dmckinno

Current genomic techniques involve humans using a lot of different software (search, ranking models, visualizations, alignment algorithms, etc.) and synthesizing the results manually into a diagnosis.

Your assumption is correct about my technique. I cloned (and expanded) this workflow into an LLM harness, so the LLM is basically orchestrating a bunch of tools that normally humans would use (and writing the conclusions and doing all the standard LLM stuff).

Thanks for sharing (I am OP). Lots of really interesting computational problems in genetics. The original speech recognition models, HMMs, diffused to genetics ahead of many other fields with which we associate machine learning. Look forward to watching this tech talk.

> Agreed. It does not automatically mean, however, that it can be significantly improved with better variant interpetation or better analysis of the same wgs data in general sense.

I wouldn't say anything is automatic or taken for granted, but it is actually relatively common for more thorough reanalysis to uncover something that the first pass missed. I hinted at this in the post, but the reason that this doesn't happen today is human bandwidth.

A core part of my thesis is that that this highly specialized human bandwidth can be scaled with AI.

It may work. It may not work. But I would feel bad if I didn't give it a try.

> Happy to see it. I wish you all the luck and will be the first one praising your solution if I see convincing results.

Appreciate that! Hopefully, they will come.

Opus-4.8/GPT-5.5 coupled with their respective harnesses will do a good job for more vanilla cases, I realized a custom harness and set of skills and MCP servers became essential as complexity increased (mostly hard SVs). I'll get an eval and more technical post out in a few weeks.

If you are open to chatting about your experience, I'd love to hear from you. I spend a lot of time learning from and supporting other rare disease families these days.

And the anti-natal thing was kind-of joking not joking. I do know lots of people with kids there now, but when my wife first got pregnant, we were alone.

This was not intended to be a technical post (obvious I hope).

I'm planning on getting one out in the next few weeks characterizing the system and how it performed on real clinical use-cases vs. alternatives and existing tools.

The TL;DR is that Gamow Labs is a harness and interface company on top of SOTA LLMs as you suggested, but my harness and interface outperforms the existing thing. While this approach would have earned me the "wrapper company" label last year, I hope the success of OpenEvidence, Harvey, Perplexity, and so on has opened minds with respect to the value here.

It was only working through clinical cases that I realized how much more I needed beyond dropping raw reads into Codex.