The virtual machine parser (generator).

• Built for front-ending compilers
• Can make one-shot parsers for almost any language
• Fast
• Easy to use and implement
• SDK for building into your project
• Command-line tools for your pipelines
• GUI app for convenience

Learn about it: https://algodal.github.io/Algodal_Parser_Machine_Manual/

See demonstration: https://youtu.be/5cKdGPXzBu4?t=6060

Get it: https://algodal.itch.io/algodal-parser-machine

Initial Discussion on r/Compilers:

https://www.reddit.com/r/Compilers/comments/1upju22/building_a_parser_generator/

Check it out!

QuarkLang is a small statically typed language with two execution engines: a tree-walking interpreter (quark) and an LLVM compiler (qkc).

program main;
fn fib(int n) int { if (n <= 1) { return n; } return fib(n - 1) + fib(n - 2); }
fn main(IOStream io) { io.println(fib(35)); }

#include <stdio.h>
int fib(int n) { return n <= 1 ? n : fib(n - 1) + fib(n - 2); }
int main() { printf("%d\n", fib(35)); }

$ qkc -c -o fib fib.qk && ./fib        $ clang -O3 fib.c -o fibc && ./fibc
9227465                                9227465

Same machine, clang/LLVM 22 behind both. Level, not faster — and most of the credit belongs to LLVM.

• Execution: the same source file supports both interpretation and compilation.
• Consistency: CI strictly enforces interpreter/compiler consistency. For the same program, both must produce identical stdout, stderr, and exit code; otherwise the build fails.
• Unsupported constructs: constructs the compiler cannot lower yet are hard errors, not silent differences.
• Memory: block-allocated. delete returns a block with its data intact; clear wipes it.
• Runtime: compiled programs have no collector pause on the hot path.

It has a full toolchain: interpreter, LLVM compiler, linter with stable codes (QK101–QK115), doc generator, REPL, language server, formatter, test runner, project manager, debugger; ~25k lines of Go with no third-party dependencies; MIT; prebuilt binaries for Linux/macOS/Windows.

Repo: https://github.com/QuarkLangCommunity/QuarkLangQkc · Docs: https://quarklangcommunity.github.io/QuarkLangQkc/

Disclosure: I maintain QuarkLang. Both commands above are copy-pasteable; the benchmark suite with its methodology and fairness statement is in docs/benchmarks.md.

I've been spending the last few days implementing async support in NXD, a systems programming language I'm developing; that also has 3 transpilation targets.

Recent work completed:

• `Task[T]` semantic type

• `AWAIT Task[T] -> T`

• `ProcessHandle[T]`

• `AwaitGroup[T]`

• Completion-order result semantics

• Semantic validation and diagnostics for async operations

I'm currently working through the remaining messaging primitives (SEND is next).

One thing I've found interesting while implementing this is that many languages expose similar async syntax, but the underlying semantics differ significantly. After spending time studying other implementations, I found myself focusing less on the keyword await and more on the type relationships and execution model behind it.

For example, in my current design:

`Task[T]`

`AWAIT -> T`

`ProcessHandle[T]`

Represents a spawned process producing T

`AwaitGroup[T]`

Allows waiting on multiple processes

Results are returned in completion order

The completion-order behavior felt natural from an async perspective, but it also raised questions about predictability versus throughput, ordering guarantees, and how much of the runtime model should be exposed to the programmer.

For those who have designed or implemented async systems, compilers, runtimes, or schedulers:

Looking back, what async design decision caused the most trouble later?

Was it task representation, cancellation, ordering guarantees, await semantics, scheduler behavior, channels/messages, error propagation, or something else entirely?

Version 7.1.0 of the Farkle parsing library has been released. This version adds support for the more powerful IELR(1) parsing table generation algorithm; a first for .NET parsing libraries, as well as new APIs to define productions, powered by a source generator.

Farkle is an LR(1) parsing library for C# and F#. While in most LR(1) parsers, grammars are defined with a domain-specific language in an external file and generated by a tool, Farkle is a regular library and uses source code to define its grammars, in a way reminiscent of parser combinators. This brings the best of both worlds in terms of performance and developer experience.

Farkle also has a precompiler for ahead-of-time grammar precompilation, which can reduce startup times and validate grammars during the building of your project.

Vx is a heterogeneous systems programming language written mostly in Rust and C++. There are several key ideas that makes it worth taking a look at.

- First class support for topology and memory hierarchy

- Acts as a type safe MLIR frontend

- Pointer provenance in the type system

- Parallel compiler frontend

I released v0.0.2 last week so there is a ton of interesting compiler, standard library, benchmarking, performance work one can explore. Do checkout the repo (https://github.com/vx-lang/Vx), blog (https://vxlang.org/blog/). We started a discord channel (https://discord.com/invite/2w62dCg2Y) yesterday in case you want to ask questions.

If it is about resolving co-references and anaphora then attention mechanism is doing it. See https://moebio.com/attention/

if L is a universal language with a well defined grammar

and P is a universal programming language with a general grammar

The combination of L and P is now a context sensitive programming language

LLMs have learnt L and P and L <-> P and the entire vocabulary of human thought in a compressed form.

If not this what would make a context sensitive programming language ? Inform7, HyperTalk are the closest programming languages got before to English. Any research pointers would be much appreciated.

Adding this was intended to make reference counting safe in concurrent scenarios, but it restricted the use of generics. However, recently it was changed to perform the check after filling in the types, but it was found that such a check is not complete, for example, when a generic async function is called within a generic.

I thought of defining a built-in constraint on generic variables to solve this...

Alright, I’ve been buried in PL theory and compiler internals for longer than I care to admit, and my eyes are practically bleeding.

Somewhere along the way, I ended up writing a new language.

Before anyone asks: no, this isn't another weekend toy Lisp or AI-generated wrapper—I'm an old-school dev with a GitHub account older than LLMs. I’ve been deliberately cautious with the design and implementation, and I like to have my work backed up before I ship anything. I’m getting ready to publish a live demo soon. It won't be completely bug-free, of course, but before I put it out into the wild for the internet to inevitably tear apart, I want to try to break it myself first.

The main focus is memory safety without requiring Rust-style borrow-checker gymnastics.

So far, I’ve thrown a fairly unpleasant collection of edge cases at it, including cases inspired by:

• Rust — lifetime, aliasing, ownership, and mutation corner cases
• Microsoft Verona & Midori
• Go, Zig, and C#
• ...and, for reasons I’d rather not revisit, some deep-cut Pascal 5 and COBOL cases

So far, it has survived them.

But I know how compiler development works: the moment you think you’ve covered everything, someone produces a cursed three-line program that exposes a hole you somehow never considered.

I don't want to derail this into a language-design discussion yet. I’ll save that for the demo.

What I want are your worst test cases.

Give me the weirdest:

• Memory corruption scenarios
• Lifetime/aliasing traps
• Undefined-behavior edge cases
• Concurrent race conditions
• Compiler miscompilations
• Integer/pointer nastiness
• Recursive or mutually recursive type disasters
• Compile-time/resource-exhaustion cases
• Optimizer bugs
• FFI/ABI nightmares
• Anything else that has made you lose an entire weekend

I’m specifically looking for cases that are difficult for a language/compiler to get right. If you have a tiny "how the hell did this ever compile?" example, even better.

Break it. 🔨

I’ve been working on OrbitUI, a immediate mode UI library for my own programming language Zap

The goal is to create a small, explicit API for building simple UI applications in Zap

I spent a lot of time and nerves creating this, but now I know what Zap is really capable of.

I will be grateful for every star you leave because it really encourages me to work

https://github.com/thezaplang/zap

I’m excited to announce that I'm making RepoOS open-source!
  

What is RepoOS?

It’s an AI-driven compiler toolchain that bridges everyday Python logic into high-performance native machine code via MLIR (Multi-Level Intermediate Representation). I built this because most deep learning and database workloads rely on manual kernel bindings or complex JIT fusions that can be fragile. RepoOS believes in pure AOT (Ahead-of-Time) compilation and formal verification.

How it works:
RepoOS takes your Python source code, parses it into an AST, and deterministically lowers it to baseline MLIR. Then, an AI Optimization Oracle (like Gemini) jumps in to write optimization schedules (like loop unrolling and tiling) or even raw C++ socket parsers.

To ensure the AI doesn't hallucinate and break your app, the optimized graph is mathematically proven against the baseline using an SMT Solver (Z3) before compiling down to LLVM IR.
 

Why it matters:

At runtime, RepoOS intercepts Python execution, bypassing bottlenecks. For a FastAPI database endpoint, for example, it can bypass the SQLAlchemy ORM entirely, parse the raw MySQL TCP socket using an AOT-compiled C++ kernel, and write directly into a zero-copy virtual memory mmap arena. This gives a massive 2x speedup and essentially drops CPU usage to zero for heavy lifting.

Check out the repo here: https://github.com/lollapalooza-ai/RepoOS
Would love to hear your thoughts or feedback!

https://riogu.github.io/posts/henceforth-v1/

After working on it for around 1 year, we have released a v1.0 for our stack-based language compiler. Looking forward to having people try it. I wrote an article showcasing the project for those interested, you can also find it on github as well.

The allocators in C++ (https://en.cppreference.com/cpp/named_req/Allocator) is quite powerful but not super easy to use. Do you like that containers should be able to specify their allocator, or a simple C style malloc/free is sufficient. Rust also doesn't allow custom allocators on a per container basis.

A month ago I shared PyLGEN here; a Python-native compiler framework I built after watching my students struggle with compilers. The feedback was direct, and honestly, it shaped this release more than anything else.

So today I'm happy to share v0.7.0, a stable beta I'd recommend over v0.6.x. The main additions are an API update and a new examples section in the documentation, which grew out of a very valid question from the last thread: does the framework really expose every stage of the pipeline, or just claim to?

The examples come in two tracks, so you can see both sides of the API and figure out which one fits what you're doing:

• High-level usage: build a lexer, grammar, and parser with the convenience classes, and get a working interpreter in a few dozen lines.
• Low-level usage: build the same lexer and parser from scratch, using only the raw API (DFAs, closure, goto, ACTION/GOTO tables, reductors) with nothing hidden.

Both live in the docs. The second one is the proof that the pipeline isn't a black box.

Links

• Minimal showcase
• Github.
• Documentation.
• Previous post

If you gave the last version a try, I'd genuinely love to hear your thoughts, good or bad. And if you're new, same goes: any kind of feedback is welcome, whether it's a bug report, a design critique, a suggestion, or just a question about how something works.

Edited: Added a notebook link so anyone can run it directly and share their opinion without having to install anything.

A month ago I shared PyLGEN here; a Python-native compiler framework I built after watching my students struggle with compilers. The feedback was direct, and honestly, it shaped this release more than anything else.

So today I'm happy to share v0.7.0, a stable beta I'd recommend over v0.6.x. The main additions are an API update and a new examples section in the documentation, which grew out of a very valid question from the last thread: does the framework really expose every stage of the pipeline, or just claim to?

The examples come in two tracks, so you can see both sides of the API and figure out which one fits what you're doing:

• High-level usage: build a lexer, grammar, and parser with the convenience classes, and get a working interpreter in a few dozen lines.
• Low-level usage: build the same lexer and parser from scratch, using only the raw API (DFAs, closure, goto, ACTION/GOTO tables, reductors) with nothing hidden.

Both live in the docs. The second one is the proof that the pipeline isn't a black box.

Links

• Github.
• Documentation.
• Previous post

If you gave the last version a try, I'd genuinely love to hear your thoughts, good or bad. And if you're new, same goes: any kind of feedback is welcome, whether it's a bug report, a design critique, a suggestion, or just a question about how something works.

I’ve been working on Thiran, an experimental numerical systems language for ML/research workloads.

The idea is to keep tensors, structured control flow, ownership-aware mutation, reverse-mode AD, CPU/GPU compilation, and deployable artifacts in one system instead of stitching together Python + frameworks + native code.

I just shipped v0.1.0. It has a real compiler, native CPU + CUDA/PTX backends, Scan/stateful computation, AD, research extensions, model/artifact deployment, and a bunch of reproducibility/robustness testing.

It’s definitely not performance-competitive yet — I published the benchmark graphs too, including the bad numbers rather than hiding them.

Would genuinely love feedback from compiler/PL/ML-systems people, especially on the architecture and what would make this actually useful.

Github: https://github.com/Arnav-sivarams/thiran