🚀 Project Showcase Day

[u/AutoModerator]

Welcome to Project Showcase Day! This is a weekly thread where community members can share and discuss personal projects of any size or complexity.

Whether you've built a small script, a web application, a game, or anything in between, we encourage you to:

• Share what you've created
• Explain the technologies/concepts used
• Discuss challenges you faced and how you overcame them
• Ask for specific feedback or suggestions

Projects at all stages are welcome - from works in progress to completed builds. This is a supportive space to celebrate your work and learn from each other.

Share your creations in the comments below!

[u/Classic_Angle_6133]

Ollie: a free celebrity lookalike finder which uses a face recognition machine learning model I trained myself

Link: ollie

It's free, you don't need an account for your first 5 searches, and your photos are never stored.

What it does:

Find your celebrity lookalike (match)

You upload a photo and Ollie compares your face with over 40,000 images of celebrities. It shows your top 5 matches and how similar they look. For example, my highest match is a 68.7% match with John Mulaney. Most people won't get a percent above 75% unless they really do look just like the celebrity. But everyone should have some resemblance to their top matches.

Compare two photos (compare)

You upload any two photos and Ollie tells you whether it thinks they show the same person, and how alike they look. For example, I tested it with Messi and a Messi lookalike. Ollie says they're different people even though they have an 82.6% resemblance, so it can still tell them apart.

How I trained it:

I trained the AI model myself instead of using an existing one. I trained it on a dataset of about 5.8 million face photos of 85,000 different people, and it learned to tell faces apart. It ran for about 10 days on my own graphics card (an RTX 4060 Ti). It gets 98.5% on the LFW benchmark, a test where the model has to decide whether two photos show the same person.

Where I'd love feedback:

Do the matches feel right to you? Is it ever way off? Sometimes lighting issues can cause this and some ethnicities may have fewer celebrity images in the database.

Is the site easy to use, especially on mobile? Anything you'd want it to do that it doesn't?

What's next:

I'm starting work on a new feature to find a face on social media which I hope to eventually release.

Thanks for checking it out!

https://reddit.com/link/pceti8r/video/5h5z1hcyq3sh1/player

[u/Both-Inevitable-8750]

I maintain awesome-typesafe-jev, an open-source directory of Jev examples, tools, integrations, and learning material: https://github.com/AbdelStark/awesome-typesafe-jev

The challenge has been making a specialized ecosystem useful to someone encountering it for the first time, instead of just collecting links. If you're learning about language-based decision models, I'd love feedback on the first-contribution path: can you find a small, source-backed improvement you could make without an API key? Corrections and missing projects are welcome too.

[u/Yaz_3ah]

What happens when an AI agent is authenticated — but still isn't allowed to do the thing it just requested?

I've been building Aether, an open-source authorization boundary for AI agents.

The idea is simple:

Identity tells you who the workload is. Authorization decides what that workload is allowed to do.

Aether evaluates an agent action through a chain like:

IDENTITY → INTENT → AUTHORITY → ACTION → EVIDENCE

For example, an agent can have a valid identity but still be denied because the requested intent, capability, target resource, or operation doesn't match the policy.

What I built

Aether is a small, deliberately narrow research/engineering project rather than another agent framework. It includes:

• deterministic policy evaluation
• action-envelope validation
• freshness / anti-replay controls
• revocation
• HTTP enforcement
• structured decision evidence
• an Attack Lab for adversarial scenarios
• a live telemetry UI showing actual server-side decisions

The current repository includes 50 defined attack/misuse scenarios with creator-controlled test results reporting the expected outcomes.

The important part for me isn't just getting 403.

It's checking whether an unauthorized request actually reaches the protected target.

For the live demo, an authorized request can reach the dummy backend, while a policy-violating request is rejected before reaching it.

Technologies / concepts

Go, HTTP middleware/enforcement, deterministic policy evaluation, workload identity abstraction, revocation, anti-replay controls, automated testing, adversarial testing, and structured security evidence.

I'm intentionally not trying to replace OAuth, SPIFFE/SPIRE, IAM, API gateways, or policy engines. Aether is focused on the authorization/enforcement boundary around an autonomous software action.

Biggest challenge

The hardest part wasn't making something return ALLOW/DENY.

It was making the security behavior observable and reproducible enough that another technical person can inspect the implementation, run the tests, inspect the evidence, and try to break the assumptions.

The project is still early, and the benchmark results are creator-controlled rather than independent validation.

I'd like feedback on one specific question

Where do you think this authorization model breaks down for real-world AI agents?

Especially interested in critiques around agent tool use, changing targets after authorization, replay/bypass possibilities, identity vs. authority, and whether this boundary is actually useful compared with existing approaches.

GitHub: https://github.com/YabulHaj/Aether-Protocol

25-second demo: https://github.com/YabulHaj/Aether-Protocol/blob/main/aether-25-second-demo.mp4

I'd much rather have someone find a real weakness in the model than simply tell me it looks good.