标签：#RAM

The conversation around Agentic AI often focuses on one goal: making agents more autonomous. More tools. More reasoning. More planning. More independence. It sounds like progress. But is more autonomy always the right answer? As software engineers, we rarely optimize for "more." We don't build distributed systems when a monolith is sufficient. We don't introduce microservices because they're fashionable. We choose architectures that balance capability with complexity. The same principle applies to AI agents. The question isn't "How autonomous can my agent be?" It's "How autonomous should my agent be?" Autonomy Is a Design Decision When people talk about autonomy, they often think of it as a feature that an agent either has or doesn't have. In reality, autonomy is a design decision. Every time we allow an agent to make another decision on its own, we are increasing its responsibility. That responsibility comes with benefits, but it also introduces new engineering challenges. More autonomy means the agent can adapt to situations that weren't anticipated during development. It can make progress toward a goal without being guided through every step. At the same time, it becomes harder to predict, validate, debug, and trust. Autonomy isn't free. Thinking in Terms of an Autonomy Spectrum Instead of treating autonomy as a binary concept, it helps to think of it as a spectrum. At one end are systems that simply generate responses. They have no authority to take action. As autonomy increases, agents begin suggesting actions, invoking tools, planning multiple steps, and eventually deciding how to achieve a goal with minimal human involvement. The important observation is that every step along this spectrum increases both capability and complexity. That's why the objective shouldn't be to reach the highest level. It should be to stop at the level your problem actually requires. More Autonomy Isn't Always Better Imagine building an internal HR assistant. Its primary responsibil

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Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

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I recently started doing some research about data oriented design and I find material mostly from gamedevs. I understand that it became popular by Mike Acton, but I think the principles could be applied to more than one domains. For example for statistics libraries and quant data analysis. Do you use this approach in non gamedev related areas. Could you please mention real world examples? TIA submitted by /u/codingbliss12 [link] [留言]

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If you've spent any time exploring backend engineering, distributed systems, or system design, you've almost certainly seen one book recommended more than any other: Designing Data-Intensive Applications , or DDIA for short. For years, I've heard experienced engineers describe it as the book that completely changed the way they think about software architecture. When the second edition was released with updated content covering modern distributed systems and cloud-native architectures, I decided it was finally time to see whether it deserved the hype. After reading it from beginning to end, I understand why this book has become a classic. It isn't another programming book that teaches a framework, a database, or a cloud platform. Instead, it teaches something much more valuable: how to think about building systems that continue working when data grows, traffic increases, and failures become inevitable. If you're a backend engineer—or want to become one—this is probably one of the best technical books you can read. This Isn't Really a Database Book The title can be a little misleading. Before opening DDIA, I assumed it would spend hundreds of pages comparing databases or discussing storage engines. Databases are certainly a major part of the discussion, but they're really just one piece of a much larger picture. The book is about designing systems that process enormous amounts of data while remaining reliable, scalable, and maintainable. Those systems happen to rely on databases, but they also involve replication, partitioning, distributed communication, stream processing, fault tolerance, consistency, messaging, and dozens of other architectural concepts that appear in modern software systems. By the end of the first few chapters, it becomes clear that the authors aren't trying to teach products. They're teaching engineering principles that remain useful no matter which technologies you're using. It Explains Why , Not Just How One of my favorite things about DDIA is

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Introducing GlintCode ✨ I've been building GlintCode , a lightweight scripting language for the browser that runs on top of JavaScript. The goal is simple: make building browser apps easier with a clean, beginner-friendly API while still using the power of JavaScript under the hood. Features 🚀 Runs directly in the browser 📝 Uses <script type="glint"> 🌐 Built-in DOM helpers 🎨 Simple UI creation functions 🔁 Built-in loop helpers 📦 Optional module system ⚡ No build tools or compilation required Hello, World <script src= "https://fast4word.github.io/glintcode/glint.js" ></script> <script type= "glint" > page ( " Hello " ) heading ( " Welcome to GlintCode " , 1 ) paragraph ( " Your first Glint app! " ) button ( " Click Me " , () => { print ( " Hello from Glint! " ) }) </script> Why GlintCode? JavaScript is incredibly powerful, but for beginners or small browser projects it can sometimes feel more verbose than necessary. GlintCode provides a set of simple, readable functions that make creating interfaces and interacting with the page easier, while still letting you use JavaScript features whenever you need them. Because GlintCode runs on top of JavaScript, you can gradually learn the underlying language without giving up access to the browser's APIs. What's next? I'm continuing to expand GlintCode with new functions, modules, examples, and documentation. Future plans include additional built-in libraries, a richer module ecosystem, and more developer tools. I'd love to hear your feedback, suggestions, or ideas for features you'd like to see! GitHub: https://github.com/Fast4word/glintcode

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Loop Engineering — เมื่อการ Prompt Agent ด้วยมือไม่พออีกต่อไป แล้ว Programmer ต้องออกแบบ Loop แทน โดย Nokka (นก-กา) | 1 กรกฎาคม 2026 TL;DR — สำหรับคนที่รีบ กลางเดือนมิถุนายน 2026 ที่ผ่านมา วงการ AI developer สั่นสะเทือนด้วยประโยค 6 คำจาก Peter Steinberger ผู้สร้าง OpenClaw: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." ประโยคนี้มียอดวิว 8 ล้านครั้งในวันเดียว และจุดกระแส "Loop Engineering" ที่กลายเป็น buzzword ร้อนที่สุดของเดือน Loop Engineering คือการเปลี่ยนจากการนั่ง Prompt Agent ทีละคำสั่ง มาเป็นการเขียน Loop (โปรแกรม) ที่ทำหน้าที่ Prompt Agent แทนคุณ โดย Loop จะเป็นคนเลือกงานต่อไป, ส่งให้ Agent, ตรวจสอบผล, ตัดสินใจว่าจะทำต่อหรือหยุด คุณไม่ได้เป็นคนขับ Agent อีกต่อไป — คุณเป็นคนออกแบบระบบที่ขับ Agent 1. Loop Engineering คืออะไร? เกิดมาจากไหน? เรื่องนี้เริ่มต้นจาก Boris Cherny ผู้สร้าง Claude Code พูดบนเวที Acquired Unplugged ต้นเดือนมิถุนายน 2026 ว่า: "I don't prompt Claude anymore. I have loops that are running. They're the ones that are prompting Claude and figuring out what to do. My job is to write loops." สองวันต่อมา Peter Steinberger โพสต์บน X ว่า "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." โพสต์นี้มียอดวิว 8 ล้านครั้ง [1] หลังจากนั้น Addy Osmani (Google Engineer, O'Reilly author) เขียนบทความ "Loop Engineering" บน O'Reilly Radar ให้คำจำกัดความว่า: "Loop engineering is replacing yourself as the person who prompts the agent." [2] และ @0xCodez ก็รวบรวมเป็น 14-step roadmap จาก "prompter" สู่ "loop designer" [3] ในมุมมองของผม Loop Engineering ไม่ใช่ buzzword ธรรมดา แต่มันคือการเปลี่ยน abstraction layer ของการทำงานกับ AI เหมือนกับที่เราเปลี่ยนจาก Assembly → High-level language หรือจาก Bare metal → Cloud แต่ก็ต้องยอมรับว่า Loop Engineering ยังเป็นแนวคิดใหม่ และยังไม่มี standard practice ที่ชัดเจน สิ่งที่ใช้ได้วันนี้อาจเปลี่ยนไปใน 3 เดือน 2. ทำไมต้อง Loop Engineering? ลองนึกภาพการทำงานกับ AI coding agent แบบเดิม: คุณพิมพ์ prompt → รอ → อ่าน dif

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

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

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

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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.

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