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Meta says usernames improve privacy, but critics question whether its safeguards can prevent impersonation.

Bonjour Je travaille actuellement sur un nouveau langage de programmation appelé Klyn . Alors je sais, certains vont me dire pourquoi encore un nouveau langage. Peut-être est-ce vrai, mais d'un autre point de vu, pourquoi ne pas être audacieux et proposer une autre lecture possible. A méditer. De plus, logiquement on ne fait pas de promo de projet sur r/programming , mais pour le coup le projet porte clairement sur la programmation et je me dis que ce post est quand même à sa place (j'espère qu'on m'en tiendra pas trop rigueur ; je suis nouveau sur Reddit). Quoi qu'il en soit, mon point de départ est que Python propose une syntaxe épurée et lisible, par contre les performances en termes de temps d'exécution, c'est pas ça. Du coup, pourquoi ne pas intégrer les bonnes idées de C++ pour les perfs dans un langage "Python-like" ? Et tant qu'on y est, Java et C# propose aussi des trucs sympa. Et puis j'ai aussi quelques autres idées sympa à tester (j'ai notamment tester des choses sur les syntaxe des collections). Et donc je suis partis à proposer Kl yn , un langage "Python-like" orienté performance . Du coup, j'en suis là : une syntaxe lisible et agréable, proche de Python, un typage statique pour la fiabilité des codes, une compilation native et implicite pour les performances, des propriétés inspirées de C#, pleins d'idées perso sur la syntaxe et les lib, une API déjà assez riche autour des collections, chaînes, fichiers, terminal, interface graphique, bases de données, thread, api llm... Etant actuellement seul sur le projet, j'ai grandement fait usage de l'IA (je préfère être cash ; clan Codex), mais franchement, je ne le regrette pas. En l'état le projet n'est pas prêt a passer en production. C'est pour ça que j'ai besoin de vos impressions (afin de préparer cette future étape). De plus, si vous êtes convaincu par la démarche, qu’attendriez vous d'autre d’un tel projet ? Site du projet : https://klyn.deepcodia.fr Tutoriel : https://klyn.deepcodia.fr/docs/tutorial/in

This is a write-up on our company blog that I wrote, sharing our perspective into Hamiltonian Neural Networks (Greydanus et al., 2019) from a differential-geometry angle rather than the usual "here's the loss function" treatment. I've been working on HNN and LNN adjacent topics for years now and I found this particular lens made the *why* click in a way the standard framing never did for me, and I've been meaning to put everything in writing for a while now. I just feel like the Noether's Theorem which shows conservations can be mapped to symmetries (and in ML context, generalization) is not getting the attention that it deserves around physics informed neural networks. Also, it's a really beautiful architecture and I just love talking about it at every opportunity. It's math-heavy, but I did my best to sprinkle some tension relievers and interactive visuals here and there and make is as easy as it is to follow. Hopefully, I did a good job. I'd genuinely love to see your thoughts and your feedback submitted by /u/FlameOfIgnis [link] [留言]

A large-scale audit of AI-as-judge evaluation — covering over half a million individual judgments — finds that AI judges are consistently reliable but not valid, meaning they give the same answer repeatedly without that answer being correct. Published work and popular benchmarks like Chatbot Arena have treated consistency as proof of trustworthiness, and the audit shows that assumption is unfounded. Key facts What: Using one AI to grade another is now common — but the biggest audit yet shows these graders are consistent without being correct. A judge that always picks "answer A" scores perfectly on consistency. When: 2026-06-19 Primary source: read the source (arXiv 2606.19544) The distinction matters: a judge is reliable if it's consistent (same question, same answer), and valid if those answers are actually correct. The audit's central finding is that AI judges are reliable without being valid, and the field has been treating the first as evidence of the second. Because consistency is easy to measure and looks reassuring, it has stood in for actual trustworthiness across a lot of published work. A new audit makes the problem stark: a judge that ignores both answers and always picks the one labeled "A" would be perfectly consistent — flawless reliability, identical verdict every time — and completely worthless, because it never read anything. Consistency is trivially easy to fake and says almost nothing about whether the judging is sound. Yet "the judge agrees with itself" has done significant reassurance work in papers and benchmarks, and the always-pick-A example shows exactly how empty that reassurance is. When the researchers corrected for the agreement you'd get by chance — as any fair test should — confident-looking scores deflated noticeably. Gaps between models that seemed meaningful shrank or blurred. Accepted folk wisdom also took a hit: the long-standing worry that AI judges are suckers for longer, wordier answers turned out to be far weaker than assumed

Pressing enter to accept model suggestions now takes less effort than scrolling past it. One keystroke, and the code is yours. Reading it, understanding it, deciding if it's actually right, that part hasn't gotten any faster. That gap, between how fast we can accept code and how fast we can actually understand it, is where things start to go wrong. The new shape of technical debt We used to know where technical debt came from. Tight deadline, cut corner, # TODO: comment that nobody ever revisits. Rushing was the cause, and we could at least point to it. Now you can build up the same kind of debt on a calm Tuesday afternoon, no deadline in sight, just six suggestions in a row accepted because they looked fine and the flow felt good. Nobody rushed you, and the code still ended up just as unexamined. Same debt, just a different excuse. "It works" is not the same as "I understand why it works" Everyone knows that debugging is twice as hard as writing a program in the first place. So if you're as clever as you can be when you write it, how will you ever debug it? — Brian Kernighan, 1974 Fifty years later, the gap got wider. Kernighan was talking about code you wrote. At least you understood it once. A suggestion that compiles, passes the linter, survives code review and even comes with passing tests can still be standing on a wrong assumption that nobody caught, because nobody was reading it as code. They were reading it as output, and output that makes sense tends to get approved. Compiling is a low bar. Passing tests is a slightly higher one, depending on whether you wrote the tests, or its suggestion shaped or created those too. If it's the second, it's like grading its homework with its own answers. None of it tells you the logic is sound, that the edge cases are covered, or that it does what you actually needed, something we already learned every time we trusted code we didn't write. Somehow it's easy to forget it the moment the code appears inline, in our own edito

A new system called Qwen-Image-Agent gives text-to-image models the ability to plan, reason, and revise across multiple steps, closing what its authors call the "context gap." Instead of converting a prompt directly into pixels, the agent wraps a language model around an image generator and runs them in a loop—breaking complex requests into pieces, writing sharper instructions, executing them, and reflecting on what worked. The result is image generation that can handle multi-part, reasoning-heavy tasks that defeat single-shot models. Key facts What: Qwen-Image-Agent wraps planning, reasoning, and memory around a text-to-image model so it can break a hard request into steps - and the local-AI crowd immediately asked whether it runs on a gaming GPU. When: 2026-06-27 Primary source: read the source (arXiv 2606.26907) The architecture follows a four-phase loop. Faced with a complicated request, the agent first plans , breaking the big ask into smaller, manageable pieces. Then it reasons about each piece, pulling in information from its own memory or outside tools and writing tighter instructions. Then it executes , calling the image-generation or image-editing tools to make or modify the picture. Finally it reflects , storing what worked in an episodic memory so the next job goes better. The contrast is direct: a single-shot image model answers in one pass; the agent sketches, steps back, reconsiders, and revises. The paper frames the advantage over ordinary text-to-image the same way a vending machine differs from commissioning a designer—one takes a request and dispenses a result with no conversation, the other asks clarifying questions, works in drafts, keeps notes on your preferences, and iterates toward what you actually meant. The vending machine is faster for a simple request; the designer is who you want for anything with moving parts. This is the same AI agents pattern—plan, act, observe, repeat—that has been reshaping text tasks, now pointed at images. To mea

Opening hook It happened during a quiet afternoon at the mosque. The imam was mid-sentence when a rhythmic, high-pitched ringtone cut through the silence like a knife. Every head turned. It was my phone. My heart sank as I scrambled to silence it, only to realize I had forgotten to flip the physical toggle before walking in. That moment of collective, disappointed glares burned. It wasn't just an annoyance; it was a total breakdown of my focus and a social failure I had accidentally caused because my phone couldn't manage itself. The problem We live in an era where our devices are supposedly 'smart,' yet they are remarkably bad at knowing when to keep quiet. We carry computers in our pockets that can calculate the exact position of the moon or stream 4K video, but they cannot inherently tell that we are in a meeting, a lecture, or a place of worship. You could argue that setting a manual schedule works, but life isn't static. Meetings run over, prayer times shift by a minute each day based on astronomical calculations, and spontaneous plans happen. I found myself constantly juggling the physical volume buttons. If I remembered to mute it, I inevitably forgot to unmute it afterward, missing urgent calls from family. If I didn't mute it, I was the person disrupting the room. I wanted a solution that respected the context of my location and the specific time of day without requiring me to touch my screen. The core friction is that Android is designed to restrict background processes to save battery, which is exactly what a silent-automation app needs to thrive. Getting the app to reliably trigger a volume change while the phone is sitting in a pocket, deep in Doze mode, became my primary development hurdle. The technical decision / implementation When I started building Muffle, I initially tried a standard Service with a Handler loop to check conditions. It worked fine while the screen was on, but as soon as the phone entered Doze mode, the OS aggressively throttled my

1. The Problem It Solves Logistic Regression is used when the outcome is a category rather than a number . Most commonly, it's used for binary classification , where the answer is either Yes or No , True or False , or 1 or 0 . Typical business problems include: Will a customer churn? Is this transaction fraudulent? Will a customer click an ad? Will a loan default? Is an email spam? Will a machine fail in the next 24 hours? Unlike Linear Regression, we're not trying to predict a continuous value. Instead, we're predicting the probability that an event belongs to a particular class. For example: A customer may have an 82% probability of churning . The business can then decide whether that probability is high enough to trigger an intervention. 2. Core Intuition Imagine you're trying to predict whether a customer will cancel their subscription. Suppose the only feature you have is how many times they opened your app this month. If you use a straight line like Linear Regression, the predictions quickly become unrealistic. A very active customer might end up with a -20% chance of churn . A completely inactive customer could end up with 140% . Probabilities obviously can't work like that. To fix this, Logistic Regression takes the linear equation and passes it through a mathematical function called the Sigmoid Function . Instead of producing a straight line, it creates an S-shaped curve . No matter how large or small the input becomes, the output always stays between 0 and 1 . That makes it perfect for probability estimation. 3. The Mathematical Model The model first calculates a linear score. Instead of using that score directly, it passes it through the Sigmoid function. Where: z = linear score p̂ = predicted probability The final output is always between 0 and 1 . For example: 0.08 → Very unlikely 0.32 → Low risk 0.65 → Moderate risk 0.94 → Very high probability Businesses can then choose a decision threshold. For example: Probability ≥ 0.50 → Predict Churn Probability

In the rush to build AI agents, we defaulted to complex vector databases. But high-traffic platforms are converging on a simpler, more robust foundation: plain files. Most long-term agent memory setups are massively over-engineered. When developers start building LLM applications, the default prescription is almost always: "Spin up a managed vector database and build a RAG pipeline." But if you look at the highest-traffic production agent platforms (like Claude Code, Manus, and OpenClaw), a quieter trend has emerged. They are bypassing the enterprise embeddings store and using plain markdown files as their primary memory substrate. This is not a regression to simplicity. Done well, it is a stronger engineering foundation because files are inspectable, diffable, portable, and git-native. But a folder of plain text notes with no structure is just a slow, poorly indexing database. To make a file-first architecture work at scale, you must follow a fundamental system design principle: separate storage from search . The Core Invariant: Storage vs. Search The single highest-leverage decision you can make in agent memory design is treating your storage layer and search indexes as completely separate systems. Storage (Canonical Source of Truth): Versioned, human-readable files (Markdown + YAML frontmatter). Search (Derived Index): Derived search structures (vector databases, full-text BM25 indexes, entity graphs, keyword indexes). In this architecture, every search index is treated as a disposable artifact. You can delete your vector embeddings database or rebuild your entity graph at any time, with zero loss of underlying memory. This buys you three advantages: Auditability for free: By storing memories in text files, you can version-control them using Git. Every memory update, supersession, or correction is diffable, attributable, and reversible without any custom database versioning logic. Algorithmic freedom: Swap your embedding models, adjust your chunking strategies, o

T-Mobile wants Broadcom to keep supporting its VMware perpetual licenses.

submitted by /u/mttd [link] [留言]

https://pymupdf.io/blog/markdown-in-pymupdf-1-28 PyMuPDF 1.28 release, introduces Markdown as a first class document in PyMuPDF. Seems useful for a variety of workflows. You can create PDFs from Markdown text with control over appearance using CSS submitted by /u/Remote-Spirit526 [link] [留言]

Xbox is making some big changes — again. On June 10th, a few months after Asha Sharma took over as CEO, she and newly-promoted chief content officer Matt Booty sent a memo to staff warning of an “Xbox reset.” The business, they said, is facing significant challenges, including a 3 percent “accountability margin,” massively higher […]

AI is great at writing tests fast, and good at writing tests that look real but verify the wrong...

AI is great at writing tests fast, and good at writing tests that look real but verify the wrong...

Touted as a less-hookup-focused Grindr, Goose is an invite-only space for gay men. The problem is the people promoting it don’t seem real.

Elon Musk says a report about a SpaceX AI phone prototype is "utterly false." The report, published on Wednesday by The Wall Street Journal, says SpaceX showed off a "handset-like prototype" to some investors before launching its record-breaking initial public offering in June. The device was "slimmer than an iPhone," and they were told it […]

"We've got time into 2027 before we're getting nervous."

submitted by /u/mttd [link] [留言]

Research appears to reveal a bug that could render the feature effectively useless.