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QBIT42 is a GDPR-compliant, no-code Generative AI platform built in Germany, designed for organizations that want to deploy AI securely without building infrastructure or hiring AI specialists. The platform enables companies to create, share, and operate AI agents (Qbots), knowledge bases, workflows, and applications โ fully hosted on European servers, with complete data sovereignty.
At its core, QBIT42 solves the three problems that block most organizations from adopting AI responsibly: uncontrolled usage, unreliable outputs, and lack of internal expertise. Through a central governance dashboard, organizations gain full visibility and control over who uses AI, how, and with what data โ eliminating Shadow IT and unauthorized tool usage overnight. Every AI response is grounded in the company's own verified internal data through a RAG-based knowledge pipeline, which means no hallucinations and full source attribution per answer. And because the platform requires zero coding, any department can build and deploy their own AI agents, apps, and workflows in minutes โ without developers, consultants, or expensive implementation projects.
QBIT42 connects to the entire system landscape an organization already uses: SharePoint, Google Workspace, ERP systems, email, Jira, GitHub, and more are unified into a single structured knowledge base. Inputs and outputs span all common formats including PDF, PPT, XLS, JPEG, WAV, and MPEG. Organizations access multiple leading LLMs โ including models via Amazon Bedrock โ through one flat per-user subscription, with no vendor lock-in and full freedom to switch or combine models at any time. All agents, knowledge bases, and data remain exportable.
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Based on our record, GitHub seems to be more popular. It has been mentiond 2466 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
// ==UserScript== // @name GitHub -> Obsidian Task // @namespace obsidian // @version 1.0 // @match https://github.com/*/*/issues/* // @match https://github.com/*/*/pull/* // @grant GM_setClipboard // ==/UserScript== (function () { 'use strict'; function getTitle() { return document.querySelector("bdi")?.textContent.trim(); } function copyTask() { ... - Source: dev.to / 1 day ago
Import requests From bs4 import BeautifulSoup From datetime import datetime Def fetch_github_trending(): url = "https://github.com/trending?since=daily" response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') repos = [] for article in soup.select('article.Box-row'): repo_link = article.select_one('h2 a')['href'] stars_today =... - Source: dev.to / 2 days ago
Git clone https://github.com//.git /opt/app Cd /opt/app Docker build -t app . Docker run -d --name app --restart unless-stopped -p 8080:8080 app. - Source: dev.to / 6 days ago
The core of the ecosystem is the official open-source server hosted on GitHub. It is written in TypeScript and implements the full MCP specification. - Source: dev.to / 11 days ago
This is why the gate needs a trace it can trust, and why AgentLens is the other half of this workflow. agent-eval scores and gates the output; AgentLens captures the trace of how the agent got there โ every model call and tool step, the resolved inputs (not the templated ones), the raw outputs. That trace is exactly the unforgeable, agent-didn't-author substrate that Tier 1+2 need to score against. Without it,... - Source: dev.to / 11 days ago
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