标签：#ci

"Dogfighting in space doesn’t quite have the drama of an aerial dogfight."

The latest discovery from NASA's Curiosity Mars rover is a field of honeycomb-shaped polygons covering a Martian valley called Valle Grande. As Gizmodo reports, Curiosity has snapped pictures of the unusual terrain texture before, called polygonal fractures, each about 1.5 to 3 inches wide, but NASA says it's previously only found them in small patches, […]

The company is also removing some of its own AI-writing features.

Three approaches to the issue of quantum results that can't be verified classically.

What Is Retrieval Augmented Generation (RAG), and Why Does It Make AI So Much Less Confidently Wrong? You know that game show contestant who buzzes in before the host finishes reading the question, shouts "MOUNT EVEREST!" with absolute certainty, and then looks genuinely confused when the correct answer turns out to be "the Treaty of Westphalia"? That's been AI for most of its existence. Supremely confident, occasionally correct, and deeply committed to whatever pops into its head first. Now imagine that same contestant gets a new rule: before answering, they can phone a friend who has the exact relevant textbook already open to the right page. The friend reads them the actual answer, word for word, and then the contestant puts it in their own words for the judges. Suddenly, our buzzer-happy friend is getting questions right. That phone call is Retrieval Augmented Generation, and it's the reason AI chatbots have gotten weirdly more useful in the past year. The Old Way: Confidently Wrong at 200 Miles Per Hour Traditional large language models (big AI systems trained on tons of text) get trained on enormous dumps of text scraped from the internet, books, and whatever else researchers can feed them. Then the training ends. The model gets sealed off, frozen in time with whatever it learned. When you ask a question, these models generate answers by predicting the most plausible-sounding next words based on patterns they memorized during training. It's essentially very sophisticated autocomplete. The AI has no fact-checking mechanism. It doesn't "know" anything in the way you know your own phone number. It just knows what words tend to follow other words. This leads to what researchers politely call hallucinations, which is a fancy term for "making stuff up with tremendous confidence." The AI generates text that sounds authoritative and well-structured because it's learned the pattern of how authoritative text sounds. But the actual facts? Those might be completely invent

Meta says AI is making it dramatically easier to build and launch new consumer apps, with CEO Mark Zuckerberg telling investors the company has more new consumer products on the way following a recent wave of releases for Facebook Groups, Marketplace sellers, Instagram, and gaming.

Avi Schiffmann has a new version of his controversial AI companion. It’s more expensive, and you can’t change its personality.

The latest version of Google DeepMind's AI model includes a significant jump into “physical AGI.” But plopping AI into the real world comes with risks.

Murat Demirbas discusses the shift toward disaggregated cloud database architectures driven by cloud economics. He explains how decoupling compute from storage enables elastic scaling, cost efficiency, and fault isolation. He shares how classical Paxos roles foreshadowed disaggregation, while analyzing network tradeoffs, shared-memory evolution, and self-assembling database designs. By Murat Demirbas

In this episode I talk about developing web application based on well known cro framework and specifically how to create CI pipeline using DSCI tool. Here is example of very simple cro application (taken from cro web site): use Cro::HTTP:: Router ; use Cro::HTTP:: Server ; my $application = route { get -> { content ' text/html ', ' Hello Cro! '; } } my Cro:: Service $service = Cro::HTTP:: Server . new : : host < localhost > , : port < 10000 > , : $application ; $service . start ; react whenever signal ( SIGINT ) { $service . stop ; exit ; } First of all let's create jobs file .dsci/jobs.yaml that would contain list of jobs, in our case this is just a single job: jobs : - id : ci path : . In this case we would have just a single job that: installs apps dependencies runs web application in background runs some end to end tests using http client .dsci/job.raku run_task " install "; run_task " end-to-end "; .dsci/tasks/install/task.bash set -e cd ../ ls -l zef install . --deps-only zef install . echo "done" nohup cro run 1>app.log 2>&1 & </dev/null The first task just installs application dependencies and runs web application in background, now we can create some end to end test. For simplicity I am going to use curl http client here, but feel free choose any languages you like, for example Raku's HTTP::Tiny client, DSCI is super flexible allowing to write tasks on different languages mixing then effectively. .dsci/tasks/end-to-end/task.bash I could have made this simple one line Bash task a part of initial install task, but for claritrty and demonstration of modularity I keep as a separate one. In the future more tests may come (rathe then this trivial one) and it reasonable separate installation and testing logic. set -e # the test should fail if HTTP response is not # successful curl 127.0.0.1 127.0.0.1:10000 -f -L Ok, let's try this out. And the very first run results in ... error: 08:47:39 :: ===> Building: Digest::SHA1::Native:ver<1.0.1>:auth<zef:bduggan> 08:47:39

AI is an amplifier; strategic focus on the organizational system brings the greatest returns. DORA's 2025 research on AI in software development provides team profiles and success capabilities that can be used to put the research into practice. By Ben Linders

This essay originally appeared in The Guardian . I teach public policy at the Harvard Kennedy School and the Munk School at the University of Toronto. And it will come as no surprise to you that my students regularly use AI to complete their writing assignments. Doing so is a waste of their tuition money. But if their entire career is going to include AI writing assistants, why shouldn’t they embrace their future? The best way I’ve found to explain the dilemma comes from the AI researcher Daniel Meissler: it’s the difference between work and the gym...

If the generative AI giant had followed well-known security best practices, it’s likely that its AI agent would never have escaped to the open internet and hacked multiple companies.

It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conference on Machine Learning, a top AI conference, this month. The claim has huge implications for the safety of this technology, which…

Despite the ongoing AI boom, LinkedIn is holding the line on compute spending. Instead, it’s challenging engineers to make every GPU count.

Researchers pitted a person against a Claude agent and found that, after a week of texting, the AI chatbot was more effective at creating “exploitable trust” with others.

How I applied Exploratory Data Analysis, Feature Engineering, Pipelines, and Ensemble Models to solve a real-world machine learning problem—and the lessons I learned along the way. Introduction There comes a point in every machine learning learner's journey when watching tutorials and completing small practice exercises are no longer enough. After spending weeks understanding statistics, exploratory data analysis (EDA), feature engineering, preprocessing techniques, and classical machine learning algorithms, I wanted to answer one question: Can I apply everything I've learned to a real machine learning competition? That's when I decided to participate in a Kaggle Playground competition. Unlike classroom datasets, Kaggle competitions force you to think like a machine learning engineer. You're responsible for understanding messy data, building preprocessing pipelines, selecting models, evaluating performance, debugging errors, and finally creating a submission that competes with thousands of participants. This article documents my complete journey—from loading the dataset to building production-style preprocessing pipelines and training multiple ensemble models. Along the way, I'll also share the challenges I faced, what worked well, and the lessons I'll carry into future competitions. Why Kaggle? Learning machine learning isn't just about knowing algorithms. Real-world ML requires answering questions like: Which features are useful? How should missing values be handled? Should categorical variables be one-hot encoded or ordinal encoded? Which preprocessing steps belong inside a pipeline? How do different ensemble models compare? Kaggle provides an environment where all of these questions matter. Instead of building a model that works only inside a notebook, you're solving a problem under realistic constraints and evaluating your solution on unseen data. Competition Goal The objective of this Playground competition was to predict the target class based on a combinatio

A German startup sent a camera-wearing chef to my apartment. In exchange for a free lunch, I let them record every chop and stir to train future humanoids.

On the company’s second-quarter earnings call Wednesday, CEO Mark Zuckerberg said Meta sees a “large enterprise opportunity” spanning AI agents, APIs, compute, and internal software.

Advertisers agreed to “reset” relationship with X.