Every engineer has a folder full of chaos: screenshots named IMG_20250314_112233. png , invoices scattered across three directories, a dozen PDFs that should be one report.
Microsoft Research has published a zero-knowledge proof system called Vega that generates a proof of age from a typical mobile driver’s license in 92 milliseconds on a commodity client device. The proof is 108 KB, verification takes 23 ms, and the prover key is 464 KB, which the authors say fits co
Peter Lee and his coauthors wrote The AI Revolution in Medicine: GPT-4 and Beyond in the months before GPT-4’s public release, working from early access to a system the rest of the world had not yet met. ChatGPT had launched in November 2022, and the book landed as a confident, deliberately optimis
Six years of free access, and what the numbers say This is the sixth edition of the annual Free Access Initiative, which 365 Data Science first ran in 2020 during the global pandemic. The 2025 edition, according to the company, was its most successful: more than 220,000 unique users from 216 countr
Most AI agents have the memory of a goldfish. The session ends, the context window clears, and everything the user said about their preferences, their project or their goals evaporates.
The reproducibility gap Reproducing an ML experiment means reconstructing a procedure from information scattered across a paper, its released code, and supporting artifacts. When something is missing, whoever is reproducing has to decide what the authors probably meant.
Ask an AI agent to improve a machine learning method and it may well produce something that looks like progress. Whether the number on the evaluation actually moves is another matter.
Ask five engineers how they build a time series pipeline in Python and you may get five different stacks. One reaches for statsmodels out of habit, another wraps everything in sktime, a third has quietly standardized on Nixtla’s tooling, and a fourth is running a deep learning model that nobody els
The loop at the center of AIIL AIIL’s overview describes a cycle rather than a single research bet. The group studies how people use AI, identifies limitations and opportunities in that usage, and then feeds those findings back into evaluation, simulation and learning.
The experiment at a glance The setup was deliberately simple. One Python script, one batch submission, one target: 10,000 pages from MDN’s documentation site.
AI research and scientific papers translated for working engineers: the method before the result, the baseline flagged when it is weak, and a link to the artifact every time.
The research that actually changes what you build.
The paper trail
