HN user

timtimmy

107 karma
Posts0
Comments27
View on HN
No posts found.

Google keeps changing their privacy and “don’t train on my data/code” options. When gemini-cli launched, there was a clear toggle for “don’t train on my code.” That’s now gone; it just links to a generic privacy page for me. Maybe something with my account changed, I can't figure it out. Deep in the Cloud Gemini console, there’s another setting that might control training, but it’s not clear what products it actually covers.

Trying to pay for Gemini-3 is confusing. Maybe an AI Ultra personal subscription? I already pay for OpenAI and Anthropic’s pro/max plans and would happily pay Google too. But the only obvious option is a $250/month tier, and its documentation indicates Google can train on your code unless you find and enable the correct opt-out. If that opt-out exists in all the products, it’s not obvious where it lives or what products it applies to.

Workspace complicates it further. Google advertises that with business workspace accounts your data isn’t used for training. So, I was going to try Antigravity on our codebase. At this point I know I can't trust Google, so I read the ToS carefully. They train on your prompts and source code, and there doesn't appear to be a way to pay them and opt out right now. Be careful, paying for Google Workspace does not protect you, always read the ToS.

Be careful with AI-studio and your Google Workspace accounts. They train on your prompts unless you switch it to API mode.

The result is a lot of uncertainty. I genuinely have no idea how to pay Google for Gemini without risking my code being used for training. And if I do pay, I can’t tell whether they’ll train on my prompts anyway.

The marketing for their coding products does not clearly state when they do or do not train on your prompts and code.

I had to run deep research to understand the risks with using Gemini 3 for agentic work, and I still don't feel confident that I understand the risks. I might have said some incorrect things above, but I am just so confused. I feel like I have a <75% grasp on the situation.

I don't have a lot of trust. And honestly, this feels confusing and deceptive. One could easily confuse it as deliberate strategy to gather training data through ambiguity and dark patterns, it certainly looks like this could be Google's strategy to win the AI race. I assume this is just how it looks, and that they aren't being evil on purpose.

OpenAI in particular has my trust. They get it. They are carefully building the customer experience, they are product and customer driven from the top.

A v2.0 update for my biology education app. I'm adding the ability to walk around cell models with billions of atoms on the Vision Pro.

I'm designing the content browser right now. I'm trying to achieve something really immersive like Apple's new Spatial Gallery app.

It's becoming quite common in S Korea for there to be a fixed tablet on a stand at each table. You order from the digital menu and then immediately pay with the integrated PoS. If there's no tablet you pay on your way out. There's a button at each table to summon the waiter. No tips.

Japan has ticket vending machines in many restaurants. You prepay and order at the front of the restaurant, it prints a little ticket, and you give that to the waiter or kitchen.

Optimizing our rendering algorithms for Apple Vision Pro. Trying to render a 300-million atom cell model at 90fps stereo. It's trivial on a 4090, it's pretty hard on a ~30W mobile GPU (W correct??). I'm thinking about a bunch of immersive mesoscale biology stuff next.

In my university days I climbed Mt Fuji at night for the sunrise in jeans, running shoes and a little tiny pen light. At the top there were people with small oxygen tanks (understandable, there are legitimate medical concerns for some). I'd do it differently today. :)

I'm ignoring the point of the article, but I'm currently in a country with a strong hiking culture. Everyone is decked out with every piece of hiking gear imaginable for a short trek up a hill (2 hour round trip?). It's a bit of a status thing. Well... maybe there is a connection to the article. Do we sometimes avoid simple tools in startups because of ego/status concerns?

"Current visualization software, such as UCSF ChimeraX6, can only render one or a few protein structures at the atomic level."

Lots of current visualization software is focused on visualizing a single protein structure (for example, ChimeraX). New visualizing and modeling systems are being developed to go up in scale to cellular scenes and even whole cells. For example, systems like le Muzic et al.'s CellView (2015) [1] are capable of rendering atomic resolution whole cell datasets like this in realtime: https://ccsb.scripps.edu/gallery/mycoplasma_model/

[1] https://www.cg.tuwien.ac.at/research/publications/2015/cellV...

I just released a biology education app very much like the preprint for the Vision Pro launch (and soon for iPad/iPhone). I worked with David Goodsell's group to integrate their whole-cell bacteria model and David wrote the content. It looks like this: https://twitter.com/timd_ca/status/1753250624677007492 Our first bit of content is a tour through a 300 million atom bacteria cell for Apple Vision Pro (>60 fps, stereoscopic, atomic resolution).

We developed the tech for iPhone, iPad and AVP mobile GPUs (UE5 doesn't support this on the devices we're targeting). iPad: https://twitter.com/timd_ca/status/1592948101144547328

The linked preprint is beautiful, and I love the pipeline. I wonder if it's possible to export to other tools like Blender? The linked preprint is part of a pretty cool field of research into mesoscale modeling and visualization. For me these are a few of the standout papers, projects and works in the area (and there are many more):

- le Muzic et al. "Multi-Scale Rendering of Large Biomolecular Datasets" 2015 [1]

- - Ivan Viola's group helped pioneer large scale molecular visualization. This reference should be in the preprint, IMO.

- Maritan et al. "3D Whole Cell Model of a Mycoplasma Bacterium" [2]

- - This is out of David Goodsell's lab and the model I'm using.

- Stevens et al. "Molecular dynamics simulation of an entire cell" [3]

- Brady Johnston's Molecular Nodes addon for Blender [4]

- YASARA PetWorld [5]

[1] https://www.cg.tuwien.ac.at/research/publications/2015/cellV...

[2] https://ccsb.scripps.edu/gallery/mycoplasma_model/

[3] https://twitter.com/JanAdStevens/status/1615693906137473030 and https://www.frontiersin.org/articles/10.3389/fchem.2023.1106...

[4] https://bradyajohnston.github.io/MolecularNodes/

[5] http://download.yasara.org/petworld/index.html

The skills are not transferable

In the immediate race towards "AR (and VR) all the things" (Meta), game developers are poised to be in high demand. Maybe we'll see the insane salaries of ML engineers directed at game developers if the metaverse takes off.

Apple engineers reading this: The painfully loud "PING" sounds on the AirPods Max hurt my ears when I switch from transparency to sound isolation mode. It's really unpleasant and I'm concerned this is damaging my hearing. The "ping" volume level should be customizable or at least quieter across the AirPods line.

I share your enthusiasm for C++ interoperability. I'm a computer graphics researcher and I have written my share of C++. I'm using Swift on my current project and there are a wealth of C++ geometry and numerical libraries that I would love to integrate without having to jump through Objective-C or an automatic binding generator. Swift is a very pleasant language to work in compared to C++ (YMMV), so being free to pull in C++ libraries natively might make it attractive to a lot of folks.

I'm excited about a few projects bringing Swift to other platforms. SwiftWasm is surprisingly painless to get working [1]. Tokamak is an early stage SwiftUI-compatible framework for building web apps [2]. It also has the goal of supporting other native platforms and to that end there is early work on a GTK backend. I have no affiliation with these projects, though I would like to contribute to them at some point.

[1] https://swiftwasm.org/

[2] https://github.com/TokamakUI/Tokamak

Is it my imagination or has Apple been quietly improving and expanding the documentation for SwiftUI over the last few months? It's becoming decent, though there are still dark corners.

Apple used to be awesome at documentation. I hope this effort extends to Apple's other frameworks.

Boids 6 years ago

I love the interactivity in your workshop sandbox. Is the bottleneck your Boid.findNeighbours function? (that was the first thing I tried on the GPU, except GPU parallelism let's us get away with 50,000 boids).

Boids 6 years ago

Sorry, I didn't mean to mislead you. This has little to do with Unreal (or Unity) and everything to do with GPU programming.

Boids 6 years ago

Wow. There are some great ideas in that paper.

Boids 6 years ago

You are right, they move insanely fast. The average speed of a medium-sized protein is about 5 m/s. Inside an E. coli, any two proteins will encounter each other at least once a second [0] (this book is incredible).

My simulation is inaccurate in terms of Brownian motion. It is slowed down and smoothed for illustrative purposes. It should be more violent. Nice idea with blurring, I'll think about that. The code is on GitHub. It requires a VR headset, and it is frankly a little clunky and "research code" in terms of the UI. But check it out :D [1]. The next version will support larger simulations on the GPU and a more friendly UI. I have friends at the Scripps institute who work on similar stuff, check them out [2].

[0]: David Goodsell (2009) The Machinery of Life https://www.amazon.ca/Machinery-Life-David-S-Goodsell/dp/038...

[1] https://github.com/timdecode/LifeBrush

[2] https://ccsb.scripps.edu/cellpaint/

Boids 6 years ago

That's a cool idea. Imagine creating a little assembly line out of vesicles, each producing its own proteins, and feeding into each other. I like it.

I find Affinity Designer is way way faster to use than Illustrator. It's not just the rendering, the whole UI is streamlined. However, it's missing many features compared to Illustrator, especially some of the stroke patterns.

Boids 6 years ago

Boids are my favourite hello world for 3D simulation environments. For the last few weeks, I've been using boids to update my GPU programming skills and knowledge of related algorithms in Unity and Unreal.

Yesterday I got 0.5 million boids simulating in Unreal Engine at 45 fps on a GTX 1080 [0]. The bottle neck is rendering so many instanced cone meshes, the simulation without rendering can do 1 million boids at 120 fps. I'm borrowing a common data structure used in GPU fluid simulations, the dynamic hashed grid, to calculate the neighbourhood of each boid (cool tutorial here with full source code [1]). We need the neighbourhoods to calculate their urges. My code is a mess at the moment, but if there is interest I can post it to GitHub (Unity and Unreal).

Boids are an example of agent-based modelling and swarm systems. I used agent-based modelling in my PhD thesis to construct and simulate realtime, interactive 3D biomolecular models of mitochondria and neuromuscular synapses [2,3]. Now that I'm free (graduated), I'm building an iPad app to teach and communicate structural and molecular biology with immersive, and realtime simulations. Our vision is to make molecular and structural biology immersive and fun. It's not a textbook, it's a little more like Neil Stephenson's young lady's illustrated primer! :D

[0] https://twitter.com/timd_ca/status/1243941167005192192?s=20

[1] https://wickedengine.net/2018/05/21/scalabe-gpu-fluid-simula...

[2] https://prism.ucalgary.ca/handle/1880/111493

[3] https://youtu.be/6CsxADBpal0