今日已更新 32 条资讯 | 累计 27207 条内容
关于我们

AI 资讯

AI人工智能最新资讯、模型发布、研究进展

14374
篇文章

共 14374 篇 · 第 373/719 页

Reddit r/programming

Building an Entity Component System: Data Oriented Hierarchies

Data Oriented Design is the practice of building code that's optimized for the hardware it runs on. Entity Component Systems help writing DOD-friendly code by laying out otherwise allocation-heavy game data in contiguous arrays. A challenge in gamedev however is that lots of game data is stored and accessed as hierarchies that change frequently, which makes them notoriously difficult to store as contiguous arrays. This article goes over a number of techniques an Entity Component System can use to integrate hierarchies with the core datamodel in a way that improves the performance of the ECS. Written mostly for gamedev, but also applies to other hierarchy-heavy applications, like UI. submitted by /u/ajmmertens [link] [留言]

/u/ajmmertens 2026-06-25 23:15 👁 5 查看原文 →
The Verge AI

Bungie hit with ‘significant’ layoffs after ending Destiny 2

Now that Bungie has moved on from Destiny 2, the game studio is being hit with its latest round of layoffs. In a statement posted on X, the studio said that "we are announcing a reduction in force as we reorganize Bungie." No specific numbers were revealed. But in a separate statement, Hermen Hulst, CEO […]

Andrew Webster 2026-06-25 22:49 👁 5 查看原文 →
Reddit r/programming

The dogma of entity-based Services and Repositories

Every day I see more projects where a layer-per-entity approach is created by default: UserService , UserRepository , OrderService , OrderRepository , and so on. My issue is not with layering itself, but with how these layers are used. In theory, a Service should represent application behavior: something that encapsulates real use cases or meaningful interactions with external systems (payments, emails, APIs, etc.). Something with enough significance to justify being isolated. A Repository , in theory, should exist when there is real complexity in data access: multiple data sources, non-trivial persistence logic, evolving storage mechanisms, etc. However, in practice, they often degrade into something else: services that simply delegate calls repositories that are thin wrappers around an ORM interfaces with no meaningful alternative implementation At that point, the abstraction starts to lose its purpose. Why not use the ORM directly when there is no real complexity to hide? Why do we default to intermediate layers that don’t truly represent either a service or a repository in their original sense? It often feels like they add files, indirection, and noise without improving clarity. I’m not saying these layers are inherently bad. I’m trying to understand why they are so often applied by default, even when they don’t serve their actual purpose. Does this approach really improve clarity, or does it just introduce unnecessary complexity? For example, instead of organizing code around generic services per model, we could structure it around explicit use cases for concrete system actions. These would still live within an application/service layer in the sense described by Fowler, but without forcing a generic ModelService structure for everything. The goal would be to design around intent rather than around structural templates—so the code reflects what the system does , not just how it is layered . submitted by /u/Character-Method-720 [link] [留言]

/u/Character-Method-720 2026-06-25 22:42 👁 5 查看原文 →
MIT Technology Review

Repositioning retail for the AI era

Artificial intelligence is rapidly reshaping retail, but not in the ways consumers might immediately notice. The biggest transformation may not be flashy virtual try-ons or chatbot shopping assistants, but in how decisions are made behind the scenes: how products surface in search results, how inventory moves through supply chains, how engineers ship code faster, and…

MIT Technology Review Insights 2026-06-25 22:22 👁 9 查看原文 →
HackerNews

Ask HN: What surprised you about Estonia e-Residency and running an Estonian OÜ?

From the official information online, joining the e-Residency program and setting up an Estonian company seems relatively straightforward. I'm considering using an Estonian OÜ for a SaaS business and would love to hear from people who have actually gone through the process. What surprised you after becoming an e-resident and establishing your company? Were there recurring costs, compliance requirements, banking/payment issues, tax complications, or other operational challenges you didn't anticip

jvilalta 2026-06-25 22:17 👁 4 查看原文 →
HackerNews

Show HN: Autofit2 – End-to-end pipeline for multilingual text classification

Hi HN, Stefan here. autofit2 is a project I have been using at my previous company and is now opensourced. It has been used extensively in automated text moderation, but can be applied to any text/document classification task. We had success modeling offensive texts in 20+ languages (cf. github.com/neospe/dataload for all the datasets). It's an integrated pipeline for lightweight multilingual text classification, covering preprocessing, training, and evaluation. It implements SetFit, a few-shot

leschak 2026-06-25 21:58 👁 4 查看原文 →
HackerNews

Tell HN: OpenAI has started putting ads on paid programs

I was on the £6.99/month program-- mainly because I didnt use it much. Last few days I've started seeing ads: 1. Ad for Financial Times 2. Add for Shein 3. Add for Amazon prime day All 3 were on a chat where I was asking about tips on a mobile game. Needless to say, I cancelled my plan. Im not paying money to see ads

shantnutiwari 2026-06-25 21:37 👁 4 查看原文 →
The Verge AI

Leica’s $6,690 SL3-P pairs 44-megapixel stills with 8K video

Following the launch of the SL3 in 2024 and last year's SL3-S that was optimized for speed and capturing fast moving subjects, Leica announced the new SL3-P today delivering some of the best features from both of its predecessors. Like Leica's previous "P" variants, the new SL3-P lacks the brand's iconic and recognizable red dot […]

Andrew Liszewski 2026-06-25 21:00 👁 11 查看原文 →
The Verge AI

Apple reveals price hikes for MacBooks and iPads

Apple is hiking prices in response to the ongoing memory shortage. On Thursday, the company adjusted the price of its new MacBook Neo, which will now start at $699 instead of $599, while the base MacBook Air will jump to $1,299 from $1,099, as reported earlier by Bloomberg. The 14-inch MacBook Pro is getting an […]

Emma Roth 2026-06-25 20:53 👁 6 查看原文 →
Dev.to

I Tracked My Body Fat for 90 Days and Built a Calculator That Actually Makes Sense

For three months, I weighed myself every morning and took body measurements every Sunday. I used a caliper, a tape measure, and a scale that probably lies to me about hydration levels. The goal wasn't to get ripped. It was to understand whether any of these measurements actually mean something day to day. The Problem With Most Health Calculators Most body fat calculators fall into one of two camps: Too simple — plug in height and weight, get a BMI number that tells you nothing about your actual composition. Too complicated — requires measurements you need a degree to take correctly, plus an email signup and a paid subscription. Neither is useful for someone who just wants to know "am I making progress?" Building Something Practical I put together a calculator that uses the Navy Method — it takes neck, waist, and hip measurements and estimates body fat percentage. The math has been around since the 80s and correlates reasonably well with DEXA scans for most people: function navyBodyFat ( gender , neck , waist , hip , height ) { if ( gender === ' male ' ) { return 86.010 * Math . log10 ( waist - neck ) - 70.041 * Math . log10 ( height ) + 36.76 } return 163.205 * Math . log10 ( waist + hip - neck ) - 97.684 * Math . log10 ( height ) - 78.387 } The inputs are simple enough that anyone can take them with a tape measure. The output gives you a ballpark number that's consistent enough to track trends over time. What 90 Days of Data Taught Me Three things stood out: Daily weight is useless; weekly trend is everything. My weight would swing 2-3 pounds daily due to water, food, and sleep. The weekly moving average was the only signal worth watching. Body fat percentage changes slowly. Like, frustratingly slowly. In 90 days of consistent training, I moved maybe 2%. But that's real — if a calculator tells you you dropped 5% body fat in a month, it's broken. Consistency beats precision. Taking measurements at the same time, under the same conditions, with the same method matter

member_ce2645ea 2026-06-25 20:50 👁 6 查看原文 →