Fly Labs
Ask FlyBot

The Mog Programming Language

Hi, Ted here, creator of Mog.- Mog is a statically typed, compiled, embedded language (think statically typed Lua) designed to be written by LLMs -- the full spec fits in 3,200 tokens. - An AI agent writes a Mog program, compiles it, and dynamically loads it as a plugin, script, or hook. - The host controls exactly which functions a Mog program can call (capability-based permissions), so permissions propagate from agent to agent-written code. - Compiled to native code for low-latency plugin execution -- no interpreter overhead, no JIT, no process startup cost. - The compiler is written in safe Rust so the entire toolchain can be audited for security. Even without a full security audit, Mog is already useful for agents extending themselves with their own code. - MIT licensed, contributions welcome.Motivations for Mog:1. Syntax Only an AI Could Love: Mog is written for AIs to write, so the spec fits easily in context (~3200 tokens), and it's intended to minimize foot-guns to lower the error rate when generating Mog code. This is why Mog has no operator precedence: non-associative operations have to use parentheses, e.g. (a + b) * c. It's also why there's no implicit type coercion, which I've found over the decades to be an annoying source of runtime bugs. There's also less support in Mog for generics, and there's absolutely no support for metaprogramming, macros, or syntactic abstraction.When asking people to write code in a language, these restrictions could be onerous. But LLMs don't care, and the less expressivity you trust them with, the better.2. Capabilities-Based Permissionsl: There's a paradox with existing security models for AI agents. If you give an agent like OpenClaw unfettered access to your data, that's insecure and you'll get pwned. But if you sandbox it, it can't do most of what you want. Worse, if you run scripts the agent wrote, those scripts don't inherit the permissions that constrain th

Hacker NewsToolAISource
0Sign in to voteCopy link

FL score

69

out of 100

Verdict

VALIDATE

high confidence

Competition

10

competitors found, emerging market, funded players

Trend

No signal yet

A new programming language designed for AI agents to write securely and performantly, but faces significant build complexity and adoption risks for a solo builder.

The pain

AI agent developers struggle with enabling agents to extend themselves with custom code securely and performantly. Existing languages are not optimized for AI authorship, leading to errors, security vulnerabilities, and performance overhead.

The gap

There is a distinct gap for a programming language purpose-built for AI agents to write, emphasizing a minimal spec for LLM context, explicit syntax to reduce errors, capabilities-based security, and native performance. Competitors are either general-purpose languages, AI coding assistants, or AI development languages not exclusively focused on AI authorship with embedded security.

Build angle

Develop Mog as an open-source, statically typed, compiled, embedded language, with its initial focus on demonstrating superior security and performance for AI-generated plugins/scripts within AI agent frameworks. Target the 'security-conscious AI agent developer' niche.

Strengths

  • Addresses a clear, specific, and growing pain point in AI agent security and performance.
  • Unique 'AI-first' language design addresses LLM frustrations directly.
  • Capabilities-based security model provides a strong differentiator.
  • The market for AI agents is rapidly expanding, indicating future demand.
  • Creator has deep expertise in language design.

Risks

  • Extremely high build complexity for a solo builder to create a new language and compiler.
  • Significant challenge in driving adoption for a new programming language.
  • Monetization model beyond open-source is not yet clear.
  • Requires substantial education and community building.
  • Funded competitors are active in the broader AI development space, though not directly in Mog's specific niche.

Next steps

  1. **Proof-of-Concept & Benchmarking:** Build a minimal, functional Mog compiler and runtime. Create clear benchmarks demonstrating superior security and performance for a simple AI-authored task compared to Python/Lua sandboxing.
  2. **Targeted Validation:** Find 5-10 AI agent developers or teams facing the exact security/performance problems Mog solves. Get their feedback on the core concept and early benchmarks.
  3. **Monetization Exploration:** While open-source, identify a clear path to generating revenue (e.g., enterprise support, premium tooling, hosted runtime, integration services) by understanding what developers would pay for.
  4. **Community Building:** Start sharing technical insights and progress in relevant AI agent developer communities (e.g., Reddit r/MachineLearning, AI developer forums, Discord servers) to gauge initial interest and gather early adopters for the open-source project.

Questions about this idea?

FlyBot reads the scoring and gives you a second opinion on “The Mog Programming Language”.

Open FlyBot