tlofreso's comments

Demo = impressed.

How's SeekStorm's prowess in mid-cap enterprise? How hairy is the ingest pipeline for sources like: decade old sharepoint sites, PDFs with partial text layers, excel, email.msg files, etc...

Having the text (for now) is still pretty important for quality output. The vision models are quite good, but not a replacement for a quality OCR step. A combination of Text + Vision is compelling too.

Are really large inputs solved at midship? If so, I'd consider that a differentiator (at least today). The demo's limited to 15pgs, and I don't see any marketing around long-context or complex inputs on the site.

I suspect this problem gets solved in the next iteration or two of commodity models. In the meantime, being smart about how the context gets divvied works ok.

I do like the UI you appear to have for citing information. Drawing the polygons around the data, and then where they appear in the PDF. Nice.

"accurate document extraction is becoming a commodity with powerful VLMs"

Agree.

The capability is fairly trivial for orgs with decent technical talent. The tech / processes all look similar:

User uploads file --> Azure prebuilt-layout returns .MD --> prompt + .MD + schema set to LLM --> JSON returned. Do whatever you want with it.

And if yes, be specific in answering. Emails are a bear! Emails can have several file types as attachemtns. Including: Other emails, Zip files, in-line images where position matters for context.

Congrats on the launch... You're in a crowded space. What differentiates Midship? What are you doing that's novel?

I came across Bert Hubert during covid because of his incredible work on this article: https://berthub.eu/articles/posts/reverse-engineering-source...

Long before Bert was writing articles on the source code of mRNA vaccines, he helped build PowerDNS. He talks about that in a three part series starting here: https://berthub.eu/articles/posts/history-of-powerdns-1999-2...

A fascinating individual...

https://fosstodon.org/@bert_hubert

https://github.com/berthubert

https://berthub.eu/

I love the effort Ford is putting into their EV offerings, and Farley seems to get it better than the other legacy auto CEOs. Ford leveraged their two most powerful brands (F-150 / Mustang) to enter the EV market. I contrast this to GM's resurgence to the space with hummer... A brand they discontinued after a tumultuous 2008.

That said, Ford aligning on 150kW chargers across their EV portfolio is a miss. I really hope 250kW is road mapped for next gen Ford EVs when they adopt NACS.

Bjorn is the GOAT for EV testing

Check out this: https://openaq.org/ Found via this: https://www.airgradient.com/blog/hidden-costs-of-air-quality...

I was excited about Colorado's law, though for the most part there's very little value. Companies either exclude candidates from that state [1], or provide cop-out salary range information of $x - $3x/yr [2]

1: https://twitter.com/digitalocean/status/1395818629657149445.

2: https://www.pwc.com/us/en/careers/coloradoifsseniormanager.h...

Nice work! I built something very similar: https://recipemincer.com

It seems you're using the same Python scraper I am: https://github.com/hhursev/recipe-scrapers

I've thought in the past, once the compute is available in a low-enough-power form, you can continually increase the resolution (frame rate) that autonomous vehicles view the world.

Essentially giving them 'The Flash' like vision. You could then have all kinds of predictive models based on various patterns.