Software Development

What happens when you open-source a tool you built for yourself? 10,000+ developers started using it. I created n8n-MCP - a tool that enables AI Agents such as Claude Desktop or Cursor to actually build working n8n workflows. I simply wanted to solve my own problem. The decision to share it publicly led to something I never expected. 🌍 The numbers tell an interesting story: • Docker images downloaded 10.1k times • npx installations: 4.6k • Repository cloned 2.6k times by 1.7k unique developers • Nearly 40k views, averaging 3k unique visitors daily • 1.7k GitHub stars ⭐ • 4 independent creators discovered the tool and created YT tutorials But beyond metrics, what truly amazes me is how the community adapted the tool for their own needs. From solo developers running local instances to entire teams deploying it remotely, each found their own use case. The tool is valued as "the first one that actually works" and "best on the market" The experience taught me a valuable, yet obvious lesson. When you have deep knowledge in certain domain and solve well particular pain point in this domain, we often solve problems we didn't know others had too. 💡 Open source isn't just about code - it's about creating tools that multiply possibilities across the community. Sometimes the most impactful contributions come from simply scratching your own itch and having the courage to share it. Have you tried it yet?

Dear Software Engineers, If your app serves 10 users → a single server and REST API will do If you’re handling 10M requests a day → start thinking load balancers, autoscaling, and rate limits /— If one developer is building features → skip the ceremony, ship and test manually If 10 devs are pushing daily → invest in CI/CD, testing layers, and feature flags /— If your downtime just breaks one page → add a banner and move on If your downtime kills a business flow → redundancy, health checks, and graceful fallbacks are non-negotiable /— If you're just consuming APIs → learn how to handle 400s and 500s If you're building APIs for others → version them, document them, test them, and monitor them /— If your product can tolerate 3s of lag → pick clarity over performance If users are waiting on each click → profiling, caching, and edge delivery are part of your job /— If your data fits in RAM → store it in memory, use simple maps If your data spans terabytes → indexing, partitioning, and disk I/O patterns start to matter /— If you're solo coding → naming things poorly is just annoying If you're on a growing team → naming things poorly is a ticking time bomb /— If you're fixing bugs once a week → logs and console prints might do If you're running production → you need structured logs, tracing, alerts, and dashboards /— If your deadlines are tight → write the simplest code that works If your code is expected to last → design for readability, testability, and change /— If you work alone → "it works on my machine" might be fine If you're in a real team → reproducible builds and shared dev setups are your baseline /— If your app is new → move fast, clean up later If your app is in maintenance hell → you now pay interest on every rushed decision People think software engineering is just about building things. It’s really about: – Knowing when not to build – Being okay with deleting good code – Balancing tradeoffs without always having all the data The best engineers don’t just ship fast. They build systems that are safe to move fast on top of.

Not a joke, many Data Engineers don’t fully understand the Medallion architecture or their caveats. Here’s a simple, crisp breakdown of the Medallion Architecture and why each layer matters: 🔹 Bronze (Raw Ingestion) - All incoming data lands here-> logs, JSON, CSV, streaming events - Data stays in its original form (think Delta Lake tables) - Use schema-on-read to keep raw JSON/XML (no forced schema yet) - Partition by ingest date/hour for fast file pruning - Add audit columns (ingest_timestamp, source_file, batch_id) for full traceability Why care? Bronze is your “source of truth.” You can recover, reprocess, or track every record. 🔹 Silver (Cleansed & Curated) - Cleaned, standardized view of Bronze data - Enforce data types, drop nulls, fill defaults (schema-on-write) - Use joins and dedupe logic (window functions help remove duplicates) - Add data profiling and constraints (NOT NULL, CHECK) to stop bad data early Why care? Silver gives you reliable, consistent tables for analytics, reports, and ML models. 🔹 Gold (Business Aggregations) - Highly curated, aggregated tables or dimensional models - Pre-compute metrics (daily active users, revenue by region) - Use Slowly Changing Dimension (SCD) for customer data - Partition and Z-order in Delta for super-fast queries Why care? Gold delivers high-performance datasets for BI tools and ML feature stores. Key Benefits Across Layers 1. Modularity & Maintainability – Keep ingestion, cleaning, and aggregation logic separate 2. Data Quality – Catch issues step by step 3. Scalability – Stream and batch workloads scale on their own 4. Governance & Lineage – Track every change with audit columns and Delta logs What else you would like to add here ? 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝟭:𝟭 𝗳𝗼𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝗴𝘂𝗶𝗱𝗮𝗻𝗰𝗲 → https://lnkd.in/gH4DeYb4 𝗔𝗧𝗦 ��𝗽𝘁𝗶𝗺𝗶𝘀𝗲𝗱 𝗿𝗲𝘀𝘂𝗺𝗲 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲 → https://lnkd.in/g-iw7FaQ Gif -> Ilum ♻️ Found this useful? Repost it! ➕ Follow for more daily insights on building robust data solutions.

The AI ecosystem is becoming increasingly diverse, and smart organizations are learning that the best approach isn't "open-source vs. proprietary"—it's about choosing the right tool for each specific use case. The Strategic Shift We're Witnessing: 🔹 Hybrid AI Architectures Are Winning While proprietary solutions like GPT-4, Claude, and enterprise platforms offer cutting-edge capabilities and support, open-source tools (Llama 3, Mistral, Gemma) provide transparency, customization, and cost control. The most successful implementations combine both—using proprietary APIs for complex reasoning tasks while leveraging open-source models for specialized, high-volume, or sensitive workloads. 🔹 The "Right Tool for the Job" Philosophy Notice how these open-source tools interconnect and complement existing enterprise solutions? Modern AI systems blend the best of both worlds: Vector databases (Qdrant, Weaviate) for data sovereignty, cloud APIs for advanced capabilities, and deployment frameworks (Ollama, TorchServe) for operational flexibility. 🔹 Risk Mitigation Through Diversification Smart enterprises aren't putting all their eggs in one basket. Open-source options provide vendor independence and fallback strategies, while proprietary solutions offer reliability, support, and advanced features. This dual approach reduces both technical and business risk. The Real Strategic Value: Organizations are discovering that having optionality is more valuable than any single solution. Open-source tools provide: • Cost optimization for specific use cases • Data control and compliance capabilities • Innovation experimentation without vendor constraints • Backup strategies for critical systems Meanwhile, proprietary solutions continue to excel at: • Cutting-edge performance for complex tasks • Enterprise support and reliability • Rapid deployment with minimal setup • Advanced features that take years to replicate What This Means for Your Strategy: • Technical Teams: Build expertise across both open-source and proprietary tools • Product Leaders: Map use cases to the most appropriate solution type • Executives: Think portfolio approach—not vendor lock-in OR vendor avoidance The winning organizations in 2025-2026 aren't the ones committed to a single approach. They're the ones with the most strategic flexibility in their AI toolkit. Question for the community: How are you balancing open-source and proprietary AI solutions in your organization? What criteria do you use to decide which approach fits each use case?

We’ve entered a strange, paradoxical AI post-hype realism stage. People are trusting AI less, but using it more, and validating it less. These were the most important signals I took from the KPMG 2025 AI Trust Landscape report, especially in advanced economies. To me, this isn’t just statistical noise. It’s a deep signal of cultural and cognitive dissonance. AI is taking root in daily workflows, decisions, and mental models, not because people trust it, but despite the fact that they increasingly don’t. In other words, AI is becoming infrastructural, even as people detach from belief in its reliability. That’s not how we usually adopt technology. Most tech earns trust slowly, through consistent benefit. But AI seems to be reversing that logic, it's being adopted faster than it’s being understood, validated, or believed. And if we’re building business processes, decisions, and institutions on top of systems people don’t trust and don’t verify, that’s not a maturity curve, that’s a fragility spiral. It’s more like we’re using AI compulsively, ritualistically, even cynically, like a slot machine that mostly pays out. It’s no longer about accuracy, it’s about the perceived cost of not using it. This is creating a strange new class of dependency, as people are outsourcing critical thinking to systems they increasingly don’t trust, not because they’re confident in the outputs, but because they’re afraid to fall behind if they don’t. Even stranger is the fact that so few are pulling back. There’s no mass rejection, no principled slowdown, just a quiet, expanding gap, between capability and comprehension, between usage and belief, between the system’s reach and our ability to reason about it. In the past, trust was a gateway to adoption, now, adoption precedes trust, and sometimes replaces it. That’s not just a shift, it’s a reversal of how we filter technology into society. And maybe the real issue is this: We’re not just building software tools, we’re building epistemic infrastructure. Systems that will guide decisions in law, education, hiring, medicine, policy. If people treat those systems as untrustworthy black boxes - but still defer to them - then we’re injecting ambient doubt into the very foundations of how society makes choices. If that’s the case, this isn’t hype anymore, it’s learned helplessness in a probabilistic wrapper. What’s eroding isn’t just trust in AI, it’s the very idea that we should know how a system works before we let it decide things for us. I remain an AI optimist, but we must not confuse momentum with maturity. Thursday musings...

Open source isn’t free. Someone else just paid the price. Behind every great library is often a tired, unpaid maintainer. Let’s talk about the tools we all rely on: ➡️ Java apps run on Spring Boot, Maven, Log4j. ➡️ Python apps lean on NumPy, Pandas, TensorFlow. ➡️ Web apps, APIs, analytics, and AI models depend on them. Most of these are maintained by developers working late nights, weekends, and holidays — without compensation. A handful of contributors are carrying the weight of global infrastructure. Big companies use these tools at massive scale… but rarely give back. ➡️ One bug in Log4j created a global security crisis. ➡️ A NumPy issue could break critical scientific apps. Meanwhile, maintainers handle security patches, bug reports, support questions, and community drama — often alone. Why does this matter? Because your code, models, and servers wouldn’t run without them. ⚠️ Burnout is real. ⚠️ When maintainers leave, critical software becomes vulnerable. How to support open source: ✅ Contribute — fix bugs, improve docs, add tests. ✅ Sponsor — via GitHub Sponsors, OpenCollective, or direct donations. ✅ Promote — highlight their work in talks, blogs, and posts. ✅ Be thoughtful — don’t treat open source like free labor. ✅ Say thanks — a kind message or tweet can mean the world. Next time you import or build, remember: There’s a human behind that code. Let’s support them. #OpenSource #DevCommunity #SoftwareEngineering #SupportMaintainers #TechResponsibility #BurnoutIsReal

There’s an underrated superpower in tech (and life): knowing who’s worth listening to. Andrej Karpathy is one of those people. Ex-Director of AI at Tesla. Founding team at OpenAI. PhD under Fei-Fei Li. If these creds don't impress you, he also coined the term 'vibe-coding'. When he took the stage at YC AI Startup School in San Francisco this week, I paid attention. Here’s what I took away: 1️⃣ Software 3.0: English as Code. He reframes software’s evolution in three eras: Software 1.0: Hand-coded logic. Software 2.0: Trained models; neural net weights are the program. Software 3.0: You program in English. Prompts are the code. Everyone who can write a clear sentence is, in theory, a coder now. 2️⃣ LLMs aren’t Utilities - they’re Operating Systems. Karpathy’s most powerful framework: we’re in the ✨ mainframe era of AI ✨ In the 1960s OS world, there was ▪️Expensive, centralized compute. Few owned mainframes, many shared them. ▪️Time-sharing. Jobs batched, users were thin clients. ▪️Command-line interfaces. No GUI, just terminals. ▪️Remote access. The computer lived in a data center, users dialed in. In LLMs today? Same story. ▪️Massive, costly, cloud-native. Nobody runs GPT-4 locally. ▪️Thin clients. We pipe requests via browser or API. ▪️No AI GUI yet. We’re typing into terminals (ChatGPT). We’re pre-personal computer. Someone still has to build the AI equivalent of the desktop, the mouse, the spreadsheet. 3️⃣ Partial Autonomy + The Autonomy Slider. Karpathy’s Tesla experience taught him what happens between flashy demos and reliable autonomy: a decade of boring, hard work. In 2013, he rode in a Waymo car that handled 30 minutes of Palo Alto driving perfectly. The demo worked. It’s 2025. We’re still debugging self-driving at scale. The same is true for AI agents. The opportunity is augmenting people with AI “Iron Man suits,” not replacing them with Iron Man robots. Cursor, Perplexity are early examples of where this is going. ▪️They package context, orchestrate multiple LLM calls, and give users GUIs to audit AI output. ▪️They offer an autonomy slider - letting humans choose how much control to give up. The future is co-pilot software - where humans steer, AI assists, and the feedback loop is fast. 4️⃣ Docs and infra need to meet AI halfway. Today’s software is built for humans and APIs. Tomorrow’s needs to be legible to agents: ▪️Ditch “click here.” Use curl. ▪️Replace PDFs with agent-friendly Markdown. ▪️Build tooling that packages context so LLMs don’t fumble their way through HTML and menus. We need to design for a new consumer: not just people, not just code, but people-like machines. We’re in AI’s mainframe era. The personal computing revolution will come. The job now is to build what comes between. And in the meantime, I guess we’ll keep typing into our terminals and hoping the prompt does what we meant.

Demystifying CI/CD Pipelines: A Simple Guide for Easy Understanding 1. Code Changes:   Developers make changes to the codebase to introduce new features, bug fixes, or improvements. 2. Code Repository:   The modified code is pushed to a version control system (e.g., Git). This triggers the CI/CD pipeline to start. 3. Build:   The CI server pulls the latest code from the repository and initiates the build process.   Compilation, dependency resolution, and other build tasks are performed to create executable artifacts. 4. Predeployment Testing:   Automated tests (unit tests, integration tests, etc.) are executed to ensure that the changes haven't introduced errors.   This phase also includes static code analysis to check for coding standards and potential issues. 5. Staging Environment:   If the pre deployment tests pass, the artifacts are deployed to a staging environment that closely resembles the production environment. 6. Staging Tests:   Additional tests, specific to the staging environment, are conducted to validate the behavior of the application in an environment that mirrors production. 7. Approval/Gate:   In some cases, a manual approval step or a set of gates may be included, requiring human intervention or meeting specific criteria before proceeding to the next stage. 8. Deployment to Production:   If all tests pass and any necessary approvals are obtained, the artifacts are deployed to the production environment. 9. Post deployment Testing    After deployment to production, additional tests may be performed to ensure the application's stability and performance in the live environment. 10. Monitoring:    Continuous monitoring tools are employed to track the application's performance, detect potential issues, and gather insights into user behaviour. 11. Rollback (If Necessary):    If issues are detected post deployment, the CI/CD pipeline may support an automatic or manual rollback to a previous version. 12. Notification:    The CI/CD pipeline notifies relevant stakeholders about the success or failure of the deployment, providing transparency and accountability. This iterative and automated process ensures that changes to the codebase can be quickly and reliably delivered to production, promoting a more efficient and consistent software delivery lifecycle. It also helps in catching potential issues early in the development process, reducing the risk associated with deploying changes to production.

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

Isabel Barberá: "This document provides practical guidance and tools for developers and users of Large Language Model (LLM) based systems to manage privacy risks associated with these technologies. The risk management methodology outlined in this document is designed to help developers and users systematically identify, assess, and mitigate privacy and data protection risks, supporting the responsible development and deployment of LLM systems. This guidance also supports the requirements of the GDPR Article 25 Data protection by design and by default and Article 32 Security of processing by offering technical and organizational measures to help ensure an appropriate level of security and data protection. However, the guidance is not intended to replace a Data Protection Impact Assessment (DPIA) as required under Article 35 of the GDPR. Instead, it complements the DPIA process by addressing privacy risks specific to LLM systems, thereby enhancing the robustness of such assessments. Guidance for Readers > For Developers: Use this guidance to integrate privacy risk management into the development lifecycle and deployment of your LLM based systems, from understanding data flows to how to implement risk identification and mitigation measures. > For Users: Refer to this document to evaluate the privacy risks associated with LLM systems you plan to deploy and use, helping you adopt responsible practices and protect individuals’ privacy. " >For Decision-makers: The structured methodology and use case examples will help you assess the compliance of LLM systems and make informed risk-based decision" European Data Protection Board