Interview Analysis

Deep interview analysis using dynamic expert routing. Automatically selects top domain thinkers based on role type to distinguish genuine capability from performance, identifying Battle Scars over Methodology Recitation. Applicable to any professional position including product management, engineering, design, operations, sales, and data science.

Science Agent Recommend - Find the Right AI Tool for Research

Get personalized AI agent recommendations for your research domain. Describe your field, task, or workflow → receive curated suggestions from 131+ projects a...

Prodmodel

Build tool for data science pipelines.

Arch Tools

61 production-ready AI API tools for data science workflows: code analysis, web scraping, NLP, image generation, crypto data, and search. REST API and MCP protocol support. [GitHub](https://github.com/Deesmo/Arch-AI-Tools)

Research Automation

Automated web research for peptides, biohacking protocols, longevity science, and trending health topics. Use when you need to discover new information, trac...

Sports Science Daily

Automated sports science intelligence engine — fetches 55+ sources (PubMed, expert blogs, wearable tech), filters noise, translates to Chinese, and syncs to...

Signal Sciences

Signal Sciences integration. Manage data, records, and automate workflows. Use when the user wants to interact with Signal Sciences data.

WoS Zotero Literature Hunter

Search Web of Science for academic literature through institutional access, filter by journal, impact factor, or date, and automatically import results into...

TPOT

Tool that automatically creates and optimizes machine learning pipelines using genetic programming. Consider it your personal data science assistant, automating a tedious part of machine learning.

Software Development Resources for Data Scientists

_Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools._