I'm a Visiting Scientist at UC Berkeley focusing on AI for ecology.

Always great to see fellow technologists in this space!

For the past few years I've maintained a free open source project called Ecodash (https://ecodash.ai) covering the world's native plants, with trait filters.

Code here: https://github.com/legel/ecodash

While I'm also an entrepreneur and *do* believe there is a lot of financial opportunity in this space, I also really just want to see ecosystems thrive.

Keep up the great work!

It’s so nice to know you’ve worked on this. I’m very curious what sources you used.

The most fun I had with Indigene was when I decided to add French ecoregions so my parents could use the app too. Most of the work is in this PR where I used a recently published study’s plant host species data to offer ecosystem value/services metrics since the Tallamy data doesn’t cover that: https://github.com/olivierlacan/indigene/pull/53

That stuff reignitated the kind of deep data hunt fever I haven’t had since my COVID Tracking Project days when I was finding random ArcGIS dashboards and extracting data from their JSON calls to find some reliable case data.

I’m bummed I didn’t find your project before. I kept looking for something when I moved from Florida to Washington after converting my entire yard there. Having to learn a whole new ecoregion was rough and it made me think the barrier should be drastically lower for people who aren’t as motivated.

On the financial opportunity side, I hoped for a while that there may be a way to align our best interest as a species (and our ecosystem) with what our modern societies value but the last decade has made it clear to me that neither governments nor private enterprise are capable to remain as focused as they need to be. Plus leveraging worldwide public-funded science to make a living seems a bit odd if I can instead use my engineering expertise to not require unnecessary waste of resources (servers, more LLM tokens, etc.) as the ecosystem rapidly degrades.

Thanks Olivier!

Very kind. Cool technical insights.

I agree 100% that learning ecoregions is too difficult. I believe that AI will help us to simulate ecosystems, just like physics equations simulate gravity.

The Natural Capital project from Stanford is impacting government policy globally, selling the argument that the financial value of ecosystems >> GDP.

In the private sector, I also see reasons to be optimistic. At Ecodash we found a 30% land value boost from biodiverse planting: https://ecodash.ai/biodiverse_gardens_roi.pdf

Please feel free to reach out ([email protected]), would love to chat further, explore collaboration.

You are wrong.

Text tokens are high-dimensional vectors, not 8 bits per character. Every token has a deep embedding, e.g. 1024 float values per text token.

DeepSeek-OCR proved 10x+ compression from visual embedding of text, which was a groundbreaking result. [1]

Very cool to see OP's project hacking on this principle. It's still not lossless, as noted in the github, but is a promising research direction.

[1] https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSe...

I kinda wonder if it's extracting usable context from 2D proximity between lines? Normal text input wouldn't have that kind of information (though it could, and it's arguably just a lookahead/behind of N characters on average).

A token is probably not a single char, and an image is probably decomposed into tokens as well (and god knows how many tokens an image is decomposed into) which probably map to similar float-hungry vectors. Your counterargument could use a bit more flesh.

And we're talking about images of texts, not images that represent complex imagery such as a very detailed scene or what have you.

People really need to read their cites and not just the summaries.

The paper notes two things:

1) While the compression ratio for visual text is better than it is for regular text, but the absolute space required is still higher for the images. OPs were talking about the space required, not the ratio.

2) The results of the OCR must still be fed into a text-based LLM for linguistic processing. Otherwise, all you have achieved is turning an image into a bunch of text.

Well, then we could presumably also add lossy compression to texts, without passing through images first

For anyone in AI, this reads like someone starting a new company called Google

Very very warm nostalgic fuzzy feelings here

I've been using Google Workspace for over a decade. Historically, after removing a user from your organization, it was always easy to migrate their data (e.g. Google Drive folders) to your own user. Suddenly, I recently had a nightmare experience where I needed to remove users no longer in my company, but I was unable to save their very important data. Google intentionally removed the feature in order to promote their new "Archive" user feature, where "data is safe" and you pay for it at continued unnecessary, extortionist rates.

Separately, I just had another terrible interaction with my own data through Google Labs Flow. That is the site that is serving the latest Nano Banana image and Veo video generations. My takeaways on the quality and value and issues with those "world models" are for another time. Here I'm pointing out another unique "dark pattern" that product managers seem compelled to apply: "if you want us to save your data in a database, then you have to let us view that data and train models on it". It's ridiculous, either I can have my data automatically deleted and have sessions be completely "dumb", or I can submit my soul for eternity and "allow any reviewer to analyze".

Beware, founders and developers relying on Google. Don't be surprised if you wake up with your data held hostage. Don't be surprised when you realize your intellectual property can either be deleted or stolen, but not simply saved.

Thanks Google.

One could use any number of LLMs on real-world problems.

Why are we still interviewing like its 1999?

Old habits die hard. And engineers are pretty lazy when it comes to interviews, so just throwing the same leetcode problem into coder pad in every interview makes interviews easier for the person doing the interview.

If you want people to interview better, you have to both allocate resources to it, and make it count on perf. It’s not laziness, it’s ROI.

As an interviewer, I ask the same problems because it makes it much easier to compare candidates.

How do you know if one candidate happened to see the problem on leetcode and memorized the solution versus one who struggled but figured it out slower?

It's very easy to tell, but it doesn't make much difference. The best candidates have seen the problems before and don't even try to hide it, they just propose their solution right away.

I try give positive feedback for candidates who didn't know the problem but could make good use of hints, or had the right approach. But unfortunately, it's difficult to pass a Leetcode interview if you haven't seen a similar problem to what is asked before. Most candidates I interview nowadays seem to know all questions.

That's what the company has decided so we have to go along. The positive side is that if you do your part, you have good chances of being hired, even if you disagree with the process.

Once upon a time, the "stupid hazing ritual" made sense.

Now it means company is stupid.

Because if you want to hire engineers then you have to ask engineering questions. Claude and GPT and Gemini are super helpful but they're not autonomous coders yet so you need an actual engineer to vet their outcome still.

I happen to have a background at this interface as well, as the founder of DeepEarth and Ecodash.ai. I can tell you that I would greatly value such experience in collaboration, although I am not currently hiring. While having such a specific interdisciplinary niche can feel limiting, I also see it as a potential superpower in excelling in a very important domain. I'll also add that machine learning and other modeling techniques are a great bridge between classical natural sciences and modern tech today, that I would look for in collaborators. More specifically from the earth sciences, "GeoAI" would be a key focus.

Just shared this myself. Very lovely!

Thanks for reporting these metrics and drawing the conclusion of an underlying breakthrough in search.

In his Nobel Prize winning speech, Demis Hassabis ends by discussing how he sees all of intelligence as a big tree-like search process.

https://youtube.com/watch?v=YtPaZsasmNA&t=1218

The one thing I got out of the MIT OpenCourseWare AI course by Patrick Winston was that all of AI could be framed as a problem of search. Interesting to see Demis echo that here.