---
title: "Show HN: Self-hosted DCF workspace using Damodaran datasets, LLM narratives"
slug: "show-hn-self-hosted-dcf-workspace-using-damodaran-datasets-llm-narratives"
date: 2026-03-11
category: community
tags: []
language: en
sources_count: 1
featured: false
publisher: AInauten News
url: https://news.ainauten.com/en/story/show-hn-self-hosted-dcf-workspace-using-damodaran-datasets-llm-narratives
---

# Show HN: Self-hosted DCF workspace using Damodaran datasets, LLM narratives

**Published**: 2026-03-11 | **Category**: community | **Sources**: 1

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## TL;DR

- A developer built a self-hosted stock valuation tool after commercial 'AI analysis' products consistently hid their math or hallucinated inputs.

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## Summary

- A developer built a self-hosted stock valuation tool after commercial 'AI analysis' products consistently hid their math or hallucinated inputs.
- 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.

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## Why it matters

A developer built a self-hosted stock valuation tool after commercial 'AI analysis' products consistently hid their math or hallucinated inputs.

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## Key Points

- A developer built a self-hosted stock valuation tool after commercial 'AI analysis' products consistently hid their math or hallucinated inputs.
- 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.

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## 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.

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## FAQ

**Q:** What is Show HN about?

**A:** - A developer built a self-hosted stock valuation tool after commercial 'AI analysis' products consistently hid their math or hallucinated inputs.

**Q:** Why does it matter?

**A:** A developer built a self-hosted stock valuation tool after commercial 'AI analysis' products consistently hid their math or hallucinated inputs.

**Q:** What are the key takeaways?

**A:** A developer built a self-hosted stock valuation tool after commercial 'AI analysis' products consistently hid their math or hallucinated inputs.. 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.

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## Related Topics

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## Sources

- [Show HN: Self-hosted DCF workspace using Damodaran datasets, LLM narratives](https://news.ycombinator.com/item?id=47332015) - Hacker News AI

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## About This Article

This article is a synthesis of 1 sources, curated and summarized by AInauten News. We aggregate AI news from trusted sources and provide bilingual (German/English) coverage.

**Publisher**: [AInauten](https://www.ainauten.com) | **Site**: [news.ainauten.com](https://news.ainauten.com)

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*Last Updated: 2026-03-11*