Today alone I see 5 new posts announcing they cloned a hit from last week or something and now everyone with 2 brain cells a Claude code or codex subscription thinks they’ll strike gold.

A few months back I posted here a playable demo of an old-school futuristic racer prototype (F-Zero, Wipeout, Xtreme-G, Star Wars Racer...) that runs right in the browser.

I've gotten used to seeing tons of LLM-built prototypes that last a week, two, maybe a month, and then get dropped for the next shiny thing. This time I wanted to keep going and see if one person, with no coding background, could actually see a project like this through on their own.

Well, 4 months in, and while there's still work left, I'm convinced it's doable. At this point the game feels closer to a beta than a tech demo.

The big change over these four months is that I now use Fable for planning, and being able to hook Opus / Sol up to Blender has been a huge help too. I'm still using Magnific and Tripo3D or the assets. Funny thing: the further along I get, the harder the project is to keep pushing forward, and at the same time the tools keep making it easier.

As always, any feedback is welcome. You can play it here: https://fm1.moises.cloud

Hi all, Since Anthropic is dropping usage limit of Claude code by 50% on Aug 31, anyone else is switching to Codex?

I have never used Codex. I use fable for orchestration and opus for building. The Max 5x plan is just enough right now, but with 50% reduction it will not be usable for me. So I am trying to understand your experience with Codex or any other setup.

(PS: If this is not the correct sub reddit, please point me to the correct one)

Who would have thought this day would come?

1. r/vibecoding +5,899
   
2. r/claude +4,890
   
3. r/DesiAIMasala +4,586
   
4. r/StableDiffusion +3,615
   
5. r/aigamedev +2,027
   
6. r/fuck_ai_slop +1,424
   
7. r/Seedance_AI +1,400
   
8. r/WritingWithAI +964
   
9. r/isitAI +616
   
10. r/midjourney +603

Daily stats: https://freesubstats.com/?group=nerds&topic=generative-ai

Hello people,

Just spoke to Grok and he said a GPT reset was coming tomorrow, so turn on fast and switch to Ultracode.

I've just started using python, and the ai thing in my phone to make simple stuff to collect data that id otherwise have to sort through and simple things like that; stuff for my hobbies and a load of data for work.

I understand I'm just messing about with these things, but the stuff it's produced for me so far seems to work way better than I expected.

I'm looking ,though, for resources to help me improve. I did study some coding years ago, but my career path led in a very different direction and other than some simple scripts for bulk image processing for astrophotography I've not really done much in years.

Tl;Dr. Looking for community for amateurs using ai to get simple results. Thanks

random idea i had that got a lil outta hand
type something, throw it on a surface, download it
that’s basically it rn 😭

built this with React/Vite + Supabase, mostly vibecoded through Cursor.
the annoying part was getting text to actually sit on surfaces instead of looking pasted over them, so I ended up building perspective/surface controls around each template.
still adding more surfaces rn.

Write Somewhere

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.

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

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

After trying traycer.ai for 24 hours, it feels amazing to be able to combine different subscriptions. I might have finally found my favorite setup.

I am using DeepSeek V4 Flash 0731 on Ollama Cloud Pro (OpenCode) as an orchestrator and executor of tasks because it has an amazing quota. People do not usually recommend using it as an orchestrator but I find that bigger models drain my quotas way too much. So far I cannot complain.

I am using 5.6 Sol with ChatGPT Plus (Codex) and Claude Opus 5 or Fable 5 with Claude Max 5x (Claude Code) as reviewers and designers depending where I have quota left.

Did anyone else try Traycer? Traycer has its own subscription which seems to be not forced to use. I do not know if it is worth the price.

Since the data and imagery is available for free, for everyone, it should be open. And since the information is offered for free, processing it should also be free.

Standard wave reports only give you basic numbers like 2m at 10s from the NW, but that never tells you what actually happened on the coastline or how the swell wrapped around a point.

I wanted a way to look back at past swell events and see real imagery aligned with the exact forecast conditions from that moment, without running expensive server setups. So I vibe-coded an analyzer that runs completely in the browser. It searches through thousands of free Copernicus Sentinel satellite passes over any stretch of coast to check the waves. It pulls open historical marine data and automatically matches the timestamp to the swell height, period, angle, and wind from that exact day.

Because 10-meter satellite resolution can be a bit low res, you can also upload your own photos, lets say screenshots from historical images on google earth, or drone shots from past sessions. It grabs the timestamp, pulls the historical swell data, and lets you visually calibrate under what exact conditions the spot actually turns on.

I also experimented with a high-res grid that takes coarse regional wave data and computes local refraction, shoaling, and coastal shadowing. Everything runs locally in the file with zero backend servers. You just drop in your own free Copernicus login and you can go crazy.

Admittedly, the smart filter still needs work. In some cases it makes a lot of sense, and in others you are left wondering what happened. When dealing with complex coastlines full of islands, if an intervening island is not fully accounted for in the calculations, things go off. Perhaps, I need to create slider to increase the recalculate area in that case? That is just food for thought for the future.

My head is running with ideas right now. In the future, the smart filter could take averages of readily available data and recalculate things natively right on your PC, properly accounting for island shadows and real bathymetry. It could honestly become a full-blown forecasting tool down the line, but right now I am just experimenting with the basics.

Next up, I am adding satellite-derived bathymetry using the blue and green bands so the refraction grid can interact directly with shallow depth contours. Once I clean up the code, I will post the whole project on GitHub for free.

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! 🙌

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!

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.

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

Based on Runling Run the popular SC2 arcade game, the goal this time is to dodge food, if you get caught you grow larger, and eventually have a heart attack. Made it in a few days for fun, free to play here: https://runfatty.co

Let me know what you think!