softcane

4 ms·

1.
by softcane 6mo ago
Certainly not the best result compared to mempalace but I was thinking something similar trough my project https://github.com/softcane/contextgc
2.
by softcane 7mo ago
https://github.com/softcane/spot-vortex-agent
3.
by softcane 7mo ago
Runs fully local: https://github.com/stockvaluation-io/stockvaluation_io
4.
by softcane 7mo ago
We kept decisions at the node-pool level and focused on operational safety signals like PDB slack, critical workload concentration, restart time, evictability, zone spread, and On-Demand headroom. If you want to push on safety, failure mode
5.
Show HN: SpotVortex helps SRE teams push more Kubernetes capacity onto Spot(github.com)
1 points by softcane 7mo ago|2 comments
6.
by softcane 7mo ago
Runs fully local: https://github.com/stockvaluation-io/stockvaluation_io
7.
Show HN: Self-hosted DCF workspace using Damodaran datasets, LLM narratives
1 points by softcane 7mo ago|1 comments
8.
Open-source DCF engine based on Damodaran's datasets with LLM narratives(github.com)
2 points by softcane 7mo ago|2 comments
9.
by softcane 7mo ago
For years, I've wanted to automate Damodaran's valuation methodology industry betas, ERP, country risk premiums, the whole thing. His data is free on the NYU page, but turning it into an actual valuation takes hours per stock. Sto
10.
by softcane 7mo ago
One thing I noticed while building this: LLMs are terrible at doing valuation math but surprisingly good at critiquing assumptions. When you let them read transcripts and compare assumptions to industry data, they often act like a skeptical
11.
I built a local-first stock valuation tool based on Damodaran's methodology(github.com)
3 points by softcane 7mo ago|2 comments
12.
by softcane 7mo ago
Building a DCF model isn’t actually the hard part. The painful part is everything around it: finding industry averages, checking current risk-free rates, and digging through earnings transcripts to justify assumptions. After repeating that