Readit News"78% of global precipitation occurs over the ocean" [1]
[1] https://gpm.nasa.gov/education/articles/nasa-earth-science-w...
On Gemini and other VLMs - we excluded these models because they don't do visual grounding - aka they don't provide page layouts, bounding boxes of elements on the pages. This is a table stakes feature for use-cases customers are building with Tensorlake. It wouldn't be possible to build citations without bounding boxes.
On pricing - we are probably the only company offer a pure on-demand pricing without any tiers. With Tensorlake, you can get back markdown from every page, summaries of figures, tables and charts, structured data, page classification, etc - in ONE api call. This means we are running a bunch of different models under the hood. If you add up the token count, and complexity of infrastructure to build a complex pipeline around Gemini, and other OCR/Layout detection model I bet the price you would end up with won't be any cheaper than what we provide :) Plus doing this at scale is very very complex - it requires building a lot of sophisticated infrastructure - another source of cost behind modern Document Ingestion services.
> Save where? With what redundancy? With what access policies? With what backup strategy? With what network topology? With what storage equipment and file system and HVAC system and...
Wow that's a lot to learn before using s3... I wonder how much it costs in salaries.
> With what network topology?
You don't need to care about this when using SSDs/HDDs.
> With what access policies?
Whichever is defined in your code, no restrictions unlike in S3. No need to study complicated AWS documentation and navigate through multiple consoles (this also costs you salaries by the way). No risk of leaking files due to misconfigured cloud services.
> With what backup strategy?
Automatically backed up with rest of your server data, no need to spend time on this.
…unless they have something to show, specifically?
Demos? Code? Details?
Nothing?
They actually revelead an interesting tidbit where they are with AI adoption and how they are positioning it now to new hires, e.g. "we made AI fluency a baseline expectation for engineers by adding it to job descriptions and hiring expectations".
It seems inevitable now that engineering teams will demand AI fluency when hiring, cuious though what they are doing with their existing staff who refuse to adopt AI into their workflow. Curious also if they mandated it or relied solely on incentives to adopt.
According to the article, we are still missing one: "David Bowie Related" 1/14/2016