标签：#RAM

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

patch-client@4.0.4 went out through the project's own github action OIDC trusted publisher today and not any stolen token or a typosquat anything, we saw that the actual release pipeline produced it. this runs on npm install, steals cloud creds and self propagates by injecting fake CodeQL workflows into repository the stolen tokens can reach. 32 packages is currently sharing the same publisher so the window of exposure isn not only just a single package. if you have anything from related to / redhat-cloud-services in your tree, 4.0.3 is the last clean version. submitted by /u/BattleRemote3157 [link] [留言]

We asked developers to spill their little dirty secrets, the lies they tell their managers and what actually creates tension in teams. One theme that kept coming up was the gap between how software development looks from the outside and what it actually looks like in practice. submitted by /u/aisatsana__ [link] [留言]

Take Aways Performance and Scalability : Pinecone is a managed machine-learning database that provides exceptional levels of performance and scaling capability due to its cloud-based design. Because of its distributed architecture and ability to do near-neighbor searches, Pinecone handles such tasks as similarity searching and anomaly detection on very large datasets efficiently. Easy to Integrate : One of the standout benefits of Pinecone is how easily it integrates through a high-level API and SDKs across several programming languages. This gives developers a real productivity boost by making vector storage, indexing and querying for machine learning applications far less complicated to implement. Strategic Factors : Pinecone brings advanced features and managed services that genuinely enhance machine learning workflows, though it does come with considerations like recurring costs and vendor lock-in. Organizations should think carefully about these factors alongside the benefits of streamlined database management and optimized performance before committing to adoption. The importance of storing and accessing information properly to build the best possible machine learning model really cannot be overstated. Pinecone addresses this directly by offering a Vector Database built specifically for ML queries, creating a strong opportunity to tap into the power of cloud databases. Designed from the ground up as a cloud-native application, Pinecone makes it straightforward to index and search complex, high-dimensional vector data — which in turn makes building state-of-the-art machine learning applications much more approachable and helps software development companies deliver more value to their clients through custom software development. What is Pinecone? Pinecone is a fully managed Vector Database that lets you store, index, and query complex vector data quickly and efficiently. Because of its vector-native design, the primary use cases for Pinecone fall within similar

submitted by /u/viks98 [link] [留言]

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

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

submitted by /u/ke2nyjks [link] [留言]

Mañana cambia el billing de Copilot: las premium requests dan paso a AI Credits calculados por tokens. Esto es lo que debe revisar un equipo técnico. El 1 de junio de 2026 GitHub Copilot empieza a migrar desde el modelo de premium requests hacia billing por uso con GitHub AI Credits. La unidad deja de ser una petición premium más o menos abstracta y pasa a reflejar consumo de tokens: entrada, salida y tokens cacheados, con precios vinculados al modelo usado. Decisión rápida Qué cambia mañana La idea de GitHub es alinear precio con coste real. Una pregunta rápida a un modelo ligero y una sesión larga de agente sobre varios archivos ya no son equivalentes. Para equipos técnicos, eso obliga a tratar Copilot como infraestructura de IA, no como una extensión de editor de coste fijo. Este artículo complementa la guía previa de AI Credits, pero se centra en el cambio operativo inmediato: qué mirar antes de que el modelo entre en vigor mañana. Briefing Qué es un AI Credit GitHub define AI Credits como una unidad de billing donde 1 AI Credit equivale a 0,01 USD. Cada interacción que usa modelos consume tokens. Esos tokens se valoran según el modelo y se convierten a créditos. En planes individuales, Copilot Pro, Pro+ y Max incluyen asignaciones mensuales de AI Credits. En organizaciones y empresas, cada licencia aporta créditos que se agrupan en un pool compartido a nivel de billing entity. La diferencia clave con el sistema anterior es que el consumo puede variar mucho dentro de una misma función. Dos sesiones de chat no cuestan igual si una es una pregunta corta y otra arrastra contexto de repositorio, varias iteraciones y generación de código extensa. Lectura práctica Qué consume créditos y qué no GitHub documenta que consumen AI Credits funciones como Copilot Chat, Copilot CLI, Copilot cloud agent, Copilot Spaces, Spark y agentes de terceros. Las code completions y Next Edit suggestions no se facturan en AI Credits y siguen incluidas en planes de pago. Esta distinción es

Hey everyone 👋 I'm exploring ideas for an AI-powered developer tool, but before building anything, I want to understand the real problems developers face every day. There are already plenty of tools that generate code. What I'm interested in is everything around coding: Debugging Code reviews Technical debt Documentation Dependency upgrades Testing Deployment Architecture decisions Learning large codebases I'd love to hear from you: A few questions: What's the most frustrating part of your workflow? What task takes more time than it should? What's something you wish AI could do for you today? Have current AI tools (ChatGPT, Claude, Cursor, Copilot, Gemini, etc.) failed you in any important way? If you could eliminate one developer headache forever, what would it be? I've also created a short 2-minute survey: 🔗 https://docs.google.com/forms/d/e/1FAIpQLSf1M5d2y-0RXEIhrbDBtS5gC900YuzWl43cJCxGUrU38MyeDQ/viewform?usp=publish-editor I'll happily share the survey results and key findings with the community once I collect enough responses. Thanks in advance for any feedback!

The use of AI Agents creates a distinctive smell... One can tell the GH Repo owner was high on...

A question for guys who into unusual programming stuff. i been programming for +12 years now .. first few years it was fun trying all sort of things from assembly x86 to neural networks and computer vision but after finishing college few years ago and getting into corporates and the job market .. i been coding only for work as a backend engineer and programming stoped being a hobby anymore .. especially with ai propaganda that is going on right now. now i want to try a completely different part of programming as a hobby again away from web/app/game development or CV and ML something that is useful and somehow low level. i thought about learning CUDA and exploring other aspects now but its hard to find super geeks now to ask after the AI boom. what other fields you guys are into that are fun, complex and unusual? submitted by /u/zeXas_99 [link] [留言]

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

I’ve been working on Keylight, a licensing layer for macOS apps, and one of the more interesting technical problems has been designing license checks that work both online and offline. The basic problem sounds simple: “Is this app allowed to run?” But in practice, there are a lot of edge cases: What happens if the user bought the app, but is currently offline? How long should an offline lease stay valid? How do you prevent a license from being copied forever? How do you handle device limits without making the app annoying? What happens when a subscription expires while the app is offline? How do you rotate SDK/API keys without breaking old app versions? How much should be checked locally vs on the server? The approach I’m using is roughly: The app validates against the server when online The server returns a signed local license lease The app can continue working offline for a limited period Device activations are tracked server-side Renewals, upgrades, and revocations update the next lease Old SDK keys can be retired gradually instead of breaking existing builds It’s a small part of the product, but it has been one of the most interesting engineering decisions so far. Curious how others would design this. Would you keep most licensing logic server-side, or allow more local verification with signed license files? And for desktop apps, what do you think is a fair offline grace period? submitted by /u/nicolas1410 [link] [留言]

Originally published at Perl Weekly 775 Hi there! I try to keep track of the Perl-related events. You can find them listed at the bottom of each edition of the newsletter and on the events page on the Perl Weekly web site. There you can also find a link to embed the calendar in your calendar program. There are a number of events scheduled for this month. Most of them online, so if your time-zone permits, you can join those events. The big in-person event is at the end of the month The Perl and Raku Conference in Greenville, South Carolina, USA. In the last couple of weeks I have been using various AI tools extensively. It still needs some hand-holding, but it already writes code that seems to be way better than the average code I've seen. So I wonder, would it be possible to ask one of the AI tools to convert Python libraries to Perl? You know, we have been complaining for many years that companies provide implementation for their SDK/API/client in several language, but not in Perl. We also saw that CPAN could not keep up with the growth of PyPI, npm and the other 3rd party library registries. So maybe some of you would like to explore the idea of converting some of these libraries to Perl using AI. Finally a personal note, I am planning a trip to Korea and Japan in September-October. If you live there and would have any travel recommendations, I'd love to get that. Enjoy your week! -- Your editor: Gabor Szabo. Articles Introducing ZuzuScript Toby Inkster created a programming language which blends a fairly JavaScript-like syntax with fairly Perl-like semantics, and a few other features that he hasn't really seen in many programming languages. ANNOUNCE: Perl.Wiki V 1.47, JSTree copy V 1.21 Teaching AI About the British Monarchy with MCP The site already exposes information through a traditional web interface and a JSON API. But those interfaces were designed for humans and developers respectively. MCP gives AI systems a much cleaner integration point. ANNOUNCE: Perl

Tuples, Sets, and Dictionaries in Python In the previous lesson, we learned about Lists and how they are used to store multiple items in a single variable. Today, we will learn about three important Python data structures: Tuples Sets Dictionaries These data structures help programmers organize and manage data efficiently in different situations. 1. Tuples in Python A Tuple is a collection of items stored in a single variable. Tuples are: Ordered Unchangeable (Immutable) Allow duplicate values Tuples are created using parentheses "()". Example languages = ( " Python " , " Java " , " C++ " ) print ( languages ) Output ( ' Python ' , ' Java ' , ' C++ ' ) Accessing Tuple Items Tuple items are accessed using indexes. Example languages = ( " Python " , " Java " , " C++ " ) print ( languages [ 0 ]) print ( languages [ 1 ]) Output Python Java Negative Indexing in Tuples Example languages = ( " Python " , " Java " , " C++ " ) print ( languages [ - 1 ]) Output C ++ Tuple Length The "len()" function returns the number of items in a tuple. Example numbers = ( 10 , 20 , 30 ) print ( len ( numbers )) Output 3 Why Tuples are Important Tuples are useful when data should not be modified accidentally. They are commonly used for: Fixed data Coordinates Database records Returning multiple values from functions 2. Sets in Python A Set is a collection of unique items. Sets are: Unordered Unchangeable items Do not allow duplicates Sets are created using curly brackets "{}". Example numbers = { 1 , 2 , 3 , 4 } print ( numbers ) Output {1, 2, 3, 4} Duplicate Values in Sets Sets automatically remove duplicate values. Example numbers = { 1 , 2 , 2 , 3 , 4 } print ( numbers ) Output {1, 2, 3, 4} Adding Items to a Set The "add()" method inserts a new item into a set. Example numbers = { 1 , 2 , 3 } numbers . add ( 4 ) print ( numbers ) Output {1, 2, 3, 4} Removing Items from a Set The "remove()" method removes an item from a set. Example numbers = { 1 , 2 , 3 , 4 } numbers . remove ( 2 ) print

submitted by /u/mttd [link] [留言]

submitted by /u/pdoherty926 [link] [留言]

I spent way too long staring at this error. If you're here, you probably are too. Your requirements could not be resolved to an installable set of packages. Problem 1 - Root composer.json requires php ^8.3 but your php version (8.2.30) does not satisfy that requirement. My Laravel 13 app needed PHP 8.3. My Hostinger server was running 8.2. composer install refused to budge. Here's exactly what happened and the one-liner that fixed it. The Setup I was deploying a Laravel 13 + Inertia + React app to Hostinger shared hosting. Laravel 13 requires PHP 8.3 minimum — and so do its locked Symfony 8.x and PHPUnit 12.x dependencies. My composer.lock had been generated on a local machine with PHP 8.3, but Hostinger's CLI was defaulting to 8.2. The hPanel showed PHP 8.3 selected under PHP Configuration . The website itself was running fine on 8.3. But SSH? Still on 8.2. $ php -v PHP 8.2.30 ( cli ) That disconnect — hPanel vs. CLI — is the trap. What I Tried First composer update My first instinct was to just let Composer resolve newer compatible versions: composer update No luck. The root composer.json itself declared "php": "^8.3" , so Composer refused before even touching the lock file. The PHP constraint wasn't just in dependencies — it was in my own project requirements. composer install --ignore-platform-reqs This flag skips platform checks and forces the install anyway. It works , but it's a lie — you end up with packages that may behave incorrectly or fail at runtime because they genuinely require PHP 8.3 features. Not a real fix. Changing PHP in hPanel Hostinger's control panel has a PHP version switcher under Hosting → Manage → PHP Configuration . I had already set this to 8.3. This controls the web server / FPM version — what runs your .php files in the browser. It does not change what php points to in your SSH terminal. That's the key distinction most tutorials miss. What Actually Fixed It Hostinger installs multiple PHP versions in parallel. They live in /opt/alt/ph

Based on real system architecture decisions. About a $660K AI platform, three AI agents that kept the dashboard green, and a P0 incident that cost $3.15M over one weekend. Act 1 · The All-Hands Meeting Wang Lei, VP of Product, stood in front of the big screen, a smile on his face. Behind him, a dashboard rolled data from the "Axon AI Client Engineering Platform — Q1 Performance Report." Numbers cascaded across the wall: Metric Axon Platform Human Team (Last Q1) Improvement Avg daily tickets processed 847 312 +171% Avg first response time 12s 4h 17m ↓ 99.92% Customer satisfaction 4.8/5 4.1/5 +17% Monthly operating cost $52K $133K −61% Twelve department heads sat in the room. Dead silence. Wang Lei planted both hands on the table and scanned the room. His eyes landed on me. "Alex. Your team processed 312 tickets last Q1. Axon processed more than that in a single day last month." He smiled. Not a friendly smile. A sentencing smile. "And Axon costs less than a third of your team's operating expense." "We invested $660K in the whole platform. At current operating costs, it pays for itself in eighteen months." "After management review — the Client Engineering technical liaison function is being fully transitioned to the Axon platform." He clicked to the next slide. "Employees in replaced roles will complete exit interviews within the week." Someone inhaled sharply. I didn't. I opened my notebook to page 37. "Wang, what dimensions are these numbers from?" "What do you mean, 'what dimensions'?" His smile tightened. "Of those 847 daily tickets — how many are auto-tagging and routing, and how many are actual technical resolutions?" The room went quiet for about five seconds. Wang Lei looked at me. "Axon's ticket closure rate is ninety-three percent." "What's the reopen rate?" He paused. "What?" "After Axon replies — how many customers reopen the same ticket within twenty-four hours?" "We're still collecting that —" "Let me save you the trouble." I turned my notebook toward th