mochi

For more context, I actually used The Algorithms as a reference when working on my own programming language, Mochi, which includes around 150–300 algorithms (I don't remember exactly) implemented directly in Mochi. These are then transpiled to over 25 programming languages such as C, Haskell, Java, Go, Scala, and more: https://github.com/mochilang/mochi/tree/main/tests/algorithm... The VM and transpiler were originally implemented by hand, and later I used Codex to help polish the code. The generated output works, though it is a bit messy in places. Hopefully, after finishing a few books, I can return to the project with more experience and add better use cases for it.

Totally agree. It is why we're building Mochi, https://github.com/mochilang/mochi a small language that treats AI, datasets, and graph queries as first-class citizens, not just targets for code generation. It's inspired by the evolution you mentioned: early compilers generating Assembly, now AI tools generating Python or SQL. Mochi leans into that by embedding declarative data queries, AI generation, and streaming logic directly into the language. Here is how it looks: type Person { name: string age: int email: string } let p = generate Person { prompt: "Generate a fictional software engineer" } print(p.name) let vec = generate embedding { text: "hello world" normalize: true } print(len(vec)) We see this as the natural next step after traditional compilers, more like intent compilers. The old "co

Repo: https://github.com/mochilang/mochi I'm building Mochi, a small programming language with a custom VM and a focus on querying structured data (CSV, JSON, and eventually graph) in a unified and lightweight way. It started as an experiment in writing LINQ-style queries over real datasets and grew into a full language with: - declarative queries built into the language - a register-based VM designed for analysis and optimization - an intermediate representation with liveness analysis, constant folding, and dead code elimination - static type inference, inline tests, and golden snapshot support Example: type Person { name: string age: int } let people = load "people.yaml" as Person let adults = from p in people where p.age >= 18 select { name: p.name, age: p.age } for a in adults { print

This thread was the final push I needed to add logic programming to Mochi https://github.com/mochilang/mochi — a small statically typed scripting language I’m building for agents and real-time data. I gave OpenAI Codex a single prompt with a sample like: fact parent("Alice", "Bob") rule grandparent(x, z) :- parent(x, y), parent(y, z) let gps = query grandparent(x, z) And it generated a working Datalog engine in Go with: - fact storage + recursive rule support - bottom-up fixpoint evaluation - unification and `!=` constraints - FFI bindings to expose `fact`, `rule`, and `query` to scripts Full thinking process: https://chatgpt.com/s/cd_684d3e3c59c08191b20c49ad97b66e01 Total implementation was ~250 LOC. Genuinely amazed how effective the LLM was at helping bootstrap a real logic layer in one go. The PR is here https://githu