I’m still kind of shocked that Dean Ball can tweet such incendiary stuff about OpenAI policy. Like presumably OpenAI would prefer it if their staff don’t pick fights with Trump administration officials.
So like oftentimes the picture will be of a church and there’s geographic coordinates for where the photo was taken. My qwen will use the geocoder to search for “church” at the coordinates of the photo and then read the Wikipedia articles about all the churches nearby and see if any of them could plausibly be the church. So far I have parsed about 2 million photos and have tagged about 800k places. My goal is to do the whole 40 million places to create a world map of open places with photos. The tool I’m using is topoloop for the geocoding
Though the power of eco tourism money doesn’t fix everything. In Tanzania the government is in the process of removing tens of thousands of Maasai tribes people from their ancestral lands to make space for more luxury safari lodges around ngorongoro. And it’s not a thoughtful move it’s a violent expulsion at gunpoint
Very few people travel to visit the Amazon especially in Peru, Bolivia, and Ecuador. In the absence of tourist money these amazing ecosystems are being turned into agriculture and logged. In the protected areas supported by eco tourism the sound of the insects is deafening. But a few miles away in the banana plantations the area is completely silent. Even on “organic” banana plantations every insect is killed.
I’ve found it helps a lot with reconciliation tasks where tools like openrefine can’t handle it. Like I wanted to tag blog posts with links to Wikipedia articles that are relevant. But the thing is whats relevant changes a lot based on context in the blog post. So like a naive reconciler will tag the article for “sky” in the blogpost title “the sky above the Notre dame shone the morning we visited” when the element that should be tagged is the cathedral.
The coolest project I’ve got this running on is improving the depicts metadata for photos on Wikipedia. A lot of times they won’t have the landmarks tagged correctly in a photo. So I will load in all the metadata that exists from each photo and the pixels of those photos and give a small qwen agent access to Wikipedia search as well as a geocoder. It does a great job of figuring out what is depicted and tagging it with the correct depicts field. Im still early on but I have been able to double the number of places that have a photo attached to them on wikimedia
One challenge/opportunity I've had is harnessing really wide running cheap agents. Any thoughts on how to move really cheap agents beyond basic summarization so we can go broader than the pricing of frontier llms allows?
The more important question than subsidy is what is the tokenomics of running the model. If it's inefficient to run on an nvl72 cluster (or whatever the heck has enough vram to run a 3T parameter model), and k3 isn't very token efficient, then it might not be that compelling of an open weights model.
It would be really interesting to redo the public benchmarks for kimi k3 but token normalize the costs. Ok so maybe k3 beats fable on terminal bench, but how many tokens did it use?
I'm a creative person so my brain requires that I make something every day. Sometimes I make stuff that isn't very good. I've been told a lot by former bosses and random people that my desire to build stuff is frivolous. I could punch myself in the face with this reality, but then I would stop building.
The problem is nobody can build a real business on models they don't control. Cursor focused on making a great AI coding experience but they didn't control Claude and got destroyed once Anthropic started explicitly training Claude to be better at using Claude code.
And then there's the cost front, a good model is soon going to be served at 90+% margin, meaning the model makers have a ton of flexibility in how they choose to subsidize their first party products. Cursor had to eat the full API price of Claude while Anthropic could sell Claude Code at a massive subsidy.
If you're an AI product then the model product synthesis is your product, and you don't get any of that from a proprietary closed model.
Currently working on wanderfugl.com