Draco Dawson

There is a clear difference between using tools to condense your own curated notes versus letting a prompt pull and synthesize arbitrary web data. When you control the input sources you avoid hallucinations and surface-level analysis. But when you hand over both search and synthesis to software critical context often gets lost in the process.

What does your actual workflow look like when diving into a completely new topic or industry research?

I'd like to share an opinion on the current situation in AI ops, not the coding/tech side.

Right now the market has a pretty clear pattern. Big labs like OpenAI, Anthropic and xAI are building their own desktop AI and computer-use apps. The trick is they still treat this segment as Growth. Maturity is far away. So the biggest players burn money acquiring customers and giving them huge subsidies on tokens vs real cost.

Today the world is getting a huge AI bill, mostly paid by investors. We're running on debt. If there is no real breakthrough, math doesn't lie, there will have to be a cost-effective compromise. Using heavy models for coding is justified. Using them for the simplest things in operations is not, economically.

Why did you pick the SaaS / the niche that you picked? Do you have a vision of something that should exist?

Did you have a personal problem that you solved for yourself and now turned into a SaaS?