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
FL score
out of 100
Verdict
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
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “The Mog Programming Language”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Mog addresses a clear, specific, and severe problem in AI agent development: enabling secure, performant, and reliable self-extending capabilities. Its unique AI-centric design offers a promising whitespace, but the buildability for a solo founder is highly challenging.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
A high potential idea in a growing market with clear value, but faces significant build complexity and competitive pressure from established AI tools/models.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A technically deep problem with a clear creator fit, but the complexity of building a language and the niche audience present significant challenges for solo execution and widespread adoption.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Mog presents a clear value proposition for a specific audience in a growing market, but it carries high risks related to adoption of a new language and a challenging path to business model validation.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Mog addresses a clear, future-critical need for AI agent developers, with strong demand and a specific target user, but requires strong evidence of AI performance and a narrow, monetizable starting point.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
A programming language that starts with Python and adds features like static typing and memory safety for increased speed and robustness, designed for AI development.
Pricing: Not explicitly stated on the website, but it's positioned as a developer tool from Modular, which offers various AI development platforms and services.
A new language created specifically for AI agents, inspired by Lisp, Elixir, and Haskell, focusing on safer control flow and natural language conditions.
Pricing: Not available on the provided source.
A statically typed, embeddable scripting language with features like classes and memory management, designed for flexibility and performance.
Pricing: Free (single .zip download available).
A lightweight statically typed scripting language that aims to offer type-safe embeddable scripting as an alternative to Lua.
Pricing: Not specified; mentioned in a Hacker News discussion.
World's first agentic AI vibe coding platform that builds full-stack web & mobile apps with English as the programming language.
Pricing: Offers a 10% discount with code PH10 for monthly/yearly plans (actual pricing not specified on the snippet).
An autonomous coding agent built for complex long-horizon tasks, turning a feature description into a fully tested implementation.
Pricing: Not specified.
An AI coding editor and CLI that allows coding with AI across a wide range of LLMs, bundled with a Chat LLM subscription.
Pricing: $10 a month for the entire Chat LLM bundle, which includes Code LLM.
APIs and tools for building AI products, including fast and efficient models like GPT-5.4 mini and nano, built for real-world AI workloads including coding.
Pricing: GPT-4o runs $2.50 input / $10.00 output per million tokens. GPT-5.4 nano is the lowest cost option, and GPT-5.4 mini is twice as fast.
An open-source LLM optimized for programming-related applications, showcasing strong performance in code generation, completion, and debugging across multiple programming languages.
Pricing: Very low cost and decent output for reasoning/summarizing through OpenRouter or DeepSeek API.
A state-of-the-art large language model designed for code generation and natural language tasks related to code, available in foundational, Python-specialized, and instruction-tuned versions.
Pricing: Free for self-hosting.
What they charge
Recent news
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Market signals
The market for programming languages designed for or optimized for LLMs and AI agents is a rapidly growing niche within the broader AI and software development market. The global market for AI agents alone is projected to grow from $7.84 billion in 2025 to $52.62 billion by 2030, indicating significant expansion. There have been numerous recent funding rounds for AI agent companies, with significant investments in startups focusing on AI agent infrastructure and specialized AI models for various industries. Key trends include the emergence of 'LLM-first' languages, designed for machine authorship rather than human authorship, and a strong focus on explicit semantics and modular structures to improve AI-generated code reliability.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI