Hey, I'm Wieland (/viːland/), I'm a computer vision researcher. You can check out my current projects on GitHub: https://github.com/w-m
---
X-Maps: Using a tiny laser projector and an event camera to estimate depth at 60 Hz on a laptop CPU. The algorithm is using only the time stamps of when the laser passes over the scene, not its color or intensity, so you can project any content you like. Fun for AR demos!
https://fraunhoferhhi.github.io/X-maps/
---
Self-Organizing Gaussian Grids: 3D data is awkward to compress. But there's plenty of solutions for compressing 2D data (images!). So let's organize our 3D data into a 2D grid, where grid neighbors are also close in 3D. That's a hard problem, but we can leverage novel assignment algorithms with GPU power for parallelizing that, to get the sorting done in a few seconds.
Ok, fair points, including the sister comment, it's likely not a drop in O2 levels.
But then why can we see problems with concentration in studies of people in poorly ventilated rooms, but not replicate that when just adding CO2 to normal air? What is the CO2 that we can measure in meeting rooms actually a proxy for?
I don't think you can cleanly compare this: In the study, they added CO2 to the room, while keeping O2 at normoxic levels throughout the experiment. In your meeting room, O2 levels will be dropping in lock-step with the CO2-levels rising. It may be the lack of oxygen that leads to drowsiness, not the additional CO2. But it's the CO2 levels that you can measure as a good proxy of overall air quality.
John Oliver had a segment on prediction markets this week. It covers insider training and opportunities for blatant manipulation like this, well worth checking out. The example in the Last Week Tonight segment was betting on dildos being thrown on court during a WNBA match. And then travelling to the match to throw the dildo.
While the trash can generation was somewhat present and around, I don't think I ever saw a cheese grater in the flesh. Did it have any users? Were there any actual useful expansion cards? Did anybody continue buying this at all, after it didn't get the M3 Ultra bump, that the Mac Studio got last year?
The original title of the document is “The future of software engineering”, which is so generic that I chose to ignore the HN rule to not editorialise, in this case, and picked one of the core insights for the title.
It takes some time to study it, but I found it time well spent: its a really good summary of the questions the community currently goes through, around the effects of LLM use in programming. Which are hotly discussed in HN every day. I think it’s helpful to gave some structured input on that.
(I’m not affiliated with the authors in any way, just found the document interesting).
I don’t think learning and understanding is hard-coupled to performing all low level steps yourself. The LLM can be a developer, sure. But it can also take on the role of rubber duck, architect, teacher or pupil.
Have a large LLM-written change set that works but that you’re not sure you fully understand? Make the coding agent quiz you on the design and implementation decisions. This can be a lot more engaging than trying to do a normal code review. And you might even learn something from it. Probably not the same amount as if you did this yourself fully. But that’s just a question of how much effort you want to invest in the understanding?
An iterative prompt with GPT-5.2 on Copilot CLI spits out a dense two-page proof for problem 10 after less than 60 minutes of working. A review of the generated proof with Claude 4.6 on Copilot attests it mathematical correctness, identifying only minor issues, mostly in the presentation.
But as a non-mathematician I'm not following any of it. How many people are there who are willing to check the generative results? And how much effort is it for a human to check these? How quickly can you even identify math-slop?
A clearly defined/testable long-horizon task: demonstrating the capability of planning and executing projects that overrun current llm's context windows by several orders of magnitude.
Single-issue coding benchmarks are getting saturated, and I'm wondering when we'll get to a point where coding agents will be able to tackle some long-running projects. Greenfield projects are hard to benchmark. So creating code or porting code from one language to another for an established project with a good test suite should make for an interesting benchmark, no?
At the current rate of progress I'm wondering how long it will take for llm agents to be able to rewrite/translate complete projects into another language. SQLite may not be the best candidate, due to the hidden test suite. But CPython or Clang or binutils or...
The RIIR-benchmark: rewrite CPython in Rust, pass the complete test suite, no performance regressions, $100 budget. How far away are we there, a couple months? A few years? Or is it a completely ill-posed problem, due to the test suite being tied to the implementation language?
Great technical demo, but the usability feels unpolished. So here's a little bit of feedback of trying this out on a piano: Just because my piano has 88 keys doesn't mean they are all useful for ear training. The very low and very high notes shouldn't be used, at least not by default. Also they don't even show up properly in the sheet.
As the melodies get longer and longer with each win, this devolves quickly into a memory game. I'd like to keep playing ear training, but I struggle with remembering what sequence of notes came at steps 8+.
This is somewhat aggravated by completely resetting the current level and replaying the whole melody after a single mistake. If I keep making a mistake in note 10, I get all the notes over and over again, which is a bit maddening.
The password and pwbuf arrays are declared one right after the other. Will they appear consecutive in memory, i.e. will you overwrite pwbuf when writing past password?
If so, could you type the same password that’s exactly 100 bytes twice and then hit enter to gain root? With only clobbering one additional byte, of ttybuf?
Edit: no, silly, password is overwritten with its hash before the comparison.
“With Series 3, we are laser focused on improving power efficiency, adding more CPU performance, a bigger GPU in a class of its own, more AI compute and app compatibility you can count on with x86.” – Jim Johnson, Senior Vice President and General Manager, Client Computing Group, Intel
A laser focus on five things is either business nonsense or optics nonsense. Who was this written for?
Just use the non-codex models for investigation and planning, they listen to "do not edit any files yet, just reply here in chat". And they're better at getting the bigger picture. Then you can use the -codex variant for execution of a carefully drafted plan.
This is a good resource. But for the computer vision and machine learning practitioner most of the fun can start where this article ends.
nvcc from the CUDA toolkit has a compatibility range with the underlying host compilers like gcc. If you install a newer CUDA toolkit on an older machine, likely you'll need to upgrade your compiler toolchain as well, and fix the paths.
While orchestration in many (research) projects happens from Python, some depend on building CUDA extensions. An innocently looking Python project may not ship the compiled kernels and may require a CUDA toolkit to work correctly. Some package management solutions provide the ability to install CUDA toolkits (conda/mamba, pixi), the pure-Python ones do not (pip, uv). This leaves you to match the correct CUDA toolkit to your Python environment for a project. conda specifically provides different channels (default/nvidia/pytorch/conda-forge), from conda 4.6 defaulting to a strict channel priority, meaning "if a name exists in a higher-priority channel, lower ones aren't considered". The default strict priority can make your requirements unsatisfiable, even though there would be a version of each required package in the collection of channels. uv is neat and fast and awesome, but leaves you alone in dealing with the CUDA toolkit.
Also, code that compiles with older CUDA toolkit versions may not compile with newer CUDA toolkit versions. Newer hardware may require a CUDA toolkit version that is newer than what the project maintainer intended. PyTorch ships with a specific CUDA runtime version. If you have additional code in your project that also is using CUDA extensions, you need to match the CUDA runtime version of your installed PyTorch for it to work. Trying to bring up a project from a couple of years ago to run on latest hardware may thus blow up on you on multiple fronts.
---
X-Maps: Using a tiny laser projector and an event camera to estimate depth at 60 Hz on a laptop CPU. The algorithm is using only the time stamps of when the laser passes over the scene, not its color or intensity, so you can project any content you like. Fun for AR demos!
https://fraunhoferhhi.github.io/X-maps/
---
Self-Organizing Gaussian Grids: 3D data is awkward to compress. But there's plenty of solutions for compressing 2D data (images!). So let's organize our 3D data into a 2D grid, where grid neighbors are also close in 3D. That's a hard problem, but we can leverage novel assignment algorithms with GPU power for parallelizing that, to get the sorting done in a few seconds.
https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/
---
https://www.3dgs.zip/ resources on 3D Gaussian Splatting from our research group.
https://survey.3dgs.zip/ - a comparison of different compression methods for 3D Gaussian Splatting scenes.