Cam Casperson

Interested in all things!

[Cam Casperson @camcasperson]

@orenmeetsworld @emergentlabs Founder

[Cam Casperson @camcasperson]

@jamesonhaslam I switched to @usemonologue (from Wispr) and I’ve really enjoyed it so far!

[tetsuo @tetsuoai]

History's first trillionaire is a guy who catches rockets out of the sky with chopsticks and beams internet to every dead zone on the planet.

Same guy ships cars that drive themselves, humanoid robots for the factory floor, brain chips that let paralyzed people move a cursor with pure thought, and an AI running on a supercomputer his team stood up in months instead of years.

And the people crashing out about his net worth are doing it on the app he owns. The same app governments spent years trying to censor.

You cannot legislate a rocket into orbit.

[Cam Casperson @camcasperson]

@Seanfrank @Seanfrank dumb question but where should I start to learn Ecom? Looks so daunting from outside.

[Cam Casperson @camcasperson]

@jamesonhaslam This has overlap with virtual inspections and probably better as a lead magnet, but a “Deck Health Score Report”. 5-10 pics uploaded, polished PDF output with with rating, estimated lifetime, top prio fixes, other deck things idk.I bet you could get reviews off it.

[Founders Fund @foundersfund]

MAFIA EP 001

[Cam Casperson @camcasperson]

Great read, not a fan of more short form but bet this will only get bigger

[Ivelin @waliet]

https://t.co/zLSUuz6xKy

[Cam Casperson @camcasperson]

Would love this direction.

[Alex Cheema @alexocheema]

A year ago at GTC, Jensen brought out a DGX Spark in one hand and a MacBook in the other.

Yesterday, at GTC Taipei, Jensen brought out NVIDIA's new RTX Spark laptop in both hands.

This is the start of a new era of personal computing - the personal AI era.

In the new era, there are two competing platforms:
- @apple with macOS / MLX
- @nvidia with Windows / CUDA

Everyone will have an always-on personal agent that runs locally, constantly looking out for you, working for you proactively, monitoring the internet and talking to other agents. This will be a personal AI agent you own, that's private, that's aligned with you (not OpenAI or Anthropic). @karpathy calls it personal computing v2.

Let's set the scene for the new era of personal computing by diving into the one thing that will matter the most - the hardware.

The best hardware for local AI isn't what's running in a data center. It's a radically different problem. Here's a breakdown of the 3 most important things:

1. Memory.
LLMs are big. To run a model locally, you need to fit the entire model into memory. Apple (with Apple Silicon) and NVIDIA (with DGX Spark + RTX Spark) have both moved towards unified memory, which puts all the memory on one chip - leveraging cheaper LPDDR5X memory - useful for making more memory accessible to the GPU. The alternative competing architecture is a disaggregated CPU/GPU architecture - which is what the DGX Station uses. It has a large pool of slow LPDDR5X CPU memory (496GB @ 396GB/s), and a small pool of high-speed HBM3e GPU memory (252GB @ 7.1TB/s). It has a high bandwidth link (900GB/s) between the CPU memory and GPU memory, enabling fast disaggregated inference e.g. Attention on GPU, FFN on CPU. This enables running really large models like Kimi K2.6 (1T parameters) by offloading experts from CPU memory to GPU memory as they are needed. You could imagine something like this in a smaller form factor.
Hardware today:
- Apple M5 Max MacBook Pro: 128GB unified memory.
- NVIDIA DGX Spark / RTX Spark: 128GB unified memory.

2. Memory bandwidth.
In a data center, multiple user's requests can be batched together, which amortizes the cost of moving model weights into memory across many requests, pushing up arithmetic intensity to compute bound territory - meaning FLOPS matters a lot. Locally, everything runs at low batch size, which is low arithmetic intensity, i.e. memory bound - so FLOPS don't matter. What matters memory bandwidth. High memory bandwidth -> fast TPS. Low memory bandwidth -> slow TPS.
Hardware today:
- Apple M5 Max MacBook Pro: 617GB/s memory bandwidth.
- NVIDIA DGX Spark: 273GB/s memory bandwidth.
- NVIDIA RTX Spark: TBC.

3. Power.
In a data center, we talk about MegaWatts. Locally, we talk about Watts. Laptops have limited battery life. The best laptop batteries have a capacity of ~100Wh. LLM inference on a MacBook Pro consumes ~140W, meaning battery life with a persistent personal agent is less than an hour. This is unusable. The game will become how long can you run a useful agent on a laptop battery. Apple and NVIDIA will compete on how long an agent can run on battery - this will become the new battery life metric. This could be where an NPU or NPU/GPU hybrid really shines. Apple ANE has about 10x better power efficiency than the GPU on Apple Silicon (but has ~4-5x less memory bandwidth, with about the same FLOPS as the GPU). There will be an entire design space of how to build energy efficient agents - this will involve co-optimizing the harness, models, inference engines together.
Hardware today:
- Apple M5 Max MacBook Pro: Consumes 140W, battery capacity ~100Wh
- NVIDIA DGX Spark: Rated for 240W, consumes 140W. No battery (direct PSU).
- NVIDIA RTX Spark: TBC.

The hardware battle will be fierce, and I expect a move towards co-design, i.e. hardware designed *with* personal agent workloads. On top of this, models are improving, we're getting more intelligence per bit/watt, and open-source harnesses like @NousResearch Hermes / OpenClaw are improving rapidly. Within the next 2 years, we'll inevitably have unmetered, private Opus-4.8 / GPT-5.5 level intelligence running locally on a future version of a MacBook or RTX Spark. I like this future a lot better than the one where OpenAI / Anthropic control the intelligence layer of the internet and can rent-seek on intelligence.

Beyond this, NVIDIA is ahead on general AI ecosystem, i.e. the CUDA moat. Apple is ahead on local AI ecosystem, i.e. models quantized/rightsized for MacBooks, native macOS apps, and ease of setup. We'll see how this might change as the new RTX Spark also brings full native CUDA to Windows-on-Arm laptops for the first time, potentially closing the gap.

There are many other factors I haven't mentioned here, but I believe I've covered the timeless, most important things for the new era of personal computing.

[Cam Casperson @camcasperson]

@yanndine OUTREACH

[Cam Casperson @camcasperson]

@dalerwills @jakemmccleary The sky is the limit! You’re struggling to customize on a CC created website?

[Cam Casperson @camcasperson]

Hmm.. with a good wrap I could see it but I also understand your worry. In UT county my best guess would be split pretty 50/50 on reactions. What’s your read? I see a ton of teslas on the road though.

I think you can pretty much do whatever you want. For me so far, the “Focus on the road” alert has been random and hard to predict. Also I still takeover a decent amount in stop-and-go residential, it’s painfully cautious/slow at times.

[Cam Casperson @camcasperson]

@ToddLlewellyn @ShaunMcKnight Hey Todd, future customer here. I did this exact deal, got it 2 weeks ago. Full self driving continues to impress. Highly recommend!

[Cam Casperson @camcasperson]

@CTW_SMB Client facing role? No reason to think they won’t do the same thing with client comms. Also lowest form of AI use haha, a direct copy paste

[tobi lutke @tobi]

@badlogicgames PI is now over 50% of agent usage at Shopify internally.

[Zain Shah @zan2434]

Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.

@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)

[Cam Casperson @camcasperson]

@StumpGuyTy Which location has the correct layout? It’s Lindon… right??

[Cam Casperson @camcasperson]

@Molson_Hart Same, driving me nuts!

[Cam Casperson @camcasperson]

@MatznerJon Just finished my first read through thanks to LL

[SemiAnalysis @SemiAnalysis_]

to be clear, NVIDIA is NOT a car