What's difficult with programming isn't the language itself, it's everything else: understanding concepts, algorithms, programming patterns, the science.
It feels a bit like trying to reinvent the language of mathematics. It'd be really inefficient writing math in plain English.
P.S. The English-to-AST though could be useful to other projects that may want natural-ish language input without having to resort to a LLM. E.g. a modify CSV tool in natural language like one posted yesterday.
It's free for individuals, orgs with < 25 people, educators, students, and non-profits, and I'm currently still working through monetization but I'm thinking of taking two paths, one being payment to get the Z3 verification feature that lets you mathematically verify that the code won't panic at runtime. The other being payment to use the tokenizer that will be built with this. If you look here you can see the lexicon to get a better idea how the english compile pipeline works. https://github.com/Brahmastra-Labs/logicaffeine/blob/main/as...
This also makes the language highly configurable as you can change any of the key-words to better suit your brain if you so chose.
Current LLM's biggest bottlenecks in my personal opinion would be the tokenizers and the way they get their info. Imagine if you got fed in random chunks of tokens the way they do. If you could create an AST of the english and use that to tokenize things instead... well at least I have some hair-brained theories here I want to test out. Standard LLM tokenizers are statistical and they chop words into chunks based on frequency, often breaking semantic units. This lexer could perform morphological normalization on the fly, an LLM spends millions of parameters to learn that the word "The" usually precedes a noun, but this parser knows that deterministically. This could be used to break things into clauses rather than arbitrary windows. Even just as a tool for compaction and goal tracking and rule following this could be super useful is my theory. A semantic tokenizer could potentially feed an LLM all parse trees to teach it ambiguity.
There is a test suite of over 1500 passing tests. I do utilize Claude, but I try really hard to prevent it from becoming slop. Development follows a strict RED/GREEN TDD cycle, where the feature gets specced out first, the plan and spec gets refined and tests get designed, then the tests get written and then implementation occurs. It is somewhat true that I can't make as many promises about the code regarding untested behavior, but I can make promises regarding the things that have been tested. The test suite is wired directly into CI. I guess it is fair that some people will feel any code written with the assistance of an LLM is slop, but everyone is still working out their workflows and personally you can find mine here: https://github.com/Brahmastra-Labs/logicaffeine/blob/main/Tr...
TLDR of it would be: 1. Don't Vibe-Code 2. One-shot things in a loop and if you fail use git stash. 3. Spend 95% of the time cleaning the project and writing specifications, spend 5% of the time implementing. 4. Create a generate-docs.sh script that dumps your entire project into a single markdown file. 5. Summon a council of experts and have them roleplay. 6. Use the council to create a specification for the thing you are working on. 7. Iterate and refine the specification until it is pristine. 8. Only begin to code when the specification is ready. Use TDD with red/green tests.
I'm always learning though, so please if you've got suggestions on better ways share them!
I'm happy to answer any questions that might pop up...
Some things of note:
Built-in P2P Mesh Networking
Listen on "/ip4/0.0.0.0/tcp/8080". Connect to "/ip4/192.168.1.5/tcp/8080". Sync counter on "game-room".
That's all you need for libp2p, QUIC transport, mDNS discovery, GossipSub pub/sub
Full conflict-free replicated data types: - GCounter, PNCounter — distributed counters - ORSet with configurable AddWins/RemoveWins bias - RGA, YATA — sequence CRDTs for collaborative text editing - Vector clocks, dot contexts, delta CRDTs
Wrap any CRDT in Distributed<T> and get: - Automatic journaling to disk (CRC32 checksums, auto-compaction at 1000 entries) - Automatic GossipSub replication to all peers - Unified flow: Local mutation → Journal → Network. Remote update → RAM → Journal. - Survives restarts, offline nodes, network partitions.
Go-Style Concurrency - TaskHandle<T> — spawnable async tasks with abort - Pipe<T> — bounded channels (sender/receiver split) - check_preemption() — cooperative yielding every 10ms for fairness
There's more... but those are my personal favorite features.
I've put a good bit of work into this, so I hope you all can appreciate and maybe find some uses for it here on my favorite place on the interwebs!
Is this in a crate in crates.io?
As a bit of background on myself and my goals/targets with this.
I started my career as an embedded software developer writing uCos-III for an RTOS working on medical devices for Cardinal Health where I primarily worked on enteral feeding pumps. From there, I spent a couple years in fintech, before trying my hand at my first startup where I co-founded a company in the quick commerce space. (Think similar to Doordash Dashmarts). When that fell apart I took a job at Hasura where I wrote GraphQL to SQL transpilers and other neat things for about 18 months. I've worked across a few different domains and the motivation behind writing this language is that I am preparing to teach my 13 year old brother how to code and wanted something I could get low level with him on, without losing him altogether.
This is a dual-purpose language and has a dual-AST, and the things I'm currently working on... having switched gears towards spending a couple days on the Logical side of things are adding an actual prover to the Logical AST. I'm getting ready to add a derivation tree struct and incorporate the ability to use a solver to do things like Modus Ponens, Universal Instantiation, etc. I also want to upgrade the engine to be able to reason about numbers and recursion similar to Lean with inductive types.
This is an early access product, it is free for students, educators, non-profits, orgs with < 25 people, and individuals.
It would make my day if I could get some rigorous HN style discussions, and even the critiques!! The feedback and discussions are invaluable and will help shape the direction of this effort.
What a lovely surprise doing my daily HN check before bed and seeing this post. :)
EDIT: I will read and respond to comments when I get up in the morning, feel free to ask questions! Or make suggestions.
It's a cute idea, though I think the consensus is that once you actually learn a programming language, it generally doesn't help to have it look like prose.
And I think that is really the point of syntax sugar: reading code, not writing code. It seems like a misconception about syntax sugar is that its primary purpose is to make code easier for beginners to learn to write. But I would contend that the real purpose is to make code easier for even experienced programmers to read at a glance, because reading code is actually far more important than writing it.
...granted a certain subsection of the population has determined that reading code is for chumps and boast about how quickly they can use a tool to write lines of code they haven't even read, and that this is the future of software development. Despite their boasts I have yet to see any software I would actually want to use that was written in this manner, though.
And it still isn't natural language, where you can paraphrase, use synonyms etc. Good job for an LLM
