AI/ML●●Solid
ML Patron – Run reproducible ML experiments with integrated funding
Funding marketplace meets reproducible ML execution with dry-run validation before GPU budget burns.
Bold BetBig BrainShip It
nblintao
212mo ago
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GitHub Repository
The Data Analyst Augmentation Framework (DAAF) is a free, open-source toolkit that turns Claude Code into a rigorous quantitative research engine with you at the helm: every step auditable, every output verifiable, every decision yours to make. Built by researchers, for researchers.
214 starsPython
DAAF – Reproducible AI-assisted data analysis for researchers
by brhkim·Feb 27, 2026·2 points·1 comment
AI Analysis
●●SolidSolve My ProblemBig Brain
Researcher-controlled AI analysis pipeline with full auditability; rivals existing workflows but for academia.
Strengths
- •Strict human-in-the-loop architecture explicitly rejects AI autonomy and prioritizes reproducibility guardrails.
- •Pre-built connectors to 40+ Urban Institute education datasets reduce cold-start friction for education researchers.
- •Comprehensive supporting materials (tutorials, video walkthrough, blog deep-dives) show genuine community intent.
Weaknesses
- •Narrow domain focus: primarily education data; extensibility claimed but unproven in practice.
- •Requires high-quota Anthropic account and 5-10 min active engagement per analysis—adoption depends on Claude Code reliability and cost.
Category
Target Audience
Academic researchers, data scientists, quantitative social science professionals
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Jupyter AI · Noteable · DataGPT
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