A light bulb, as screwed into a lamp to light a room. Depicted as an incandescent bulb with a silver base, often shown with filament and a soft, yellow-white glow. Commonly used to represent ideas (as over a head in a cartoon), thinking, and learning, often as paired with 🤔 Thinking Face or 💭 Thought Balloon. May also represent various senses of light and brightness.

The Easy Read Generator Is Live

Posted by jfrant

Click here to download an Easy Read version of this blog post.

Five years ago, a team of NDI colleagues pitched an idea called “Right To Know” at an internal innovation competition, the culminating project of an internal course on Democracy and Technology (DemTech 1000) I organized. The concept, led by Whitney Pfeifer, was straightforward: build a tool that could translate complex civic documents into Easy Read format—short sentences, plain language, paired with clear illustrations—so that people with intellectual disabilities could access the same information as everyone else. The team won, the idea got a small innovation grant, and what followed was a long, winding road to a working product that I’m only now finally able to share.

The Easy Read Generator is now officially a thing!

What Easy Read Is

Easy Read is a method of presenting information in a format that’s easier to understand. It combines simple language with images that reinforce the meaning of each sentence. It’s valuable for people with intellectual disabilities, low literacy levels, or limited fluency in the language being used—but it’s also just good communication practice more broadly.

Article 21 of the UN Convention on the Rights of Persons with Disabilities guarantees the right to accessible information. In practice, though, Easy Read materials are expensive and time-consuming to produce, which means they’re rarely created—especially in lower-income countries where the need is greatest and the resources are thinnest.

An Idea Ahead of Its Time

The “Right To Know” pitch happened in 2021—more than a year before ChatGPT launched and kicked off the modern era of generative AI. The team envisioned a tool that could take dense policy language and automatically simplify it, but the technology to do that reliably didn’t exist yet. When ChatGPT arrived in late 2022, the concept Whitney’s team had imagined suddenly became technically plausible. With the innovation grant, we built a first version: a static site at easyread.demcloud.org with detailed instructions on how to use generative AI tools to accelerate Easy Read document creation.

In October 2024, I traveled to Nairobi, Kenya, to facilitate a human-centered design workshop with representatives from disabled people’s organizations (DPOs) including the United Disabled Persons of Kenya, the Kenya Association of the Intellectually Handicapped, the Down Syndrome Society of Kenya, and several others. Over two days, we tested assumptions about accessible information, explored what generative AI could and couldn’t do, and collaboratively designed the features an Easy Read generator tool would need.

One moment from that workshop has stayed with me. A teacher who supports students with Down Syndrome said: “I wish I knew about this before. This will help a lot. I struggle to break down complex jargon into understandable information. With this tool, that work becomes easier.”

Continuing After the Layoff

In January 2025, I was laid off from NDI after nearly 11 years. The Easy Read Generator was not finished. The workshop participants had given us a clear mandate and a thoughtful design, and I had made commitments to them and to the DPOs we were working with. I continued the work on my own and worked with University of Maryland students who contributed concepts for Easy Read Generator’s UX redesign.

A mosaic of prototype screens from the Easy Read Generator redesign—an accessibility-focused civic tech tool reimagined by UMD students to better serve users with diverse cognitive and digital literacy needs.

The Image Problem

Most people who encounter Easy Read for the first time assume the images are supplementary—nice to have, but not essential. They’re not. In Easy Read, each illustration exists to support the comprehension of a specific sentence. If the image doesn’t clearly represent the concept in the text, it can actually make the document harder to understand, which is the opposite of the goal.

When I first tried to build the generator, I assumed AI image generation would handle this, but current AI image generators are weak at producing the kind of clear, simple illustrations that Easy Read requires. The images they generate tend to be too detailed, too stylistically inconsistent, too prone to visual noise, and often imbued with cultural biases that undermines comprehension. Closing that gap would have meant training a custom image generation model—far beyond what I could take on as a solo developer working on a civic tech side project.

That failure stalled the project for months. I tried multiple approaches, multiple tools, multiple prompting strategies. None of them produced images I’d feel comfortable putting in front of the people this tool is meant to serve.

Selecting Instead of Generating

The thing that eventually unblocked the project was a shift in approach. Instead of asking AI to generate images, I started asking it to select them.

I built a keyword-mapped image library—a JSON file containing 564 keywords mapped to 186 unique illustrations drawn from three open-licensed sources:

Mulberry Symbols—a widely-used symbol set designed for augmentative and alternative communication (AAC), licensed under CC BY-SA 2.0 UK
OpenMoji—an open-source emoji library with clean, consistent line art, licensed under CC BY-SA 4.0
NDI’s Easy Read Online Dictionary—illustrations collected through NDI’s own Easy Read program, licensed under CC BY-SA 4.0

When a user pastes text into the Easy Read Generator, the LLM does two things: it simplifies the language into short, clear sentences, and it matches each sentence to the most appropriate illustration from the library using the keyword map. The AI isn’t creating images—it’s making selections from a curated set of symbols that were designed for this purpose by people who understood accessible communication.

The library doesn’t cover every possible concept, and some matches are better than others. But every image in the output was created by designers who understand accessibility, not hallucinated by a model optimizing for visual plausibility.

Where This Leaves Me

The tool I shipped is not what I originally envisioned. It’s simpler, more constrained, and more honest about what current AI tools can and can’t do. I think it’s better for it. My earlier attempts were too ambitious, and the image generation requirement exceeded what the technology could responsibly deliver. Stripping back to the core problem—simplify text, match it to existing illustrations—turned out to be enough.

After contributions from countless people, I’m relieved that I was finally able to deliver a working prototype. The Easy Read Generator will remain free to use, no login required, as long as I’m able to host and improve it. If this tool is useful to you or your organization, consider supporting the project.

Scene from office space movie. The "two Bobs" interrogate Tom: "what would you say you do here?"

The End of the Product Manager (As We Knew It)

Posted by jfrant

There’s a scene in Office Space where two consultants, the infamous “Two Bobs,” ask Tom Smykowski a deceptively simple question: what would you say you do here? Tom bristles. He explains that he talks to customers, translates between teams, and keeps things from falling apart. “I have people skills,” he says, and still fails the interview. Initech is bloated, inefficient, and badly run; the Two Bobs are there to reduce headcount, strip out bureaucracy, and show quick savings. But their logic only worked because technology and process standardization were already absorbing coordination and oversight work. What once required multiple roles could now be combined, or eliminated.

The film predates the economic shifts now underway with generative AI, but the pattern is familiar. As tools become more capable, work that once required multiple specialized roles begins to recombine. The work itself isn’t disappearing, but the categories we use to describe it are breaking down as responsibilities coalesce. Product management isn’t going away; it’s becoming more demanding and more technical. As AI tools absorb coordination and translation work, product managers are increasingly responsible for judgment, ethical tradeoffs, and hands-on experimentation. The historical boundary between defining software and building is collapsing—and product managers are increasingly expected to operate on both sides of that line.

For more than a decade, I worked in international democracy and civic technology programs at the National Democratic Institute, where product work rarely looked like Silicon Valley product management. Budgets were tight, users were diverse, failure carried political and reputational consequences, and technology had to function inside institutions that moved slowly and had low risk tolerance. I was often cast as “the people person,” responsible for translating between program teams, technical constraints, and real-world use. I served as product manager for the DemTools suite—a set of open-source tools NDI hosted and maintained as a shared service for civil society and political actors—defining roadmaps and requirements, managing vendors, and taking responsibility for whether tools actually worked in practice, not just in theory. This was product management in the classical sense, shaped by the realities of international development and democracy support.

While my perspective is grounded in the international development, non-profit and government sectors, the consolidation of product roles is equally applicable to the for-profit and tech industries. Indeed, tech-sector product managers are likely the vanguard in this trend, being among the first to face the need for deeper technical capabilities as AI tools mature.

When the Trump Administration abruptly ended most foreign assistance, I was laid off, along with many others in my sector. That moment forced a reevaluation of my value in the job market—which kinds of work remained in-demand as institutions retrenched. It also created space. For the first time, I could spend sustained time working directly with tools now accelerating this consolidation. At NDI, I had been invited into an internal AI working group, but hands‑on use of contemporary AI coding tools was largely prohibited in day‑to‑day work. Outside those constraints, the shift was clear: even without formal computer science training, these tools have allowed me to expand what product management itself entails. And this experience reflects a broader market trend: as software development becomes more accessible, roles consolidate, and product managers are increasingly expected to build, not just define, the tools they own.

Building Without a Buffer

After my layoff, I began experimenting seriously with AI‑assisted coding tools to solve problems I had previously only managed indirectly. Working inside an integrated development environment (IDE)—the software workspace where code is written, run, and debugged—with a coding agent that can read my codebase, refactor logic, and respond to tightly scoped instructions, I was able to move from defining requirements to implementing and testing them myself.

I took on work I had previously only specified or reviewed: writing data-cleaning scripts to normalize inconsistent datasets; building small backend services and database schemas; wiring together APIs, authentication, and basic front-end components; and deploying a functioning open-source web application. Work that once required contracts, budgets, and months of coordination now happens in days. As a result, I spend less time coordinating handoffs and more time interrogating outputs—testing assumptions, pressure-testing model behavior against real-world constraints, and deciding where automation ends and responsibility begins. That experience has given me a clearer sense of how to embed institutional policies into practical system behavior: shaping product direction, advising teams on appropriate uses of AI, and setting guardrails that organizations can actually stand behind.

AI hasn’t turned me into a senior engineer, and I wouldn’t ship production‑level code without review. But it has allowed me to turn conceptual understanding into working systems while retaining responsibility for product decisions. At the same time, these tools hollow out traditional entry points on the engineering side. Junior‑level work—boilerplate, scaffolding, translation between systems—is increasingly easy to automate. The developer, product manager, and project manager roles aren’t vanishing; rather they’re collapsing inward, concentrating responsibility in fewer hands.

A Failure That Taught Me More Than the Wins

My first serious attempt to build something more ambitious—an Easy Read generator tool—failed for a number of reasons. First, I started with a product mistake. Instead of defining clear, minimal functional requirements and testing a narrow MVP, I tried to build everything I thought the tool eventually needed to be. I collapsed “prototype” and “platform” into the same effort before validating the core idea.

That mistake collided with a harder constraint. I ran into a real technical limit: current AI tools are still extremely weak at generating Easy Read–style images that actually support reading comprehension for people with intellectual disabilities. The requirement exceeded what the technology can responsibly deliver today—and it also exceeded my abilities as a solo developer. Closing that gap would have required orders of magnitude more time and effort, up to and including training a custom image-generation model—well beyond the practical scope for this project.

The failure wasn’t just technical; it was conceptual. Building directly with AI tools made that misalignment impossible to ignore. There was no vendor buffer and no sprint cycle to hide behind—the system simply stopped cooperating. When you work this close to implementation, bad assumptions fail immediately. Either the requirement was flawed, or I lacked the technical depth to solve it. In this case, it was both.

Human Connection Still Matters

As roles collapse and responsibilities concentrate, human collaboration becomes even more critical. In my own work, this has taken a few concrete forms: regular collaboration with former colleagues who are practicing software developers, and reaching out to others working on similar problems. Sometimes this looks like show-and-tell; other times it takes the form of short, informal working sessions to compare approaches. The emphasis isn’t on tools for their own sake. It’s on clarifying what we’re actually trying to build, catching weak assumptions early, deciding what not to attempt, and making sense of rapidly changing technology together.

Those interactions do work that AI tools don’t. Coding agents accelerate implementation, but they don’t independently challenge framing, surface blind spots, or carry context across decisions. When you’re simultaneously acting as developer, product manager, and project manager, peer-level human feedback becomes the primary check on overconfidence and misjudgment. AI may compress roles, but it also reduces opportunities for feedback. As those feedback loops shrink, collaboration has to become more intentional. Without it, the risk is the accumulation of unrecognized mistakes—problems you don’t realize you’re creating until they surface downstream.

Conclusion (As We Know It)

When I talk about the end of the product manager, I’m not predicting the disappearance of a job title. I’m describing the collapse of a boundary. As tools change the economics of building, the old division of labor—between defining work and implementing it—no longer holds. What’s ending isn’t product work itself, but the idea that it can remain insulated from the act of building.

AI-assisted coding compresses the distance between intent and execution. Product managers who can’t get close to the code risk losing contact with reality; developers who can’t reason about requirements inherit decisions they didn’t make. Responsibility concentrates, feedback loops shrink, and mistakes surface later without intentional human collaboration.

This isn’t a story about replacing expertise or celebrating lone builders. The tools only work when grounded in real technical understanding—and they fail fast when that foundation is missing. What changes is who is expected to carry that understanding, and how early.

The end of the product manager isn’t the end of product work. It’s the end of pretending that thinking and building can be cleanly separated. What comes next belongs to people willing to hold both sides of that responsibility at once.

Forked, Not Finished: Mentoring Civic Tech the Open Source Way

Posted by jfrant

This spring, I had the opportunity to support several student-led civic tech projects through the University of Maryland’s iConsultancy program. The partnership was originally facilitated through my role at the National Democratic Institute (NDI), but when NDI’s participation was disrupted by a sweeping freeze on U.S. foreign assistance programs, I continued advising the students in a personal capacity.

What started as a straightforward mentorship experience became a much more fluid—and in some ways more meaningful—engagement, shaped by shifting roles, student initiative, and a shared interest in public-interest technology. In many ways, it reminded me of the spirit of open source: people stepping in, adapting to change, and contributing however they can. NDI itself has long embraced open source platforms like Decidim and CiviCRM as part of its commitment to digital democracy—tools that reflect the values of transparency, adaptability, and shared ownership.

Three Projects, Three Distinct Challenges

Each iConsultancy team focused on a different scope of work—specifically related to Decidim, an open-source platform for democratic participation, and a new tool that NDI was designing to make information more accessible to people with intellectual disabilities. These projects were all rooted in the open source ethos: building in the open, iterating in real time, and aiming for impact beyond the immediate team.

1. Decidim Alternate Deployment Methods

This team explored ways to simplify and modernize how Decidim is deployed across different environments. The official Heroku option had become outdated, and the manual installation process was prohibitively complex for non-expert users.

The students conducted a technical evaluation of Docker and Heroku deployment methods, tested them across operating systems, and ultimately created an updated Docker configuration tailored for production environments. Their contributions were submitted to the Decidim GitHub repo. These additions make it significantly easier to deploy Decidim in a production environment using Docker Compose. Like many open source contributions, their work advanced on community-maintained tools, with the potential to be picked up and improved by others.

2. Easy Read Generator UX Redesign

The second team focused on redesigning the user interface for NDI’s Easy Read Generator project, a tool that simplifies complex civic documents to make them more accessible for individuals with intellectual disabilities and those with lower literacy levels.

Drawing on user research, accessibility guidelines (like WCAG), and competitive analysis, the students developed a high-fidelity prototype and detailed UX recommendations. While I had envisioned an iterative redesign of existing wireframes, the team pushed the concept further—exploring new features such as login options and donation functionality. Their willingness to experiment expanded the conversation about what this tool could become.

3. Manual Installation Documentation Enhancements

The third project aimed to unify and improve Decidim’s manual installation documentation. English-language instructions were incomplete, and more robust Spanish-language documentation had yet to be translated or standardized.

The team was tasked with consolidating and testing these disparate guides, streamlining the process for deploying Decidim with all its intended features. Documentation is the connective tissue of any open source ecosystem, and while this team faced challenges in delivering their final product, the importance of the task—and the gaps it sought to fill—remains clear.

Lessons from the Field

Each project reflected the realities of open collaboration: sometimes productive, sometimes messy, always instructive. The teams that stayed organized and engaged produced genuinely useful outputs that could be built upon by others. In other cases, student groups struggled to balance their workload or needed more support to stay aligned with the project’s goals.

To be clear, this isn’t a critique of the iConsultancy model—student-led learning is, by design, exploratory. But like any open source initiative, success is rarely the result of individual effort alone. It depends on a thoughtful mix of initiative, shared norms, and an ecosystem of support. Civic tech projects, especially those aiming for real-world relevance, demand a working knowledge of community context, accessibility, and technical infrastructure—all challenging to fully absorb in a single semester. And just as open source contributors rely on documentation, mentors, and community to navigate complex codebases, student teams benefit from structured feedback, clear goals, and a culture that rewards asking questions. Those ingredients can turn short-term projects into lasting contributions.

Why I Stayed

Even after my layoff from NDI, I chose to remain involved because my commitment to the projects didn’t depend on a formal title. The UMD students brought real energy and fresh ideas. And continuing to mentor them gave me a sense of continuity and purpose at a time when many other structures were unraveling.

In civic tech, we often talk about resilience, distributed leadership, and decentralization. These principles are foundational to the open source ecosystem, where no single person or entity controls the project and leadership often emerges organically from contributors. This experience reminded me that these values aren’t just theoretical—they show up in how we navigate change. Open source projects are a fitting metaphor: they can survive the loss of their initial stewards, thriving as new contributors pick up the thread. Our work, too, can have a life beyond any single job or institution. Even when a formal role ends, the ideas, tools, and momentum we create can continue evolving—adapted, expanded, and reimagined by others who care.

Using AI to Strengthen Democratic Inclusion

Posted by jfrant

Participants develop a list of features they would like to be included in an Easy Read generator tool. They then used this list to design a prototype tool.

From the 15 percent of people around the world who live with a disability, 8 in 10 reside in developing countries. Although Article 21 of the United Nations Convention on the Rights of Person with Disabilities (CRPD) grants them the right to accessible information, people with disabilities often face communication barriers due to a lack of information accessibility. Access to information is essential for democratic and political participation, which enables people to make informed decisions and influence policies that affect their lives. If people with intellectual disabilities have greater access to easy-to-read information on political processes or policies and the necessary assistance using it, they will be better equipped to advocate for themselves and participate in democracy. By reducing communication barriers through Easy Read and other accessible formats, societies can foster inclusion, making it possible for people with disabilities to engage fully in civic life.

With these circumstances in mind, the National Democratic Institute (NDI) organized a two-day workshop in Nairobi, Kenya, to bring people with intellectual disabilities, caretakers, civil society representatives, government officials, and accessibility experts together to test and design tools for creating Easy Read documents. The workshop began by reviewing the results of a remotely-conducted activity to test assumptions about how to best address barriers to accessible information in Kenya. Participants then explored the possibility of using generative AI tools, like ChatGPT, to facilitate the creation of accessible information. To ensure that everyone could participate, NDI provided accessibility accommodations, such as sign-language interpretation, an expanded time frame agenda to allow for ample participation, and illustrations to enhance comprehension and retention.

Easy Read is a method of presenting information in an easy-to-understand format. Easy Read materials are especially beneficial for people with disabilities, those with low literacy levels, non-native language speakers, and individuals experiencing memory difficulties. Easy Read combines short sentences that are clear and free of jargon with simple images to help explain the written content. Easy Read is essential not only for people with intellectual disabilities but also for making information accessible to everyone, particularly in a democratic society. Accessible information enables all citizens to participate in civic processes, make informed decisions, and understand their rights and responsibilities. By utilizing Easy Read, NDI seeks to support inclusive democratic participation and enable people to actively engage in their communities.

Alice Mundia, Chairperson of the Differently Talented Society of Kenya (DTSK), discusses barriers faced by persons with intellectual disabilities, specifically with regard to accessing information.

Twenty representatives from various disabled people’s organizations (DPOs) and other civic groups contributed their diverse perspectives and expertise to advance information accessibility in Kenya. These groups included the United Disabled Persons of Kenya (UDPK), the Kenya Association of the Intellectually Handicapped (KAIH), Kenya ICT Action Network (KICTANet), Differently Talented Society of Kenya (DTSK), Black Albinism (BI), Ubongo Kids, Down Syndrome Society of Kenya (DSSK), Kenya Sign Language Interpreters Association (KSLIA), the Kenya National Association of the Deaf (KNAD), and the Directorate of Social Development under the Ministry of Labour and Social Services. The event fostered collaboration and laid the foundation for further development of accessible digital tools in the country.

On the first day, participants reflected on the structural challenges that restrict access to information for people with intellectual disabilities. Alice Mundia, Chairperson of the Differently Talented Society of Kenya (DTSK), led a discussion on the barriers to creating and distributing Easy Read materials. Participants then explored NDI’s Easy Read website, provided feedback on navigation and usability, and used generative AI tools to draft Easy Read documents. Working in small groups, they refined these drafts, exploring the potential and challenges of using AI for accessible content creation.

“I wish I knew about this before. This will help a lot,” said a teacher who supports students with Down Syndrome. “I struggle to break down complex jargon into understandable information. With this tool, that work becomes easier.”

During the second day, participants focused on mapping key stakeholders involved in creating and disseminating Easy Read documents and developing a prototype for an Easy Read Generator tool. Participants collaborated to design user flows, interfaces, and features for the tool by sketching visual prototypes. This hands-on session ensured that the tool would meet the diverse needs of people with intellectual disabilities and their supporters. The concept for an Easy Read Generator originated during a pitch competition in 2021, where NDI staff proposed tech solutions to democracy challenges. The winning idea, the “Right To Know” project, envisioned an Easy Read translator, anticipating the development of generative AI technologies like ChatGPT, which has enabled computers to simplify complex documents quickly.

Through the workshop, participants found that while ChatGPT is a powerful tool for generating and simplifying text, the unpaid version has several limitations that hinder its generation of accessible content. These include browsing limitations and the inability to upload documents or generate images.

Following this workshop, NDI has begun exploring two avenues to address these limitations and improve access to accessible information for people with intellectual disabilities. First, NDI is reaching out to companies that provide Generative AI chatbots to explore the possibility of allowing NGOs that support people with intellectual disabilities to access paid services for free or at a reduced cost. Such a program could enable disability rights advocates, caregivers, and organizations to leverage the most advanced tools to generate Easy Read content. This would significantly enhance their ability to reach and support individuals who depend on these accessible materials.

NDI is also exploring avenues for developing the prototype Easy Read Generator that participants designed into a working application through future programs. This tool would not only improve the experience of using Generative AI tools to create Easy Read documents, it could also be offered for free to select partner organizations, eliminating cost as a barrier to generating easy-to-read information.

This illustration captures the second day of the workshop, which focused on designing an Easy Read AI chatbot.

Through this workshop, participants from diverse backgrounds collaborated to explore generative AI’s potential for making information accessible for all. The workshop provided an invaluable opportunity to address challenges, share insights, and develop solutions. NDI remains committed to expanding these programs to ensure that all citizens have access to information in formats they can understand and use.

Author: Jesper Frant, Senior Technology Projects Manager for NDI’s Democracy and Technology team

NDI’s engagement with this program is implemented with the support from the National Endowment for Democracy (NED) program.

Persons With Disabilities Enhance Civic Engagement in Jordan

Partnering with the Disability Community and Parliament to Promote Inclusion

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NDI is a non-profit, non-partisan, non-governmental organization that works in partnership around the world to strengthen and safeguard democratic institutions, processes, norms and values to secure a better quality of life for all. NDI envisions a world where democracy and freedom prevail, with dignity for all.

This story was originally posted on ndi.org.