Burak Prompter

Sharing AI Prompts, Systems, Tips & Tricks

Human + AI = ⚡Superpowers

[Burak Prompter @burak_prompter]

One important detail before the engineers arrive with pitchforks 😂:

The animation is obviously not a literal engineering reconstruction of Titanic’s machinery.

Titanic’s actual reciprocating engines were enormous, but each used four cylinders, not the giant wall of pistons shown here.

Each main reciprocating engine was about 63 ft (19 m) long and weighed roughly 720 tons.

And then they added a turbine because apparently two building-sized steam engines still weren’t enough.

AI visualization: 10/10 spectacle
Historical accuracy: the comments section is now open 😂

[Burak Prompter @burak_prompter]

GPT-6 Astra looked at the Titanic and said:

“Needs more cylinders.” 😂

This AI visualization basically turns its engine room into a mechanical skyscraper.

The funny part?

The real Titanic was already ridiculous enough:

**2 massive four-cylinder triple-expansion steam engines

1 low-pressure Parsons turbine
3 propellers
~46,000 horsepower**

All to move a 46,000-ton ocean liner at around 21 knots.

1912 engineering was basically:

“What if the entire building was the engine?”

[Burak Prompter @burak_prompter]

@gdb https://t.co/QAUIJKGave

[Burak Prompter @burak_prompter]

The next frontier AI wave is already taking shape.

These are the 4 models I’d watch most closely right now:

1. Gemini 3.5 Pro
Google’s delayed flagship.
The original June launch window was missed, with reporting pointing especially to coding performance as an area Google wanted to improve. It’s still one of the biggest unreleased models to watch.

2. Gemini 4
Google has already started work on its next major generation, but there’s still no public release date. Recent reporting says the flagship remains unreleased even as Google keeps shipping faster Flash models.

3. Claude Mythos 5.1
Anthropic’s most capable model for areas like cybersecurity and biology — but access is still restricted to vetted organizations because of the capability risk.

4. GPT-6 Astra
Already announced, but broader access is still rolling out. OpenAI positions it for its hardest end-to-end work across coding, research, computer use and complex multi-step tasks.

The interesting part:

the competition is no longer just “who has the smartest chatbot.”

It’s becoming:

Who can build models that can actually work for hours, use computers, write software, operate tools — and finish entire workflows?

[Burak Prompter @burak_prompter]

@gdb Finally ChatGPT will write emails that sound exactly like me… including the part where I type ‘sent from my phone’ even when I’m sitting at my desk, and then immediately regret the all-caps outburst I had at 2 a.m.

[Burak Prompter @burak_prompter]

Gemini turned the human body into a firework. GPT-6 turned it into an actual product.😂
Same prompt. Same 2 minutes. One of them is ready for med school, the other is ready for a screensaver.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​💣

[Burak Prompter @burak_prompter]

The black hole looks great, but that’s not the point. Astra builds the scene, Blender makes it editable, Higgsfield turns it into a film shot.
The scarce skill is shifting from “can you produce the asset?” to “do you know which shot is actually worth making?

[Burak Prompter @burak_prompter]

GPT-6 Astra built the black hole in Blender.
Higgsfield turned it into a cinematic flyby.

That workflow is the interesting part.

Prompt → 3D scene → Blender → cinematic video.

What used to be separate modeling, lighting, camera and video-production stages are starting to connect into one AI-assisted creative pipeline.

And the final shot looks far more like a film sequence than a technical demo.

3D creation is getting compressed very quickly.

[Burak Prompter @burak_prompter]

The pattern across all four is pretty clear:

Gemini 3.5 Pro / Gemini 4
→ coding + frontier reasoning

Claude Mythos 5.1
→ extreme specialist capability

GPT-6 Astra
→ autonomous end-to-end work + computer use

Meanwhile, Google’s newest Gemini 3.8 Flash is already pushing hard on software engineering and agentic tasks, which makes the pressure on its delayed Pro-class models even more interesting.

My bet:

The next major benchmark won’t be:

“Which model answers the hardest question?”

It’ll be:

“Which model can finish the hardest job without you babysitting it?”

[Burak Prompter @burak_prompter]

The next frontier AI wave is already taking shape.

These are the 4 models I’d watch most closely right now:

1. Gemini 3.5 Pro
Google’s delayed flagship.
The original June launch window was missed, with reporting pointing especially to coding performance as an area Google wanted to improve. It’s still one of the biggest unreleased models to watch.

2. Gemini 4
Google has already started work on its next major generation, but there’s still no public release date. Recent reporting says the flagship remains unreleased even as Google keeps shipping faster Flash models.

3. Claude Mythos 5.1
Anthropic’s most capable model for areas like cybersecurity and biology — but access is still restricted to vetted organizations because of the capability risk.

4. GPT-6 Astra
Already announced, but broader access is still rolling out. OpenAI positions it for its hardest end-to-end work across coding, research, computer use and complex multi-step tasks.

The interesting part:

the competition is no longer just “who has the smartest chatbot.”

It’s becoming:

Who can build models that can actually work for hours, use computers, write software, operate tools — and finish entire workflows?

[Burak Prompter @burak_prompter]

The bigger shift isn’t that AI can make a black hole.

It’s that different creative systems can now hand work off to each other:

Astra → build the scene
Blender → editable 3D environment
Higgsfield → cinematic interpretation

Higgsfield is already pushing deeper into Blender with scene-building, 3D, animation, image and video workflows inside the tool.

If this pipeline keeps improving, the scarce skill may stop being:

“Can you produce the asset?”

and become:

“Do you know what shot is worth making?”

[Burak Prompter @burak_prompter]

GPT-6 Astra built the black hole in Blender.
Higgsfield turned it into a cinematic flyby.

That workflow is the interesting part.

Prompt → 3D scene → Blender → cinematic video.

What used to be separate modeling, lighting, camera and video-production stages are starting to connect into one AI-assisted creative pipeline.

And the final shot looks far more like a film sequence than a technical demo.

3D creation is getting compressed very quickly.

[Burak Prompter @burak_prompter]

@bridgemindai 10 dakika zor dayanıyor hemen bitiyor

[Burak Prompter @burak_prompter]

Open-source voice AI just got surprisingly serious.
OmniVoice clones voices and generates speech in 600+ languages — with control over accent, age, pitch, and even whispering.
Creators report speeds up to 40× real-time.
Already 10K+ GitHub stars.
Apache-2.0 licensed, Python-based, supports both zero-shot voice cloning and voice design.
A serious open-source alternative to closed TTS platforms.
GitHub: https://t.co/uJ1MyaAEcF

[Burak Prompter @burak_prompter]

Open-source voice AI just got surprisingly serious.

OmniVoice can clone voices and generate speech in 600+ languages — with control over accent, age, pitch and even whispering.

Its creators report speeds up to 40× real-time.

And it already has 10K+ GitHub stars.

[Burak Prompter @burak_prompter]

The real question isn’t whether AI can generate the assets anymore.

It’s what becomes the bottleneck when:

concept → 3D model → code → playable world

gets compressed into one workflow.

Coding? 3D art? Game design? Or simply taste?

I think the scarce skill is shifting toward knowing what’s actually worth building.

[Burak Prompter @burak_prompter]

The distance between a concept image and a playable 3D character is collapsing.

This demo chains:

img2threejs × GPT-6 Astra × Hyper3D

Image
↓
3D asset
↓
Three.js component
↓
Playable browser world

And the result isn’t just a static model — the character ends up moving inside a live 3D environment.

The interesting part isn’t the monster.

It’s how much of the pipeline between “I have an idea” and “I can interact with it in 3D” is starting to disappear.

[Burak Prompter @burak_prompter]

There are two useful pieces underneath this workflow:

img2threejs is designed to turn reference images into website-ready Three.js components, including GLB output, LODs, React Three Fiber / vanilla loaders, camera + lighting setup, and performance information.

Hyper3D Rodin can turn images or text into textured 3D assets and export formats including GLB, FBX, OBJ and USDZ.

One important distinction: the video presents img2threejs + GPT-6 Astra + Hyper3D as a creator workflow. I wouldn’t describe it as an official partnership unless the creators explicitly confirm that.

The bigger question:

If generating the first usable 3D asset becomes this cheap, does modeling remain the bottleneck — or does world design become the scarce skill?

[Burak Prompter @burak_prompter]

A better way to use the list:

Foundations
→ Python-100-Days
→ ML-For-Beginners
→ AI-For-Beginners

LLMs / GenAI
→ LLMs-from-scratch
→ Generative AI for Beginners
→ OpenAI Cookbook

Agents / production AI
→ AI Agents for Beginners
→ Pathway LLM App

Vision / generative models
→ Segment Anything
→ Stable Diffusion

Microsoft’s GenAI course currently contains 21 lessons; its agent course has expanded to 18 lessons covering areas including tool use, agentic RAG, context engineering, computer use, local agents and agent security.

Pathway’s repo is particularly useful once you move beyond tutorials: it focuses on ready-to-run RAG, AI pipeline and enterprise-search templates connected to live data sources.

[Burak Prompter @burak_prompter]

If I had to rebuild my AI engineering bookmarks from zero, I’d start with these 10 GitHub repos.

They cover almost the entire learning stack:

Python → ML → LLMs → Generative AI → Agents → RAG → Computer Vision.

And several already have 70K–180K+ GitHub stars.

Save this list. ↓

jackfrued/Python-100-Days
microsoft/generative-ai-for-beginners
rasbt/LLMs-from-scratch
microsoft/ML-For-Beginners
openai/openai-cookbook
microsoft/ai-agents-for-beginners
CompVis/stable-diffusion
microsoft/AI-For-Beginners
pathwaycom/llm-app
facebookresearch/segment-anything

The interesting part isn’t the star counts.

It’s that you could build a surprisingly complete AI engineering curriculum using almost nothing but these repositories.

The first three alone currently sit around 186K, 119K and 104K stars respectively.

[Burak Prompter @burak_prompter]

The interesting part: it’s Apache-2.0 licensed, Python-based, and supports both zero-shot voice cloning + voice design.
Another serious open-source alternative to closed TTS platforms.
OmniVoice on GitHub⬇️ ⁠
https://t.co/jbbWpgr0jf

[Burak Prompter @burak_prompter]

Open-source voice AI just got surprisingly serious.

OmniVoice can clone voices and generate speech in 600+ languages — with control over accent, age, pitch and even whispering.

Its creators report speeds up to 40× real-time.

And it already has 10K+ GitHub stars.

[Burak Prompter @burak_prompter]

https://t.co/ffyrcIvboq

[Burak Prompter @burak_prompter]

You can try GPT-5.6 Luna, Claude Sonnet 4.6 and DeepSeek V4 Flash for free right now.🔥

Vyce AI drops $50 in credits the moment you sign up. Grab the API key and drop it into Cherry Studio, Claude Code, or any OpenAI-compatible client.

I just tested it — $50 sitting in the account.

Which model are you trying first?👀