Search Results for "software without code" - Page 6
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326 projects for "software without code" with 2 filters applied:
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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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vJEPA-2
PyTorch code and models for VJEPA2 self-supervised learning from video
VJEPA2 is a next-generation self-supervised learning framework for video that extends the “predict in representation space” idea from i-JEPA to the temporal domain. Instead of reconstructing pixels, it predicts the missing high-level embeddings of masked space-time regions using a context encoder and a slowly updated target encoder. This objective encourages the model to learn semantics, motion, and long-range structure without the shortcuts that pixel-level losses can invite. The... -
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Bolt.new
Prompt, run, edit, and deploy full-stack web applications
...Bolt.new is designed to significantly lower the barrier to entry for software creation, making it accessible not only to developers but also to product managers, designers, and non-technical users who want to quickly prototype or launch applications. -
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Loop Engineering
Practical patterns, starters & CLI tools for loop engineering with AI
Loop Engineering is a practical reference repository for designing loop-based workflows with AI coding agents. It focuses on replacing repeated manual prompting with systems that prompt, verify, schedule, and hand off work over time. The project is aimed at developers using tools such as Grok, Claude Code, Codex, Cursor, and other coding agents. It explains core building blocks such as scheduling, worktrees, skills, plugins, connectors, sub-agents, and persistent memory or state. The... -
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Lagent
A lightweight framework for building LLM-based agents
...Instead of using LLMs only for text generation, Lagent enables developers to transform models into agents capable of performing actions such as retrieving data, executing code, or interacting with APIs. The system includes modular components that allow developers to connect different models and tools within the same agent architecture. Its design emphasizes simplicity and flexibility so that developers can experiment with different agent workflows without needing a complex infrastructure setup. Lagent can also be deployed as a web service to support distributed or multi-agent applications. -
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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The Pope Bot
Autonomous AI agent that you can configure and build
The Pope Bot is an autonomous AI agent framework that lets users configure and run an AI-powered agent that can perform tasks continuously, day in and day out, by leveraging GitHub Actions, commit history, and secure workflows. It’s designed so that every action taken by the agent is logged as a git commit, giving users complete visibility into what the agent did, why it did it, and when, which makes actions auditable and reversible. The framework treats the repository itself as the agent’s... -
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Vision Transformer Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA
...It breaks down the model into patch embedding, positional encoding, multi-head self-attention, feed-forward blocks, and a classification head so you can understand each component in isolation. The code is intentionally compact and modular, which makes it easy to tinker with hyperparameters, depth, width, and attention dimensions. Because it stays close to vanilla PyTorch, you can integrate custom datasets and training loops without framework lock-in. It’s widely used as an educational reference for people learning transformers in vision and as a lightweight baseline for research prototypes. ... -
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Preswald
Python tool for browser-based interactive data apps in one file
Preswald is an open source Python-based framework and static-site generator designed for building interactive data applications that run entirely in the browser. It packages application logic, data processing, and user interface components into a single self-contained output, enabling easy sharing and deployment without requiring local dependencies. Preswald leverages a WebAssembly runtime along with technologies like Pyodide and DuckDB to execute Python code directly in the browser environment. This approach allows developers to create dashboards, reports, notebooks, and data tools that are portable, fast, and capable of running offline. Preswald emphasizes a code-first workflow where users define applications entirely in Python while using built-in UI components such as tables, charts, and forms. ... -
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Agent Lightning
The absolute trainer to light up AI agents
...The project emphasizes minimalist integration, so you can drop this into existing systems without extensive rewrites, focusing instead on iterative performance improvement. -
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JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
JEPA (Joint-Embedding Predictive Architecture) captures the idea of predicting missing high-level representations rather than reconstructing pixels, aiming for robust, scalable self-supervised learning. A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. This makes learning focus on semantics and structure, yielding features that transfer well... -
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TimesFM
Pretrained time-series foundation model developed by Google Research
TimesFM is a pretrained time-series foundation model from Google Research built for forecasting tasks, designed to generalize across many domains without requiring extensive per-dataset retraining. It provides a decoder-only model approach to forecasting, aiming for strong performance even in zero-shot or low-data settings where traditional models often struggle. The project includes code and an inference API intended to make it practical to run forecasts programmatically, with options to use different backends such as Torch or Flax depending on your environment and performance needs. ... -
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Segment Anything
Provides code for running inference with the SegmentAnything Model
Segment Anything (SAM) is a foundation model for image segmentation that’s designed to work “out of the box” on a wide variety of images without task-specific fine-tuning. It’s a promptable segmenter: you guide it with points, boxes, or rough masks, and it predicts high-quality object masks consistent with the prompt. The architecture separates a powerful image encoder from a lightweight mask decoder, so the heavy vision work can be computed once and the interactive part stays fast. A... -
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OneFileLLM
Specify a github or local repo, github pull request
...Instead, the entire runtime environment, model interface, and application logic are bundled together into a single executable artifact. This design allows developers to share AI tools in a format that can be easily distributed and executed across different machines without complicated installation procedures. Such packaging strategies help make AI software easier to use in educational settings, demonstrations, and lightweight deployments. -
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Obsidian Skills
Agent skills for Obsidian
Obsidian-Skills is a repository of agent skills tailored for use with Obsidian and any Claude-compatible agent that follows the standard Agent Skills specification, enabling AI assistants to better understand and interact with Obsidian content. These skills are markdown-driven specifications that teach Claude Code (or similar agents) how to perform context-aware tasks within Obsidian’s unique environment, such as interpreting different file types and workflows, automating workflows tied to... -
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II Agent
A new open-source framework to build and deploy intelligent agents
...The platform allows users to interact with multiple AI models within a single environment while connecting those models to external services and knowledge sources. Through a unified interface, users can switch between models, access specialized tools, and execute tasks that require information retrieval, code execution, or file analysis. The architecture focuses on transforming traditional software tools into autonomous assistants capable of completing tasks independently based on user instructions. II-Agent supports integration with modern AI services and can coordinate interactions between different models and capabilities within the same workflow. -
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Microsoft Learn MCP Server
Official Microsoft Learn MCP Server, powering LLMs and AI agents
...Rather than relying on training data that may be outdated or incomplete, MCP servers let agents like GitHub Copilot, Claude, or other LLM-based tools search and pull context directly from up-to-date Microsoft Learn content, including Azure, .NET, and other tech docs. By connecting to the MCP endpoint, coding agents can answer questions, retrieve code examples, and offer best practices grounded in authoritative sources without requiring API keys or manual browser searches. This capability helps eliminate hallucinations, improve accuracy, and streamline developer workflows by keeping relevant tech guidance close at hand. -
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Anthony Fu's Skills
Anthony Fu's curated collection of agent skills
Anthony Fu's Skills is an open-source collection of agent skills — modular instruction packages that teach AI coding assistants how to perform specific tasks automatically when relevant. These skills are typically simple, human-readable files that contain structured steps, rules, examples, and workflow logic, letting tools like Claude Code or Copilot CLI load and run them only when they apply to the user’s input. By offloading detailed task patterns into discrete skill modules, developers can greatly extend what coding agents can do without retraining the underlying language model itself. The project serves as a curated registry of utilities that save time, standardize best practices, and encode expertise across domains, while still being easy to customize or extend. ... -
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FISSURE
The RF and reverse engineering framework for everyone
FISSURE is an open-source radio frequency analysis and signal intelligence framework built to support software-defined radio research, wireless security experimentation, and protocol reverse engineering. The project brings together tools for capturing, inspecting, decoding, replaying, and analyzing RF signals across a wide range of wireless technologies. It is designed as a practical environment for researchers and operators who need to move from raw spectrum observation to structured investigation without stitching together too many separate utilities by hand. ... -
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FlowLens MCP
Open-source MCP server that gives your coding agent
...The MCP server then loads this captured “flow” and exposes it to the AI agent via the Model Context Protocol (MCP), letting the agent examine, search, filter, and reason about the session just as a human developer would, without needing the agent to re-run the flow or rely on minimal reproduction data (logs, screenshots). -
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Compound Engineering
Official Compound Engineering plugin for Claude Code, Codex, Cursor
...The plugin integrates with multiple AI coding environments, including Claude Code and other tools, enabling consistent workflows across platforms. It also supports automated code review and ideation processes, leveraging multiple agents to enhance quality and decision-making. By codifying patterns and learnings, it creates a feedback loop that improves productivity over time. -
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SimpleHTR
Handwritten Text Recognition (HTR) system implemented with TensorFlow
...The system uses a combination of convolutional neural networks and recurrent neural networks to extract visual features and model sequential character patterns in handwriting. It also employs connectionist temporal classification (CTC) to align predicted character sequences with input images without requiring character-level segmentation. The repository provides code for training models, performing inference on handwritten text images, and evaluating recognition accuracy. SimpleHTR is commonly used as an educational example for understanding how modern handwriting recognition systems operate. -
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ai-cookbook
Examples and tutorials to help developers build AI systems
...Developers can learn how to construct applications like intelligent assistants, automation pipelines, and AI-powered data analysis tools through step-by-step tutorials and ready-to-run scripts. The code examples are designed to emphasize practical architecture patterns that are commonly used in production environments, helping developers understand how to integrate AI services into software products. -
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DFlash
Block Diffusion for Ultra-Fast Speculative Decoding
...The project includes support for multiple draft models, example integration code, and scripts to benchmark performance, and it is structured to work with popular model serving stacks like SGLang and the Hugging Face Transformers ecosystem. -
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indie-hacker-tools-plus
Here comes a selection of technology stacks and tool repositories
...It also includes code examples and practical guidance that help developers move from an idea to a working product more efficiently. The collection prioritizes tools that are widely used, cost-effective, and validated by the developer community. By aggregating these resources in a single location, the project reduces the time required to research and select technologies for new products. -
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Midscene
Vision-based AI framework for cross-platform UI automation tasks
...Instead of relying on traditional selectors, DOM structures, or accessibility attributes, it uses a vision-first approach where screenshots are analyzed by visual-language models to identify interface elements and perform actions. It allows developers to automate interactions on web applications, desktop software, and mobile devices without needing platform-specific automation logic. Developers can describe tasks such as clicking buttons, filling forms, or extracting information, and the system interprets these commands to interact with the interface accordingly. Midscene.js includes SDKs, scripting options, and integration capabilities that allow automation workflows to be written in JavaScript, TypeScript, or YAML-based scripts. ... -
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DeepSeek-V3.2-Exp
An experimental version of DeepSeek model
...The key innovation in this version is DeepSeek Sparse Attention (DSA), a sparse attention mechanism that aims to optimize training and inference efficiency in long-context settings without degrading output quality. According to the authors, they aligned the training setup of V3.2-Exp with V3.1-Terminus so that benchmark results remain largely comparable, even though the internal attention mechanism changes. In public evaluations across a variety of reasoning, code, and question-answering benchmarks (e.g. MMLU, LiveCodeBench, AIME, Codeforces, etc.), V3.2-Exp shows performance very close to or in some cases matching that of V3.1-Terminus. ...