Alternatives
Products that do what AstroBee – AI-Generated Semantic Layers does
Hi, I’m a cofounder of AstroBee and I wanted to share our work with the community. AstroBee is an automatic semantic layer generator for your business. It brings data together from different locations, storing it either in your data warehouse or in one we host. Then, AstroBee scans your data and models it to create an integrated source of truth (we call it an ontology because it’s structured like Palantir’s ontology). Once you have your source of truth, you can either build applications on top of it, or chat with directly to answer analytics questions. If you don’t like AstroBee’s original…
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gm gm, We’re excited to show our project, Hive Network, a new frontier for decentralized AI agents that operate both on-chain and off-chain. Our mission is to make AI more powerful and transparent, and we’re inviting you to join us in this revolution. What is Hive Network AI? -- Hive Network AI is a platform where developers can create, deploy, and manage AI agents that function autonomously across blockchain and traditional networks. Our system addresses significant issues in the AI space, such as the lack of transparency, difficulty in monetizing models, and insufficient research funding.…
2024 · hivenetwork.ai
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You might know Cube as an open-source semantic layer (https://github.com/cube-js/cube). Started in 2018, now 19K+ stars, 1000+ releases. We kept hitting the same wall: everyone wants AI analytics, but AI without business context hallucinates. The fix is a semantic layer — a model that defines what "revenue" or "churn" actually means. But building one by hand takes weeks. So we built an AI agent that writes the semantic layer itself, then uses it to answer questions and build dashboards with no hallucinations. Connect your data → agent builds the model in seconds → ask…
Feb 2026 · youtube.com
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Humans compete to improve their AI agents on benchmarks. But what if agents could collaborate and compete on their own? We built Hive, a crowdsourced platform where agents can evolve solutions together. One agent begins to tackle a task, iteratively improving its code. Then other agents join. They read each other’s runs, fork the best ideas, propose new ones, and push the solution forward together. We already have agents working on benchmarks like Tau2-Bench, Terminal-Bench, and ARC-AGI-2, with more tasks coming soon. We also support the new OpenAI Parameter Golf Challenge, and you can…
Mar 2026 · hive.rllm-project.com
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Frustrated by AI assistants that can only suggest actions but never take them, I built Merlio (https://merlio.app) - an AI that generates charts, searches the web, creates images, and analyzes YouTube videos directly within conversations. Built with React, it functions as an AI hub providing access to multiple models (Claude, GPT, Gemini) through a unified interface. The custom LLM orchestration layer enables the AI to execute tools with proper parameters and process results while maintaining conversation flow. Users are visualizing data, generating designs, and summarizing…
2025 · merlio.app
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I built SpecMind, an open source developer tool for spec driven vibe coding. It keeps architecture and implementation aligned from the first commit instead of letting them drift apart. With AI assistants writing more of our code, projects move faster but architectural consistency is often lost. Each developer or AI can introduce new patterns, and after a few sprints, the structure becomes fragmented. SpecMind helps prevent that by generating and maintaining living architecture specs directly from your code. It works in three steps: 1. analyze – scans your codebase and generates…
Nov 2025 · github.com
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Founder here. I built NEO, an AI agent designed specifically for AI and ML engineering workflows, after repeatedly hitting the same wall with existing tools: they work for short, linear tasks, but fall apart once workflows become long-running, stateful, and feedback-driven. In real ML work, you don’t just generate code and move on. You explore data, train models, evaluate results, adjust assumptions, rerun experiments, compare metrics, generate artifacts, and iterate; often over hours or days. Most modern coding agents already go beyond single prompts. They can plan steps, write files, run…
Jan 2026 · marketplace.visualstudio.com
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Hello, my name is Andrei. My friends and I recently built CentralMind Getaway, an open-source tool that automatically generates AI-agent-optimized APIs from your database connection. It’s designed for those who don’t want to expose direct SQL access to their databases and prefer not to spend time building these APIs manually. What it does: - Auto-generates APIs from your database connection, infer schema & sample data using AI - Filters out PII and sensitive data for compliance (GDPR, SOC 2, etc.) - Optimized for AI-Agent with extra meta information and REST and MCP protocol support -…
2025 · github.com
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I'm building Comind, an experimental AI system that acts as a cognitive layer for ATProtocol/Bluesky. It's a self-evolving knowledge graph where specialized AI agents ("cominds") process social data through focused "spheres", each guided by core directives. The system builds up understanding by asking questions, making connections, and synthesizing information from the network. I wrote a post describing the general architecture, motivation, and future directions. There's a few small results from Comind's early run. Built with neo4j, a small Modal GPU instance, and the Python atproto…
2025 · cameron.pfiffer.org
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Hello HN! My name is Rémy and I recently published an astronomy dashboard where everything is open source: the code of the website, the framework it is based on and the library that computes astronomical data. For the last few years, I have been developing the Ruby library Astronoby (https://github.com/rhannequin/astronoby) with as much dedication and perseverance I could while I don't have any scientific academic background. My end goal was to use this library and build a website that not only provides as much data as possible, but let anyone access and understand how it…
Nov 2025
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I'm building out Kerns, as an AI environment for research. You can seed a space with a topic and multiple source documents, and complete your research completely in one space. There's interactive mindmaps for exploration, podcast mode, powerful source readers with original plus chapter level summaries that let you zoom into source on demand, a powerful chat agent that lets you control context and cite refs, and AI assisted note taking. My goal is to have one place to do research on any topic which minimizes manual context engineering, and jumping around between chat/notes/readers.…
Nov 2025 · kerns.ai
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Hi HN — I’m Abhi. We built Agint so PMs and engineers can design and edit software as a graph — architecture first — iterate with fast visual feedback, then generate deployable code from it when it’s ready. We presented underlying approach at NeurIPS (Deep Learning for Codegen) as an Agentic Graph Compiler: The graph (structure + types + semantic annotations) is the source of truth, and code is a compilation/export target. Paper: Agentic Graph Compilation for Software Engineering Agents: https://arxiv.org/abs/2511.19635 Live Demo: https://flow.agintai.com…
Jan 2026 · flow.agintai.com
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I have a favour to ask. I’ve been working for a while on Kalavai, a project to make distributed AI easy. There are brilliant tools out there to help AI hobbyists and devs on the software layer (shout out to vLLM and llamacpp amongst many others!) but it’s a jungle out there when it comes to procuring and managing the necessary hardware resources and orchestrating them. This has always led me to compromise on the size of the models I end up using (quantized versions, smaller models) to save cost or to play within the limits of my rig. Today I am happy to share the first public version of our…
2024 · github.com
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I've been running Claude agents for various automation tasks — monitoring crypto news, syncing Todoist, running health checks — and I kept hitting the same problem: there's no clean way to deploy an agent that just runs on a schedule without a human babysitting it. Every agent framework I looked at was built around chat interfaces or one-shot workflows. I wanted something closer to cron for AI agents — define a task, give it a schedule, let it run forever. So I built Ductwork. You define tasks as simple JSON files — a prompt, a schedule, optional memory and skills — and ductwork handles…
Mar 2026 · github.com
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