IdeaWell

Project ideas from Hacker News discussions.

Nanolang: A tiny experimental language designed to be targeted by coding LLMs

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Here is a summary of the three most prevalent themes from the Hacker News discussion on NanoLang:

1. Feasibility of LLMs on Novel Languages

The core debate centers on whether an LLM can effectively learn and generate code for a new language with minimal training data. Proponents argue that LLMs understand grammar structures and can succeed with proper documentation and iterative feedback, while skeptics believe that without a massive corpus of existing code, the models lack the statistical probability to generate correct output.

  • LLMs can learn via grammar and feedback: > "LLMs understand grammars really really well. If you have a grammar for your language the LLM can one-shot perfect code." > — nl
  • Without training data, LLMs are limited: > "Where are the millions lines of code needed to train LLM in this Nanolang? LLM are like parrots. if you dont give them data to extract the statistic probability of the next word, you will not get any usefull output." > — Surac
  • Empirical evidence suggests success with context and tooling: > "The thing that really unlocked it was Claude being able to run a file listing... and then start picking through the examples that were most relevant to figuring out the syntax." > — simonw

2. The Value and Practicality of Required Testing

NanoLang's requirement that every function have tests at compile time sparked discussion on whether this is a beneficial constraint or unnecessary noise. Some view it as a way to enforce quality and catch errors early, while others worry about "code pollution" and the sheer volume of boilerplate test code required for every single function.

  • Testing enforces discipline but creates overhead: > "I think that a real world file of source code will be either completely polluted by tests (they are way longer than the actual code they test) or become... [boilerplate] to please the compiler." > — pmontra
  • It encourages good habits: > "One novel part here is every function is required to have tests that run at compile time." > — spicybright

3. Human Readability and Language Design

Beyond LLMs, users evaluated NanoLang purely as a human programming language, focusing on its syntax (prefix notation/S-expressions) and structure. While some found the syntax "jarring" or "unholy," others appreciated the clarity and simplicity, highlighting the trade-offs between terseness, expressiveness, and ease of reading.

  • Syntax preferences divide users: > "I find Polish or Reverse Polish notation jarring after a lifetime of thinking in terms of operator precedence." > — noduerme
  • Some found the design surprisingly clean: > "Really clean language where the design decisions have led to fewer traps (cond is a good choice)." > — sheepscreek
  • Others saw it as a mix of existing paradigms: > "It’s peculiar to see s-expressions mixed together with imperative style." > — sheepscreek

🚀 Project Ideas

LLM-Safe DSL Transpiler

Summary

  • A tool that lets users define a Domain-Specific Language (DSL) for their specific problem space, which is then used to generate targeted, constrained LLM prompts.
  • Core value proposition: Instead of teaching an LLM a completely new general-purpose language, it provides a structured, high-level interface to generate valid code in a target language (like Rust, Python, or even NanoLang) while minimizing the "noise" and potential for hallucination.
Key Value
Target Audience Developers building complex agentic workflows or using LLMs for specific, repetitive coding tasks (e.g., UI component generation, API client creation).
Core Feature A declarative configuration file defining the DSL grammar and constraints, paired with a transpiler that converts DSL inputs into optimized prompts for LLMs.
Tech Stack Rust (for the transpiler), JSON/YAML (for DSL definition), LLM API integration (OpenAI, Anthropic, etc.).
Difficulty Medium
Monetization Revenue-ready: SaaS offering with a free tier for personal use and paid tiers for team collaboration and higher token usage.

Notes

  • This directly addresses the skepticism in the thread (e.g., verdverm: "This seems like a research dead end to me") by acknowledging that teaching a new language to an LLM is hard. It shifts the burden from the LLM to a structured tool, acting as a translator.
  • abraxas suggested using a "pure AST representation" or Lisp; this tool allows users to define their own lightweight AST-like structure without needing to learn Lisp or build a full parser.
  • It provides practical utility by allowing developers to constrain the output space of the LLM, reducing the debugging loop highlighted by jkh99 (debugging line numbers and syntax).

Agentic Sandbox IDE

Summary

  • An Integrated Development Environment (IDE) specifically designed for coding with LLM agents, prioritizing feedback loops and safe execution environments over raw text editing.
  • Core value proposition: It treats the LLM not as a text generator but as a programmer that needs a standardized environment. It automatically captures compiler output, test results, and execution traces to feed back into the LLM context, minimizing the "context window reset" problem.
Key Value
Target Audience "Vibe coders" and developers building software primarily through AI assistance (e.g., users of Claude Code, Cursor, or generic API wrappers).
Core Feature A containerized sandbox where code runs immediately. Features include automatic trace generation, a "diff" view of LLM changes, and prioritized error logs formatted specifically for LLM consumption.
Tech Stack Electron/VSCode Extension (frontend), Docker/Containerd (sandboxing), Node.js/Python (orchestration).
Difficulty High
Monetization Revenue-ready: Subscription model for managed sandbox instances and compute credits for running tests/execution.

Notes

  • This solves the frustration mentioned by nl about the "bootstrapping" cost of context. By providing a persistent, interactive environment, the IDE maintains state and feedback without requiring the user to manually paste error messages back into a chat.
  • It addresses measurablefunc's demand for evidence by providing a closed loop where the LLM generates code, the system executes and verifies it, and the results are used to refine the next generation.
  • nxobject's point about "introspection/reproducible input" is a core feature here—capturing the execution state to guide the agent.

Static Analysis for Agent-Generated Code

Summary

  • A linter and static analysis tool specifically tuned for common patterns of errors found in LLM-generated code, rather than human coding conventions.
  • Core value proposition: Standard linters catch human mistakes; this tool catches LLM hallucinations and structural weaknesses. It flags issues like redundant variable creation, inefficient algorithmic choices, and likely security flaws based on common LLM output distributions.
Key Value
Target Audience Teams integrating LLMs into their development pipeline who need quality assurance and security auditing for AI-generated code.
Core Feature Custom rules engine that identifies "LLM-style" inefficiencies (e.g., over-nesting, redundant imports, hallucinated API calls) and security vulnerabilities common in generated code.
Tech Stack Rust (for performance), Tree-sitter (for parsing multiple languages), LLM API (for semantic analysis of comments/intent).
Difficulty Medium
Monetization Revenue-ready: CLI tool with a paid license for enterprise teams, potentially integrated into CI/CD pipelines.

Notes

  • This addresses bevr1337's observation that agents often slip in todo! macros or hacks to get code to compile. The tool would automatically flag these or suggest safer alternatives.
  • It offers a solution to fragmede's concern about the "bugged-ed-ness" of generated code by providing a safety net that understands the specific failure modes of LLMs, not just syntax rules.
  • It bridges the gap between generation and verification, making the codebase more "auditable" as requested by loeg.

"Spec-to-Test" Framework

Summary

  • A tool that takes a high-level natural language specification (or pseudo-code) and generates a comprehensive suite of executable tests before writing the implementation code.
  • Core value proposition: It enforces the "testing discipline" discussed in the thread (referencing Pyret) but in a way that fits into existing workflows. The LLM is tasked with writing tests that define the desired behavior, and only then is it prompted to write code that satisfies those tests.
Key Value
Target Audience Developers who want to leverage LLMs for feature implementation but are concerned about correctness and specification drift.
Core Feature A template system that converts user requirements into standard test frameworks (e.g., pytest, Jest, Rust tests) with placeholders for implementation.
Tech Stack Python (for the framework), Generic Test Runners (Jest, Pytest, Go test), LLM API.
Difficulty Low
Monetization Hobby (Open Source) with potential for a premium hosted dashboard for visualizing test coverage and spec adherence.

Notes

  • This directly implements spicybright's observation about NanoLang's "compile-time tests" but makes it applicable to existing languages. It shifts the focus from "write code + tests" to "define behavior (via tests) + write code."
  • It mitigates pmontra's concern about "pollution" by keeping the test logic in separate, standard files rather than shadow functions, while still enforcing the discipline.
  • It leverages the LLM's strength in understanding natural language (deepsquirrelnet's point about specifications) and its ability to generate code, creating a pipeline that ensures the code matches the intent.

Read Later