Alternatives
Products that do what QCX does
Artificial General Intelligence.
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General Compute▲315AI models that run on an inference cloud optimized for speed
May 2026 · generalcompute.com
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Hey HN, I don’t know who else has the same issue, but: Textbooks often bury good ideas in dense notation, skip the intuition, assume you already know half the material, and get outdated in fast-moving fields like AI. Over the past 7 years of my AI/ML experience, I filled notebooks with intuition-first, real-world context, no hand-waving explanations of maths, computing and AI concepts. In 2024, a few friends used these notes to prep for interviews at DeepMind, OpenAI, Nvidia etc. They all got in and currently perform well in their roles. So I'm sharing. This is an open & unconventional…
Feb 2026 · github.com
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Hey HN community! We're excited to introduce UnionX, an AI Copilot for Work designed to revolutionize the way you perform research, write long documents, and extract insights. Our platform is perfect for PMs, students, lawyers, finance professionals, and more. UnionX is built on cutting-edge technology, leveraging OpenAI's GPT4 model and Jupyter notebook-style workflows to create a seamless, AI-powered experience. Our block-based editor allows you to easily upload documents, analyze them, and generate new insights or docs with the help of AI. If you don't have ideas about what to do with…
2023 · unionx.io
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Hey HN, We are excited to announce that we’ve open-sourced AGX, a fast and lightweight data explorer for ClickHouse. It is designed for developers and analysts who want to leverage SQL-native querying in an IDE-like environment. Here’s what makes AGX stand out: - Built on ClickHouse (accessible via embedded chdb or remote instance). - IDE-like user interface: features tabs, a Monaco editor, and a fast keyboard workflow. - Zero setup required: no infrastructure or complex dashboards to manage. - Ideal for exploring live blockchain data, while also flexible enough for general ClickHouse usage…
2025 · github.com
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2025 · github.com
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Hey HN, we built an Econ+Finance database to let AI agents do investment research. We spend a lot of tokens to organize macro releases and SEC filings into a clean format, so that your agents have more context to do actual analysis. The problem AI agents are great at data analysis. But they become ineffective if most of their context window is spent on gathering and cleaning data, instead of validating hypotheses. Data in the wild is messy and rarely standardized. Definitions and measurements change over time. This problem is compounded by a fragmented data universe. Point solutions exist…
Jul 2026 · github.com
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