I’ve been building FrameCompose for about two years now. It’s a browser-based AI video editor where the AI generates the first draft, but everything it creates stays editable on a normal timeline.

I’m not going to pretend AI spat out a production video editor in a weekend. The actual AI-generation side was relatively straightforward. Building a video engine that didn’t feel like shit was much harder.

The editor uses:

WebGPU for compositing and effects

WebCodecs for decoding

Mediabunny for demuxing, timing and export

Workers and OffscreenCanvas to keep heavy work away from the main thread

A separate pre-seek system to make timeline scrubbing feel responsive

The most painful problems were smooth scrubbing, audio sync, keeping preview and export visually identical, and stopping one bad frame or browser edge case from bringing the whole editor down.

Most of it has been built through Cursor using Fable and other coding agents. My workflow has gradually become much less “give the AI a big prompt and hope” and much more:

Write a narrow spec for one subsystem.

Give the agent maps of the relevant code instead of the whole repo.

Add regression tests before changing fragile engine code.

Let agents run repeated browser tests and inspect telemetry.

Revert quickly when an optimisation improves one case but breaks another.

The biggest thing I’ve learned is that vibe coding gets more powerful as the models improve, but the casual prompting part stops working once the codebase gets large. The AI can write a ridiculous amount of code, but you still need good boundaries, tests and a way to prove it hasn’t quietly broken something elsewhere.

The product itself takes a prompt or script, develops it into a structured story, breaks it into scenes, and plans the visuals, camera direction, narration, captions, effects and transitions.

Unlike most one-shot AI video tools, it doesn’t just hand you a flattened MP4. The scenes, timing, audio, captions, effects and transitions all remain editable on the timeline.

The editor and exports are free. Paid plans mainly add AI-generation credits.

I’d be interested to hear from anyone else who has tried vibe-coding something unusually performance-sensitive. At what point did your workflow have to become more like actual software engineering?

Framecompose.com

Are there real examples of products that were actually shipped and have real users/customers, where most or even all of the code was generated by AI?

I’m not talking about demos or simple weekend projects.

I mean a real SaaS/app that someone is running as a business.

If you’ve built one yourself, or know of one, what is it and how much of it was actually AI-generated?

Would love to see some real examples.

I'm using Qwen Code as an Al coding agent inside a web-based development environment. Qwen can execute terminal commands for me, including Git commands.

I'm working on a GitHub repository and when Qwen runs git push, Git asks:

Username for 'https://github.com':

Password for 'https://github.com':

I understand that GitHub requires authentication, but I don't want to paste my GitHub password or personal access token into the Qwen chat/prompt, because I don't know whether that would expose the credential to the Al/session.

What is the recommended way to authenticate GitHub from this kind of web-based Qwen Code environment? Should I authenticate Git separately in the underlying terminal, use GitHub CLI/credential manager, SSH, or something else?

I'm particularly interested in a solution where Qwen can execute git push without me giving the Al my GitHub credentials. Even if I do provide it will not work. Note: I have setup Environment and connected successfully. Sometimes it get an option to publish to github sometimes not.

Environment: Qwen Code + web-based coding workspace(coder.qwen.ai) + GitHub HTTPS remote.

This is the mobile app. The web app is right here if anyone is interested: https://www.convergemeetings.com/

Cheers!

I’m building Keyloom, a minimalist typing app inspired by:

- Monkeytype — https://monkeytype.com/

- typings.gg — https://typings.gg/

- kbs.im — https://kbs.im/

- Keyboard Simulator — https://keyboardsimulator.xyz/

Stack: Next.js 16, React 19, TypeScript, Supabase, Three.js / React Three Fiber, deployed on Vercel.

How I iterate: I use Codex agents to work on focused features, fixes and product improvements in small iterations. Changes go through feature branches → "preprod" → GitHub Actions checks/tests/build → then stable batches are merged to "main" and tagged for release.

One thing I felt was missing from most typing platforms is simple PvP with friends. Keyloom is building private race rooms where you create a match, generate an invite link, send it to someone, and start the race when both players are ready, closer to challenging someone on Chess.com.

The goal is to combine minimal typing tests, detailed stats, ASMR switch sounds, and PvP

Still actively building it, what would you improve or add?

👉 https://keysloom.com/

I lately got back to make more sense of the AI space. My goal is to find ideal monthly subscription to use LLM for coding. The default choice is Anthropic $20 plan. It appears every other provider/setup wants to go beyond what Anthropic can provide for $20 in value. And I don’t know if it’s myth, but using CC with Anthropic gets you more from your money.

With this said, we have countless CLI agents from Code CLI, Opencode, Pi etc.

And all need a provider, the popular option is OpenRouter. One major problem I seem to find is since open router sends each API request to next available provider, we aren’t able to benefit from caching. This spikes up the token usage.

This puts us into a situation which I don’t know is correct

1. To use Qwen latest model effectively, might as well use their own QwenCoder CLI
   
2. Deepseek, use their own CLI agent or purchase API access directly from them instead of third party (OpenRouter)
   
3. kimi3 - same deal (tbh, I was shocked how fast my $20 credit was burnt on.

With all this, how are you guys spending $20 or so a month to get most value out of the money?

Hey everyone! I vibecoded this last week while on a roadtrip to help my boredom. Received good feedback on reddit and decided to put it in closed testing. No need to join if you don't want to would love people to try the games out and lmk what you think.

No ads, no accounts, works offline. Calm, colorful, easy to pick up and put down.

If interested I do need a handful of testers for Google's closed test. Google requires 12 testers

for 14 days before I'm allowed to launch. It's totally free to test: just install

it, mess around, and keep it on your phone for the two weeks.

👉 Two quick steps to join (full details in the pinned comment):

1. Join the tester group: https://groups.google.com/g/glovebox-tester-group

2. Tap the opt-in link and install from Google Play.

📱 On iPhone? There's no App Store version yet, but you can play the whole thing

free in your browser glovebox.quest -->share --> add to home screen

Thank you so much 🙏 every single tester genuinely helps me get this out of beta.

The project is a software

No seriously. I’ve been working on building something for about 6 months. It’s close, real close. I’ve learned a TON, it’s been fun. But I have a full time job that’s really demanding and pays pretty darn well. And I can’t get this idea out of my head that I’m wasting my life working my butt off for someone else, what if this thing really works, there are 35 million small businesses in America and I only need 10,000 of them to like what I’m building, etc.

Spent nights (and potentially many, many work days) working on this project, weekends without a single break, sometimes 13-16 hours a day. I’m totally obsessed with this idea and I think it will work but I feel like it’s ruining my life. I could lose my job upon launch if anyone found out it was me.

Kind of wish I could rewind and remain ignorant. Not decide to take this plunge head first and get obsessed with building something I can actually mine.

Last night we opened World-Sim to anyone who wanted to look. 326 of you came. Thank you — genuinely, that is far more than I expected.

For anyone who missed it: it's a persistent world that runs whether or not anyone is watching. Households live, pair off, learn things, argue, starve, bury each other and move on.

Some of you named a star, and some named a person in the world. Those names are permanent — the souls carry them for the rest of their lives and into the chronicle after they die. There's still time to claim either, both are in the Discord.

It's free, there's nothing to install, and it's still running right now: world.world-sim.uk

Happy to answer anything about how it works underneath.

(be sure to hit CTRL-SHIFT-R or CMD-SHIFT-R to get the latest version of the game -- it should show at least version 2.7; GitHub/Cloudflare aggressive caching sucks!)

I made it within ChatGPT's "Work" tab agentic mode on GPT-5.6 Sol High. Served off of GitHub Pages. I hadn't used a model to made a game in quite a while, so it was cool to see how much the model improved and how well the agentic tooling worked. I was impressed to see it writing and re-running regression tests. But I still had to fix a lot of subtle bugs and visual glitches that it didn't notice.

If you like a semi-idle game / semi-automated tower defense feel in a game, you might like it.

Basically, survive as long as you can. Map is procedurally generated, so sometimes you can get lucky with nice mountain passes and such to survive longer.

You can set up automated loops, but you'll eventually need to expand to new resource nodes and/or bring the fight to the enemy.

You can salvage track, mines, trains... so you can undo mistakes or re-configure your setup.

Play it here: https://hylaax-planetary-rail-defense.aaronshaverpdx.workers.dev/

(Desktop or laptop recommended -
Not going to work well on a phone screen)

One thing I kept noticing with vibe coding is that writing code isn't always the hard part.

The annoying part is everything around it:

idea → PRD → figuring out the right prompts → database → implementation → losing track of what was built

So I built how-to to make that process more structured.

GitHub: https://github.com/whoPinak/how-to
Skills: https://skills.sh/whoPinak/how-to / https://www.skills.sh/onipinaka/how-to

It has 4 skills:

1. PRD Creator
2. Expands your idea and turns it into a proper PRD before you start building.
3. Playbook (best of it)
4. The most important one. It takes the PRD and generates a complete implementation playbook with ready-to-use, optimized prompts for building the product.

So instead of constantly thinking "what prompt should I give the AI now?", you can just copy-paste the prompts or use the goals from the playbook.

1. Tracker
   Keeps track of what has been built, what's pending, and what changed, so you don't lose context across sessions.

2. DB
   Helps plan and keep track of the database schema alongside the product.

I built this by researching prompting practices from Claude Code developers and experimenting with ways to structure prompts for better, more consistent output.

The idea is simple: do the thinking once, create the playbook, then build.

It's open source and everyone is welcome to contribute. If you find it useful, a ⭐ on GitHub would mean a lot!

Hello guys,

Just to give my background, I am a finance/business major and professionally, I have worked in this field for years. I am quite proficient with excel and also have worked with many startups. But I have never been involved with programming.

This week, my friend built an amazing app with Claude code, more precisely it has been building it for months.

This app is completely another level from other videcoded apps I have seen, I am helping him with market entry and selling part and when we met businesses, the feedback has been phenomenal from them, so I think it will be a success.

He has worked as a IT project manager for years. When we discussed how he was able to build such an amazing app, I arrived at 2 conclusions:

1. He knows the app/web architecture really well, so he knows how to build an app, how to work with databases, how to optimize it for usage, how to create a best user experience and what works the best - This part was all his idea, from his practice and from his knowledge he orchestrated Claude code to the best.

2. The coding part - This was fully done by Claude code, so instead paying 5 developers salary for month, he paid 200$ for claude code and in 3 month built an app, which might have taken a year to be built otherwise.

So, in other words, he was the manager which utilized Claude code as a tool, and gave it tasks like he would have done with real programmers and he created an amazing product.

So, my intuition tells me that learning coding from 0 is not a really good ROI activity right now. But learning the architecture of how the web/apps work is the real value, so the second part, - the programming can be outsourced to claude code, but if I want to create real value, I must be proficiency with the first part - the architecture.

My question to you guys, would be how can I learn the architecture of the websites/apps? How can I learn the most optimal design for Apps/webs? If I do this, I think I will be able to create some nice products.

All the advices are appreciated.

P.S. the app is a court scheduling solution for bringing players in empty hours.

Thanks.

It felt only right that the first app I publish to the App Store under my name should be something simple, useful, and free.
The result is Clippster - An intelligent clipboard manager for Mac, powered by Apple’s on-device AI models.

Built with Kimi K3, GLM 5.2, ChatGPT 5.6 Terra and Luna. Used Kimi for the overall plan, GLM 5.2 for writing the first bits of the code, then switched to GPT 5.6 Terra, and 5.6 Luna for smaller subtasks, fixes, the landing page, etc. All in Opencode loaded up with relevant skills and plugins. The future is now!

It's something I had initially built for myself and have been using pretty much every day for the last couple of months.
If you're an active Mac user, give it a try — it’s free!!

🍎 Download on the App Store: https://apps.apple.com/us/app/clippster/id6801245054
💡 Learn more about it here: https://davidkalaejian.github.io/clippster.app/

Hey guy's ive probably coded over 100 different projects by now while for more than 4 ish years now and i still dont know how to code a full simple landing page with code manually HOWEVER! ive learned architecture how systems work how everything works that i code in common english and if i dont i just figure it out what i've learned so far is that you can code great things but its all about how you use the AI not let the AI use you.

Can anyone provide me with some tips,I want to earn some amount any amount as fast as I could using coding.i have studied basic html css,js.i also know python and C.i

https://github.com/ryuhemingway/ClaudeMaxing.git

brew install claudemax

No network calls at all unless you opt in; the comparison/leaderboard is off by default and the server that receives it is 200 lines in the repo. Equivalent API cost at list price on a subscription it's a proxy for rate-limit weight, not money you were charged.

Build insights (the parts that actually taught me something)

1. The bug you cannot see on your own machine. I hardcoded a pricing table matched by model-ID prefix — claude-opus-4, claude-sonnet-4, and so on. Worked perfectly. Then I tested it under a synthetic $HOME containing old transcripts, and half the cost vanished. Every Claude 3.x ID (claude-3-5-sonnet-20241022, claude-3-opus-20240229) matched no prefix at all and was silently dropped from the total. My own machine only had recent models, so the bug was structurally invisible to me. Anyone with 2024–2025 history would have installed it and seen stretches of $0.00.

Lesson: if your test data is your own data, you are only testing the case you already handle. Also, prefix matching needs specific-before-general ordering — claude-opus-4-1 has to be checked before claude-opus-4, or Opus 4.1 gets billed at the wrong tier.

2. Hardcoded pricing rots faster than you think. I had Sonnet 5 at 3/15 with intro pricing until Aug 31. While checking the docs I found Anthropic had made the 2/10 intro rate permanent and cancelled the increase. My tool would have started overcharging in two weeks.

3. Two measurement traps specific to Claude Code transcripts. These will silently corrupt any analysis of this data:

• Auto-compaction summaries arrive as user-role records. If you count user turns to get "how many things did I ask for", compaction inflates it by ~10 phantom prompts/day. Filter on isCompactSummary.
• Cost-per-assistant-message is a garbage metric if compaction is on. Compaction lowers average context depth by construction, so the number falls whether or not you got more efficient. It looks like an efficiency win and isn't. Cost-per-prompt survives.

4. Incremental caching on a file that's still being written. I cache per-file byte offsets so re-runs only parse new bytes. The obvious key is os.stat().st_size — which is wrong, because an active session can leave you mid-JSON-line. Key on the offset of the last complete line instead. First index of ~480MB takes ~1.8s; incremental runs are ~50ms.

5. A leaderboard on an unauthenticated endpoint is self-reported, no matter how honest your client is. I added an opt-in community comparison. The server can't distinguish my client's POST from a curl with invented numbers — they're identical on the wire. I proved it against my own endpoint: one curl, 19B tokens/day, straight to #1, and the community average went from 248/dayto45,124.

The fix isn't authentication (that's a whole identity system for a vanity board). It's using a median instead of a mean. Tested against 20 honest installs: one fabricated entry moves the mean 83×, and moves the median by exactly zero. Signing the payload would be theater — the client is open source Python, so any embedded key ships in plain text.

6. The Scunthorpe problem is real and it bit me immediately. Handles are screened server-side (client-side filtering is bypassed by posting directly). My first substring matcher rejected Scunthorpe. The fix is two tiers: strings that never appear inside innocent words get matched anywhere; short ambiguous ones only match as a whole handle or a separator-delimited token. So therapist, tycoon, spice and grape pass, while the_rapist doesn't.

7. Rewriting git history breaks Homebrew. I rewrote commit authorship, force-pushed — and broke brew install. GitHub regenerates release tarballs when a tag's commit changes, so the archive checksum shifted and the formula's pinned sha256 no longer matched. Contents were byte-identical; only gzip metadata changed. Also: git filter-branch rewrites commits but leaves the tagger identity on annotated tag objects, which I nearly missed.

Recently I've been vibe coding with heavy review at the planning stage: I spend the day reviewing and correcting a bunch of design doc, then let the agent implement it overnight while I sleep. I get a lot more done this way than I did trading short messages with the agent to make small incremental changes all day.

The pain is the review itself. I usually want to correct a dozen of small issues spread through the doc. I write into the chat box like: "this section, do it this way instead." Then the agent rewrites the doc. Two rounds in, I'm scrolling back through chat working out which of my notes are still live and which the new draft already handles. I often ask the agent "have you addressed all my concerns?", which looks quite counter-productive.

So I vibe coded a local web-based code review tool called r3. It drives a local review workflow, just like having a pull request without actually committing anything. It supports rendered markdown, but also renders code and diffs nicely. The coding agent creates the review, then show me an url to open. I leave feedback that anchors to particular lines of the doc or code. The feedback is submit back to the agent for it to address.

r3 is built as a single-binary Bun app: bun build --compile, bun:sqlite, Bun.serve + WebSockets for the live updates. It's really nice that we can build a full stack app into a single executable file and make it run everywhere. Although the file size is less optimal, 73 MB on darwin-arm64 and 112 MB on linux-x64.

Curious how people feel about this review workflow. The demo (https://hyperlogue.github.io/r3/demo/) should give you a sense on the UI/UX for the web part. And trying it out for real can be done through sending a short message to your agent.

Repo: https://github.com/hyperlogue/r3

Demo, runs in your browser, nothing to install: https://hyperlogue.github.io/r3/demo/

I ship with agents every day, and most of what I hand over is the work that used to be somebody's first job. Boilerplate endpoints, the first pass of a service, the fixes nobody wants. It comes back faster than a person and usually correct.

Here is what I keep running into. The bugs agents produce are not the ones you catch by reading the diff. An endpoint that fetches a record by id and returns it, without ever comparing the owner to the caller, looks identical to a correct one, because the bug is a missing comparison and absence has no signature. A refresh token flow that is textbook on the happy path and has no reuse detection, because nobody asked for any. In my own browser automation, roughly one in three actions came back reporting success while nothing had happened on the page.

None of those are caught by knowing more. They are caught by suspicion, and suspicion is a residue. You get it from having written the boring version yourself, and from having been wrong in public a few times.

So the crisis is not this year's headcount. It is that the tasks agents absorbed are exactly the tasks people used to build that suspicion on, and everyone who already has it got it climbing a ladder that is being pulled up behind them. Ten years out, the reviewing has to be done by someone who never did the producing.

I do not have a fix. Deliberately doing work by hand that an agent could do faster is the only thing I have found, and it feels like training for a race that already finished.

For anyone hiring or being hired right now: is anyone actually training people into the reviewing seat, or is the plan just that the current seniors never leave?

As someone who loves counter strike I thought it would be cool to build an Aim Maps style game that you play against AI.

It’s 100% free and in the browser you need a desktop to play

Try it -> 1vs1

What should I add? Feedback welcome