What the author demonstrates here is a powerful principle that dates back to LISP's origins but remains revolutionary today: the collapse of artificial boundaries between program, data, and interface creates a more direct connection to the problem domain.
This example elegantly shows how a few dozen lines of Clojure can replace an entire accounting application. The transactions live directly in the code, the categorization rules are simple pattern matchers, and the "interface" is just printed output of the transformed data. No SQL, no UI framework, no MVC architecture - yet it solves the actual problem perfectly.
The power comes from removing indirection. In a conventional app, you have:
- Data model (to represent the domain)
- Storage layer (to persist the model)
- Business logic (to manipulate the model)
- UI (to visualize and interact)
Each boundary introduces translation costs, impedance mismatches, and maintenance burden.
In the LISP approach shown here, those boundaries disappear. The representation is the storage is the computation is the interface. And that direct connection to your problem is surprisingly empowering - it's why REPLs and notebooks have become so important for data work.
Of course, there are tradeoffs. This works beautifully for personal tools and small-team scenarios. It doesn't scale to massive collaborative systems where you need rigid interfaces between components. But I suspect many of us are solving problems that don't actually need that complexity.
I'm reminded of Greenspun's Tenth Rule: "Any sufficiently complicated program contains an ad hoc, informally-specified, bug-ridden, slow implementation of half of Common Lisp." The irony is that by embracing LISP principles directly, you can often avoid building those complicated programs in the first place.
Rich Hickey's "Simple Made Easy" talk explores this distinction perfectly - what the industry calls "easy" (familiar tools and patterns) often creates accidental complexity. The approach shown here prioritizes simplicity over easiness, and the result speaks for itself.
Over time I’ve come to see LISP less as the natural collapse of artificial boundaries but the artificial collapse of natural ones. Where and how data is stored is a real concern, but where and how the program is stored isn’t. Security boundaries around data and executable code are of paramount importance. Data storage concerns don’t benefit from being mixed with programming language concerns but from computer and storage architecture concerns (eg column stores).
In toy programs, such as this one, those concerns can all be discarded, so LISP is a good fit. But in serious systems it’s soon discovered that what is offered is in fact “simplistic made easy”. That’s not to say that traditional systems don’t suffer from all the ills Hickey diagnoses in them, but that we differ on what the cure is.
The whole post was about how that doesn't solve the problem perfectly - there is no way to interactively edit the output.
> by embracing LISP principles directly
This could just as easily have been javascript+json or erlang+bert. There's no lisp magic. The core idea in the post was just finding a way for code to edit it's own constants so that I don't need a separate datastore.
Eventually I couldn't get this working the way I wanted with clojure and I had to write a simple language from scratch to embed provenance in values - https://news.ycombinator.com/item?id=43303314.
> It doesn't scale to massive collaborative systems where you need rigid interfaces between components. But I suspect many of us are solving problems that don't actually need that complexity.
Here's the issue. Starting out you almost certainly don't need that rigid interface. However, the longer the app grows the more that interface starts to matter and the more costly retrofitting it becomes.
The company I currently worked at started out with a "just get it done" approach which lead to things like any app reaching into any database directly just to get what it needs. That has created a large maintenance issue that to this day we are still trying to deal with. Modifying the legacy database schema in any way takes multiple months of effort due to how it might break the 20 systems that reach into it.
My take on what the issue is, is primarily in the ramifications of Conway's law and how our social structures map to systems.
When the system is small, it makes a great deal of sense to be an artisan and design simple automations that work for exactly that task, which for the most common things is always supported by any production-oriented programming environment - there's a lot of ways in which you can't go wrong because the problem is so small relative to the tools that any approach will crush it. "Just get it done" works because no consequence is felt, and on the time scale of "most businesses fail within five years", it might never be.
When it's large, everyone would prefer to defer to a common format and standard tools. The problems are now complex, have to be discussed and handled by many people, they need documentation and clear boundaries on roles and responsibilities. But common formats and standards are a pyramid of scope creep - eventually it has to support everyone - and along the way, monopolistic organizations vie for control over it in hopes of selling the shovels and pickaxes for the next gold rush. So we end up with a lot of ugly compatibility issues.
In effect, the industry is always on this treadmill of hacking together a simple thing, blowing out the complexity, then picking up the pieces and reassembling them into another, slightly cleaner iteration.
Maintenance can be done successfully - there are always examples of teams and organizations that succeed - but like with a lot of infrastructure, there's an investment bias towards new builds.
>> No SQL, no UI framework, no MVC architecture - yet it solves the actual problem perfectly.
No SQL but in it's place is some code. The point of SQL was to standardize a language for querying data. This is just using a language other than the standard. A UI is a way for people to avoid writing code.
Sure doing your own custom thing results in something easy for the programmer. Nothing new about that.
> Of course, there are tradeoffs. This works beautifully for personal tools and small-team scenarios. It doesn't scale to massive collaborative systems where you need rigid interfaces between components. But I suspect many of us are solving problems that don't actually need that complexity.
Spreadsheets. If you squint the right way, they embody the lisp principles for the non-programmers' world.
> "Any sufficiently complicated program contains an ad hoc, informally-specified, bug-ridden, slow implementation of half of Common Lisp."
I have never seen that in practice (30+ years of industry experience working on very large applications).
I think it is one of those statements that fans of Lisp love to quote (a lot) without having any empirical data to back it up.
And yes I am sure that there are examples out there. You can probably find examples of anything if you look hard enough. But that doesn't make it a general rule.
This makes no sense to me. CL is not an astral technology. It’s just a parentheses-rich language that is worse than almost any other until you get to macros, continuations and psychotic polymorphism. Which are cool to talk about at hacker parties, no /s, but don’t do much business-wise.
This example elegantly shows how a few dozen lines of Clojure can replace an entire accounting application. The transactions live directly in the code, the categorization rules are simple pattern matchers, and the "interface" is just printed output of the transformed data. No SQL, no UI framework, no MVC architecture - yet it solves the actual problem perfectly.
The power comes from removing indirection. In a conventional app, you have: - Data model (to represent the domain) - Storage layer (to persist the model) - Business logic (to manipulate the model) - UI (to visualize and interact)
Each boundary introduces translation costs, impedance mismatches, and maintenance burden.
In the LISP approach shown here, those boundaries disappear. The representation is the storage is the computation is the interface. And that direct connection to your problem is surprisingly empowering - it's why REPLs and notebooks have become so important for data work.
Of course, there are tradeoffs. This works beautifully for personal tools and small-team scenarios. It doesn't scale to massive collaborative systems where you need rigid interfaces between components. But I suspect many of us are solving problems that don't actually need that complexity.
I'm reminded of Greenspun's Tenth Rule: "Any sufficiently complicated program contains an ad hoc, informally-specified, bug-ridden, slow implementation of half of Common Lisp." The irony is that by embracing LISP principles directly, you can often avoid building those complicated programs in the first place.
Rich Hickey's "Simple Made Easy" talk explores this distinction perfectly - what the industry calls "easy" (familiar tools and patterns) often creates accidental complexity. The approach shown here prioritizes simplicity over easiness, and the result speaks for itself.