Neuralk AI | Field Engineer | Full-time | Hybrid (Paris/London)
We are a seed stage startup building Tabular Foundation Models (TFM). This is an emerging space with huge potential.
LLM's like Claude take text as input and produce text.
Model's like nano banana take text as input and produce images.
Tabular Foundation Models take rows/columns as input, and produce predictions.
The world is sitting on huge amounts of structured data that isn't being used to its potential. Making predictions is hard. Today, most folks are building, training, maintaining their own models. A bit like you'd need to have done back in 2019 with LLM's.
We want to change that. We're looking for an early career Field Engineer to help build out demos, docs, resources, integrations as well as driving product adoption directly with customers. Someone who is passionate about building, has shipped real software (or predictions), has experience with Python and data science fundamentals.
If you're looking for something a bit different than just "hands on keyboard" engineering, and enjoy business as much as tech, this could be for you.
I run AssetRoom, which sends AI summaries of SEC filings. Found myself wanting a quicker way to discover interesting companies and hear what others think about them.
So I added a weekly poll. I pick a stock, show some key facts, and let people vote and discuss whether it’s overvalued or a hidden gem. Takes a few minutes, usually learn something.
By default free plans can run 5x concurrently on self-hosted, 20x minimum for all paying customers, and yes there's a "talk to sales" for >20x on the pricing page
Can you send a link to the page you’re looking at? Thanks!
Pricing should mostly just be users + build minutes (for cloud runners) + storage. There is a few other optional, feature specific costs. Self hosted runners are free, but you need to self host caches/workspaces - our native ones have an egress bill to self hosted runners.
I'm working on AssetRoom, a free service to email you noise-free, easy to digest summaries of SEC filings from companies you're interested in.
I often read about interesting public companies (from an investment perspective or otherwise), but fail to then keep up with them over time (sometimes reading many months/years later how successful they were - or not!). I built this to make an easy way for me to follow updates from said companies.
That is absolutely absurd that you have built something many have tried and failed to do with millions of dollars of venture capital behind them, all on your own.
Genuine kudos to you, you should be an inspiration to any indie hacker.
The S3 SDK allows you to generate a URL that can only be used to upload to a specific path (+ there are other constraints you can enforce, file size / mime type etc)
Correct, I have no need for the original filename in most cases. If I did want this info, for example if I was building a file browser type thing (ala dropbox), then sure I'd keep that in the db.
Personally I'm uploading directly from the browser to S3 using presigned URL's. All files get uploaded to a /tmp directory in my bucket. This bucket is configured so that all files in /tmp are deleted after 1 day (to remove any unsaved uploads). When a form is submitted, I pass the key to the temporary file in the form (via e.g <input type="hidden" name="s3_key">) and create the associated database record. I then move the file from its temporary location to its permanent one upon saving said record.
Feel free to email me to continue this discussion - email address is on my profile.
Perhaps I'm missing something about your use case, but I only create buckets per application, or sometimes file category (videos, profile images, whatever).
We are a seed stage startup building Tabular Foundation Models (TFM). This is an emerging space with huge potential.
LLM's like Claude take text as input and produce text. Model's like nano banana take text as input and produce images. Tabular Foundation Models take rows/columns as input, and produce predictions.
The world is sitting on huge amounts of structured data that isn't being used to its potential. Making predictions is hard. Today, most folks are building, training, maintaining their own models. A bit like you'd need to have done back in 2019 with LLM's.
We want to change that. We're looking for an early career Field Engineer to help build out demos, docs, resources, integrations as well as driving product adoption directly with customers. Someone who is passionate about building, has shipped real software (or predictions), has experience with Python and data science fundamentals.
If you're looking for something a bit different than just "hands on keyboard" engineering, and enjoy business as much as tech, this could be for you.
Feel free to reach out at [email protected]