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@@ -18,7 +18,9 @@ Previously, MHF created [TestRAMP](https://www.testramp.org/), an ambitious effo
 
 In this interview, Mike speaks candidly about the challenges and opportunities of deploying AI in nonprofit settings, and the lessons learned from building responsible, high-impact technology during moments of rapid change.
 
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 ## The Appetite for AI in the Third Sector 
 
@@ -44,7 +46,9 @@ From day one, we knew getting anything onto a nonprofit’s website would be dif
 
 Behind the scenes, Knowbot is written mostly in Python and uses LLMs from Anthropic, OpenAI, Meta, Google, and Perplexity. We don’t develop our own models — few organisations on Earth have the budget for that. Instead, we “build the car around the engine,” and it’s a slightly different car for each non-profit that we work with. 
 
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 ## Ethics by Design: Domain Restriction as a Safety Mechanism
 
@@ -64,7 +68,9 @@ I think we have been really lucky in terms of timing. To do what we're doing now
 
 We’ve also been lucky in that  LLM technology has improved fast enough that we’ve been able to incorporate new functionality almost as quickly as our nonprofit partners have requested it. For example, we now allow partners to restrict Knowbot to particular sections or topics within a website, or to sit across multiple websites. This has only become practical as models and retrieval tools have matured. Development has become faster, too: for example, we can now submit an entire codebase into our coding LLMs and ask questions about it. By comparison, when ChatGPT first launched we could only submit a small section of a program at a time.
 
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 ## Impact: What Knowbot Is Changing