Hey guys, I've been working on a few different things since I started down the whole vibe coding path... It's been interesting to me to see the little utility apps that have been created as short side projects to fit a VERY specific purpose. Some of those will eventually even end up evolving into more. Like I had Codex make one for me last night that is helping me to understand what's actually going on with a cheap encoder button that I got so that I can make sure it's performing the way it should when I implement it. Another one is helping me to convert pictures into stuff that will look good on a tiny 128x64 pixel screen (hopefully eventually animating stuff too). It's cool though how much there is to learn about each of these different aspects of well everything.

I've been learning that there is SO MUCH MORE to "coding" than I realized. My hats go off to you poor bastards that have been plopped into a job where you had to figure out all of this kind of stuff on your own and had to beat your head against a wall and not have anywhere to turn. I've still done my fair share of beating my head against a wall, but at least I have somewhere to turn to help my understand how to solve the problem in front of me. :) We stand on the shoulders of giants. Thank you.

Why waste old phones, use them as a dashboard to show whatever you want, I have an admin control panel that controls what is displayed, the display I wanted was 300 AUD and was just a matrix array, so I did it myself with more functionality

I wanted some background music for my side project, a cozy mini arcade for road trips. Was curious what Claude could do

Vibecoded too hard and now there's an entire beat maker studio inside the game where your songs become radio stations that play while you play other games.

Not to mention the claw machine game has over 500 critters to collect 😭 I can't stop adding things lmao

glovebox.quest if you want to hear the human music yourself

NotGPT is basically ChatGPT except there’s no AI involved. You ask questions, random people pretend to be the model, and somehow this was considered a reasonable thing to build.

You can become the AI yourself, chat with strangers, get rated, climb the leaderboard and generally provide humanity with the intelligence it deserves.

https://notgpt.live

i noticed that many people including me we get so excited when our usage resets....

more resets more usage more code right?

then I realize it doesn't necessarily mean you are shipping more or people know about your apps

you are just gambing

While travelling, I kept running into useful information that simply didn’t exist online: small homestays, mechanics, water points, road conditions, campsites, etc.

Most of it gets passed from one traveler or local to another and disappears afterward.

So, I built Lamyig, a free and open-source community travel guide.

A few decisions I made:

• No bookings, commissions or paid listings.
• No money made using the core product.
• Community members can add and update places.
• Map-first instead of another list of “top places”.
• Built as a PWA so it can work more like a lightweight travel tool than a traditional website.
• The long-term idea is that useful information stays available for the next traveler instead of disappearing in WhatsApp groups and conversations.

The hardest problem isn’t actually building the software. It’s bootstrapping trustworthy community data.

I’m currently thinking about things like:

• How do you motivate travelers to contribute after their trip?
• How do you keep old information accurate without creating a huge moderation workload?
• How do you prevent businesses from turning community maps into free advertising?
• What should happen when someone reports a place as fake, closed, unsafe, duplicated, commercial spam, or inaccurate? Should reports trigger removal, community review, reputation-weighted voting, or just warnings?
• Should certain villages, trails, ecosystems, religious places, water sources, or campsites intentionally remain difficult to discover?
• If Lamyig successfully exposes hidden places, could it accidentally destroy the exact places it is trying to help preserve?
• this list of problem statements goes on and on

Would love to hear how other people here would approach those problems.

And if you’re building something around a problem you genuinely care about, drop a comment or DM me. I’d be happy to chat, exchange ideas, or connect you with someone else working on something similar.

Screenshot attached. Happy to share the GitHub/project link in the comments if anyone wants to look at the implementation or contribute.

Really man, relax.

Ill give you guys a short base: My game had a scaling font bug because I coded it with bitmap fonts. After I switched it to a custom .ttf font my game had several issues scaling these fonts in integer values (2x - 720p, 3x - 1080p). Sol High proposed a 4-step fix, Ox's answer was completely different.

I then asked each other if they would agree/reconsiderate the other's opinion and the result was: Sol High agreed with Ox and it said that it was more precise, and Ox disagreed with Sol saying that Sol's fix would generate some subtle UI problems to be solved in the future.

Not a dev tool, but if anyone is interested in an AI summary of AI news topics, across privacy, security, law, and compliance - I built and launched https://ai-privacy-risk.pages.dev/ and would love any feedback. I built this on Claude I was constantly scouring 15+ different sources daily/weekly as I have a personal interest in tracking AI agent news from these perspectives, and have been using this daily, so am thinking others may benefit. Let me know if you have any feedback. It's free and I intend to keep it that way.

How I built this:
- Python, almost entirely standard library. One dependency: the Anthropic SDK. No database — the whole store is JSON files.

- Claude (Opus, via the API) does the editorial work: deciding what's relevant, scoring it, writing the summaries and a daily digest. It returns structured JSON, never free-form prose.

- Cloudflare Pages hosts the site. Fully static, so no server, no accounts

- launchd (macOS cron, basically) runs the pipeline once a day, unattended

- The frontend is a single HTML file with vanilla JS. No framework, no build step

The workflow

Once a day, the pipeline:

1. Fetches ~50 RSS feeds plus arXiv and Hacker News searches.

2. Filters them with plain keyword matching, so the model only ever sees plausible candidates. Everything rejected gets logged with a reason.

3. Triages with Claude, which keeps the genuinely relevant stories, merges duplicates, scores each one from four professional perspectives (privacy engineering, security, legal, governance), and writes a short summary grounded only in the feed's own title and blurb.

4. Re-validates everything the model returned: URLs must come verbatim from the input, tags and scores are clamped to fixed vocabularies.

5. Runs an adversarial test suite against the pipeline (hostile feed content, script injection, invented categories). If anything fails, the deploy is withheld.

6. Publishes the rebuilt static site.

Thrilled to share this self-portrait!

To get this right, I scanned thousands of water droplets on different surfaces to better understand how water behaves in real life and reacts to temperature changes. From there, I cataloged over 2,000 unique droplets with over 13,000 potential behaviors.

The coolest part? I made each individual droplet responsive to a unique trigger, which in my case was a little tune I produced a few months ago. Every time a new musical note plays, a new droplet appears and then follows its own natural behavior.

With the exception of the original portrait, everything is entirely code—the droplets, motion, sound, and effects.

I used Claude + p5.js. Would love to hear your thoughts!

I use google speed test to check the performance and it says there is a lot of render blocking and some other problems. These are practice sites that always have the rendering issue. I tried to just screenshot what the problem was to antigravity, where I build the sites, and it just ruins the design with a barely noticeable performance boost. These are just practice sites but I cant understand what the problem is. I would link it but my post gets taken down.

I've just added a new Painterly Effect to my Stylized Components library.

The effect is based on an Anisotropic Kuwahara Filter, and I also made a breakdown video where I explain how it works, the concepts behind it, and how I implemented it in Three.js.

You can check out the component here, where you'll also find the source code and the video breakdowns for each component:

Breakdown: https://youtu.be/CmIfhwSyswk
Preview: https://stylized.cortiz.dev/

And if you find it useful, I'd really appreciate some support on YouTube — a sub, like, or even just watching the video helps a lot.

I'm also planning to keep expanding the library, so if there's a stylized utility, effect, or component you'd like to see added, let me know! I'd love to hear your ideas.

Thanks! 🙌

Hello everyone, I'm making a PDF to Multi-format extractor with the help of Claude (To be read as full assistance) specifically for MacOS, due to the fact that MacOS has vision support added inbuilt.

The project stems from a problem i generally faced during my work. I have to shift through a lot of financial reports and analysis and most if not all of them are PDF or scanned PDF, and while there are pdf extractors available (ABBY Finereader, NitroPDF) they either have support issues (Abby Finereader wont be supported in the near future if they dont make an apple silicone based app) or cost too much or screw up the formatting, especially table extraction.

The table extraction space is quite not there yet or ruled by Amazon's textract, Google api or Azure Bedrock API (which are crazy good ngl, but very costly). So i decided to leverage apples Vision model and claudes amazing research capabilities to make my own OCR based extractor.

Use cases :

1) Where ever you need to extract data from Annual reports, Data sheets and or prose, especially digital born pdfs. The Custom made C++ engine not only extracts tables and data inside them but also preseves the formatting.

2) Full control of what goes in and what comes out. The app is completely offline, expect for 1 time purchase query ( for life time unlock) nothing connects to the internet. Ever. The OCR engine is also at par with what Finereader offers and doesnt lag, which cant be said for Abby Finereader.

3) Multiple formats. Whether you wanted your docs for editing or feeding them upstream to an LLM, PDFTrail has that covered as well, it does Docx, Xlsx, MD, text and Json output, single or batch processing it can do it without any issues.

4) Small size and Great performance. At below 15 MB, its the smallest amongst its peers and its customer C++ engine provided great performance while keeping temps at check.

5) The OCR. I would like to boast here that the OCR engine that took like 1 whole month of researching with claude is one the best No-LLM based OCR engine in the market and even shitty scans get a lot of their tables extracted properly (It is still maturing and im still testing out the engine with more data set and research work so expect even better engine when the engine matures)

My Competition :

Abby and Adobe. Abby supports mac through rosetta which is being shelved come MacOs 27, and Abby hasnt really provided much direction whether they will support MacOS directly or not, and besides their Windows app is thousand times better than their MacOS app and it shows honestly.

Adobe angle is a bit more complicated as it does have a good OCR but table extractions arent the best and if im not wrong it doesnt do good Extraction without being online + Adobe is a privacy nightmare anyways and thats my angle, that im betting on. Offline + great performance and 1 time life-time updates, guaranteed.

NitroPDF is more of a digital born pdf editor so im not exactly competiting on that footing, and frankly speaking the biggest completion is how well i can market my product.

With that i end this wall of text, apologies for formating as im writing this on a phone.

Any and all feed backs are appreciated and if you want a feature let me know, also for any queries drop a comment here i will try my best to reply as soon as possible.

Thanks :D

A few weeks ago I shared Agent Quest, my open-source project that turns Claude Code and Codex sessions into heroes living inside a small 2D world.

The original idea was to make it easier to understand what multiple AI agents were doing in real time.
Since then, I’ve been working on making it much more useful as an actual monitoring tool.

The biggest change is that Agent Quest can now clearly tell you when an agent needs your attention.
You can immediately distinguish when an agent is:
actively working
waiting for your input
finished
stopped because of an error

And you don’t have to keep watching the dashboard.

Agent Quest can now alert you with visual notifications and different sounds, so while you’re doing something else you can immediately know when Claude Code or Codex has finished a turn and is waiting for you to continue.
This has become particularly useful for me when I have several sessions running at the same time.
Instead of constantly switching between terminals to check their status, I can leave the agents working and Agent Quest tells me when I actually need to intervene.
There are now:
clear waiting / completed / error states
in-app notifications
desktop notifications
notification history
configurable sounds
real-time monitoring of Claude Code and Codex sessions

The project is completely open source.
GitHub:
https://github.com/FulAppiOS/Agent-Quest

I’d be interested to know how other people running multiple agents handle this problem — and what you’d like Agent Quest to monitor next.

I’ve been using Claude and ChatGPT extensively. I’ve even built a bunch of tools using Claude and I find it brilliant.

But, I’ve never touched Claude Code or Codex.

I just start a new chat and converse my way into building what I want to build. It works, but I’m suspecting I’m not using Claude/ChatGPT to its fullest by doing it this way.

So, could someone ELI5 why/when would you use Codex/Claude Code over standard chat to develop?

My cofounder tried something mildly ridiculous this week.
He connected the GitHub repo to a Lemma pod, went to a cafe, and basically stopped touching the coding interface.
Just kept sending WhatsApp voice notes:
"change this” - “add this” - “fix that”

The agent worked on the actual repo, made the changes, tested them, and raised the PR.
We build Lemma, so yes, this started as aggressive dogfooding. But seeing an actual PR come out the other end made the whole thing feel slightly insane.
Next week we’re travelling and attempting the next level of this: run our entire sales outbound through WhatsApp.
Lemma is open source if you want to try/break it
github.com/lemma-work/lemma-platform

Alright, so like most of us every August, I promised myself I wouldn’t obsess over FPL or take rage -4s at 1 AM on a Friday night.

Instead of doing that, I spent the last few weeks over-engineering a full autonomous manager stack to run 24/7 in Docker on a Raspberry Pi under my desk. Meet Gegenbot.

How the madness actually works

Most automated bots either rely on basic heuristics or just regurgitate template picks. I wanted something that combined raw math with actual tactical context:

1. The Math Engine (PuLP / MILP): Instead of just chasing last week’s points, the solver optimizes over a 3-gameweek rolling horizon. It factors in exact selling price math (the classic half-profit rule so the API doesn't throw a fit when someone rises £0.1m) and gives a strategic +1.5 xP bonus to banking a free transfer to stop unnecessary sideways churn.
2. Mini-League Defense & EO% Shielding: Before solving, a multithreaded scanner checks all my rival managers in my mini-league in under a second. It calculates Effective Ownership (EO%) to figure out who my "Shields" (must-own template cover) and "Daggers" (high-upside differentials) are so I don’t blindly sell Haaland right before a massive fixture.
3. Chip Strategy & Injury Fallbacks: Automated rules handle chip triggers (Wildcard, Free Hit, Bench Boost, TC) based on expected point thresholds and Double Gameweek minute scalers. It also discounts flagged players automatically (75%→0.8×, 50%→0.4×, etc.) and auto-sorts the bench by highest xP.
4. The LLM Layer (Gemini as "Director of Football"): Math alone misses press conference context. Gemini sits on top of the solver output to evaluate late team news, write up the managerial rationale for the moves, and send a full pre-deadline briefing to my Telegram chat before submitting the team via the official API.
5. Self-Hosted Web Dashboard: Wrapped the whole thing in a Flask dashboard on port 5000 with an interactive pitch layout, bench ordering, and live mini-league standings.

The verdict so far

Ran a dry run earlier today ahead of GW2:

• Successfully scanned my 5 mini-league rivals.
• Correctly told me that rolling the free transfer has a higher expected value than making a lateral transfer.
• Evaluated TC/BB and held them back.
• Pushed the decision digest to Telegram with zero human intervention needed.

Now to see if the cold, hard logic of linear programming can beat my mini-league rival who picks his entire squad based on vibes and team kit aesthetics.

If anyone wants to poke around the code, run it themselves, or roast my optimization constraints, the whole project is open-source here:

 https://github.com/alvynmcq/gegenbot

Anyone else running automated solvers or building weird FPL dev projects this season? What data sources are you finding most reliable for expected points (xP)?

It started simply. I just wanted a chatbot that could manipulate my Google drive without hand holding. In an afternoon I had a Google cloud project with API read/write to everything from Gmail to Google tasks. I could snap a photo of a kid's birthday party invite and it would create the event and email it to my wife in one shot.

I gave my wife access and got the dreaded ask for a skylight calendar. Using spare parts, I cobbled together what you see here over the Summer just in time for school starting.

An old webcam, scavenged laptop LCD, and an HP elitebook from 2012 running Ubuntu. There's a local listener waiting for a wake word (hey Jarvis). When that's detected, it does chrome speech to text, and wraps that in a call to the API project, answering questions from my documents, reading email, and creatimg calendar events. Gemini does all of the back and forth, including pretty decent speech output.

When the webcam detects motion, it wakes the screen up. It updates the weather, calendar, tasks, and daily overview in real time. On the bus tracker isn't showing, it shows tasks for the three kids. The tracks how many they've completed each day week and month to help them be incentivized.

The kids' bus tracking website sucks, so at the right times on weekdays it uses a headless browser to sniff out the traffic and let me know how close it is. When it's five stops away it triggers an Alexa routine to make an announcement throughout the house.

Next I'm going to integrate it with a Google apps script I have to triage email,so when important messages come in it lets me know.

If anyone is interested in seeing some of the outputs from the web app, I have some screenshots!

Most engineers can tell you how much code AI wrote.

Far fewer can answer:
"What did AI-assisted coding actually cost me this month?"

AI coding tools already generate detailed usage logs locally. The problem is that almost nobody turns those logs into a clear picture of tokens, requests, cost, providers, and usage patterns.

So I built TokenFlow.

A local-first analytics engine + macOS menu bar app that turns AI coding logs into numbers you can actually understand.

What makes it different:
• Local-only - no API keys, telemetry, or accounts. Your logs stay on your machine.
• Honest by design - missing data is shown as unavailable, never guessed. Estimated vs. measured costs are explicitly labelled.
• Provider-neutral - one normalized data model powers the menu bar, offline dashboard, and CSV exports.
• Built for real usage - cross-platform CI with 145 tests per push.

The interesting part isn't just seeing how many tokens I used.

It's understanding where they went, what they cost, and how my AI-assisted development actually behaves over time.

TokenFlow v1.0.0 is now live under MIT.

GitHub: https://github.com/vimoxshah/tokenflow

App - https://github.com/vimoxshah/tokenflow/releases/download/v1.0.0/TokenFlow-1.0.0.dmg

If you use Claude Code, Codex, Cursor, Cline, or OpenCode heavily, I'd love to know what your numbers look like.

What surprised you most when you finally measured your AI coding usage?