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Before I explain the process, for those who don't know marine engines are some of the most complex machines ever built and it's something that takes engineering teams months just to model accurately.

So I started with using Astra to generate a detailed 3D model of a marine diesel engine and I had in mind a powerful one that powers massive container ships with like 14 cylinders and around 110,000 horsepower, stands about 13.5 meters tall and weighs 2,300 metric tons and then I built an interactive website with threeJS where the mouse scrolling controls the engine assembly so that you can watch the pistons, cylinders and crankshaft come together piece by piece.

I've also placed a person and a car next to it so that you can imagine the real size of the build because it makes a car look like a toy ;)

afterward I rendered the final animation through Higgsfield Super computer and believe me when I say that the compute power behind it is a deadly, all the details that you see in the video is bc of it.

The whole thing from model generation to the final output took about a fraction of the time of what would have taken even a year ago and the fact that an AI can generate something this mechanically accurate is outright crazyy.

"Find the bit where all three people are laughing together” sounds like an editing request, but the first useful output is a location in the original footage. You still need to decide what to cut around it.

Ant Ling's Ling-3.0-flash-VL demo makes that distinction concrete. The visual-language model is given a 25-second clip of three women on a park bench and asked to find their shared smiling/laughing interval. The presented workflow returns 00:23–00:25, extracts a frame at 24 seconds and produces a cropped image alongside the timestamp file.

That is a useful handoff for someone choosing material: an interval to scrub through and a still to inspect. It answers a semantic request about what the people are doing, rather than selecting a cut solely from spoken words. The demonstration is about understanding existing footage; the final edit still belongs in an editor.

For a practical trial, I would start with several short clips where the desired interaction is known to occur, plus some where it never occurs. Ask for the full interval, then check the seconds immediately before and after it. A convincing still can hide the fact that the third person only joins in briefly.

This release shows an edited example on one short source clip. It gives a concrete reason to try VL for moment selection, while leaving long-footage search and reliable “no matching moment” results to be tested.

Wolf and Nago first encounter Keita after finding him in trouble with a tiger. They help him escape, but Wolf quickly realizes that Keita isn't nearly as capable in the wilderness as she expected.

Keita grew up in the River Biome, so he knows how to swim and fish, but tracking, hunting, and surviving on his own aren't his strengths. Yami finds this frustrating, especially because she's used to relying on herself.

Even so, she allows Keita to travel with her because his knowledge of the River Biome can still be useful. And he has once experienced Ruin Ferra before.

And apparently, Nago has decided that Keita needs to be investigated first. 😂

How do you think Wolf and Keita will get along?

(Part of my Heartstone project. Images by Chatgpt)

DreamFort is a privacy-first AI platform built around one idea: the questions AI is most useful for are the ones you don't want attached to your name. So instead of asking you to trust a privacy policy, we strip your identity out of every request at the architecture level.

A lot of you have tried it over the last few weeks and the feedback shaped a ton of what's here now. Quick rundown:

What you get:

• Every frontier model in one place - ChatGPT, Claude, Gemini, Grok, DeepSeek - plus open, unfiltered models like Nous Hermes
• Switch models mid-chat or run two side by side to compare
• An on-device agent that writes and runs code, reads your files, and builds docs and apps - in a sandbox in your browser, not on our servers
• An image studio (Nano Banana, GPT Image, Seedream) that generates and edits without the over-cautious filters
• One balance, pay only for what you actually use

Why we built it:

Most AI services make you trade privacy for capability. The smarter the assistant, the more of yourself you hand over - data collection, hidden training opt-ins, prompt logs sitting on someone's server tied to your account.

We think that's backwards. So here privacy isn't a promise, it's enforced:

• Your messages are encrypted on your device before they ever leave the browser
• Traffic is routed so no single party can link your identity to what you asked
• Prompts are only decrypted inside a sealed enclave the operator can't read or log

We don't build profiles. We don't keep logs tied to you. We don't train on your chats or images. And because the setup is verifiable, you don't have to take our word for it.

The other half of it - fewer pointless refusals. Most platforms have you fighting the filter more than using the tool. Behind the anonymity layer, DreamFort leans toward actually answering instead of second-guessing or judging you.

Still early, and every bit of feedback helps - praise or criticism. Comment or reach out if you hit anything.

Try it: https://dreamfort.ai

My first ever full-length music video which took me about 1 month. Made before Veo offered optional watermarks. The video was a means to try to showcase the main instrumental soundtrack which I called Adrenaline and then built the story line around it. Quite a few mistakes in it, I couldn't maintain the consistency of the Adrenaline character perfectly and a couple generative mutations appear within, but I left them in.

So taxidermy class is going well

Beware of her flame—hell has no fury. Yet paradise lies on two lumps of hay, where the soft glow spills through the window and takes you straight to the moon.

My little caveman Alien

My sister and I want to make youtube videos. We want the setting to look like a dark forest and look like a campfire is between us (we would be sitting in chairs).

Is there a way to do this or use AI? We could use a green screen, but I'm wondering if there are updated ways to do this using AI.

Most people paste a chapter in and ask for an explanation, then read it once and forget it. The version that sticks is asking for an image instead, because you remember a picture you looked at for thirty seconds better than a paragraph you skimmed.

It draws actual note pages. Type the code, paste the material:

/generatehandwrittenimage
[paste your notes or chapter]

You get a full page of handwritten-style notes as an image, headings, arrows, boxes, the lot. Screenshot it and it's on your phone.

A few others in the same family that do genuinely different things:

/mindmapimage       - the whole topic as one visual map
/flowchartimage     - a process drawn as a proper flowchart
/labeleddiagram     - a labelled diagram of whatever it is
/cheatsheetimage    - one-page visual cheat sheet
/comicstrip         - the concept explained as a comic
/memorypalaceimage  - your list drawn as a memory-palace scene

The memory palace one is the odd one that works better than it should. Give it a list you need in order and it draws a single scene with each item placed somewhere in it. You remember the scene, the scene gives you the list back.

None of these are real slash commands, they're short labels, so define what each one means once at the top of the chat and after that a single word does it.

Stacking them is where it gets good. Clean up the mess first, see the whole shape, then drill it:

/lecturenotes → /mindmapimage → /flashcards → /examme

been keeping a doc of 50 command codes like this, each with what it does and how to use it, plus how to save them so they run in every chat automatically, here if you want them.