlings
Learn docker in your terminal, with bite sized exercises
Dockerlings is an interactive learning tool and game that helps users learn Docker fundamentals directly in their terminal through bite-sized exercises and challenges. Designed for both beginners and developers who want a hands-on introduction to containerization, it gamifies common Docker tasks like managing images, containers, networks, and volumes so you can practice repeatedly without needing external tutorials or sandbox environments. The tool runs as a terminal application that... -
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Anki
Anki is a smart spaced repetition flashcard program
Anki is a free, open-source spaced repetition flashcard application designed for efficient long‑term memorization. It supports a wide variety of media types (text, images, audio, LaTeX), advanced scheduling algorithms (SM‑2, FSRS), and extensibility via add‑ons. It’s widely used for education, language learning, medical training, and more. -
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EvoAgentX
Self-evolving AI agent framework for automated workflows
...This allows agents to adapt dynamically instead of relying on fixed logic. It is designed for researchers and developers who want to automate complex agent systems and improve performance through continuous learning cycles, reducing manual orchestration and enabling more efficient development. -
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Ornith-1.0
Ornith-1.0 is a self-improving open-source models for agentic coding
Ornith-1 is an open-source family of agentic coding models from DeepReinforce AI. It is designed for coding agents that need to solve software engineering tasks through iterative tool use and solution rollouts. The project presents 9B dense, 31B dense, 35B mixture-of-experts, and 397B mixture-of-experts variants. These models are post-trained on top of Gemma 4 and Qwen 3.5 foundations. Its training approach uses reinforcement learning to optimize both the solution and the scaffold that... -
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Artificial Intelligence for Beginners
12 Weeks, 24 Lessons, AI for All
...The curriculum is intentionally beginner-friendly while still exposing learners to widely used frameworks such as TensorFlow and PyTorch. It also supports many languages, making the material accessible to a global audience. Overall, the project functions as a complete self-paced learning pathway for students, educators, and developers who want a practical introduction to modern AI concepts. -
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Recommenders
Best practices on recommendation systems
The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several... -
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Learn AI Engineering
Learn AI and LLMs from scratch using free resources
...The curation recognizes modern AI realities, including data pipelines, evaluation, prompt engineering, retrieval-augmented generation, and cost/performance trade-offs. It’s equally useful for refreshers—dipping into a specific module before a project—as it is for a full, self-directed curriculum. By centralizing the best references in one place, the repo reduces the overhead of finding, filtering, and sequencing resources, letting you focus on learning and building. -
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Saeba's Blog
Where Saeba writes his blog, he plans to write four series: JavaScript
Blog is a large Chinese-language frontend engineering knowledge base maintained by mqyqingfeng. It collects in-depth articles on JavaScript internals, practical JavaScript topics, ES6, React, and broader frontend development ideas. Rather than being a software package, it functions as a structured educational repository for developers who want to understand how JavaScript features work under the hood. Many articles focus on concepts such as execution context, scope, prototypes, closures,... -
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autoresearch-macos
AI agents running research on single-GPU nanochat training
autoresearch-macos is a macOS-focused adaptation of autonomous research loop systems inspired by the autoresearch paradigm, enabling AI agents to iteratively improve machine learning models through self-directed experimentation. The system follows a structured loop in which an agent modifies a training script, executes a fixed-duration experiment, evaluates performance metrics, and decides whether to keep or revert changes. It is designed to operate efficiently within macOS environments, making it accessible for developers working outside traditional high-performance GPU clusters. ... -
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Pro Workflow
Claude Code learns from your corrections: self-correcting memory
Pro Workflow is a productivity framework for Claude Code that introduces self-improving workflows through memory, context engineering, and structured agent orchestration. The system learns from user corrections over time, storing feedback and refining its behavior across sessions to improve accuracy and efficiency. It supports advanced development setups such as parallel worktrees, enabling multiple tasks to be handled simultaneously without interference. The framework includes a collection... -
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33 JS Concepts
33 JavaScript concepts every developer should know
33-js-concepts is a curated collection of essential JavaScript concepts that every developer should understand to strengthen their knowledge of the language. The project was originally inspired by an article by Stephen Curtis and has since grown into a community-driven learning resource. It serves as a roadmap for developers to review and master core principles such as closures, promises, prototypes, event loops, and other critical topics. While not a strict curriculum, it provides a...
