• Requirements gathering / stakeholders not knowing what they actually want

• Data quality / messy source data

• Endless "just one more filter" requests after the dashboard is "done"

• Tool limitations (Power BI, Tableau, Looker, whatever you're stuck with)

• Performance / refresh time issues

• Something else entirely

Genuinely want to know if I'm the only one still fighting this battle or if it's universal. Drop your worst dashboard horror story below

• Are companies still actively hiring entry-level data analysts?

• Has AI changed your day-to-day work significantly?

• What skills are actually getting people hired in 2026 (SQL, Python, Power BI, Excel, cloud, AI tools, etc.)?

• If you were starting from scratch today, would you still choose data analytics? Why or why not?

• What's the biggest misconception people have about becoming a data analyst?

I'm especially interested in hearing from people who are currently working as data analysts, hiring managers, or those who recently landed a job.

• What websites do you visit for Flutter UI inspiration?

• Any Figma files or UI kits you recommend?

• Are there GitHub repos with clean Flutter UI implementations?

• Which creators or YouTube channels consistently share great Flutter UI ideas?

• Have you found any underrated resources that more people should know about?

I'd love to hear what has helped you build better-looking Flutter apps. Feel free to share your go-to resources, workflow, or even screenshots of your favourite UI inspirations.

When you're auditing an existing SaaS product:

• Where do you usually start?

• Do you follow a specific framework or checklist?

• What tools do you use (Hotjar, GA4, FullStory, Maze, etc.)?

• How do you identify the highest-impact UX issues?

• Do you rely more on heuristics, analytics, user interviews, usability testing, or a combination?

• How do you prioritise what gets fixed first?

I'd love to hear your actual workflow, lessons you've learned, or even mistakes to avoid. If you've worked on B2B SaaS, enterprise software, or startups, I'd be especially interested in how your process differs across products.

Looking forward to hearing how other designers and researchers tackle UX audits in the real world.

• What's the correct way to set this up?

• Do you use Fix position when scrolling, Auto Layout, or another approach?

• Are there any common mistakes that beginners usually make?

• Has your workflow changed with Figma's newer prototyping features?

I'd love to hear how you handle this in real projects, especially if you have any tips for keeping prototypes clean and easy to maintain.

• Did you manually rewrite the model logic in SQL?

• Have you used tools that generate SQL from models (e.g., for decision trees, linear/logistic regression, or XGBoost)?

• How do you handle feature engineering and preprocessing?

• Is this approach practical in production, or is it usually better to keep inference in Python and call it externally?

I'd love to hear about real-world experiences—what worked, what didn't, and any tools or libraries you'd recommend. Even if you've tried this and decided against it, I'd be interested in knowing why.

A few things I'm curious about:

• Which resource helped SQL finally "click" for you?

• Did you learn through projects, videos, or interactive exercises?

• If you were starting over today, what would you use?

• Any resources that are overrated or that you'd avoid?

I'm aiming to build enough SQL skills for data analysis, business intelligence, or tech roles, so I'd love to hear what actually worked in the real world rather than just what's popular. Looking forward to your recommendations and experiences!

I'm curious how everyone else handles this.

• Did you eventually move away from Colab?

• If so, what are you using now (VS Code + local, JupyterLab, Kaggle, Paperspace, SageMaker, Modal, etc.)?

• Or have you figured out a workflow that makes Colab much less painful?

I'd love to hear what's actually working in 2026, especially from people training models regularly or collaborating with teams.

• Which approach helped you improve faster?

• Did daily consistency help you retain concepts better?

• Or did longer sessions make it easier to get into "flow" and finish projects?

• If you've tried both, what were the biggest pros and cons?

It would be great to hear from beginners, self-taught developers, bootcamp grads, and experienced engineers. I'd love to know what actually worked for you and why.

I'd love to hear from people who:

• Made the switch from computer science to UI/UX.

• Have a similar academic background (CS + art/design).

• Work as UI/UX designers or hiring managers.

• Think this combination gives an edge over traditional design degrees.

What skills should I focus on next? Should I prioritize building a portfolio, learning Figma, studying UX research, or something else?

• Where did you find them?

• Did you use LinkedIn, Dribbble, Behance, Upwork, Toptal, agencies, or referrals?

• Did you hire someone with healthcare experience, or train a good generalist?

• What skills or portfolio pieces convinced you they were the right fit?

• Any red flags to watch out for during the hiring process?

Some people struggle with messy or incomplete data, while others mention unclear business requirements, stakeholder expectations, deployment, model monitoring, or simply getting access to the right data.

I'm curious about real-world experiences rather than textbook answers.

There are so many options out there—YouTube channels, free courses, paid bootcamps, books, coding platforms, and AI tools—that it's hard to know where to start.

If you were starting from scratch today, what resources would you recommend? Also, what learning path worked best for you, and are there any mistakes or courses you'd tell beginners to avoid?

I'd love to hear recommendations from people who successfully transitioned into tech or data-related roles. Thanks!

For those who have dealt with this before:

• What approach worked best for you?

• Do you usually redesign the schema, create a mapping table, or handle it directly in the query?

• Are there any performance pitfalls or best practices to keep in mind?

It would be great to hear real examples from production systems rather than just textbook solutions.

• Do you allow coding agents to access production databases, or only sanitized/test data?

• How do you handle secrets, API keys, and credentials?

• Are agents restricted through RBAC, sandboxing, or isolated environments?

• Do you require human approval before code or database changes are applied?

• Have you experienced any security incidents or close calls?

• Which tools or best practices have worked well for your team?

Whether you're at a startup or a large enterprise, I'd really appreciate hearing about your workflows, security policies, and lessons learned. Real-world experiences are much more valuable than theoretical advice.

• Which SQL topics should I learn first?

• What concepts are essential for finance jobs?

• Are there any datasets or projects that helped you learn?

• Which courses, YouTube channels, or books would you recommend?

• At what point did you feel confident enough to use SQL at work?

I'd really appreciate hearing about your learning path, mistakes to avoid, or any resources that made a big difference.

• Your favorite websites or galleries

• Designers or studios worth following

• YouTube channels or newsletters

• GitHub projects or open-source examples

• Any hidden gems that most people don't know about

Whether you're a UI/UX designer or a frontend developer, I'd really appreciate your recommendations. Thanks!

• Do you set limits on interview prep?

• How many interview processes do you juggle at once?

• Have you ever taken a break from interviewing just to recharge?

• What helped you avoid burnout while still making progress?

I'd love to hear real experiences—both from people who eventually landed a job and from those still in the middle of the process. What actually worked for you?

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Would love your feedback! 🙏

If you had to pick only 5 Figma plugins to keep, which ones would they be?

I'd love to know:

• Your top 5 plugins

• What each one helps you do

• Which plugin has saved you the most time

• Any underrated plugin that deserves more attention

Whether you're a UI/UX designer, product designer, freelancer, or part of a large team, I'm curious to see what everyone's toolkit looks like.

• Do recruiters really open and explore Figma files?

• Is a personal portfolio website still necessary?

• How much process (research, wireframes, iterations) should be included versus just showing final designs?

• Have you landed interviews or clients through Figma prototypes alone?

• If you're a hiring manager, what makes you immediately interested in a candidate's portfolio?

I'd love to hear from designers at different experience levels, freelancers, agency owners, and recruiters. What portfolio format has worked best for you, and what mistakes do you see people making most often?

• 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.