Video summary
The 2026 Coolest Tool Award celebrates innovation within the Wikimedia ecosystem by recognizing tools that simplify complexity and remove barriers to contribution. Last year's winners, including the Abuse Filter Analyzer and Paulina, demonstrated how evolving infrastructure can help communities navigate moderation effectively. This year, the focus shifts even further toward making participation more intuitive and impactful for contributors worldwide through a tool designed to turn curiosity into meaningful action without requiring users to sift through overwhelming amounts of data.
The award-winning project is the Microtask Generator, created by Mercy Oyelakin, an open-source developer from Lagos, Nigeria. Powered by Wikimedia machine learning models, this innovative software helps editors discover specific editing tasks related to articles or topics they care about most. Rather than simply recommending edits, the tool explains why those changes matter for Wikipedia's quality and reliability, thereby lowering barriers for new contributors while allowing experienced users to focus their efforts more effectively on areas that need urgent attention such as missing citations or images.
Developed during Outreach Year Round 31 at the Wikimedia Foundation, Microtask Generator allows organizers and editors to enter a language code and select topics or lists of articles within seconds. The system then analyzes these inputs to generate tailored recommendations based on metrics like quality scores, page views, and translation coverage, identifying exactly what each article needs from missing references to stronger categories. Users can further refine results by filtering by geography or task type, export assignments as wikitext for tracking progress, and access direct links to help documentation that makes learning common editing tasks easier.
Ultimately, the tool represents a vision where machine learning and human collaboration work together to strengthen participation rather than replace it. By automating the discovery of quick improvements across thousands of articles daily, Microtask Generator ensures contributors spend less time searching for tasks and more time making an impact. Mercy Oyelakin's achievement highlights how technology can empower communities globally by providing focused work lists for campaigns and edit-a-thons, marking a significant step forward in expanding the boundaries of what Wikimedia tools can accomplish.
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Knowledge grows through contributions
and contributions are powered by people
[music] and the tools that support them.
Across Wikimedia, developers and
volunteers are building technology that
simplifies complexity, removes barriers,
and helps more people turn curiosity
into contribution. Last year, [music]
the winning projects were Abuse Filter
Analyzer, Database Report, and Paulina.
They helped communities navigate
moderation more effectively,
demonstrated the power of evolving
infrastructure, and expanded the
boundaries of what a Wikimedia tool
[music] could be.
This year, we celebrate even more
innovation.
Today, we [music] recognize the tools
making participation more intuitive,
more accessible, and more impactful for
contributors around the world.
Welcome to the 2026 Coolest Tool Award.
This year's most innovative awards goes
[music] to Microtask Generator, created
by Mercy Oyelakin, a software developer
and open-source contributor from Lagos,
Nigeria. Powered by Wikimedia machine
learning models, Microtask Generator
helps contributors discover editing
tasks related to the articles, topics,
or countries they care about most.
Instead of asking contributors to sift
through large amounts of information,
the tool surfaces actionable
opportunities where help is needed most:
a missing citation, a page needing
improvement, or another valuable task.
What makes the tool impactful is the
experience creates. Microtask Generator
does not simply recommend edits. It
explains why those edits matter [music]
for Wikipedia's quality, reliability,
and growth. In doing so, it lowers
barriers for newer editors [music] while
helping experienced contributors focus
their efforts more effectively.
The project was developed by Mercy
Oyelakin doing Outreach Year Round 31 at
the Wikimedia Foundation where she
worked on tools that helped organizers
and contributors [music]
discover quick improvements across
Wikipedia. Her work reflects an
important vision for Wikimedia's [music]
future, one where machine learning and
human collaboration work together to
strengthen participation, not replace
it. Every day, thousands of Wikipedia
articles could be improved, [music] but
finding where to start can be a
challenge. That is where the Microtask
Generator comes in.
Let's say you're an editor, organizer,
or someone preparing for an edit-a-thon.
Enter a Wikipedia language code [music]
and either provide a list of articles or
select a topic to explore. Within
seconds, the tool analyzes those
articles and generates recommended
editing tasks.
For each article, contributors can view
metrics such as quality scores, page
views, translation coverage, and editing
activity. [music]
More importantly, the tool identifies
exactly what each article [music] needs.
An article may be missing references. It
may need images, an infobox, stronger
categories, or additional content.
Selecting an article opens a breakdown
showing where improvements are needed
and how much progress has already been
made.
Recommendations connect directly [music]
to Wikimedia help documentation, making
it easier to learn common editing tasks.
The tool can also sort and filter
results by topic, geography, popularity,
or task type,
>> [music]
>> helping organizers build focused work
lists for campaigns and edit-a-thons. If
you do not already have articles in
mind, Microtask Generator can generate
recommendations directly from a
category. Choose a topic, select the
number of articles to analyze, and the
tool creates a tailored list
automatically. Results can be exported
or copied as wikitext, making it easy to
[music] share assignments, track
progress, and measure impact. With
Microtask Generator, contributors can
spend less time searching for tasks.
Congratulations [music]
to Mercy Oyelakin for winning the 2026
Coolest Tool Award [music] for most
innovative.
>> Hello everyone. My name is Mercy
Oyelakin from Nigeria. I'm a software
developer and open source contributor. I
would like to say thank you to the
Wikimedia community for the recognition
of my tool, the microtask generator, as
one of the winners of the Coolest Tool
Award for this year 2026 in the
innovative category.
>> [snorts]
[music]