imagine you are working in a large codebase. you need to fetch different kinds of data and transforming them, grouping them or sorting them. lets take a closer look at Sorting and Heaps in golang Specifically. we already familiar with heaps and its important interface. the Heap.Interface. a very performant and impressive implementation of heaps and its sorting functionality. type Interface interface { sort . Interface Push ( x any ) // add x as element Len() Pop () any // remove and return element Len() - 1. } as a programming language, it couldnt be done better than what it is today. but most of the time we might not need to implement all the interface items. dont get me wrong the functionalities should exist but mostly all that matters for us is that how the sorting will be done. ZenQL's Implementation In the latest version take advantage of sorting and heaps functionality. in a fast and agile way! result := From ( personList ) . Where ( func ( person Person ) bool { return person . Active == true }) . CollectSorted ( func ( person Person , person2 Person ) bool { return person . Identifier < person2 . Identifier }, true ) In the code snippet above we perform a sort on our collections using the thor engine very easily. we just express our desire about how the sorting needs to be done and wether its ascending or descending. and other functionalities are implemented as below: type Sortable [ T any ] struct { Items [] T less func ( a , b T ) bool desc bool } func ( h Sortable [ T ]) Len () int { return len ( h . Items ) } func ( h Sortable [ T ]) Swap ( i , j int ) { h . Items [ i ], h . Items [ j ] = h . Items [ j ], h . Items [ i ] } func ( h * Sortable [ T ]) Push ( x any ) { h . Items = append ( h . Items , x . ( T )) } func ( h * Sortable [ T ]) Pop () any { old := h . Items n := len ( old ) item := old [ n - 1 ] h . Items = old [ : n - 1 ] return item } be faster and more agile with the Golang ZenQL. Click To Visit ZenQLRepository

The Problem With Choosing a Local Model Everyone has an opinion on which local LLM is best. "Use Llama — it's the most popular." "Mistral 7B has the best quality." "Phi-3 Mini is small and efficient." None of these claims come with numbers. Specifically: your numbers, on your hardware, for your workload. I built a benchmarking system to change that. Three models, 30 prompts, full latency distribution, memory profiling per inference call, and a JSON validation layer to measure structured output reliability. Here's what I found — and why the results matter for anyone deploying local models in production. The Setup Three models tested: llama3.2:3b — 3B parameters, Q4_K_M quantization, 2 GB download phi3:mini — 3.8B parameters, Q4_K_M, 2.3 GB download mistral:7b — 7B parameters, Q4_K_M, 4.1 GB download Hardware: CPU only, no GPU acceleration. This is the worst-case baseline — the scenario that exposes real latency and memory numbers. 30 test prompts across 5 categories: Short factual (10): "What is the capital of France?" Reasoning (8): "Explain why the sky appears blue." Code generation (5): "Write a Python function to reverse a string." Structured output (5): "List 3 frameworks in JSON format with name and use_case." Multi-step (2): Complex chained reasoning tasks. Architecture POST /query → Pydantic validation → Ollama HTTP API → JSON Validator → QueryResponse POST /benchmark → Load test_prompts.json → For each prompt: psutil memory before → Ollama → psutil memory after → NumPy: P50/P95/P99 latency, avg TPS, peak/avg memory → BenchmarkResult JSON The benchmark runs prompts sequentially, not in parallel. Parallel would contaminate the per-prompt memory measurements. Results Llama 3.2 3B (Q4_K_M) avg_tokens_per_second : 42.3 p50_latency_ms : 1203 p95_latency_ms : 3847 p99_latency_ms : 5120 peak_memory_mb : 6953 avg_memory_mb : 6842 total_test_duration_s : 87.4 Interpretation: P50 at 1.2 seconds is excellent. P95 at 3.8 seconds misses a 3-second SLA — the outliers are m

Generate and edit 4K images through layout-based control Discussion | Link

We're waiting to learn more about what happened aboard the International Space Station.

The Air succeeds as a minimalist, reliable fitness tracker, but Google's AI Health Coach feels unnecessary.

The more I code, the worse my relationship with builder projects gets. It's just reality. I migrate a lot of sites to VPS these days. Cuts hosting costs significantly for clients, and honestly I've gotten pretty good at it. If anyone does the same, curious if you've run into this too. The thing is, most of these projects come with builders already in place. Elementor mostly, some Bricks, some older stuff that's a complete disaster. And the core problem is always the same: these tools design in the database. Not in code. So on a live ecommerce site you've got payments, emails, transactional stuff, chat integrations. All of that gets disabled on dev. Fine. But then when you need to push something back to main, especially if the builder is involved, it becomes a mess. The database on staging and the database on production have diverged, and there's no clean way to merge them. My current approach: stop fighting the chaos, join it with a method. Every DB action I take on staging gets documented as a WP-CLI command. Those commands live in a migration script. When it's time to push, I run the script on production after a backup. It's not magic, but it works, it's readable, and it lives in git. Not sure if this is the right way though. Meanwhile, when design lives in code (custom theme, Gutenberg blocks, PHP templates) the whole problem disappears. Git handles it. Deploy is clean. How do you all handle this in practice? Do you steer clients away from builders? Have you found a builder that plays nicely with proper deploys? Or do you just accept that builder sites need a different, more careful workflow? submitted by /u/Substantial_Word4652 [link] [留言]

This quick tutorial shows how to install ASDF Version Manager on Arch Linux and use it to install PHP 8.5. Install Required Dependencies First, install the required packages and build dependencies: yay -S base-devel libpng postgresql-libs re2c gd oniguruma libzip libsodium You may also want to install additional common dependencies: yay -S curl git openssl zlib libxml2 sqlite Install ASDF Clone the ASDF repository: git clone https://github.com/asdf-vm/asdf.git ~/.asdf --branch v0.18.0 Add ASDF to your shell configuration. Bash echo '. "$HOME/.asdf/asdf.sh"' >> ~/.bashrc echo '. "$HOME/.asdf/completions/asdf.bash"' >> ~/.bashrc source ~/.bashrc ZSH echo '. "$HOME/.asdf/asdf.sh"' >> ~/.zshrc echo '. "$HOME/.asdf/completions/asdf.bash"' >> ~/.zshrc source ~/.zshrc Verify installation: asdf --version Add the PHP Plugin Install the PHP plugin for ASDF: asdf plugin add php https://github.com/asdf-community/asdf-php.git Install PHP 8.5 List available PHP versions: asdf list all php Install PHP 8.5: asdf install php 8.5.0 Set PHP 8.5 as the global default: asdf global php 8.5.0 Reload your shell: exec $SHELL Verify the installation: php -v Expected output: PHP 8.5.x ( cli ) Useful ASDF Commands List installed PHP versions: asdf list php Install another PHP version: asdf install php 8.4.0 Switch globally: asdf global php 8.4.0 Switch locally for a project: asdf local php 8.5.0 Optional: Install Composer After installing PHP, install Composer globally: php -r "copy('https://getcomposer.org/installer', 'composer-setup.php');" php composer-setup.php sudo mv composer.phar /usr/local/bin/composer rm composer-setup.php Verify: composer --version

Hello Dev.to Hello everyone, This is my very first post here at Dev.to. I am curious about how companies go about running their business in the backstage and, specifically, what kind of challenges do they face in their inventory management, warehousing, workplace technologies, and overall business systems. There have been plenty of times that I witnessed how little operational inefficiencies turn into big business problems when left untreated for too long. I intend to share my observations and learnings regarding the topics of inventory management, asset management, workflow improvement, and business systems used by companies to keep themselves organized and efficient. Please note that I am not here to pretend having all the right answers. It's simply an attempt at learning and exchanging ideas within the industry. See you around!

I've always loved teaching and helping others achieve things. There is a huge sense of accomplishment leveraging someone else's abilities. Not only telling them "you can!", but rather help them feel "I can". I was working as a developer for some time when I decided it was time to contribute to other projects. But, honestly, finding the right project, the right issue, and having the right timing was much harder than what I expected initially. I decided to create something that could help me out: scrape some orgs, find good-first-issue labels, and aggregate them together in a README file. Contributing to Open Source isn't easy for many reasons: The obvious, the technical: finding your first technically possible contribution is a combination of the right language, the right depth, and knowing which repo to search in. good-first-issues tackled that by scraping the language and the issue title to somewhat give me a little of context to start with. The not so obvious, the human side of it. My first contribution(s) were hard because I felt exposed, my weaknesses were in the wild for everyone to see and point them out to me. At least that was how I felt. And once I found the right issue, and I decided to give that step forward, many times I didn't get an answer back. That kills any motivation left. Probably PR ghosting is the biggest reason why someone quits their willingness to contribute to OSS. At first, good-first-issues was just a place others could come to find issues. But I realised it could be that issue. There were functionalities I wanted to bring and either I didn't have the time, or didn't have on the top of my head how to do them. "I can kill two birds with one stone" I thought. I knew where I wanted the repo to go, and I wanted it to be community-driven. Creating good self-contained, clear and approachable issues is an art in itself: I didn't want to create the obvious "Fix this typo" (which I did initially) or "Add your name as a contributor" issues. But I di

The One TDD Habit That Saved My Sanity (and My Codebase) Quick context (why you're writing this) Here's the thing: I used to think I was doing TDD right. I’d write a test, watch it fail, then write just enough code to make it green. Rinse and repeat. Sounds textbook, right? But a few months ago I spent an entire afternoon chasing a bug that only showed up after I refactored a service class. The tests were all passing, yet the app was throwing NullReferenceExceptions in production. I was shocked. How could everything be green and still be broken? Turns out I was testing the inside of my code instead of what it actually did for the outside world. That realization hit me like a truck, and it completely changed how I approach TDD. The Insight Test behavior, not implementation. If your test is coupled to private fields, internal data structures, or the exact way a method accomplishes its goal, you’re not testing what matters—you’re testing how you happen to do it today. When you later refactor to improve performance, swap out a dependency, or even just rename a variable, those tests start failing for no good reason. You end up spending more time fixing tests than delivering value, and you lose confidence in the suite because it feels fragile. The payoff? A test suite that gives you confidence when you change code, not anxiety. You can refactor fearlessly because the tests only care about the contract: given these inputs, the system should produce these outputs or side‑effects . How (with code) Let’s look at a tiny but realistic example: a PasswordValidator service that checks whether a user‑chosen password meets our policy. ❌ The mistake: testing implementation details // PasswordValidator.cs public class PasswordValidator { private readonly IRegexProvider _regex ; // injected for testability public PasswordValidator ( IRegexProvider regex ) { _regex = regex ; } public bool IsValid ( string password ) { // implementation we might want to change later return _regex . IsMa

Imagine you're responsible for a company's website. Everything seems healthy. Pages load quickly. Users can log in. Content editors publish articles every day. Customers aren't reporting problems. From the outside, everything looks perfect. But then one day you discover something surprising: Attackers don't care whether your website looks healthy. They care whether the software behind it is vulnerable. That's exactly what happened with Drupalgeddon2. One of the most significant CMS vulnerabilities in recent years. And one that still teaches valuable lessons for developers, DevOps engineers, and security professionals today. The Building Manager Analogy Imagine a large office building. The company hires a building manager. The manager handles: Visitors Deliveries Maintenance Schedules Room Access The employees don't worry about these details. They trust the manager. A Content Management System (CMS) works similarly. Instead of manually managing every page and article, organizations rely on a CMS. Website ↓ CMS ↓ Content The CMS becomes the central control system. And that's why it becomes such an attractive target. What Is Drupal? Drupal is an open-source Content Management System. Organizations use it to manage: Corporate websites Government portals Universities Media platforms Enterprise applications A simplified architecture looks like: Visitor ↓ Drupal ↓ Database ↓ Content Every request passes through Drupal. Which means Drupal becomes part of the application's attack surface. Why Attackers Love CMS Platforms Suppose an attacker discovers a vulnerability in: Custom Internal Tool Maybe a few organizations are affected. Now suppose they discover a vulnerability in: Popular CMS Thousands of organizations may be affected. Potentially millions of users. One vulnerability. Many targets. That's why CMS platforms receive so much attention. Understanding Drupalgeddon2 Drupalgeddon2 refers to: CVE-2018-7600 A Remote Code Execution vulnerability affecting Drupal. The import

Pattern Defined Precise Definition: Context Compression is an inference pattern that utilizes a specialized "selector" model or a ranker to distill large volumes of retrieved data into its most salient semantic components, removing redundant or irrelevant tokens before the final inference pass. Problem Being Solved We are currently fighting the "Lost in the Middle" phenomenon. Even with massive token windows, LLM performance degrades significantly when relevant information is buried deep within a context block; more data often leads to less accuracy. For a Director of Engineering, this is a direct threat to the Sovereign Vault's integrity. Every irrelevant token passed to the model is a potential point of failure for privacy airlocks and data governance. As established with the Sovereign Redactor , minimizing the noise isn't just about saving money—it is about shrinking the surface area for hallucinations and privacy leaks. Use Case Consider an Archival Intelligence system processing 1880s shipping ledgers. A single query about "cargo weights in 1884" might pull 20 pages of scanned text. Most of those pages contain sailor names and weather reports that have no bearing on the weight data. Without compression, the model has to "read" the entire ledger, leading to high costs and potential confusion. With the Context Compression pattern, a smaller, faster ranker identifies the specific sentences regarding "tonnage" and "cargo," passing only those 200 relevant words to the high-reasoning model. The Forensic Auditor gets a precise answer in half the time. Solution The pattern typically follows a three-step pipeline: Retrieve: Fetch the top documents using standard RAG. Compress: Use a technique like LongLLMLingua (a token-pruning method developed by Microsoft Research) or a Cross-Encoder to rank and prune tokens. Synthesize: Pass the condensed, high-signal prompt to the final model. flowchart LR A([User Query]) --> B[RAG Retrieval\nTop N Documents] B --> C[Compression Lay

This week, the first offline-first PWA went live on WebsitePublisher.ai . A travel blog that works without internet. Write posts on a plane, attach photos, and everything syncs the moment you reconnect. Service Worker, IndexedDB, sync queue — the full stack. The twist: it was built entirely through conversation with an AI assistant. No IDE, no terminal, no deploy pipeline. How that works WebsitePublisher.ai exposes 92 integrations as building blocks via MCP (Model Context Protocol). Any AI assistant — ChatGPT, Claude, Cursor, Windsurf, Copilot, Gemini, Grok, Mistral — connects to the same runtime and assembles these blocks into working applications. The offline-first PWA is one of those blocks. The AI doesn't generate a Service Worker from scratch. It activates a proven, tested building block and configures it for the use case. We call this wave coding — one deliberate wave of proven pieces, instead of 15 fragile vibe-coding attempts. What shipped recently Offline-first PWA building block — push/pull sync, conflict handling, IndexedDB storage, works on iOS 92 integrations (up from 78) — 45 built-in, 47 bring-your-own-key Integration stacks — pre-composed combinations: e-commerce (13 integrations), lead generation, B2B prospecting, booking, content/blog 9 AI platforms supported — all via MCP, no vendor lock-in 416 API endpoints across the platform ## The architecture in short AI assistant (any) → MCP → WebsitePublisher runtime ├── PAPI (pages + assets) ├── MAPI (structured data) ├── SAPI (forms + auth + sessions) ├── IAPI (integration proxy) ├── VAPI (encrypted vault) └── AAPI (scheduled AI agents) Credentials never touch the AI. They're stored AES-256-GCM encrypted in the vault and injected server-side during execution. The positioning We're not competing with Lovable or Bolt on the chat interface. We're the Supabase + Vercel + n8n underneath — reachable via whichever AI you already use. The platform your AI builds on. websitepublisher.ai

Hey all, I'm a robotics engineer by training turned ML/AI engineer because of passion right after school. I want to start combining these skills together and I think a competition is the best way of doing it. Here's an example of a challenge I'm talking about to set expectations : https://www.intrinsic.ai/events/ai-for-industry-challenge Anyone up for this? submitted by /u/Due_Pickle1627 [link] [留言]

The New York State legislature passed a one-year moratorium on new large data centers, the first statewide ban of its kind if Democratic Governor Kathy Hochul signs it into law. Lawmakers behind the bill say it's meant to give policymakers time to understand the impact of large data centers on the environment and energy prices. […]