Vegavid
AI, AI Agent and Blockchain development services
What it does
Vegavid Technology is an AI-first engineering firm dedicated to building the next generation of autonomous business systems. While our roots are in deep tech, our mission is centered on Artificial Intelligence and Agentic Workflows. We help enterprises transition from manual processes to intelligent automation through custom LLM integration, RAG architectures, and AI Agent development.
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- AAAn AI agent that learns your product and guides your users2025 · frigade.ai · ▲69
Hey HN! My name is Christian, and I’m the co-founder of https://frigade.ai. We’ve built an AI agent that automatically learns how to use any web-based product, and in turn guides users directly in the UI, automatically generates documentation, and even takes actions on a user’s behalf. Think of it as Clippy from the old MS Office. But on steroids. And actually helpful. You can see the agent and tool-calling SDK in action here: https://www.youtube.com/watch?v=UPe0t3A1Vpg How is this different from other AI customer support products? Most AI "copilots" are really just…
- ABAutohive – Build AI agents the easy way for everyday teams2025 · autohive.com · ▲29
- AAAI Agents for engineering use cases like debugging, LLD,testing etc.2024 · github.com · ▲26
Hi I am Aditi and I co-founded Potpie AI with my college mate Dhiren. We are building an open-source infrastructure to create custom agents for engineering use-cases like debugging, system design, integration testing, PR review etc. The agents are powered by a knowledge graph built on your code base to provide better context and memory, leading to better planning and execution. Currently we offer 6 ready-to-use agents but you can also build your custom agents. You can tune agent parameters like purpose, goals, background etc. and they are also empowered by pre-built tooling like code…
Artifex - Graph Based GPU Harness for AI Agents23d ago · gatewai.studio · ▲7Artifex is a machine-first, headless CLI runtime built for autonomous coding agents to author, validate, and render media node graphs locally. The agent talks to Artifex through a structured CLI interface. Workflows are DAGs, and each node is a plugin that can implement its own execution logic.. Each node has capability to inject logic into graph processing, WebGPU rendering, audio processing and their own SKILL.md file. Nodes can also inject their react components (not available with CLI) - which will be available with the desktop app. Execution is topological and supports checkpoint…
More ai this month
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com