Web Summit 2017

At the beginning of November I had an i.n.t.e.n.s.e. week.

Selfie in front of the many Summit signs that were placed all over the city

I spent almost a full week in Lisbon to attend one of the biggest technology events out there: Web Summit!

Here’s a summary of why I went, what I experienced and what I learned.

Why I went

It all started with some marketing work magically making it through my sub-consciousness about two years ago through social networks and emails. Then the positive feedback about the event from friends and colleagues who had attended previous editions added to this black magic. It seemed something one had to do, you know? Attend this big event to hear the most brilliant minds from the tech industry giving inspiring talks and sharing their top-notch technology work, research, ideas, predictions, fears and promises. I wanted to see it with my own eyes! Plus, it was happening in my favorite city of all times where we had also happened to open a new office! It all seemed like a perfect match.

The colleagues from Pipedrive with whom I flew to and from Lisbon to attend the Summit

On March 6, 2017, I got my women in tech ticket to go! Yay! So exciting! Later I heard some other Pipedrive colleagues were planning to go, too, so I joined them and with our lovely Pipedrive employee supportive platform we made our journey to Lisbon and spent a few days together getting inspired (but also getting overwhelmed with the noise pollution, dizzy from overcrowded subway stations, bloated from overdoses of white bread, happy from glasses of vinho tinto and vinho verde, and angry at each other from logistical disagreements).

What I experienced and learned

The event and the venue

The event was gigantic. It consisted of

  • Altice Arena (capacity of 20K people) where the Opening Night was held, and main presentations given during the following days
  • 4 exhibition pavilions (the FIL International Fair of Lisbon) where some smaller parallel conferences and company exhibitions happened
  • A couple more pavilions where private discussions and night events happened
  • another bunch of venues here and there

You could technically attend talks from a bunch of different conferences happening in one pavilion or another. Some people chose to do that. I tried, but failed miserably so I focused on sitting at one conference at a time and listened to talks for 2-3 hours in a row each time.

Shopping Center Vasco Da Gama, a few meters away from the event venue

There were also food trucks, lounge areas, company expositions and booths, and people all over the place.


These are the conferences I attended:

Main topic: Data

 Main topics: AI & Robotics

 Main topic? You guessed it! SaaS!


Talks and key takeaways

Stephen Hawking talking about AI in the Opening Night of the Web Summit

Day 0: Artificial Intelligence was written all over the place. The first talks in the Opening Night set the tone.

“Our systems must do what we want them to do for the benefit of humanity”

These were the words of Stephen Hawking (yes! the same one!) in what seemed a prerecorded video for the summit.

Bryan Johnson, founder of Braintree and founder and current CEO of https://kernel.co/ followed with the idea that the brain was really underestimated and that was were we needed to capitalize AI on in the next few years, not to enhance our individual abilities a la superhero, but to help do things like eliminate the concept of enemy. He said:

“I am more concerned about the power of human behavior than I am about the power of AI”.

Margrethe Vestager, a Danish politician and currently working in the EU Commission, discussed democracy, transparency and freedom in the context of the impact brought by tech companies. We are talking here about a woman who has initiated legal cases against giants like Google, Amazon, and Apple on topics of taxation and antitrust. She said:

“We have to take back our democracy and renew it, because society is about people and not about technology”.

On day 1 I spent the morning sitting in SaaS Monster, and from the talks I heard here, the one I enjoyed the most was that of Des Traynor from Intercom. The title was “Getting Product Strategy Right”. Traynor talked about what was it that made some companies stay around for long while most really just died.

He landed on the topic of brand versus product. It has to be the brand that drives the product and not the other way around. The brand is the promise we are making to our customers and that promise has to stand the test of time. The people working on making that a reality is something that cannot simply be copied by others, while the material make up of a product can (e.g. copying CSS).

What I liked the most was the emphasis he made on NOT planning your product around a specific technology. Technology is constantly changing after all. The main question to ask ourselves was if the problem will still be around when the technology changes.

Another question he posed was about the market. Paraphrasing: If there was a gap in the market but there was not market for that gap… would we stand a chance of succeeding?

In short, getting product strategy right was about getting the right problem identified, tackled by the right solution, sitting on strong brand that has a gap in the market that wants to be served.

[We took a break here to go to the office for a meeting].

Back in the venue in the afternoon I jumped into Talk Robot where Aimee van Wynsberghe, an ethics and robot professor, was talking about what it meant if we really had robots everywhere in our homes. I made a bit late to this one so all I managed to get to was the example of Sophia, a robot that recently got a nationality, which posed the question on whether this robot had any rights, since that’s part of what comes with having a nationality.

Later Max Tegmark, a professor and scientist, took on the stage for “Life 3.0: A conversation with Max Tegmark“. The discussion driven by journalist Andy O’Donoghue was trying to get to the answer of whether AI was going to outrun us or make us be better. No matter what question Andy asked, Professor Tegmark answered almost every single one of them with something around the words of it all depends on what kind of society we want to create. Do we want to own technology or do we want it to own us? From his point of view we can use all the future progress to help us flourish as a society (pretty much in line with what I am currently reading: Abundance: The future is better than you think. He also admits that as a tech nerd he is not the right person to turn to when looking for answers to big questions like how should the legal system should work, but we must involve everybody. I like this point particularly because it rings a bell with this topic very close to me which is diversity, and how building solutions that work for all require all being involved.

On day 2 I had decided I would not jump from conference to conference, so I sat all morning in binate.io. The first talk was highly expected because one of the speakers was Chess Master Garry Kasparov which everyone seemed to be very excited about except me. All I could hear in this talk was buzzwords: big data, computer processing power increasing, algorithms, image recognition, self-driving cars, yada yada yada.

The second talk, though, was totally different. Emil Eifrem, founder and CEO of https://neo4j.com/, came to save the day by sharing four specific stories that showed how data was being used or could be used to solve real problems. The first story was related to the Paradise Papers date leaked case in which data patterns helped identify indirect connections that brought to light how certain politicians were involved in mishandling of money. The second story was about the potential to find a cure to cancer through connecting separate silos of data rather than analyzing them separately which is the challenge and, in this way, again, find patterns that could connect one with the other guiding to a solution. The third story was about the Lessons Learned NASA database which is a set of 50 years of data that allow to look again at the links between them rather than having to look at documents one by one. The fourth and last story was a hypothetical one. In here Eifrem talked about the potential there is if we think about what can be really done with machine learning. Not about they hype around it, but about the real potential of problems that can be solved through graphs (not too blame, since that’s what his company is all about :)). His main point was that knowledge is based on context. Context is how we make sense of the world. Graphs help give context to things making it easier for us to understand them. To him, the most interesting database out there are our brains, exactly the thoughts that Bryan Johnson shared in his Opening Night speech.

The third talk I heard this day was by a very American dude with a very Estonian name: Kalev Leetaru, from Google. He is behind GDELT Project which was the main driver of his talk. He discussed what is behind this which is the intention of understanding and predicting big social events to, for example, save lives through the gathering and analysis of data (images, text, etc.) from all around the world as opposed to that from a single source no matter how “big” we consider it (like social media sources). One of the examples he brought up: watching a labor protest through data as it moves in real time and understand through tracking multiple events like this, what are the trends so we could predict where the next civil unrest could come from or why they are happening. He says that through understanding patterns, machines could monitor the entire world without getting tired and they could help us understand early signs of a crisis. The talk: What doe sit look like to compute the entire planet?

Dr. Chitra Dorai: how we can use AI to reimagine business processes

After Leetaru I heard Dr. Chitra Dorai talk about how we can use AI to reimagine business processes. She first made a differentiation of terminologies: Machine Learning (part of AI referred to statistical analysis used to make predictions), Artificial Intelligence (apart from patter recognition, it’s also about listening, planning ahead) and Cognitive Computing (using AI and ML techniques to solve real world deep domain issues). The examples that Dr. Dorai shared where about professions where AI could help do ordinary work in an extraordinary way. For example, if you have a procurement specialist dealing with a supplier, we can put an AI system at the hands of this professional to help put together external data about this supplier and analyzing it to help make the right decision, rather than have to look for this information manually. She talked about 3 ways in which AI is being used at the core of business processes: engagement ( to interact and assist through understanding of both content and context), decision (to provide bias-free advice semi-autonomously) and discovery (create new insights and new value).

Brenda Darden Wilkerson from the Anita Borg Institute

The last talk that I actually paid attention to on this session was that of Brenda Darden Wilkerson from the Anita Borg Institute. She talked about the way we can use AI to help understand the job gaps we have in the IT industry and also to help solve the problem. If we have such a big deficit of IT professionals, and we address parts of the population that are currently underrepresented in the industry we could both solve the problem of the deficit and also the one of lack of diversity in an industry that is mainly dominated by white men (*in the US). Did you know that Hispanic women make up only 1% of the computer-related workforce in the US? (That means I am a rarity!) Why is this a problem? You ask yourself. To paraphrase Brenda, if we collect data to analyze it and build solutions and make decisions based on this, but this data is representing only one part of the population, can you imagine what is the impact of those solutions and decisions on those underrepresented people that actually make up a significant part of the population affected? Her talk: How data can drive industry-wide culture change.

There is another handful of interesting talks I heard on the last day at the Talk Robot conference, but just as I was tired by day 3 of the Summit, I am tired at this point of transcribing my keynotes to this blogpost, so I figured you could just stop by the Web Summit Facebook page and listen to them yourself 🙂

Here are those I had the pleasure of watching live (they linked to the video recording so you can click on them to go there and watch)

IoT 2.0:

VR that blows your mind:

The next evolution of intelligent assistants:

What does an AI-powered society look like?


I heard from multiple sources (including the founder of the Web Summit in his opening night speech) that networking was one of the main things to get value from during the event. However, I never had enough energy to start a conversation with strangers (I suppose I have lived in Estonia for that long) so instead I focused on networking with people I already knew but that I had not been in touch with for a while or in real life at all! I caught up with Ines and Mariana from our Pipedrive Lisbon office and introduced them to another friend of mine, Ines (yes, popular name in Portugal :)), and we discussed the topic of diversity in the tech industry, and what the panorama looked like in Lisbon. We talked about things we could do together to contribute to the change of this situation.

I also organized with my friend Ines (well, mostly she did) spontaneous gatherings with the women in tech community attending the event. That is how we got to meet some interesting figures before the event had started and even after it was over.


Spontaneous networking with other attendees on Sunday evening, before the official start of the event

Networking with old friend and volunteers at the event

The Web Summit signs around the city


One last networking event that I sort of helped organize, but did not attend

Talking with colleagues about the diversity tech situation in Lisbon

The side events

Coincidentally some other things related to my work where happening while we were there. The Pipedrive Marketplace was launched in a private party where I stopped by to say hello and have a drink. We also had several spontaneous gatherings with people from different Pipedrive offices who were in the city for specific meetings. It was a pleasure to be able to see some of our Lisbon colleagues faces again, and some for the first time.

Pipedrive private feature launch party

Dinner with colleagues (just so you can stretch your neck)

Overall, it was an intense and very enriching event. I will try to repeat next year! Except next time I will charge my batteries more heavily to step out of my box and talk a bit more to strangers out there working with product and other tech areas.

Refresh Conference 2017: Key Takeaways

I spent last Friday at Refresh, a conference for Product, Design and Front End people that takes place every year in Tallinn since 2015.

I don’t go to conferences often, but when I do I like to come back with the feeling that I learned something new and that I interacted with fine, like-minded people. I like going home with a new vision on an old topic, a new topic to dig more into, or a new idea to try out.

Conference Swag: A notebook, pen and pencil which I used straight away to take my notes. A shirt, a cloth back, a card holder with RFID and stickers.

Conference Swag: A notebook, pen and pencil which I used straight away to take my notes. A shirt, a cloth bag, a card holder with RFID and stickers.

Taking handwritten notes is my way of making sure I can focus even when there’s a difficult or boring presentation, and it is how I can take out specific bits of useful information. I tweet some of that during the event to share the love with the online community; and when I return to work, I also write a quick summary of these things to share with my work mates (like this post). This last bit is something many of us are putting into practice in the Product & Design department at Pipedrive as a way of sharing knowledge.

I had been to the first edition of Refresh in 2015 with very little to no expectations and I left with a positive attitude. Presentations, content, venue, food and people were great. I couldn’t make it to the 2016 one, but this time around they managed to deliver again. While in 2015 the presentations were in one single stage mixing up all the topics, this time around they had two parallel tracks: one for Product & Desing; another for Front End. I spent the full day at the… guess? Product & Design one, of course.

Below is a summary of the key things I learned during my three favorite presentations. Things I assessed in these:

  1. The topic: was it of my interest and did the content deliver on it?
  2. The presenter: did she/he did a good job delivering the message?
  3. The slides: where they digestible and well-prepared?
  4. Applicability: Can I do something with this when I get back to work?

Here they are, from OK to the best:

Number 3: How to design Experiments that Don’t Suck

Presenter: Willie Tran, Growth Product Manager @ Dropbox

Willie is a very dynamic presenter (spoke too fast at times) and I personally like that style. I am interested in experimenting and research in general, since this gives great direction to my work as a Product Manager. Willie delivered 3 specific lessons all contextualized in his own experience running experiments at Dropbox. He started from emphasizing the importance of being methodic when running experiments, and he did so by quoting the Mythbusters:

He dissected the definition of the hypothesis and how getting it right is basically getting your experiment design right, since a hypothesis contains in a single paragraph the [execution] of your experiment, the [metric] you think will be affected by it and the [assumption(s)] you have. If you get those wrong, your experiment will not work.

While there were three lessons, I caught only two. I get easily distracted (which is why handwriting helps me as well) and I seemed to have been mentally elsewhere when he mentioned the second one. But here are the ones I got:

Lesson #1: Do not blindly follow successful experiments by others. Just because it worked in their context does not mean it is the right experiment for you to run. Basically, all those out-of-the-box experiment tools are not real sh*t.

Lesson #2: (dreaming of the next coffee break…).

Lesson #3: When do you give up experimenting? When you have stopped learning.

The main message of Willie is that experiments are useful not necessarily if they pass, but if you learn from them and, my own addition: if you apply those learnings. Experiment, learn, iterate, rinse and repeat. Quit when there’s no learning.

Number 2: Designing for Learning Applications

Presenter: Jonn Galea, Lead Designer at Lingvist.

Jonn was a very articulate and clean presenter. His presentation was neatly organized and went from one point to the next in a very logical way. This was great, but maybe because his tone of voice was too even all along or because it was the second part of the day, towards the end of his turn, it got hard to follow.

I chose to pay attention to this presentation because teaching is very dear topic to me (I was a teacher for five years). I could also relate with his approach and the work we do at Pipedrive.

Jonn talked about the importance of understanding what the process of teaching was like when building a learning application. He broke it down into steps that he then correlated with the process of product design. He also talked about the app as a teacher, and the user as a student. I could see the common elements with the work we do at Pipedrive, and I cannot imagine a product manager or a designer doing a good job without understanding the process of the work done by the professional or the person for which they are trying to solve a problem, or the role they play when using the app, or the app in itself. It is that bit of knowing the area of business in which you are that make some product managers or designer strongers than others.

I am not strong at all in sales, but if there’s a topic I have learned a lot about in the last two years that’s the one. The same happened when I was working for fintech software companies, and I’ll repeat it in whatever business area comes next.

Jonn’s bottom line was that to have a great foundation to build a (winning educational) app you need to have:

  • A product concept
  • Strong back-end, data,…
  • A great team
Coffee break

Coffee break

Number 1: Machine Learning from the Product Perspective

Presenter: Inga Chen, Product Manager at Squarespace

Inga was by far my favorite. She was dynamic, with a perfect pace (not too fast, not too slow), delivered clearly applicable information with beautifully designed and digestible slides.

Although many presenters are great at making Machine Learning sound like a couple of buzzwords that get randomly squeezed into their presentations, Inga delivered on what she promised. She covered:

  • What Machine Learning is
  • What problems are good to solve with it
  • What can product managers do when considering to work on a product with ML
  • Why we should care about ML

She quoted Arthur Samuel’s definition from 1959:

Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.

Data is critical to building a Machine Learning Model, and this data should be ideally labelled. There are several problems that ML can help solve, but mainly:

  • Decisions with many outputs
  • Decisions with many inputs
  • Decisions at scale

One example she brought and that I noticed myself while using the app a few weeks ago were the Slack Highlights:

Machine Learning used in the Highlight feature from the Slack app

Machine Learning used in the Highlight feature from the Slack app

My favorite part was how she made clear what product managers needed to bring into the table when considering using machine learning in a product:

  1. The problems: we bring the problems that we think can be solved with the help of ML
  2. The data. We need to make sure we have the data needed to feed the model
  3. Alternatives: we need to consider alternative non-ML solutions, because this is a time consuming expensive approach. In most cases we will be able to solve the same problems with simpler tools / techniques
  4. This is what Inga highlighted as the most important bit if we went down the ML path: Designing for when the ML Model fails.

She also covered how a product development cycle was different when it was being built using ML and a bit that I much wanted to bring with me but could not capture (my mind was boiling with ideas at that point) was what were the converging trends that indicated why we should care about ML.


It was a lovely event and I feel I came with ideas and refreshed knowledge. I’ll see about next year, but first I need to get these new ideas and knowledge rolling.