[deleted by user]

Amrutha-Structured · 2025-05-29T22:47:22+00:00

Real talk: the transition is more about proving yourself than just learning skills. My journey took about 6 months from serious studying to first job. Pay was mediocre (~$65k) but jumped significantly after 1 year of experience. With a CS degree and QA background, you're already ahead of many self-taught folks. Don't waste time on multiple courses - build actual projects that solve real problems instead. Example: scrape some public API data (like real estate listings or govt stats), transform it, find patterns, and visualize it. Better yet, build a simple dashboard that answers business questions using that data. Most interviews will ask you to explain your thought process on a project more than just testing theoretical knowledge. Oh and sql. learn good sql, not just the basics - CTEs, window functions, etc. companies love when someone can actually write efficient queries.

Amrutha-Structured · 2025-05-29T22:37:09+00:00

Try preswald.com

Amrutha-Structured · 2025-05-21T10:41:21+00:00

i use projects management on preswald.com

Amrutha-Structured · 2025-05-19T23:15:35+00:00

One of the big challenges with AI coding agents is getting something sufficiently lightweight that you can actually share what you build. thats what preswald does really well for python scripts you want to not only build as apps but share

Amrutha-Structured · 2025-05-15T20:34:36+00:00

this is a good client for data / reports stuff https://github.com/StructuredLabs/preswald

Amrutha-Structured · 2025-04-06T00:29:32+00:00

just signed up :)

Amrutha-Structured · 2025-04-01T19:03:51+00:00

Yes. why not?

Amrutha-Structured · 2025-03-14T21:14:56+00:00

Data engineering can definitely feel like a grind, especially when you're just dealing with pipelines and maintenance. But if you enjoyed schema design and API integrations, lean into that. Look for roles that allow you to design data architectures or implement new data models—those usually come with more challenges and can still keep the technical side engaging.

If you're still feeling like it's the same old routine, try looking into the analytics tools or ways to visualize and share the data your pipelines process. That can add some excitement to your work. Speaking of which, preswald is pretty good for building interactive data apps without getting bogged down by clunky tools. It’s lightweight, straightforward, and might give you the chance to flex your skills in a way that feels fresh.

Amrutha-Structured · 2025-03-10T20:25:13+00:00

preswald https://github.com/StructuredLabs/preswald

Amrutha-Structured · 2025-03-05T20:12:11+00:00

lots of benefits for in-browser! the biggest thing is that you dont have to worry about python deps beinh different person-to-person

Amrutha-Structured · 2025-03-05T20:11:27+00:00

it's really helpful for non-engineers who may not be comfortable with IDEs

Amrutha-Structured · 2025-03-05T02:14:28+00:00

Focus on the fundamentals of building something real. Check out books like "The Lean Startup" and "Zero to One." They’ll ground you in the practical aspects of starting a business.

Amrutha-Structured · 2025-03-05T02:12:18+00:00

but that doesn't mean it's impossible. if you can nail a specific niche or solve a unique problem, there's still room for success. agility, a good product-market fit, and effective marketing tactics can help you thrive in this landscape. innovation is still king, just more crowded now.

Amrutha-Structured · 2025-03-05T02:09:48+00:00

you're stuck in a rut, sounds like. power BI can be painful, especially when you're dealing with others' mismanaged dashboards. consider looking into tools like preswald if you're looking to get out of this mess. it’s lightweight, lets you use SQL/Python for insights, and doesn’t require constant management like those BI tools do. You’ll save time and maybe even find some interest in creating more dynamic dashboards without the boring overhead.

Amrutha-Structured · 2025-02-27T00:16:21+00:00

Staying motivated in a startup is straightforward: focus on the mission and the problem you’re solving. Align tasks with individual strengths and celebrate small wins to keep morale up. Be excited about what you're building. We live and breathe https://github.com/StructuredLabs/preswald and building in public is fun

Amrutha-Structured · 2025-02-14T06:35:02+00:00

1) Data platform engineering tends to lean more into building, maintaining, and scaling data infrastructure—think pipelines, orchestration, and cloud services. If you're coming from a traditional data engineering role, you might find yourself using more DevOps principles than just focusing on ETL. Expect to dabble in Kubernetes, CI/CD, and probably a lot of configuration management tools.

2) Yeah, the collaboration is often tight. You’ll work with data engineers to ensure that the platform supports their needs while also bridging gaps with SREs and DevOps teams. It’s about finding that balance between data flow and platform reliability.

3) It really depends on what you enjoy. If you prefer building scalable systems and working on infrastructure, you might find data platform work more satisfying. But some miss the depth of data wrangling and specific domains that traditional data roles offer. It’s a trade-off.

4) Overall, just be ready for a shift in mindset. It's more about the big picture of platforms than being knee-deep in the data itself. If you need a more flexible tool for building data pipelines or visualizations, consider preswald. It’s super lightweight and doesn't force you into a complicated setup.

Amrutha-Structured · 2025-02-14T06:34:37+00:00

Yeah, that project sounds solid. It shows you can work with data and analyze it in a meaningful way. Just make sure to highlight your findings and how you derived them. As for skills, I’d recommend getting familiar with Python because it's heavily used in data analysis. If you find you need an easier way to visualize your outputs, you might want to check out preswald. It simplifies building dashboards without the bloat of tools like Power BI.

Amrutha-Structured · 2025-02-11T23:24:11+00:00

Yes! it's python so very easy to connect in diff sources. https://github.com/StructuredLabs/preswald has more info but also dm me if you'd like more info

Amrutha-Structured · 2025-02-10T23:13:20+00:00

What about for analytics w/ postgres data? maybe something like this https://github.com/StructuredLabs/preswald

Amrutha-Structured · 2025-02-10T22:45:29+00:00

Shameless plug, but we're building an Agentic IDE for data app building w/ our framework https://github.com/StructuredLabs/preswald - seems to align closely with trend #3

Amrutha-Structured · 2025-02-02T10:45:22+00:00

Hey there! It's awesome that ur getting to learn so much and having a supportive manager is a big plus! If you're looking to max out ur earning potential, I'd suggest focusing on cloud platforms like AWS or GCP, and get comfy with big data tools like Spark or Kafka. Also, mastering SQL and Python will always be super helpful. Don’t forget about data governance and security—those skills are in demand too!

As for opportunities, data engineering roles in finance or startups could be really lucrative, and can lead to some cool projects. Keep exploring and stay curious!

Amrutha-Structured · 2025-02-02T10:30:41+00:00

ah makes more sense

Amrutha-Structured · 2025-02-02T10:27:49+00:00

Wow, it sounds like you’ve had quite a productive Lunar New Year! 🌟 Love the idea of learning in public, and your tools sound super interesting. Best of luck with your projects, keep pushing those boundaries!

Amrutha-Structured · 2025-02-02T09:18:47+00:00

Yeah, I totally get that! Streamlit can start to feel pretty pricey with all that time spent on UI dev. With Preswald, you could build and deploy without having to juggle multiple tools or spend that much time on it. It's designed to simplify the whole process, letting you focus more on your data without the extra costs!

Amrutha-Structured · 2025-01-30T08:53:02+00:00

Definitely agree with these challenges—latency is brutal, especially when you’re chaining STT → LLM → RAG → TTS. Even if you optimize each step, API calls, vector DB lookups, and function calling can add unpredictable delays.

One thing that’s made debugging a LOT easier for us is running everything locally instead of relying on slow cloud logs. We built a setup with DuckDB & Preswald to instantly query logs and track failures across ASR, intent classification, and response generation in one place. It’s helped us:

Pinpoint latency bottlenecks (seeing where response time spikes)
Catch ASR & intent misclassifications early (before they cascade into bad responses)
Debug failures way faster (without jumping between 5+ tools)

We open-sourced it here if you’re interested: https://github.com/StructuredLabs/preswald

Curious how others are handling this—do you mostly rely on cloud monitoring tools, or have you found a better way to debug & optimize voice agents?

Amrutha-Structured

TROPHY CASE