But won't it be fun when we can cloud burst-parallel a grid/tiled sampling of multiple code implementations/architectures, and interactively explore navigate/blend-points-in the latent design space. Multiples embodying different trade-offs, styles, clarity vs performance, etc. Code as generative art. What might the software engineering equivalent of designer moodboards be?
Fwiw, one case where I've wanted rollback has been input fusion over interface devices with diverse latencies. You might have 10 ms for a keypress, 100 ms for optical tracking, and 1000 ms for speech. So given click+"the red one"(spoken), you might start running click+"the one in front"(default), and almost a second later rollback and rerun with "the red one". Or for real example, keypress event handling might branch on optical "pressed where on the keycap" and "by which finger", which won't become available for several frames.
> Get any modern undergraduate Intro Biology textbook [...] These are fantastic books
Curious how perspectives vary. I would have said there's basically nothing available, textbooks being horribly wretched.
I don't know of anything which takes a "bottom up", rough quantitative, engineering first-principles intro to cell bio, let alone to biology. No whys and hows of building close to thermal noise energy levels. No focus on pervasive multi-scale cross-cutting strategies for localization and compartmentalization. No energy budgets, not feel for reasonable numbers, no... sigh. When you see a nifty foundational insight mentioned in passing in a research talk, it's a really good bet it won't be in textbooks any year soon. One of the causal threads leading up to TFA, the Harvard bionumbers database, was born out of someone's 'it's absurdly hard to find numbers'.
Chatting with a cell bio tome publisher years ago, about what absurdly implausible resources would be needed to do something transformatively better, the snark for "but it has 100 authors!" was "nifty - and how many for the second page?". Maybe now with AI we can start nibbling away at this faster.
Very true, these books are qualitative. There's a bit of basic math around delta-G for reactions and Chi-sq tests for genetic associations, but the conventional undergraduate introductory biology course is 99% descriptive.
There are reasonable arguments for taking that approach. These courses are foundations for subsequent study, with the intended outcome that students have a broad but shallow understanding of core basic ideas. Lots of biology makes intuitive, mechanistic, and visual sense, much like introductory computer science and introductory chemistry.
Obviously applied math plays a key role in biology but it tends to address specific needs like protein structure prediction, dynamic modeling of transcription/translation and metabolism, inferring phylogeny, high-throughput 'omics analysis, network simulation of epidemic outbreaks, and so on. These are great to study, but without the broader context the understanding would be relatively fragmented, lacking the big picture.
Rereading OP's question:
> good modern starting points to someone who would want to learn more about how living beings work (from bottom up)?
I interpret that as wanting a general understanding, starting with chemistry and working upwards towards evolution and populations. That's all in the standard two-semester introductory course, hence my book recommendation.
If that's wrong and OP wants a math-centric approach, here are a few gems:
First, thank you for the comment. It prompted fruitful reflection. I note LLMs as nifty for this.
For me, success means "robust structural intuition". Perhaps frame it as understanding that's robust to adversarial noise? To fuzzing testing? If you fuzz content, changing numbers, inserting negations and lies, how extreme before there's a "wait, that doesn't make sense"?
Merely quantitative isn't sufficient. An Ideal Gas Law chapter problem, with numbers for solid Argon - mindless plug-and-chug - is not this success. But a sense of reasonable values, yes. Contrast the first-tier med student, asked for red blood cell size, who failing to recall it as a factoid, is quantitatively lost, retreating to "really really small".
Similarly, "descriptive" can be deep structure and constraints of a domain, focused on building structural intuition, or at least trying for it, or an embrace of "stamp collecting" focused on regurgitation.
I nod to "foundations for subsequent study, with the intended outcome that students have a broad but shallow understanding of core basic ideas. Lots of biology makes intuitive, mechanistic, and visual sense, much like [...] introductory chemistry. [...] without the broader context the understanding would be relatively fragmented, lacking the big picture." But then contrast it with content presenting a not-broad and quite-shallow take, that pervasively fails to engage with the domain's core structure. And then, even on its own shallow terms, still fails outcome-wise: First-tier institution students, coming to intro genetics from intro bio, lacking even a firm grasp of central dogma? Stoichiometry students not even thinking of atoms as real physical objects? So I see "wonderful books" and think "wat?!? - how about profoundly and pervasively dysfunctionally unhelpful books?".
Perhaps at root, there might be different visions of what a "big picture map" best looks like??? Maybe picture a human surface map, vs a USGS topography and geology one. Do details clarify by exposing patterns, or obscure as clutter? Does underlying structure? Do year-to-year research insights provide opportunity and motivation for frequent rewrites, or is there relative stability and slow evolution? Are labels and vocabulary treated as foundational, or as relatively unimportant? If you haven't seen part of the map, how important is being able to sketch it in plausibly? If fragmented into puzzle pieces, how important is being able to fit them together? How important is seeing why things are the way they are?
Maybe the contrast between a slim tourist guidebook, versus walking in conversation with someone who deeply understands the history and society and structure of a city? Both are accessible experiences. Conversation that's numerate will be richer than non. But while the guidebook can provide a bit of orientation, it's not even trying to leave you insightful and deeply clued in.
Thanks again. I'd not thought of the fuzzing analogy before.
> For me, success means "robust structural intuition". Perhaps frame it as understanding that's robust to adversarial noise? To fuzzing testing?
There was a flurry of papers in the early 2000s that aimed to generalize biological robustness, borrowing from ideas and math from engineering. You might find these interesting:
> An Ideal Gas Law chapter problem, with numbers for solid Argon -
Ha!
> First-tier institution students, coming to intro genetics from intro bio, lacking even a firm grasp of central dogma?
Yeah, unfortunately this is a real problem: Foundational biology courses (intro, genetics, cell bio) overwhelm students with a firehose of facts that must be learned or you flunk out. Later, in upper-level undergrad and grad school, those facts start connecting, and biology becomes lots more interesting and actually easier to study.
> Are labels and vocabulary treated as foundational, or as relatively unimportant?
Vocabulary is a big deal in biology. Many terms carry associated meaning, for example polymerase chain reaction helps describe the mechanism, and TAQ polymerase reminds you that heat is important. Bone morphogenic protein says a lot.
That said, plenty of biology terms are pretty useless. Ribosome doesn't provide much intuition other than RNA is involved, and Golgi apparatus is even worse. Many gene names are arbitrary, reflecting a lack of knowledge at the time of discovery. Some are just dorky like sonic hedgehog.
Good undergrad biology books have big, carefully written glossaries in the back, these are absolutely invaluable.
> How important is seeing why things are the way they are?
It's important to internalize: 1) Biology is just physics and chemistry. 2) Millions of years of evolution and randomness produced all these arbitrary biological systems with their endless complexity. That's why living organisms are nothing like rational engineered systems, despite all the shared physics.
> If you haven't seen part of the map, how important is being able to sketch it in plausibly? If fragmented into puzzle pieces, how important is being able to fit them together?
For me, studying any big subject with lots of details, context really helps. It's easy for me to get lost in the details and lose motivation unless the ideas plug into some bigger picture. That's true even if I only want tourist-level knowledge.
A cautionary note: the jiggle can be misleading, making one think motion is fully represented. These are traditional 3D animations, with their profoundly misleading handling of motion, just with a "jiggle filter" added.
For illustration, consider the classic animation of a walking kinesin towing a vesicle. One could jiggle-ify it. But that won't convey that during every step, the vesicle has done a "balloon in a hurricane" exploration of every possible position it can reach while remaining tethered. Won't clarify that the very very misleading "I'm just a peaceful barge" vibe is entirely animation fantasy. Secondary content could have been added to defuse this negative educational impact, but the choice was made to optimize for, and I'm quoting, "pretty".
Jiggle-ification takes perhaps the biggest educational downside of these animations, and makes it even more misleading.
In case anyone else wondered about using gptel to edit thinking (eg vis Qwen3.6's `preserve thinking`), [1] explains:
> In a multi-turn request, from the time you run `gptel-send`, everything the LLM sends is passed back to it [...during tool calls...] includes multiple reasoning blocks. [...But...] subsequent gptel-send calls read their input from the buffer contents (or active region, etc), so the reasoning blocks in the buffer will not [] be sent as "reasoning_content".
But in org mode, those are apparently `#+being_reasoning` blocks (`gptel-include-reasoning`?), so editable thought might be an easy addition?
A caution, fwiw, that any llms which respond with interleaved content and reasoning blocks, currently only work when not streaming, and fixing that is non-trivial.[also 1]
The xkcd color survey[1] was 16 years ago now. With the data available, there were various follow-ups. Many, including xkcd's own "take it for fun here" link there in [1], are now 404. But the strata[2] and word cloud[3] are still up, and relevant here.
The data remains available. It was a TidyTuesday ("social data project") for 2025-07-08.[4] This response[5] looked at TFA. (This week's TT is ag tariffs.[9])
I just did a quick and sloppy search, so there's likely more out there. Curiously, I found AIMode and Bing/chat less immediately helpful than I'd have guessed.
Years ago I used the xkcd data for a prototype web interactive for kids, which shader filtered video to selected colors... but it was overlapping rather than a partition, and I'd never leave out cyan. Hmm, maybe a vibe coding target.
Just to stir thought, I note the TiddlyWiki[1] community (wiki as a self-modifying single html file; 20+ years old) has of course been exploring AI tooling... though not necessarily as an agentic environment. There's a markdown plugin, and others to make the file executable, or into a self-serving web app. Git is more problematic. So hypothetically, one could have a single-file agentic wiki wandering around and self-editing.
For the single-file configuration you describe, there are already several LLM connectors — e.g. [1]. The appeal is exactly what you note: no dependencies, no installation, trivially archivable. A single-file agentic wiki wandering around and self-editing isfeasible today.
For something closer to Karpathy's LLM Wiki pattern, I've been working on twillm[2], which uses TiddlyWiki's Node.js configuration. That setup saves tiddlers as individual files, so you can point it at an existing Markdown vault and work alongside tools like Claude Code.
Some benefits of TiddlyWiki for this:
* Open source, so you can be confident it'll remain usable indefinitely.
* Web-based, so accessible anywhere.
* Computed views replace materialised index files. Karpathy's setup relies on an index.md that the LLM has to keep in sync as it adds notes — something LLMs are bad at, with staleness creeping in across sessions. TiddlyWiki views are live filter expressions: "tiddlers tagged concept, sorted by rating" computes its contents at render time.
* Frontmatter becomes queryable structure. Obsidian renders YAML frontmatter as boxed metadata at the top of a note. TiddlyWiki promotes frontmatter fields into first-class tiddler fields you can filter, sort, and aggregate over.
* LLM-authored applets, not just content. Beyond Markdown notes, the LLM can drop in wikitext tiddlers (.tid) that act as small interactive live views: dashboards, browse-by-tag tools, journal indexes, glossary pages.
There's a meme, "Why develop one's own expertise? It's a poor investment. When you need it, you can hire it." Does AI make us all trust-fund kids?
An intro-physics educator at a first-tier university, observes that their entering students, having attended such well-funded schools, with such highly-skilled teachers, presenting material so clearly... widely lack both the skills and inclination to wrestle with a body of knowledge to extract their own understanding. To the detriment of their early-college education. The sci-ed snark version is, raising all students to the level of these, would be both an unimaginably immense triumph, and an ongoing profound failing to teach well. Will AI give everyone material presented so clearly?
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But won't it be fun when we can cloud burst-parallel a grid/tiled sampling of multiple code implementations/architectures, and interactively explore navigate/blend-points-in the latent design space. Multiples embodying different trade-offs, styles, clarity vs performance, etc. Code as generative art. What might the software engineering equivalent of designer moodboards be?
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