r/theVibeCoding

r/StableDiffusion, r/comfyui, r/Cursor, r/SideProject

TL;DR: Paid Cursor workspace + Python pipeline for character libraries. One identity file, each new pose chains from the last registered image, outfits dress the reference instead of redrawing. Includes 7 characters with real PNGs. No cloud API — GenerateImage runs in your Cursor subscription.

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I kept running into the same problem:

• New prompt → new face
• New pose → outfit breaks
• New angle → accessories vanish

So I stopped treating every generation as a fresh character and built a character library workflow instead.

What it actually does

AI Character Production System — create one character once, reuse it forever. http://mskt.gumroad.com/l/jygfmp

Not another image generator. A repo you open in Cursor:

• One identity file (SSOT) — species, materials, proportions locked in YAML
• Pose chaining — each new pose builds from the last registered active.png
• Outfits — dress the reference, don't redraw from scratch
• One change per generation — pose or outfit or angle, not all three at once

Outputs are yours to take anywhere:

• YAML prompts → paste into Midjourney, ComfyUI, Flux, etc.
• active.png per pose → img2img, video, your own app
• Pick front / side / full body so accessories stay coherent

What's in the bundle

Piece	Detail
Cursor workspace	Slash commands: /new-character, /add-new-pose-hamster, /character-pose-grid, …
Docs + agent rules	Step-by-step workflows, not vibes
Python pipeline	Register, lint, 59 tests — scripts never call a model API
Image gen	GenerateImage in Cursor — your subscription

7 ready-made characters (real YAML + PNGs, not empty scaffolds):

• hamster — 15 images (richest example: poses + outfits)
• stork — 10 (costumes)
• frog, lion, tiger, tanned_girl — 9–13 each
• scooter — 3 (minimal bootstrap)

Totals: 7 characters · 55 pose prompts · 69 canonical PNGs · 92 saved generations · 11 pose grids

What it's not

No kitchen scenes, full compose pipeline, cloud hosting, or unlimited auto-gen. Character libraries only — by design.

Day one in Cursor

Paste this after opening the folder:

> Get familiar with this project. Read README.md and AGENT_START_HERE.md, then start creating new characters or experiment with the existing ones.

Disclosure

This is a paid bundle I'm selling (Gumroad / store link in comments). I'm sharing because character consistency comes up constantly in these subs and I wanted to show the approach, not drop a link with zero context.

Happy to answer workflow questions in comments — even if you never buy it. What do you use today for keeping characters consistent across poses?

I just open-sourced Context Warp Drive, a continuity engine for LLM agents.

Repo: https://github.com/dogtorjonah/context-warp-drive

Right now, the industry has two bad ways of dealing with long agent horizons:

1. Just ride the 1M-2M context window.
2. Use an LLM to summarize older messages ("compaction").

LLM summaries are inconsistent, they burn an extra model round-trip, they quietly drop the exact identifiers your agent needs (UUIDs, paths, hashes), and worst of all, they constantly rewrite the prefix—which trashes your provider prompt cache.

This library takes a different approach: deterministic folding.

As the agent works, older context is folded into deterministic skeletons. Instead of linearly bloating to the ceiling, the active context sawtooths—building up efficiently, then dropping back down to a clean floor without losing continuity.

Why not just use the 1M token window?

Because 95% of what an agent carries with it on a long task isn't needed right now. It's looking for the needle in the haystack, but massive context windows force it to carry all the hay.

A larger window raises the ceiling, but it doesn't move the floor where models reason best. Long-context evals keep showing the same thing—models do not use giant contexts as cleanly as the marketing numbers imply:

• Lost in the Middle — models degrade when needed information is buried in the middle of long context.
• RULER — large drops as context length and task complexity increase, even for models advertised as long-context.
• Context Length Alone Hurts LLM Performance Despite Perfect Retrieval — length itself hurts performance even when retrieval succeeds.
• Intelligence Degradation in Long-Context LLMs — models can collapse past critical context thresholds even when input remains relevant.

By keeping the agent deterministically folding with a warm cache and a low context band, you keep it snappy, cheap, and focused. You leave the hay behind until it's actually needed.

How Context Warp Drive works:

• The Rebirth Seed: The continuity package that makes the full reset possible. It carries the recent user and AI messages, what the agent was actively working on and editing, its execution plan state, preserved exact identifiers from the full trace, and episodic context from earlier work. It is not a vague summary—it is a structured, deterministic snapshot the agent can wake up from and continue seamlessly.
• Cache-Hot Appending: As the agent works, older turns fold into compact bands that append onto the rebirth seed. The context builds up over time, but because the seed stays byte-identical, you pay for cheap cache reads turn after turn instead of expensive fresh inputs.
• The Sawtooth Reset: You can't append forever. When measured input pressure hits your configured ceiling, the engine performs the full sawtooth—the context drops back to a fresh rebirth seed and the cycle continues from a low-context floor.
• Zero-LLM Folding: Raw chat history stays preserved as the source of truth, but the model sees a deterministic compact view. Tool calls, paths, receipts, retained reasoning, and exact identifiers are all preserved without asking another model to summarize anything.
• Episodic Recall: When the agent re-touches a path or concept from before the reset, the engine pages the relevant folded detail back in. The agent doesn't carry all the hay—it pulls it back when it matters.
• Task Rail: I also included a portable execution primitive called TaskRail. It keeps long-horizon plan state outside the prompt: steps, progress, acceptance criteria, and serializable checkpoints. Combined with folding and rebirth seeds, the agent stays low-context while still knowing exactly where it is in a multi-step workflow.

What's in the repo:

• Core folding engine, provider-agnostic across Anthropic content blocks, OpenAI-style tool_calls, and Gemini parts.
• Anthropic prompt-cache breakpoint helpers to maximize read-hits.
• Raw rebirth seed renderer.
• Model-aware context budget resolver.
• Fold recall and episodic recall (with an optional SQLite episode store).
• Portable Task Rail state machine.
• Gemini CLI and Codex CLI folding adapters.

There are a lot of knobs you can tune, but the core philosophy is the same: use the 1M window as safety headroom, not as the operating band.

(Not on npm yet—install from source for now.)

I've been running this in my own multi-agent orchestration stack for months and completely dropped LLM compaction. The difference is fundamental: the agent stops treating context as a giant backpack and starts treating it like a paged working set—small, hot, recoverable, and always grounded in the raw trace.

If you vibe-code agents that browse, they probably die at Cloudflare. I open-sourced (BSD-3-Clause) a Chromium fork that fixes that. It corrects the fingerprint in native C++, not injected JS, so it holds up across iframes and workers. Real Chromium on raw CDP, drops into Playwright.

pip install tilion-fortress

Clears CreepJS and Sannysoft in my tests. Does nothing for your IP though.

github.com/tiliondev/fortress

What keeps hitting bot walls for you?

Hi vibecoding!

I was thinking it would be nice to see just a listing of true vibe coded projects, and see what others thought about them via reviews and some data points.

I put together VibeCoding.is to give a place where other vibecoders could submit their projects for a quick review and a potential place to showcase them.

Submission is not painful: Some details, an image, a live link, and an email confirmation code. If you have a product ready for review, please feel free to submit yours today!

Your submission should be an active, usable, and vibecoded project. Does it need to be 100% vibed? Of course not. Mine are various degrees of vibed and other tool sets, depending on the needs. Explain it in the description, that's to help others understand your project.

Feedback is appreciated, as I am just getting this open to submissions. If you see a problem, please let me know. If you hate it, let me know that as well. If you love it, please take the time and submit your projects, and review others on there.

And yes, it does look very generically vibecoded. I had choices of layouts of course, and spent some time with a couple folks who don't really know about vibecoding, and this was the overall direction that tested well. Go figure.

Because the site is fresh, I do have a few Mock projects out there that will be removed. The live submissions are simple vibe coded projects, be gentle.

EDIT: Thanks very much for feedback so far, almost all of it has been incorporated in the latest build. Please deal with us as we sort out emails. Some are going to spam, which I'm guessing shouldn't be a surprise for a new domain, so if you submitted a project or a review, please check your spam folder for validation emails, thanks!!

Mock projects have been removed, these are all live VibeCoded things, from Boxing to Hotel Booking, we've got some really interesting things!

I built DoneCheck, a tiny zero-dependency proof-of-done gate for AI coding agents.

When Codex, Claude Code, Cursor, or OpenCode says "done", DoneCheck makes it leave evidence: changed files scanned, the verification command, exit code, recent output, and a DONECHECK receipt. It also catches obvious AI-code misses like TODO placeholders, skipped verification, stale receipts, and swallowed exceptions.

Install from GitHub:

pipx install git+https://github.com/AtharvaMaik/donecheck
donecheck --cmd "pytest -q"

Repo: https://github.com/AtharvaMaik/donecheck

It is not a linter or a test framework. It is the cheap first gate before an agent, PR, or CI run claims the work is finished.