112 karma · joined September 27, 2021
The way this works is that it stores workstreams and session state in a local SQLite DB, and links each ctx session to the exact local Claude Code and/or Codex raw session log it came from (also stored locally).
What do you mean by prompt caching?
One solution could be to get experts to write similar press releases so that the text itself is out of distribution or if an actual quant firm has internal models, they can just make sure that there is a cutoff date to the pre-training data.
I'm curious, when you ran a quant fund, what was your approach?
I'll give it a read to learn more. Thanks for the note!
Appreciate the comment though! I generally agree with your sentiment!
>> First, your business model isn't really clear, as what you've described so far sounds more like a research project than a go-to-market premise.
This is not really a core component of our business but more so was just something cool that I built and wanted to share!
>> Computational pathology is a crowded market, and the main players all have two things in common: access to huge numbers of labeled whole-slide images, and workflows designed to handle such images. Without the former, your project sounds like a non-starter, and given the latter, the idea you've pitched doesn't seem like an advantage. Notably, some of the existing models even have open weights (e.g. Prov-GigaPath, CTransPath).
We have partnerships with a few labs to get access to a large amount of WSIs, both H&E and IHC, but our core business really isn't building workflow tools for pathologists at the moment.
>> Second, you've talked about using this approach to make diagnoses, but it's not clear exactly how this would be pitched as a market solution. The range of possible diagnoses is almost unlimited, so a useful model would need training data for everything (not possible). My understanding is that foundation models solve this problem by focusing on one or a few diagnoses in a restricted scope, e.g. prostate cancer in prostate core biopsies.
I agree with you in that I don’t necessarily think this is really a market solution at the current state (it isn't even close to accurate enough), but I think that the beauty of this solution is the general-purpose nature of it in that it can work not only across tissue types, but also different pathology tasks like IHC scoring along with cancer sub typing. The value of foundation models is in the fact that tasks can generalize. For example, part of what made this super interesting to me was the fact that the general purpose foundation models like GPT 5 are able to even perform this super niche task! Obviously there are path-specific foundation models too that have their own ViT backbones, but it is pretty incredible that GPT 5 and Claude 4.5 can perform at this level already.
Yes to the best of my knowledge, most FDA-approved solutions are point solutions, but I am not yet convinced this is the best way to deploy solutions in the long-term. For example, there will always be rare diseases where there isn't enough of a market for there to be a specialized solution for and in those cases, general-purpose models that can generalize to some degree may be crucial.
1. Can an LLM navigate a slide effectively (i.e find all relevant regions of interest)? 2. Given a region of interest, can an LLM make the correct assessment?
I need to come up with a better test here in general but yep I'm thinking about this
I have been running into some problems with memory management here as each later frame needs to have a degree of context of the previous frames... (currently I just do something simple like pass in the previous frame and the first reference frame into context) maybe I can look into video compression and see if there is any inspiration there
I should probably first clarify here, the disease classification tasks are about subtyping the type of cancer (i.e classifying a case as invasive ductal carcinoma of the breast) rather than just binary malignant/benign classification so random guessing is much more difficult and makes this model performance more impressive.
> Would also be curious how the LLM compares to this and other approaches.
There aren't a lot of public general purpose pathology benchmarks. There are some like (https://github.com/sinai-computational-pathology/SSL_tile_be...) but focus on just binary benign/malignant classification tasks and binary biomarker detection tasks.
I am currently working on self-hosting the available open-source models.
> this seems like the sort of task where being right 90% of the time is not good enough, so even if the LLM beats other approaches, it still needs to close the gap to human performance
Yep, your intuition is right here, and actually the expectation is probably closer to mid-high 90%, especially for FDA approval (and most AI tools position as co-pilots at the moment). There is obviously a long way to go, but what I find about interesting about this approach is that it allows LLMs to generalize across (1) a variety of tissue types and (2) pathology tasks such as IHC H-score scoring.
While we hope to share similar DXs, our fundamental difference is that we are focused on a BYOC-first platform instead of a serverless Postgres platform. We realized that developers who were doing strict tenant-isolation were only doing it as a means to meet their customer's demands to close deals. Often, database isolation is not the only requirement; there are requirements on which cloud a database can be hosted on, or even asks to host the database on a private cloud. For these reasons, we thought that the BYOC angle gave us more flexibility to solve these problems as well as providing a easy-to-use interface.
We still definitely need to work on our language to best communicate this; we'll work on keeping it more concise and straightforward to best highlight what we offer.
We initially targeted startups at the moment that they are moving from a 3rd party DBaaS to databases on their own cloud. However, we just realized this was super tough to time. We experimented a bit with enterprise actually too but many of them already have huge systems in place.
We are shifting our focus to SaaS developers. We know that often, thinking about data isolation is a factor on SaaS developers' minds. We want to build a platform where they can have an incredibly simple DX while also trusting that their customer data will be isolated.
We've seen most SaaS companies use some sort of tenant_id column and this is definitely the most popular method that developers currently use.
We want to provide a few things for SaaS developers. For one, many SaaS companies will face the need to create a completely new isolated database instance and may need to deploy this instance on a specific cloud (we know that Azure is really popular for healthcare).
Further, we want to spare the dev-experience of using WHERE clauses and/or setting up RLS. We aim to provide a seamless DX that abstracts over where the tenant data actually is and provide a unified platform that developers can trust to provide native isolation. We are pretty early but want to hear whether these resonate with you!