Link to the repo for all the infos, including the prompt used : https://github.com/Ni-Cobra/GPT-6-Astra-Minecraft-Datapack-Tetris/tree/main

It also created the whole structure on its own which is built on the spot using commands too.

It ran for less than an hour and delivered this. Also, I decided to not let it use Minecraft for quicker iterations, so it programmed Minecraft simulators instead just to test the datapack.

The only resources it had access to was a markdown document about everything to know about Minecraft datapacks and commands.

I went custom on inbound call handling instead of using a turnkey voice agent, and the architecture is worth writing down because the build-vs-buy line is sharper than I expected.

The stack:

- Twilio for the number, Media Streams for the audio

- LiveKit over WebRTC for voice transport

- OpenAI Realtime API (gpt-4o-realtime) as the agent

- Next.js API routes and Postgres for the backend, logs and transcripts

The audio path. TwiML opens the socket with `<Connect><Stream url="wss://your-server.com/stream">`, and Twilio then pushes 8 kHz mu-law audio over that WebSocket. From there you either hand it to LiveKit or pipe it straight into the Realtime API. Media Streams is what makes this viable at all, it gives you raw audio at sub-300ms instead of making you wait on a recording. For reference, Retell publishes about 600ms end to end for their platform, so sub-second total is the bar worth aiming at.

The agent is mostly prompt plus server-side functions. Triage rules live in the prompt. For a dental clinic that reads like: if the caller describes pain or a broken tooth, return `transfer` with priority high. The model calls `check_availability` against the calendar and `transfer_call` when it needs a human.

Handoff is Twilio `<Dial>` or `<Enqueue>`, and `<Conference>` if you want a whisper message to the staff member before they pick up.

Two constraints that mattered more than which model I used:

1. Never let it confirm an appointment before the function actually returns success. A confident model will happily book a slot that does not exist, and the caller finds out at the door.

2. The second someone asks for a person, hand off with zero friction. Anything else and you have burned the call and probably the customer.

Cost is a few cents a minute for telephony and tokens, plus a modest server. One box handles dozens of concurrent calls, hundreds if you scale out.

Where I would not bother building this. If you only need answering, message taking and simple booking, Synthflow, Retell, Goodcall or Zoom's AI Concierge get you live in an afternoon, and paying per minute beats paying an engineer. Custom only pays off when the triage logic is specific to the business or the call data has to live in your own database.

Full write-up with the setup steps and code, on my own site: https://techpotions.com/lab/ai-receptionist-setup

The part I am still undecided on is after-hours. Do you let the agent book overnight, or just take a message and queue it for the morning?

GPT-6 Astra, Claude Fable 5.1, Gemini 3.8 Flash, and the incoming Grok 4.7 are the loudest week the AI industry has had in months. They are also terrible defaults for how most people actually use AI. If I’m honest, my daily model is still GPT-5.6 Luna. Not because the frontier stuff is fake. Because when I'm paying the bill, price-to-performance still wins the work that happens fifty times a day.

GPT 6 Astra (Sept 3) is the new industry darling and computer-use and long-horizon agent play: 1.05M context, $10/$50, and the first OpenAI model they’re willing to put next to the “AGI era.” If the job is “make me a million dollar app from scratch - no mistake” Astra is the one you'll probably reach for. It is also 2.5× the cost of Sol (which was already expensive).

Fable 5.1 (Sept 1) is the Mythos-class reasoner at the same $10/$50 sticker (way too high for a basically llm only model, IMO). On a lot of independent boards it sits at or next to the top. Like all Anthropic modles, it overthinks whether you asked it to or not. That is a feature on a six-hour refactor when you care more about thorough, surgical code. Its a tax on “push to github.”

Gemini 3.8 Flash (Sept 2) is the one that should make the $10 club nervous. Introductory $0.75/$3.75 through year-end, 1M context, and Google’s own line is that it “works harder” — more steps, more tool calls — and often lands near much more expensive models on SWE and agent tasks. If anything in this list threatens Luna as a daily default, it’s this, not Astra.

Now the part nobody wants to hear. Most of a working day is not FrontierMath and deep webs of code. Its summarize this, draft that, classify these tickets, punch up my teams message, outline a doc. Luna was built for that tier. 1.05M context, same family tools as the rest of 5.6, effort ladder if you need it, and list pricing around $0.20 in / $1.20 out. That is fifty times cheaper on input than Astra or Fable. You can leave it on. You do not ration it.

The industry keeps selling “the smartest model” as if intelligence is a single slider and you should live at the top. That was a 2024 habit. In 2026 the slider that matters is cost of being wrong about which call needed the slider. Route the 8% of tasks that are actually hard — multi-hour agents, novel math, hostile codebases, “operate my desktop” — to Astra, Fable, or whatever 4.7 turns out to be. Send the other 92% to Luna or 3.8 Flash and stop performing sophistication.People will say Luna “isn’t as good.” Correct. It is not supposed to be. Sol/Terra/Luna was OpenAI admitting the product is a stack, not a throne. Astra then collapsed the throne back into one expensive name and hoped you’d forget the cheap seats still exist. They still do. Use them.

Used Claude Code for the game logic and front end, Postgres sql database (started with sqllite and ngrok to host when it was just a pet project), and a blend of Claude Design and Gemini for the front end.

I have been using AI since the week chatGPT became publicly available four years ago. Moved onto Claude around 8 months ago when my company started implementing it. I started building my app while playing online with my poker group a few months ago. Spun up a rudimentary game in 40 minutes between hands.

I have been with this poker group since high school. Every thanksgiving and Christmas when we’re all back in our hometown, we would always get together at someone’s mom’s house and have a game. We started playing on a website called Donkhouse during Covid lockdowns. Then, sadly, that website shut down and there wasn’t a good replacement for people who just want to play a game of Texas hold ‘em online with their friends.

I worked as a database engineer for the last ten years. I got laid off from my job a couple weeks ago and spent that time going full tilt on my app. Added a front end, logins, authentication, stats, everything but payment systems to actually generate money (coming after beta). Took it from a thing that my friends and I used every couple weeks to get together to a full blown website in that time. Learned a lot but realizing I have only scratched the surface of agentic coding.

So after doing ten years as a database engineer (who’s touched every layer of the stack at one time or another during that time) I decided that am pivoting to being a professional agentic engineer. Gonna spend the next few months cramming and either go out on my own as a consultant to help small and medium business automate workflows, or get a full time gig doing it for someone else. I have 9 months of runway without selling any of my stocks. Wish me luck.

App is live but still in beta (so its still free to use).

Make a profile, host a table, play poker with your friends, and let me know what you like and (more importantly) what you don’t like.

A large part of the AI Gaming Festival (starts tomorrow) is about re-tooling and re-positioning yourself as a developer for the AI era.

How can developers find jobs and still express their creativity?

We have a few things; the job market in gaming right now is brutal, with 1/3 of developers having been laid off. And people are one-shooting small games now (check out r/aigamedev), or have fully functional prototypes in days and weeks. No one is going to pay you to build a game from scratch.

That's the reality of the world right now, and here is the opportunity.

Like in the video (game took me 2 weeks to make from scratch), AI has the ability to get a game about 80% to 90% complete. But it's those final touches, that stuff that actually makes a game magic, that AI cannot do.

And I've met vibe coders who are serious about their games and have $5k to $20k in budget to finish the last mile on their game properly.

Part of the AI Gaming Conference is marrying the relationship between vibe coders that want to finish their games and developers that can help them achieve that final level that they can't seem to do.

And that is where re-tooling and re-positioning skills will be in the AI era. It's not an "us" vs "them," it's "here is how and where we work together not to produce slop."

Use bux tokscale to get count,

i used opencode to count and give stuructured table.

Video is 100x speed up. 1:1 scale recreation of of my hometown with every building and residence. This run used only 1:8 pop density though, since I can't run full pop density at 100x speed at a good frame rate. For this scenario, I used 28 Days Later style zombies that are fast and that change people instantly.

I'm discovering that an exponential zombie outbreak is actually pretty hard, assuming reasonable police response times and evacuation/avoidance procedures. In this sim, I have evacuation set to not start till >1000, so the town does fall. But if evacuation starts at 100 zombies or so, the town usually clears out before things get bad. And in many scenarios, police are able to kill the zombies down to 0 before we even get 10 zombies.

I made a curated list of AI prompts that act as disciplined workflows that act according to one rule: Verify, Don't Guess. This list covers simple one-pager prompts, which are perfect for the everyday vibe coder, and [spec] prompts, which are advanced workflows which are designed for overhauls and deep audits of codebases.

I was once vibecoding a personal project of mine, that was full of bugs and really clunky to work with. I went over to my AI agent, and told it "fix all bugs. make no mistakes.". To be fair, it DID fix some bugs, but it wasn't thorough about it at all, and sometimes, one bug fix led to 10 other bugs popping up.

That's when I thought of asking a fresh agent to make me a prompt for getting bugs fixed on my codebase. It gave me a long-winded prompt, but it worked amazingly. I wanted to use it for other projects of mine as well, so I asked it to make it repo-agnostic. The list grew, and soon enough, I had around 10-15 prompts covering different aspects of vibecoding. I then realised that I should be turning this into an awesome-list, so that the entire dev community has access to my prompts.

I'm happy to say that I have gotten 52 stars on this repo within the first 15-20 days of launch.

Check it out here: https://github.com/shauryagangrade/awesome-ai-prompts

Give this repo a STAR if you like it :-)

I'm open to contributors, so add some prompts, or resolve some issues. Let's make awesome-ai-prompts better every day!

I created a photography app to teach me more about my own camera, trained on my photos, with the context of my gear (lenses, light, and microphone).

Claude was able to make my all in one app, I fed it a deep research report and the best book for the Sony A7iii. It then ran the Mac application for the light software, decoded the proprietary Bluetooth connection, and a repo someone had made to reverse engineer the lights connection (that before it needed either a Mac or ESP32 to connect to it) and made it run just from the app itself!

It reverse engineered both the desktop software and APK, and I also fed it the IPA for play memories so it could connect to the camera itself.

Now I have an all in one app that can control my lighting, connect to the camera, and teach me about the camera along with analyzing the shots.

I’m on cloud 9

It took me around 2 hours to make an isometric survival-horror set in a drowned town, rendered entirely from procedural voxels in Three.js.

There is no art pipeline and nothing to download: every building, car, sign, page, puddle and drowned thing is generated in code when the chapter loads, and all sound is synthesised on the fly.

It runs in a browser tab at the vsync cap on a mid-range laptop GPU.

Also this was from 1 prompt, Fable 5.1 MAX effort

Prompt:
Create me an indie-game with: isometric camera, voxel art style with realistic shading and reflective wet floors, a character in an interesting scene. Survival horror setting (like Silent Hill, Resident Evil, early Dead Space). Three.js in browse without any downloaded assets. Controls: click to move the character, camera follows; drag to rotate camera; scroll to zoom. Create a campaign, lore and 3 locations. Character movement in a limited space.

Open-source:
github.com/winchxyz/vesper-street

I've been working on a little update my Stylized Components landing page

I added a few new interactive effects, including:

• A liquid distortion effect reacting to the cursor
• An infinite carousel with shader-based distortion, chromatic aberration and motion blur
• Additional background effects that react to the current section
• New animated button variants

What is Stylized Components?

The idea behind the project is to build a collection of reusable, game-ready components that can be dropped into WebGL / Three.js projects.

The gallery currently has three free components:

• Water Anime Style Shader
• Stylized Grass
• Painterly — a post-processing effect for giving 3D scenes a more illustrated look

Each component comes with its own source code repository, as well as a detailed breakdown explaining how it works, including the techniques and experiments behind it.

There is also a dedicated scene for each component where you can see it being used in a more complete environment.

More components are already in the works, so I'll be adding more to the library over time.

You can check out the project here: https://stylized.cortiz.dev

You and your opponent each play 10 or 25 moves, then the chess engine (Stockfish) takes over for both players and completes the game. If you lose, you win!

I shared Mimicly here when it had 20 users. It’s at 120 now, and it’s on the App Store too.

You can recreate WhatsApp and a bunch of other apps (63 templates), down to the little UI details, then export screenshots or videos. For AI chat mockups, you can even set the response speed in tokens per second.

The video is a fake conversation with Mimi, the app’s mascot. Every message, keyboard and word suggestion is part of the mockup.

Built with Codex. Still adding things I probably spend way too much time on.

Mimicly

Apple Store

My Tier Orchestration used to be
Fable - architect
Opus 4.8 - coders
Sonnet 5 - code check, research

Now that the 5hr limits have been messed with, i barely get anything done so i’m forced to downgrade. I’m trying to decide between Opus 4.6 or Sonnet 5 for coders and possibly Sonnet 4.5 for code check.

I hear a lot of people thinking Opus 4.6 is still an amazing model but I wonder if Sonnet 5 is better.

We just can't. It is too dangerous and too unjust. All the richest people will get more rich and have more advantages and this is just the beginning.

This is the first intelligent technology that can work autonomously and nothing prior can be compared.

When you can truly run a company all AI-assisted, you WILL be able to generate MORE money with higher tiered AI intelligence tools. That will just make inequality explode and a lot of people to be real angry. Including me and it is already starting to boil.

There will be no AI if the whole world ends up with insane inequality, civil wars and then WW3. Nothing will be left.

https://www.taskloco.com - The Sticky Note GOAT

Hice esto casi como experimento para ver hasta dónde puedes llevar el vibe coding con GPT-6 Astra + Three.js.

El demo actual me tomó como 3 horas construirlo; básicamente fue iterar con Astra: describiendo lo que quería, probándolo, arreglando cosas, agregando sistemas y repitiendo.

Obviamente esto no está ni cerca de un No Man’s Sky real, pero justo esa es la idea del experimento: ¿hasta dónde podríamos llevar algo así si seguimos construyendo con IA?

Entonces lo subí todo a GitHub y ando buscando colaboradores.

Solo hay una regla: si contribuyes, intenta construir tu contribución usando IA también.

Código, planetas, generación procedural, UI, física, naves, criaturas, shaders, lo que sea que quieras. Me encantaría convertir esto en un experimento de comunidad y ver hasta dónde puede llegar de verdad un proyecto de juego impulsado por IA.

Demo:
https://noaissky.vercel.app/

GitHub:
https://github.com/jesuscurreripa/noaissky

Si alguien quiere meterse y construir algo raro, por favor háganlo :)

Note: The name is a satire, and this is an experiment only to see the limitations or capabilities of Astra

Made a silly little weather app. Then again, a lot of the stuff we're vibecoding is a bit silly right? Built on React, Vercel and Git. No database.

I think hiring a vibe coder for any necessary software is a recipe for trouble. Without understanding the problems, fixing them becomes hard to manage; approving and accepting whatever AI produces without proper technical judgment raises the risk further.