On their website, when I enter a digit, the cursor advances to the next input box automatically. Deleting a digit with backspace also jumps back to the previous box. Seems to work perfectly for me. Maybe fixed since yesterday?
I used to love browsing GitHub Explore Trending but in the last two years it is almost exclusively AI apps that are slight improvement on the previous trending page of AI apps.
GitHub explore could be way more interesting with a simple filter algorithm.
Yes, I searched for the same. No evidence this has anything to do with the European Union. More like a vibe-coded landing page with user signup form.
Edit: I am certain this is one or two people vibe coding then will pitch to VCs when the waitlist has 1000 people.
Listing major company logos in their banner:
“The organizations listed here use similar technology (Nextcloud) as part of their operations. Their inclusion is for illustrative purposes only.”
If astronomers announced that a large asteroid might strike Earth in twenty years, and that we currently had no way to deflect it, nobody would respond by saying, “Come back when you already have the rocket.” We would immediately build better telescopes to track it precisely, refine its trajectory models, and begin developing propulsion systems capable of interception. You do not wait for the cure before improving the measurement. You improve the measurement so that a cure becomes possible, targeted, and effective.
Medicine is no different. Refusing to improve early, probabilistic diagnosis because today’s treatments are modest confuses sequence with outcome. Breakthroughs do not emerge from vague labels and mixed populations. They emerge from precise, quantitative stratification that allows real effects to be seen. The danger is not that we measure too early. It is that we continue making irreversible clinical and research decisions using imprecise, binary classifications while biological insight and therapeutic tools are advancing rapidly. Building the probabilistic layer now is not premature. It is how we make future intervention feasible.
This post describes a third approach, grounded in computer science. Before any interpretation, systems should record whether verifiable evidence is actually available.
The standard adds a small but strict step. Each rule first reports whether it could be checked at all: yes, no, or not evaluable. Then the evidence is used in reverse, not to confirm the result, but to try to rule it out. If removing or negating that evidence would change the outcome, it counts as real evidence. If not, it does not.
Crucially, this forces a simple question: could the same result have appeared even if the evidence were absent or different? Only when the answer is no does the result actually count as evidence.
The idea comes from genomics, where hospitals, companies, and research groups need to share results without exposing proprietary methods, but it applies anywhere systems reason over incomplete or black-box data.
I have worked 100% in 3 comparable systems over the past 10 years. Can you access with ssh?
I find it super fluid to work on the HPC directly to develop methods for huge datasets by using vim to code and tmux for sessions. I focus on printing detailed log files constantly with lots of debugs and an automated monitoring script to print those logs in realtime; a mixture of .out .err and log.txt.
This topic is fascinating to me. The Toy Story film workflow is a perfect illustration of intentional compensation: artists pushed greens in the digital master because 35 mm film would darken and desaturate them. The aim was never neon greens on screen, it was colour calibration for a later step. Only later, when digital masters were reused without the film stage, did those compensating choices start to look like creative ones.
I run into this same failure mode often. We introduce purposeful scaffolding in the workflow that isn’t meant to stand alone, but exists solely to ensure the final output behaves as intended. Months later, someone is pitching how we should “lean into the bold saturated greens,” not realising the topic only exists because we specifically wanted neutral greens in the final output. The scaffold becomes the building.
In our work this kind of nuance isn’t optional, it is the project. If we lose track of which decisions are compensations and which are targets, outcomes drift badly and quietly, and everything built after is optimised for the wrong goal.
I’d genuinely value advice on preventing this. Is there a good name or framework for this pattern? Something concise that distinguishes a process artefact from product intent, and helps teams course-correct early without sounding like a semantics debate?
Yes, I spend a majority of my professional life on similar systems writing code in vim and running massive jobs via slurm. Required for processing TBs of data on secured environments with seamless command line access. I hate web-based connections or vscode type system. Although open to any improvements, this works best to me. It’s like a world inside one’s head with a text-based interface.
Graphical data exploration and stats with R, python, etc is a beautiful challenge at that scale.
I’ve just finished reading Walter Isaacson’s biography of Steve Jobs. His vision was extraordinary, recognising that even the design of the stores was integral to the product itself. Every layer of engineering was deeply intertwined with aesthetic design. I’ve always shared that belief, but I’m now fully committed to pursuing it without compromise in my own products. It’s proving even more challenging than I’d imagined to make highly technical things feel simple and intuitive for users.
I was recently thinking the exact same thing as the author here; as a teen I got my ipod and instantly respected the graceful design and felt shocked how shoddy my previous cheap mp3 player was in comparison.
I am also convinced that he was fully responsible for keeping Apple on this path and that it is almost impossible to stop others from diluting the craftsmanship towards mediocrity as the group size grows. Big CEOs get labelled as greedy exploiters in a single brushstroke by people who don’t seem to care to read up.
Non-AI experts gives their opinion about AI, noting that the data is messy. The goal of the method was to train and work on messy data. The quote is basically pointless.
Ironically, giving the original scientific article to AI for a summary and critique (chatGPT) would have provided more detailed info.
It reminded me of something from childhood. It’s no comment on this story - just a personal anecdote.
We were on a family road trip, and I was wearing a new pair of cheap sunglasses, feeling way too cool for a kid. As we turned a corner, the setting sun blinded my father. But through my tinted lenses, I saw the wall coming. He didn’t. We crashed hard. My sunglasses flew off, and in that moment, all I cared about was catching them. For a few seconds, I thought that was the only emergency.
That moment left a mark. Now, whenever I start to feel too cool or overconfident, I get a quick flash, like a reflex, to check myself. How stupid will this look if things suddenly go wrong, especially if I could have seen it coming?
It’s made me quietly grateful for all the small, tedious safety rules. Not because they prevent every disaster - but because they sometimes do.
Doesn’t that make perfect sense? First test in animals. If carcinogenic in animals then don’t move to humans. A lack of studies in human is hardly a basis for ruling it safe.
That was a sensible simplified version of the logic during my training for regulation in drugs and medical devices, at least.
To note, the placebo group is typically getting the best current existing treatment. Rather than nothing. Although in some cases the best treatment is still nothing, which sucks.
I would disagree with this. Regulators that I have worked with in US and EU knew what it meant. Maybe not the most amazing technical people I’ve ever worked with, but they were as competent as any random sample of pharma/tech types. Their jobs is to verify that you have completed all the valid documentation to demonstrate that your drug does what you claim it does. Their opinion on personal emotions are irrelevant. They are supposed to be guideline checkers.