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Lessons learned from organising our first global AI hackathon

Learn from our experience and best practices when organising your own hackathon

In June 2023, a first global hackathon was organised at Adevinta, bringing together people from our marketplaces LeboncoinKleinanzeigenMilanuncios and Global teams. The goal was twofold: To bring together people from different sites and countries and have fun building something useful for the company using generative artificial intelligence (AI).

So how did the hackathon turn out? In a nutshell, pretty well! Overall, participants’ feedback was positive, both during the event and in the satisfaction survey sent afterwards. In addition to meeting colleagues from other Hubs, they were able to learn about generative AI tooling and its stack.

But now that some time has passed, we’d like to take a step back to identify what went well and what could have been done differently. There is always room for improvement in every project, especially when we only had a month and a half to prepare for it, and none of us were experts in this kind of event planning.

But before we examine the lessons learned from this experience, let’s give you some more context.

Setting the scene

The setup of the hackathon in our Paris office
The setup of the hackathon in our Paris office

A few stats

This Adevinta AI hackathon was held over two days in Paris, with physical attendance required. There were 110 participants, plus 30 organisers, external partners, jurors and stakeholders. In total four countries were represented: France, Germany, Spain and the Netherlands.

Note: The hackathon was only open to those Adevintans working on a specific internal transformation project, not everyone employed by the company.

Teams and topics

The participants were free to propose any AI topic they wanted. After the kick-off meeting one month before the event, a Miro board was created for people to post ideas. Then individuals organised themselves into teams based on the topic they found the most interesting.

There were some criteria set for team formation:

  • It was recommended that teams not exceed six members.
  • A minimum of two countries/marketplaces needed to be represented in each team.
  • The aim was for each team to have a mix of Product Management, Engineering, Data/machine learning (ML) and UX/UI people.

The recommendation regarding the maximum number of people per team was not always followed, resulting in only 12 being created out of the 110 participants.

AI platforms

The generative AI platforms used were Hugging FaceAWS and OpenAI. Hugging Face and AWS representatives were present during the whole hackathon to assist teams. A dedicated table was set up, but they also circulated and directly checked in with teams at their tables.


We chose to make the pitch sessions really short: four minutes and no more. Each team had to use the same slide deck. They were also offered help with practicing their pitches.


There were two votes to reward the best projects: a jury vote and a popular vote.

A total of six people were involved with the jury vote, using four criteria for their decision-making:

  • Impact (30%): Financial impact or indirect/strategic impact.
  • Innovation (30%): Is the idea unique?
  • Feasibility (30%): Does the hack have a realistic chance of succeeding with the time, skills and technology we have?
  • Collaboration (10%): Best group picture, with bonus points for teams representing more than two marketplaces.

As with the teams, the jury was composed of members from multiple marketplaces and countries, with a variety of roles represented (CTO, CPO, Data/ML, and Product Management). Grades were given for each category using a scale of 1 to 5. The results for each criterion were then added up.

The popular vote was conducted by the participants themselves.


The winners of the jury vote received smart watches, invitations to AI conferences and the opportunity to pitch to high-level management.

Snack boxes were delivered at home or the office for the team that won the popular vote.

With all the details about the hackathon now covered, let’s take a look at the lessons learned.

Key lessons learned: What worked well

Participants working together
Participants working together

Building a team you can trust

Ensuring you surround yourself with motivated and aligned team members makes a huge difference! Our project manager was assisted by one person on site who was responsible for logistics, one for communication and design and one for AI expertise. Two of us had already organised hackathons, but none were experts. We communicated weekly in quick synchronisation meetings, which became twice-weekly as the deadline approached.

We received the full confidence of senior management to organise this event successfully. They did not require regular updates. Even though the initial request came from the top, we took this project on and made it our own, and we could organise things however we wanted.

Picking a quick-to-build area

Hackathons usually last a few days, so participants should have access to easy-to-use technologies to be able to build their demos as quickly as possible.

AI is a good example of an area that has become more accessible. Generative AI is much easier to use than the previous ML generation as models don’t need to be trained (which took ages). Using foundation models makes AI more accessible for participants, since the main concern is integrating it, not building models.

We were happily surprised to see how many teams were able to have a demo working by the end of the 2 days.

Providing the same experience for everyone

We were convinced from the start that every participant should have the same experience. This meant that no remote work was allowed: Everyone had to be physically present at the Leboncoin offices in Paris. Moreover, all the participants worked in the same room rather than being dispatched throughout the building.

The external partners followed the same logic. Hugging Face and AWS representatives were present in person because we felt the participants would not be comfortable/confident enough to contact them by Slack or email. As more than 50% of respondents said they were “very satisfied” with the help of the external partners, we know now it was the right decision.

In addition to providing a sense of unity, having everyone in one location allowed us to provide massages, showers, nap locations, catering services and even a marching band. This also made it easier to organise an icebreaker before hacking sessions: The participants were split into groups of two teams, with each being given a card with pictures of different items on and instructions to act out the items, charades-style, to the other team. Also, in the evening of the first day of the hackathon, a dinner was arranged so that the participants could enjoy a meal together.

We’re not saying that online hackathons don’t work, but from our experience we recommend saving them for groups of people who already know each other.

Participants discussing their project
Participants discussing their project

Putting learning first

The more people can learn from the experience, the more willing they are to give their time. That’s why we provided assistance for participants to learn as much as possible about generative AI.

Special sessions were held during working hours two weeks before the event to allow people to ask questions. Additionally, we helped them set up their AWS, Hugging Face and OpenAI accounts.

An introductory talk about AI was given the first morning of the hackathon, and throughout the event a total of about 15 people from AWS and Hugging Face attended at various times to provide assistance.

We recommend choosing tools that participants already know or are used within the company to avoid having to spend too much time on the learning curve.

Giving participants the freedom they need

By definition, hackathons are about breaking the rules and doing things differently. So even if you define some ground rules, don’t be too strict about them. Also, don’t worry too much if things don’t go as planned and let people self-organise as much as possible.

During this hackathon, people were free to use any AI tool they wanted, even if AWS, Hugging Face and OpenAI accounts had been set up.

We recommend applying the same approach to choosing topics. It’s important to let people express their opinions even if the topic isn’t 100% what you had envisaged being part of the session. Because hackathons are untimed momentum sessions meant for exploration, you also shouldn’t be too concerned about implementing a particular hack. In the same way, we don’t recommend defining specific use cases for people to follow. Overall, don’t expect hackathons to answer management’s questions.

In addition, be flexible about the hours: Don’t force participants to start at 9AM if they prefer working late.

Backing up logistics as much as possible

Let’s be honest, our biggest fear was that the Wi-Fi would go down on D-Day and ruin the event. In order to prevent this from happening, we worked with an external company, who provided an ethernet connection for every table. But they ended up doubling Wi-Fi because not many people used the RJ45 cables.

Defining the objective criteria for the votes

The best way to avoid bias is to grade the pitches based on criteria that have been defined in advance. Our four criteria (impact, innovation, feasibility and collaboration) worked pretty well. This system prevented good grades being awarded to a team just because they knew a jury member or two. In the event, the majority of the jurors arrived at the same conclusion regarding the winning team.

So let’s review what could have been done better now that we’ve reviewed the best practices for our hackathon.

Things we can improve upon for the next iteration

Surprise band performance with a member of the leboncoin team
Surprise band performance with a member of the leboncoin team

Giving innovation more room

The hackathon was definitely a first step in attempting to generate more innovation around AI within Adevinta. Having the Adevinta CPTO give the introductory speech demonstrated that this was an important event for the company, and soon after it was held, the winning team (based on jury votes) pitched its idea to management. However, no commitments were made that the hackathon ideas would be incorporated into the existing roadmap.

The success of this first global hackathon has led us to wonder how to integrate a culture of innovation in the company. Right now, Adevinta doesn’t have an R&D department with people working outside the roadmap. It does not mean that teams are not doing R&D: some engineers are using new tools to develop new things, and we are putting together two teams to introduce recent AI innovations to the rest of the teams. However, we still need to find a way to spread AI within teams with senior management working on two-year roadmaps that don’t leave much room for innovation.

Focusing even more on the learning experience

Some feedback we received from participants was that the hackathon was a bit short on time and so they weren’t able to learn everything they wanted to about AI. Extending the event to three days and adding more breaks could be the solution for a potential next edition. Organising more AI-related initiatives before the hackathon would certainly have been beneficial as well.

Welcoming non-tech and non-product people

Since our hackathon focused on generative AI, we only invited people with tech and product backgrounds, which included Engineers, Data/ML experts, Product Managers, and UX/UI Designers.

But we are now wondering whether inviting people from sales and marketing teams would bring more creativity. Innovative ideas sometimes come from people outside the area of expertise.

Providing better catering services

Food was the subject of most of the negative feedback we received. Participants complained about the lack of inclusivity, such as vegan food not being clearly labeled as such, and the location where food was served, as well as the amount of food served.

Working on a better ending

Some people expected a better ending after the pitching sessions. Perhaps we could have saved some surprises for the end.

Going further

Picture of the all participants
Picture of the all participants

In conclusion, Adevinta’s first global AI hackathon was a huge success, bringing together participants from different countries and marketplaces to explore generative AI and build innovative solutions.

The hackathon demonstrated that AI is one of the biggest technological breakthroughs, which will change how we think about user experience and coding. It is our responsibility as a company to prepare our employees for the future by connecting them with AI experts and training them.

The biggest challenge we have for the future is to find a way to incorporate innovation into our processes. A hackathon like this proved that disruptive events can speed up innovation within an organisation. The next step is to figure out how to inject an enduring culture of innovation within the company.

Want to see what it looked like? You can watch the recap video here.

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