April and May 2024 Short-Term Project Updates

By Kathy Davis

We’ve got several updates to share from our Q1 and Q2 project developers. Check out the latest in their April and May reports.

clj-merge tool: Kurt Harriger
Compojure-api: Ambrose Bonnaire-Sergeant
Instaparse: Mark Engelberg
Jank: Jeaye Wilkerson
Plexus: John Collins
Lost in Lambduhhs Podcast: L. Jordan Miller
Scicloj: Daniel Slutsky


Clj-merge: Kurt Harriger

Q2 2024 Report No. 1. Published May 15, 2024


This tool aims to reduce unnecessary conflicts due to whitespace and syntax peculiarities by using a more semantic approach to diffing and merging. I’m grateful for the support from ClojuristsTogether and the invaluable feedback and support from the Clojure community.

Milestones Overview

The project was structured around several key milestones:

  1. Development of the MVP.
  2. Enhancement of diff handling and presentation.
  3. Community engagement and feedback integration.
  4. Performance optimization and cross-platform compatibility.

Milestone Progress

  1. Development of the MVP

    • Goal: To create a minimal viable product using editscript and rewrite-clj.
    • Progress: The MVP was successfully developed and demonstrated its capability in resolving basic merge conflicts. Initially, extensive diffs suggested a complete rewrite of editscript might be necessary. However, implementing isomorphic translations between rewrite-clj node representations proved to be a sufficient workaround for now.
    • Next Steps: The focus will now shift towards refining this MVP, enhancing its error handling, and improving the user feedback system to make the tool more robust.
  2. Enhancement of Diff Handling and Presentation

    • Goal: To improve the readability and utility of diffs for developers.
    • Progress: The generated diffs are more machine-readable than human-readable, early use of the tool has underscored the importance of easily interpretable diffs emphasizing the need for better visualization.
    • Next Steps: I plan to continue working on improving the presentation of diffs, making them easier to understand and act upon.
  3. Community Engagement and Feedback Integration

    • Goal: To actively engage with the community to gather detailed feedback and real-world merge conflict examples.
    • Progress: Some feedback and bug reports have been received, such as an “index out of range” error; however, collecting comprehensive examples has proved challenging.
    • Next Steps: I aim to increase efforts to engage the community and hope to present at one of the clojure meetups in the near future.
  4. Performance optimization and cross-platform compatibility

    • Goal: Simplify the installation process
    • Progress: The project has been successfully compiled with GraalVM for fast startup. However, these are built during the install process and require GraalVM to be available at install.
    • Next Steps: CICD process to publish the binaries for download.

Future Vision and Calls to Action

Moving forward, enhancing diff visualization will be my primary focus. A more intuitive representation of changes will not only improve the tool’s usability but also its adoption. I encourage everyone in the Clojure community to try clj-mergetool, especially in challenging merge scenarios, and share any issues or feedback. Your contributions are crucial for refining the tool and expanding its capabilities.

Thank you for your continued support and contributions to the clj-mergetool project.

Compojure-api: Ambrose Bonnaire-Sergeant

Q2 2024 Report No. 1 & 2. Published April 30 & June 7, 2024

Report 1: April 2024


I have released ring-swagger 1.0.0, compojure-api 1.1.14 and 2.0.0-alpha33 which all include a critical fix to prevent this memory leak.

Rajkumar Natarajan proposed OpenAPI3 support and I have been reviewing it.

1.0.0 (30.4.2024)


My two main focuses with compojure-api have been to make 2.x backwards compatible with 1.x and implement performance improvements.

I have drafted some further performance ideas as issues and the remaining tasks for 1.x compatilibity are here.

2.0.0-alpha33 (2024-04-30)

2.0.0-alpha32 (2024-04-20)

Report 2: May 2024


This month I concentrated on making compojure-api 2.x backwards compatible with 1.x. I approached this by both backporting features to the 1.x branch and reestablishing 1.x features in the 2.x branch. My hope is when feature-parity is achieved, I can release a stable 2.x and retire the 1.x branch.

This proved to be challenging and resulted in many false starts. The main success this month was adding support for 1.x coercions in 2.x.

My initial conceptualization of the problem was that the move from ring-middleware-format to muuntaja in 2.x was the main breaking change, and backwards compatbility would involve restoring ring-middleware-format support in the 2.x branch. I am reexaminining this assumption, as I realized I conflated 1.x coercions with ring-middleware-format after successfully adding 1.x coercions in 2.x, however this assumption directed most of my development this month.

I initially copied the 1.x test suite to the 2.x branch and attempted to directly change the 2.x branch until the tests pass. This proved overwhelming and highlighted that I did not deeply understand compojure-api’s internals. I abandoned this approach.

To better understand the changes from 1.x to 2.x, I created a PR to compare the 1.x and 2.x branches. Using this PR, I backported many of the changes made in 2.x to 1.x until the major refactoring around coercions was the main difference.

From there, I attempted to backport 2.x coercions into 1.x. This proved very difficult to reason about since the code paths around coercion completely changed in 2.x.

It was a useful exercise, however, and I realized that 1.x coercions could unintrusively be added to 2.x, which was a key stumbling block in my initial attempts at having the 1.x test suite pass in the 2.x branch.

Next month I’m planning to continue on backwards compatibility. I think I may not need to add ring-middleware-format support which addresses a user concern, so I will probably back out any ring-middleware-format related changes I added in 2.x.

Instaparse: Mark Engelberg

Q1 2024 Report 2. Published May 31, 2024

Thanks to funding from Clojurists Together, I have continued to review instaparse pull requests that have been submitted over the past couple of years.

The most interesting and useful issue, which had languished among the pull requests for over two years, was a suggestion to incorporate namespaced non-terminals, so I am pleased to report that this feature has now been implemented, tested, documented, and deployed in instaparse version 1.5.0. The pull request wasn’t quite usable out of the box, as it relied on a feature unique to Clojure 1.11, and I always strive for instaparse to be backwards compatible to Clojure 1.5. But it provided a great starting point for implementation. I think the community will find this feature to be useful.

For the final third of my Clojurists Together time, I have my eye on a bug with negative lookahead that was discovered shortly after instaparse’s initial release. It’s something I have always wanted to fix, but have never had the time to solve it as the problem is quite subtle, and any fix will require in-depth testing since it could have wider ramifications.

Jank: Jeaye Wilkerson

Q2 2024 Reports 1&2. Published April 30 & May 31, 2024

Report 1: April 30, 2024

This quarter, I’m being funded by Clojurists Together to build out jank’s lazy sequences, special loop* form, destructuring, and support for the for and doseq macros. Going into this quarter, I had only a rough idea of how Clojure’s lazy sequences were implemented. Now, a month in, I’m ready to report some impressive progress!

Lazy sequences

There are three primary types of lazy sequences in Clojure. I was planning on explaining all of this, but, even better, I can shine the spotlight on Bruno Bonacci’s blog, since he’s covered all three of them very clearly. In short, we have:

  1. Per-element lazy sequences
  2. Chunked lazy sequences
  3. Buffered lazy sequences

This month, I have implemented per-element lazy sequences, along with partial support for chunked lazy sequences. Chunked lazy sequences will be finished next month. By implementing even per-element lazy sequences, so many new opportunities open up. I’ll show what I mean by that later in this post, so don’t go anywhere!


Prior to this month, jank supported function-level recur. As part of this month’s work, I also implemented loop* and its related recur. When we look at how Clojure JVM implements loop*, it has two different scenarios:

  1. Expression loops
  2. Statement loops

If a loop is in a statement position, Clojure JVM will code-generate labels with goto jumps and local mutations. If the loop is an expression, Clojure JVM generates a function around the loop and then immediately calls that. There is potentially a performance win of not generating the function wrapper and calling it right away, but note that this particular idiom is commonly identified and elided by optimizing compilers. It even has its own acronym: IIFE. (see this also)

jank, for now anyway, simplifies this by always using the IIFE. It does it in a more janky way, though, which is interesting enough that I’ll share it with you all. Let’s take an example loop* (note that the special form of loop is actually loop*, same as in Clojure; loop is a macro which provides destructuring on top of loop* – now you know):

(loop* [x 0]
  (when (< x 10)
    (println x)
    (recur (inc x))))

Given this, jank will replace the loop* with a fn* and just use function recursion. Initial loop values just get lifted into parameters. The jank compiler will transform the above code into the following:

((fn* [x]
   (when (< x 10)
     (println x)
     (recur (inc x)))) 0)

jank code-generates function recursion into a while(true) loop with mutation on some locals for each iteration, similar to Clojure.

However, loop* is tricky, since it can also do anything let* can do. For example (also note: no recursion):

(loop* [a 1
        b (* 2 a)]
  (println a b))

Since we’re using a in the binding for b, we can’t know b until we’ve calculated a, and doing so can involve any arbitrary expression. Agh! This can’t work if we just dump those into the positional parameters of the IIFE. So jank gets around this by actually just wrapping it in a let*. 🙃

(let* [a 1
       b (* 2 a)]
  ((fn* [a b]
    (println a b)) a b))

This could be done in a macro, but since it’s a language-level feature, the compiler does it for us. This means you can still use loop* even if you’re running without clojure.core. As mentioned, this is potentially slower, in the scenario of the loop being in statement position. We can return to this when the performance of loops is the most important thing to tackle. Right now, parity with Clojure and getting jank onto your machine are most important.


Clojure supports all kinds of fancy destructuring of sequences, maps, and keyword arguments. We use destructuring in let, defn, and loop, primarily. One interesting thing about this destructuring is that there’s no compiler support for it at all; it’s not a language-level feature. It’s a library feature, done entirely in macros. The amazing thing about it is that, as long as we support all of the core functions required, we can support destructuring. The actual destructure function is huge, but you can see it in Clojure’s source here.

This month, I implemented all of the missing functions required for the destructure function to be ported over to jank. Largely, once all those functions were implemented, the port just meant updating Java interop in a few places to be C++ interop. Now jank supports all of the fancy destructuring Clojure does, in all the same places. This helps demonstrate how much closer jank is to being a complete Clojure dialect, since complex functions like this can almost just work.

New clojure.core functions

So, to support lazy sequences and destructuring, I needed to add several new core functions. While adding those, I tended toward implementing any similar or surrounding functions as well. I got a little carried away, to be honest. Let’s take a look at the new functions jank now supports.

take (no transducer) cycle
take-while (no transducer) repeat
drop (no transducer) seq?
filter (no transducer) concat
identity ->
constantly ->>
into (no transducer) cond->
mapv zipmap
filterv last
reduce butlast
nthrest map?
nthnext key
partition val
partition-all dissoc
partition-by ident?
dorun simple-ident?
doall qualified-ident?
when-let boolean
when-some nth
when-first loop
split-at peek
split-with pop
drop-last for
take-last chunk-buffer
chunk-append destructure

That’s 52 new functions/macros! That alone amounts to around 10% of all the functions in clojure.core jank will be implementing. A few of these will need some updates once jank fully supports chunked lazy sequences and transducers, but they’re all very usable today. You may also note that for is in there, which was one of the goals this quarter.

Migration from Cling to Clang

jank is much closer to running on Clang’s JIT compiler than it was a month ago. Some recent patches have landed which partially address a blocking bug with pre-compiled header handling in Clang’s internal C++ JIT compiler. I have identified another small reproduction case for what I hope to be the rest of the issues. Part of my work this month involved getting jank running on LLVM 19 and updating filling out the related CMake system to be able to flexibly bring in LLVM on any system.

Once jank moves away from Cling in favor of Clang, building and distributing jank will be significantly easier. Developers won’t need to compile a custom Cling/Clang/LLVM stack. On top of that, Clang’s JIT compiler has recently landed support for loading C++20 modules, which can serve as an less-portable equivalent to JVM’s class files, allowing jank to load pre-compiled modules very quickly. This will drastically optimize jank’s startup time, but will require some work to get going. I’ll keep you updated!

What’s next?

I’m well ahead of schedule, for the quarter, but I need to finish up chunked sequences and doseq. I’ll have time after that and I’d like to get atoms working, since most Clojure programs have some form of state. From there, I can look into strengthening native interop and making jank more easily distributable, but let’s not get ahead of ourselves.


Report 2: May 31, 2024

Hey folks! I’ve been building on last month’s addition of lazy sequences, loop*, destructuring, and more. This month, I’ve worked on rounding out lazy sequences, adding more mutability, better meta support, and some big project updates.

Chunked sequences

I’ve expanded the lazy sequence support added last month to include chunked sequences, which pre-load elements in chunks to aid in throughput. At this point, only clojure.core/range returns a chunked sequence, but all of the existing clojure.core functions which should have support for them do.

If you recall from last month, there is a third lazy sequence type: buffered sequences. I won’t be implementing those until they’re needed, as I’d never even heard of them before researching more into the lazy sequences in Clojure.

Initial quarter goals accomplished

Wrapping up the lazy sequence work, minus buffered sequences, actually checked off all the boxes for my original goals this quarter. There’s a bottomless well of new tasks, though, so I’ve moved onto some others. So, how do I decide what to work on next?

My goal is for you all to be writing jank programs. The most important tasks are the ones which bring me closer to that goal. Let’s take a look at what those have been so far.

Volatiles, atoms, and reduced

Most programs have some of mutation and we generally handle that with volatiles and atoms in Clojure. jank already supported transients for most data structures, but we didn’t have a way to hold mutable boxes to immutable values. Volatiles are also essential for many transducers, which I’ll mention a bit later. This month, both volatiles and atoms have been implemented.

Implementing atoms involved a fair amount of research, since lockless programming with atomics is not nearly as straightforward as one might expect.

As part of implementing atoms, I also added support for the @ reader macro and the overall derefable behavior. This same behavior will be used for delays, futures, and others going forward.

Meta handling for defs

Last quarter, I added support for meta hints, but I didn’t actually use that metadata in many places. Now, with defs, I’ve added support for the optional meta map and doc string and I also read the meta from the defined symbol. This isn’t a huge win, but it does mean that jank can start using doc strings normally, and that we can do things like associate more function meta to the var in a defn, which can improve error reporting.


There will be many jank projects and I’ve known for a while that I want them all to be in one git monorepo. This makes code sharing, searching, refactoring, and browsing simpler. It gives contributors one place to go in order to get started and one place for all of the issues and discussions. It’s not my intention to convince you of anything, if you’re not a fan of monorepos, but jank is now using one.

This started by bringing in lein-jank, which was initially created by Saket Patel. From there, I’ve added a couple of more projects, which I’ll cover later in this update.

New clojure.core functions

Following last month’s theme, which saw 52 new Clojure functions, I have excellent news. We actually beat that this month, adding 56 new Clojure functions! However, I only added 23 of those and the other 33 were added by madstap (Aleksander Madland Stapnes). He did this while also adding the transducer arity into pretty much every existing sequence function. I added volatiles to support him in writing those transducers.

dotimes chunk
chunk-first chunk-next
chunk-rest chunk-cons
chunked-seq? volatile!
vswap! vreset!
volatile? deref
reduced reduced?
ensure-reduced unreduced
identical? atom
swap! reset!
swap-vals! reset-vals!
compare-and-set! keep
completing transduce
run! comp
repeatedly tree-seq
flatten cat
interpose juxt
partial doto
map-indexed keep-indexed
frequencies reductions
distinct distinct?
dedupe fnil
every-pred some-fn
group-by not-empty
get-in assoc-in
update-in update
cond->> as->
some-> some->>

New projects

At this point, I was thinking that jank actually has pretty darn good Clojure parity, both in terms of syntax and essential core functions. So how can I take the best steps toward getting jank onto your computer?

Well, I think the most important thing is for me to start writing some actual projects in jank. Doing this will require improving the tooling and will help identify issues with the existing functionality. The project I’ve chosen is jank’s nREPL server. By the end of the project, we’ll not only have more confidence in jank, we’ll all be able to connect our editors to running jank programs!

nREPL server

nREPL has some docs on building new servers, so I’ve taken those as a starting point. However, let’s be clear, there are going to be a lot of steps along the way. jank is not currently ready for me to just build this server today and have it all work. I need a goal to work toward, though, and every quest I go on is bringing me one step closer to completing this nREPL server in jank. Let’s take a look at some of the things I know I’ll need for this.

Module system

jank’s module system was implemented two quarters ago, but since there are no real jank projects, it hasn’t seen much battle testing. To start with, I will need to work through some issues with this. Already I’ve found (and fixed) a couple of bugs related to module writing and reading while getting started on the nREPL server. Further improvements will be needed around how modules are cached and timestamped for iterative compilation.

Native interop

Next, jank’s native interop support will need to be expanded. I’ve started on that this month by making it possible to now write C++ sources alongside your jank sources and actually require them from jank! As you may know, jank allows for inline C++ code within the special native/raw form, but by compiling entire C++ files alongside your jank code, it’s now much easier to offload certain aspects of your jank programs to C++ without worrying about writing too much C++ as inline jank strings.

jank’s native interop support can be further improved by declaratively noting include paths, implicit includes, link paths, and linked libraries as part of the project. This will likely end up necessary for the nREPL server.

AOT compilation

Also required for the nREPL server, I’ll need to design and implement jank’s AOT compilation system. This will involve compiling all jank sources and C++ sources together and can allow for direct linking, whole-program link time optimizations (LTO), and even static runtimes (no interactivity, but smaller binaries).


Finally, both jank and the nREPL server will need distribution mechanisms for Linux and macOS. For jank, that may mean AppImages or perhaps more integrated binaries. Either way, I want this to be easy for you all to use and I’m following Rust/Cargo as my overall inspiration.

I hope I’ve succeeded in showing how much work still remains for this nREPL server to be built and shipped out. This will take me several months, I’d estimate. However, I think having this sort of goal in mind is very powerful and I’m excited that jank is far enough along to where I can actually be doing this.

nREPL server progress

Since I have C++ sources working alongside jank source now, I can use boost::asio to spin up an async TCP server. The data sent over the wire for nREPL servers is encoded with bencode, so I started on a jank.data.bencode project and I have the decoding portion of that working. From there, I wanted to write my tests in jank using clojure.test, but I haven’t implemented clojure.test yet, so I looked into doing that. It looks like clojure.test will require me to implement multimethods in jank, which don’t yet exist. On top of that, I’ll need to implement clojure.template, which requires clojure.walk, none of which have been started.

I’ll continue on with this depth-first search, implementing as needed, and then unwind all the way back up to making more progress on the nREPL server. Getting clojure.test working will be a huge step toward being able to dogfood more, so I don’t want to cut any corners there. Once I can test my decode implementation for bencode, I’ll write the encoding (which is easier) and then I’ll be back onto implementing the nREPL server functionality.

Hang tight, folks! We’ve come a long way, and there is still so much work to do, but the wheels are rolling and jank is actually becoming a usable Clojure dialect. Your interest, support, questions, and encouragement are all the inspiration which keeps me going.

Plexus: John Collins

Q2 2024 Report 1. Published May 15, 2024

1. Much better Loft Algorithm.

The previous loft algorithm simply mapped vertices between one-to-one between cross sections. This meant that lofted cross-sections had to have the same number of vertices. The new loft algorithm is much more general and now supports many-to-one and one-to-many vertex mappings. Loft is a very important operation that is not available in many programmatic CAD tools, so I’m excited to have support for this.

I go over this a bit more in a blog post: http://www.cartesiantheatrics.com/2024/04/09/perfect-loft.html

2. Text rendering Support

I wrote a first-cut at support for text rendering in companion library to plexus called clj-manifold3d. There is not now a simple function called text that takes a .ttf font file, a string, and a few other parameters and renders the string to a 2D cross section that can be extruded like any other cross section. This is done in the c++ by interpolating freetype2 glyphs.

Aside from built-in support for textures, this is the last major feature that clj-manifold lacked compared to OpenSCAD.

3. N Slices

There is now a highly efficient algorithm to slice a manifold into N evenly spaced 2D cross sections. This is a primitive that can be used to implement custom slicers. In the future, I will likely be writing a slicer that gives a high level of control over the G-code generated.

4. Three Point Arcs

You can now create arcs given three points. This lets you draw circles or arcs in a similar way that is often done in graphical CAD systems. It is useful for many complex polygon constructions.

5. Progress on Navigation on Manifolds

This is totally unfinished and unproven, but I have been experimenting heavily with a somewhat unique way doing custom texturing of manifolds. It works by creating a set of primitives that make it easy to “draw on” arbitrary 3D manifolds as if you’re drawing on a 2D plane. I’m hoping achieve something more fine-tunnable than common texturing methods (like UV mapping) and that could integrate seamlessly with Plexus’s abstractions. It has proven to be challenging unclear how well it’s going to work out.

While continuing to tinker with this approach, I’ll likely add support for more standard texturing.

6. Example projects

I improved and updated example projects that demonstrate using plexus for real-world problems. These are:

I will try more examples in the future.

7. Better support for 2D affine transforms

2D affine transforms (3x2 matrices) are now fully supported. They support the same API as 2D cross sections. Having affine transforms as independent objects is a big advantage over OpenSCAD.

8. Tests, Docs, Code Cleanup

Documentation is still sorely lacking, but README’s have been improved. There are more tests now and somewhat better API documentation in the code. There are also now github action pipelines in an effort to bring a bit more transparency and professionalism to the dev process.

Lost in Lambduhhs Podcast: L. Jordan Miller

Q2 2024 Report 1. Published May 15, 2024.

Progress Overview

I have made significant progress on my new podcast series, thanks to the support from Clojurists Together. Here are the key milestones I’ve achieved so far and my plans moving forward:

Platform Subscription

Guest Coordination

Recordings and Editing

Release Schedule

Next Steps


I am on track with my project timeline and excited about the content I am creating. I will continue to provide updates as I progress further.

Scicloj: Daniel Slutsky

Q1 2024 Report 3. Published April 30, 2024

April 2024 was the last of three months on the Clojurists Together project titled “Scicloj Community Building and Infrastructure”.

Scicloj is an open-source group developing Clojure tools and libraries for data and science. As a community organizer at Scicloj, my current role is to help make the emerging Scicloj stack easier and more accessible for broad groups of Clojurians. I collaborate with a few Scicloj members on this.

While this is the last update under the Clojurists Together 2024 Q1 support, the project will, of course, continue.

Below are the sub-projects that were addressed during April 2024. They are listed by their proposed priorities for the coming month.

The new real-world-data group is ranked highest for its impact on community growth. This means the following. Assuming this group will (hopefully) grow well and demand attention, the goals of other projects will receive less attention and will be delayed. However, some of them (e.g., required extensions or bugfixes to libraries) will receive more attention if the real-world-data group requires them.

The real-world-data group

The real-world-data group is a space for Clojure data and science practitioners to bring their data projects, share experiences, and evolve common practices.

April summary

May goals


The Noj project bundles a few recommended libraries for data and science and adds convenience layers and documentation for using them together.

April summary

May goals

translating books

In this project, we are renewing previous efforts to systematically review data science books in other programming languages and convert them to Clojure.
The goal is twofold: figuring out what common data science needs are still missing in the Clojure stack and creating well-polished documentation of this stack. It is also an opportunity for Clojurians to get involved in the data science community and learn from books they are curious about.

April summary

May goals

visual-tools group

This group’s goal is to create collaborations in learning and building Clojure tools for data visualization, literate programming, and UI design.

April summary

May goals

Clojure Data Scrapbook

The Clojure Data Scrapbook is intended to be a community-driven collection of tutorials around data and science in Clojure.

April summary

May goals


Clay is a minimalistic namespace-as-a-notebook tool for literate programming and data visualization.

April summary

May goals


Kindly is a proposed standard for requesting data visualizations in Clojure.

April summary

May goals


Cmdstan-clj is a draft library for interop with Stan (probabilistic modeling through Bayesian statistics).

April summary

May goals


ClojisR is a bridge between Clojure and the R language for statistical computing. During this Month, @generateme released the first non-beta version of the library and announced it as stable after 4.5 years of usage.

April summary

May goals

The Scicloj website

April summary

May goals

Your feedback would help

Scicloj is in transition. On the one hand, quite a few of the core members have been very active recently, developing the emerging stack of libraries. At the same time, new friends are joining, and soon, more people will enjoy the Clojure for common data and science needs.

If you have any thoughts about the current directions, or if you wish to discuss how the evolving platform may fit your needs, please reach out.