 That room. The tape drives in their cabinets along the far wall, the operator's console in the centre, the line printer to the right. It looks like something from Kubrick: the same antiseptic geometry, the same monolithic seriousness, the same implication that whatever happens here is important enough to require its own architecture. This is a Philips P1100. In 1973, I was twelve years old, and this is where I started programming. I got the opportunity through school. The P1100 was a Dutch mainframe, not widely known outside Northern Europe, and the fact that a twelve-year-old was permitted to approach it probably says more about 1970s Scandinavian attitudes to children than about any pedagogical intention. But approach it I did. COBOL and FORTRAN, mostly. Punch cards. Line printers. Typewriter console input. Thirty-six-bit words and 32K words of core memory – actual magnetic cores, the kind you could theoretically see with a magnifying glass. Spinning tape stations and disk units. The whole physical apparatus of computation as it existed before anyone thought to make it personal.  P1100 product reveal What the photographs convey, and what no description quite captures, is the physicality of early computing. These were rooms, not screens. You walked into the machine. The tape drives were furniture. The noise was constant. Programming in that environment carried a particular weight; there was nothing casual about it, nothing disposable, nothing you could do from bed with a laptop. The seriousness was built into the space. I don't mention this for nostalgia. I mention it because something from that period has stayed with me through fifty years of subsequent work, and I think it matters for understanding why I build the way I build. A Language That Trusts You Six years after the P1100, in 1979, I encountered Lisp. I was eighteen, and the encounter came through the legendary August 1979 issue of BYTE magazine, devoted entirely to Lisp. I've written about this before, so I won't repeat the full story here; but the experience deserves its place in this genealogy because it changed the direction of everything that followed. I should say what computing felt like in the late 1970s, for anyone who wasn't there. The dominant paradigm was procedural: FORTRAN, COBOL, Pascal, various assembly languages depending on your hardware. Programming meant telling the machine what to do, step by step, in a language designed for the machine's convenience as much as the programmer's. The programmer was an operator. The metaphor was industrial. I had spent six years in that world. It was what programming was. Lisp was different in kind, not just in degree. What I encountered was a language that didn't tell me what to do. It waited. It provided a set of primitives so minimal they seemed almost absurd; seven or eight special forms, a way to define functions, a way to combine them, and the extraordinary implication that this was sufficient for anything I could imagine. The language was smaller than what I'd been using, and it could do more. That paradox has never stopped being interesting to me. What Lisp offered, and what I wouldn't have the vocabulary to articulate until years later, was freedom. Not freedom as a marketing term or a political slogan, but freedom as an engineering condition: the ability to reshape the language to the problem rather than reshaping the problem to fit the language. In Lisp, the programmer was not an operator. The programmer was a maker of tools, a builder of languages, a creative agent trusted with the full power of the system. The culture around Lisp carried this same ethos. The people who shaped it (Steele, Gabriel, Kiczales, Norvig, among others) were not priests. They were builders, hackers, improvisers. They valued expressiveness, wit, elegance in the mathematical sense, and above all, the conviction that the person at the keyboard was intelligent enough to be trusted with dangerous tools. The community was irreverent, argumentative, often brilliant, and entirely uninterested in policing anyone's tone or orthodoxy. You were judged by what you built, not by which catechism you recited. I mention these names because they are not abstractions to me. I was eighteen. I was reading their papers. I was trying to implement what they described. Immediately after that BYTE issue, I implemented a Lisp in 6502 assembler, using an assembler I had written in BASIC. I tell you this not to impress but to convey the intensity of the response. When something is that good, you build it yourself to understand it. CLOS and the Workshop TraditionThe progression from basic Lisp to the Common Lisp Object System (CLOS) in the mid-1980s was not, for me, a move from one paradigm to another. It was an expansion within the same tradition. CLOS is difficult to explain to anyone whose experience of object-oriented programming was formed by Java, C++, or Python. In fact, some of us maintained even then that CLOS was not object-oriented at all; the name was a concession to the terminology of the period, not a description of what it actually was. Those languages treat objects as containers: data and methods bundled together, accessed through interfaces, extended through inheritance. The programmer works within the object. CLOS transcends this entirely. Methods don't belong to objects; they belong to generic functions. Dispatch can happen on any combination of argument types. Method combinations (:before, :after, :around) let you layer behaviour without modifying existing code. The Metaobject Protocol (MOP) lets you redefine how the object system itself works. The result is a system of extraordinary power and openness. You could override anything. You could extend anything. You could reshape the behaviour of the system at any level, including the level at which behaviour is dispatched. CLOS treated the programmer not as a user of the object system but as its co-author. The system was a workshop, not a cathedral, and the door was always unlocked. This mattered to me practically, not just aesthetically. When I built HotLisp for the Royal College of Music in Stockholm (a Common Lisp implementation written in C that treated MIDI events as first-class citizens), I was already integrating music directly into the computational core. When I later built Igor Engraver, CLOS became the substrate on which the entire semantic model rested. The ability to extend, compose, and reshape; to add new types of musical objects that participated seamlessly in existing operations; to layer specialised behaviour onto generic frameworks without breaking them: these weren't theoretical luxuries. They were the reason the software worked. I taught algorithmic composition using Lisp at the Royal College. My students, who were composers, not computer scientists, understood the freedom immediately. You could express musical ideas directly, without the mediation of a language designed for someone else's problems. The gap between thinking and making was as small as I have ever seen it in any medium. Having Been ThereI am, as of this writing, sixty-four years old. I have been programming for over fifty years. I say this not as credential but as context, because having been there changes what you see. I was there for the AI winter. I watched a field that had promised the moon retreat into a decade of funding cuts and reputational collapse, dragging Lisp's standing down with it. The language didn't deserve the association; it was punished for the sins of researchers who had over-promised. I kept using it anyway, because the language hadn't changed, only its fashionability. This was the first time I learned that technical merit and cultural standing are largely unrelated. I was there for the object-oriented conquest of the 1990s. I watched as C++ and then Java established OOP as orthodoxy, complete with design pattern catechisms and UML diagrams that looked more like religious iconography than engineering documentation. CLOS, which had solved the problems these languages were struggling with years earlier, was invisible because it ran on the wrong platform and belonged to the wrong tribe. The industry chose what was marketable over what was good. This was the second time I learned that lesson. I was there for the web's arrival, which changed everything about deployment and nothing about the fundamental problems of building complex software. I was there for the enterprise Java years, when 'architecture' meant XML configuration files and dependency injection frameworks and a peculiar conviction that more abstraction was always better, regardless of what it cost in comprehensibility. I was there for the functional programming renaissance, which rediscovered principles that Lisp had embodied since the 1950s, often without acknowledgement and frequently with a missionary zeal that replicated the very dogmatism it claimed to reject. I am here for the current AI explosion, which rhymes with the original AI boom in ways that make my skin prickle. I have seen this arc before: extraordinary technical promise, sweeping claims about imminent transformation, and a market that moves faster than the underlying science. Time will separate what is durable from what is fashionable. None of this is complaint. It is observation. When you have been programming for fifty years, you develop a particular relationship with trends: you have seen enough of them arrive, dominate, and recede to know that the arrival tells you very little about the value and the dominance tells you nothing about the longevity. What remains after the trend passes is what was true about it. Immutability was true about functional programming. The JVM was true about Java. Interactive development was true about Lisp all along. The thing that has remained true for me, through every cycle, is the freedom principle. The conviction that the programmer should be trusted, that the tools should serve the maker rather than constrain them, that the ability to reshape your instruments is not a luxury but a precondition for doing serious work. I did not learn this from a manifesto. I learned it from a mainframe in 1973 and a Lisp prompt in 1979, and everything since has confirmed it. What Was Lost Igor Engraver was the fullest expression of this principle I had achieved. Eighteen people, $7.5 million in investment (2024 equivalent), a music notation system built entirely in Common Lisp that introduced semantic modelling to the field. Conductors were interested. Composers were interested. The architecture was sound. The project ended in 2002 after a combination of venture capital pressure, management-imposed feature creep, and the collapse of M&A activity following September 11, 2001. The technical architecture was sound; the business environment was not. I won't rehearse the full history here; it's documented elsewhere. What matters for this essay is what was lost and what survived. What was lost was the software, the team, and the years of accumulated work. What survived was the knowledge: of what works in music notation architecture, of what CLOS makes possible, of what fails when business logic overrides engineering discipline. And what survived, underneath all of it, was the conviction that the freedom principle was correct and that the work deserved to be done properly. Most people, having had a project of that scale taken from them, do not try again. I understand why. The rational calculus says the risk exceeds the reward. But the rational calculus doesn't account for the fact that you still know how to do it, that you've learned what you didn't know the first time, and that nobody else is going to build this. The ReturnWhen I came to Clojure, I came home. I don't mean this sentimentally. I mean it technically. Clojure is a Lisp; it thinks in the same shapes, provides the same minimal-but-sufficient primitive set, and trusts the programmer with the same radical freedom. But it adds things that Common Lisp never had and that my work desperately needed: immutable data structures by default, Software Transactional Memory for concurrent coordination, and the entire JVM ecosystem for deployment, GUI development, and cross-platform reliability. I've written extensively about these on the blog, so I won't rehearse the technical details here. What matters for this essay is the genealogical thread. I've called this 'Clojure for closure', and the wordplay is deliberate. It is a return to a native language after decades away, carrying everything those decades taught me. The CLOS patterns came with me. Ooloi uses Clojure's multimethods with the Methodical library to achieve CLOS-like method combinations: :before, :after, :around modifiers that layer behaviour exactly as they did in Common Lisp. The hierarchical type system uses Clojure's `derive` mechanism to create inheritance-like relationships without classes, mirroring CLOS's approach to multiple inheritance through composition rather than rigid hierarchy. Generic functions dispatch on argument types just as they did in CLOS, with operations defined outside the data they manipulate. Rich Hickey, Clojure's creator, made choices that resonate specifically with the tradition I come from. 'Mutability in bounded contexts is fine'. 'Solve problems first'. The emphasis on what you can build, not on what catechism you recite. Clojure didn't inherit the Lisp purity culture that never really existed in the first place; it inherited the Lisp builder culture, the one that judged you by what you shipped rather than which monads you could name. But Clojure also gave me something new. Immutability as a default changes how you think about time, state, and coordination. In Common Lisp, you could write immutable code, but the language didn't push you toward it. In Clojure, immutability is the grain of the wood. This inversion turned out to be profound for music notation specifically, because a musical score is a temporal structure where the meaning of any element depends on everything that precedes it. The semantic engine that results from this is something Igor Engraver, for all its strengths, could never have achieved. Where the Genealogy ArrivesOoloi's plugin system is, in a specific sense, the culmination of everything this essay describes.
The core engine is written in Clojure and architecturally closed. I've written about this elsewhere as the 'monastic core' principle. The freedom is at the perimeter: any JVM language can write plugins, and the plugin API speaks each language's idioms natively. A Java developer writes Java. A Kotlin developer writes Kotlin. Nobody is forced to learn Clojure or to understand the internals. The benefits of the architecture propagate without requiring conversion. This is the CLOS principle, expressed architecturally. The system trusts the developer. The developer works in their own language, with their own patterns, and the underlying architecture handles the coordination. The monastery uses Latin internally because Latin is precise; but when teaching farmers improved agriculture, you speak their language. This is not compromise. It is proper boundary design, and it is the lesson of fifty years in the workshop tradition. When I sit at this keyboard today, fixing a macOS menu bug or stripping debug symbols from a jlink runtime, I am using skills accumulated across the full span of what I've described. The mainframe taught me that serious work requires serious infrastructure. Lisp taught me that freedom is an engineering condition, not a slogan. CLOS taught me that the system should trust the programmer. The loss of Igor Engraver taught me that good architecture survives the business that funded it, if someone is stubborn enough to rebuild. Clojure taught me that immutability changes what's possible. The whole genealogy is present in every design decision, whether or not it's visible. I don't know if Ooloi will succeed, whatever that is. I know the architecture is right. I know the foundations carry what needs to be built on top of them. And I know where the freedom comes from, which is what I wanted to write down.
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 'Well then, alone!' – Elektra's cry when Chrysothemis refuses to help her. Not triumphant independence, but desperate necessity fused with unwavering resolve. The isolation isn't chosen; it's forced by the impossibility of finding anyone who shares her singular purpose. Orestes isn't likely to materialise. There was a functional programming conference in Stockholm recently. I'm sure it was excellent. I didn't attend. I should probably have been there – Ooloi is built in Clojure, after all, and finding collaborators would be useful – but I felt conflicted, and that conflict revealed something I'd been avoiding: the FP community cannot help me, and I don't need it anyway. Sect DynamicsI'm disappointed with the functional programming community. I was expecting higher-level thinking – freer thinking, commensurate with the intellectual freedom Clojure itself offers – but the atmosphere proved to be a shallow puddle of sectarianism. That probably has its reasons – being marginalised as a community is probably one of them – but the end result remains unchanged. The patterns are unmistakable. Knowledge as gatekeeping: the endless monad tutorial phenomenon, where every advocate believes they can explain monads better than everyone else, typically through increasingly baroque metaphors involving burritos, space suits, or elephants. This isn't pedagogy; it's ritual initiation. The complexity serves a social function – maintaining boundaries between insiders and outsiders. Purity as virtue signalling: debates about whether using `IO` makes you impure, whether exceptions violate functional principles, whether mutation in bounded contexts is acceptable. These discussions frame technical trade-offs as moral categories, as though architectural design were a moral discipline rather than an engineering one. The language reveals it – clean, pure, disciplined versus dirty, impure, undisciplined. This is religious vocabulary applied to software engineering. Terminology as tribal marker: deliberate retention of academic terminology when simpler terms exist. Endofunctor, catamorphism, anamorphism when 'map over containers', 'fold', 'unfold' would suffice. The obscurity is the point – it establishes hierarchy and demonstrates membership. The emphasis falls on mathematical elegance rather than problem-solving. The question isn't Does this help ship software but Is this theoretically sound. People who can recite monad laws but have never shipped a product receive more status than developers applying functional patterns to solve actual problems. Then there's the missionary behaviour: the conviction that imperative programmers need conversion. The framework isn't Here's another useful tool but You're doing it wrong until you see the light. This creates antagonism rather than adoption. Being marginalised as a community probably explains some of this – defensive posture manifesting as increased boundary enforcement, which creates insider/outsider distinctions, which enables status hierarchies based on doctrinal purity. But understanding the cause doesn't change the result, and it doesn't make the behaviour intellectually rigorous or practically useful. The Clojure Exception Clojure largely escaped this because Rich Hickey explicitly rejected purity culture. 'It's acceptable to use Java libraries'. 'Mutability in bounded contexts is fine'. 'Solve problems first'. The Clojure community focused on what you can build, not on arcane knowledge as status marker. This produced broader adoption without compromising functional principles. This is why I chose Clojure for Ooloi in the first place. But even within that pragmatic oasis, the broader FP community dynamics leak through. The conference I didn't attend would have featured both kinds of people – those interested in building things and those interested in doctrinal purity – and I couldn't predict which would predominate. The Intersection Problem Here's the substantive issue: finding Ooloi collaborators in FP communities is statistically improbable because very few people occupy my intersection point between various disciplines. Music notation requires an understanding of compositional structure, engraving conventions, and how musicians actually work. Functional architecture requires a sophisticated understanding of immutability, higher-order functions, transducers, STM transactions, and compositional patterns. Backend infrastructure requires a willingness to work on unglamorous problems like endpoint resolution and temporal traversal rather than visible features, and in Ooloi's case, an understanding of server technology and secure cloud operations. The population at that intersection is approximately one. FP communities might yield people who appreciate my transducer implementations or STM transaction handling. But they won't understand why endpoint resolution for slurs matters, how temporal traversal serves musical structure, or what makes intelligent engraving different from geometric placement. The domain expertise is orthogonal to FP community concentration. The inverse holds equally: musicians who understand notation deeply rarely have the architectural sophistication to work on Ooloi's core, and even fewer would find satisfaction in building infrastructure rather than using tools. I've worked outside the FP community all my life. Functional programming is a tool, not a (monadic) religion. (And why are monadic and nomadic so similar?) Why join the community now, when the benefits are unclear and the costs palpable? Consilium LatinumThe technical response is what I call the Latin strategy: making Ooloi's core a stable foundation for a plugin ecosystem. Build the architectural core once in Clojure, then let developers in other JVM languages contribute via plugins without needing to understand the underlying functional implementation. I've written about this approach in Penitenziagite!, so I won't rehearse it here. Elektra or Quixote?  The psychological question is whether this makes me Elektra or Don Quixote. Elektra confronts a real murder, real injustice, a legitimate need for action that others refuse. The isolation comes from their cowardice or pragmatism, not from her misunderstanding of reality. The task is achievable and gets completed. The tragedy is the psychological cost, not the validity of the purpose. Quixote confronts imaginary problems with obsolete ideals, mistaking windmills for giants. The isolation comes from a fundamental disconnect with reality. The task is impossible because it's based on delusion. The comedy (later tragedy) is that the quest itself is meaningless. The distinction depends on whether the problem is real. Do musicians actually need what Ooloi provides? If existing notation software genuinely fails at problems Ooloi solves, then Elektra. If musicians are adequately served by current tools, if the architectural sophistication I'm building doesn't translate to problems they actually experience, then Quixote. But there's a third option beyond tragic obsession and delusional quest. I'm building something architecturally excellent because I can, because it interests me, because functional approaches to musical structure are intellectually satisfying. The architecture might be elegant, but it's not worth psychological dissolution. The Latin model suggests I've already chosen this third path. I'm building core infrastructure well, documenting it properly, then making it available via plugin architecture that assumes others might have different needs. That's craft separated from identity. Not Dancing to DeathElektra's tragedy is total consumption by purpose. She becomes nothing but the task, and when it completes, there's nothing left because she permitted no existence beyond vengeance. She dances herself to death. I'm certainly not doing that. Ooloi is a project, not my entire existence. Sustainable completion means finishing the backend, documenting it clearly, releasing it, and then moving on. The work stands independently; I remain separate from it. I'll finish Ooloi's core architecture working alone, not because I prefer isolation, but because collaboration at this intersection point is impractical. The resolve comes from accepting reality rather than pretending community exists where it doesn't. The backend is complete. The transducer-based timewalker is fast, tight, and efficient. Endpoint resolution handles slurs and ties correctly. Nearly nineteen thousand tests pass. Vector Path Descriptors enable elegant client-server communication. Then comes plugin architecture, and seeing whether anyone finds Ooloi useful. If they do, excellent. If they don't, I built something architecturally sound and learned what I needed to learn. Either way, the work speaks for itself. And I continue existing beyond it. Nun denn, allein!   I'm one of the world's most committed anti-religious people. Despite decades at organ consoles in churches and cathedrals, I stand with Hitchens: religion is humanity's adolescent phase, something we need to outgrow. Its influence is fundamentally harmful. But when I read something like How Lisp Became God's Own Programming Language, I completely understand the reverence the author describes. There's something about Lisp – and Clojure – that creates what you can only call a transcendental response. Nothing actually transcendental happens, of course, but the feeling is real. What Lisp gives you is freedom. I've written about 'windsurfing through parentheses' before, and the metaphor sticks because it captures something essential. Most programmers are chained to the oars of enterprise slave galleys, with CTOs yelling 'RAMMING SPEED!' like that brilliant scene from Ben-Hur. Meanwhile, those of us who've found Lisp are windsurfing in circles around them, enjoying a freedom they can barely imagine. The discovery feels like Dave Bowman meeting the monolith: 'My God... it's full of stars!' That vertigo when you realise this thing's inner dimensions vastly exceed its outer ones. Lisp isn't transcendental, but it works like a star gate in both senses. The language doesn't get in your way, and it opens new ways of thinking. At the same time, it's so simple that complexity becomes manageable. I remember that August 1979 BYTE magazine perfectly. The cover promised mysteries, the articles delivered. I couldn't wait to start implementing what they described – eventually doing it in 6502 assembler, using an assembler I'd written in BASIC. Everything clicked, even as a teenager. This was real freedom, expressed as code. Years later, I wrote HotLisp (or 'HotLips' – M.A.S.H. was huge then) for the Royal College of Music in Stockholm. It was incredibly ambitious: a full Common Lisp that treated MIDI events as first-class citizens. Looking back, I see this as the beginning of what became Igor Engraver – integrating music directly into the computational core. We used it to control our Synclavier and MIDI synths whilst teaching algorithmic composition to advanced students at the Royal Academy. The Two-Bit History article nails something important about Lisp's mystique. It traces the evolution from McCarthy's 'elegant mathematical system' through AI research, Lisp machines, and SICP's role in making it the language that 'teaches you programming's hidden secrets'. Each phase built the reputation. What the article doesn't cover is the educational betrayal that followed. Computer science departments got it right for a while – they taught Scheme as a first language because it let students focus on learning algorithms rather than wrestling with syntax. Pure freedom to think about problems. Then Java Enterprise was foisted upon the world, the departments caved in, and they started churning out galley slaves instead of computer scientists. I see this as nothing short of high treason. But here's what really matters: that freedom has evolved in Clojure. Rich Hickey didn't just bring Lisp to the JVM – he solved problems that even Common Lisp couldn't handle elegantly. Those immutable data structures aren't academic toys; they're game changers that eliminate whole categories of bugs whilst making concurrency and parallelism natural instead of terrifying. The effects ripple out: undo/redo becomes trivial, and the JVM gives genuine multi-platform reach. This isn't just improvement – it's architectural breakthrough disguised as evolution. Clojure keeps Lisp's essential quality (that feeling of discovering how programming should work) whilst solving modern problems McCarthy couldn't have anticipated. The poor souls in corporate Java shops keep rowing, occasionally granted small mercies as functional concepts trickle in – hints of the freedom they're missing. I wish they could experience what we know: programming doesn't have to feel like industrial labour. There's a way of working where ideas flow directly into code, where the language becomes transparent, where you stop fighting tools and start windsurfing through solutions. Maybe that's the point. As McCarthy noted in 1980, Lisp survives not because programmers grudgingly accept it as the best tool for each job, but because it hits 'some kind of local optimum in programming language space'. It endures even though most programmers never touch it, sustained by reports from those who've experienced its particular form of computational enlightenment. Until we can imagine God creating the world with some newer language – and I doubt that day is coming soon – Lisp isn't going anywhere. Read the full article at Two-Bit History: https://twobithistory.org/2018/10/14/lisp.html   How solving a real music notation problem revealed the perfect transducer use case The Problem That Started It All I found myself confronting what appeared to be a deceptively simple requirement for Ooloi: 'Resolve slur endpoints across the musical structure'. Rather straightforward, one might assume: simply traverse the musical structure and locate where slurs terminate. But then, as so often is the case, the requirements revealed added complexity:
 It became apparent that I needed a general-purpose piece traversal utility: something handling temporal coordination whilst remaining flexible enough for multiple applications. Rather than construct something bespoke (and likely regrettable), I researched the available approaches within Clojure's ecosystem. That's when I recognised this as precisely what transducers were designed for. The Architecture RecognitionAllow me to demonstrate the pattern I anticipated avoiding. Without a general traversal utility, each application would require its own approach:  Three functions, identical traversal logic, different transformations. Exactly the architectural smell I wanted to avoid from the outset. This was precisely Rich Hickey's transducer insight made manifest: "What if the transformation was separate from the collection?" The Transducer RevelationWhat if I could write the temporal traversal once, then apply different transformations to it?  Objective achieved: one traversal algorithm, many applications. But its architectural reach turned out to be even more profound. The Architectural InsightThe design decision hinged upon recognising that I was conflating two distinct concerns: the mechanism of traversal and the logic of transformation. This wasn't merely about avoiding the tedium of duplicated code (though that would have been reason enough) but rather about establishing clean architectural boundaries that would serve the system's long-term evolution. Consider the conceptual shift this separation enabled: Rather than thinking in terms of specific operations upon musical structures:
The transducer approach encouraged thinking in terms of composed processes:
The traversal thus became reusable infrastructure, whilst the transformation became pluggable logic. This distinction would prove invaluable as the system's requirements expanded. The Broader ApplicationsWhat I hadn't anticipated was how broadly applicable the resulting abstraction would prove. After implementing the piece-walker for attachment resolution, I discovered it elegantly supported patterns I hadn't originally considered, each demonstrating the composability that emerges naturally from separating traversal concerns:  Each is built from simple, testable pieces. And they all inherit the same temporal coordination guarantee. This composability emerged naturally from the transducer design: a pleasant architectural bonus. The Performance Characteristics As one would expect from a well-designed transducer, memory usage remained constant regardless of piece size: a particularly crucial consideration when dealing with the sort of orchestral scores that might contain hundreds of thousands of musical elements. Consider the alternative approach, which would create intermediate collections at each processing step:  The transducer version processes one item at a time:  Same result, constant memory usage. This exemplifies what Rich meant by 'performance without compromising composability'. Demystifying Transducers Transducers suffer from an unfortunate reputation for complexity, often relegated to 'advanced topics' when they needn't be. This is particularly galling given that they're fundamentally straightforward when you encounter the right use case, which the musical domain provides in abundance. Think of transducers as 'transformation pipelines' that work with any data source, much as one might design AWS data processing workflows that operate regardless of whether the data arrives from S3 buckets, database queries, or API streams:  The pipeline stays the same. The data source changes. In Ooloi:  Why This Matters Beyond MusicThe piece-walker solved a universal software problem: How does one avoid duplicating traversal logic whilst maintaining performance and composability? This pattern applies everywhere:
Transducers provide the infrastructure for "traverse once, transform many ways." The Bigger PictureBuilding the piece-walker demonstrated that transducers aren't an abstract functional programming concept. They're a practical design pattern for a specific architectural problem: separating the concerns of traversal from transformation. The musical domain made this separation particularly clear because the temporal coordination requirements are so explicit. When you need the same traversal logic applied with different transformations, transducers provide the elegant answer. This separation makes code:
What's Next?The piece-walker is documented thoroughly in our Architecture Decision Record for those wanting technical details. But the real value lies not in the musical specifics but in observing how transducers address genuine architectural challenges with apparent effortlessness. The next time you find yourself contemplating similar data processing logic across multiple contexts, you might ask: 'What if the transformation was separate from the collection?' You may well recognise your own perfectly suitable transducer use case. References and Further ReadingRich Hickey's Essential Talks
Official Documentation
Educational Resources
Advanced Topics
Claude & Clojure It's no secret that I use Generative AI, specifically Claude Sonnet, to assist with the Ooloi project. I use it for writing Clojure tests TDD fashion, for generating Clojure code, for generating documentation, READMEs, architectural design documents and much more. Above all, I use Claude for exploring architectural strategies before coding even begins. It's somewhat reminiscent of pair programming in that sense: I'd never just task GenAI with generating anything I wouldn't scrutinise very carefully. This approach works very well and allows me to quickly pick up on good design patterns and best practices for Clojure. Claude & Python Overall, working with Claude on Clojure code works surprisingly well. However, this is not the case when I try to involve Claude for coding in Python, the main language I use as an AWS Solutions Architect. Generative AI struggles with creating meaningful Python tests and code – especially tests, which rarely work at all. This hampers its use as an architectural discussion partner and a TDD assistant. In fact, I've given up trying to use Generative AI for coding in Python. DifferencesI have a deep background in Common Lisp and CLOS, dating back to the 1970s. I've written Common Lisp compilers and interpreters, as many Lispers did in those days. The standard practice was to write a small kernel in assembler or C or some other low-level language, and then use it to write an optimising compiler on top of it to replace the kernel in an iterative fashion, sometimes using transformations of source code based on lambda calculus. (I still remember that paper by Guy Steele.) I see Common Lisp essentially as a big bag of good-to-haves (a really excellent one, mind you). As such, it was designed by committees over a period of decades. Clojure, on the other hand, is much tighter and rests solidly on consistently applied computer science design principles. Common Lisp is pragmatic and eclectic and thus somewhat sprawling in design. Clojure, in comparison, is smaller and much more focussed, even opinionated in nature, and for clear reasons. People attracted to Common Lisp and Clojure tend to be pretty well versed in computer science, especially Clojurians who generally have a good understanding of functional programming and immutable data structure concepts. Thus, the public code "out there" on sites like GitHub tends to be fairly advanced and of high quality. Python is an entirely different ballgame. It's one of the most commonly used languages today, and the public code base is absolutely enormous. This also means that the quality of that public code varies considerably. Also, Python is not functional at heart, and its data structures aren't immutable: side effects is the name of the game. Python programmers, though much greater in number than Clojure programmers, range from script kids to computer scientists. Thus, public Python code is much more all over the place and of varying quality. This may make it harder for a large language model to reason about. I wondered whether these differences accounted for the discrepancies in the usefulness of Claude and other LLMs as coding assistants in Clojure and Python. Asking ClaudeSo I decided to ask Claude 3.7 itself. I shared the Clojure codebase with it and asked why it thought results were so much better for Clojure than for Python. This is what it said:
It then continued, quoting the code base:
I guess this answers my question about Clojure vs Python. It's not just the functional and immutable aspects of Clojure, it's also specifying the domain stringently through design, architectural discussons, specs, and tests. The Ooloi project in particularWith all that out of the way, I then went on to describe how I use Claude as a discussion partner before code is generated, and the TDD approach I'm using where the tests of course describe the domain. Claude was almost embarrassingly enthusiastic:
I continued: "I also use Claude to create ADRs, READMEs, introductions for newcomers, Dev plans, etc. I'm quite open about this; transparency is paramount in an open-source project, and the idea is to facilitate collaborators' understanding of the Ooloi project as much as possible. This means that adherence to best practices, the use of sane architecture, abstracting away the complex stuff without compromising on performance or flexibility, etc, all are central." Claude responded:
We then went on discussing open-source strategies, tutorials and onboarding materials for contributors and much more which I'll spare you from for now. Finally, I asked it to summarise its views on Ooloi as a project:
Now, this level of enthusiasm and praise from an anthropomorphic-presenting LLM (essentially a glorified Markov chain) always makes me deeply suspicious – I'm absolutely allergic to toxic positivity, especially the kind from the USA – so I asked it "Are you sure you're not overly enthusiastic just to please me?". It replied:
I then asked Claude 3.7 to comment on my journey from Common Lisp to Clojure and on the influence from Common Lisp and CLOS on Ooloi:
And finally, I just couldn't resist asking: "What would Rich Hickey say?"
I guess that hypothetical assessment is good enough for me at this point. Still not quite sure that Claude isn't giving me a friendly reach-around, though... ;)
 As I windsurf through parentheses on my holiday, reviving the spirit of Igor Engraver in the form of FrankenScore, I'm struck by a profound realisation: this is how programming should always feel. Free. Uplifting. Intellectually stimulating. A far cry from being shackled to the oars of enterprise galleys, with some middle manager shouting "ATTACK SPEED!" at bewildered code monkeys. But why should this freedom be a holiday exception? As programmers (not "developers," please!), we should be grounded in computer science thinking. We need to regularly return to these ancient founts of wisdom, like Lisp, and apply their lessons to our everyday work. Otherwise, we're just highly paid button-pushers in a digital sweatshop. Remember when computer science curricula started with Scheme? It wasn't about the language; it was about learning to think algorithmically. Then Oracle, in its infinite wisdom (read: hunger for "cannon fodder"), saw Scheme replaced by Java Enterprise. And thus began the great shitshow that's lasted for decades. Yet, for all its faults, we must tip our hats to Java for gifting us the JVM. And here's where Clojure enters, marrying Lisp's elegance with the JVM's robustness and interoperability. It's like finding out your eccentric uncle and strait-laced aunt had a brilliant love child. But thanks to the JVM, your weird uncle can now fit into the enterprise world. Diving into Clojure led me to Rich Hickey's talks. The man veers into philosophical territory faster than a Silicon Valley startup pivots to blockchain. He ponders things like what names are, and why we use them - essential musings for any first-class programmer. It reminds me of my friend Niklas Derouche, architect and coder extraordinaire, who insists you must read Derrida to be a proper architect. Because nothing says "I understand this codebase" like a healthy dose of deconstruction theory. And he is right. Make no mistake. In three weeks of holiday hacking, I've made more progress and felt more fulfilled than in months of enterprise work. It's a stark reminder of what's possible when we shed unnecessary constraints and return to first principles. So, fellow coders, I challenge you: When was the last time you felt truly free in your programming? Perhaps it's time we all took a holiday to rediscover the Lisp arts. Who knows, you might just find your programming parentheses - I mean, paradigms - shifted. P.S. If you're about to comment that 'modern' languages and frameworks are just as good, save your breath. I'd sooner believe in the tooth fairy than in the supposed superiority of JavaScript or the 'agility' of SAFe. P.P.S. If you missed the Ben Hur reference (you uncultured git), this is sprint execution according to SAFe, with the CTO watching: |
AuthorPeter Bengtson – SearchArchives
April 2026
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