momojo

Did you chew on monetization at all? I'm building something similar but for bio-imaging scientists, where I've packed everything I can into a single HTML (and even some modern segmentation models like Cellpose3 and StarDist that fit under Cloudflare's 25MB free tier limit haha.

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.

This blew my socks off. Theres something so satisfying about knowing everything I need is in a single file. I know the caveat is *1. Have a modern browser*, but this is my go to format when trying out random ideas. I even have a macro called `try` that sends me directly to a new folder called `scratch/YYYYMMDD_<try_bento>`.

Great work! I hope to see more and more apps like this in the spirit of "Toss it all into context"

My company does whole-brain scans of mice on a Zeiss Z.1. Lower resolutions are typically in the low hundreds of GB. Higher-resolutions and multichannel staining can get you in the TB range. When a typical client is doing 10's of brains it definitely adds up. But even for us (and I consider us a smaller operation) we aren't output PB's. So I'd consider the above claim still pretty impressive.

I'm no fan of Facebook or its social effects but I can't deny the wonderful downstream effect of their open source.

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/

PSA: Apple menu > System Settings > Accessibility > Motion

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?"

I got chills reading this. The last time I felt this way was reading "Project Hail Mary" and that was for a fictional cell!

It's crazy to me that we are still discovering these microscopic yet crucial parts of nature around us.

Seems like one of those situations where the outcomes should be measured, not the implementation. If you've created a product that is leading to hundreds of hours of wasted time (and the users themselves are sick of the amount of time they spend) maybe its time to investigate?

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.

I've had so much fun writing small apps in pure JS and HTML witj Gemini (no harness or agent, just the free web chat) because it's forced me to keep my index.html below 1000 lines. I love the forced constraints. It's liberating. My day job is wrangling production-level codebases of a monolith service, so my tiny web apps let me live out the fantasy of cutting features instead of adding them.

How long has this been your daily driver? How has this setup worked for you compared to enterprise models? Which models?

Surfacing for the thread:

Armin Ronacher's "Absurd Workflows: Durable Execution With Just Postgres" https://lucumr.pocoo.org/2025/11/3/absurd-workflows/

Poetry. I'm not Buddhist but I find the 'metaphysics' of their prayer wheels fascinating.

Add microgpt to that list

And you get sandboxxing for free! My company got tailscale recently, and its just the final cherry on top: `tailscale serve` my `/tools`, and I don't even have to worry about auth!

Hash tables are one of those miraculous data structures that, once grokked, take you up a step-function in understanding modern computer science. I'm not talking about the 2018 leetcode meme of "Just solve everything in a hash table". It's more about Array chapter in CS1 where you're told "...and therefore, can retrieval ever go below O(N), or O(logN) if sorted?" and you sit there scratching your head, assuming thats reality and then BAM, the professor reveals hash tables and O(C) retrieval and drops the mic.

I might have to look into mojo.

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)

Sorry, I'm reposting a comment I made yesterday that seems fitting:

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".

When exploring a new idea or tool, my go to prompt is

``` 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!"

I work in bioimaging. What kind of bioinformatics are you doing that requires mojo level power?

I need to hear more stories, I'm begging

1. I admit my job is not a good sample of 'job most mortals work' 2. I still think 'most mortals' software devs aren't Antirez, and work in industries or fields that still require a human to bridge the ambiguity gap.

Models are starting to get good at ambiguity

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.

This reminds me of Antirez's "Don't fall into the anti-AI hype" [0]

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

Check out Airflow and Dagster.

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.

Is this a demo? A hobby? How long have you been at this?

Thanks for the story! Was this company similar to Plasmidsaurus in terms of industrial-scale sequencing?

I disagreed, then re-read your post, then re-read the OP, and now I've come full circle to apologize; I think you make a fair point.

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).

I know I'm bandwagoning but just to make sure the signal beats the noise:

- 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".

How did you eventually get funding after those initial rejections? What changed?

I was wondering the same thing. Maybe with the way gemma4 and intelligence density is going, they don't predict the need for NPU's?

I bet it's more about what he didn't do. Like how a stable marriage seems boring but is the accumulation of many many right (by necessarily genius) decisions.