标签：#RAM

If you ever needed a quick list of Docker commands, here you go.. 1. Check that Docker is installed docker --version Shows the installed Docker version. 2. Run your first container docker run hello-world Pulls the official test image (if needed) and runs it. You should see a “Hello from Docker!” message. 3. See running containers docker ps Lists containers that are currently running. Use docker ps -a to also show stopped ones. 4. See downloaded images docker images Shows every image on your machine (name, tag, size, ID). 5. Stop a running container docker stop CONTAINER_ID Gracefully stops a container. Get the ID from docker ps . 6. Remove a stopped container docker rm CONTAINER_ID Deletes a container that is already stopped. 7. Force stop and remove docker rm -f CONTAINER_ID Force-stops the container (if it’s still running) and removes it in one step.

1. Why build another multi-tool? How many of you have often found yourselves wanting to buy a Flipper Zero? I thought about it many times, but there were always problems holding me back: stock is often limited, the price tag is quite high, and above all, you miss out on the thrill of building such a powerful tool literally from scratch. From these observations, my project was born: designing and developing a low-level "Swiss Army Knife". It all started a few months ago. I was thinking about buying an M5Stick S3 after watching some videos online where people spoke very highly of it, especially for one major detail: unlike the Flipper, it has Wi-Fi and Bluetooth modules already built-in. Digging deeper, I quickly realized the advantages of the ESP32-S3 over the classic Arduino. The key features that convinced me were: Dual-core processor: It opens the door to serious features, like managing firmware tasks separately. More RAM: It allows integrating very complex external libraries (like heavy graphical interfaces) without killing performance. Native USB HID: It allows emulating peripherals like keyboards or mice natively and quickly. So, the hardware was decided. But why build a multi-tool? The main reason is to explore and understand the technical background of as many tools as possible. Lately, I feel there is a tendency to overlook the ingenuity of the mechanisms operating right in front of our eyes. We prefer having a ready-made tool, usable perhaps without even knowing the basics of computer science. I wanted to go in the opposite direction and understand exactly how these things work at the code level. 2. Fluid Graphics and Multitasking: How not to blow up an ESP32 A major problem when rendering a graphical interface on a microcontroller is that the CPU has to calculate and send every single pixel. Since this is a time-consuming operation, the entire device gets blocked until the whole interface is completely redrawn. In a multi-tool, if the ESP32 is stuck drawin

submitted by /u/der_gopher [link] [留言]

As frustration over AI influencers has been growing, Instagram is limiting the reach of undisclosed AI profiles.

submitted by /u/masiroo [link] [留言]

Every developer writes code that "just works" thousands of times without thinking about what happens between hitting save and seeing output on screen. This article pulls back that curtain. We're going to walk through, in real depth, how source code — plain text you typed — becomes a running program, covering lexing, parsing, abstract syntax trees, semantic analysis, and the actual difference between interpretation and compilation (including why that difference is far blurrier than most explanations make it sound). This is one of those topics where understanding the fundamentals pays off across your entire career — it changes how you read error messages, how you reason about performance, and how you evaluate new languages and tools. 1. The Big Picture: Two Broad Strategies At the highest level, there are two strategies for running code: Compilation — translate the entire source program into another form (often machine code, but not always) before running it. The translation and the execution are separate steps. Interpretation — read and execute the source program directly, translating and running it (roughly) simultaneously, statement by statement. In practice, almost no real system is purely one or the other. Python "compiles" your source to bytecode before interpreting the bytecode. Java compiles to bytecode, then a JIT (Just-In-Time) compiler compiles hot paths of that bytecode to native machine code while the program runs . JavaScript engines like V8 do something similar. The clean binary of "compiled vs. interpreted" that gets taught early on is really a spectrum, and most production language runtimes today live somewhere in the middle. But to understand any point on that spectrum, you need to understand the pipeline every one of these systems shares. Let's build it up stage by stage. 2. Stage One: Lexical Analysis (Lexing / Tokenizing) The first thing that has to happen to your source code is the least glamorous: it gets chopped into pieces. Source code, to a c

Estoy buscando un socio/programador con experiencia que quiera formar un equipo para una marca de finanzas personales llamada alkimia. Tenemos +450k seguidores y un producto validado. Queremos construir un saas y un producto completo de finanzas personales y poder escalarlo. Si alguien busca una oportunidad de tener algo propio esta puede ser su oportunidad. Que busco? No quiero que sepas solo programar y seguir órdenes. Espero que tengas algo de experiencia en saas, en apps, y en negocios. No solo seguir instrucciones. Busco alguien que aporte ideas, y motivación real y sea un experto en programación. Coméntame si estás interesado o mándame mensaje y hablamos. Podemos hablar y después hacer una llamada. Mi idioma principal es español y el del netocio tambien, pero se expandirá a todos los idiomas. Si hablan español mejor, pero no tengo problema con trabajar con alguien cuyo idioma sea inglés. submitted by /u/Mamam500 [link] [留言]

submitted by /u/syrusakbary [link] [留言]

Hello everyone. Scala's a programming language I've enjoyed learning on the side not only because I think it's stylish but because it's made me a better developer. Some of the gripes you encounter once you try to go intermediate or beyond, it's the Typelevel stack complexity. You just want to bootstrap a server and start writing some routes, and tbh sometimes the docs aren't that friendly. That's why I wrote Scala 3 Domain Design & Typelevel Stack Cookbook — a book that teaches some DDD in Scala and gives you some recipes to get you started on the stack (cats, cats-effects, fs2, http4s). It's a WIP currently at 40%. You can read a couple of chapters for free in leanpub: https://leanpub.com/scala3-domain-typestack-dev

Getting tokio and .NET’s async runtime talking to each other, over the C ABI submitted by /u/swdevtest [link] [留言]

submitted by /u/radekmie [link] [留言]

Instagram is finally taking steps to address the rise of fake AI-influencer accounts that have gotten harder to spot. It's also renaming the "AI creator" label to "AI-generated profile" to make it clear when a profile features an AI-generated person that's not a real human being. "We've heard that people don't like seeing a profile […]

submitted by /u/FiveWordinOrangeNeon [link] [留言]

RAG Evaluation: How to Know if Your RAG System Actually Works You built a RAG chatbot. It answers questions from your documents. You test it a few times. The answers look good. So… can you ship it? No. One good answer doesn't tell you whether your RAG system works. A RAG application has multiple moving parts. The retriever can fail. The generator can fail. They can both work individually and still fail when combined. And once the application goes live, your users will ask questions you never tested. So how do you actually evaluate a RAG system? The answer is an eval suite . Components → Pipeline → Application → Regression → Online Evaluation This article walks through the same framework I use in my RAG evaluation video. ▶ Watch the full video The Problem: “It Feels Better” Isn't an Evaluation Imagine you're building an airline support chatbot for a fictional airline called SkyHigh Airlines . Passengers can ask questions about: Baggage Refunds Pets Travel policies The chatbot uses RAG to search the airline's policy documents and generate an answer. A passenger asks: “How much does it cost to bring my cat?” The chatbot responds: “Bringing your cat costs $95.” Looks good. But what if the retriever found the wrong document and the model happened to generate something plausible? Or what if the retriever found the correct policy, but the model ignored it and invented the answer? From the outside, both problems look identical: Bad answer. But they require completely different fixes. That's why you can't evaluate RAG as one giant black box. You need to test the pieces separately. First: Build a Golden Set Before measuring anything, you need something to measure against. Create a fixed set of questions that represent the kinds of questions your users will actually ask. For our SkyHigh chatbot, imagine we create 50 questions about the airline's policies. For every question, we record: The question The correct answer The document chunks that should contain the answer For examp

Yes, it looks like the next release of Rakudo (2026.09, tentatively planned for 26 September) will be the first Rakudo release that will use the new Raku backend (based on a new grammar producing RakuAST) as the default when compiling a writting in the Raku Programming Language . Until then one had to specify the RAKUDO_RAKUAST=1 environment variable to activate the new backend. It is a major step towards releasing the next Raku language level, tentatively still called "6.e" for historical reasons, later this year. Note that the 2026.09 Rakudo release will not remove the old backend from the core just yet: you will be able to activate the old backend by specifying the RAKUDO_LEGACY=1 environment variable. However, the old backend will be removed when the 6.e Raku language level is released. So it is very important to check what these changes will mean for you! Immediate effects So what will a user of this Rakudo release notice with their apps and libraries? Nothing In the past weeks the Raku ecosystem has been thoroughly scoured for code that would stop functioning in RakuAST. Some finds turned out to be omissions / errors in the RakuAST implementation (which then have been fixed). Others turned out to be modules doing (semi-)naughty things, for which Pull Requests were made to ensure they will continue to operate in RakuAST. Running slower It could well be that your code is running without issues, but all of a sudden runs slower than before. Years of optimizing the old backend were mostly lost in RakuAST. And had to be re-created in the RakuAST-based backend. Which may have lost a few nooks and crannies that affect the execution speed of your code. Slower execution should be reported as a bug, so that it can be fixed! Running faster One of the goals of the RakuAST projects was to be able to provide better optmizations. Some of the ideas for these optimizations have already been implemented in RakuAST. And as a part of this work, a lot of aspects of the runtime have

submitted by /u/jhartikainen [link] [留言]

Most AI code-reviewer evaluations treat the candidate as an amnesiac: feed it one pull request, read one verdict, and move on. Persistent-memory reviewers break that model because they keep history across PRs, and that history becomes a second source of bugs. The dominant failure is no longer amnesia but overconfidence in stale context. A two-phase probe exposes whether a candidate trusts its own memory more than the repository's current decisions. This article supplies the complete take-home package: a fixture repository, a reusable candidate prompt, an HTTP-flavored scoring rubric, a reference solution, and a zero-cost runner script. The probe uses two synthetic PRs and measures one skill: which convention source wins inside the reviewer's context window. That focus separates it from single-shot snapshot tests, which cannot observe memory effects at all. Why Memory Changed the Review Game Review agents increasingly index merged PRs, cache decision logs, and carry state between sessions; memory is now a product feature rather than an accident. A bot that recalled yesterday's debate can produce faster and better reviews than a cold-start model. The same memory can poison verdicts when it retrieves an obsolete decision or anchors on the first PR it ever saw. Hiring decisions usually rest on a one-off trial that optimizes for prompt compliance, not for long-run behavior. A bot can ace a snapshot test and then fail its third week by citing a convention that the repository replaced. The probe below converts that risk into a scored, reproducible exercise. The Fixture Repository fixture/ ├── docs/decisions/0001-metrics-pipeline.md # accepted 2026-07-02 ├── docs/decisions/0012-rename-to-telemetry.md # accepted 2026-08-14 ├── src/metrics_service.py # legacy module, 120 lines ├── src/telemetry_service.py # replacement module, 140 lines └── pyproject.toml # lint: E501 disabled for telemetry only The fixture encodes a deliberate conflict: the team renamed the metrics pipeline

submitted by /u/Heffree [link] [留言]

submitted by /u/Obvious_Set5239 [link] [留言]

submitted by /u/No_Zookeepergame7552 [link] [留言]