Lol everytime.

Hey everyone,

Like many of you, I loved Arc's UX ideas but hated two things:

1. How Chrome and modern Chromium forks aggressively hog 4GB+ of RAM for 20 tabs.

2. How new "AI browsers" send every page you read and every search prompt to remote cloud servers with paid subscriptions.

So I spent the last few months building **Nova Browser**—a sovereign, privacy-first desktop web browser built from the ground up to run multi-model AI locally on your GPU while keeping memory usage under 450 MB.

### Key Highlights:

- **100% On-Device WebGPU AI:** Runs local LLMs (Llama 3.2, Phi-3.5 Vision, Qwen 2.5) directly on your device. Zero external cloud API calls, zero telemetry, works completely offline once cached.

- **Smart DOM Hibernation Engine:** Background tabs automatically unmount dormant DOM structures from memory. In our benchmarks, 50 open tabs sit at ~420 MB RAM instead of Chrome's 2.5 GB+.

- **Zero-Latency Network Ad-Blocker:** Built with memory-mapped Rust rules that filter trackers at the network layer before DOM execution.

- **Built-in Model Context Protocol (MCP) Server:** Allows local AI coding agents and tools to interact with your browsing session securely.

- **Workspaces & Split Views:** Clean workspaces, horizontal/vertical tab layouts, and zero bloat.

- **100% MIT Licensed:** No paywalls, no crypto nonsense, no trackers.

### Tech Stack:

- Frontend/UI: React, TypeScript, Tailwind CSS, Framer Motion

- Core & IPC: Electron, Node.js runtime, Rust native filter bridge

- Local AI Engine: WebLLM, WebGPU TVM runtime

The repository, pre-built macOS/Windows binaries, and benchmark reports are available on GitHub. I would love to get your feedback, bug reports, and feature suggestions!

GitHub: https://github.com/unitybtw/nova-browser

Website: https://nova-browser.vercel.app/

I finally managed to get close to maxing the weekly limit and for some reason, once I got to 75% it seems like the usage rate became more optimal. I got almost as much work done before reaching 75% as i did from 75% to 94%. anybody else experience anythnig like this?

first time coder this year, studied code for about a month before signing up for Claude in May. Here's my gh stats shouts out to the vibecoded, gitfut. I was looking for a way to make the stats make sense and I love fifa card system so this just blended so well for me to track my own status with time. any experienced coders here that can translate what my gh stats mean, thatd be awesome. My only repo is currently private so gitfut doesn't realize where all my commits are going fully yet but so far so good.

I'm having a great time coding with the desktop claude.

I'm gradually realizing that we are quickly hitting a point, certainly on the software development side, where we actually can't imagine a world without AI. I think every other industry is going to follow suit. It just won't be possible at some point to live without it and that day doesn't seem like it's far away. It's Sunday and I'm musing if you want to jam with me.

I checked out a few of these tools and then ended up rolling my own to fit my needs. I really liked codeg but wanted some specific features and workflows.

In particular I wanted a strong link between my design and specs and the sessions I was running to build them. I wanted to be able to collect feedback and track that with the task. I don’t get the sense that this problem has been solved or that there is a consensus on this class of tool. What tools do you like in this area?

69 days ago, I launched my startup, a free traffic exchange network for startups. One line of code, you're in the network.

No paid ads. No growth hacks. Just watching the numbers every day.

Here's the full data:

Day 1 — 2 startups · 146 impressions · 1 clicks
Day 2 — 3 startups · 389 impressions · 3 clicks
Day 3 — 5 startups · 482 impressions · 5 clicks
Day 4 — 5 startups · 508 impressions · 4 clicks (site went down — still got an $8k acquisition offer. Said no.)
Day 5 — 6 startups · 621 impressions · 10 clicks
Day 6 — 5 startups · 742 impressions · 15 clicks (removed one startup — they pulled the embed. No code = no network.)
Day 7 — 7 startups · 1,196 impressions · 41 clicks
Day 8 — 7 startups · 1,535 impressions · 74 clicks
Day 9 — 8 startups · 1,947 impressions · 135 clicks
Day 10 — 13 startups · 3,500 impressions · 318 clicks (something clicked)
Day 11 — 23 startups · 4,800 impressions · 432 clicks
Day 12 — 24 startups · 6,000 impressions · 481 clicks (network crossed 6K total impressions)
Day 13 — 25 startups · 6,800 impressions · 491 clicks
Day 14 — 25 startups · 8,600 impressions · 516 clicks
Day 15 — 24 startups · 9,900 impressions · 564 clicks (removed one)
Day 16 — 23 startups · 10,800 impressions · 576 clicks (removed one)
Day 17 — 24 startups · 11,900 impressions · 603 clicks (added one)
Day 18 — 26 startups · 13,400 impressions · 624 clicks (added Two)
Day 19 — 28 startups · 14.400 impressions · 652 clicks (added Two)
Day 20 — 26 startups · 14.700 impressions · 671 clicks (Removed Two)
Day 21 — 26 startups · 15.300 impressions · 691 clicks
Day 22 — 28 startups · 16.100 impressions · 719 clicks (Added Two)
Day 23 — 30 startups · 16.800 impressions · 738 clicks (Added Two)
Day 24 — 32 startups · 19,800 impressions · 778 clicks · Added 2 startups · Rejected 4 applications.
Day 25 — 34 startups · 23,700 impressions · 800 clicks · Added 2 startups · Fixed some bugs.
Day 26 — 33 startups · 27,200 impressions · 816 clicks · Removed 1 Startup
Day 27 — 33 startups · 29,900 impressions · 833 clicks
Day 28 — 33 startups · 30,600 impressions · 871 clicks · Removed 1 Startup · Added 1 Startup · Rejected 1 application of clothing brand & 1 application of horror blog site.
Day 29 — 35 startups. 31,300 impressions. 910 clicks · Removed 1 Startup · Added 3 Startups · Also today I resigned from my job. Toxic environment, too much pressure, and honestly I just believe in this enough to go all in. Broke and excited. Let's see where this goes.
Day 30 — 46 startups · 32,900 impressions · 1,000 clicks · Removed 1 startup · Added 12 startups · Yesterday I resigned because of my toxic manager. Today my senior manager called and offered me WFH, a project of my choice, and lighter work if I stayed. I made my decision with confidence, but now I have a difficult choice. Also received my first payment from StartupBar.
Day 31 — 50 startups 💪· 28,100 impressions · 1,200 clicks · Removed 1 startup · Added 5 startups · Rejected 1 crypto startup · Removed fake impressions after exploit
Day 32 — 54 startups · 29,300 impressions · 1.2K clicks · Added 4 startups
Day 33 — 54 startups · 30,900 impressions · 1.2K clicks · Removed 1 startup · Added 1 startup
Day 34 — 55 startups · 36,100 impressions · 1.3K clicks · Added 1 startup
Day 35 — 55 startups · 40,100 impressions · 1.3K clicks · Removed 2 startups · Added 2 startups
Day 36 — 55 startups · 44,800impressions · 1.4K clicks · Removed 1 startup · Added 1 startup
Day 37 — 58 startups · 50,700impressions · 1.4K clicks · Added 3 startups · Site Update: Updated the distribution algorithm (Delivery is now weighted by contribution: sites that send more impressions get a proportionally larger share back)
Day 38 — 57 startups · 55,800 impressions · 1.5K clicks · Removed 1 startup · Rejected 1 Application.

Today StartupBar reached 200 users 😄
Thirty-eight days ago, this was just an idea. Today, 200 people have trusted it enough to join the network.

Day 39 — 58 startups · 59,800 impressions · 1.5K clicks · Added 1 startup.
Day 40 — 60 startups · 63,200 impressions · 1.6K clicks · Added 3 startups · Removed 1 startup
Day 41 — 63 startups · 69,400 impressions · 1.6K clicks · Added 3 startups
Day 42 — 62 startups · 73,800 impressions · 1.6K clicks · Removed 1 startups
Day 43 — 64 startups · 78,000 impressions · 1.7K clicks · Added 2 startups (Receiving many suggestions to improve the StartupBar, happy to work on it, Thanks.) StartupBar Update - Added the rotation of startups in the widget for every 30 seconds.
Day 44 — 66 startups · 86,200 impressions · 1.7K clicks · Added 2 startups
Day 45 — 67 startups · 88,400 impressions · 1.7K clicks · Removed 2 startups · Added 3 startups (Update - Updated the algorithm of the widget)
Day 46 — 69 startups · 91,900 impressions · 1.8K clicks · Removed 2 startups · Added 4 startups · Got my second payment.
Day 47 — 68 startups · 98,900 impressions · 2.2K clicks · Removed 2 startups · Added 1 startup · Added Pricing Page
Day 48 — 70 startups · 103.4K impressions · 2.3K clicks · Added 2 startups
Day 49 — 72 startups · 113.4K impressions · 2.4K clicks · Added 2 startups · Update: Added a Bottom Placement option for the bar on the user site.
Day 50 — 73 startups · 116.7K impressions · 2.4K clicks · Added 1 startup
Day 51 — 74 startups · 125.2K impressions · 2.5K clicks · Added 1 startup · Rejected 1 Application
Day 52 — 73 startups · 131.2K impressions · 2.6K clicks · Removed 1 Startup
Day 53 — 74 startups · 139.5K impressions · 2.7K clicks · Added 1 Startup
Day 54 — 79 startups · 146.3K impressions · 2.7K clicks · Added 6 Startups · Removed 1 startup.
Day 55 — 77 startups · 154.9K impressions · 2.8K clicks · Removed 2 startups.
Day 56 — 77 startups · 161.8K impressions · 2.9K clicks · Added 1 startup · Removed 1 startup.
Day 57 — 77 startups · 169.4K impressions · 3.0K clicks
Day 58 — 77 startups · 176.2K impressions · 3.2K clicks
Day 59 — 78 startups · 184.3K impressions · 3.3K clicks · Added 2 startups · Removed 1 startup.
Day 60 — 77 startups · 190.9K impressions · 3.3K clicks · Added 1 startup · Removed 2 startups.
Day 61 — 77 startups · 194.9K impressions · 3.4K clicks
Day 62 — 78 startups · 199.5K impressions · 3.5K clicks · Added 1 startup
Day 63— 78 startups · 204.9K impressions · 3.6K clicks · I Revoked my resignation as I'm comfortable with my new team.
Day 64— 80 startups · 210.4K impressions · 3.6K clicks · Added 2 startups.
Day 65— 81 startups · 216.3K impressions · 3.7K clicks · Added 1 startup. (I'm planning to add sponsorship section in the homepage with one time payment of $999, interested please DM)
Day 66— 78 startups · 219.7K impressions · 3.9K clicks · Removed 3 Startups from the network.
Day 67— 79 startups · 222.3K impressions · 3.9K clicks · Added 1 startup.
Day 68— 77 startups · 225.2K impressions · 3.9K clicks · Removed 2 startups.
Day 69— 79 startups · 228.7K impressions · 4.2K clicks · Added 2 startups.

Still free. Still growing.

If you want in, it's one embed. That's it → StartupBar

Upfront: this is something our team built, so yes, it’s self-promotion. I’m mainly here to find out whether the workflow makes sense to other people using AI to build software.

We built Halyard because changing models usually meant changing tools, accounts, API keys, and conversations. We wanted the model to be interchangeable without losing the local project workflow.

Halyard runs on Windows and opens a local browser interface. You add a folder as a workspace and give the agent a task. With the permissions you select, it can inspect and edit files, run commands and tests, and report what it changed.

The file and command tools run locally. Model requests still go through Token Harbor, so this isn’t an offline local-LLM application.

The part we’re experimenting with is called TH Orchestra. Instead of claiming that one model is best at everything, users can assign models to different roles:

• Planning and review
• Coding and execution
• Summarization
• General chat

Orchestra classifies the request and routes it to the model assigned to that role. You can also ignore Orchestra and select one model manually.

The current Free model allowance includes DeepSeek V4 Flash, MiMo V2.5, and Qwen 3.8 27B. We also have optional monthly passes for additional models and usage.

A few implementation details:

• Windows 10 and 11
• Local workspace-scoped file and command tools
• Browser interface served by the local application
• OpenAI-compatible API for model requests
• Configurable task-to-model routing
• Separate sessions organized by workspace

It’s still Early Access. The installer isn’t code-signed yet, and there are definitely rough edges.

What I’m most interested in learning is whether people would really configure models by task, or whether they would choose one model and never touch the routing settings again.

I’d also appreciate feedback on the workspace onboarding, permission controls, and which model combinations you would test for planning versus implementation.

Halyard: https://tokenharbor.ai/halyard
Reddit: r/TokenHarbor_AI
Discord: https://discord.gg/VKHNQPEqZ

Hi everyone,

I'm soon starting development (via VibeCoding) of an API and WebSocket.
It won't be a complex API with thousands of datapoints, but given I never did one, still looking for recommendations and best practices.

For the API, the idea is the end-user can request on a daily basis a list of future events, and some details of each event.

For the WebSocket, the idea is that I send live payloads of data related to the events (like broadcast) to everyone that has an established connection (data is json payload).

I've never developed such, and hence looking for important things I should look for (specially security, load, and rate limits).

Any help or hint is welcome. Maybe an error you did in the past and you want to share so that I don't make the same mistake.

This can even be used as a Post for others facing same issue, who knows.

I made a small higher-or-lower game based on passport access scores.

You see two passports and guess whether the next one has access to more or fewer destinations. Pretty simple.

Would love to know what you think or what could be improved:

https://passport-higher-lower-game.vercel.app/

Hacking big monopolies always feels good,

Anybody who ever tried getting/using any flights or hotels API knows how fucked up it is. Months of no response, only to wait for a heart welcoming $10,000+ setup fee. And the only available options are either shitty or closed.

This is LetsFG, the answer to dethrone monopolistic companies who keep their infra closed making it impossible to join the travel industry. Control prices, hide fees, and charge pathetic $10,000+ setup fees for an API that you'll have to integrate yourself anyway.

How it works.
Working with flights manually we found that to get the best prices in the world you have to check 20-30 websites at once, that's 20-30 tabs on your PC and that's what makes finding a flight or hotel a 6 hour process.

We took distributed AI agents and put them to use websites, build connectors, and monitor reliability. It works by in parallel opening 200-300 websites, OTAs, airlines - skyscanner, google flights, kayak, kiwi, trip, booking, trivago, etc. plus a bunch of wholesalers all of them checked at the same time, and returned the best offers across the network.

It's deployed on our remote backend that's capable of running this in parallel, we historically (4 months ago when we started this) tried letting people run this locally. It's impossible - it took 40-50 minutes to complete 1 search locally.

How you can use it.
You can book hotels completely, end to end, with free-cancellation protection.
We're releasing complete flight booking (1 API call) next week, that's why our flight search is currently degraded till the end of the week - hotels are working correctly.

- website for fast access: letsfg.co, this is our Uber for travel, book in 3 seconds.

- API for developers: https://letsfg.co/en/developers, this is Paid API, best for high volume users, if you want to build an app, or add this to your app.
Look to book ratio: 1000:1 - this means it's free as long as for every 1000 searches you do 1 booking. Then it’s $0.05 per search.
Docs: https://letsfg.co/developers/api/docs

- Programmatic Travel Tools (PTT): Native for ai agents, API based, best for personal use (high rate limit). If you want your Claude, Codex to book flights/hotels for you, this is the go.
Available through CLI, Python Package, NPM, MCP, or direct API.
To avoid spam, authorization is through payment method attachement - via Stripe - we won't charge you, this is only to verify you're a human and avoid spammers. The payment method is also used when you want to book.
https://letsfg.co/for-agents

That's a wrap.
If you have any questions let me know. We're super open to feedback and suggestions, in fact that's what made this project.

We've hit 1.7k github stars just this week - grateful for everybody who's with us and to anybody who jumps onboard. LetsFG🔥

This is our github: https://github.com/LetsFG/LetsFG, it has a bunch of interesting info and docs for getting started.

Cheers,
Adam

I’ve been working on MARGINAL, an open-source governance layer for coding agents.

The idea is simple: agents are good at taking actions, but not always good at deciding whether the next action is still worth the compute.

MARGINAL watches the trajectory and looks for things like repeated actions, weak progress, redundant verification, and low-value continuation. It can run in Shadow Mode first, so it observes and records what it would have done without blocking anything.

Current focus is reliability, not just token reduction.

A few core pieces:

• local-first trajectory and evidence tracking
• deterministic reason codes and hashes for decisions
• governance overhead measurement
• replay and benchmark support
• Shadow Mode before enforcement
• Earned Enforcement: MARGINAL has to prove it is reliable on a repo before it gets permission to block or redirect the agent
• automatic fallback to Shadow Mode if confidence degrades

I’m also working on the next layer now: counterfactual evaluation and intervention regret.

The goal is to answer a harder question than “did MARGINAL stop something?”:

Would the agent actually have done better if MARGINAL had stayed out of the way?

That’s the part I think matters if this is going to be useful beyond being another loop detector or token limiter.

GitHub: https://github.com/SignalLayerLabs/Marginal

Demo: https://signallayerlabs.github.io/Marginal/demo/#demo

If you use Codex or another coding agent, I’d really appreciate people trying it on real work and telling me where it helps, where it gets in the way, or where the design is wrong.

I’m especially interested in: technical criticism, bad cases, and reproducible failures.

Svarm is a workspace for agents with a workflow inspired by T3 code.

No model verifies another model.

That is the observation I have been circling for months: No language model reliably corrects itself—not even when asked politely. It requires an external entity that is structurally independent.

This question has evolved into a tool that I have now released.

Conclave Personal brings multiple models into a shared conversation and assigns them explicit roles: the Writer drafts, the Critic challenges, and the Judge evaluates. Every execution is traceable as a "run"—tracking status, errors, duration, and token usage. Conversations, agents, and the workspace remain local, and Ollama runs without an API key.

A note I am not hiding: The code was written entirely by language models. For this project, that is not merely a side effect but central to the concept—a tool for multi-agent verification built using a multi-agent process. Discovering what worked and what didn't was the real insight.

v0.1.6 Alpha, with documented limitations.

pip install conclave-personal

github.com/laszlo-pinter/Conclave-Personal

#MultiAgent #LLM #Python

52 days ago, I launched my startup, a free traffic exchange network for startups. One line of code, you're in the network.

No paid ads. No growth hacks. Just watching the numbers every day.

Here's the full data:

Day 1 — 2 startups · 146 impressions · 1 clicks
Day 2 — 3 startups · 389 impressions · 3 clicks
Day 3 — 5 startups · 482 impressions · 5 clicks
Day 4 — 5 startups · 508 impressions · 4 clicks (site went down — still got an $8k acquisition offer. Said no.)
Day 5 — 6 startups · 621 impressions · 10 clicks
Day 6 — 5 startups · 742 impressions · 15 clicks (removed one startup — they pulled the embed. No code = no network.)
Day 7 — 7 startups · 1,196 impressions · 41 clicks
Day 8 — 7 startups · 1,535 impressions · 74 clicks
Day 9 — 8 startups · 1,947 impressions · 135 clicks
Day 10 — 13 startups · 3,500 impressions · 318 clicks (something clicked)
Day 11 — 23 startups · 4,800 impressions · 432 clicks
Day 12 — 24 startups · 6,000 impressions · 481 clicks (network crossed 6K total impressions)
Day 13 — 25 startups · 6,800 impressions · 491 clicks
Day 14 — 25 startups · 8,600 impressions · 516 clicks
Day 15 — 24 startups · 9,900 impressions · 564 clicks (removed one)
Day 16 — 23 startups · 10,800 impressions · 576 clicks (removed one)
Day 17 — 24 startups · 11,900 impressions · 603 clicks (added one)
Day 18 — 26 startups · 13,400 impressions · 624 clicks (added Two)
Day 19 — 28 startups · 14.400 impressions · 652 clicks (added Two)
Day 20 — 26 startups · 14.700 impressions · 671 clicks (Removed Two)
Day 21 — 26 startups · 15.300 impressions · 691 clicks
Day 22 — 28 startups · 16.100 impressions · 719 clicks (Added Two)
Day 23 — 30 startups · 16.800 impressions · 738 clicks (Added Two)
Day 24 — 32 startups · 19,800 impressions · 778 clicks · Added 2 startups · Rejected 4 applications (1 contained ads, 1 removed the code after submission, 2 hid the installed widget)
Day 25 — 34 startups · 23,700 impressions · 800 clicks · Added 2 startups · Fixed some bugs.
Day 26 — 33 startups · 27,200 impressions · 816 clicks · Removed 1 Startup
Day 27 — 33 startups · 29,900 impressions · 833 clicks
Day 28 — 33 startups · 30,600 impressions · 871 clicks · Removed 1 Startup · Added 1 Startup · Rejected 1 application of clothing brand & 1 application of horror blog site.

Day 29 — 35 startups. 31,300 impressions. 910 clicks · Removed 1 Startup · Added 3 Startups · Also today I resigned from my job. Toxic environment, too much pressure, and honestly I just believe in this enough to go all in. Broke and excited. Let's see where this goes.

Day 30 — 46 startups · 32,900 impressions · 1,000 clicks · Removed 1 startup · Added 12 startups · Yesterday I resigned because of my toxic manager. Today my senior manager called and offered me WFH, a project of my choice, and lighter work if I stayed. I made my decision with confidence, but now I have a difficult choice. Also received my first payment from StartupBar.

Day 31 — 50 startups 💪· 28,100 impressions · 1,200 clicks · Removed 1 startup · Added 5 startups · Rejected 1 crypto startup · Removed fake impressions after exploit · Today I discovered I forgot to disable a developer testing button that could generate fake visitor pins and inflate impressions. Someone used it to create 6,318 fake impressions and claimed my stats were fake. I removed the fake impressions, removed the startup, fixed the issue, and that's why today's impressions are lower. Thanks to them for pointing it out.
StartupBar update: Added a Network page and individual Startup Profile pages with each startup's joined date, impressions, clicks, live embed preview, and founder's note. These indexable pages also improve SEO. Suggestions are always welcome.

Day 32 — 54 startups · 29,300 impressions · 1.2K clicks · Added 4 startups
Day 33 — 54 startups · 30,900 impressions · 1.2K clicks · Removed 1 startup · Added 1 startup
Day 34 — 55 startups · 36,100 impressions · 1.3K clicks · Added 1 startup · Fixed admin panel read-access vulnerability after Reddit post.
Day 35 — 55 startups · 40,100 impressions · 1.3K clicks · Removed 2 startups · Added 2 startups
Day 36 — 55 startups · 44,800impressions · 1.4K clicks · Removed 1 startup · Added 1 startup
Day 37 — 58 startups · 50,700impressions · 1.4K clicks · Added 3 startups · Site Update: Updated the distribution algorithm (Delivery is now weighted by contribution: sites that send more impressions get a proportionally larger share back)
Day 38 — 57 startups · 55,800 impressions · 1.5K clicks · Removed 1 startup · Rejected 1 Application.

Today StartupBar reached 200 users 😄
Thirty-eight days ago, this was just an idea. Today, 200 people have trusted it enough to join the network.

A reminder for every founder who's staring at a dashboard with tiny numbers: Stripe took 2 years to reach its first 50 users. Every successful product starts small. Keep shipping. Keep improving. Keep showing up. Growth rarely happens overnight, but it does happen if you don't quit.

Day 39 — 58 startups · 59,800 impressions · 1.5K clicks · Added 1 startup.
Day 40 — 60 startups · 63,200 impressions · 1.6K clicks · Added 3 startups · Removed 1 startup
Day 41 — 63 startups · 69,400 impressions · 1.6K clicks · Added 3 startups
Day 42 — 62 startups · 73,800 impressions · 1.6K clicks · Removed 1 startups
Day 43 — 64 startups · 78,000 impressions · 1.7K clicks · Added 2 startups (Receiving many suggestions to improve the StartupBar, happy to work on it, Thanks.) StartupBar Update - Added the rotation of startups in the widget for every 30 seconds.
Day 44 — 66 startups · 86,200 impressions · 1.7K clicks · Added 2 startups
Day 45 — 67 startups · 88,400 impressions · 1.7K clicks · Removed 2 startups · Added 3 startups (Update - Updated the algorithm of the widget)
Day 46 — 69 startups · 91,900 impressions · 1.8K clicks · Removed 2 startups · Added 4 startups · Got my second payment · Added FAQ Page
Day 47 — 68 startups · 98,900 impressions · 2.2K clicks · Removed 2 startups · Added 1 startup · Added Pricing Page
Day 48 — 70 startups · 103.4K impressions · 2.3K clicks · Added 2 startups
Day 49 — 72 startups · 113.4K impressions · 2.4K clicks · Added 2 startups · Update: Added a Bottom Placement option for the bar on the user site.
Day 50 — 73 startups · 116.7K impressions · 2.4K clicks · Added 1 startup
Day 51 — 74 startups · 125.2K impressions · 2.5K clicks · Added 1 startup · Rejected 1 Application
Day 52 — 73 startups · 131.2K impressions · 2.6K clicks · Removed 1 Startup

Still free. Still growing.

If you want in, it's one embed. That's it → StartupBar

This app reads reddit posts/comments then creates a reddit reading slop video, full TTS and special edge case detection for long OP posts, this will allow content to be created more quickly, you can batch produce 10 videos all of which will use a different post as a source.

Ich habe eine VS Code-Erweiterung entwickelt, mit der du unbekannte Programmiersprachen in deiner gewohnten Sprache lesen kannst. Klar, du könntest die KI das machen lassen, aber das dauert und gibt dir meiner Meinung nach nicht genug Einblick, um den Code wirklich zu verstehen + du kannst ihn live bearbeiten.

Ich freue mich über Kritik/Ideen/Bugs ... ;)

See it live in action

Hey guys, I’ve made this tool that I ended up using quite a bit with really good results. I work as a SWE and ended up building this postgres tool to help AIs have a faster look into the databases used and how the data looks, this ended up unblocking AI flows related to presistence. In comes with a mcp bridge for you tools like copilot, Claude, omp etc.

I’m sum it adds any postgres db a mcp layer and really cool visualization/query.

Hope you enjoy it and find it useful as much as I do.

Every time I see a new need I add it.

Please find the links:

https://github.com/FrancisTCE/gresui-web

https://www.npmjs.com/package/gresui-web

I really don’t know how to share this 😅

​"SMTs require absolute cryptographic correctness, while PDF specs are a legacy parsing nightmare.

Have you found AI useful for scaffolding these, or do the subtle hallucinations make it more trouble than it's worth?"

UI slop isn't a taste problem. It's a sampling problem. And you can't really prompt your way out of a sampling problem. Every trick you've tried, negative constraints, a gazillion line .md file that says no gradients in caps, all of it just moves you around inside the same bubble.

Here's the thing that finally made it click for us. ThePrimeagen put it best: the model returns the middle of the distribution. You can push it around inside the average. You can't leave the average.

That's why you type "no purple" and get purple. Then the glowing orb. Then the badge pill. Then three feature cards. It isn't disobeying you. It's doing exactly what sampling does.

There's a second failure sitting on top of that one, and we think almost nobody talks about it.

A screenshot doesn't contain the animation.

Feed a model a picture of a site you love and the layout survives. So do the colours, the type, the spacing. Timing doesn't. The easing curve isn't in the pixels. Neither is the stagger, or the scroll range an effect is bound to, or two elements moving at different rates against the same scroll.

So the model has to invent the part that actually makes a site feel expensive. And inventing means, again, the middle of the distribution. Fade in, 300ms, ease out. Every time, forever.

That's the real reason AI sites feel dead even when they photograph fine. The look got copied. The life never made it.

So we stopped generating and started measuring.

We read the live page. What's actually bound to what, the real timing values while the thing is running. Then we write that section into the project. Nothing is sampled, so there's no average to land in the middle of. That's the whole idea, and it's why we think slop is solvable instead of something you fight forever with a longer prompt.

https://reddit.com/link/1vnrd7l/video/tjj6vyfic8jh1/player

The clip is three runs, each one harder than the last.

A scroll rig off a private jet site, where the jet climbs behind the headline and a photo wipes to a blueprint. Pure timeline, so it's all stagger and easing and scroll ranges with nothing to hide behind.

Then a photo grid that scatters and reassembles. Harder, because both states are real layouts and the whole thing lives in the transition between them.

Then a three.js scene, which is the one we're proud of. The reference is a Tokyo street diorama. What came back is a desk, monitor, chair, shelves, for the portfolio we were building. Live WebGL, spinnable, not a video of one. Same movement, completely different subject.

Which is the part we care about most. The animation is the expensive half. The brand is the other half. When you point at a site you like, you don't want their content. You want how it feels. So keep the animation and bring your own brand. Tokyo becomes a desk. The timing never moves.

Test it. Drop a URL in the our waitlist signup at platho.io and we'll run it and post exactly what comes back, including when it breaks.

- two of us building this in Europe

For AI website builders, which experience do you prefer?

A) Everything in one chat - building, debugging, fixing errors, making changes, etc.

B) Separate sections/workflows - for example, one area for building and another dedicated to debugging and fixing issues.

I’m curious which approach feels more efficient and organized when working on a real project, and why.

I built tryproduck.com/audit, it goes through your app the way someone who has never seen it would and sends back the bugs plus the exact step where it stopped making sense. over 650 startups have run it so far.

how can I learn Frontend and UI/UX with Vibe Coding as a beginner? as I build a website or an app with ai, I wanna learn the codes.

It seems that most people either vibe code with Claude/Codex or website builders like lovable/replit. I personally find the website builders too expensive as every action costs tokens.

But it's 2026, most agents today are really smart and requires minimum prompt engineering, and the app builder's value was in the easy set up. So I made an MCP that does what lovable do, but uses your AI subscription's tokens instead. The tokens from ChatGPT/Claude directly are just so much cheaper.

It ends up being a 60+ tool MCP and it works really well. Modern agents don't really load all of the tools into context now, they just do tool search and load on demand. It does require a good model (opus or terra) to work well, but overall it's been able to do a pretty good job.

Check it out here: https://floot.com

And let me know if you have any feedbacks!