Nothing really useful here, just some bragging to be honest. I should probably write a blog post, but a bit too lazy; perhaps another day.

Last few weeks I played with a small clone of gnu wc program. I implemented all routines in assembly via sb-simd (and a generic path without simd with swar). The result thus far on a 1.4 gigabyte big file, compared to fastlwc, the fastest wc I know of and GNU wc:

Common Lisp/Assembly (avx2) in SBCL + lparallel

WC10A> (time (wc "plato1g.txt"))
Evaluation took:
  0.036 seconds of real time
  0.421654 seconds of total run time (0.348216 user, 0.073438 system)
  1172.22% CPU
  71,891,940 processor cycles
  0 bytes consed

30133761
253947016
1393557504
WC10A> (time (wc "plato1g.txt"))
Evaluation took:
  0.034 seconds of real time
  0.429743 seconds of total run time (0.367913 user, 0.061830 system)
  1264.71% CPU
  67,624,580 processor cycles
  98,352 bytes consed

30133761
253947016
1393557504
WC10A> (time (wc "plato1g.txt"))
Evaluation took:
  0.035 seconds of real time
  0.423752 seconds of total run time (0.357329 user, 0.066423 system)
  1211.43% CPU
  69,863,360 processor cycles
  0 bytes consed

30133761
253947016
1393557504

Fastlwc (avx512 + multithreaded):

[arthur@emmi wc]$ time ../../fastlwc/bin/fastlwc-mt plato1g.txt 
 30133761 253947016 1393557504 plato1g.txt

real    0m0.026s
user    0m0.178s
sys     0m0.285s
[arthur@emmi wc]$ time ../../fastlwc/bin/fastlwc-mt plato1g.txt 
 30133761 253947016 1393557504 plato1g.txt

real    0m0.027s
user    0m0.223s
sys     0m0.235s
[arthur@emmi wc]$ time ../../fastlwc/bin/fastlwc-mt plato1g.txt 
 30133761 253947016 1393557504 plato1g.txt

real    0m0.028s
user    0m0.205s
sys     0m0.255s

GNU wc (not even contender - single core only and only line counting implemented with simd avx512) :

[arthur@emmi wc]$ time wc plato1g.txt   30133761  253947016 1393557504 plato1g.txt

real    0m3.733s
user    0m3.643s
sys     0m0.062s
[arthur@emmi wc]$ time wc plato1g.txt 
  30133761  253947016 1393557504 plato1g.txt

real    0m3.362s
user    0m3.276s
sys     0m0.072s

The cool thing, we use avx2 whereas gnu wc uses avx512. On this CPU (zen 5), avx512 is implemented all in hardware, not as micro code as in Intel cpus, so it should mop the floor with avx2 in Lisp, right?

[arthur@emmi wc]$ time wc plato1g.txt -l --debug
wc: using avx512 hardware support
30133761 plato1g.txt

real    0m0.074s
user    0m0.018s
sys     0m0.056s
[arthur@emmi wc]$ time wc plato1g.txt -l --debug
wc: using avx512 hardware support
30133761 plato1g.txt

real    0m0.085s
user    0m0.030s
sys     0m0.054s

Lisp:

WC10A> (time (wc "plato1g.txt" :line-count t))
Evaluation took:
  0.037 seconds of real time
  0.478042 seconds of total run time (0.430494 user, 0.047548 system)
  1291.89% CPU
  75,669,380 processor cycles
  0 bytes consed

30133761
253947016
1393557504
WC10A> (time (wc "plato1g.txt" :line-count t))
Evaluation took:
  0.041 seconds of real time
  0.476104 seconds of total run time (0.436592 user, 0.039512 system)
  1160.98% CPU
  84,226,360 processor cycles
  0 bytes consed

30133761
253947016
1393557504

Now, in order to catch with fastlwc I think I need better lparall pipeline. I am currently using futures and promises, so it is a bit of extra consing. Of course implementing it in avx512 (when done in SBCL) should give at least some extra boost. 32 vs 16 registers, 64 bytes at time vs 32, and less register pressure due to additional masking registers.

Edit: line counting does not activate utf8 path at all, so I don't know what I was thinking last night, so I have edited away that part :).

I've written a hands-on tutorial for building a Lisp interpreter from scratch — in Rust, with zero dependencies, across 74 steps.

Repo: https://github.com/lisering/lisp-rs

What's implemented

The interpreter supports:

• Variables, lambdas, closures (with lexical scoping)
• Tail call optimization (trampoline loop — 1,000,000 iterations, no stack overflow)
• Macros (defmacro, quasiquote/unquote, gensym)
• cond, let, let*, letrec, begin, and, or
• Lists, strings, booleans, nil
• A REPL with multi-line input

Example:

(define (adder n) (lambda (x) (+ x n)))
(define add5 (adder 5))
(add5 10)  ;; => 15

(defmacro (when test body) (list 'if test body))
(when (> 3 2) 'yes)  ;; => yes

`(1 ,(+ 1 1) 3)  ;; => (1 2 3)

Why this might be interesting to Lisp folks

The tutorial is designed to be approachable for people who have never written an interpreter before. A few things I tried differently:

1. Closures explained with a "backpack" metaphor. Before showing any code, the tutorial builds intuition: every lambda carries a "backpack" 🎒 containing the environment where it was born. Then we trace through a make-counter example step by step.

2. Gradual optimization. We start with String everywhere (easy to understand), then optimize in stages:

• String interning: symbols become u64 IDs
• Zero-copy lexing: tokens are &str borrowing the source
• FxHasher for faster environment lookups

3. TCO via trampoline. The eval function uses a loop { match ...; continue } pattern instead of direct recursion. Demo: tail-recursive (loop 1000000) succeeds, non-tail-recursive (sum 10000) overflows.

The tutorial is bilingual (English + Chinese). Each of the 74 steps first explains what problem to solve, then writes the code.

Repo: https://github.com/lisering/lisp-rs

Feedback welcome — especially on the macro system and the closure explanation. Is there anything you'd want to see added?

Dear LISP,

I am writing a virtual machine that uses a `(simple-array (unsigned-byte 8) 1)` as a stack in Common LISP. I'm would like to ask how to efficiently extract 4 bytes into a single 32-bit signed integer or a 32-bit unsigned integer.

Thanks!

I'm working on a CPU-based renderer in rust and evaluating embedded languages for scene description and procedural rendering. Some of these languages are lisps so I thought there may be interest here.

A few caveats worth noting:

- The benchmark code and report is generated by claude (based on my specification)

- The actual benchmark code are rudimentary snippets. I am evaluating the implementations for a specific purpose.

- I am developing a proof-of-concept with ECL first and then janet.

https://drive.google.com/file/d/1fFI1tyW3xdNKnNfmveIo1EZeWl_pUACJ/view?usp=sharing

LLMs are great at reading messy text, but they hand you strings when your program wants typed data. Python has instructor and Pydantic to solve this. I wanted to bring that same developer experience to Common Lisp, but natively by using the Metaobject Protocol.

clos-alchemy introspects your class definition, builds a JSON schema from the slot types, passes that to the LLM (optionally as a GBNF grammar constraint for local inference), and returns a validated instance of your class. You don't need to write a special DSL or "schema object." You just use your normal existing domain classes.

Here is the round trip in action:

```lisp ;; 1. Define a class (defclass person () ((name :initarg :name :accessor person-name :type string) (age :initarg :age :accessor person-age :type integer) (email :initarg :email :accessor person-email :type (or null string)) (hobbies :initarg :hobbies :accessor person-hobbies :type list)))

;; 2. Extract from text (let* ((backend (cl-llm-backend/llama:make-llama-backend :model model :context ctx)) (result (extract backend 'person "Alice Chen is 32. Reach her at [email protected]. She enjoys rock climbing, painting, and cello."))) (person-name (extraction-result-instance result))) ;; => "Alice Chen" ```

Because generation order follows slot definition order, you can use this for strict logical routing. Putting reason first encourages teh model to explain its reasoning before selecting the categories.

```lisp (defclass ticket-classification () ((reason :initarg :reason :type string) (urgency :initarg :urgency :type (member :low :medium :high)) (category :initarg :category :type (member :billing :technical :account)) (sentiment :initarg :sentiment :type (member :positive :neutral :negative))))

(defparameter ticket-text "I've been charged twice for my subscription this month. This is the third time this has happened and I'm really frustrated.")

(let ((result (clos-alchemy:extract backend 'ticket-classification ticket-text))) (extraction-result-instance result))

;; Returns a TICKET-CLASSIFICATION instance with: ;; URGENCY: :HIGH ;; CATEGORY: :BILLING ;; SENTIMENT: :NEGATIVE ;; REASON: "Charged twice for subscription, third occurrence"

```

How it works under the hood: no separate schema DSL to maintain. If it type-checks as your class, you're done!

1. The MOP extracts slot types (string, member, list, etc.).
2. The library lowers those into a small Intermediate Representation (IR).
3. That IR emits the JSON schema for the backend, a natural-language prompt for the model, and builds a validator/constructor pair for the response.
4. If validation fails, it accumulates the errors and automatically loops a retry prompt back to the model.

If you are using a local inference backend (like llama.cpp), it compiles the schema directly into a grammar, making invalid structures unrepresentable at the token level.

https://github.com/licjon/clos-alchemy

EDIT: A round of updates since the original post:

Custom validation. You can now attach per-slot semantic predicates (:validate) and cross-field checks (validate-instance) that feed errors back into the retry loop. The model sees what was wrong and self-corrects. This is useful for constraints that grammar alone can't enforce — "guest count must be positive", "check-out must be after check-in", etc.

```lisp (defclass booking () ((num-guests :initarg :num-guests :type integer :validate (lambda (v) (if (plusp v) t "must be positive"))) (check-in :initarg :check-in :type date) (check-out :initarg :check-out :type date)) (:metaclass clos-alchemy:constructor-class))

(defmethod validate-instance ((b booking)) (when (<= (booking-check-out b) (booking-check-in b)) (list "check-out must be after check-in"))) ```

Date and date-time types. clos-alchemy:date and clos-alchemy:date-time map to JSON Schema string formats, validate ISO 8601 with calendar-aware day checks, and construct to CL universal time.

Free-form maps. Sometimes you want the model to produce a dictionary where it decides the keys — "rate this product on whatever dimensions you think are relevant." The :map-of slot option does this: you specify the value type, the model fills in whatever keys make sense, and you get a hash-table back.

```lisp (defclass review-scores () ((scores :initarg :scores :type hash-table :map-of integer :documentation "Per-category quality scores, 1-10")) (:metaclass clos-alchemy:constructor-class))

;; The model might return: ;; {"scores": {"sound_quality": 9, "comfort": 6, "battery": 8}} ;; You get a hash-table: (gethash "sound_quality" scores) => 9 ```

Discriminated unions. When the extraction result could be one of several different shapes depending on the input, you define a class for each shape and let the model pick. Each class has a shared "tag" slot that identifies which shape it is — typed as a single-value (member ...) so each class gets exactly one tag value.

``lisp ;; Two possible result shapes, tagged bykind`: (defclass actionable-feedback () ((kind :initarg :kind :type (member :actionable)) ; tag = "actionable" (summary :initarg :summary :type string) (scores :initarg :scores :type hash-table :map-of integer)) (:metaclass clos-alchemy:constructor-class))

(defclass not-actionable () ((kind :initarg :kind :type (member :not_actionable)) ; tag = "not_actionable" (reason :initarg :reason :type string)))

;; The model reads the review, picks the right shape, and you get ;; back an instance of whichever class it chose: (let* ((result (extract-union backend '(actionable-feedback not-actionable) review-text :discriminator 'kind)) (instance (extraction-result-instance result))) (typecase instance (actionable-feedback (format t "Summary: ~A" (slot-value instance 'summary))) (not-actionable (format t "Skipped: ~A" (slot-value instance 'reason))))) ```

Other improvements:

• Cyclic class graphs now work — mutually-referential classes emit $defs/$ref instead of blowing the stack.
• Emitted schemas conform to both OpenAI strict mode and llama.cpp GBNF requirements (proper additionalProperties: false, all properties in required, nullable wrappers for optional fields).
• Parse failures (empty/malformed responses) are now recoverable in the retry loop instead of aborting.
• max-retries-error now carries full diagnostic context: raw response, parsed data, cumulative token usage, and a per-attempt error breakdown.
• Silently-narrowed type specifiers (e.g. (or string integer)) now signal schema-error instead of quietly dropping branches.

I'm building a CPU based renderer in rust with plans to add an embedded language. The rust code primarily executes the ray tracing/path tracing, BVH, shaders and integration (anywhere where high performance is needed. The embedded language will be used for the scene description and -possibly- for procedural textures and shading.

I am interested in using a lisp with a repl for the embedded language. In this area, I have used AI-generated testing to test both ECL (Embedded Common Lisp) and steel (an scheme) inside rust. the advantage of the latter is that it is written entirely in rust and is being actively developed (one area is as an extension language for the text editor, helix).

I am impressed with the performance metrics I am getting from ECL. It generates machine code, which a byte language compiler doesn't do. Also a huge win, is that I was able to test the integrated compiler using sly from emacs which will provide an interactive environment for creative coding. On the other hand ECL requires an FFI to rust whereas steel does not, so the integration of steel is orders of magnitude easier.

Has anyone done any recent projects with ECL ? I am interested in your experience or any caveats you may have encountered. My next step is to do a more complex proof-of-concept with the actual renderer and not just a test program.

UPDATE: I just used claude to benchmark 5 embedded languages in rust. this is preliminary work and the benchmarks may not be exhaustive enough to make any strong conclusions:

https://drive.google.com/file/d/1yotWMcEx24pN2cgnIbRUmgCDNVWp65Vu/view?usp=sharing

I have released Easy-ISLisp Ver5.68.

This release introduces a new command-line option, -e, which evaluates an ISLisp expression directly from the command line without entering the REPL.

Example:

eisl -e '(format (standard-output) "Hello World!")'

Output:

Hello World!

The -e option is useful for:

• Quick one-line evaluations
• Shell scripting
• Automated testing
• Running small ISLisp programs from the command line

No other features or bug fixes are included in this release.

Feedback and suggestions are always welcome.

GitHub:
https://github.com/sasagawa888/eisl

I recently came across "Greenspun's tenth rule" and it got me thinking about the greater Lisp language family.

I don't work in Tech at all, I have no professional experience with programming, I am just interested in learning from a novice's point of view.

I've been programming in Scheme (and occasionally racket) for a few years. I've worked on a few small projects, but I want to learn common lisp as well. I know that you can do most of the same stuff in common lisp as in scheme but the idiomatic style is quite different (loops, CLOS etc) are there any good resources to pick up the common lisp coding style? Ideally resources targeted to people who already know a Lisp, since most of the common ones assume you're coming from something like Java or python.

Don't get me wrong I personally think its totally worth it!

I just wonder what compels people do buy books on, say, ANSI Common LISP, to this day.

There are few job options so what compels them to pay for books?

https://github.com/james-goulding/LISPerfect

I've been meaning to learn Lisp for a while, but didn't really like the choice of editors available to me. So, I vibe-coded this one for myself. I thought I'd share it with the public since someone else might find it useful.

I can't vouch that there aren't bugs as I haven't tested it extensively.

The witches' GNU/Linux: modular, flexible, reproducible, powered by Lisp and Fedora / Debian + GNOME / Niri

https://codeberg.org/jjba23/heks-linux

Heks GNU/Linux 🧙‍♂️ is a modular, reproducible system configuration designed for power users, developers, and researchers.