Tootfinder

I posted a long email to 9fans, the Plan 9 mailing list.
Not sure if they will love or hate me. I guess both, so I might be doing it right lol...
This one may as well go out to other communities.
I may transform it into a blog post for reference.

The Python library "YFinance" decided to force an upgrade. They did this by making the old version report a rate-limit error on every request.
Confusing.
Would have been better if they reported a "old version not supported" error or something instead. So that wasted some time.
That upgrade had dependencies which have dependencies upon a newer version of Python, so needed a whole OS upgrade really.
Which failed. Bricking the Rasp PI it was running on.
Oh well, complete rebuild of the whole machine and software it runs from scratch then.
That took all day yesterday. At the end I notice that the touch-screen doesn't touch. Needs drivers.
The drivers haven't been upgraded in six years. They brick the machine again when trying to install them on Debian Trixie.
Luckily, I kept good notes and could rebuild it all again much faster with no mistakes and knowing what to do and all the required custom software changes already made and saved.
So now I spent a whole day on annoying upgrade work because a single Python library decided to break the old version, and my Rasp Pi has no touch-screen. Which isn't ideal for a machine mostly operating as a fancy light switch for all the LED strips in the house.
This happens all the time in software. Millions of man hours wasted, so much hardware dumped because the drivers get abandoned.

In other news: Microsoft abandons Windows 10 any day now. Good luck to everyone faced with doing that lap on the upgrade treadmill.
I still have more work to do to bring up this RaspPi's software to where it was, but it'll have to wait, other things to do. At least it's back to sending me the nightly finance report and controlling the LED strips. If without a touch screen now.
#software #upgradeTredmil #python #microsoft

Experimental and simulative study on laser irradiation of 3D-printed micro-structures at intensities relevant for inertial confinement fusion
M. Cipriani, F. Consoli, M. Scisci\`o, A. Solovjovas, I. A. Petsi, M. Malinauskas, P. Andreoli, G. Cristofari, E. Di Ferdinando, G. Di Giorgio
https://arxiv.org/abs/2506.04017

Experimental and simulative study on laser irradiation of 3D-printed micro-structures at intensities relevant for inertial confinement fusion
Inertial confinement fusion requires a constant search for the most effective materials for improving the efficiency of the compression of the capsule and of the laser-to-target energy transfer. Foams could provide a solution to these problems, but they require further experimental and theoretical investigation. The new 3D-printing technologies, such as the two-photon polymerization, are opening a new era in the production of foams, allowing for the fine control of the material morphology. Deta…

Tensor network method for real-space topology in quasicrystal Chern mosaics
Tiago V. C. Ant\~ao, Yitao Sun, Adolfo O. Fumega, Jose L. Lado
https://arxiv.org/abs/2506.05230

Tensor network method for real-space topology in quasicrystal Chern mosaics
Computing topological invariants in two-dimensional quasicrystals and super-moire matter is an exceptional open challenge, due to the absence of translational symmetry and the colossal number of sites inherent to these systems. Here, we establish a method to compute local topological invariants of exceptionally large systems using tensor networks, enabling the computation of invariants for Hamiltonians with hundreds of millions of sites, several orders of magnitude above the capabilities of con…

CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection
Fanxiao Li, Jiaying Wu, Canyuan He, Wei Zhou
https://arxiv.org/abs/2505.23449

CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection
Multimodal large language models (MLLMs) have demonstrated impressive capabilities in visual reasoning and text generation. While previous studies have explored the application of MLLM for detecting out-of-context (OOC) misinformation, our empirical analysis reveals two persisting challenges of this paradigm. Evaluating the representative GPT-4o model on direct reasoning and evidence augmented reasoning, results indicate that MLLM struggle to capture the deeper relationships-specifically, cases…