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This was largely inspired by the work of Sydney Brenner, which became the basis of my brennerbot.org project.

In particular, I became very fascinated by phages, which are viruses that attack bacteria. They're the closest thing to the "fundamental particles" of biology: the minimal units of genetic code that do something useful that allows them to reproduce and spread.

They also have some incredible properties, like having a structure that somehow encodes an icosahedron.

I always wondered how the DNA of these things translated into geometry in the physical world. That mapping between the "digital" realm of ACGT, which in turn maps onto the 20 amino acids in groups of 3, and the world of 3D, analog shapes, still seems magical and mysterious to me.

I wanted to dig deeper into the subject, but not by reading a boring textbook. I wanted to get a sense for these phages in a tangible way. What are the different major types of phages? How do they compare to each other in terms of the length and structure of their genetic code? The physical structure they assume?

I decided to make a program to explore all this stuff in an interactive way.

And so I'm very pleased to present you with my open-source Phage Explorer:

phage-explorer.org

I probably went a bit overboard, because what I ended up with has taken a sickening number of tokens to generate, and resulted in ~150k lines of Typescript and Rust/Wasm.

It implements 23 analysis algorithms, over 40 visualizations, and has the complete genetic data and 3D structure of 24 different classes of phage.

It actually took a lot of engineering to make this work well in a browser; it's a surprising amount of data (this becomes obvious when you look at some of the 3D structure models).

It works fairly well on mobile, but if you want to get the full experience, I highly recommend opening it on a desktop browser in high resolution.

As far as I know, it's the most complete informational / educational software about phages available anywhere. Now, I am the first to admit that I'm NOT an expert, or even that knowledgeable, about, well, ANY of this stuff.

So if you’re a biology expert, please take a look and let me know what you think of what I've made! And if I've gotten anything wrong, please let me know in the GitHub Issues and I'll fix it:

https://github.com/Dicklesworthstone/phage_explorer

It's a 9M Conformer-CTC model trained on ~300h (AISHELL + Primewords), quantized to INT8 (11 MB), runs 100% in-browser via ONNX Runtime Web.

Grades per-syllable pronunciation + tones with Viterbi forced alignment.

Try it here: https://simedw.com/projects/ear/

https://github.com/omodaka9375/peerweb

I’m a builder and user of Obsidian, validating a concept called Concerns. Today it’s only a landing page + short survey (no product yet) to test whether this pain is real.

The core idea (2–3 bullets):

- Many of us capture tons of useful info (notes/links/docs), but it rarely becomes shipped work.

- Instead of better “organization” (tags/folders), I’m exploring an “action engine” that:

  1.detects what you’re actively targetting/working on (“active projects”)

  2.surfaces relevant saved material at the right moment

  3.proposes a concrete next action (ideally pushed into your existing task tool)

My own “second brain” became a graveyard of good intentions: the organizing tax was higher than the value I got back. I’m trying to validate whether the real bottleneck is execution, not capture.

Before writing code, I’m trying to pin down two things:

- Project context signals (repo/PRs? issues? tasks? calendar? a “project doc”?)

- How to close the loop: ingest knowledge → rank against active projects → emit a small set of next-actions into an existing todo tool → learn from outcomes (done/ignored/edited) and optionally write back the minimal state. The open question: what’s the cleanest feedback signal without creating noise or privacy risk? (explicit ratings vs completion events vs doc-based write-back)

What I’m asking from you:

1.Where does your “second brain” break down the most?

capture / organization / retrieval / execution (If you can, share a concrete recent example.)

2.What best represents “active project context” for you today?

  task project (Todoist/Things/Reminders)

  issues/boards (GitHub/Linear/Jira)

  a doc/wiki page (Notion/Docs)

  calendar

  "in my head"

Which one would you actually allow a tool to read?

3.What’s your hard “no” for an AI that suggests actions from your notes/links? (pick 1–2)

  privacy/data retention

  noisy suggestions / interruption

  hallucinations / wrong suggestions

  workflow change / migration cost

  pricing

  others

Agents get a bash-like shell and can only call tools you provide, with constraints you define. No Docker, no subprocess, no SaaS — just pip install amla-sandbox

https://twitter.com/karpathy/status/2017296988589723767

also Moltbook is the most interesting place on the internet right now - https://news.ycombinator.com/item?id=46826963

https://archive.md/bzPSR