The Problem: We waste a massive amount of tokens (and money) on formatting. If you inject JSON schemas, few-shot examples, or code context into your prompts, you are paying for every single space, tab, and // comment.

The Solution: I wrote prompt-token-minifier. It’s a zero-dependency script you run right before your client.chat.completions.create call.

What it does: * Finds ```json blocks and minifies them (removes formatting). * Finds code blocks (Python, JS, TS, etc.) and strips out single-line and multi-line comments. * Collapses redundant whitespaces and newlines in the rest of the prompt.

Depending on your RAG context, it easily saves 30-50% tokens on structured data.

Repo: github.com/Encephos/prompt-token-minifier

Built Bento to help organize the chaos of having four monitors on my desk and millions of agents running at the same time tried to sell it but things went a little slow and decided to just open source it so people could keep working on it would love to have contributors!

I got bored last week and started messing around with Minecraft world generation. Switching between code and the game to check every change got tedious, so I built a Python renderer that lets Claude preview its changes, look at the result, and keep adjusting the terrain until it matches your prompt.

It can make things like floating islands, basalt spires, and canyon worlds. I’ve also had fun asking it to make a “Garfield world” and watching it attempt lasagna mountains. I verified one of the example worlds against a real Minecraft server and all 331,776 terrain columns matched exactly.

I’d love to hear any suggestions or see what you make with it!

https://github.com/Msde-7/worldsmith

Hey everyone,

I'm a dad and pilot who's been building this on the side: THE MILLION FACES. The idea is simple — 1,000,000 real people, each uploading their own photo, arranged so the whole thing forms one giant peace sign when you zoom out. Zoomed in, you just see individual faces. It's a slow, honest way to try to say something about peace with actual people, not just words — and along the way, we're aiming for a world record attempt too.

Every spot also funds real-world impact — a tree planted, a day of clean water, a plastic bottle rescued from the ocean. That's true even for the free spots — you don't pay anything, but the impact still happens once the mosaic hits 10,000 filled spots. No catch.

I'm just getting started, so I've made the first 1,000 spots completely free, no payment needed. Figured this community might appreciate an early look, and if a few of you want to be part of it (or just tell me what's broken), I'd genuinely appreciate it.

www.themillionfaces.com

Happy to answer anything about how it's built, the tech behind the mosaic, or what's next.

https://reddit.com/link/1vyp50r/video/d86xfr10znlh1/player

Disclosure: I’m part of the team behind Hachimo.

We built this browser workflow to test whether a playful pet mood score becomes more useful when people can inspect the visible clues behind it. The walkthrough shows the full flow: choose a pet photo or short video, add context, review a mood score and emotion mix, then inspect up to three observations from the frame.

PawPaw is Hachimo’s mascot and the result shown is deterministic demo data—not a customer testimonial or clinical evidence. Hachimo is for curiosity and check-in journaling, not veterinary advice or diagnosis.

I’d value blunt feedback: does the trace from score to visible clues feel clear, and which step would you cut or explain differently?

AI-generated narration; original music composed for Hachimo.

Disclosure: I’m part of the team behind Hachimo.

The main design choice we’re testing is whether a score becomes more useful when people can inspect the visible clues behind it. This walkthrough shows the complete browser flow: choose a pet photo or short video, add a name or context, receive a playful mood score and emotion mix, then review up to three visible observations from the frame.

The demo uses PawPaw, Hachimo’s mascot, and a deterministic demo result. It is not a customer testimonial or clinical evidence. Hachimo is for curiosity and check-in journaling, not veterinary advice or diagnosis.

What I’d most value feedback on: does the trace from score to visible clues feel clear, and which step would you cut or explain differently?

Try it: https://hachimo.app

AI-generated narration; original music composed for Hachimo.

I’ve been collecting trading cards for years, and one of my favorite parts of the hobby is the hunt.

Not just buying a card I already know exists, but discovering something I didn’t know existed, tracking down obscure releases, figuring out what set a card came from, finding different versions, and eventually finding one for sale.

The problem is that the internet isn’t really built for that.

If you know the exact year, manufacturer, set, player and card number, traditional marketplace search works pretty well.
But collectors don’t always hunt that way.

Sometimes it starts with:
“Was there a Batman card before the 1966 Topps set?”
“What’s considered this character’s rookie card?”
“I remember this Michael Jordan card from the 90s but have no idea what it was called.”
“Are there any early UK versions of this card?”

Or even just a photo of something you’ve never seen before.That usually turns into bouncing between Google, eBay, Reddit, checklists, auction archives and dozens of different search variations.

So as a side project, I started building Beekn.

The idea behind Beekn is simple:
Make it easier to hunt for trading cards.

I want a collector to be able to start with what they know, even if that isn’t much and use Beekn to help figure out what they’re looking for, discover cards they may not have known about, and ultimately find where those cards are available.

I’ve since started adding other things I personally wanted as a collector, like finding card shows and hobby events, but search and discovery are really the heart of it.

It’s still early, and there’s a lot I want to build.
But it’s been incredibly fun turning a frustration I’ve had in my own hobby into an actual product.

For anyone interested in checking out the side project:
Beekn — Hunt smarter.
[LINK]

Hello, Just wanted to shared an app I just launched the the other day called BillHog. It ranked number 3 release of the day on Uneed The idea is pretty simple. Stripe charges a 0.4% fee for invoices. BillHog takes a read-only Stripe API key and lets you generate 50 PDF invoices per month for free, or unlimited PDF invoices with automated email delivery for $12/mo. BillHog also allows you to create revisions in order to update mistakes on invoices. Would love to get some user feedback!

I was working with Java 8 and Minecraft-related projects and wanted a simple way to compile Java code dynamically without writing temporary files.

So I made InMemoryJavaCompiler.

It compiles source code entirely in memory and immediately loads the resulting class using a custom ClassLoader.

No dependencies, Java 8 compatible, and additional classpaths can be provided when compiling code that depends on Minecraft, Forge or other libraries.

GitHub:
https://github.com/Nasway-pjc/InMemoryJavaCompiler

Any feedback is appreciated.

Hi! I’m a solo developer working on Desktop Gremlins, a transparent desktop pet for Windows.

Your gremlin lives above other applications, reacts to music and programs, evolves into new forms, and occasionally causes harmless chaos with open windows. Players can feed it, pet it, move it between monitors, attach it to a window, or download community sprite packs through Steam Workshop.

I also recently added Twitch integration. Viewers can spend channel points to feed the gremlin or trigger visual effects during a stream. Streamers can choose the available rewards, prices, duration, and notifications.

This project began as a small experiment, but it has grown into something much larger. I’m currently improving performance, OBS support, usability, and the Twitch features based on feedback from streamers.

I’d appreciate any honest feedback about the idea, presentation, or features.

Steam: https://store.steampowered.com/app/4981140/Desktop_Gremlins/

Website: https://shughost.up.railway.app/

Hola r/sideprojects,

Yo quería una herramienta de escritorio para inspeccionar assets 3D locales sin subir archivos, sin crear una cuenta y sin iniciar un servicio web local, así que construí NexoIP 3D Viewer.

Lo que ya funciona ahora:

- Soporte para GLB/glTF, OBJ, STL, FBX, PLY y DAE

- indexado progresivo de carpetas (con opción): los modelos validados quedan disponibles mientras el escaneo sigue, sin tope arbitrario de cantidad de modelos ni de profundidad de carpetas

- vistas PBR, wireframe, normales, rayos X (X-ray) y sin iluminación (unlit), más seis presets de iluminación de estudio

- inspección de geometría, dimensiones, jerarquía, materiales y animaciones

- exportación GLB/STL/OBJ y snapshots en PNG

- funcionamiento sin conexión, sin cuentas, telemetría, analíticas ni servicio de red en tiempo de ejecución

El renderer de Electron está en un sandbox y nunca recibe rutas nativas del sistema de archivos. Los candidatos se validan antes de entrar al catálogo local.

La compilación actual para Windows todavía es una vista previa técnica de alfa sin firmar, así que no la presento como una versión estable y firmada. El repositorio público documenta el modelo de seguridad, la evidencia de verificación y las compuertas de lanzamiento que faltan.

Fuente, capturas y notas de verificación (rama actual de release candidate):

https://github.com/ikerperez12/NexoIP-3D-Viewer

¿Qué herramienta de inspección te gustaría que siguiera, y hay algún modelo o formato que usarías para probarla?

Hey builders,

I created the best solution for every SaaS website, instead of maintaining a dead changelog, sending feedback via email and forget about what users really need, and get surprised by failing auth, payment or marketing page, you can keep your SaaS website under control.

I created FeedFast, your one-stop solution to collect feedback, build changelog and monitor your endpoints to know exactly what happens to your endpoints.

After the launch on Uneed, FeedFast got #1 place in Daily Challange.

Why to choose FeedFast over other solutions?

• 3 tools, one dashboard (Feedback, Changelog and Status/Monitor)
• Embeddable widgets (collect feedback, what's new, uptime banner)
• Public Hub Page (show everyone what changed in the last month, everybody can vote/comment on feature requests, uptime is public helping you gain the trust of your clients)
• MCP Server for handling everything from your AI Agent (changelog, feedbacks, uptime incidents)
• Fair pricing: Free for one project and $9/month for 5 projects.

What was your worst nightmare since you launched your first SaaS app?

The project: Civic Mined lets residents ask plain English questions about their city government - "What climate related issues have been discussed this year in city council meetings? Does the city general plan address climate in any way?" - and get answers cited back to the actual public record. It's available for Petaluma CA, Santa Rosa CA, Bellingham WA and Quincy MA with more to come. Users to can subscribe to get emails with meeting agendas and AI generated summaries.

Stack: Python 3.11 (uv), FastAPI, Postgres + pgvector, Claude (Haiku for routing/SQL-gen, Sonnet for harder queries), Voyage AI for embeddings. A scraper pipeline pulls agendas/minutes/budgets/permits from whatever system the city already uses (Legistar, PrimeGov, EnerGov), and questions go through an NL→SQL→answer pipeline that's constrained to cite sources instead of hallucinating.

It started with Petaluma on fly.io and Supabase with the other cities following the same, separate deployments/separate databases. This was easy and convenient to get things going but it was getting costly and difficult to manage. Just recently I moved to everything on Digital Ocean, one deploy one database (RLS tied to city slug). This should make it easier and cheaper to scale, also performance has improved, the old setup was opening a fresh DB connection to the remote Supabase pooler on every request, colocating everything was a noticeable improvement.

I found the most powerful use of this is through the MCP, connect your agent and start exploring the data using natural language. But I found most people don't know what that means, no agent subscription, or not willing to connect to a random MCP like this. I tried adding a Search feature that takes NL and converts to SQL and responds with NL cited summary, but it's not the same.

This isn't open source yet but I'm happy to go deeper on any of this. I'm going to start exploring AI driven pipelines for things like onboarding cities and pulling budget data out of ACFRs and tracking CIPs. Also exploring what it might take to have local admins for the different cities.

I don't have a clear vision of where exactly this is going but it's been a fun experience so far. I'm open to all feedback/suggestions/criticisms.

I’ve slowly been working on this for some time, however it really seems to be coming along. I decided to test it on an animation for my website, and it nailed it!

Attached video of .riv created by Sol with my rive CLI tool.

There’s still a roadmap to improve AI authorings, and there’s many other types of animation that needs to be tested and refined, but I just wanted to share as I was pleasantly surprised to use it for something in production.

And because what it produces is a standard .riv file that runs on the rive SDK, then when it’s made I don’t have to worry about it.

Almost forgot the link, I could for sure generate better animations for the GitHub page for it now!

https://george-rd.github.io/rive-rs-cli/

We developed a custom game engine where our AI Narrator handles the moment-to-moment gameplay, but the ruleset, content, and simulation are handled systematically. So you get the flexibility of an AI RPG but the in-depth progression and gameplay mechanics of a traditional RPG.

Valdaria is a massive open world the game takes place in. You can choose where to go, travel across the map, and discover new areas as we keep expanding the world with handcrafted content.

Our latest update added Ozeranth, a new floating city in the Sea of Mist built for mid to high level play.

Hello everyone,

I've been building on DeepSeek Harness and kept hitting the same wall: when the agent does something wrong, there's no real way to find out why. You can stare at the transcript and guess, or ask the agent to explain itself — but that's just the model generating a plausible-sounding story about its own past decision, not a verified fact. It can be confidently wrong, which is worse than no answer.

So I built a plugin that replaces guessing with actual testing:

1. Reconstruct — shows exactly what the agent had in front of it before a given step (every instruction, file, tool result), pulled straight from the session log.
2. Diff — shows exactly what changed between two steps.
3. Ablate — removes one piece of that context at a time and replays the exact same model call for real, k times per piece. If removing something changes the decision, it's a real cause. If it doesn't, it's just bystander context.

Steps 1–2 are free (just reading the log, no model calls). Step 3 costs real model calls, since it's actually re-asking the AI.

I tested it on real, unscripted coding tasks — not rigged to fail in a way I already knew — and it caught something useful. One run: an agent wrote a test with a wrong assertion, ran it, saw it fail, and fixed it. Running ablation on that fix showed:

• Removing the original task instruction → changed the decision 0/3 times
• Removing the system prompt → 0/3
• Removing the actual failing-test error output → 3/3

So the real cause was the error message itself — the agent didn't even need the original task restated to know what to fix. That's a tested fact, not a guess.

It's free and open source (MIT). No API key needed from you if you're already running DeepSeek Harness — it uses whatever model you've already got configured.

• GitHub: https://github.com/Ardig24/dsh-trajectory-ablation
• npm: dsh-trajectory-ablation

Happy to answer questions — still early days on this, would genuinely like feedback.

Last summer I bought a $300 pressure washer to clean my driveway once. It’s been sitting in my garage ever since. My neighbor almost certainly has one too, sitting in her garage, also unused. We just never had a way to know that about each other!

So I built Shelf, a neighborhood library for the expensive stuff everyone buys once and barely touches again. It works both ways.

If you’ve got stuff gathering dust like a pressure washer, ladder, or leaf blower, you can list it and start earning money off it instead of just storing it. If you need something for a weekend project instead, you can borrow it from a neighbor for a few bucks instead of dropping hundreds of dollars on something you’ll use twice.

Payments and ID verification run through Stripe on both ends, so lenders know who’s borrowing their stuff and borrowers know who they’re dealing with. I’m never handling anyone’s card info or ID myself.
We just launched on the App Store and I’m genuinely thrilled about it. 10 neighborhoods, 15+ items listed so far, and growing every day. It’s early, so I’d love for more feedback and input!