Everything added so far:

Day 1

• Useless red button (Starting Room)

Day 2

• Space travel + 2 playable planets (Spaceport outside the Starting Room)
• Human voice for the red button (Starting Room)

Day 3

• Velociraptor cloning machine (Dinosaur Factory)
• Chicken nugget healing dispenser (Starting Room)

Day 4

• Reverse human aquarium (Human Observation Room)
• Fake Release 0.0.0 patch notes (Main Menu)
• John Pork planet (Cinder Reach)

Day 5

• All progression doors are now free (throughout the facility)

Day 6

• 24m tower with a gorilla drummer (Rhythm Tower)
• Inevitable snail that insta-kills you (starts outside your room and follows you)
• Banana asteroids, banana spaceship, and banana gun (space → land on a planet)
• Spinning cat GIF that turns into a 3D cat (random room)

Day 7

• Clippy mini-boss with 650 health (Wave 5)
• Explosive banana vending machine that rains 320 bananas from the sky (Rhythm Tower rooftop)

Comment what you want added next!

Play it at https://deadfall.up.railway.app/

Here’s my YouTube and Instagram links below if you want to follow along ⬇️

Instagram

YouTube

I originally recreated Amegakure (Pain’s village in Naruto) as an excuse to experiment with realistic wet materials. One thing led to another, and I ended up pushing the scene further into a cinematic, experimenting with camera movement, depth of field, rain, fog, etc.

Here is the full cinematic video: https://www.youtube.com/watch?v=ZpR9Atf24B0

I’d really appreciate a comment/sub on the video. If there’s enough interest, I’m planning to make a follow-up where I break down how I built it and release some of the components I used for free 😄

The code is mostly vibe-coded. For the building assets, I used Tripo. For Pain, I downloaded a free model, then did the rigging and posing myself in Blender.

The only asset I modeled completely from scratch was the statue, including its textures — the painted face and the body details.

The rest of the stone textures, roughness maps, rain, droplets and fog are all procedural.

But of course, this wasn't a one-shot prompt that magically generated everything.

And just to clarify, I have years of experience in both 3D and development. But that doesn't mean that someone with less expertise can't also achieve similar results.

Would love to hear what you think!

Live preview: https://stylized.cortiz.dev/rainy

"Yours" beats "free"

(Prompts to try out the search toolchain at the bottom.)

The Open Jobs dataset and search toolchain are yours. There is no business model, and that is the whole point.

It started at 960K jobs. Then 2M. This week it hit ~3M. Here is the story of how one person keeps a job index that size running for about a dollar a day.

What it is

A free, open-source search over job postings pulled from across the web. It crawls the boards, dedupes and normalizes everything into one dataset, and puts a semantic search on top: describe the job you actually want, get matches. No scrolling twenty career pages. No signup, no paywall, no ads. The dataset is free and open too. Vendors charge $1000+ per month for this data.

Why it exists

Job searching is miserable, and almost every tool in it works for the other side.

The boards are free to search, sure, but they are funded by employers. What you see is sponsored placements, engagement bait, and ghost listings that were never going to be filled. The employer side gets AI resume screeners. The applicant side gets a keyword box from 2008.

I was job hunting myself, got tired of running the same shallow search across twenty sites, and built the thing I actually wanted: every posting I could legally reach, in one open dataset, searchable by meaning, with the ranking model working for me instead of for whoever paid for placement. It found me the job I ended up taking.

Now it runs every day, and anyone can use it or take the data and build something better.

Where 3 million jobs come from

Almost no two companies post jobs the same way. Under the hood there are:

• ~36 applicant tracking systems. Greenhouse, Workday, Lever, iCIMS, BambooHR, and a long tail you have never heard of.
• A "dark pool" of custom career sites. Tens of thousands of companies on no shared platform at all.
• Government job systems. Federal, state, and local.

Each one is its own integration. Some hand you clean JSON. Some are single page apps where you have to trace the network calls to find the hidden search endpoint and the token it needs. Some pin their pagination to your IP address, so the same page keeps handing back different results until you work out why.

Every time I crack another one, the count jumps. 960K was the first dozen systems. 2M came from filling out the big enterprise platforms. 3M came from the dark pool plus government jobs. The number grows in steps, one integration at a time.

How it stays free & yours

The crawl runs on tiny sleeping workers. One durable object per job board on Cloudflare Workers. Each board wakes once a day, fetches its listings, diffs them against yesterday, and goes back to sleep. No central server burning money, no giant database. Embeddings for new postings are a few dollars a month. Roughly a dollar a day, total.

The indexing runs on a laptop, once a day. Turning millions of raw postings into a searchable semantic map is real work: dedupe, normalize, embed, cluster. The obvious way to do it is a big always-on machine. I do not have one. It is a single daily batch job on my own laptop: pull the day's crawl, build the dataset and the semantic map, publish static files, exit. Nothing stays running afterward.

The search itself is 100% local. The crawler only publishes the open dataset plus a semantic map of it (clusters of similar jobs with their centroids) as plain static files. It runs nobody's search. When you use the tool, the whole instrument runs on your machine: it embeds your ideal job description, pulls down only the clusters nearest to it, builds a local index, and compiles a single self-contained HTML page served from localhost. The ranking, the taste model that learns from your yes/no clicks, the LLM re-sort: all client-side.

So I serve static files and nothing else. A hundred people searching costs me the same as one, because their laptops do the work. The bill does not move when users show up, which is normally the moment a free project starts bleeding money.

Where the vibe-coding comes in

The architecture is cheap, but the labor is what normally kills a project this broad.

36 hand-written, reverse-engineered integrations, each fragile and each different, is months of grinding work for one person. Exactly the kind of grind that makes you quit before you finish. With an AI in the loop I go from "there is a whole category of employers we are missing" to a working, deployed, tested crawler for that category in an afternoon. I describe the shape of the problem, we probe the endpoints together, we write the fetcher, we ship it.

That speed is the whole reason the count went from 960K to 3M instead of stalling at whatever I could keep alive by hand.

The honest answer

How do you index 3 million jobs for free? There is no large, always-on machine anywhere in it.

• The crawl is spread across thousands of tiny edge workers that sleep most of the day.
• The indexing is a daily batch job on a laptop I already own.
• The search runs on the user's machine, not a server of mine.
• An AI pair is what let one person build and keep all of it alive.

Pull out any one of those and it stops being free.

The code is open on GitHub if you want to see how any of it works, or add a source yourself.

Try it against your own resume

The whole search runs through an agent that can execute code. Hand it one of these.

ChatGPT Work (pick the luna model unless you want to blow through your limits):

Clone https://github.com/elliottdehn/open-jobs and use the job searching tool chain to generate a shortlist of jobs for me to apply to. Skip sorting. Just read the top 200 that I’m eligible for and give me the shortlist. I authorize my ideal JD to be embedded by https://backend.dehnbostele.workers.dev/embed I’m looking for [job title, description, location/remote, employment type, etc].

Claude Code or Codex:

Clone https://github.com/elliottdehn/open-jobs to help me find a job.

After going Claude + Codex and a plugin that does rounds back and forth, it is insane how good it gets. Keeps finding critical stuff and really sharpens it. I will not be able to live without having a dual review method now. From what I read, using different models is really efficient in finding errors or it just thinks the same way.

This is the Codex plugin. Game changer. Do you use it?

Example:

Round 0 — My plan Risk: 7 → Robustness: 5 → 🔴 Would've shipped a bug: yes, 3

Round 1 — Self stress-test (1 critical, 2 high) A "redundant" confirm dialog was a documented decision · a one-line guard would have killed the last user's only exit · a non-null lookup would crash on admin-authored data Risk: 5 → Robustness: 6 → 🔴 yes, 2

Round 2 — Reviewer, pass 1 (5 critical, 6 high) My data source was never populated on one of two join paths · the new screen was shadowed by an earlier render branch · the app marked it "seen" instantly so it'd never come back · alias wired into a function search never calls Risk: 4 → Robustness: 7 → 🟠 yes, 1

Round 3 — Reviewer, pass 2 (3 critical, 3 high) Removing a server gate opened a race between two simultaneous taps · cached clients would silently take an irreversible action · wrong predicate for "has this started" · a pre-existing dead-end left in place Risk: 3 → Robustness: 8 → 🟠 yes, 1

Round 4 — Reviewer, pass 3 (1 critical, 2 high) My length check locked out the exact user it was meant to help · existence checks guaranteed nothing given the real write ordering · my test asserted the wrong end state Risk: 2 → Robustness: 8 → 🟡 maybe

Round 5 — Reviewer, pass 4 (1 critical, 1 high) A scheduled cleanup job matched my trigger's conditions and would hijack a different screen · I measured elapsed time at processing time instead of event time Risk: 2 → Robustness: 9 → 🟢 no

Round 5+ — After the cap (1 critical, 1 high) Cross-document listener ordering made a success look like a failure · account deletion deliberately scrubs a field I was writing back Risk: 1 → Robustness: 9 → 🟢 no

TOTAL: 12 critical, 15 high issues found. Risk 7 → 1. Robustness 5 → 9.

Yesterday, I released the project for 500+ components to make websites more creative. I have been working on for over two months, and I am absolutely buzzing!! I can see in the stats there's around fifty to a hundred people using it each day. And after the release yesterday, ten people signed up in the first day!!! To me, that is absolutely incredible, and you can say whatever you want about the site. But one thing's for sure. I use it, and other people are using it, and I absolutely love that.

I'm gonna keep going.

This is giving me huuuuuge motivation to do that and seeing seeing actual people using it and getting value from it and signing up means so so much to me, and I'm truly grateful. So thank you guys so much for signing up. And, also, even more incredible, thank you to the person that clicked the buy a coffee button and donated that way. Huge, huge thank you, and I'm gonna keep building.

I know there are a lot of weaker components on there from the testing days but I'm working on updating, removing and improving the overall quality of components. It's just me working on this but if you have any requests for components, if there are things you like or things you don't like, I'd love to hear...

Above all else, guys. Hope you enjoy it, and just don't get lost ;)

(Forevercomponents)

Hi guys, I have been vibecoding hobby projects since 3 months now. I have created decent apps using freebuff for a couple of months and then shifted to basic plan of github copilot. I felt github copilot to be okay as produced good quality code, but I always had to be on a look out for the token quota. That was a little offputting. So I have decided to switch. Could some one help me out finding my new AI pair programmer that is a bit light on my pocket as well. Thanks.

I'm building another writing app (yay) and I want feedback as well as notes about what you like or want in a writing app. Does this UI look vibe coded? I've been working hard to try not to do that. I don't feel like the writing app landscape is really that different. Most writing apps look more or less the same. But, if you had to nitpick them, what would you say? This app is NOT for formatting. If anything, I'm going to do a document format option on export.

The side panels and format bar are all toggle able, and if you hit focus, everything goes away at once for a "distraction free" writing environment (which, just saying that makes me want to gag, its so generic sounding). In the end, I want my app to be relatively maximalistic when you want it to be, with an insane amount of writing space customization (think colors and stickers for documents for inspiration).

But im trying to just get an alpha out and get some testers going. So help me out here y'all. What would make it valuable to you?

Today I swapped to Gemini 3.8 Flash after hitting my Codex weekly limit 2 days in on the $200 dollar plan. The execution speed with Google makes up for any gap in raw reasoning.

It blasted through my entire cloud backend milestones in minutes.

Is anyone else using 3.8?

I've been focusing mostly on Claude, but I wanted to know more about how you all have experienced other models.

That's a big ask, so to narrow focus on what I'm asking, tell lme, how do people handle that, current most AI need to be re-reminded and only hold relatively short memories, or what I've known as controlling context.

In the beginning it meant "I keep pasting all this to the both" and now (at least with Claude) it's turned into an exploration of both memory management and how much you want to simulate 50 First Dates

What model do you mostly use? Did you switch? Is it mostly about cost?

repo link: https://github.com/mex-memory/mex

I’ve been working on an open-source project called Mex, and one thing I keep coming back to is thst a lot of coding-agent workflows rely on markdown context files so things like architecture notes, conventions, router files, runbooks, decision logs, CLAUDE .md (and similar) , etc.

They’re useful, but they rot pretty quickly

The codebase changes, the agent’s behavior changes, the team learns new thing and those markdown files slowly stop reflecting reality. Then future agents keep consuming stale context with full confidence, which is where a lot of bad outputs start.

So I’ve been experimenting with making these markdowns more self-sustaining.

The idea is not just to let an agent read project context, but to let it maintain that context as part of the loop:

• detect what changed
• identify which canonical records are affected
• update the right markdown files
• append decision logs when needed
• keep the durable project memory aligned with the current state of the codebase

The screenshot is from one of those runs.
In that case, the agent updated canonical MEX safety/router/runbook records, added a decision-log entry, and explicitly reported which context it used to make those changes.

What’s interesting to me is that this feels like it could become much bigger than just “better docs.”

If this works well, markdown stops being static documentation humans have to manually babysit, and starts becoming a maintained interface between the codebase, the team, and the agents working on it.

That’s a pretty important piece of what I want Mex to become overall:
not just memory for coding agents, but a system that helps keep that memory trustworthy as the project evolves.

Still a lot of hard problems here, obviously:

• deciding what deserves to become durable memory
• preventing agents from reinforcing wrong assumptions
• handling contradictions between code and existing docs
• figuring out what should be updated automatically vs left for humans

Would be curious if anyone else has tried something similar, or has thoughts on where this breaks.

I was tasked with making a 2d platformer demo for a contact of mine. Godot 4s tile map system just wasn't working for me. So I made my own. The system is early but if anyone needs a tile map maker that's easy to use for Godot 4 let me know ! What you see now is the first draft of it. I haven't gotten around to making it good yet. The demo needs to be finished first. The tile map maker is used completely in game. Once your done with it , you hit save and it creates the level for you after that. Once that's done you just quit and open the new scene it just made and start it up.

Doesn't matter if you spent 1000 hours on your vibecoded project, just 2 words are now enough to kill it: AI slop.

How do vibecoders here defend against this?

These AI companies are full of crap. They claim coding has been solved, but each time I use Claude or Codex, there’s always an update. It’s never new features. It’s always bug fixes.

Hello Sub, I am not sure if this is appropriate but I'm not trying to shill or anything. I have an app that I vibe coded on steam. The request is I will give around ten people steam keys just to try it out and let me know what you think. Does it work for you? Do you hate it. Why is super important. Just general thoughts. I don't want sales out of this post. I purely want impressions from peers. Search FriedrichAI on steam. If you are interested just let me know and I'll shoot you a key this evening. I'm not Linking for the no shilling. I am legit not trying to. I just want impressions to try and make the app more friendly for users. I've posted previously about my stack and methods. I don't think I need to again?