Every time you create a new Laravel application, the installer asks a series of questions: - Which starter kit would you like to use? - Which database are you using? SQLite, MySQL, or PostgreSQL? - Would you like to install Boost? - Would you like to use Pest? - Would you like to install and build NPM dependencies? - Would you like to initialize a Git repository? These questions are useful, but if you're creating Laravel projects regularly, answering them repeatedly becomes tedious. If your preferred setup includes: - SQLite - Pest - Boost - NPM dependencies installed and built - Git initialized You can skip all the prompts and create a new project with a single command: laravel new --database=sqlite --boost --npm --pest --silent --git my-project Prefer MySQL or PostgreSQL? Just replace sqlite with mysql or pgsql. You can make it even more convenient by creating an alias: alias laravelx="laravel new --database=sqlite --boost --npm --pest --silent --git" Then create a new project like this: laravelx my-project One command. No questions. Your project is ready. Small productivity improvements may not seem important individually, but they save a surprising amount of time when repeated every day. Keep this one handy. It can make your Laravel workflow a little smoother. #Laravel #PHP #WebDevelopment #Programming #DeveloperProductivity #SoftwareDevelopment

💻 LLM-Augmented Software Engineering & Internal Developer Productivity — the powerful force multiplier that lets engineering teams write code 2–5× faster, ship higher quality, reduce boilerplate, accelerate onboarding, and deliver more value with existing capacity. Just read this excellent capstone technical white paper from @aasaitech on LLM-augmented workflows across the SDLC. Key highlights: • 7-step workflow: Understand → Generate → Review → Improve → Test → Document → Deploy continuous feedback • High-value use cases: IDE assistance, code/test generation, refactoring, code review, documentation • Metrics that matter: Code acceptance rate, PR cycle time, bug escape rate, test coverage, time-to-market, developer satisfaction • Enabling infrastructure: Internal LLM gateway, codebase indexing (RAG), prompt management, safety/security, CI/CD integration, observability LLMs don’t replace engineers — they amplify impact. Perfect for building robust internal platforms, agentic systems, and edge/industrial AI products. Full white paper infographic: x.com/aasaitech/status/20656… How are you using LLMs to augment your own engineering productivity — GitHub Copilot-style assistants, full internal RAG-powered coding agents, or structured review workflows? #LLMAugmentedEngineering #DeveloperProductivity #AgenticAI #IndustrialAI #InternalLLMPlatform #SoftwareEngineering #EdgeAI

Repo and writeup: juanjofuchs.github.io/ai-dev… if something breaks or you want a different insert format, tell me. #ClaudeCode #DeveloperProductivity

Story time 📖 Imagine you've spent months building the perfect internal platform. The tooling is elegant. The docs are solid. But no one is using it. Sound familiar? Research in platform engineering shows that voluntary adoption requires a deliberate strategy — and the 'Three Asks' framework is emerging as a practical model to guide teams from awareness to active engagement. Here's what the data says about adoption metrics for optional platforms: ✅ Early adopters signal platform health ✅ Measuring 'asks' reveals friction points ✅ Optional platforms build long-term trust Dive into the full conversation with Steve 👇 lckhd.eu/BgL6Fb #PlatformEngineering #InternalDeveloperPlatform #EngineeringLeadership #DevOps #PlatformTeams #DeveloperProductivity

Who's really writing your code in 2026? Swipe through. Then tell us: does this match your team's reality? #AICoding #DeveloperProductivity #SoftwareEngineering #GenerativeAI #CodeQuality

I added one line to our AI prompts. It stopped us from repeating the same mistakes. The line: Lessons Captured — append to lessons.md That's it. At the end of every implementation or code review task, the AI asks: Did we learn something worth remembering? * A workaround. * A security issue. * An assumption that turned out to be wrong. * A pattern that broke. If yes, it appends a single lesson to a shared file in the repo. Most AI workflows don't automatically learn from previous tasks. The same architectural mistake gets rediscovered. The same edge case gets missed. The same bad pattern gets suggested again. A small lessons.md file changes that. Every completed task leaves behind a tiny piece of judgment that future engineers — and future AI sessions — can reuse. Most lessons live in one engineer's head. The person who fixed the auth bug. The person who learned not to touch a certain shared utility. The person who discovered a deployment pitfall at 2 AM. When those lessons are captured automatically and stored in the repo, they stop being tribal knowledge. A new teammate gets them on day one. A new AI session gets them on task one. Asynchronous mentorship. No meeting required. The trick is keeping it small. A 200-line lessons file gets ignored. A 15-line one gets read. Rules: • One lesson = one concrete sentence • No duplicates • Remove stale lessons regularly • Promote universal rules to your main context docs Every bug fixed once becomes a lesson. Every lesson becomes context. That's how your AI workflow gets smarter without changing models. #AI #SoftwareEngineering #AIAgents #DeveloperProductivity #PromptEngineering

Amazon is using Generative AI to transform software development productivity… ✅ 66% increase in developer throughput ✅ 4x faster ramp-up for new developers joining projects ✅ AI-assisted writing, coding and documentation reducing time spent on repetitive tasks Developers spend less time on admin work, onboard faster, and ship software more quickly. A great example of AI creating measurable productivity gains across engineering teams. Read their AI use case here 👉👉 headofai.ai/ai-industry-case… Ready to drive ROI faster with AI? Explore how at headofai.ai?utm_source=twitt… #AI #HeadofAI #GenerativeAI #SoftwareDevelopment #DeveloperProductivity #EngineeringLeadership #DevOps #SoftwareEngineering #ProductDevelopment #DigitalTransformation #Innovation #EnterpriseAI #Productivity #Automation #TechLeadership #FutureOfWork #MachineLearning #EngineeringManagement #Technology #BusinessTransformation headofai.ai/ai-industry-case…

Hidden code issues = missed deadlines. 😬 CodeScene spots risky hotspots, team coupling, and tech debt, before they become bottlenecks. 💻⚡ Predict. Refactor. Ship better code. 🚀 👉 Try CodeScene: aiagents.saastrac.com/ai-age… #CodeScene #DevTools #DeveloperProductivity #SoftwareEngineering

9Router: A Unified AI Routing Layer for Coding Tools, Cost Optimization, and Model Failover 🤖💀 💡 Stop paying for unused AI quotas and switching models manually. 9Router is an open-source AI router that connects coding tools like Claude Code, Cursor, Codex, Cline, and OpenClaw to 40 providers with smart fallback, token optimization, and quota tracking. ✅ Save 20–40% tokens ✅ Auto-switch between providers ✅ OpenAI-compatible endpoint ✅ Free and self-hostable 🔗 github.com/decolua/9router #AIEngineering #OpenSource #ClaudeCode #CursorAI #LLMOps #DeveloperProductivity

AI coding just had its “cloud bill shock” moment. GitHub Copilot’s usage-based billing is now live, and the early reaction from developers should make every CIO, CISO, CTO, and engineering leader pause. This is not just a pricing update. It is the end of the “AI feels unlimited” era. GitHub confirmed that Copilot usage is now billed through GitHub AI Credits. Your monthly subscription includes a credit allowance. Once those credits are consumed, your team either buys more or usage gets constrained. Important detail: Basic code completion is still included. But the expensive part is where modern AI development is heading: Agent mode. Code review. Large repo chat. Multi-file edits. Long-running coding sessions. Agents reading, reasoning, testing, and rewriting across codebases. That is where the cost model changes fast. Industry reports are already showing sticker shock. Developers are reporting Copilot costs jumping from around $29/month to nearly $750/month. Others are reporting credit burn that could turn small AI subscriptions into serious engineering budget line items. And GitHub itself says Copilot code review now consumes AI Credits under the new model. For Business and Enterprise customers, GitHub is cushioning the transition with temporary promotional credits: Business: extra $30/user/month Enterprise: extra $70/user/month But those credits only cover June, July, and August. In September, the real run-rate begins. So the question is no longer: “Are our developers using AI?” The real question is: “Do we know what our AI engineering usage actually costs when agents start working across real codebases?” Because AI tooling is starting to look a lot like cloud infrastructure: Easy to adopt. Powerful at scale. Dangerous without governance. Expensive when no one is watching. The flat-fee era of AI developer tools is fading. Usage-based AI is the new normal. And Copilot may not be the exception. It may be the preview. #AI #GitHubCopilot #Copilot #AICoding #AgenticAI #SoftwareEngineering #DevOps #CyberSecurity #CloudCosts #FinOps #AIEngineering #DeveloperProductivity #AIGovernance #GenAI #EnterpriseAI #TechLeadership #VibeCoding

The future developer is not just someone who writes code. It is someone who designs systems where AI can safely produce, test, and improve code. #LoopEngineering #AIAssistedEngineering #AIEngineering #AgenticAI #FutureOfCoding #SoftwareEngineering #DeveloperProductivity #AIForDev

[GN⁺] AI 시대, 가장 가치 있는 개발자는 장인이면서 빌더인 사람이 될 것 (by 9bow님) d.ptln.kr/10605 #geeknews #vibecoding #aicoding #developerproductivity #aigeneratedcode #softwarecraftsmanship

AI coding tools have become developer muscle memory. After moving to GitHub AI Credits, we gave employees ~5K credits each. Many burned through them in 3–4 days. Now productivity feels stuck. AI is no longer just a tool. It is part of the engineering workflow — and every prompt has a cost. #AI #GitHubCopilot #DeveloperProductivity #SoftwareEngineering

Quick tip for Claude Code users: You can mine your old Claude Code transcripts to extract recurring corrections, preferences, and lessons, then convert them into better project rules. This helps Claude stop repeating the same mistakes across sessions. Use a prompt like this: --- Mine my Claude Code session transcripts for recurring lessons, corrections, and preferences. Turn them into compact rules. This instruction is the authorization. Do not re-ask unless file access fails. SOURCE: <path-to-your-claude-project-transcripts>/*.jsonl Important: Do not read all raw files fully. These files can be large. Work in these steps: 1. Shrink first Only extract user turns from each JSONL transcript. Look for lines where: type == "user" The text may be in: message.content as a string or message.content as an array of blocks. In that case, extract each block.text. Inspect one file first to confirm the shape, then process the rest. Prioritize turns containing correction signals such as: "no" "don't" "again" "I told you" "as I said" "stop" "revert" "wrong" "every time" "do not" "instead" "repeat" 2. Process in batches Split the transcript files into batches. For each batch, extract only lessons, not transcript dumps. Each lesson should be: one line only action-first deduped within the batch include recurrence count if repeated 3. Merge and dedupe Merge all batch results. Aggressively collapse near-duplicates. Keep only lessons that are recurring, emphatic, or clearly useful. Each final lesson should be one compact rule. 4. Classify lessons Classify every lesson as: GENERIC: useful across any repo/project PROJECT-SPECIFIC: only useful for this repo/project 5. Compare against existing rules Before writing anything, read existing Claude rules/memory files, such as: ~/.claude/CLAUDE.md and the relevant project memory file. Do not add rules that already exist. 6. Write only net-new rules Add GENERIC lessons to global Claude rules. Add PROJECT-SPECIFIC lessons to project memory. Avoid bloat. Do not include private paths, file names, project names, or transcript details in global rules. 7. Report briefly Return: number of global rules added number of project rules added number of lessons skipped because already covered If there are more than 15 net-new candidates, show the candidate list before writing so I can trim it. --- This is a useful way to turn repeated frustration into durable coding-agent memory. #claude #ClaudeCode #AICoding #CodingAgents #AIWorkflow #DeveloperProductivity #PromptEngineering #AgenticCoding #DevTools #BuildInPublic #SoftwareEngineering

Constant context switching crushes coding flow. A dedicated, distraction-free zone unlocks deep work. More output. Less burnout. #DeveloperProductivity

Cursor is powerful. But out of the box, it doesn't know your stack. Your conventions. Your patterns. Your team's opinions. It hallucinates imports. Ignores your architecture. Writes code that works but doesn't belong in your codebase. .cursorrules files fix that. And @PatrickJS built the definitive collection — 39,800 GitHub stars, 500 rules, fully public domain. Here's what a .cursorrules file actually does: → Drop a file in your project root — Cursor reads it on every request → Tell the AI your exact stack: Next.js TypeScript Zod shadcn/ui → Block deprecated imports before they're ever suggested → Enforce your naming conventions, file structure, and patterns → Prevent hallucinated decorators in NestJS — rules block them at source → Define how errors should be handled in your codebase → Set tone: "always write TypeScript, never use any, prefer interfaces" → Combine multiple rules — framework styling state management layers → New .mdc format: globs let rules auto-attach only to matching files → Works with the VSCode Cursor Rules extension — install in one command → CC0-1.0 licensed — fully public domain, zero restrictions Rules available for: React, Next.js, Angular, Astro, TanStack, NestJS, WordPress, Android Jetpack Compose, Python, Go, Rust and more. Same AI. Completely different output. One file. Discovered on OSSphere : ossphere.dev/PatrickJS/aweso… What's the one rule you'd put in every project's .cursorrules file? Drop it below 👇 #CursorAI #OpenSource #AITools #BuildInPublic #DeveloperProductivity #CursorRules #DevTools

Getting code from AI is easy. Getting a complete, working solution? Much harder 👀 The biggest gains with AI don’t come from generating more code they come from building better systems. Because great software isn’t built from snippets. It’s built on structure, context, and execution ⚡ That’s how modern development is evolving and what Code Studio is built for. Follow for more real-world AI workflows. Explore: syncfusion.com/code-studio/ #CodeStudio #Syncfusion #AIIDE #AIDevelopment #SoftwareArchitecture #AIEngineering #DeveloperProductivity

Phase 3 and 4 of my MSc dissertation: shipped. I built an instrument that puts AI coding tools on a scoreboard. Same spec, five conditions, five metrics. No vibes, no anecdotes — numbers. Today's drop: ↳ Recorded 1,315 keystrokes of me hand-coding a spec in VS Code ↳ Ran Anthropic Claude Code against the same spec — 123 lines of FastAPI, all 6 features, all governance rules respected ↳ First real CSV: humans backspace 52.8 times per 1,000 keys. Agents backspace zero. The whole thing: keystroke capture → vendor adapters → 5 analysers (incl. live SonarCloud) → CSV → dashboard. End-to-end. This is the prototype I'm taking into my PhD application at the Aston University Aston-Capgemini Centre of Excellence for Enterprise AI. If you're building or researching in this space — AI evaluation, dev productivity, enterprise AI guardrails. — my DMs are open. Code's on GitHub. Repo and dashboard screenshots in the comments. #BuildingInPublic #AI #DeveloperProductivity #PhD #ClaudeCode

OpenAI, Mac platformunda çalışan yazılımcıların ve profesyonellerin günlük iş akışlarını kolaylaştırmak için Codex asistanına "Appshots" adında yenilikçi bir özellik ekledi. #OpenAI #Codex #MacOS #Appshots #AIAgents #DeveloperProductivity #Apple airehber.com.tr/timeline/6mN…

Quick question for CTOs and Engineering Leads: What percentage of your monthly Claude or OpenAI API spend is actually generating new code versus just re-reading old files for context? In our latest enterprise benchmarks, we found that stateless agents are wasting up to 70% of their context window re-discovering the same file relationships turn after turn. We built GrapeRoot to give these agents a local, persistent structural map so they stop guessing. DX is moving from model optimization to context optimization. #AIEngineering #DeveloperProductivity #Tokens #ContextWindow #LLMs #cto #techlead #aicost