At this point I think Apple just needs to simply not do anything stupid and these small model makers are going to hand them models
457 karma · joined January 4, 2022
Maybe I'm thinking too far ahead but I'm hoping maybe I can cover the (future?) compute costs by charging for the heavy duty ML that would have to be run non-locally but IDK. This isn't critical yet but would love to hear your musings.
Great work! I hope to see more and more apps like this in the spirit of "Toss it all into context"
Popular microscopy models like Cellpose[0] have leaned heavily on the cornucopia of open and SOTA power. I have no doubt thousands of biologists have benefitted from the capabilities these models bring. I think it was unthinkable just 5 years ago that a single biologist with just a laptop could do mass-segmentation at this kind of fidelity.
Then there's Napari and it's plugin ecosystem[1] that wouldn't exist without the Chan Zuckerberg Initiative. Again, I'm not trying to glaze them but as someone in the biotech/microscopy space I can't understate how often I use and benefit from their open source.
[0] https://cellpose.readthedocs.io/en/latest/models.html [1] https://chanzuckerberg.com/rfa/napari-plugin-grants/
These cool demos come up now and again and it always takes me a minute to remember I have reduced motion. But for that minute I was disappointedly clicking around thinking "Whats the big deal?"
It's crazy to me that we are still discovering these microscopic yet crucial parts of nature around us.
I guess I'm agreeing with your point that its hard to pre-emptively define what "bad UI" is when it's often so close to "efficient UX", so just go downstream.
Armin Ronacher's "Absurd Workflows: Durable Execution With Just Postgres" https://lucumr.pocoo.org/2025/11/3/absurd-workflows/
What's "alignment" in your context. In bioimaging it usually refers to aligning something to a reference atlas (like the Allen Reference Mouse Brain Atlas) or aligning two microscope channels (like the red channel and green channel)
> This reminds me of Antirez's "Don't fall into the anti-AI hype". In a sentence: These foundation models are really good at optimizing these extremely high level, extremely well defined problem spaces (ie multiply matrices faster). In Antirez's case, it's "make Redis faster".
``` In a single index.html, no dependencies, sparse styling, create an app that <idea> ```
Even before AI, it's how I built small tools, and there's something lovely about being able to email my friends the tool, and tell them "If you want to make a change, toss it to your LLM!"
That's fair, and something I've observed too. I wish I had written "the rest of us shouldn't freak out and quit software today".
But here's another data point: At the biotech I work for, writing good code has never been the bottleneck. I actually told my boss that a paid Claude vs free subscription wouldn't be that much value because even if it took every piece of code or algorithm we've ever written and 10x-ed the hell out of them, we'd still be bottlenecked by the biology and physics which dictates that we wait 24 days for our histology assay pipeline.
I have a hunch most fields outside of software are this way. And I'm personally not planning to quit anytime soon.
In a sentence: These foundation models are really good at optimizing these extremely high level, extremely well defined problem spaces (ie multiply matrices faster). In Antirez's case, it's "make Redis faster".
There have been two reactions: "Oh it would never work for me" and "I have seen months of my life accomplished in an hour", and I think they're both right. I think we should be excited for Antirez, (who has since been popping off [1]), and I think the rest of us should rest easy knowing that LLM's can't (and maybe were never meant to) tackle the tacit-knowledge-filled, human-system-centric, ambiguously-defined-problem-space jobs most mortals work.
[0] https://antirez.com/news/158 [1] https://antirez.com/news/164
I've used Dagster but I can't compare to airflow. But in terms of DX, I've found Dagster pretty easy to use. Instead of writing their own DSL, they have a python library that lets yo tag your pre-made methods as @ops and and string them together into a DAG.
I work at a biotech. We spent who knows how much time and money trying to develop a 'lab technician bot' to automate one of our critical assays. Turns out, a 6-figure machine still isn't as economical as my coworker Y, one of the veteran lab-technicians. Sure she takes the occasional sick day but even at our volume (and we do industrial-level, multiple clients batched into a single assay pass) it won't be economical to replace her for a very long time (if we even reach that scale).
- This looks like vibe-code + fake github stars - There's a difference between "Summarize this verbose report from my the PM so I can get the gist" and "ELI5 this complex subject so I feel like I understand it".