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?
GitHub explore could be way more interesting with a simple filter algorithm.