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35,738 results · Page 220 of 993

DailyStack

AI office

DailyStack is a daily morning briefing app that connects to work tools such as Gmail, Outlook, Linear, Calendar, Jira, Asana, GitHub, Todoist, Notion, and others. Every morning it automatically reads information from each tool and generates an AI briefing that can be read or listened to, presenting only what truly matters, such as tasks that need handling, meetings, and emails awaiting replies, helping users quickly grasp the day's priorities before standup or their first meeting. The official description says connections require no engineering support, users can set their own sync time and timezone, and new context is pulled once per day rather than through continuous polling. The free version supports 1 integration (Google or Outlook), scheduled sending of the daily AI briefing, standard audio briefings, and up to 30 items per day, free forever with no credit card required. Paid plans offer a 14-day free trial; Pro includes all integrations, multiple Google and Outlook accounts, connection of up to 5 integrations, 100 items per integration per day, custom timezone scheduling, HD audio briefings, and more; the Enterprise version additionally includes team dashboards and collaboration, enterprise security review, and a dedicated customer success manager. The official description emphasizes encrypted transmission, read-only access to connected tools, and that user data is not used to train models.

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Daloopa

AI finance

Daloopa is a financial data platform serving the investment research sector, providing fundamental data with source links for analysts, hedge funds, mutual funds, and large banks. The platform covers more than 6,000 stocks globally, offers 14 years of historical data, has 4 to 10 times the number of data points per company compared with other vendors, and averages over 99% accuracy. Daloopa can help analysts save about 70% of modeling time during initial coverage and save an average of 2 hours per stock when updating models during earnings season. Its core feature is that every number is hyperlinked to its original source, and the output is auditable. The product suite supports integration with Excel, automated data updates, programmatic access via API, and auditable financial data infrastructure for LLMs and AI agents.

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data-to-paper

AI agents

data-to-paper is an open-source automated scientific research framework developed by Technion-Kishony-lab, designed to enable multiple AI agents to collaborate on the entire research process from raw data to human-verifiable research papers. The framework systematically guides interactive LLMs and rule-based agents along the traditional scientific path: data exploration, literature search and ideation, data analysis and interpretation, through to the step-by-step writing of a complete paper. Its core feature is the generation of "data-chain" manuscripts, in which any numerical value can be clicked to trace back to the specific line of code that generated it, enabling backward traceability and verifiability. The platform supports two modes: Autopilot fully autonomous operation and Copilot human-guided operation. Users can supervise, inspect, and guide the research, set goals or let the AI autonomously propose and test hypotheses, provide reviews or invoke AI reviews, roll back to previous steps, record and replay runs, and track API costs. Code guardrails cover standard statistical packages to reduce common LLM coding errors. The project includes installation documentation; after installation, simply run the data-to-paper command. It is suitable for relatively simple research goals and datasets, especially tasks that require proposing and testing statistical hypotheses. Use is at your own risk; users bear full responsibility for the rigor, quality, and ethics of generated manuscripts, require human supervision and domain expert review, should be mindful of API token costs, and must not remove the AI-generated watermark contained in manuscripts.

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