Building in Public for Women in Tech: Ultimate Career Insurance
Stop Waiting for Perfection: How Building in Public Boosts Your Tech Career
We are living through a massive, fast-moving shift in the tech industry. Between restructuring, ongoing layoffs, and the relentless pace of AI development, it is easy to feel an underlying hum of anxiety about whether your skillset is keeping up.
When faced with this disruption, the default reaction for many high-achieving women is to over-prepare. We buy books, take courses, and stack up certifications, quietly waiting until we are 100 percent sure we are ready before we raise our hands or speak up in meetings.
But in a tight, competitive market, certifications are no longer enough to make you stand out. What actually acts as your ultimate career insurance is Proof of Work.
Recently on the Women Who Ship podcast, I sat down for Part 2 of my conversation with Tanushree Panigrahy, founder of the Stay Ahead of the Curve community and substack, to discuss how she broke out of the over-preparation trap and built unstoppable career confidence by building publicly.
Overcoming AI anxiety by building in public
Earlier in the AI wave, Tanushree found herself on the same roller coaster of anxiety that many of us are riding. She was stretching herself thin, trying to learn every new framework, and constantly feeling like she was falling behind.
The turning point came when she decided to stop trying to master everything and instead focus on building one small thing, learning from it, and moving to the next.
By shifting her goal from "knowing everything" to simply "making something real," she broke the cycle of self-doubt. She realized that confidence does not magically appear from a textbook or a certification stamp; it is built on action. You get that confidence from the daily habit of building, messy and imperfect as it may be.
Overcoming the fear of sharing code on GitHub
For many high-achieving women, the idea of sharing "half-baked" work in public is terrifying. We are socialized to hold back, to minimize our contributions, and to wait until we can present a beautifully polished final product.
But as Tanushree points out, people in tech are not looking for perfection; they are looking for authenticity, collaboration, and real ideas.
To get past her own hesitation, Tanushree dusted off a GitHub account she had opened for a data science course twelve years prior and had completely forgotten about. She committed to publishing her code repositories publicly, even when they were incomplete or in progress.
The result? She started sharing her builds on LinkedIn, and before long, a VP-level executive at a major cable company sent her a direct message. He was not judging her or looking for a finished product; he wanted to discuss her ideas and fork her repository to adapt it for his own team. That direct, real-world connection was all the proof Tanushree's brain needed to know her work was highly valuable.
4 practical AI projects you can build today
You do not need a multi-million-dollar budget or a massive engineering team to build powerful proof of work. Tanushree shared several of her own builds, illustrating how easy it is to start small and scale up:
The Local Personal Financial Analyst: When making a family financial decision, Tanushree wanted to analyze her mortgage and bank statements, but she was not willing to upload her private data to the cloud. Instead, she used Ollama with an open-weight model to build a private, local desktop assistant. This experiment proved to her how easily enterprises can leverage local AI to keep their data safe.
The School Memo Summarizer: Faced with a massive, overwhelming document from her child's school, she built a quick summarizer that compiled parent-teacher meeting notes into quick, readable bullet points. It was immediately validated and used by other busy working parents.
The Deal War Room (Enterprise): Drawing on her consulting experience, she built a multi-agent system simulating a 5-person deal pursuit team. The agents worked in parallel to analyze parameters, and a final agent consolidated the data to produce a partner-level deck ready for client presentations.
The Procurement Assistant (Enterprise): To test the flip side of the deal war room, she built a local tool using Ollama to analyze public sector RFP responses. By testing the tool on public datasets, she discovered a surprising pattern: every single vendor utilized identical, diplomatic evasion patterns in their answers, which the local AI easily flagged.
Be sure to check out Tanushree’s work on her Stay Ahead of the Curve Substack and connect with her directly on LinkedIn to follow her journey. She shares concrete AI builds, career experiments, and lessons learned that are especially powerful if you are navigating your own transition.
How to create your own proof of work strategy
If you are dealing with AI anxiety right now, Tanushree's advice is clear: do not wait for direction or a permission slip from your current job. You may not get picked for the next big project, and your current title does not define your capabilities.
Take matters into your own hands. Pick one annoying, recurring problem in your life and build a rough, basic version to fix it. It does not have to be code; it could be a simple prompt, a custom checklist, or an automated workflow. Spend a couple of weeks working on it, and then share it with someone.
Once you push past the fear of being judged, you will realize that the tech community is eager to collaborate, share ideas, and build together.
If you want help actually building that file and turning your experiments into concrete proof of work, grab my free Get Recognized at Work: 5 Steps to Document and Show Your Impactplaybook. It walks you through a simple weekly “Recognition Retro” so you can turn your day‑to‑day work into clear impact statements you can use in 1:1s, performance reviews, and promotion packets.
Let's go ship some good stuff together.
