Apple is working on a "revamped" version of its entry-level MacBook Pro that it could launch as soon as the first half of 2027, Bloomberg reports. The company is also testing four new iPad Pros that are set to launch in the spring with a focus on "internal improvements." The updated MacBook Pro, which will […]

The co-founders of Bending Spoons, the Italian company quietly buying beloved, ailing Internet brands, learned big lessons from their own startup's failure.

Fable 5 is back (for a week) but I just noticed that all the Claude models suddenly are failing with all the Tools ( search, fetch, read, write). Anyone else experienced the same?

Cody Spencer, the co-owner of the small games retail chain Pink Gorilla Games, put it well when I asked about the impact of Sony's recent announcement that it will stop making discs for new games starting January 2028. "It's sad to see. This decision is only a negative for gamers. We're losing the ability to […]

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

GitHub热门项目 | 🚀 A real-time monitoring dashboard for Claude Code, built with SQLite3, Node.js, Express, React, Vite, TailwindCSS, and WebSockets. It tracks sessions, agent activity, tool usage, and subagent orchestration, providing live analytics, a Kanban status board, status notifications, a cute buddy, and an interactive web UI/MacOS/Windows native app. | Stars: 734 | 162 stars this week | 语言: TypeScript

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]

Everyone keeps saying AI will let a solo developer take down the giants. And everyone keeps saying the giants will just absorb everything. Both takes are wrong , and I spent a while reading the actual 2025 data to figure out why. I pulled from four of the biggest developer datasets of the year: DORA 2025 State of AI-Assisted Software Development (Google Cloud, ~4,867 respondents) Stack Overflow 2025 Developer Survey (49,009 respondents) GitHub Octoverse 2025 (behavioral data across 180M+ developers) JetBrains State of the Developer Ecosystem 2025 (24,534 developers) Here's the honest synthesis. It's more useful than either hype narrative. The one-sentence thesis AI collapsed the cost of writing software to near zero. It did not collapse the cost of distribution, trust, support, or being liable when it breaks — and those are ~80% of what a software business actually is. So the effect isn't "solos beat giants." The effect is that the middle got hollowed out . The 10-person, VC-funded, me-too startup building a feature is the loser of this era — squeezed from below by a solo who ships the same thing for free, and from above by a giant who bundles it. Solos and giants both survive. The undifferentiated middle doesn't. "AI is an amplifier, not an equalizer" This is the single most important finding of 2025, and it comes straight from DORA: "AI's primary role in software development is that of an amplifier. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones." Read quickly, that kills the "AI levels the playing field" fantasy. AI rewards whoever already has good practices — not whoever is scrappiest. But read one layer deeper and it becomes the best available argument for the small team. DORA found the key enabler is independence of action — "the ability to develop, test, and deploy value independently, with little or no coordination cost." In an Adidas pilot they cite, teams in loosely-coupled architectures saw 20–30% produ

Turn to-dos into scheduled tasks Discussion | Link

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