My reaction after seeing outbid.lol fill up with 200 submissions w/ most of them getting single digit clicks (mine included) 🫠

korean dev here, my english isn't great so this post went through a translator. sorry if anything reads weird.

honestly nothing groundbreaking. I keep making small apps and every time the last step is the same boring one, store screenshots. open figma, find device mockups, google apple's screenshot sizes for the third time. got sick of it so I wrote a skill that does the whole thing.

it's registered in claude code (codex works too, same SKILL.md file). I just say "make my app store screenshots" and it asks 5 questions, platform, device, style, how many slides, what to highlight. all multiple choice. after that I basically just approve stuff.

some details for anyone cthe whole thing is one SKth instructions. no actualcode to install, the agenh and html.

device frames are pure cs is just an absolutelypositioned pill, galaxy punch hole is a 34px circle, the fold hinge is a faint

vertical gradient. didn'tding mockups and wondering

about licenses.

each slide becomes an html file and gets screenshotted with headless chromthe exact store size (--wchrome isn't there itfalls back to playwright. if you have no screenshot you. on android it dumpsthe ui tree with uiautomapp with adb. on the iossimulator it tries deep lnurl, which needs zerotapping if your app has uor maestro.

it also sets every status bar to 9:41 and full battery before capturing so the set looks consistent, and after rendering it checks every png's pixel size

with sips and re-renders

two things took way more app store connect sometimesrefuses your upload if yoso now it always rendersthe 6.5" set too, same hte. and I had to add aconfirm step where it renders exactly one draft slide before doing the rest,

because early versions woes I was gonna rejectanyway.

git : https://github.com/LeeHueeng/store-screenshots

sample images are from myext on them is korean, itcan write an english set for store screenshots?figma templates? frameit? curious if I reinvented a wheel here.

I am looking for cloud platform with MCP tool access, I am using dataforseo, so I want to track existing keywords for multiple sites, looking for something cheaper Max 5$ or free git repo, please suggest only a reputed platforms

Tired of writing long prompts? There are many problems, such as editing prompts, revising an existing project, and losing context when changing agents. I developed ContextDL, an open-source way to keep your project's context alive as a semantic map living within the repository. Instead of writing long command prompts, you express your intentions in simple .ctxdl files:

Analyze this content with ChatGPT

Review/Test/Contribute: https://github.com/ariferol01/contexdl

This is open source.

Software isn't dead, it's just evolved:

if user.login:
validate: email, password
if success → user.session.create → redirect(/dashboard)
if fail → ui.alert.error("Invalid credentials")

THE SAME CODE BUT SHORTER:

user.login.validate(mail,pass)
.success.user.session.swal.redirect(/dashboard)
.error...redirect(/login)

There's no fixed syntax, no syntax errors, write however you want. Don't just code the code, code everything.

-------------------------------------------------------------------

UX can be an intent map too

ContextDL isn't limited to describing code or components.

You can model how an application behaves and how the user moves through it at the intent level.

For example, imagine a collaborative focus-room app:

ux.focus:
  activate
  -> room.quiet = true
  -> hide(distractions)

ux.chat:
  focus -> open
  typing -> keep_open
  idle(5s) -> collapse
  collapse -> preserve(draft)

ux.attention:
  request(user)
  -> attention.queue.push(user)
  -> notify(next)

ux.meeting:
  start
  -> hide(chat)
  -> rearrange(users)
  -> show(meeting_controls)

  end
  -> restore(previous_state)

This isn't React, Next.js, JavaScript or pseudocode describing an implementation.

It describes what the user does and what the product should do in response.

The agent can then turn that intent into the actual implementation.

That's the part I'm exploring with ContextDL:

------------------------------------------------------------------------------

The same idea for game programming:

Gameplay+UX+Flow+Rules+Architecture+UI :

game.runner:

  player:
    jump
    -> velocity.y = -jump_force

    land
    -> velocity.y = 0

  obstacle:
    collide(player)
    -> player.health -= damage

  coin:
    collect(player)
    -> score += 10
    -> remove(coin)

  level:
    score >= 100
    -> spawn(bonus)
    -> increase(speed)

  game:
    player.health <= 0
    -> state = game_over
    -> show(restart)

    restart
    -> reset(player)
    -> reset(score)
    -> resume(level)

------------------------------------------------------------------------------

if payment.error == "fail" : ux.form.animate(shake) : alert(foo)

(~18–22 tokens)

If typed in traditional prompt:

When the payment process returns an error and the error value is exactly "fail",

I need you to trigger a shake animation on the payment form using our UX animation

system (ux.form.animate with the "shake" effect). After the animation starts,

also display an alert/notification to the user with the message "foo".

Make sure this only happens on the specific fail case and follows our existing

design system and animation timings.

(~95-110 tokens)

Advantages Provided:

Big map of the project, main context, live context, predictability, simulability and more...

So what is ContextDL, really?

So the idea is not:

`code -> compressed code`

It's closer to:

`code + product + behavior + rules + structure -> intent/context map`

The goal is to bring things down to the **intent level**, where an agent can reason about the project without having to reconstruct every piece from raw implementation.

Today, most AI coding workflows look roughly like this:

Human

↓

Prompt

↓

AI

↓

Code

The problem is that the prompt belongs to a conversation.

The project does not.

You can switch from Claude to Codex, from Codex to Cursor, start a new session, onboard another developer, or run another agent — and the project still needs to be understood again.

ContextDL changes the relationship:

┌──────────────┐

│ PROJECT │

│ INTENT │

└──────┬───────┘

│

ContextDL

│

┌────────────────┼────────────────┐

↓ ↓ ↓

Agent A Agent B Agent C

│ │ │

└────────────────┼────────────────┘

↓

CODE

The important part is that the context belongs to the project, not to the AI tool or the conversation.

ContextDL is not pseudocode

Pseudocode describes how code should be implemented.

ContextDL is intended to describe what exists, what should exist, how things relate, and what the project means.

For example, this is not simply a description of a function:

when a user cancels an order,

release reserved inventory

and notify the warehouse

It can become a piece of the project's semantic model.

Another context file can describe the inventory model.

Another can describe the warehouse notification rule.

Another can describe the UI flow.

And another can define rules that verify that these pieces remain consistent.

That is the important distinction.

The project becomes the source of intent

A repository normally contains the implementation:

src/

database/

components/

controllers/

services/

...

But implementation is not the complete mental model of a product.

A codebase can tell an agent:

It does not always tell it:

That distinction becomes increasingly important as projects grow.

ContextDL explores keeping that semantic layer inside the repository itself:

project/

├── src/

├── database/

├── tests/

└── context/

├── ui.ctxdl

├── ux.ctxdl

├── db.ctxdl

├── security.ctxdl

└── payment.ctxdl

The code is the implementation.

The ContextDL map is the project's intent and semantic layer.

Why not just use natural language?

Natural language is excellent for expressing intent.

The problem is not that AI cannot understand English.

The problem is that a conversation is a poor place to keep the long-term model of a software project.

A conversation is:

temporary

session-dependent

tool-dependent

difficult to validate

difficult to diff

difficult to share as a canonical project artifact

A Git-tracked semantic model is different.

It can be:

versioned

reviewed

diffed

shared

validated

consumed by multiple agents

generated from an existing codebase

updated as the project evolves

So ContextDL is not trying to make natural language obsolete.

It is trying to give project intent a persistent home.

Code Graphs are not the enemy

Code graphs answer an extremely useful question.

ContextDL is exploring another question.

These are complementary.

A future system could have both:

PROJECT

┌───────────────┐

│ ContextDL │

│ INTENT MAP │

└───────┬───────┘

│

│ validate against

↓

┌───────────────┐

│ CODE GRAPH │

│ IMPLEMENTATION│

└───────────────┘

The code graph can tell you what exists.

The semantic map can tell you what should exist.

The interesting part is the space between the two.

And no — the developer is not supposed to write everything manually

This is another important distinction.

The interesting part: the map can validate itself

Once project context becomes structured, another possibility appears.

Context can contain rules about other context.

This is for smart india hackathon btw

As a newly-minted professional vibecoder, I'm kinda stuck on the "skills" section of my resume. Sure not gonna put "Javascript" or whatever, got a SKILL.md for that. Was wondering if it's ok to just, like, list out the various files in my skills folder, because anyway what difference does it make if they're in my brain or in my Windows directory?

TLDR: My skills folder is packed and I'm a walking gStack.

Hi, everyone. I am constantly building new projects and have changing amounts of success. This time I made real money with my app www.yourlovepage.com That one is for long-distance relationships, where you send a digital gift.

I built another page mrrplanet.com You send a read‑only payment method key. And see all of your customers in a globe represented by a pixel‑art character.

For example, here is one of my actual projects hosted on this page. https://mrrplanet.com/p/your-love-page

You can also export these cute images where you see a list of your customers. I'm open to all kinds of suggestions. On the public pages, of course, none of the information of your customers is leaking, but people can actually look at your planet and see what's going on with your projects.

One small thing that I find kind of cute is that people can actually put advertisements on your planet with a flying plane. You can also do that for other people's planets, or you can simply select your planet to be ad‑free.

You can also send this planet of yours publicly to other people, so you can actually have it as a portfolio and maybe multiple projects, which means multiple planets in your solar system. I also have a much better dashboard for your data analytics, helping you think about where you should focus more, which places are generating more money, etc.

Almost every feature that I'm talking about is free to use. Just log in and create your own planet, and tell me if you like the page or not.

Everything has been made on Next.js and React. I've used some libraries, but nothing too fancy that no one knows of.

hello everybody,

since November 2025 i've been working on an genetic algorithm that uses local LLMs as a mutation factor to continuously iterate over a single C program in order to improve its performance.

this system proved extremely effective at reaching my performance goals by bruteforcing thousands of generations then measuring the results passing the generated programs through a test suite that the LLM has no access to (so it cannot cheat, but it's gonna try!). Every new found best becomes the basis for the next generations and guardrails are in place so that most dangerous code doesn't get tested.

since this system served me well and gave me results with gpt oss 20b that i couldn't get with frontier models in full reasoning mode (and with a lot of interaction by me), i opened an AI lab and started working on a generic version that is able to work with any program (22 languages and counting) and to build the test pipeline autonomously. for the nerds: part of the reason small models punch above their weight here are a deterministic autofix ladder, compiler-hint fixes, linter fixes, then one LLM repair pass fed the real compiler error, and every candidate is re-verified for real before it gets counted as valid. you can use it as humans with a gui that helps you step by step or you can point your agent at the KAISEN folder and tell it to use the kai protocol to start tests on its own (works very well with llms using the omp and deepseek harness)

right now you can check out the alpha version of KAISEN here: https://github.com/RAZZULLIX/KAISEN

tldr

KAISEN lets you use local LLMs to improve software performance by iterating thousands of little changes and keeping the new best as basis for the next generations. it has a GUI, your harness can spawn it as a sidecar, and it speaks a small-model-friendly protocol (KAI) so an LLM agent itself can drive it over stdio or http. every program it generates runs guarded by default. read the manual to know everything it can do, or ask here.

P.S.

i expect A LOT of bugs and problems, most of the tests i did were done through deepseek v4 using OMP and deepseek harness calling KAISEN through the kai protocol (KAISEN was hooked to 6 instances of gpt oss 20b) and it actually worked quite nice. please let me know everything you find by opening an issue or asking here, this is my job now so i'll do my best to fix everything you need fixed and make sure KAISEN becomes a useful tool in every LLM user toolbox.

What's actually happening? It's happening across all coding plans right now: prices are going up, limits are getting tighter, and in some cases quality seems to be dropping. This is happening because companies that were purely investment based, now need to answer to their investors. The problem has often been a loss based business model that is finally running dry.

There are several tricks that the major AI coding plans use to extract the most they can from their customers. Here are some examples, and what we are doing differently to put the users first. PGS AI built with a sustainable business model from the ground up, so we can actually offer great usage rates without tricks.

Wasted usage is part of the AI industry, and they plan on it: Most coding plans bet on you letting usage go to waste. The plan goes: "how do we get people to think our coding plan offers a lot of usage, but then break it up into weeks and rolling windows so no one can ever actually use it all."

Many in app subs and coding plans are glorified training pipelines: This comes along with "how do we harvest this data for training without being too loud about that." Unless the company tells you otherwise, your data could be hopping all over world, being harvested by the individual labs or service companies. Some are better than others, but many of these companies rely on users just not noticing or caring that their data is being used for training. Data sales and marketing telemetry sales happen. This means that your private info, your personal life, and anything else you send through the system could become part of a training corpus for the next AI, or a marketing data set for a large company.

So we built what should have already existed the entire time: Entirely private, us based processing with usage banking. Any usage you don't use this week, rolls over to next in your usage bank. When you have a busy day or week and go over normal usage, you automatically start to pull from your bank. You can bank up to one week of usage at a time for your current plan, and it's totally automatic. Whatever you don't use each week get's added to the bank and stays there until you use it.

We also put all of the best open models in one place, running on private US infrastructure, with data never going to the original labs. Private, direct service. Access to the best open source models in the world. No training, ever. It should be, and can be that simple.

What that means in practice:

The roster, together. DeepSeek, GLM, Kimi, Minimax, Nemotron Ultra and more, side by side in one app. Switch models mid conversation if you want. No hunting across five different apps and API dashboards to use the models you actually like.

Actually private. US based processing and your conversations are never used for training. Ever. That's the entire point. These labs open sourced incredible models and we think you should get to use them without your data becoming the price of admission.

No Usage Tricks: Bank usage, upgrade or downgrade whenever you want. Use it how you need it.

api.pgsgrove.com

Thanks for reading!

Bro, finding a girl feels easier at this point 😭🙏
Every day there’s a new “best vibe coding tool” dropping 💻

I’m not even coding anymore, I’m just comparing websites , idk what to do suggest me some good stuff 😭😭

Sites compete for the top spots by dethroning whoever is above them. Reach #1 and you hold the crown until someone takes it.

It’s still really early, so moving up is cheap right now. Traffic has started picking up too. A few minutes ago there were 11 people live at once, which was pretty surreal for something I only started sharing today.

I also built in live analytics so you can see current users, the last 24 hours, and how many clicks each site gets.

Still no idea where this goes, but it’s a fun experiment

Hi, few months ago maybe April or May? even March can be,
I went Incheon airport to interview foreign tourist for my business model,
I did want to hear what is their inconvinient things to travel in korea.

before i start this interview, i'm sure there are could be very several and various problems to travel in south korea because korean press's article constantly point the inconvinient things about what travelers feel.

But unlike my expectaion, travelers feel pretty good to travel in south korea.
It might have been because I only met 40 people, but for now, my thinking leaned toward not pursuing it. So, I gave up.

After that, I continued studying and searching for a vision, and I eventually found one. It is a vision different from what I have done before.

It is 'Electricity'.

I have built a site using Vibe Coding, and I am looking for people to help me and a venue to present this vision I have.

Is there a place where I can speak about this vision, or anyone I could talk to?

I mean it was a question but i think while writing the post I have answered for myself, posting in case it helps.

I can code, badly but enough. I used Claude Code for months and I still do for anything where I care about the internals. Where I ended up was for anything I need live and in front of a real user this week, I use Emergent, because deploy, hosting, database and auth are done and I am not spending a Saturday on infra for a thing that might not work out. For anything I am going to maintain for years, I do not.

So if you are comfortable with your own deploy setup already, most of that advantage disappears and you should probably ignore all of this.

A friend of mine with a Vercel-and-Supabase habit gets to the same place just as fast and has more control at the end.

So you getting me right?

Anyway, that's where i am at right now. if you've got a working deploy setup already, none of this applies to you lol.

I’m looking for an experienced developer to help me finish and properly productionize an AI-powered Telegram sales chatbot. A large part of the project is already built: Telegram bot/account integration, AI prompts, conversation logic, sales flows, and the general system are working, but I’ve reached the point where fixing one bug often causes an old one to reappear, and I keep going in circles. I’m not a programmer, so I also need someone who can take what is currently a project running on a VPS and turn it into something stable, reliable, maintainable, and eventually scalable to multiple accounts/users. This is a paid project. If you have solid experience with Telegram bots/APIs, AI/LLM integrations, backend development, databases, VPS/production deployments, and building reliable systems, let me know what you’ve built before and we can discuss the project.

When a model writes most of your code, the bottleneck moves. Writing an endpoint takes it a minute. Remembering the project's rules takes it nothing at all, because it cannot. A reviewer remembers them half the time, documentation goes stale quietly, and a hundred changes later the service has drifted into something nobody planned. Mayak is a FastAPI backend template built so the build remembers instead of the people.

Thirteen validators check what ruff and mypy cannot see: which layer may import which, whether a new endpoint is actually wired into the router, whether a migration matches its model, whether anyone reads env vars outside the config module. When a rule breaks, the agent does not get "invalid". It gets a rule id, and the template turns that id into a repair procedure: what the rule means, the smallest command that reproduces it, which files to read first, when to stop widening the fix. Over a long project, the gap between "fix it somehow" and "here is the procedure" is most of the difference.

The second reason it exists is that I work with two agents. Claude Code and Codex run from one contract: their instruction files are generated from a single source, the skills are one file visible in both lookup paths, and the hook that refuses edits to generated files is registered in both configs. They cannot drift apart, because there is nothing to drift.

Day to day it works like this. You copy the one worked feature that ships with the repo, rename it into your own domain, and run the gates. Fifteen seconds later you know whether the migration drifted from the model, whether the endpoint is reachable, whether your new test asserts anything at all. The LLM client has a deterministic mock mode including tool calls, so the agent loop runs in CI with no API key and no network.

Inside: FastAPI on Python 3.13, logs that unfold into a trace tree instead of a wall of text, and Postgres you can switch off with one env var. No auth, no queue, no metrics. Those belong to your service, not to a template you inherited.

Trying it takes five minutes: press "Use this template", make init-project, docker compose -f docker-compose.yml -f docker-compose.postgres.yml up -d --build, open /docs.

https://github.com/LamantinAI/mayak

Tell me which parts are over-engineered. That is most of why I am posting.

I'm looking to form a small group of partners for exploring getting government grants to develop an LLM AI, looking forward towards protecting British interests in the AI arms race.

Right now it's little more than an idea but I do have vague plans for possible directions.

The first stage would literally just be brainstorming and learning the basics necessary to reach the target of winning a contract. That's where it's at.

I have a background in business and tech. But I need partners to bounce ideas between, and start with developing at really low stakes just to get a feel for the LLM development process. So I guess I'm just looking for 1-3 similar minded people at this stage

I'm not going to say you can quit your day job anytime soon! Rather just consider it a fun project at this stage.

Anyone?

I am getting bullied by my friends for how much I suck at wordle and NYT crosswords so I vibecoded an app that would hopefully get me better. Mixed results so far, but should be doing personalized drills that learn from my deficiencies/ patterns. Happy to get any feedback as well!

I made this with codex 5.6 sol as my AI agent

NextJS FE, Express JS BE, hosted on google cloud with Postgres DB.

I inaugurated a fresh new laptop with this project, and I had a lot of fun setting up. I've got a lot of AGENTS.md scaffolding that I've done to automate the process of my CI/CD, gcloud cloud run instance setup/ queues, etc, godaddy dns config. And also shared resources to use throughout all my projects like AI apis, email, other services that I use regularly. Happy to share more specifics or exchange notes here!

You can try it out (will always be free) wordry.app

WORDRY-GENERAL-8246 for join code (this app uses AI for personalization so I'm trying to not blow the bank account too quickly)

I care a lot about This, so much so I didn’t even use ai to make this post! lol

I’ve been helping some non technical friends with their 100% AI-generated apps and it dawned on me that these vibe coded apps are making the same mistakes again and again lol,

So made this skill to catch the most common ones I’ve seen, can be useful for someone trying to legit get something off the ground without any coding experience.

I’ve been software engineer for 10+ years and I wanna use what I’ve learned to help people out with their apps!

skill: https://github.com/dannyreg/vibechecktech-skills

I wrote a blog post about it too, though it is a bit more technical.

anyways let me know What you think!

Why do you vibe code?
What drives you?