On September 23rd, 1846, Urbain Le Verrier (calculation) and Johann Gottfried Galle (observation) discovered Neptune.
The story begins with Uranus, which had been puzzling astronomers for years. Its orbit showed strange irregularities that didn't fit existing models.
Using Newton's law of universal gravitation, Le Verrier analyzed the perturbations in Uranus's orbit and worked backward to determine the hidden planet's likely mass, orbit, and position. The calculations were extraordinarily complex—all done by hand without modern computational tools.
Le Verrier published his preliminary findings in June 1845, then spent months refining his work throughout 1846. He didn't just predict the planet's existence; he pinpointed its precise location in the sky.
When Galle pointed his telescope to Le Verrier's predicted coordinates, Neptune was there waiting.
Most of the planets of our solar systems have been found by direct observation. Only Uranus where found with technology assisted obsvervation1. Neptune was the first planet found through pure mathematical modeling.
While the analogy will sound peculiar to some, I feel we have been through the same pattern recently.
Le Verrier worked backward from Uranus's orbital irregularities to find Neptune.
Context engineers work backward from inconsistent AI outputs to identify what contextual structures might stabilize performance.
Prompt engineering was the obvious. Then we enter context engineering which is more thoughtful and methodical.
A natural follow up question I'm sure you asked yourself is "how to build context then?".
I'm not talking about the first order context: the one we feed our synthetic PRDs ultimately digested by our best models. I'm talking about the one we build upfront.
I don't have a master's in cognitive science or linguistics, but my gut feeling here is we're using these new models to uncover just how powerful language really is—and how it contains computational structures we're only starting to grasp2.
Put it in another way: there's a call here to focus more on literature, linguistics, and cognition. The actual disciplines allowing us - humans - to develop larger, thorougher, newest, contexts.
📡 Expected Contents
Obituaries, a Way of Creativity
My summer - nerd - reading is Metaphors We Live By. Likely my favorite book of the year so far.
Metaphors are very powerful and most of the time underestimated. Exploring uncommon ones is good for creativity as highlighted in this post:
Gentner calls this property mapping — when people borrow attributes like texture or shape from one concept and apply them to another. It’s a kind of remote association, and clearly more creative than imagining a standard school desk. But Gentner identified something even more powerful: structure mapping. This happens when you transfer the relational structure of one concept to another. Say you combine “pony” and “chair” and picture a chair shaped like a pony — that’s still property mapping, just more elaborate. But if you imagine a small chair, you’ve made a bigger leap. That’s structure mapping: drawing on the idea that a pony is smaller than a horse, and applying that relationship to redefine the size of a chair. These kinds of mappings — especially when the underlying relations are abstract or non-obvious — tend to produce the most original and surprising combinations.
A big part of this newsletter is driven by exploring metaphors; this one post resonates a lot:
The Illusion of Causality in Charts
Again a call for "corellation isn't causation". Our industry is stuck somehow in a local optima: the ETL era. Into "observational data."
While causality cannot be directly inferred from observational data, different degrees of plausibility can be derived from it.
But we might also lift our new tools to move forward on proper A/B tests and methods designed since decades to run causality experiments.
Using AI for Data Modeling in dbt
I'm working with an analytics engineer at Kestra, and he surprised me very early in the onboarding process: he set dbt with some AI plugin in VS Code. This took less than 2 days and it drove the whole analytics motion.
dbt with an AI copilot will likely be the default setup when it comes to analytics engineering. Even niche code editor like Nao are emerging to support that project.
When to Design For Emergence
I can't recommend this post more. Likely promoted in a previous issue (shame on me if not). Reading it again in the plane right now and it highlight something we might need in the vibe-coding phase.
Yes, building very specific product that fit a niche problem is good. But to sustain a tool letting users find solutions by themselves is another game.
Again, I'm thinking about Malloy here, and globally speaking any tool or DSL that let users create new meaning their own way.
📰 The Blog Post
Very proud to release that one with Julien. We were very exicted to write and explore on the semantic layer and the future of analytics here.
Please read it quickly, you'll be surprised how far you can go with a simple semantic layer definition, a MCP, and proper prompt techniques.
🎨 Beyond The Bracket
Quitting programming as a career right now because of LLMs would be like quitting carpentry as a career thanks to the invention of the table saw.
Not that much to add. But it’s a good reminder for the current time. The new paradigm in set.
Our end goals as engineers will likely be the same. We will just use better tools.
And so again, the same loop… This is a call to focus on what matters.
Yes, you should learn to use the new tools.
But what are you doing with these tools? What are you focusing on right now?
Hope it’s not tool specific.
Hope you try to build a house, like it always been.
Not a wooden toy for kids you don’t have.
Writing this issue in the plane toward Portland. I'm quite proud of my reading setup here: my printed magazine of my favorite blog posts, a book, and Obsidian on my Macbook. I don't any WiFi, so the latter is very welcomed as I can access my own librairie of thoughts, and project I save and nurture since years now.
Can't wait to land on that new territory. The other side of the planet for me.
Hope the world on your side is doing good.
Uranus was found thourhg conducting a systematic survey of stars with a telescope.
I might be the average literate guy, so I'm maybe just discovering advanced literacy...