One of the issues being explored is that although US radar is aged, surface vehicles can be equipped with ASDE-X transponders to be more visible to ATC systems. https://www.faa.gov/air_traffic/technology/asde-x
The vehicle that crashed into the plane did not have one and thus no automated alert was triggered.
Yeah that's odd. It seems like you'd want an n-1 dimensional grid on the surface of the unit sphere rather than an n dimensional grid within which the sphere resides.
Looking at the paper (https://arxiv.org/abs/2504.19874) they cite earlier work that does exactly that. They object that grid projection and binary search perform exceptionally poorly on the GPU.
I don't think they're using a regular grid as depicted on the linked page. Equation 4 from the paper is how they compute centroids for the MSE optimal quantizer.
Why specify MSE optimal you ask? Yeah so it turns out there's actually two quantization steps, a detail also omitted from the linked page. They apply QJL quantization to the residual of the grid quantized data.
My description is almost certainly missing key details; I'm not great at math and this is sufficiently dense to be a slog.
Yes. Great catch. I simplified the grid just for visualization purpose.
I've updated the visualization. The grid is actually not uniformly spaced. Each coordinate is quantized independently using optimal centroids for the known coordinate distribution. In 2D, unit-circle coordinates follow the arcsine distribution (concentrating near ±1), so the centroids cluster at the edges, not the center.
Is there an error in the visualization? It shows that every vector is rotated the same amount. My understanding was that they are randomized with different values, which results in a predictable distribution, which is easier to quantize.
That's actually correct and intentional. TurboQuant applies the same rotation matrix to every vector. The key insight is that any unit vector, when multiplied by a random orthogonal matrix, produces coordinates with a known distribution (Beta/arcsine in 2D, near-Gaussian in high-d). The randomness is in the matrix itself (generated once from a seed), not per-vector. Since the distribution is the same regardless of the input vector, a single precomputed quantization grid works for everything. I've updated the description to make this clearer.
Thanks. However, from this visualization it's not clear how the random rotation is beneficial. I guess it makes more sense on higher dimensional vectors.
I believe they are all rotated by the same random matrix, the purpose being (IIUC) to distribute the signal evenly across all dimensions. So effectively it drowns any structure that might be present in noise. That's essential for data efficiency in addition to avoiding bias related issues during the initial quantization step. However there are still some other issues due to bias that are addressed by a second quantization step involving the residual.
That said, I don't believe the visualization is correct. The grid for one doesn't seem to match what's described in the paper.
Also it's entirely possible I've misunderstood or neglected to notice key details.
“””
For the full technical explanation with equations, proofs, and PyTorch pseudocode, see the companion post: TurboQuant: Near-Optimal Vector Quantization Without Looking at Your Data.“
Anthropic has zero problems with API billing, there's no chance they told him to rip that out.
Reading through his X comments and GitHub comments he is behaving immaturely. I don't trust what he's saying here. Ripping out Claude API support was just throwing a tantrum. Weird given his age - he's old enough to be more mature.
> Mr Cannon-Brookes told investors he “couldn’t be more bullish” about the opportunities ahead, despite relentlessly selling his own shares in the company daily. The Nightly reports he kept selling 7665 shares on a daily basis even in the month prior to the results at prices ranging from $US161.11 (AU$227) a share on January 8 to $US105.14 on February 4.
> While ordinary Aussies are asked to make big changes, the 46-year-old decided to treat himself to a ritzy new private jet late last year, admitting to a “deep internal conflict” over the carbon-heavy method of travel.
> The Atlassian co-founder and CEO bought a Bombardier 7500 and will use it to travel across his vast business operations, which include a minority stake in the Utah Jazz NBA team and a sponsorship deal with Formula 1.
Engineering including CTO. The makeup of Australian staff cannot be immediately decided due to local workplace rights laws requiring genuine consultation.
> The union representing Atlassian workers, Professionals Australia, said impacted employees were told on Thursday, with a consultation process to last until 19 March and final termination expected on 2 April.
> More than 900 affected positions were involved in software research and development, a spokesperson said. Most of Atlassian’s employees work in software engineering and design, accounting for over 50% of its 13,813 full-time workforce in June 2025.
Seems to me like this detail is particularly important though:
> [Atlassian] is not profitable and has recorded millions in losses every year since 2017, including a net loss of US$42m in the last three months of 2025, up from US$38m the prior year.
The vehicle that crashed into the plane did not have one and thus no automated alert was triggered.
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