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infocollector

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This might be reflected in upcoming metrics reported on Friday if the IPO goes through. It seems aligned with improving subscriber growth metrics, smoothing out upfront hardware costs into predictable monthly revenue, and generally increasing adoption by lowering the barrier to entry. Curious if anyone closely following the IPO story has more context on this.

AirPods Max 2 4 months ago

I’ve been using AirPods Max since they first launched, and over the years I’ve tried several times to switch to Bose.

However, the Bose headphones just haven’t worked as smoothly for me from a software and integration standpoint. I tend to run into more glitches and small issues compared to the AirPods Max. I’m not sure whether that’s primarily a Bose issue or something related to Apple’s ecosystem, but my guess is that other high-end headphones probably face similar integration challenges when paired with an iPhone.

On the bright side, Max is very reliable.

GLM-4.7-Flash 6 months ago

Maybe someone here has tackled this before. I’m trying to connect Antigravity or Cursor with GLM/Qwen coding models, but haven’t had any luck so far. I can easily run Open-WebUI + LLaMA on my 5090 Ubuntu box without issues. However, when I try to point Antigravity or Cursor to those models, they don’t seem to recognize or access them. Has anyone successfully set this up?

It’s misleading to cite two government-funded supercomputers as evidence that NVIDIA lacks monopoly power in HPC and AI:

- Government-funded outliers don’t disprove monopoly behavior. The two AMD-powered systems on the TOP500 list—both U.S. government funded—are exceptions driven by procurement constraints, not market dynamics. NVIDIA’s pricing is often prohibitive, and its dominance gives it the power to walk away from bids that don’t meet its margins. That’s not competition—it’s monopoly leverage.

- Market power isn't disproven by isolated wins. Monopoly status isn’t defined by having every win, but by the lack of viable alternatives in most of the market. In commercial AI, research, and enterprise HPC workloads, NVIDIA owns an overwhelming share—often >90%. That kind of dominance is monopoly-level control.

- AMD’s affordability is a symptom, not a sign of strength. AMD's lower pricing reflects its underdog status in a market it struggles to compete in—largely because NVIDIA has cornered not just the hardware but the entire CUDA software stack, developer ecosystem, and AI model compatibility. You don't need 100% market share to be a monopoly—you need control. NVIDIA has it.

In short: pointing to a couple of symbolic exceptions doesn’t change the fact that NVIDIA’s grip on the GPU compute stack—from software to hardware to developer mindshare—is monopolistic in practice.

The problem I see with investing in such technologies:

  1. Satellites are too far to fight anything on ground (Power per unit area (i.e., intensity) decreases as the square of the distance)
  2. If the Satellites are relaying to things on the ground, they are also relaying their location (easy adversarial targets)
  3. In a war (they mention Ukraine in the article), first thing that is toast is these satellites.
I don't think this is the right replacement for GPS. Perhaps someone here can correct me if I am wrong?

I believe there's a balance to be struck between writing your own code and relying on dependencies. If Armin (the author of this post) is the one writing the code, I would highly recommend using that dependency!

The Nvidia Way 2 years ago

A different perspective from the outside: Nvidia's journey features three pivotal milestones that shaped its success:

- First, the 2006 acquisition of ATI by AMD solidified Nvidia's position as a dominant player in the industry.

- Second, Geoffrey Hinton's groundbreaking work on deep learning over 25 years laid the foundation for AI advancements that aligned perfectly with Nvidia's GPU capabilities.

- Third, Nvidia's recognition of its leadership in AI has propelled it to the forefront of the technology revolution.

Thanks for writing this! This nicely breaks it down into boxes that OpenAPI deals with.

I still think OpenAPI usage is a bit confusing in general. For example, I am still waiting for a better explanation of this diagram with relation to a choice of backend (Python WSGI) + frontend (JS) combinations. Perhaps someone here has a pointer for me to read?