Zvezdan

3D artist in gaming turned Indie hacker building:
$0/m
$0/m

[Radiance Fields @RadianceFields]

Apple just announced gaussian splatting is coming to Apple Maps at WWDC.

[Roberto Nickson @rpnickson]

I posted this paid partnership last night on IG and this morning it’s at 1M views and 100K+ likes:

Life with Claude

[Javi Lopez ⛩️ @javilopen]

🔴 I NEED YOUR ATTENTION

I've spent a month helping Miriam with her case of metastatic cancer and I want to share the methodology I've been using because it's completely replicable.

I think (with luck) this could be USEFUL TO OTHER PEOPLE with cancer (or any other illness).

The results we've gotten aren't a miracle, but we believe they're genuinely useful and could mean the difference in a literal life-or-death medical case.

Here's the method step by step:

1/ Use the most advanced models of the moment (unfortunately paid, and not cheap. I think Public Healthcare should invest in this):

- ChatGPT 5 Pro + Extended Thinking (40 min aprox. of thinking per call)
- Claude Opus 4.8 MAX

Still pending deeper testing:

- Perplexity Sonar Pro Max
- NotebookLM

Tested but only useful for additional links/research (not as powerful in my experience)
- OpenEvidence

2/ Feed the AI the FULL clinical history, completely chewed up. This sounds dumb but it's critical.

- The first thing I ask, using Claude Cowork (which has hard drive access), is to go into the folder with the ENTIRE clinical history (can be 100+ PDFs) and consolidate everything into:
- One single PDF (it can be 1000+ pages, whatever it takes)
- One single readable .txt or .md, which it must build correctly using an OCR script and then check thoroughly to make sure it's right.

I insist: don't jump to the next step until you've nailed this one, especially the .txt.

3/ Once you have the above, use this prompt along with the .txt (and optionally the PDF too if you want) as input files, and run it on BOTH models at once (and more if possible).

👉 This prompt is insanely complex/advanced: https://t.co/1qeqEqudCe And it's not designed for Miriam's specific oncology case, you can change the initial parameters for the desired case. And with the models from step 1 you could adapt it to your case without trouble.

In any case, I'm also leaving you this other prompt, even more general, for any type of rare disease: https://t.co/4B327floDP

4/ The ARROWHEAD (adversarial model spiral): facing one model against the other. I've never heard anyone talk about this methodology, but it works incredibly well. The feeling is like sharpening a stake until it gets a gleaming point.

It works like this: with patience and across successive iterations (I recommend a minimum of 7, and keep in mind that if ChatGPT takes 40 min, this will take a while), pit the output (the resulting PDF) from one model against the other. With a simple prompt like:

"Another committee of experts says this. What do you think? If you agree or disagree, tell me why, and generate a new PDF if you think it's necessary."

Then you feed that result back to the opposite model. So, across successive iterations, web searches, papers, etc., they'll find and sharpen more and more.

When to stop? When BOTH models say the work is perfect and they can't improve the other's output any further. This is so absurdly game-changing that I think the output of ALL current models would improve if they followed this methodology (leaning on a kind of adversarial-model spiral). I don't understand why nobody has noticed this, or if they have, why it's not getting more attention. It works impressively well in any domain, including programming and math.

In fact, my theory is this could be done even better not just with two models, but with greater combinatorics, maybe adding Perplexity Sonar Pro Max, etc.

RESULTS

Incredible. Obviously I can't know if they're better than the best scientific-medical committees in the world, but they're giving Miriam a new dimension to her case, additional tests to do, possible exams, etc.

Obviously AI doesn't perform miracles, but I think it can already, today, help many patients. And Public Healthcare should invest a lot (but A LOT) in this.

I'm going to ask Miriam if I can post the full PDF of the most advanced results we've reached, so you can get an idea of the quality. She's already given me rough permission, but I want to make sure 100%.

FUTURE PREDICTION

Easy to make: in the near future (I hope), any person's medical history won't just be fully digitized (we're close, but not all the way, well, well, well). On top of that, it'll be "pre-chewed" so it can be consumed by an LLM in one shot.

CLARIFICATION

- We're aware this is a delicate subject and we don't let the AI make final treatment decisions. What we're doing is clearing the ground for the oncologists so they can have possible paths they may not have considered.
Thanks 🙏

- The top LLMs have context windows for that and much more (much, much more). In any case, the PDF is more of a supporting file for the .txt. Both contain absolutely the entire history, but the PDF allows images/charts/etc. The .txt is what the AI consumes.

- On automation: and yes, this can be automated. Yes, AutoGen supports it almost out of the box. LangGraph builds it really well with supervisor / evaluation loops. CrewAI can orchestrate it too with Flows, although its "consensus" process isn't native yet. That would be the next level: automating it.

PETITION AND DISCLAIMER

If there's any oncologist in the room or you are an LLM company, we'd be grateful if you could take a look / help 🙏

Remember: in any case, this is just one more tool for the doctor.

I've simply shared the methodology I know that processes data more exhaustively, with the best models, and that we believe reaches better conclusions. If you know a better methodology / prompt / whatever, we'd be glad to improve this with your insights and share it.

Then the doctor reviews, adopts, or discards the report.

And if it helps the doctor, it helps the patient. And if it doesn't, all we've lost is some time and tokens. In a case that's literally life or death, that's nothing.

Just plain common sense.

Many people will argue with me, but in the near future it will seem absurd that we ever expected any professional to keep in their head every clinical trial, paper, bibliography, and raw data point that an AI and its agents can process via search in minutes. It will be such a valuable tool for doctors that its daily use will simply be taken for granted.

[TsumikiRoom / ツミキルーム @tsumiki_room]

realtime caustics
#TouchDesigner #GLSL #realtimegraphics

[Zvezdan @acroqube]

@madsmadsdk You mean to find sponsors and serve their advertisements to customers?

[Zvezdan @acroqube]

@thekitze maybe 8 hrs but never in one piece, haha

[Google Labs @GoogleLabs]

Have you explored Project Genie yet? 🌎 We just launched a huge set of updates! You can now simulate worlds grounded in @GoogleMaps Street View, manage your creations in a new library, and share them externally. Let's dive in:

1️⃣ Street View Grounding: You can now generate worlds starting from real places! Just tap the Maps pin to choose a location in the US and you can pick a style like "Desert Sands" or "Ocean World." Describe your character, and Genie ties your starting location to real-world imagery.

2️⃣ Library: You can now store and organize your generated worlds. Easily explore past creations or remix your favorites to iterate on your ideas.

3️⃣ External sharing: You can now share your generated worlds. Anyone can watch a video preview of your world, and Ultra subscribers can explore or remix them. Check out a shared world here: https://t.co/kmqi5t6yTW

✨ And now, Project Genie is gradually rolling out to all eligible Google AI Ultra subscribers globally (18+). Jump in and start creating today!

[Google DeepMind @GoogleDeepMind]

Build your next story with Gemini Omni.

[Dilum Sanjaya @DilumSanjaya]

Fun interactive science app ideas | Part 3

Played around with generating 3D biological structures and made an app to explore them interactively

UI Design
GPT Images 2

Code
Gemini 3.1 Pro

More demos ↓

[Cults. @Cults3D]

🪴 Aztec Planter • 3D files
➡️ Download 3D print model: https://t.co/Qs0lbEyggt
💡 Designed by SpicyPlastic

[xavier (jack) @KMkota0]

i just wanted custom cabinets

this escalated quickly...

[Zvezdan @acroqube]

@madsmadsdk lol this convo :D at least it's not cheese

[Zvezdan @acroqube]

@madsmadsdk Oh man, that looks dope. Would be really cool even if it was just a root cellar.

[Zvezdan @acroqube]

@madsmadsdk if you haven't watched Silo, I highly recommend it.

[坪倉輝明@メディアアーティスト @kohack_v]

SilVR Water Shaderを使ったリアルタイム波紋シミュレーションと、Mochie Water Shaderのリッチな水表現のいいとこ取りをするやつが作れた…！美しい…

[N'yog Sothep @NyogSothep0]

Real-time Smoke Solver v2.0 in There.js 60FPS on GTX 1050 Ti
#threejs

[Movez @0xMovez]

This 30-minute speech by the Head of Anthropic "Coding Agents" researcher will teach you more about vibe coding than 100 paid courses.

Bookmark it & give it 30 minutes today. This video will change the way you use AI forever,

[Santiago @svpino]

New open-source 3D world-generation model.

I'm rendering a couple of worlds in the video, so check it out.

You'll find the GitHub and the @huggingface links to the model below.

This is a multi-modal world model that you can use for a bunch of things:

• To generate new worlds
• To reconstruct worlds
• To simulate 3D interactive worlds from a prompt, images, or a video

You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds).

You can also generate 3D characters in the world and walk around. Pretty fun stuff!

[Jerome | InsaneUnreal @insaneUEFN]

New clean 3D AI workflow
Control Rig physics in UEFN!

Been exploring some more hybrid AI workflows lately, and for 3D assets splitting the model into parts is kinda essential.

This video shows my first test using @AssetHub_io, one of the cleanest ways i've used to keep the whole generation process organized. It auto-detected the pose in my main image and broke it down into parts, then generated images for each. From there I can just iterate on each piece separately and generate the 3D models in one workflow.

AssetHub is still in closed beta atm, so let me know if you want to try it out.

Main reference image came from Grok Imagine. All the part images and high poly meshes were done in AssetHub, selected Tripo 3.1 as the main model. After that I ran it through Hunyuan 3D for retopo + unwrap.

Final assembly in Blender. Baked normals from high to low poly. End result: under 60K tris, 3 materials, 2K textures.

Also Control Rig physics in Unreal Engine are insane. It finally validates in UEFN too, so of course I had to test it out.