Tag: JUnit in Action

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Merhaba arkadaşlar. Bu yazıda Kotlin programlama dilinde kullanıcıdan veri alma işleminin nasıl gerçekleştiğine değineceğim.Continue reading on Medium »

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Ever scroll through a coding forum, secretly comparing your skills to everyone else’s? It’s easy to feel like you’re behind, like you’ve…Continue reading on Medium »

Teknolojinin hızla gelişmesiyle birlikte, her geçen gün daha güçlü, hızlı ve verimli uygulamalar geliştirilmeye başlanıyor. Bu yazıda…Continue reading on Medium »

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I've been experimenting with Lovable.dev and Bolt.new for a couple weeks now and I'm genuinely impressed by how quickly they can generate a working app from a description. The demos and simple apps I've built have been fun to create. For those using these tools regularly in actual work (not just demos), I'm curious about your experience: Have you found any limitations when building more complex apps with these tools? I'm particularly interested in how they handle domain logic that goes beyond basic CRUD. For those who've maintained an app generated by these tools for more than a month - how's that experience been? Does the architecture hold up when you need to add features? Do you use them for production applications or mainly for prototyping? What kinds of projects seem to work best with these tools? I'm trying to understand where we are in the evolution of AI coding tools. The "AI will replace programmers" headlines are everywhere, but I'm more interested in the nuanced reality from people actually using these tools day-to-day. Are we truly approaching a point where these tools can handle the complexity of real-world applications, or are we still in the early stages where they're mainly useful for MVPs and simpler projects? Would love to hear your honest experiences - both positive and negative! submitted by /u/Unique_Working6841 [link] [comments]

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Not mine. I always wanted to do something with this, but it never matched personally or professionally. submitted by /u/esdraelon [link] [comments]

Hi all I have built this project lblprof to be able to very quickly get an overview of how much time each line of my python code would take to run. It is based on the new sys.monitoring api PEP669 What my project Does ? The goal is to be able to know very quickly how much time was spent on each line during my code execution. I don't aim to be precise at the nano second like other lower level profiling tool, but I really care at seeing easily where my 100s of milliseconds are spent. I built this project to replace the old good print(start - time.time()) that I was abusing. This package profile your code and display a tree in the terminal showing the duration of each line (you can expand each call to display the duration of each line in this frame) Example of the terminal UI: terminalui_showcase.png (1210×523) Target Audience Devs who want a quick insight into how their code’s execution time is distributed. (what are the longest lines ? Does the concurrence work ? Which of these imports is taking so much time ? ...) Installation pip install lblprof The only dependency of this package is pydantic, the rest is standard library. Usage This package contains 4 main functions: start_tracing(): Start the tracing of the code. stop_tracing(): Stop the tracing of the code, build the tree and compute stats show_interactive_tree(min_time_s: float = 0.1): show the interactive duration tree in the terminal. show_tree(): print the tree to console. ​ from lblprof import start_tracing, stop_tracing, show_interactive_tree, show_tree start_tracing() #Your code here (Any code) stop_tracing() show_tree() # print the tree to console show_interactive_tree() # show the interactive tree in the terminal The interactive terminal is based on built in library curses What do you think ? Do you have any idea of how I could improve it ? submitted by /u/PhilosopherWrong6851 [link] [comments]

When I came across a study that traced 4.5 million fake GitHub stars, it confirmed a suspicion I’d had for a while: stars are noisy. The issue is they’re visible, they’re persuasive, and they still shape hiring decisions, VC term sheets, and dependency choices—but they say very little about actual quality. I wrote StarGuard to put that number in perspective based on my own methodology inspired with what they did and to fold a broader supply-chain check into one command-line run. It starts with the simplest raw input: every starred_at timestamp GitHub will give. It applies a median-absolute-deviation test to locate sudden bursts. For each spike, StarGuard pulls a random sample of the accounts behind it and asks: how old is the user? Any followers? Any contribution history? Still using the default avatar? From that, it computes a Fake Star Index, between 0 (organic) and 1 (fully synthetic). But inflated stars are just one issue. In parallel, StarGuard parses dependency manifests or SBOMs and flags common risk signs: unpinned versions, direct Git URLs, lookalike package names. It also scans licences—AGPL sneaking into a repo claiming MIT, or other inconsistencies that can turn into compliance headaches. It checks contributor patterns too. If 90% of commits come from one person who hasn’t pushed in months, that’s flagged. It skims for obvious code red flags: eval calls, minified blobs, sketchy install scripts—because sometimes the problem is hiding in plain sight. All of this feeds into a weighted scoring model. The final Trust Score (0–100) reflects repo health at a glance, with direct penalties for fake-star behaviour, so a pretty README badge can’t hide inorganic hype. I added for the fun of it it generating a cool little badge for the trust score lol. Under the hood, its all uses, heuristics, and a lot of GitHub API paging. Run it on any public repo with: python starguard.py owner/repo --format markdown It works without a token, but you’ll hit rate limits sooner. Repo is: repository Also here is the repository the researched made for reference and for people to show it some love. Researcher repository Please provide any feedback you can. I’m mainly interested in two things going forward: Does the Fake Star Index feel accurate when you try it on repos you already know? What other quality signals would actually be useful—test coverage? open issue ratios? community responsiveness? submitted by /u/WelcomeMysterious122 [link] [comments]

Here is a summary of how netflix is built on java and how they actually collaborate with spring boot team to build custom stuff. For people who want to watch the full video from netflix team : https://youtu.be/XpunFFS-n8I?si=1EeFux-KEHnBXeu_ submitted by /u/stealth_Master01 [link] [comments]

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For those of you that are still writing C in the age of memory-safe languages (I am with you), I wanted to share a little library I made that helps with one of C's most annoying quirks - the complete lack of array metadata. What is it? MIDA (Metadata Injection for Data Augmentation) is a tiny header-only C library that attaches metadata to your arrays and structures, so you can actually know how big they are without having to painstakingly track this information manually. Revolutionary concept, I know. Why would anyone do this? Because sometimes you're stuck maintaining legacy C code. Or working on embedded systems. Or you just enjoy the occasional segfault to keep you humble. Whatever your reasons for using C in 2024, MIDA tries to make one specific aspect less painful. If you've ever written code like this: c void process_data(int *data, size_t data_length) { // pray that the caller remembered the right length for (size_t i = 0; i < data_length; i++) { // do stuff } } And wished you could just do: c void process_data(int *data) { size_t data_length = mida_length(data); // ✨ magic ✨ for (size_t i = 0; i < data_length; i++) { // do stuff without 27 redundant size parameters } } Then this might be for you! How it works In true C fashion, it's all just pointer arithmetic and memory trickery. MIDA attaches a small metadata header before your actual data, so your pointers work exactly like normal C arrays: ```c // For the brave C99 users int *numbers = mida_array(int, { 1, 2, 3, 4, 5 }); // For C89 holdouts (respect for maintaining 35-year-old code) int data[] = {1, 2, 3, 4, 5}; MIDA_BYTEMAP(bytemap, sizeof(data)); int *wrapped = mida_wrap(data, bytemap); ``` But wait, there's more! You can even add your own custom metadata fields: ```c // Define your own metadata structure struct packet_metadata { uint16_t packet_id; // Your own fields uint32_t crc; uint8_t flags; MIDA_EXT_METADATA; // Standard metadata fields come last }; // Now every array can carry your custom info uint8_t *packet = mida_ext_malloc(struct packet_metadata, sizeof(uint8_t), 128); // Access your metadata struct packet_metadata *meta = mida_ext_container(struct packet_metadata, packet); meta->packet_id = 0x1234; meta->flags = FLAG_URGENT | FLAG_ENCRYPTED; ``` "But I'm on an embedded platform and can't use malloc!" No problem! MIDA works fine with stack-allocated memory (or any pre-allocated buffer): ```c // Stack-allocated array with metadata uint8_t raw_buffer[64]; MIDA_BYTEMAP(bytemap, sizeof(raw_buffer)); uint8_t *buffer = mida_wrap(raw_buffer, bytemap); // Now you can pretend like C has proper arrays printf("Buffer length: %zu\n", mida_length(buffer)); ``` Is this a joke? Only partially! While I recognize that there are many modern alternatives to C that solve these problems more elegantly, sometimes you simply have to work with C. This library is for those times. The entire thing is in a single header file (~600 lines), MIT licensed, and available at: https://github.com/lcsmuller/mida So if like me, you find yourself muttering "I wish C just knew how big its arrays were" for the 1000th time, maybe give it a try. Or you know, use Rust/Go/any modern language and laugh at us C programmers from the lofty heights of memory safety. That's fine too. submitted by /u/LucasMull [link] [comments]

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OpenSearch 3.0 is out (first major release since the open source project joined the Linux Foundation), with nice upgrades to performance, data management, vector functionality, and more. Some of the highlights include: Upgrade to Apache Lucene 10 and JDK 21+ Pull-based ingestion for streaming data, with support for Apache Kafka and Amazon Kinesis Separate reads and writes for remote store for granular scaling and resource isolation Power agentic AI with native MCP (Model Context Protocol) support Investigate logs with expanded PPL query tools, backed by Apache Calcite Achieve 2.5x faster binary quantization with concurrent segment search submitted by /u/horovits [link] [comments]