Shmungus

In the early 2010s, sneaker culture was just coming alive and merging with drill rap. There has never truly been a game to capture that world. Now there is! Join Chief Keef and make your empire from scratch.

Your friends won't. Your users definitely won't. They just leave.

So I built something that will.

Paste a link. Get a number out of 100.

Mine said 54%. Yours will be worse.

Link in comments. Don't cry.

Shorter, dumber:

Paste your website.

Get a number.

Cry.

Humans really are better at coding than AI.

That's why I wrote this handmade Python script.

It validates whether this script has been executed prior to the current execution context being instantiated.

It is Enterprise-grade and runs with 0 dependencies.

I really wanted to show the world just how valuable handwritten code really is.

Link -> https://github.com/Mattbusel/pre_execution_validator

I had built a rendering engine in Rust where the fundamental primitive is a particle, not a triangle. There are no meshes. No skeletons. No polygon budgets.

Every surface is a continuous matter field held together by spring-force physics. When something takes damage it doesn't play an animation, the forces holding it together get overcome and it physically falls apart. Destruction resolution is infinite because particles don't have polygon limits.

Cloth, fluid, soft-body, volumetric light, they all come from the same particle substrate. Different spring constants, not different systems. Every other engine fakes these as separate modules. This one doesn't fake any of them.

Geometry is SDF. That means analytical normals, mathematically exact ambient occlusion from the SDF gradient, and subsurface scattering from thickness probes. No baked textures. No approximations.

Current demo runs 50 million particles in real time. 650k lines of Rust. OpenGL 3.3 renderer with 23 post-processing passes. Full visual editor with a compiled Windows .exe on the releases page.

Im glad to hear!

Thank you!

100%, I hope you get some good use out of it!

Built this over a weekend because every C++ project I worked on needed LLM features and the options were either "wrap the Python SDK somehow" or "write the curl SSE parsing yourself again."

26 libraries across four categories — Core, Data, Ops, App. Each one is a single .hpp file. Drop in what you need, define one implementation macro, ship. No Python runtime, no package manager, no build system changes beyond linking libcurl where needed.

llm-stream has 83 unique clones already with zero promotion. Figured the rest of the suite was ready to share. Happy to answer questions on design decisions, the single-header pattern, or anything else.

The previous documented record for simultaneous Claude Code agents is 16, set by Anthropic's own engineering team building a C compiler.

Tonight I ran 24. What made this possible: I built a Tokio-native LLM orchestration pipeline in Rust that routes inference through a local Mistral 7B model running on an RTX 4070. The agents coordinate through governance docs (CLAUDE.md, AGENTS.md) that define module ownership and test ratios, they don't communicate directly, they communicate through the codebase.

The recursive part: the orchestrator was built using the same multi-agent workflow it enables. The MCP server that lets Claude call the pipeline natively.

Technical details:

683 tests, 0 failures 1.53:1 test-to-production ratio enforced by pre-commit hooks

9-stage deduplication pipeline with circuit breakers

Sub-millisecond pipeline overhead (inference dominates at 3.6s on Mistral 7B)

Repo: github.com/Mattbusel/tokio-prompt-orchestrator

Raise it again?

I launched a solo financial intelligence tool 2 weeks ago at $9.99/mo. Caught 6 major market moves in the first week (including a $9.9B acquisition and a +137% move). Got users in 46+ countries organically.

Then I tripled the price to $29.99 Pro / $149.99 Ultra.

Conversion behavior didn't change. If anything, traffic to the pricing page increased.

I think the problem was that $9.99 signaled "toy" to options traders who routinely risk thousands per trade. The higher price actually built trust.

Anyone else found that raising prices early improved conversion? Curious if this is specific to finance/trading products or a broader SaaS pattern.

Product: rot.trading (165K lines of Python, built solo in 9 days, $5/mo infrastructure)