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@holyjak
holyjak / defdecorator.clj
Last active October 15, 2021 13:59
Macro to create a decorator (wrapper) for a objects implementing a Java interface
;; A macro to create a decorator (wrapper) for a objects implementing a Java interface
;; Disclaimer: The code most certainly is not perfect and does not handle some corner cases
;; License: The Unlicense http://unlicense.org/
(require '[clojure.string :as str])
(defn type->tag [parameter-type]
(let [array? (-> parameter-type name (str/ends-with? "<>"))
primitive? '#{int long float double short boolean byte char}
type (if array?
(-> parameter-type name (str/replace #"<>$" "") symbol)
@ssrihari
ssrihari / clojure-learning-list.md
Last active November 11, 2025 03:07
An opinionated list of excellent Clojure learning materials

An opinionated list of excellent Clojure learning materials

These resources (articles, books, and videos) are useful when you're starting to learn the language, or when you're learning a specific part of the language. This an opinionated list, no doubt. I've compiled this list from writing and teaching Clojure over the last 10 years.

  • 🔴 Mandatory (for both beginners and intermediates)
  • 🟩 For beginners
  • 🟨 For intermediates

Table of contents

  1. Getting into the language
@hiredman
hiredman / ui.cljc
Created December 2, 2022 21:17
#!/bin/sh
#_(
true; exec clj -J-Xmx256M -J-XX:-OmitStackTraceInFastThrow -Sdeps "`sed -n -e '/;;(DEPS$/,/;;DEPS)$/p' $0`" -M -i $0 -e '(user/main)'
)
(ns user
#?(:cljs (:require-macros [user :as m]))
(:require
#?@(:clj ([cljs.build.api :as cljs]
@VictorTaelin
VictorTaelin / dps_sup_nodes.md
Last active June 28, 2025 09:16
Accelerating Discrete Program Search with SUP Nodes

Fast Discrete Program Search 2

I am investigating how to use Bend (a parallel language) to accelerate Symbolic AI; in special, Discrete Program Search. Basically, think of it as an alternative to LLMs, GPTs, NNs, that is also capable of generating code, but by entirely different means. This kind of approach was never scaled with mass compute before - it wasn't possible! - but Bend changes this. So, my idea was to do it, and see where it goes.

Now, while I was implementing some candidate algorithms on Bend, I realized that, rather than mass parallelism, I could use an entirely different mechanism to speed things up: SUP Nodes. Basically, it is a feature that Bend inherited from its underlying model ("Interaction Combinators") that, in simple terms, allows us to combine multiple functions into a single superposed one, and apply them all to an argument "at the same time". In short, it allows us to call N functions at a fraction of the expected cost. Or, in simple terms: why parallelize when we can share?

A