
Founder @dotproduct3d Prev. Founder @ Enkin AR (acq‘d by Google) SLAM / Sensor Fusion / 3D Capture GPU poor
Joined June 2013
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- Following1.4K
- Followers2K
- Likes84.9K
Rafael Spring retweeted
【全世界がこの瞬間に感動した?】
#dot3d と #drogger 最新状況
dot3dがdroggerのGパッケージに対応
希望者またはPRO版の方は30日限定のテストライセンス番号を交付して貰えるので、まずは公式サイトから申し込みしてください
※PRIME版は1年間$999てすが、月額$139の契約もあるので、まずは必要な時に月額サブスク契約するのもやり方もありですね
@DotProduct3D
#dot3d 更新キター❗️
まだBETA版ですが #drogger を機種選択できません😭
PRIME版に契約しないいけないの?
PRO版で動かないと動作テストも出来ないよ😵
最初から難題が起きて躓いてしまった…🤦
どうすれば試用できるのでしょうか?
@DotProduct3D
Rafael Spring retweeted
Replying to @unusual_whales
Rafael Spring retweeted
Y Combinator just published the list of startups it wants to fund next winter. One of them is AI for City Hall. In some American cities, YC writes, getting a building permit takes longer than building the house. By its own math, if every permit in the country were approved in a tenth of the time, cities would collect $16 billion more a year in property tax. There are 19,000 cities in America, and almost all of them still check drawings against the code by hand.
I like the idea. My first company, while I was still at Columbia in the 1980s, redeveloped buildings in Tribeca and SoHo, so I have stood on the other side of that counter.
But look at what the waiting actually costs. In 2021 the National Association of Home Builders surveyed more than 2,000 builders and found that regulation accounts for $93,870 of the price of an average new home, 23.8% of it. Only about $2,400 of that is the pure cost of delay. The rest is fees, building codes that keep changing, design standards beyond the ordinary, and land the government takes or forces you to leave empty.
So AI can make City Hall faster. It cannot make it cheaper. A model that approves a bad rulebook in one day still leaves the same $94,000 inside the average new house, just sooner.
The startup I would fund does both: it reads every permit, and it also measures what each rule costs the family that will buy the house. Then it publishes the numbers and lets voters decide which rules are worth paying for.
Faster bureaucracy is progress. Less bureaucracy is reform.
Rafael Spring retweeted
Can it really be that easy?
Can it really be that fast?
Actually yes.
The real numbers:
Prep: 0 min
Scanning: 13 min
Optimization: 3.5 min
Software: Dot3D Prime
Hardware: iPhone Pro Max + General Laser GLRM RTK
Constraints: 1 AprilTag for loop closure, MaCORS RTK corrections
Rafael Spring retweeted
Replying to @jbulltard1
"Yeah bro, I had Muse fill out my extension for unemployment. Shit's fire."
Rafael Spring retweeted
Replying to @Rafael_L_Spring
@Rafael_L_Spring COLMAP with no install. Drop images or a video into this unofficial browser port and watch keypoints, matches, camera poses and a sparse point cloud appear. Nothing leaves your computer. We made a 30s intro video for it.
offlinetools.io/colmap-landi…
OK, launch day!!
Over the past few weeks I built a fully browser-resident implementation of COLMAP, using WASM and WebGPU (-- link in reply --).
No uploads, no installs, no setup. Just drop images or video and run the pipeline. Then export and drop results into a 3DGS tool of your choice.
The first naive implementation was about 10x slower than native but with many optimizations it's now faster than the original!
Go give it a try!
Rafael Spring retweeted
What people are missing about the French debt crisis is that it is greatly worsened by the currency union. Yes, the US is $40T in debt, and it will pay like all countries in those circumstances do, print the currency & inflate its way out. Destroy the debt via inflation.
1/
Rafael Spring retweeted
this is what a european datacenter looks like
Replying to @GuillaumeLample
ML4 was trained on 3,800 NVIDIA Grace Blackwell GPUs in our European datacenters -- our cluster in Bruyères-le-Châtel built using our Series B fundraise.
We are investing heavily in infrastructure, with our Series C and D clusters coming online soon. This additional compute will let us train longer, pursue more ambitious post-training, and iterate faster.
We expect large and rapid improvements in the weeks and months ahead.
2/n
Readers added context they thought people might want to know
The posted photo shows the Breuillet - Bruyères-le-Châtel train station in France, not a datacenter. Mistral AI's cluster is in a separate modern modular facility operated by Eclairion at the location.
fr.wikipedia.org/wiki/Gare_de_B…
datacenterdynamics.com/en/news/mistra…
usinenouvelle.com/article/ce-dat…
baxtel.com/data-center/ec…
Rafael Spring retweeted
Arguing that "consciousness arises from a computational substrate, and since AI models are computation, they are likely conscious," is as meaningless as arguing that "living creatures are made of atoms, and since this rock is made of atoms, it is likely alive."
Everything is computation. Sometimes incredibly sophisticated computation, like a chess engine, AlphaGo, google3, or the Linux kernel. That does not make it conscious. A rock being made of atoms does not confer it any of the properties we associate with living things, also made of atoms. Likewise a static input-output program that has *none* of the properties we associate with conscious beings (e.g. information integration, interoception, temporal binding, embodiment, etc.) has no more reason to be presumed conscious than a rock has to be presumed alive.
Humanity has not yet created a conscious computer program, and there are no signs we are close to doing so. When we get close, the case for machine consciousness will be backed by evidence and corroborated by consciousness science (which is a thing), not by evidence-free wishful thinking and cargo culting.
Rafael Spring retweeted
serious question: who is paying for this coverage? what PR firm is placing these stories?
Anthropic CEO Dario Amodei's handpicked AI watchdog has deep ties to woke Effective Altruism movement: 'a complete joke' trib.al/IuA3uHW
Rafael Spring retweeted
I’ve been watching tennis since I was a kid and although it has been exciting to see the sport reach new levels of popularity amongst younger audiences and the media in recent years, a huge downside to this is that the entire sport has become increasingly more poisoned by the worst parts of online culture: constant obsession with victimhood, incessant race-baiting, braindead complaints about “representation.”
Tennis is unique because of the decorum, elegance, and respect at its core. The sport transcends the low-level oppression Olympics that resides on TikTok. We don’t want players pretending they are Rosa Parks just because they are funded by Ben Crump Law and they’re looking for their social justice moment of fame.
We grew up watching Serena and Venus Williams dominate the sport, without having to listen to the condescending speeches about why their win was more historically important than Maria Sharapova’s because of the color of their skin. It was only in recent years that everything was twisted into a constant, trashy race war.
Rafael Spring retweeted
This post looks like the start of a VERY sophisticated and well-funded PR operation to get support for Democrats to regulate AI into oblivion. Let me show you how it works:
1.) This guy, with minimal followers and no previous account activity, goes to the Wall Street Journal which publishes an exclusive with quotes from him on his resignation 18 minutes BEFORE this post goes up. Planning was clearly done in advance.
2.) Within hours, it has tens of thousands of reposts and the account has 100k+ followers. The post is punchy, quotable, it almost seems professionally written. The first three accounts to quote tweet it all do so within 15 minutes of the initial posting. Remember, this account had basically zero engagement beforehand, so an organic reach explanation seems unlikely.
According to Grok those accounts are @_NathanCalvin (General Counsel at Encode AI), @peterwildeford (Head of Policy at the AI Policy Network), and @DKokotajlo (Head of the AI Futures Project), all of which are up-and-coming AI-Doomer policy advocacy nonprofits.
The AI Futures Project website says it is funded “primarily” by the Survival and Flourishing Fund, which says on its own website that it has advised Jaan Tallinn, Skype creator and one of the leading investors in Anthropic, to grant over $2.5 million to the AI Futures Project since 2024.
Encode AI says on its website that it is ALSO funded by the Survival and Flourishing Fund, which in turn says that it told Anthropic investor Jaan Tallinn to grant $516,000 to Encode AI in 2025.
And wouldn’t you know it, the Survival and Flourishing Fund ALSO says it told Jaan Tallinn to grant $2 million to the AI Policy Institute, the 501(c)(3) affiliate of the AI Policy Network, as well.
What are the odds that the first three quote tweets of Coxon’s post would all be major AI-restriction policy advocates funded generously by the same donor, who also happens to be one of the leading investors in, and a board member of, Anthropic, the company Coxon was resigning from? And all within 15 minutes of posting (two within ten)?
3.) Jacob Coxon doesn’t have much of a resume, but we do know that, in 2022, he got a $20,159 scholarship for the “long term future scholarship program” from the Good Ventures Foundation, one of the philanthropic vehicles of Dustin Moskovitz, a notorious AI-doomer who has spent tens if not hundreds of millions on policy advocacy to strictly regulate AI, while also being an Anthropic Investor himself.
It also just so happens that the 14th person to quote Coxon’s post was @MaxNadeau_ (27 minutes after posting) who is the program officer for the Technical AI Safety team at Coefficient Giving, another of Moskovitz’s philanthropic spending vehicles. Max is not a frequent poster, his last posts before quoting Coxon were before Labor Day, but he was remarkably quick off the mark for this one.
4.) Basically every major Democrat politician and candidate has suddenly glommed on to this post, and conveniently, as the people cry out foe answers, Bernie Sanders already has a bill written to “ban super intelligence” and regulate AI into oblivion, and will be releasing later this week. The bill, among many other things, will create “a new cabinet-level federal agency to safeguard the public from the dangers of artificial intelligence” that will be “advised by an Artificial Intelligence Advisory Board comprised of experts on artificial intelligence.” Do you think, perhaps, Anthropic and its many investors who fund AI policy advocacy might have interest in getting to place a pet “expert” on the board of an entity that dictates what AI is and isn’t allowed to do? And isn’t it fortuitous that this whistleblower came forward with his oh-so scary stories so close in proximity to the release of the most radical piece of AI legislation ever introduced?
Rafael Spring retweeted
What is the role of academic computer vision research in the age of increasingly powerful large models? Is GPT-6 Astra a step change? How can a researcher have an impact today in academia?
These are the questions I ask myself as I head off to ECCV 2026, a conference I’ve attended since 1992. One of my papers this year is VIGA, a method that takes an image as input and outputs a 3D Blender scene that represents that image. This is a classical inverse-graphics task and VIGA was the first method to solve it using an agentic approach.
The idea is now several years old and the first version of the paper was rejected. This delayed publication significantly. After it was accepted at ECCV, it was quickly surpassed by people using Claude Code for the same purpose. Today GPT-6 Astra blows away all previous results. But we still head off to ECCV to tell the community about our invention that is now fully out of date.
The way academic work often progresses is that one reads recent papers, notices that they have limitations, comes up with a new idea, explores this, publishes it, etc. Any published paper I read today is based on ideas that are at least a year old. And those ideas were based on the literature of the time, which was also a year old. That means that any paper I see at ECCV is likely two years out of date. In AI today, two years means your work is likely irrelevant.
At CVPR this summer I noticed that many authors have not gotten the message. They continue to work on “old” problems that have a long history. This history is based on assumptions about how the “vision problem” will be “solved”. The truth is that it is being solved in a very different way and many of these problems are no longer relevant. Another group of papers focuses on very niche problems where large models likely fail because of insufficient data or lack of business interest. The impactful papers were largely from industry and had long author lists and massive data+compute behind them. These papers were also out of data, describing systems that had been released months before, but at least they served to provide the community with more complete documentation and analysis of commercial systems.
So what should academics do? First, we need to put aside the tools we’ve used for years and start from scratch. Every project should start by trying really hard to solve the problem with existing tools. I would like to see every paper begin with a detailed experimental analysis of how existing models perform and why they fail (if they do). This gives the kind of insight we need today. Then, assuming current models fail, the solution should provide some fundamental insight that will outlive the next release of such models.
Reviewers today still focus on technical novelty. This pushes people to focus on tweaking architectures rather than clearly moving the field forward. Papers need to be judged based on their novel insight and not their novel technical contribution. This is a real shift in thinking but it focuses us on what matters - progress of the field.
If we want there to be a “field” of computer vision, then it can’t become a marginal backwater, focusing on esoteric problems. If you haven’t tried using Astra (or whatever comes next) to solve your problem, then you have not done your homework. This omission should be seen as negatively as not having a previous work section.
Concretely, I think papers should include a new section analogous to “Related Work” where that related work is current models and how they perform on the task. Reviewers should start asking for this and expecting authors to be able to articulate their insights about the limitations of existing large models.
I'm interested in your thoughts.
Rafael Spring retweeted
probably the biggest misconception today is that everything will be chat
chat is bad for discovery, chat is bad for repeating usecases, bad for multiplayer
people love GUI. people love being passive. people love to scroll and tap-tap-tap vs. type. voice only works when you're by yourself. the list goes on.
build a Mac, not MS-DOS!
Rafael Spring retweeted
Please tell me I am not the only one who tries to preserve the moment of inertia of the egg carton







































