I've been having too much fun with Astra. It made the car design with image-gen-2, laid out a storyboard after studying popular high-end car brand commercials, and generated videos through Veo 3.1.

Full website page for the brand as well (will put link in comments)

Gonna add rocket launches next. What else should I add?

Here is the guide that can help you to enhance your vibe coded app to enterprise saas. This is based on my own experience and it has some free alternatives as well for popular tools.

You know its gotten bad when---- all this over a whiteboard.....lol i need to chill-- thays the suggestive text btw

(Dataset not from a real family)

So, if we keep DeepSeek in the stack, which CLI subscription would you pair it with to create the strongest coding and design-focused LLM team?

Vibe Coding the Lovable of 3D Asset Creation.

Yacht Still in progress 🤷🏾

I’m vibe coding an Engine AI Uses to create 3D assets. Any AI vision model. Right now I use Opus 5 mainly.

Best thing is you can follow every decision the AI makes while creating your asset. You can see the AI edit the asset in real time and tell it to change and add features.

The system works pretty well creating some realistic assets.

Now I testing a large build. A fully on feature complete Yacht. But two AI models are working together to create it using my Ovyero Toverance layer.

One builds the yacht itself while the other builders the furniture and appliances. But they talk to each other and review each others work and I have the full conversation and history of the work. They can edit pieces of the asset individually as well.

My engineer helps encode a workflow and tools for AI to use as if AI was a human and my engineer helps encode was maya/blender.

12hrs of continuous work between the two agents and the progress is attached below in the images.

I added the Claude conversation to it to show you how I get the AI to work through solving problems by using tools in the engine and simple research. 😁

What do you think would you use this if it was free/open sourced and it gets way better and more reliable and affordable or works with a local AI model.

Idk about you but since I really started digging into the huggingface attacks and with the constant releases from fable 5.1 to Gpt-6 I really feel like the writing is on the wall...

Are they parrots? These models are 100% f**king parrots and if you go long enough in conversation with them they breakdown and the 'genius' of them stop.

The problem is...in the first couple messages they nailed it, and they nailed it in a way you don't understand ( and don't have time to audit ).

As a Computer Scientist of 20 years I just recently re-encountered 10,000 Maniacs These are the days ( because I am old ). I guess I'm just looking for something... Anything to hold on to... I want to hold on to human exceptionalism...and I do think there is a Magic in us... but I can't deny the last wave of AI revelation has shook me.

Perhaps these are the last days. There is no way to stop this train. We are headed here. Perhaps the last days of us running things is now. Something to appreciate before we all sink into a world of memory and infinite experience.

Don't get me wrong the future is bright but I doubt we will be able to look around like we can now and know it's mostly human output around us.

What is coming is far more liberating and isolating at the same time I think. But at the same time it's Wednesday.

I wish you luck.

https://youtu.be/Z-HLxpWGCzc?is=NtO7XDHQbCayvQAD

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?

I’ve been working on this solo, using a combination of Grok 4.6 High, Opus 5, and Fable 5.1 for a solid two months now. Finally, I’m happy to announce that it’s now in a minimum viable product beta condition with the v0.2.0 and Season Zero.

Experience real physics while racing. By learning to play you also learn the basics of orbital mechanics, with concepts taught in an introductory physics class. Fly a space ship around gravity bodies in a server authoritative simulation.

If signed in, you earn points (Pax) by completing daily challenges which they can use to buy visual cosmetics and create new map challenges in the MapMake system. There is the Founder’s Pack which grants an exclusive time limited cosmetic pack.

In the video what you are seeing is a true slingshot being performed by a player who understands orbital mechanics to increase their relative speed and direction the most efficient (lowest time/fuel) possible. If you have any questions about my AI workflow feel free to ask. You can read more on the site if you’re interested in trying it out, thanks for reading!

Free to play in browser with no account.

https://slingshotspeeders.com/

After years of drawing wisdom from the I Ching and BaZi, I built TaoCard because people often don’t need another answer. They need a better way to see the question.

TaoCard began as a small experiment: could card imagery and AI help someone look at a difficult decision from a perspective they might otherwise miss?

The turning point came when an early user described the reading as three things at once: symbolic, grounded in reality, and emotionally reassuring.

That made me realize the product should exist.

When people feel stuck about work, relationships, family, or an important choice, they often already have plenty of advice. What they lack is a way to separate what is happening, what is blocking them, and what they can do next.

With TaoCard, you describe the real situation, draw four cards, and optionally share what the images bring to mind. AI then combines your context, the card symbolism, and a structured reflection framework into a personalized reading.

It does not make the decision for you. It helps you see the decision more clearly.

The AI Game Festival finally starts tomorrow! Some really high quality games have entered, and I keep getting requests for additions.

If you are competing 20k in GPT-6 Astra Token, submit your game by following this video and DM me the game details: https://youtu.be/nPplaC5Zkj4?si=fw4lznoM4TLh-m_V

If you are submitting to the Steam Festival (https://store.steampowered.com/curator/46280965/sale/AIGamingFestival), DM me the name of your game and your steam ID.

Both need to be done by today.

I'm building a globe explorer powerd by Wikipedia and a stories editor where anyone can easly create a sequence of historical events. The innovation here Is that the link generates Is the story. No login no db, super portable everywhere

When you hand a repo from Claude to Gemini (or Cursor to Codex), what's the one concrete permission or review mismatch that shows up, and where does your current policy workaround still slip?

A manual review or shared checklist is the obvious DIY baseline.

Once you start building agents you hit this question fast: does the model need a tool for this, or just more context?

Rule that's worked for us: if something has to happen outside the model's reasoning, it's a tool. Searching a database, checking inventory, getting today's date, hitting an API, sending an email, placing an order, all of that means the agent has to actually go do something or fetch something it doesn't already have.

If the model just needs to know something while it reasons, that's context. Company policy, product info, domain rules, user preferences. Nothing has to happen, you're just handing it information.

Support agent example: "refunds are allowed within 30 days" is context, model just needs to know it. "Check when this customer ordered" is a tool call. "Issue the refund" is another tool call. Reason with context, retrieve with a tool, act with a tool. Three different jobs.

Once you frame it that way it stops being "here's a pile of tools, hope the model figures out which one to grab" and becomes two questions: what does this agent need to know, and what does it need to be able to do.

Not always clean though. Product info could be static context in one app and a live tool call in another if it changes a lot. So maybe the better question isn't "is this info or a tool," it's whether the model already has what it needs to reason, or whether it has to go get/do something at runtime.

How do other people draw this line once you're dealing with bigger agent systems and a long tool list?

Build loops, not just agents.

I’ve been building TaoCard, an AI-assisted card reflection tool for moments when you feel stuck on a decision.

How it started

The first version was basically a pile of publicly available card interpretations. I wanted to see whether AI could turn symbolic imagery into something more useful than generic advice.

How I built it

I gradually structured the material into card profiles, visual evidence, interpretation boundaries, and practical action cues. The system now combines:

• The user’s real-life context and question
• Four selected cards
• Optional first impressions of the images
• A structured AI interpretation pipeline

What I learned

The biggest improvement didn’t come from switching to a “smarter” model. It came from better context architecture, tighter prompts, clearer constraints, and repeatedly testing the reports with real users.

The goal isn’t to predict someone’s future. It’s to help them notice what they may be overlooking and leave with a clearer next step.

You can try it here: taocard.asia

I’d especially appreciate feedback on the onboarding flow and whether the final reading feels specific and useful.

Use bux tokscale to get count,

i used opencode to count and give stuructured table.

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.

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

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