Albert Díaz Benitez

You must master these 11 concepts. If you want to succeed with development in 2025... It's not optional anymore. If it were a game... Using GitHub Copilot is basically level 1. While top product teams are already at level 50. And the rules are changing fast, So mastering them is critical for staying competitive. Here are the 11 must-learn AI concepts 👇 (Master all of them or get left behind) 1. Context Management ↳ Building structured project specs for consistent AI output. 2. Prompt Patterns ↳ Proven templates that improve AI coding consistency. 3. MCP (Model Context Protocol) ↳ Protocol connecting AI models to external tools and data. 4. RAG (Retrieval-Augmented Generation) ↳ AI system that reduces hallucinations using external sources. 5. Agentic Development ↳ AI agents that autonomously write and modify code. 6. AI-Assisted Programming ↳ Real-time collaboration between developers and AI. 7. Vibe Coding ↳ Natural language programming for rapid prototyping. 8. AI Guardrails ↳ Safety controls preventing harmful code generation. 9. Monitoring & Evaluation ↳ Measuring AI performance in development workflows. 10. Cost Optimization ↳ Smart model selection and caching to control expenses. 11. Model Routing ↳ Using appropriate AI models based on task complexity. You need to master EVERY concept. Missing even one puts you at a disadvantage. Save this list and refer back to it... Your product team's efficiency depends on it. ♻️ Repost to help others level up their AI game Follow Alex Barady for practical AI strategies

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One of the biggest misconceptions I hear about serverless is this: "It’s just a spaghetti of Lambda functions calling each other." Yes, I’ve seen that happen. When it does, it’s ugly, and it usually comes from inexperience and lack of design. But that’s not what serverless architectures are supposed to be. It’s like saying "cats are animals that poop on your bed", sure, accidents happen (…probably 😹), but that’s not the norm or the expected behaviour! So, what does a well-designed serverless architecture actually look like? From a bird’s-eye view, pretty much the same as what you'd build with containers or EC2: • Separate accounts per team/workload • System is decomposed into independent services • Every service owns its own data (no shared DBs) • Services are loosely coupled through events • Centralised logging and observability Whether I have an API (synchronous communication) or use events (asynchronous communication) does not depend on whether I use Lambda vs. containers. A serverless architecture doesn't have to be event-driven. Equally, an event-driven architecture can run on containers or EC2. Those are orthogonal architectural choices. Inside each service, I use the serverless-first mindset to decide on my tech stack, e.g. • Prefer DynamoDB over RDS • Prefer API Gateway of ALB • Prefer Lambda functions over containers or EC2 • Prefer EventBridge over Kafka The guiding principle is simple: pick the service that does the most heavy lifting. And with serverless technologies like Lambda, you get built-in multi-AZ redundancy; scalability; reduced attack surface; no need to manage infrastructure; simplified deployment; and, pay-per-use pricing. So no, you don’t expose "a bunch of Lambdas" as your service boundary. That’s not the goal. That’s just a mistake.

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🚀 New Release from ComfyUI - Subgraph Publishing & Selection Toolbox Redesign ComfyUI 0.3.63 update unlocks two powerful enhancements that make working with this tool even more fluid and modular: ✨ What’s New ⁉️ 1. 𝗦𝘂𝗯𝗴𝗿𝗮𝗽𝗵 𝗣𝘂𝗯𝗹𝗶𝘀𝗵𝗶𝗻𝗴 You can now save any subgraph as a reusable component in your node library. Use the publish icon or menu in the Selection Toolbox, then find it under Node Library → Subgraph Blueprints. When you need to tweak the behavior, just edit and update it like a regular node. This change elevates composability and reusability in your workflows. ComfyUI 2. 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗧𝗼𝗼𝗹𝗯𝗼𝘅 𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻 The UI of the selection toolbox has been refreshed with clearer icons and an expandable menu. This redesign isn’t just aesthetic — it paves the way for future enhancements and more flexible workflows. ComfyUI Blog 🔍 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝘔𝘰𝘥𝘶𝘭𝘢𝘳𝘪𝘵𝘺 & 𝘙𝘦𝘶𝘴𝘦: Subgraphs can now live as first-class entities—no recreating from scratch each time. 𝘉𝘦𝘵𝘵𝘦𝘳 𝘜𝘟: Cleaner UI and menu structure help speed up experimentation and iteration. 𝘌𝘹𝘵𝘦𝘯𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺: The foundation is laid for even richer features down the line. As someone who values efficient, scalable design in creative and engineering workflows, I’m excited to see how this deepens what’s possible in ComfyUI. #ComfyUI #UIUX #subgraphs #ai #monks

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Frameworks like Node.js lack the primitives for safe applications. Postgres has had them for decades. It should be at the core of our designs. Almost a decade after this paper's publication, concurrent bugs are still plaguing applications. Even seasoned programmers get caught. (And new coding assistants like Claude Code also struggle.) That's because it takes very little to create a concurrency bug: 2 concurrent actions... What's really sad is that these problems have been solved by databases like Postgres decades ago. We need libraries that bring transactional guarantees into application code: steps that always run at-least-once or exactly-once, in order, backed by the database. Look at the findings of the paper (consistent with other studies). The root causes of bugs in Node.js: - 65% atomicity violation (no transactions in Node.js...) - 35% order violation (no synchronization primitives in Node.js...) Note that 40% of bugs came from interacting with external APIs. For example, check figure 4 in the paper: this is a typical example of un-orchestrated calls to an external API leading to data losses. When this happens, teams often reach for heavy orchestration frameworks like Airflow or Temporal, even though a simple Postgres-backed SAGA could suffice. The paper also finds that 50% of concurrency bugs are caused by API mis-use. i.e., people get fooled by async/await. So, what do we do? Node.js is very popular and for good reasons: javascript/typescript are much more approachable than traditional backend languages like C# or Java. Many devs enjoy using the same language for both frontend and backend. But twisting the framework to make concurrent programs safe is really, really hard in Node.js. One approach is to bring ACID properties to the application by embedding a RDBMS-backed library to the framework. For example, if you provide a "step" abstraction, with automatic ID generation and checkpointing, you can ensure that each step is executed at-least-once or exactly-once and that they order is always the same. Happy to chat more about that idea :) #postgres #nodejs

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With vibe coding, it's quick and easy to build apps. (But integrations take a while to set up, until now.) To set up integrations, you typically need to: → Create a developer account with the service → Find and manage API keys → Share credentials with your team → Repeat for every new app you build This can take several hours or days. Luckily, there’s a new and better solution… Replit just launched Connectors to streamline this entire process. ↳ OAuth-based integrations where you just log in to a service once and start building - no developer accounts, no API keys, no manual OAuth setup required. Here's the new workflow: → Tell Replit's Agent what you want to build → Log in to the service (Salesforce, Dropbox, etc.) → Agent immediately starts building with your data That's it. One login. Unlimited apps using that service. With Replit Connectors: ✅ Integrations feel native - no credentials to manage for OAuth services ✅ Faster time-to-value - create apps with external data in minutes ✅ Safer by default - no manual credential management ✅ Enterprise governance - assign connections to specific users and roles Connectors work with the tools you already use: → Google (Sheets, Docs, Calendar, YouTube) → Salesforce, HubSpot, Notion, Linear → GitHub, Jira, Confluence, Asana → Dropbox, Box, OneDrive, SharePoint → Discord, Spotify, Outlook Log in once to any service, and your entire team can build apps on top of that data. Some apps you can build in plain English: • “Pull my YouTube analytics, spot trending topics, and plan them in Google Calendar.” • “Scan Salesforce opportunities, flag renewal risks, and draft outreach emails.” • “Read a Confluence PRD and turn it into Jira epics, stories, and tasks.” That’s the shift: from integrations as a blocker to integrations as a starting point. Build apps & automations on top of your data with Connectors. 📌 Try Replit Connectors today: https://replit.com/ 🔄 Repost to help others discover faster integration workflows #AI #BuildingApps #AppIntegrations #SponsoredByReplit #ReplitPartnership  

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💥 Postman has just released a collection of 𝗳𝗿𝗲𝗲, 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝗯𝗲 𝘂𝘀𝗲𝗱, 𝗼𝗳𝗳-𝘁𝗵𝗲-𝘀𝗵𝗲𝗹𝗳 𝗔𝗴𝗲𝗻𝘁 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝗗𝗲𝘃𝗢𝗽𝘀 𝘁𝗮𝘀𝗸 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻𝘀. There are plenty to choose from, my favourite - Slack-to-Jira Filer Agent. Here is the situation I was unfortunate to be part of for too many times: 👉 You discover a bug or consider a new feature. 👉 You open a Slack thread for discussions. 👉 50 messages later you come to the conclusion. 👉 You 𝘀𝗽𝗲𝗻𝗱 𝟭 𝗵𝗼𝘂𝗿 to read through the thread and create a Jira ticket. Here is how Slack-to-Jira Filer Agent fixes the above: ✅ Connect the agent to your Slack and Jira once. ✅ Type a predefined emoji in the thread. ✅ The agent summarises the conversation, creates a Jira ticket with all relevant information. ✅ You 𝘀𝗮𝘃𝗲 𝟭 𝗵𝗼𝘂𝗿 of tedious work. Find this and other available templates here: https://fnf.dev/44TxZBW Thank you to Postman for collaborating on this post and helping to share such useful resources with the community! What other templates would you like to see to help with DevOps automation? Let me know in the comments 👇 #Agents #AI #DevOps

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I stopped using Cursor for coding. I use Claude Code now. It's 10x better. Here are 5 tips on how we use Claude Code in my team: 1. Create a CLAUDE.MD file to include project goals, tech stack, folder structure and best practices 2. Don't ask Claude to code first. Instead, ask it to explore first, then plan, then implement features. 3. Use /clear command often to reduce hallucinations and save tokens 4. Build custom slash commands to automate repetitive tasks like debugging 5. Paste screenshots into Claude to better describe errors, layouts and bugs. A picture says more than 1000 words To learn more Claude Code tips, check out my latest post: https://lnkd.in/daBja6sC AI won't replace software engineers. But an engineer using AI will.

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We just added something nerdy to our tool, as treemaps seems to be hot in #JavaScript + web #performance world 🔥 When using RUMvision - Core Web Vitals monitoring to track #UX in real-time, we already show site owners a third party dashboard. But we now added a treemap-view as well: → sort by events This will get the third party with most ocurrences at the top left. In this case #GTM. However, the color+opacity coding is telling us that it doesn't come with biggest INP impact 💡 → sort by JS time To get the one with highest JS time at the top left. This newly introduced view should help all shareholders (even less/non technical ones) to start discussions around third parties and make the right decisions. For example: where should we start optimizing? In this case: 1️⃣ probably cookie-script because of high-ish amount of events 2️⃣ then new relic being in the middle with high JS time 3️⃣ and for example nosto + hotjar 4️⃣ consentmanager + intercom are very red as well, is what mouseover will reveal

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Amazon walked back their variation theme removal approach. Original plan: Remove all deprecated themes Sept 2-Nov 30 Updated plan: Only remove themes with zero sales in 12 months What this means for you: Critical themes (size, color, style) are safe Existing variations keep operating normally Much smaller impact than first announced Still worth auditing your themes This is a good reminder that Amazon's initial announcements often get refined. The key is having systems to adapt quickly either way. Free audit tool still available for anyone who wants to check their inventory.

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I was checking out Sonnet 4.5 (via Claude) — hyped as one of the best coding models right now. 🧵 Then, just saw this Reddit post where a developer tried using Claude Sonnet 4.5 for a simple coding task — and got burned. 👉 https://lnkd.in/dQbVTswV They asked it to add a file upload feature. Instead of reusing an existing helper function from the codebase, the model generated new logic — blindly and confidently. Classic case of what people now call vibe coding. It looks clean. It sounds smart. But under the hood, it’s chaos. No context. No reuse. No accountability. This isn't the first time I've seen LLMs do this — confidently skipping over edge cases, ignoring constraints, and hallucinating structure. And yet, vibe coding isn't all bad. ✅ It's great for: – Quick POCs – Exploring unfamiliar code – Learning new stacks – Getting unstuck ❌ But it fails at: – Stability – Debugging – Legacy integration – Team handoff Vibe coding is fine... until you try to ship it. So here’s my take: Use AI to move fast — but don't skip the real engineering. Curious — how do you use LLMs in your workflow? Where do you draw the line between vibing and building? Follow Shubham Vora to learn more about AI agents and AI tools.

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🚨 Backend Devs, You Know This Struggle 🚨 You build the API. You test it in Postman. Everything works flawlessly. ✅ No errors. No surprises. Smooth sailing. But then… 🚨 The frontend team integrates it, and suddenly—𝗯𝗼𝗼𝗺—nothing works. You double-check: 🔸 Request body? Correct. 🔸 Headers? Fine. 🔸 Auth token? Looks good. So what went wrong? 🧠 These are the kinds of bugs that haunt us: • Postman bypasses browser-level CORS restrictions • The frontend might structure the JSON 𝘴𝘭𝘪𝘨𝘩𝘵𝘭𝘺 differently • Content-Type headers could be missing or incorrect • Or maybe… a sneaky middleware is quietly sabotaging the request 📌 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Just because it works in Postman doesn’t mean it works in the browser. Welcome to the joys of backend debugging. 😅 Please check 🌍 https://lnkd.in/gfZJqPTR and follow ✅ https://lnkd.in/gZ6myPbn Please follow Chanchal Kumar Mandal #BackendDevelopment #Postman #APIIntegration #DeveloperStruggles #DebuggingTips #SoftwareEngineering #CodingLife #WebDevelopment

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The Web is more powerful than you think. Modern Web APIs are bridging the gap between browsers and native apps. A few examples you’re probably already using without noticing: 1️⃣ Clipboard API – Copy/paste across apps and browsers. → Use case: Google Docs syncing your clipboard with OS seamlessly. 2️⃣ Web Share API – Share links/files directly to native apps. → Use case: A news site letting you share an article to WhatsApp in one tap. 3️⃣ Device APIs (Camera, Geolocation, Bluetooth) → Use case: Scanning QR codes for payments, connecting fitness trackers, live navigation. 4️⃣ File System Access API – Read/write local files securely. → Use case: Figma in browser saving files directly to your machine. These APIs unlock “native-like” experiences inside the browser. But here’s the catch, System Design matters more than ever. 👉 How do you handle permissions safely? 👉 How do you scale features across browsers & devices? 👉 How do you degrade gracefully when an API isn’t supported? The power is there, but the architecture decisions determine if your app feels magical or broken. If you want to go deeper, I highly recommend exploring Namaste Frontend – System Design by Akshay Saini 🚀 & Chirag Goel. ❓What’s the Web API that impressed you the most? Regards, Chirag Goel

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ChatGPT 5 vs Claude Opus 4.1 for development… and why this update isn’t just for developers. The new ChatGPT drop isn’t only about writing code. It’s shaping up as a full creation and execution surface for product teams. For Development: ChatGPT 5: stronger repo-scale reasoning, cleaner tool use, faster scaffolds, smarter tests, and refactors. Great for spinning up prototypes and integrating with workflows. Claude Opus 4.1: excellent spec reading, careful code edits, solid architecture notes, reliable docstrings, and explanations. Great for large design reviews and safety-sensitive changes. Beyond Developers: ChatGPT 5 now leans into product and ops: data analysis, slide outlines, site copy, UI drafts, lightweight agents, and quick connections to the tools teams use daily. Claude Opus 4.1 shines for research memos, strategy docs, policy language, and extracting structured insights from long materials. How would I choose??? - If you need speed to prototype end-to-end features, then try ChatGPT. - If you need meticulous reasoning and clear writing, consider using Claude. Best stack uses both. Standardize prompts, add guardrails, and route tasks by strength. Bottom line: This ChatGPT update expands the audience from “developers only” to “whole product teams.” PMs, designers, analysts, marketers—everyone gets a lift.

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