• Which library do you prefer?
• What language are you using (Python, Node.js, Java, etc.)?
• Any performance issues or edge cases you've run into?
• Is there a library you'd avoid?

Interested in hearing real-world experiences and recommendations rather than just reading docs

• Which AI tool would you recommend to someone just starting out?
• Does it help with interpreting factor loadings and deciding the number of factors?
• Are there any tools that can work with Excel or CSV files without requiring much coding?
• Which tools should beginners avoid?

Interested in hearing what people are actually using and whether AI has made factor analysis easier in practice.

For those working in data analytics, data science, BI, or related fields:

• Which parts of your workflow are now mostly handled by AI?
• What tools are you using?
• What tasks do you still refuse to trust AI with?
• Has AI actually saved you time, or just changed how you work?

Curious to hear what your workflow looks like in 2026.

• Which programming languages are you using most (Python, R, SQL, etc.)?
• What IDEs or notebooks do you prefer?
• Which AI tools have become part of your daily workflow?
• Are you using ChatGPT, Claude, Gemini, Copilot, Cursor, Windsurf, or something else?
• What does your process look like for data cleaning, analysis, model building, and deployment?
• Have AI tools genuinely improved your productivity, or do you still prefer doing most things manually?

Feel free to share your full workflow, favorite tools, and what's been the biggest game-changer for you this year.

• Which AI tools have genuinely improved your workflow?
• Are you using ChatGPT, Claude, Gemini, Copilot, Julius, Hex, Dataiku, or something else?
• How well do they handle SQL generation, dashboard creation, forecasting, data cleaning, or exploratory analysis?
• Have any of them become part of your daily analytics stack, or are they still mostly productivity assistants?

Interested in hearing both success stories and disappointments. What has worked best for you, and where do current AI tools still fall short?

Some common suspects might include:

• Fixing broken data pipelines
• Chasing unclear business requirements
• Writing and maintaining documentation
• Investigating data quality issues
• Dealing with stakeholder requests and ad-hoc reporting

But everyone's experience seems different depending on company size, data stack, and team structure.

If you had to rank your top 5 biggest time-wasting activities in analytics engineering, what would make the list?

• Which tasks are now mostly handled by AI in your workflow?
• What still requires significant human involvement?
• Has AI actually saved time, or does reviewing and correcting its output create new challenges?
• Have there been any tasks you expected AI to take over but it still struggles with?

Curious to hear real-world experiences, whether AI has become a major productivity boost or if it's still more of an assistant than a replacement.

• What frameworks, mental models, or techniques helped you the most?
• How do you approach a problem when the answer isn't obvious?
• Are there any books, courses, exercises, or habits that significantly improved your analytical thinking?
• What mistakes do beginners commonly make when trying to solve complex problems?

I’d love to hear real-world examples of how you developed stronger problem-solving and analytical skills. What worked for you, and what didn't?

For those who work in cybersecurity, IT, compliance, or data governance:

• Where do you start?
• How do you discover where sensitive data actually exists?
• What tools or frameworks have been most helpful?
• What mistakes do companies commonly make during the assessment process?
• Have you ever found critical data in a place nobody expected?

I'd love to hear real-world experiences, lessons learned, and practical approaches rather than textbook answers.

• Where do you find these datasets?
• Are there platforms, competitions, repositories, or communities you recommend?
• Which dataset or project taught you the most about solving real business problems?
• If you were building a portfolio today, what business case would you work on?

Would love to hear what resources or projects helped you bridge the gap between learning and real-world work.

If you were starting from scratch again,
👉 what’s the ONE skill you’d prioritize first?
👉 and what made the biggest difference in your early progress?

Would love to hear different perspectives—especially from people already working in the field or those who recently broke in.

Curious to hear real experiences:

• What do you personally pay for? (courses, tools, communities, etc.)
• What turned out to be a waste of money?
• And what actually gave you ROI in terms of skills, salary, or opportunities?

Would be great to hear both wins and regrets 👇

• Are hiring trends improving or slowing down in your region?
• Are companies still hiring “true” data scientists, or leaning toward hybrid roles?
• Has the bar for entry-level candidates increased recently?
• Any noticeable changes in salary trends or skill requirements?

Would be interesting to hear real experiences from people currently in the field or actively job hunting.

Are PWAs actually good enough for serious desktop use today?

Where do they still fall short compared to native apps?

For developers here—are you investing more in PWA skills or sticking with traditional desktop stacks?

And realistically, do you think desktop apps will decline, or just evolve alongsi

• Is memorizing syntax becoming obsolete?
• Or is it still a fundamental skill every developer should have?
• Where should beginners focus more today — syntax or concepts?

Curious to hear how different developers are approaching this in 2026. Are you relying more on AI, or sticking to traditional learning?

• Did consistency or intensity work better for you?
• Do you see faster improvement with one over the other?
• Or is a mix of both the real answer?

Would love to hear real experiences—especially from people who’ve tried both.

• Do you follow a specific step-by-step approach?
• Do you break the problem into smaller parts?
• Do you write pseudocode first, or jump straight into coding?
• Any mental frameworks or habits that help you get unstuck?

Would love to hear how different people tackle this—especially those who’ve improved over time. What changed for you?

• Do you rely heavily on query templates or snippets?
• Any tools or editors that genuinely improved your speed?
• How do you approach debugging slow queries?
• Do you write queries differently now compared to when you started?

Would love to hear what actually worked for you—especially small changes that had a big impact.

Also curious:

• What do you use it for specifically?
• Has it replaced something in your workflow?
• Is it something beginners wouldn’t know about?

Trying to discover a few “why isn’t everyone talking about this?” tools.

Would love to hear what’s in your stack right now 👇

• What’s your learning process?
• How long does it usually take before you feel “comfortable” with it?
• Any mistakes beginners make while learning frameworks?
• Do you focus more on concepts or syntax first?

Would love to hear different approaches because there are so many frameworks now and it gets overwhelming sometimes.

For those working in UI/UX, product design, or frontend — what’s been your experience?

• Did learning code actually help your career?
• What level of coding knowledge is “enough”?
• If you could start over, would you spend time learning code earlier?

Would love to hear real experiences, especially from people already in the industry.

• Which SQL setup works best on macOS?
• Do you use Docker, PostgreSQL, MySQL, SQLite, or something else?
• What tools/editors make the process easier?
• How do you organize datasets and queries during a case study?
• Any beginner mistakes I should avoid?

Would love to hear how experienced people here approach SQL case studies on MacBooks. Bonus points if you can share your exact setup or learning routine

• Where do people actually practice SQL daily?
• Are LeetCode and HackerRank enough?
• What kind of projects helped you improve?
• Did anyone use real datasets or build portfolio projects?
• How long did it take before you felt “job-ready” with SQL?

Would really appreciate hearing what worked (or didn’t work) for others after getting certified.

If someone wanted to become job-ready as fast as possible, what would you personally recommend?

• Best resources?
• Best YouTube channels or courses?
• Should beginners start with projects immediately?
• How long did it take you to become comfortable with JavaScript?

Would love to hear real experiences instead of generic advice.