I remember two years ago, when I actually got into using graph data structures, wondering if maybe the "space" of available reactions for any given starter and target molecules could be mapped as a graph, with intermediates as nodes and reactions as weighted directed edges, so synthesis becomes pathfinding through chemical space.

Turns out, it’s a thing! [^0]

Edit: Makes you wonder how much interesting stuff is sitting in plain sight, waiting for someone with the right cross-domain awareness / knowledge / whatever to notice it.

[0]: https://pmc.ncbi.nlm.nih.gov/articles/PMC9574932/

Yeah. And also resurface relevant old notes, ideas, and web clippings I have forgotten. Two recent example[^0][^1].

> it could be an interesting synthesis/writing exercise to try to connect concepts that are far removed in your own mental model.

As well as different takes on or applications for those concepts, same for sources.

[0]: https://imgur.com/a/jhxUV9i

[1]: https://imgur.com/a/gmaq9Yw

But being able to tie related notes together, and see at the bottom of one which other notes reference it is interesting.

Even more now that a LLM can take care of the actual tending and pruning.

[0]: https://hackernoon.com/the-factors-prompting-a-judge-to-issu...

Quite a bit of false positives, but it hasn’t had any ill-effect so far. Aside from increased quota usage.

Which somehow ended up being a very convincing argument for more frugal engineering, leading to a sort of "mind the user’s fridge" policy, "Fridge-Driven Development".

A policy that has been dutifully and scrupulously observed by all agents since, across all projects. Unlike my original clear, comprehensive, infrastructure guidelines.

On top of that, we have been running multi-model AI reviews on every PR through their respective GitHub integrations (Codex, Gemini, Copilot, Greptile, CodeRabbit).

They never fully overlap, and yet they somehow usually all miss the same things. The most significant improvement came from having agents commit their plan along with their work.

On the upside, it means I get to focus my reviews on different things.

Is that really the consensus? There’s been a bit of literature lately on that. Can’t find the one about looking into whether or not the harness had a greater impact than the models (for comparable models), but there’s this one: https://arxiv.org/html/2605.23950

Pointing out past suboptimal / failing behaviours to new opus sessions would almost always actually create a sort of "anchoring bias" that would drive the agents towards exhibiting the failure mode (often while mentioning how it wouldn’t fall for it).

As far as I can recall, Fable has been the first model to discover the documented failure modes, comment on them, and just… keep going, actually avoiding them. Quite a surprise.

And there’s no reason evals can’t be done on multi-turn agents in a loop (or not): it’s pretty much what all these benchmarks do.

:/

- https://maderix.substack.com/p/inside-the-m4-apple-neural-en...

- https://news.ycombinator.com/item?id=47257931

Not sure if they do that (yet), but no reason an app couldn’t expose "Here’s what you can use to present data of shape X", or "here’s a UI for process doing y".

It feels like turning the common approach inside-out. But it works.

Edit: you could even imagine, in that world, apps that only expose surfaces, composable UI libraries, multi-step flows, declaring what they’re for, what kind of inputs they take, and what output they produce. Without ever owning any of the data (eg flights data, hotels inventory, booked trips, financial data, etc) or capabilities (eg book a flight).

Bundles that provide data and capabilities to iOS and Siri? And perhaps libraries of UI components to display and interact with said data?

But then, if that works really well, and gets strong adoption, why ever open the app? What’s the point of having navigation flows inside an app? Could one make entire apps solely dedicated to providing a set of data, capabilities, and UI components to the system?

In that world, what drives user retention, for such apps? What even is an app? App engagement disappears as well.

And that’s not even diving into the use-case of Siri, say, planning a trip across five different apps (flights, hotel, restaurants, whatever) using just App Intents. If done well.

In that world, do most apps just become plugins, providers for Siri?

This place is incredible.

Sometimes I daydream that the end goal demo would be to set one up in a VM and let a sort of ransomware lose in it, and see if the agent can identify what’s going on and react in time.

But for now I’m fighting Apple notarization to enable local notifications on macOS.

[0]: https://github.com/lightless-labs/descartes