📅 เขียนเมื่อ: กรกฎาคม 2026 ⚠️ อ้างอิงจากเอกสารต้นฉบับของ Bell Labs และบันทึกของผู้พัฒนา ถ้าผมถามว่า "ระบบปฏิบัติการที่เจ๋งที่สุดในโลกคืออะไร" — คุณคงตอบ Linux, macOS, หรือ Windows แต่ถ้าถาม Ken Thompson และ Dennis Ritchie — สองคนที่สร้าง Unix ขึ้นมา — พวกเขาจะตอบว่า Plan 9 Plan 9 คือระบบปฏิบัติการที่ Bell Labs สร้างขึ้นในช่วงปลายยุค 80 ถึงต้นยุค 90 โดยทีมเดียวกับที่สร้าง Unix แต่มันไม่ใช่แค่ "Unix เวอร์ชันใหม่" — มันคือการเริ่มต้นใหม่ทั้งหมด และถึงแม้วันนี้แทบไม่มีใครใช้ Plan 9 — แนวคิดของมันแทรกซึมอยู่ในทุกระบบปฏิบัติการที่คุณใช้อยู่ จุดเริ่มต้น — "Unix เริ่มแก่แล้ว" ปัญหาที่ Unix สะสมมา Unix เกิดในปี 1969 — ตอนนั้นคอมพิวเตอร์คือเครื่องเดียวที่มี terminal ต่อพ่วง พอยุค 80 มาถึง โลกเปลี่ยน — network กลายเป็นเรื่องปกติ, graphics เริ่มสำคัญ, distributed computing เริ่มเกิด แต่ Unix ไม่ได้ถูกออกแบบมาเพื่อสิ่งเหล่านี้ มันถูก patch, extend, retrofit — จนกลายเป็นระบบที่ซับซ้อนเกินกว่าที่ผู้สร้างจะภูมิใจ Ken Thompson เคยพูดประมาณว่า: "Unix เริ่มต้นด้วยความเรียบง่าย แต่มันค่อย ๆ สะสมความซับซ้อนเข้าไปเรื่อย ๆ — ถึงเวลาที่ต้องเริ่มใหม่" ทำไมต้องชื่อ Plan 9 ชื่อ "Plan 9" มาจากหนัง B-movie สุดคลาสสิกเรื่อง "Plan 9 from Outer Space" (1959) ของ Ed Wood — ที่ได้ชื่อว่าเป็น "หนังที่แย่ที่สุดตลอดกาล" แต่ชื่อนี้ไม่ได้หมายความว่า OS นี้แย่ — มันคือมุกวงในของทีม Bell Labs ที่ชอบตั้งชื่อแปลก ๆ (Unix เองก็เป็นมุก — มันล้อ Multics) หัวใจของ Plan 9 — "ทุกอย่างคือไฟล์" จริง ๆ จาก Unix สู่ Plan 9 — ทำให้สุดทาง Unix มีแนวคิด famous: "everything is a file" filesystem เป็นไฟล์ → /home/user/document.txt devices เป็นไฟล์ → /dev/sda , /dev/tty processes เป็นไฟล์ → /proc/1234 แต่มันมีข้อยกเว้น — network sockets, graphics, window system — สิ่งเหล่านี้ไม่ใช่ไฟล์ใน Unix Plan 9 เอาแนวคิดนี้ไป สุดทาง — ใน Plan 9, ทุกอย่างคือไฟล์ ไม่มีข้อยกเว้น 9P — โปรโตคอลเดียวที่เชื่อมทุกอย่าง หัวใจของ Plan 9 คือ 9P — โปรโตคอลที่ทำให้ทุกอย่างสื่อสารกันผ่าน filesystem หน้าต่าง GUI → mount เป็นไฟล์ network connection → mount เป็นไฟล์ เครื่องอื่นใน network → mount เป็นไฟล์ ลองนึกภาพ: คุณ ls ดูไฟล์ในเครื่องคนอื่นได้เหมือน

Introduction Your agent guesses. It matches names as text and hopes the right line was in...

CleanSlate is an open-source platform for running coding agents across your local machine and the cloud. Today, it works through an IDE, CLI, and SDK. Agents can understand a codebase, make changes, run commands, browse the web, and verify their work. We are now building longer-running autonomous cloud agents that can continue working without keeping your machine active. CleanSlate supports multiple model providers and is not tied to a single ecosystem. GitHub: https://github.com/TheWariend/CleanSlate Website: https://thewariend.com/cleanslate The project is still early, and I would appreciate honest feedback from developers who use coding agents.

This is part of my Building Fluentic Style series, where I’m writing down the design decisions, tradeoffs, and small surprises from building Fluentic Style . It is one thing to make a styling library feel good in a client-side app. It is another thing to make it feel good in Next.js App Router. In a simple SPA-style development setup, most of the styling loop lives in one place: component renders in the browser Fluentic style chain resolves atomic CSS rule is inserted DevTools can inspect the generated rule sourcemap points back to authored code That is already a lot of work. But at least the browser is the main place where the style is produced and consumed. Next.js App Router changes the shape of the problem. Now the page can involve: server rendering React Server Components client components streamed HTML hydration client-side navigation HMR Webpack or Turbopack development sourcemaps production extraction So the hard part is not just “can Fluentic run in Next.js?” The hard part is: Can Fluentic keep the same CSS debugging experience when styles cross the server/client boundary? That is what this post is about. Docs for the Next.js integration are here: Next.js Integration DevTools And Sourcemaps Runtime And Dev Debug Without Getting Lost The Goal Was Not A Special Next.js API I did not want Fluentic to have one mental model for client apps and another one for Next.js. This should still be normal Fluentic: const card = style ({ padding : 16 , borderRadius : 12 , }). hover ({ boxShadow : ' 0 12px 30px rgb(15 23 42 / 0.16) ' , }); export function Card () { return < section css = { card } > Hello </ section >; } And this should still be normal Fluentic too: const buttonStyles = { root : style . slot ({ display : ' inline-flex ' , border : 0 , }), label : style . slot ({ fontWeight : 700 , }), }; const danger = style . scope ([ buttonStyles . root ({ backgroundColor : ' #dc2626 ' , }), buttonStyles . label ({ color : ' #ffffff ' , }), ]); The Next.js integration shou

Mainline-friendly products are designed so their board support lives in upstream Linux, U-Boot, and standard build systems instead of a vendor fork. The decision is architectural, not aspirational: it is made when you choose the SoC, design the add-on connectors, and write the device tree — not when the product is already shipping. This article gives the strategic case, the product design rules that follow from current kernel work on hot-pluggable add-on boards, a vendor checklist for tech leads, and the concrete steps to upstream your own board support. We have covered why silicon vendors are moving to upstream-first BSPs . This article covers the product team's side of that shift: what you should do about it. Building mainline-friendly products means making a set of design decisions — SoC selection, connector design, device tree structure, and an upstreaming plan — so that mainline Linux and U-Boot treat your board as a normally supported board rather than as a permanent private port. Each section below turns one of those decisions into rules you can apply on your next board. Why mainline-friendly products are a strategic decision The cost of a vendor-fork BSP is not paid at bring-up; it is paid for the life of the product. Every kernel upgrade becomes a forward-port of private patches. Every security fix arrives on the vendor's schedule, not the kernel's — and for devices in scope of regulations such as the EU Cyber Resilience Act, patch latency is now a compliance question, not just an engineering one. Hiring is harder, because engineers must learn your fork before they can touch it, and the knowledge they build does not transfer in either direction. Board support that lives in mainline inverts each of these. New kernels are more likely to boot your board without forward-porting private support patches, because your board is part of the kernel's own build-and-test surface; LTS security fixes are easier to consume because the code paths you depend on are already

This is a submission for Frontend Challenge - Comfort Food Edition, Perfect Landing 🍳 Pixel Chef AI — A Memory Kitchen That Learns Your Taste What I Built Pixel Chef AI is an interactive AI cooking companion built around a simple idea: Food is not only about recipes. It is about memories, habits, emotions, and personal taste. Instead of being a traditional recipe generator, Pixel Chef AI creates a complete AI-powered cooking journey: 🧊 Enter the Memory Kitchen 🥬 Choose ingredients 🤖 Let AI analyze flavors and nutrition 🔥 Cook with real-time AI guidance 🍽️ Reveal your final dish 🧬 Build your personal Taste DNA Every cooking session becomes a memory. Over time, the AI learns your cooking preferences, flavor choices, and habits to create a more personalized kitchen experience. The core question behind this project: What if your AI assistant could remember how you cook and become your personal kitchen companion? ✨ Features 🧠 AI Taste Intelligence Pixel Chef AI is designed around the idea that cooking decisions are personal. The AI analyzes: Ingredient combinations Flavor balance Nutrition information User preferences It can: Predict flavor direction Suggest ingredient improvements Recommend better combinations Adapt suggestions based on cooking goals 🤖 AI Cooking Companion A pixel AI chef accompanies users throughout the entire cooking process. The AI provides: Ingredient analysis Flavor recommendations Cooking suggestions Real-time guidance during cooking Personalized feedback The goal is to make AI feel like a kitchen partner, not just a chatbot. 🧊 Interactive Pixel Kitchen The experience starts inside a cozy pixel-art kitchen. Users can: Open the fridge Select ingredients Create their own combinations Watch AI analyze their choices The kitchen becomes a place where users interact with AI through cooking. 🔥 AI Cooking Simulation Cooking becomes an interactive experience instead of a simple result page. During cooking: A cooking timeline controls progress Different coo

The answer arrives clean, well-formatted, and certain. You run it. The config flag doesn't exist. Nothing in the response told you the difference between the parts it was sure about and the parts it filled in. The syntax was right. The explanation was reasonable. The flag had a sensible name and a clear purpose. It just wasn't real. This is a calibration problem, not a trust problem. "Don't trust AI" is useless advice. It tells you to doubt everything, which is the same as doubting nothing, because you can't actually work that way. What you need instead is a way to tell which parts of an answer are grounded and which were filled in. There are patterns to look for, and once you know them, they are hard to miss. 🔍 Why this affects beginners specifically This isn't about intelligence. It's about experience. An engineer with a few years behind them has already lost time to a function that didn't exist. They have wasted an afternoon on a config parameter that turned out to belong to a different engine. They have built up a collection of "that looked right but wasn't" memories, and those memories now activate automatically. A small feeling that says check this one before they run it. Beginners haven't had those afternoons yet. The pattern recognition that comes from repeated failures doesn't exist, because the failures haven't happened. Everything arrives in the same confident tone, and without past mistakes to compare against, there is no internal signal separating a grounded answer from a made-up one. That's not a character flaw. It's missing experience, and it can be partly replaced with three concrete things to look for. ✅ The three signs These aren't perfect. They are a minimum standard, and they catch the common cases. I'll be clear about what they miss at the end. Sign 1: Very specific details with no source When the answer includes a precise detail (a specific config flag, a particular function signature, an exact version number) and doesn't say where that detail

Noticed a pattern: people switch from GPT to Claude, upgrade to a newer version, try a bigger model and the output barely changes. If that's happened to you, the issue usually isn't the model. It's what you handed it before asking the question. Broke it down to three things context actually needs to supply, and most disappointing outputs are missing one of these, not all of them: Current facts the training data can't know: your pricing, this quarter's numbers, a customer's actual history. Leave this out and the model doesn't leave a blank, it quietly invents something plausible. A concrete example of what "good" looks like: not "professional tone," an actual paragraph to pattern-match against. Descriptions get interpreted, examples get copied. What already happened earlier in the task: a correction you made two messages ago. If you don't restate it, it's gone. The model isn't ignoring you, it just doesn't re-read messages you haven't pointed it back to. The counterintuitive part: the most common mistake isn't giving too little context, it's dumping in too much unfiltered. The model has to weigh every token, and irrelevant material competes for attention with what actually matters. Forty pages when the task needs three paragraphs makes the right answer harder to find, not easier. Wrote up a longer breakdown with a concrete before/after example (same task, same model, only the context changed): https://medium.com/@nagatomopedro05/good-ai-starts-with-good-context-design-77496f7b9eb6 Curious if others here have run into this, model-swapping as a first instinct instead of fixing the input. submitted by /u/ClickOk5811 [link] [留言]

I'm building an app with Claude Code right now. The setup is a little unusual. One "commander" session writes the instructions, and separate "worker" sessions implement them in parallel. The commander never touches the keyboard itself. Its whole job is to turn "what to do next" into a handoff prompt that a worker can read and just run with. And that handoff prompt is quietly the scariest thing in the whole loop. The moment I hand it over, the worker trusts it as the spec and starts sprinting. If the spec is wrong, the wrong thing gets built. Fast, and with confidence. Before handing it over, I did my usual ritual I've picked up a habit lately. Before I throw the instructions at a worker, I run them past a subagent whose entire job is to demolish them. It's defined to never approve, to hunt for holes, and to never, ever close with "this looks broadly reasonable." What was different this time: this thing didn't just read the prose of my prompt. It went and read the actual code. And the reply it came back with made my stomach drop a little. Objection 1: I wrote "make the test pass (go green)", but that test's green meant nothing In the instructions, I'd written this as a definition of done: "Get the XX test passing (green)." The demolition agent's answer: "That test goes green even when the thing it's testing fails. " I read it. It was true. The test only checks "did the process run all the way to the end." It never checks the one thing that matters: did it succeed? Fail, and as long as it "returned a failure result and finished running," green. On top of that, the batch path was swallowing exceptions, so no matter what blew up, still green. So even if a worker reported back "DoD met, tests green!", nothing was actually proven. The completion criterion I wrote myself was an empty pass. Objection 2: I wrote "just flip a flag", but that switch didn't exist One more. I'd written " flip a config flag and it swaps in the real component ", as if it were a feature that alread

Disclosure: I build Schemity , a desktop ERD tool - this post is from our blog and uses it for the examples. TL;DR: Database views carry real responsibilities - reporting layers, security boundaries, API surfaces - but ERD tools either leave them out entirely (DBML has no view support despite requests since 2022) or draw them as if they were ordinary tables. Schemity displays views and materialized views as read-only entities with italic names and a bold view or mview token in the entity footer, so derived relations are distinguishable from base tables at a glance, and they can be imported into context views like any other entity. A database view belongs in your ERD, but not disguised as a table: it is a derived, read-only relation, and the diagram should say so at a glance. Schemity draws views and materialized views as read-only entities with italic names - present on the canvas, visually distinct from the base tables they are built on. That sentence would be unremarkable if the rest of the tooling world agreed with it. Mostly, it does not. In most ERD tools your views are simply absent, and in the rest they are dressed up as something they are not. The reporting layer your diagram pretends does not exist Views are not decoration. They are where schemas put their public face: the reporting layer that joins five tables into one readable relation, the security boundary that exposes a subset of columns to an application role, the compatibility shim that survives a refactor. On Supabase , views are how you shape what PostgREST exposes as an API. A materialized view may be the single most performance-critical object in an analytics schema. Whoever reads your diagram to understand the system needs to see them. Yet the diagram usually cannot show them. DBML - the schema language behind dbdiagram.io - has no syntax for views at all: a user proposed designing views with join definitions in December 2022, others were still upvoting the request in July 2024, and there has be

Article URL: https://www.science.org/content/article/deep-sea-vehicles-spot-alien-sharks-deep-beneath-waves-pacific# Comments URL: https://news.ycombinator.com/item?id=49140599 Points: 32 # Comments: 13

Semisort Reorder an array so that identical keys are grouped contiguously Keys do not need to be in sorted order Stable Partition Group by predicate, preserve order K-smallest selection, single Get the k-smallest key from the array K-smallest selection, list Get the k-smallest key from the array, and everything smaller (or equal), in any order K-smallest selection, list sorted Get the k-smallest key from the array, and everything smaller (or equal), in sorted order submitted by /u/Grouchy-Trade-7250 [link] [留言]