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?

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?

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

Innovation knows no gender. Reflecting on my journey as an engineer over the past 25 years, from stepping into the workforce to witnessing the remarkable strides women have made today, I am struck by both the progress achieved and the many challenges that persist. When I started my career in the late 90s, women engineers were a handful and today, I'm heartened to see more women not only entering the field but also pioneering innovations and driving meaningful change. ➡️ However, looking at the numbers, in 2023, men outnumbered women in global engineering by 86.3% to 13.7%. And despite the demand for tech skills, women constitute only 28% of engineering graduates globally. In STEM fields, they make up 33% of researchers but hold just 12% of national science academy memberships. ➡️The leaky STEM pipeline begins early and persists over time. It is not just enough to keep feeding the pipeline by increasing the number of female students. It is imperative to work towards breaking gender stereotypes through early investment in reskilling and the promotion of STEM education. Apart from making STEM education more fun and engaging, introduction to female role models and mentors can help change stereotypical perceptions related to these subjects and inspire more girls to choose and work in the area. ➡️I see technology as an enabler here. Achieving equal representation of women in the tech industry requires a collaborative effort from organisations, academia, and government bodies. At the organisational level, tech firms should focus on creating supportive structures that not only attract but also retain and nurture female professionals. Flexible working policies, improved leave and well-being benefits, and support networks serve as key factors in promoting women in the workplace. Investing in training and mentorship programs is essential to equip high-potential women technologists with the necessary skills for leadership roles. Initiatives like involving female employees in the recruitment process, hosting career fairs, and offering internship programs can help organisations move towards a more gender-balanced workforce. The future of engineering is bright, and women are an integral part of that future. By continuing to support and celebrate women in engineering, we are investing in a world where innovation knows no gender, and where the contributions of all are valued and recognized. #InternationalWomenInEngineeringDay 🎉✨

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.

What Would Happen If The AI Industry Overlooks Women's Contributions? "A recent New York Times article released a list of people 'behind the dawn of the modern artificial intelligence movement' – and not a single woman was named. It came less than a week after news of a fake auto-generated woman being listed as a speaker on the agenda for a software conference. Unfortunately, the omission of women from the history of STEM isn’t a new phenomenon. Women have been missing from these narratives for centuries. In the wake of recent AI developments, we now have a choice: are we going to leave women out of these conversations as well – even as they continue to make massive contributions to the AI industry? Doing so risks leading us into the same fallacy that established computing itself as a 'man’s world'. The reality, of course, is quite different. A More Accurate History: Prior to computers as we know them, 'computer' was the title given to people who performed complex mathematical calculations. These people were commonly women. English mathematician Ada Lovelace (1815–1852) is often referred to as the first computer programmer. She was the first person to realize computers could do much more than just math calculations. Her work on the analytical engine – a proposed automatic and fully programmable mechanical computer – dates back to the mid-1800s. By the 1870s, a group of about 80 women worked as computers at the Harvard Observatory. They catalogued and analyzed copious amounts of astronomic data for astronomer Edward Charles Pickering (who exploited the fact they’d work for less money than men, or even as volunteers). By the late 19th century, increased access to education meant there was an entire generation of women trained in maths. These woman computers were cheaper labour than men at the time, and so employing them significantly reduced the costs of computation. During the first world war, women were hired to calculate artillery trajectories. This work continued into the Second World War, when they were actively encouraged to take on wartime jobs as computers in the absence of men. Women continued to work as computers into the early days of the American space program in the 1960s, playing a pivotal role in advancing NASA’s space projects. One of these computers was Katherine Johnson, who was responsible for quality-checking the outputs of early IBM computers for an orbital mission in 1962." #WomenInSTEM #GirlsInSTEM #STEMGems #GiveGirlsRoleModels https://lnkd.in/eDkSmjdG

Production changes everything. What worked in a demo starts breaking at scale. That’s where real AI systems are tested. Here are the concepts that actually matter 👇 - Prototype vs production A demo works in controlled conditions, while production systems deal with scale, failures, and messy edge cases. - Training vs inference Training happens occasionally to build the model, while inference runs continuously to serve real users. - Batch vs real-time inference Batch is cost-efficient for large workloads, while real-time is critical when user experience depends on instant responses. - Accuracy vs reliability Accuracy looks good on test data, while reliability shows consistent performance under real-world conditions. - Guardrails vs validation Guardrails prevent unsafe outputs, while validation ensures correctness. Both are needed for safe and dependable systems. - Offline vs online evaluation Offline testing uses past data, while online evaluation measures real user impact. One doesn’t guarantee the other. - Data drift vs model drift Data drift changes inputs, while model drift shows performance degradation. Detecting this early avoids silent failures. - Monitoring vs observability Monitoring tracks known issues, while observability helps you understand unknown failures and system behavior. - Model hosting vs model serving Hosting deploys the model, while serving handles scaling, routing, and real-time requests. This is where complexity grows. - RAG vs fine-tuning RAG brings in fresh external knowledge, while fine-tuning embeds knowledge into the model. One adapts, the other is fixed. - Latency vs throughput Latency is response speed, while throughput is volume. Systems often fail because latency becomes too high. - Prompting vs fine-tuning Prompting shapes behavior through instructions, while fine-tuning changes model weights. Many real systems rely more on prompting. Understanding these trade-offs is what makes AI systems actually work. Which of these has been the toughest in your production setup?

Git Lifecycle for Data Engineers: Think in Pipelines ⚙️ From dev to production, Git is the “Data Lineage” for your infrastructure. If you build data pipelines, you already understand Git. The flow is almost the same. 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗗𝗶𝗿𝗲𝗰𝘁𝗼𝗿𝘆 Your raw zone. Files change, experiments happen, nothing locked yet. 𝗦𝘁𝗮𝗴𝗶𝗻𝗴 𝗔𝗿𝗲𝗮 git add marks what should move forward. Like selecting the clean batch before loading. 𝗟𝗼𝗰𝗮𝗹 𝗥𝗲𝗽𝗼 git commit -m "msg" stores a snapshot. Clear history. Easy rollback. 𝗥𝗲𝗺𝗼𝘁𝗲 𝗥𝗲𝗽𝗼 Shared source of truth. git push sends your work. git pull syncs with the team. Know these common commands you’ll use daily: • git add → stage changes • git commit -m → save snapshot • git commit -a -m → stage + commit tracked files • git push → send to remote • git fetch → download updates only • git pull → fetch + merge • git merge → combine branches • git diff → inspect changes anytime Image Credits: Brij kishore Pandey Follow the Data engineers rule: Commit like pipeline checkpoints — small, clear, reversible. Version control isn’t just for devs. It’s how data teams ship with confidence. 🔁

Reliability, evaluation, and “hallucination anxiety” are where most AI programmes quietly stall. Not because the model is weak. Because the system around it is not built to scale trust. When companies move beyond demos, three hard questions appear: →Can we rely on this output? →Do we know what “good” actually looks like? →How much human oversight is enough? The fix is not better prompting. It is a strategy and operating discipline. 𝐅𝐢𝐫𝐬𝐭: ⁣Define reliability like a product, not a vibe. Every serious AI use case should have a one-page SLO sheet with measurable targets across: →Task success ↳Right-first-time rate and rubric-based acceptance →Factual grounding ↳Evidence coverage and unsupported-claim tracking →Safety and compliance ↳Policy violations and PII leakage →Operational quality ↳Latency, cost per task, escalation to humans Now “good” is no longer opinion. It is observable. 𝐒𝐞𝐜𝐨𝐧𝐝:  evaluation must be continuous, not a one-off demo test. Use a simple loop: 𝐏lan: Define rubrics, datasets, and risk tiers 𝐃⁣o: Run offline evaluations and limited pilots 𝐂heck: Monitor drift and regressions weekly 𝐀ct: Update prompts, data, guardrails, and workflows Support this with an AI test pyramid: →Unit checks for prompts and tool behaviour →Scenario tests for real edge failures →Regression benchmarks to prevent backsliding →Live monitoring in production Add statistical control charts, and you can detect silent degradation before users do. 𝐓𝐡𝐢𝐫𝐝: reduce hallucinations by design. →Run a short failure-mode workshop and engineer controls: →Require retrieval or evidence before answering →Allow safe abstention instead of confident guessing →Add claim checking and tool validation →Use structured intake and clarifying flows You are not asking the model to behave. You are designing a system that expects failure and contains it. 𝐅𝐨𝐮𝐫𝐭𝐡: make human-in-the-loop affordable. Tier risk: →Low risk: Light sampling →Medium risk: Triggered review →High risk: Mandatory approval Escalate only when signals demand it: low confidence, missing evidence, policy flags, or novelty spikes. Review becomes targeted, fast, and a source of improvement data. 𝐅𝐢𝐧𝐚𝐥𝐥𝐲: Operate it like a capability. Track outcomes, risk, delivery speed, and cost on a single dashboard. Hold a short weekly reliability stand-up focused on regressions, failure modes, and ownership. What you end up with is simple: ↳Use case catalogue with risk tiers ↳Clear SLOs and error budgets ↳Continuous evaluation harness ↳Built-in controls ↳Targeted human review ↳Reliability cadence AI does not scale on intelligence alone. It scales on measurable trust. ♻️ Share if you found thisuseful. ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI #AI #AIReliability #TrustAtScale #OperationalExcellence

Recently, while fine-tuning analytics for my dashboards, I stumbled upon some of SQL's unsung heroes: 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 𝐒𝐞𝐭𝐬 𝐚𝐧𝐝 𝐂𝐮𝐛𝐞𝐬 - they make advanced analytics feel like a breeze. 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 𝐒𝐞𝐭𝐬 are the tailor of SQL, allowing us to custom-fit our data aggregation with precision. This feature is a godsend when you need to aggregate data across multiple dimensions but want to avoid the clutter of unnecessary combinations. Imagine wanting to see sales totals by product, by region, and then both together without running separate queries for each view. Grouping Sets let you do just that in a single query, streamlining your analysis and saving valuable processing time. 𝐂𝐮𝐛𝐞 takes the concept of Grouping Sets further by exploring every possible aggregation combination within specified dimensions. It's like setting off on an expedition across your data landscape, uncovering every insight along the way. If Grouping Sets tailor your data, Cube weaves an intricate tapestry, showcasing the full picture of your data's potential relationships and patterns. 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 While both tools enhance our analytical capabilities, Grouping Sets offer more control and flexibility, allowing us to specify exactly what we're looking for. This precision makes it invaluable for targeted analysis, where only certain data combinations are relevant. On the other hand, Cube provides a broader view, ideal for when you're in the exploratory phase of your analysis, seeking insights without preconceived notions of what you'll find. In my journey, leveraging these functions has not only optimized our dashboards but also enriched our data storytelling, offering both the bird's-eye view and the detailed close-ups where needed. The ability to tailor our approach to data aggregation, choosing between the meticulous customization of 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 𝐒𝐞𝐭𝐬 or the comprehensive exploration with 𝐂𝐮𝐛𝐞, has been a game-changer. Integrating these powerful SQL features with tools like Apache Superset, however, does present its set of challenges, like navigating through a fog of null values in unions. But, there's always a lighthouse in the fog. A straightforward use of 𝐂𝐎𝐀𝐋𝐄𝐒𝐂𝐄 to assign default values to these nulls ensures our dashboards run as smoothly as a well-oiled machine, keeping our data voyage on course. Have you dived into the world of Grouping Sets and Cube in your SQL queries? How have they transformed your analytics and dashboarding strategies? Let's exchange insights and elevate our data game together! #DataAnalytics #SQL #GroupingSets #Cube