#python #large_language_models #llms #long_video_understanding #multi_modal_llms #rag #retrieval_augmented_generation

Vimo is a desktop app that lets me chat with any video, from short clips to hundreds of hours, in simple natural language. I can drag and drop videos, ask questions, find exact moments, compare multiple videos, and export useful insights, all on macOS, Windows, or Linux. Powering this is the VideoRAG algorithm, which deeply understands visual, audio, and contextual information, giving accurate answers even for very long videos. This helps me save time, understand complex content faster, and turn large video libraries into searchable, usable knowledge.

https://github.com/HKUDS/VideoRAG

#javascript #agent #agentic #agentic_ai #ai #ai_agents #automation #cursor #design #figma #generative_ai #llm #llms #mcp #model_context_protocol

Cursor Talk to Figma MCP lets Cursor AI read and edit your Figma designs directly, using tools like `get_selection` for info, `set_text_content` for bulk text changes, `create_rectangle` for shapes, and `set_instance_overrides` for components. Setup is quick: install Bun, run `bun setup` and `bun socket`, add the Figma plugin. This saves you hours by skipping context switches, automating repetitive tasks like text replacement or override propagation, speeding up design-to-code workflows, and keeping everything in sync for faster, precise builds.

https://github.com/grab/cursor-talk-to-figma-mcp

#rust #ai_gateway #ai_gateway_support #envoy #envoyproxy #gateway #generative_ai #llm_gateway #llm_inference #llm_proxy #llm_routing #llmops #llms #openai #prompt #proxy #proxy_server #routing

Plano is an AI-native proxy server that handles key tasks for agentic apps like routing between agents, smart LLM model selection, safety guardrails, and automatic traces for observability. Define agents in simple YAML, write basic HTTP code in any language, and start Plano to run multi-agent systems without custom plumbing or framework lock-in. You benefit by building and shipping reliable agents to production much faster, focusing on core logic while gaining safety, low latency, and easy scaling.

https://github.com/katanemo/plano

#python #agent_memory #financial_forecasting #future_prediction #knowledge_graph #llms #multi_agent_simulation #public_opinion_analysis #python3 #social_prediction #swarm_intelligence

MiroFish is a simple AI tool that predicts anything by creating a digital world from your data like news, policies, or stories. Upload seed info and describe what you want to predict; it builds thousands of smart agents with personalities and memories to interact, simulate futures, and give you a detailed report plus chat access. You benefit by testing decisions risk-free—like policy impacts or story endings—making smart choices or fun ideas win through safe, accurate previews.

https://github.com/666ghj/MiroFish

#python #agent #agents #ai #anthropic #claudecode #llm #llms #openai

Open SWE is a free, open-source framework to build internal coding agents like those at Stripe, Ramp, and Coinbase. Trigger it via Slack, Linear, or GitHub (@openswe) to research codebases, plan tasks, code, test, review, and auto-open PRs in secure cloud sandboxes—running parallel jobs without your machine's resources. Customize models, tools, and workflows easily. You benefit by automating routine coding, slashing review cycles and production time by 30-50%, freeing you to focus on high-value work while ensuring safe, high-quality changes.

https://github.com/langchain-ai/open-swe

#typescript #ai #ai_agents #llms #on_device_ai

Thunderbolt is an open-source AI client you control fully—you pick models like Ollama or llama.cpp, own your data, and avoid vendor lock-in. It runs on web, iOS, Android, Mac, Linux, and Windows, with enterprise features for on-prem deployment. Still early (needs your model providers and optional backend), but self-host via Docker to test. This benefits you by giving private, flexible AI access anywhere without big-tech dependency, cutting costs and risks.

https://github.com/thunderbird/thunderbolt

#jupyter_notebook #agent #agent_framework #agents #ai_agents #deployment #genai #generative_ai #langgraph #llm #llms #mlops #production #python #tutorials

Agents Towards Production is a free open-source guide for building AI agents that work in real products. It gives runnable tutorials on memory, tools, search, deployment, security, monitoring, testing, and user interfaces. You can use it to learn faster, build with less guesswork, and move from a simple prototype to a more reliable, scalable agent system.

https://github.com/NirDiamant/agents-towards-production

#html #awesome #awesome_list #generative_ai #interview_questions #large_language_models #llms #notebook_jupyter #vision_and_language

This guide is a free, one-stop place for generative AI learning. It gives you papers, courses, roadmaps, interview prep, code notebooks, and tools, so you can quickly learn the basics, build apps, and prepare for jobs. It helps you save time by putting many useful resources in one place and showing what to study next.

https://github.com/aishwaryanr/awesome-generative-ai-guide

#python #cfg #generative_ai #json #llms #prompt_engineering #regex #structured_generation #symbolic_ai

Outlines is a Python library that guarantees large language models produce structured outputs (like JSON, XML, or custom schemas) directly during generation, not after. By simply specifying your desired output type using Python syntax (e.g., `int` or a Pydantic model), it mathematically ensures the result matches your structure exactly. This benefits you by eliminating broken JSON, parsing errors, and fragile regex code, making your AI applications reliable, predictable, and ready for production use without post-generation fixes.

https://github.com/dottxt-ai/outlines

#kotlin #agent #agentic_ai #agents #ai #ai_agents #aiagentframework #genai #generative_ai #java #kotlin #llms #multi_agents #multi_agents_orchestration #multi_agents_system #spring

Embabel is a Java and Kotlin framework for building AI agents on the JVM with Spring, using typed goals, actions, and conditions instead of hard-coded prompt chains. It plans and replans at runtime, which helps you build more flexible, testable, and reusable agent flows while still fitting into existing enterprise code. The benefit to you is faster AI app development with stronger type safety, easier testing, and simpler use of LLMs, tools, and domain models in one system.

https://github.com/embabel/embabel-agent