r/VibeCodersNest

I developed Weather World because I wanted a simpler, more helpful way to stay ahead of the forecast. I truly believe that a weather app should be a tool that makes your life easier, not a source of distraction with ads and confusing menus.

How it helps you: The core of the app is all about visual clarity. I’ve focused on creating intuitive graphs that let you see temperature shifts and precipitation trends at a single glance. Instead of reading through long lists of numbers, you can visualize exactly how your day will unfold. It’s minimalist, lightweight, and built for speed—perfect for anyone who values a clean Android experience.

I’d love your support! Please give it a try and see if it helps your daily routine. If you find it useful, please recommend it to your friends! As a solo developer, your support and word-of-mouth are what help me improve and grow.

In compliance with the community rules, I’ve shared the link via IndieAppCircle. Check it out there and let me know what you think!

Find it here: https://play.google.com/store/apps/details?id=com.danie.pocasisveta

I got tired of network monitors that look like they were designed for a BBS, so I built flow. It's a real time bandwidth monitor with Braille grid waveforms, spring smoothed numbers, and glowing borders that react to traffic load.

What it does

It shows live download and upload throughput with units that auto scale from B/s up to GB/s. The waveform is a high res Braille grid scrolling at 30fps, and the borders glow brighter as traffic picks up, going from a dark idle state to bright cyan and emerald under load. Numbers are spring interpolated so they glide instead of jumping around. It tracks session peaks, flashing white when you hit a new record, and keeps a running daily total.

There are three views that adapt to your terminal width. Hero is the full dashboard. Compact strips it down to numbers only. Tiny is a single line built for tmux status bars.

Philosophy

If a feature doesn't help you understand your network in under a second, it doesn't make the cut. No CPU panels, no packet counters, no multi pane clutter. Just download and upload throughput, done well.

Usage

flow                        # hero view, auto interface
flow --compact              # numbers only
flow --tiny                 # tmux status bar
flow --json                 # one-shot JSON for scripts
flow --once                 # one-shot plain text

tmux integration

set -g status-right "#(flow --tiny --no-color)"
set -g status-interval 1

Install

go install github.com/programmersd21/flow/cmd/flow@latest

or AUR:

yay -S flow-network-monitor-bin

or homebrew:

brew install programmersd21/flow/flow

Pre-built binaries for Linux, macOS, and Windows (amd64/arm64) are on the releases page.

It works with zero config out of the box. If you want to tweak the refresh rate, history length, or units, there's an optional TOML config at ~/.config/flow/config.toml.

Platform support

It runs on Linux (/proc/net/dev), macOS (sysctl), and Windows (GetIfTable2, no admin needed). Idle CPU stays under 1%.

Links

Source and demo: https://github.com/programmersd21/flow

---

Would love feedback, especially on the tiny/tmux mode. Curious if the info density is right for people running it in a status bar all day.

Introducing LeakScope, again.

we've been updating it : )

LeakScope is a security scanner built for Supabase applications. Paste your app's public URL, and it checks what an attacker can learn from the outside—from exposed keys and public data access to weak RLS, leaked credentials, and insecure frontend configuration.

We've introduced two scanning modes:

Light Scan — Paste a public app URL to instantly check for exposed keys, public data exposure, leaked credentials, weak RLS, and risky frontend configuration. No account required.

Deep Scan — Authenticate to validate Row Level Security, test BOLA/IDOR, analyze JWT security, and generate detailed reports for real security validation.

Whether you're a solo founder, indie hacker, or vibe coder shipping MVPs at 2 AM, LeakScope gives you a fast way to see what your app is exposing before everyone else does.

1,936 websites scanned.
13,679 security findings identified.

Try it out at leakscope[.]tech

Hello everyone,

Wanted to share LyteNyte Grid, which IMO has some incredibly innovative capabilities when turbocharged with AI.

Building data grids for dashboards, admin panels, internal interfaces, etc. takes a really long time and can be tedious. All this takes away from the time you would much rather spend on your app.

The financial options dashboard above was made with Claude Code using LyteNyte Grid Skills.

The prompt:

Create an options trading dashboard using LyteNyte Grid. data.ts contains options contracts – ticker, type, strike, expiry, IV, and full Greeks.

Enable row grouping by ticker and type, sorting across all columns, and master-detail rows that show the full Greek breakdown when expanded. Use Vite + Shadcn. Dark mode by default.

That was it, one prompt. You can expand it, group it, sort it, filter it. It is also fully accessible.

The reason it works so well is that the grid is declarative and type-safe. AI can verify the result without running the code.

Tokens burned were minimal. Since all the AI had to do was declaratively configure LyteNyte Grid and run tsc to check for errors, there were none.

Other grids are imperative, with heavy abstractions and wrapping layers, making them unreliable for coding agents. If they work, it's usually after 20 prompts, using enough tokens that you might as well just wire it up yourself… or reach your Claude limit.

We will continue work on this, but with LyteNyte Grid, you can now build any type of dashboard for a data-intensive workflow, in literally minutes.

Our API is not opinionated at all, making it really easy to integrate with your favorite UI / charts libraries

Install Skills: npx skills add 1771-Technologies/lytenyte

If you’re unfamiliar with LyteNyte Grid, it’s a 40kb React data grid with 150+ features. I would shamelessly plug its benefits. If you’re interested, they’re listed in the repo.

All our code is available on GitHub: https://github.com/1771-Technologies/lytenyte/commits/main/

I'd love to hear your feedback (maybe showcase your creations?). Feature suggestions and contributions are always welcome.

If you find it useful, please consider leaving a star ⭐ on GitHub to help us grow!

GitHub

Live Demo

Your mouse is slowing you down.

Every time your hand leaves the keyboard, you lose focus.

I switched to a keyboard-only workflow on macOS, and it's surprisingly addictive. The app is made using Swift, and it is created with the help of AI.

Try it yourself:

👉 https://github.com/bhavesh164/mouseless

You'll wonder why you ever reached for a mouse.

Note: This app is made with the help of LLM (AI)

Two weeks ago, I challenged myself to see how fast I could go from idea to App Store using Claude Opus 4.8 as my development partner. Instead of spending months building a single game, I focused on rapid iteration, gameplay, polishing, App Store assets, and shipping. Fourteen days later, I had launched four iPhone games: Word Bucket, Word of Treasure, Oddz, and Find That Word. None of them existed before this experiment.

The biggest lesson wasn’t that AI can write code. It was realizing that building is becoming easier while taste, creativity, and execution matter more than ever. AI helped me move faster, but deciding what makes a game fun, what to cut, and what players will enjoy was still the hard part. We’re entering a world where a solo developer can test ideas at an incredible speed, and I’m excited to see where this goes next. Curious, which of the four games would you try first?

🪣 Word Bucket

🏴‍☠️ Word of Treasure

🤔 Oddz (Odd One Out)

🔎 Find That Word

I've spent the last few months building something and I'm finally at the point where I want to share it properly rather than just quietly hoping people find it.

The idea came from a frustration I kept seeing (and feeling myself): SQL tutorials teach the syntax fine but there's never a reason to care about the answer. You filter a table called employees, get a result, and nothing happens. Your brain doesn't bother keeping it.

I wanted to try a different approach. QueryCase teaches SQL through detective investigations. You get a briefing from Chief Fox (our mascot), a real database to query, and a mystery to crack. The JOIN matters when a suspect has an alibi. The WHERE clause matters when you're trying to find who entered the building at 22:13. The SQL is the tool for solving something, not the point in itself.

Here's what's actually in it:

• A structured learning path across 54 cases, going from Recruit through Rookie, Detective, Senior Detective, and Chief Detective. Each rank has drills and a level exam to pass before you progress.
• Sandbox mode where you can explore real datasets (IMDB movies, Spotify, sports stats, Steam games) and run whatever you want with no pressure and no mystery attached. Just free exploration against actual data.
• Everything runs in the browser using DuckDB WASM so there's nothing to install.

I'm a solo developer and this is genuinely early days. I'm sharing here because this community is exactly the kind of people I built it for, and I'd rather get honest feedback now than find out later I've built the wrong thing.

What's missing? What would make you actually stick with something like this versus what you've used before?

querycase.com if you want to take a look.

Any feedback appreciated!

👋 Hey n8n community,

A few of you asked for more detail after my Purchase Order extractor post yesterday. The build looked simple on the surface, but a handful of things bit me along the way. Here are the five that cost me the most time, in case they save you some.

1. Only numbered rows are real articles. The POs had little note lines wedged between items ("2 Box", a location note under a lock, etc). My extraction kept swallowing those into the neighbouring product name, so one row would come out as two items mashed together. The fix wasn't in the workflow at all, it was in the document description I gave the extractor: spell out that only rows with a number in the No. column are real articles, and that unnumbered lines are notes belonging to the row above. That one paragraph cleaned up every merge. Lesson: when a document extraction misbehaves, describe the document better before you touch the nodes.

2. Keep numbers as text until you've checked for empties. Tempting to pull quantity and price straight as numbers. Don't, at least not yet. A missing field comes back as the string "null", and if the field is typed as a number that signal turns into a 0 or a real null and you lose the ability to spot the miss. I keep everything as text, run my checks, then cast to a number afterwards. Clean signal in, clean data out.

3. One helper to catch every flavour of empty. "Empty" is never just one thing. Across the docs I saw real null, the string "null", empty strings, and whitespace. I stopped writing one-off checks and made a single isMissing() helper that catches all of them, then used it everywhere. If a field is missing, the workflow flags it on the form's completion screen with the document name and which field failed, so my friend knows exactly which PO to eyeball instead of trusting the sheet blindly.

4. The Google Sheets checkbox that quietly broke everything. This one nearly broke me. New rows kept landing at the very bottom of the sheet instead of the top empty row. Turns out I'd added a checkbox column with Insert > Tick box, which silently writes a FALSE value into every one of the 1000 cells in the column. The Append node counts those as data, so it always appended below them. If you want checkboxes, add them through Data → Data validation instead, which draws the box without writing a value. Hours lost to a column that looked empty but wasn't.

5. Batch size 1 is not optional here. The extractor node bundles every input item into a single call, so if you hand it two PDFs at once you get one jumbled result. Wrapping it in a Loop Over Items with batch size 1 means each document gets its own clean pass. Small setting, big difference, and easy to miss until your two-file test comes back merged.

The whole thing runs on the easybits extractor node for the actual PDF reading, the rest is stock n8n. If you want to try the Purchase Order extractor yourself, feel free to grab it here:
https://github.com/felix-sattler-easybits/n8n-workflows/blob/c38749a68fd6ea4ae6ebff41789d35cceaacdef1/easybits-purchase-order-extractor-workflow/easybits_purchase_order_extractor_workflow.json

What's the dumbest time-sink you've hit on a build that looked easy? Always happy to hear how other builders tackle this stuff.

Best,
Felix

👋 Hey n8n community,

Last week I posted a workflow I built for a friend who runs an online shop (find it here). He called me again a few days later with a new headache: he's drowning in Purchase Orders. Every single one gets opened by hand, the data typed into a Google Sheet, and that sheet uploaded into his ERP to update his numbers. Hours a week, pure copy-paste.

So I built him something to kill that step. He uploads the PO PDFs through a simple n8n form, and a structured Google Sheet comes out the other end. He just downloads it and pushes it to his ERP.

How it's set up:

The form accepts multiple PDFs at once, so he can batch a whole stack instead of doing them one by one. Each PO loops through on its own so nothing gets jumbled.

The extraction runs on the easybits Extractor node (@easybits/n8n-nodes-extractor). I set the field structure up in two parts: the header fields that appear once per PO (PO number, PO date, delivery date, mark for, PR number, reference no), plus an articles array for the line items, each holding article name, unit and quantity. That array is the key bit, it gives you one entry per row of the PO table, and I flatten it into one sheet row per article with the header details repeated on each.

Two things I added because real documents are messy:

Error flagging. If any field comes back empty, the completion screen lists which document and which field didn't extract cleanly, so he knows exactly which PO to double-check instead of trusting it blindly.

Document name column. The original filename lands in the sheet next to every row, so if a number looks off he can jump straight back to the source PDF.

Workflow JSON is on GitHub: https://github.com/felix-sattler-easybits/n8n-workflows/blob/c38749a68fd6ea4ae6ebff41789d35cceaacdef1/easybits-purchase-order-extractor-workflow/easybits_purchase_order_extractor_workflow.json

I also made a short video showing how the workflow works.

Anyone else automating document-to-sheet data entry? Curious how you're handling the messy multi-line rows – that was the trickiest part to get right.

Best,
Felix

About 8 months ago I built this app that's actually doing decent.

My first app idea literally came from remembering a viral X post about turning drawings into YouTube thumbnails with AI.

I searched the App Store. Nothing.

Looked on the web and found a the websites but nothing for mobile.

So I just copied the concept and built it into an app.

The app is now live on the App Store and recently crossed $1,100 in total sales.

A few stats:

• $1,000+ in sales
• $130 MRR
• $860+ in proceeds
• iOS Only

Most of the growth came from ASO and organic tiktok/youtube shorts videos. I haven't really explored influencers, affiliate programs, ASO optimization, or creator partnerships yet.

The biggest surprise was that building the app was actually easier than I expected. Getting people to consistently discover it is the hard part.

The code isn't perfect. The design isn't perfect. The marketing definitely isn't perfect.

Still a long way from life changing money, but seeing strangers spend money on what originally felt like a pretty dumb idea was a cool milestone.

Curious to hear from others here:

What surprised you most after launching your first app?

**

I use Gemini's NanoBano to generate the images and it costs me around $30/month

Been building this for a while and finally put out a first release. Not going to oversell it, just going to describe what it actually does.

The core idea came from being tired of AI tools that give you one confident answer and leave you to figure out if it's right. So I built something where the output you see has already been challenged internally before it reaches you. Not the same model second-guessing itself. A genuinely separate process with a different job, specifically designed to find problems with what was just produced.

There are two sides to the app.

The first is a council mode where you load local AI models and assign them different roles. One role breaks down your task and makes a plan. Another executes against that plan. A third receives both the plan and the result and checks one against the other. For coding tasks it actually runs the code before the reviewer sees it, so problems get caught by execution rather than by a model guessing whether it looks correct. If problems are found it either patches the specific issues or rewrites entirely depending on how bad it is. What you get at the end has been through all of that.

It also has session memory that builds up as you work, a document pipeline that processes files into structured knowledge before you start asking questions, task history, a diff view showing exactly what changed between the original output and any revision, and confidence labels on every result.

The second is a normal chat mode that runs Python, JavaScript, C#, Java and PowerShell inline and shows execution results inside the conversation. Web search with full page content extraction, LaTeX math rendering, a thinking mode, document attachment, and chat branching where you can fork from any point in the conversation.

Both modes run locally on your machine using GGUF models. If you don't want to manage model files there is a cloud mode through OpenRouter using their free models, same full pipeline, no local setup needed.

No account. No signup. No subscription. Open the app and use it.

MIT licensed. GitHub: github.com/YoMosa2009/Axiom

Happy to answer questions about anything.

Hey everyone,

I’m building a small tool called RedditHub.

The idea came from a problem I keep running into as a builder: Reddit has tons of complaints, broken workflows, “I hate using X” posts, and people asking for alternatives — but manually going through all of that is slow and messy.

RedditHub is not meant to magically “generate validated startup ideas.”

The goal is more practical:

It helps you find repeated pain points on Reddit, group similar complaints, see the original evidence, understand how often the problem appears, and turn that into a SaaS opportunity worth testing.

For example, instead of saying:

“This is a great SaaS idea.”

It would say something closer to:

“Several people are complaining about this same workflow, here are the threads, here are the existing alternatives, here is a possible MVP angle, and here are questions you can ask to validate it further.”

I’m especially interested in helping vibe coders avoid building random ideas just because they sound cool.

The real goal is to build from repeated user pain, not from imagination only.

Would this be useful for people here who build fast with AI tools?

And be honest: is this actually helpful, or is Reddit already too noisy to be a good signal?

Hello friends!

I've released a claude-assisted TV remote app for android and it's tough to get testers across a variety of phones, TVs, boxes, etc so any help you can provide would sure be appreciated. Fwiw I do think it has potential to be a very good free ad-free tv remote app. Any and all feedback welcome, thanks for any you provide

I've used Claude Code CLI and occasionally Codex to code with Gemini writing some design docs and more detailed prompts, tested on device (Pixel 9), and used Gemini for marketing materials/store content.

There are a ton of custom button types and you can use one for free (and change it as many times as you like). For unlimited custom buttons there's a charge which may or may not be the right way to do it please let me know what you think

Play store ->

I also plan to release on iOS fwiw

___

Compatibility list (partially tested, please help!)

Google / streaming ecosystem
- Android TV & Google TV (Sony, TCL, Hisense, Philips, NVIDIA Shield, onn.,
etc.) — full remote, on‑screen keyboard & voice
- Chromecast with Google TV / Google TV Streamer — full remote, keyboard &
voice
- Google Nest Hub & smart displays — volume & media‑playback controls

Smart TVs
- Roku — Roku TVs & streaming players (remote + app shortcuts)*
- Samsung Smart TVs — Tizen, 2016 and newer (remote + app shortcuts)
- LG Smart TVs — webOS (remote + app shortcuts)
- Vizio SmartCast — remote + app shortcuts (4‑digit PIN pairing)
- Panasonic Viera — 2017 and earlier (remote)
- Sharp Aquos — IP‑control models (remote)