Show HN: Self-hosted DCF workspace using Damodaran datasets, LLM narratives
TL;DR
A developer built a self-hosted stock valuation tool after commercial 'AI analysis' products consistently hid their math or hallucinated inputs.
Key Points
- The tool computes intrinsic value via DCF using Damodaran industry datasets — betas, equity risk premiums, country risk premiums.
- Every assumption is exposed: cost of capital, reinvestment rate, terminal value. LLMs generate bull/bear narratives with cited sources but cannot silently alter the numbers.
- Runs fully local with a single Docker command; code is open source on GitHub.
- Known rough edge: handling high-growth names where DCF structurally breaks down.
Nauti's Take
This is the right way to use AI in financial analysis: a hard separation between what is calculated and what the model merely narrates. Making it structurally impossible for the LLM to silently change the numbers is not a minor detail — it is the most important architectural decision in the whole project.
The DCF breakdown on high-growth names is a real limitation, but that is a fundamental methodology problem that professional analysts routinely paper over too. Using Damodaran datasets as the foundation gives this tool more credibility than most paid products on the market.
Sources