Nonprofit leaders have the desire to adopt best-in-class technologies, but the lack of talent, tooling and resources often gets in the way.
At AlleyCorp, our Nonprofit ENG(INE) initiative provides $1 million worth of engineering and tech support to four nonprofits each year. The hours of work donated help nonprofits operate more effectively, and in turn, our engineers reap the rewards of helping others.
3 Benefits of Pro Bono Work for a Nonprofit
- Applying your expertise to a nonprofit can make a meaningful difference to their mission.
- Applying your technical skills in a totally different business context sharpens your decision making.
- Helping in the community can inspire creativity and new ways of approaching your day-to-day work.
In 2023, we worked with Teaching Lab, Podsie, Magpie Literacy and Trans Lifeline to help these nonprofits rethink their tech stacks and provide meaningful ways to scale their mission through tech. My hope is that the lessons and examples from that work can inspire engineers and top talent to donate their own time, expertise and energy to helping important causes.
Teaching Lab & Magpie Literacy: Supporting Experimentation
Teaching Lab, an organization that trains teachers and incubates edtech ideas, wanted to broaden its tech infrastructure to incorporate AI.
We initially set out to build a set of features that allowed one engineering team within Teaching Lab to use large language models (LLMs) to pilot edtech tools and test them through research-based guidance from teachers and students. However, we soon realized that Teaching Lab’s needs around LLMs would continually need to evolve. The speed with which Teaching Lab’s engineering team needed to move, redesigning features based on the feedback they were collecting in real time, required a better development experience.
So, instead of focusing on a sole set of features, we began building tooling for experimenting with LLMs, making a number of additional improvements that gave their team the ability to test a wider set of hypotheses. This meant their developers could ship products faster with each subsequent rebuild.
With Magpie Literacy, an organization that develops digital literacy tools for kids pre-kindergarten to fourth grade, we similarly set out to build a machine learning model to solve a specific task — mapping a text to the literacy skills required for reading it and vice versa. We quickly realized, however, that training the model would be an ongoing need, and that the organization would benefit from human-in-the-loop tooling as well as experiment tracking infrastructure.
In both cases, investment in infrastructure allowed the organizations to expand on what we’ve built with them and even develop entirely new applications with AI.
Podsie: Prioritizing Design
Better design can often solve a problem that you think is purely technical.
The tech team at Podsie, a personalized and automated learning tool for spaced repetition (a learning techinque) in the classroom, had asked us to create a platform that generated early high school math equations. The solutions to these were working about 98 percent of the time. We could have spent months, honestly maybe even years, improving that code to get it to a perfect 100 percent, but all we really needed to do was rethink our design through the lens of the user.
That design feature was a feedback loop. By encouraging Podsie to introduce a feedback loop to its platform, where students could report on incorrect question/solution sets to bring those prompts out of rotation and call the teacher’s attention to the issue, we were able to solve for those 2 percent of inaccuracies.
The cost of this design solution was also much lower than it would have been had we solved through engineering or rewriting code. It helped validate the ultimate value of the feature (rather than perfecting functionality forever), and allowed the Podsie team to rethink the internal structure of its team and recognize the need for a full-time designer.
Sometimes, the best thing engineers can do is move over and ask for a designer to reframe the problem altogether. Many of us have learned that lesson the hard way, and it underlines the importance of building out a balanced, well-structured team of both engineers and designers.
Trans Lifeline: Tech Mentorship
Our work with Trans Lifeline, an organization connecting trans people to community support, was centered on completely revamping its tech stack. What made this partnership especially unique, though, was the way our support translated into an even more impactful result from a technical mentorship perspective.
Our team spent hour upon hour helping Trans Lifeline’s three engineers choose a new technology stack based on their business needs, operational budget and their existing knowledge and preferences. Trans Lifeline’s team got more value out of the partnership as our team guided theirs through the process, explaining the variables of every key decision and the steps of implementation that needed to occur after. By spending time assisting them with this sort of decision making, the team would be able to grow their new platform in a self-sustaining way.
In the first four months of our work with the Trans Lifeline team, our single biggest impact didn’t come in the form of a newly built tech network. Instead, it emerged in the form of pairing our engineers with theirs. It was an inspiring lesson to see how a non-technical impact could be made when partnering with a nonprofit.
Many philanthropic organizations can’t afford the high salaries and costs of tenured engineers with decades of experience. That means teams sometimes lack the guidance they need to develop emerging talent and sustain an organization for years to come.
Technical mentorship matters, and it’s an accessible way for any seasoned engineering leader to help a nonprofit.
