Bruno Pedro

Choosing the best programming language for your eCommerce site is a decision that can impact scalability, talent acquisition, and long-term maintainability. And unfortunately, there's no one-size-fits-all solution. The best approach depends on your requirements, existing infrastructure, and team composition. Some considerations on key implementation factors: 1. Project Requirements Consider JavaScript (JS) or TypeScript (TS) for dynamic, interactive frontends. Python, Java, and Go are strong contenders for robust backends. Ruby can be advantageous for rapid prototyping. 2. Scalability Java and Go excel in high-traffic, large-scale environments. PHP can face challenges at scale but remains popular for its ease of use. 3. Developer Talent Pool JavaScript/TypeScript offer the largest talent pool. Python and Java also have substantial communities. Ruby and Go may present challenges in finding experienced developers. 4. Long-term Maintainability - TypeScript improves upon JavaScript's maintainability - Java and C# offer robust tooling and framework support - Python's readability aids in long-term maintenance. 5. Performance Go offers excellent performance for backend services, Java provides a good balance of performance and scalability. Node.JS (TypeScript) is also super fast and robust for Backends 6. Ecosystem and Libraries JavaScript (Node.js) boasts a vast ecosystem for both frontend and backend. Python excels in data processing and AI-driven features, while PHP has a strong presence in CMS-based eCommerce platforms. My Recommendations: -For Startups and SMEs: Consider JavaScript/Node.js or Python (Django) for rapid development and ease of finding talent. While Node.js is excellent for I/O-bound operations, keep in mind that it may be less efficient for CPU-bound tasks compared to Java or Go. -For Large-Scale Operations: Java and TypeScript offer robust performance and scalability. Go is also a strong option for high-performance backends, but be aware that finding experienced Go developers might be more challenging. -AI-Driven eCommerce: Python's extensive ecosystem makes it an excellent choice for integrating AI and data processing features into your eCommerce platform. -Enterprise Integration: If you're already within the Microsoft ecosystem, C# provides seamless integration along with robust tooling and framework support. Need help to choose the best option for your specific case? Send me a DM with “CODE” and we can talk! ✉️ #management #ai #retai #ecommerce #digitaltransformation #RemoteNative

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🚀 OpenAI DevDay San Francisco is today! 🎉 But let's manage our expectations: 📌 This is a developer-only event 📌 No livestream available 📌 Videos will be published after the event 📌 Focus on API updates only 📌 No major model announcements expected All of this has been confirmed by the OpenAI development team in recent days. So, what API updates are you hoping to see today? Don't forget, there are two more DevDays coming up: 🇬🇧 London 🇸🇬 Singapore #OpenAI #DevDay #AIDevelopment #TechEvents

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“Bingo. Replacing method calls and module separations with network invocations and service partitioning within a single, coherent team and application is madness in almost all cases. “ My take: No doubt where the author lands with respect to microservices. This is too focused on the technology. Don’t start with the technology, start with the businesses opportunity and work back to the solutions, build teams who are deep in enterprise and solution architecture, who know design patterns, and provide the principles to guide your decisions. The solution may be classic three tier, SOA or microservices. We ran SOA/SOAP long after it was cool to do so and the system performed great. #EA #SA

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🚀 Exciting News: Stability AI's Cutting-Edge Models Now on Amazon Bedrock! 🎨🖥️ Have you heard? Amazon Bedrock just got a major upgrade with the addition of Stability AI's latest image generation models! As an AI enthusiast, I can't wait to explore these in my side projects. New models available: 🔹 Stable Diffusion 3 (SD3) Large 🔹 Stable Image Ultra 🔹 Stable Image Core These models promise enhanced image quality, improved prompt understanding, and faster generation times. Whether you're into creating hyper-realistic images or pushing the boundaries of digital art, these tools are game-changers. 💡 Potential use cases: - Rapid prototyping for UI/UX designers - Custom illustrations for marketing campaigns - Generating concept art for game development Who else is excited to test these out? What innovative applications can you envision? 🔗 Learn more in the blog post #ArtificialIntelligence #AmazonBedrock #StabilityAI #MachineLearning #AIInnovation

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About Malte Ubl (Vercel's CTO) talk on "Framework Defined Infrastructure (FDI) – an Evolution of Infrastructure as Code (IaC)". Some thoughts: - FDI seems to be a specialized approach than a direct evolution of IaC. - The core idea behind FDI - allowing devs to work within familiar framework contexts - highlights the growing demand for more abstracted infrastructure interfaces. - FDI and similar concepts demonstrate the need for IDPs that can sit on top of IaC, providing a more developer-friendly experience without replacing the underlying IaC principles. While I'm not convinced FDI is the next step after IaC, it reflects the ongoing efforts to make infrastructure management more accessible to developers and obvious demand for IDP experience. 🔗ref https://lnkd.in/gU-9wT5J #InfrastructureAsCode #DeveloperExperience #FDI #DevOps #IDP

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As per my experience with Copilot, Codium, and Cursor... it does, and at scale. Why do programmers need AI to generate code in the first place? Because 1) too lazy to write the code, 2) they don't know how to write the code. Either way, you can imagine it will be a disater if a team does not incorporate best practices when adopting AI-generated code. Not to mention associated with unintended code leaks, such as exposing API keys. In my opinition, the technology is not mature enough for enterprise-grade products teams, but extreamly beneficial for startups where codebase is more disposable. Let me know your thoughts!

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You may not have noticed, but the ZenUML team has spent tens of hours, if not more, ensuring that every pixel looks right in all scenarios. #zenuml #diagramming Soon you will be using AI to generate ZenUML diagrams at https://diagramly.ai

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One week until we will gather in Madrid on October 15th for Kafka Horror Stories! While Apache Kafka is a powerhouse for event streaming, but it comes with its fair share of challenges. Here are the top three pain points many users face: ⚙️ Operational Complexity Managing Kafka clusters isn’t easy—handling brokers, partitions, replication, and scaling requires precision. Plus, zero-downtime upgrades can feel impossible. 🐢 Consumer Lag & Throughput Consumer lag happens when message processing slows down, usually from poor partitioning or configuration issues. 📊 Monitoring & Observability Even with tools like Prometheus or Grafana, getting full visibility into Kafka’s health—consumer lag, broker failures, system metrics—can be tricky. We'll be hearing from speakers from Lenses.io, AWS, and Adidas. Come to share your knowledge and get your questions answered. The event will be from 6:30-9:00pm at the Celonis Office (Pl. de Manuel Gómez-Moreno, 2, Tetuán, 28020 Madrid). Registration link in the comments-

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1 Mistake That Cost This SaaS Entrepreneur Millions (𝐀𝐧𝐝 𝐇𝐨𝐰 𝐖𝐞 𝐅𝐢𝐱𝐞𝐝 𝐈𝐭): We worked with a SaaS company, that faced a critical bottleneck: 👉 their outdated single-tenant architecture. 🚩 𝐓𝐡𝐞 𝐈𝐬𝐬𝐮𝐞: Each client had their own server. Simple, right? Except it multiplied their costs like bunnies—operational expenses ballooned by 300% in just two years! 🤦‍♂️ Scaling became a financial sinkhole. Every new client setup was a mini-expedition to the bank. 💫 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐝𝐢𝐝: 1️⃣ We consolidated their systems into one sleek multi-tenant platform. Hello, simplicity and goodbye, unnecessary costs! 2️⃣ We tweaked resource use to cut server stress and slash operational expenses by 40%. 3️⃣ Set up auto-scaling to smartly handle growing demands. No manual tweaks needed. 4️⃣ Implemented a CI/CD pipeline for quick, efficient feature rollouts—boosting update speed by 30%. 🚀 𝐍𝐨𝐰, 𝐭𝐡𝐞𝐢𝐫 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐟𝐨𝐫 𝐭𝐡𝐞𝐦—𝐢𝐭’𝐬 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐨𝐯𝐞𝐫𝐭𝐢𝐦𝐞. Ready to ditch the tech headaches and supercharge your growth? Shoot me a "⚡" and let’s talk transforming your setup!

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I think that AWS is making a really strategic move with introduction of the Sagemaker Lakehouse: - companies already using AWS Lakehouse Formation will benefit from it immediately - companies that keep a lot of their data in Redshift Managed Storage will be able to process it with very performant engines like Spark without any extra ETL processes - S3 Tables - the new buckets with automatically managed Iceberg will be the powerful storage core of the ecosystem - Iceberg APIs introduced to many AWS services like DMS or Firehose will simplify ETLs, as well as zero-ETL services that AWS offers, plus federated queries to Snowflake and BigQuery - Redshift Spectrum (Serverless Engine) running against Iceberg auto-optimized tables might be a really good choice for analytics - And PySpark is great choice for data engineering and ML - allowing for a writing reusable, testable, clean code with automated unit / code / data quality testing - All data operations in single catalog and ecosystem - data engineering, analytics, and AI is a great pattern I’m a huge Databricks enthusiast and it was Databricks that in my opinion really introduced the concept of a lakehouse to data world but it’s quite fascinating to observe how popular it becomes across major cloud providers :) #AWS #Lakehouse

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Performance-wise, the new protocol has reduced dataplane latency by 11% on average, and by 17.4% at the P90 mark. In addition to making SQS itself more performant, this change benefits services that build on SQS as well. For example, messages sent through Amazon Simple Notification Service (Amazon SNS) now spend 10% less time “inside” before being delivered. Finally, due to the protocol change, the existing fleet of SQS hosts (a mix of X86 and Graviton-powered instances) can now handle 17.8% more requests than before. #AWS #AmazonWebServices #AWSBlogs #Cloud #CloudComputing #Serverless #SQS

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New OpenAI features (GPT-4 Omni) in a nutshell - Deutsch 👇 Impressive news from the OpenAI Spring Update: 👉 Native Clients for ChatGPT 👉 New Model called "GPT-4o" (o is the letter O like omni:-)) 👉 Improved modalities for voice (you can interrupt the model, no lags) and vision (integrated recognition) 👉 ChatGPT with GPT-4o free for everybody - so everybody can also use the GPT Store (pro users get 5x bigger capacity limit) 👉 API access to GPT-4o 👉 Real time translations - the Demo was impressive! Nothing about improved agents support - but I am sure not too far away ;-) -- Beeindruckende Neuigkeiten aus dem OpenAI Spring Update: 👉 Native Clients für ChatGPT 👉 Neues Modell namens "GPT-4o" (o ist der Buchstabe O wie omni :-)) 👉 Verbesserte Modalitäten für Sprache (man kann das Modell unterbrechen, keine Verzögerungen) und Sehen (integrierte Erkennung) 👉 ChatGPT mit GPT-4o kostenlos für alle - damit kann auch jeder den GPT Store nutzen (Pro-User bekommen 5x größere Kapazitätsgrenze) 👉 API-Zugang zu GPT-4o 👉 Übersetzungen in Echtzeit - die Demo war beeindruckend! Nichts über verbesserte Agentenunterstützung - aber ich bin mir sicher, dass es nicht allzu weit her ist ;-)

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Comparison and ranking of API provider performance for over 100 AI LLM Model endpoints across performance key metrics including price, output speed, latency, context window & others. https://lnkd.in/d7trmruE

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