AIMinds #002 | Samiur Rahman, CEO & Founder at HeyDay

About this episode

Samiur Rahman is the CEO and Co-founder at Heyday. He has 12+ years of experience building AI and ML applications. Born in Bangladesh to a diplomat father, he spent his years frequently moving from one country to another. His early years saw him living in the United States, Saudi Arabia, England and Iran, along with his native Bangladesh. With him at Heyday they are focused on using AI/software to automate the mundane/repetitive tasks people have to do so that people have the most time/space to do their creative best.

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In the second episode of AIMinds, we had a conversation with Samiur Rahman. This discussion delves into the strategic evolution of Heyday as an AI thought partner, specifically tailored for executive coaches.

During the talk, Samiur underscored the deliberate approach Heyday has taken in transitioning from a general knowledge base to a specialized tool for executive coaches. Their focus on aiding coaches in maintaining a presence during sessions by automating note-taking and transforming valuable insights into structured content illustrates the practical application of AI as a collaborative thought partner.

Samiur's emphasis on pinpointing real problems and creating bespoke solutions underscores the significance of understanding user needs to drive the effective integration of AI technology. His insights offer compelling perspectives on the challenges and opportunities inherent in leveraging AI to augment professional workflows.

If you're interested in learning more about how Heyday is transforming executive coaching with AI thought partnerships, we encourage you to listen to the full episode.

Fun Fact: Heyday initially emerged as a personalized search tool, indexing public browsing, email, Google Calendar, Slack, and other sources to help users organize and access relevant information. This evolved to assist executive coaches with note-taking, session prep, and prompting insightful conversations with AI-generated notes and relevant content through a browser extension.

Show Notes

00:00 Learned a lot at AWS, embraced smaller companies.
05:38 Struggles with user conversion, search engine success.
09:09 Narrowing down target audience for product.
10:54 Entrepreneur uses AI to automate coaching processes.
15:17 AI helps coaches manage client interactions efficiently.
17:42 AI writing assistant aids in content creation.
20:51 Semantic search revolutionizes content discovery, yet challenges remain.
24:01 Expresses gratitude for guest's presence on show.

More Quotes from Samiur

Heyday is also featured on our AI Apps page here!

Transcript

Demetrios:

Samiur, it's great to have you here. I'm really excited to talk to you. You are the CEO and founder of Heyday. You're also a participant in our Deepgram startup program. Heyday is doing some pretty rocking stuff. I mean, let me just break down the short, short blurb about what you all are doing and then you can get into it a little bit more because I would love to hear your story and how you created it. But for those who do not know, Heyday is an AI thought partner. That is for knowledge workers, and you're primarily working right now with executive coaches.

Demetrios:

So that being said, that was a bit of a mouthful. I want to know, though, before we learn more about heyday and how you came up with the idea. What's your story?

Samiur Rahman:

I mean? Probably most startup founders have like a weird story, but typically weird story. My dad was a diplomat. I was born in Bangladesh, so I moved around a lot growing up, so lived in the US when I was super young, but then moved to Saudi Arabia, back to Bangladesh, to England, to Iran, Bangladesh again. And then my dad very unexpectedly died when I was 14 and my mom decided to move the whole family to New York City. So weirdly, I consider moving around a lot in my life. New York is like the place that I consider home. So I don't know how. I have a very clean american accent because I learned English all over the place at some point I had a british accent.

Samiur Rahman:

I don't know what happened to it. It comes out when I get drunk. So that's like the start of that. I actually studied electrical engineering, wireless signal processing with a side in machine learning. And at the time, this is like 14 years ago. So what machine learning was back then kind of started getting into neural networks. Started my career off at Amazon, was doing some very early machine learning stuff with them, like doing recommendations on accessories. I was working on the retail side for a little bit.

Samiur Rahman:

What HDMI cable should you buy with this tv? That was like the life, fulfilling work. Amazing work. It's crazy though. So every tv would have like a set of recommended products before we used any machine learning for it. It would be a human picking out. For every tv there was like a team of humans picking that stuff out. So that's how weird it was back then. And I was like a new employee and I had to convince multiple levels of people that why are we doing this with a team? And then I eventually worked at AWS for a little bit, but honestly realized that I really didn't like working at big companies like multiple times.

Samiur Rahman:

I learned a lot from AWS, but multiple times, bureaucracy getting the way of doing things, even at a company like Amazon was just like, you know what, screw it, I just want to work at smaller companies. So I was up in Seattle, I worked at another company doing consulting work with products like the Nike Fuel band, Disney's Magic band. There was like a bunch of stuff. It was around using small ML algorithms in devices, so to predict steps and stuff like that. Super interesting work. But then realized I loved actually being part of the product development process, being a client, being a sole development shop. It was like Nike or whoever would just tell us, we want to build this. This is what needs to happen.

Samiur Rahman:

So I moved down to work at a small startup, and I've been in the Bay area for like nine years now. Started another company before heyday that I shut down. So I'm technically a second time founder, even though it feels like it's just as hard anyway, maybe a little bit easier. But yeah, primarily my machine learning work has been in text and natural language processing. And so I've been basically building that kind of stuff for years, basically my whole career. I did do some signal processing based prediction algorithms, but that was not that much. But yeah, I've been working on search and natural language processing and text for a long time.

Demetrios:

Yeah. So you recognized the love for developing products and being on the product side of things and understanding what the user would potentially want. And that got you into the startup game. How long ago did you start heyday and what was the motivation?

Samiur Rahman:

So heyday was two years ago, and that was like right after within a couple of months of shutting down a previous company because I think there's a startup. Founders are crazy psychotic people, I'd say, and I'm one of them. Instead of taking a break just being like, well, I got another thing. Why don't we just jump into the, that's how Heyday started. And Heyday started with a lot of the mix of the first company journal. We started as like, hey, what if you could have a Google search for all your own stuff so it would.

Demetrios:

Integrate with, sounds very familiar.

Samiur Rahman:

These like, well, I feel like we have a lot of learnings from that, so I can share that. It could either just be execution failure or actually learning that people think it's cool but then won't pay for it is we integrated with all the things like Google, Google Docs, Gmail, Dropbox box at the time slack, and then we would be one place for you to search for everything. And we got to like 20,000 users in a freemium way, but very few of them were actually converting. The paying users couldn't figure out the business model, and we still had an incredible search engine. We basically built our own at the time, built our own vector DB because there was no vector DB. So it would have been nicer to not have to spend like six months building our own vector database and just use pine cone or what the hell exists now. So many things.

Demetrios:

You've got Qdrant, you've got Weaviate.

Samiur Rahman:

Yeah, we could have made that into the business also. No one would have wanted that at that point. So we were too early for a lot of things, I would say. But also, I think we learned that we were building a really general product without a lot of direct magical workflows. And honestly, even that, we didn't quite carry over into learning, like applying that learning directly with heyday. With heyday, one of the first things was we got to be solving a real use case. I'm very passionate about leveraging information knowledge. I'm slightly ADHD and also care about automating away anything that humans don't need to work on.

Samiur Rahman:

I'm very passionate about building tools that help allow people to focus on the things that they're either excited about creating. They have a unique human potential to create. And so all the things that I've worked on are kind of related to that. Probably why I'm excited about machine learning and AI in general. So with heyday, we wanted to do the same kind of thing but be even more AI forward, not just like Search. So could we figure out ways to be assistive without people prompting us? Prompt is a word that's overloaded these days, but I don't mean just like prompt in the OpenAI type sense or whatever language model sense. I just mean the ideal AI for me can sense when I need something and just provides it. And we weren't doing that.

Samiur Rahman:

People would have to basically change behavior and come to journal to do that. So from the get go. Hey, Dave, we started by, hey, could we serve alternate answers to things alongside Google search results? So it would be, oh, hey, you're looking for some way to tips on fundraising, like creating a pitch deck. And turns out you read like three articles about that before, or you even took notes about it, and it was like years ago. And our goal would be surface that right next to the Google search result to be like, oh, this is like stuff from your memory, from your previous research. And then that started doing better. It was paid from the get go. We had like four or 500 paying users.

Samiur Rahman:

But we then got stuck with another thing where it's like, it's still too general. There weren't enough people. There were people who were enjoying it, but no one was like, oh my God, this has changed my complete, this has changed my life. And so our hypothesis was we really need to build. So we wanted to focus on a single Persona and we kind of looked into who was already getting value from heyday. There's like a cluster of journalists, marketers, investors, startup founders and executive coaches for some reason. So we kind of dug in and evaluated across a bunch of different how likely are we to help them? How much do they care about this problem? How good are they as early startup customers? Journalists are on the opposite end of the spectrum because they don't really make buying decisions themselves. So terrible at buying tools.

Samiur Rahman:

So based on a lot of that, we went with executive coaches and have been working with them for the last six months and launched a product that's more focused. Well, the product's been launched for long, but the features that are focused around coaches, we launched that like a month or I guess two months ago now, and that's been going really, really well, but we're still in the phase of figuring out how to get it in front of more coaches.

Demetrios:

So can you tell me more about this idea around magical workflows that you said you learned back at journal and you didn't quite put it into production or understand or internalize that learning when you started heyday, but it feels like now you have.

Samiur Rahman:

Yeah, it's one of those things that I'm like, how have I been a startup founder for like five, six years but didn't do this, where we spent like basically three to four months. We're building AI ML products. So we set up a process. We're like, okay, we're going to work with ten to 15 coaches who sign an NDA with us and our goal is to learn what we can help them with and then be a human service that does the work for them. While we're doing that, our job is to automate away the work that we're doing using AI. So instead of a human doing it, eventually it becomes like the AI starts doing it and then we have like a tuning process. So we basically learned on our own work and learned in the process what are the problems that are most kind of impactful to solve for coaches, it also lines up to the types of problems we're solving are very general in some ways, so in weird ways, it's going to be easy to go to the next Persona. We think a lot of consultants have the same problem.

Samiur Rahman:

Coaches are basically like a small subset of the consultants that we could go after suit after. But this idea, or at least our hypothesis is going Persona at a time. We can talk to real people and find out what their problems are and then balance this. We want to be in the long run a thought partner, an AI thought partner for everyone. But in the short term, how can we build things that can help more people in the long run but focused on this group of people? So it's still like a concrete problem that we're helping someone solve, but with an eye to well, let's not do really coach specific things that only coaches need, but let's find the general problems that they need help with that we can automate away.

Demetrios:

So talk to me about how the product has evolved from the beginning and then becoming coaches. You said that in the beginning it was something like, oh yeah, you've actually researched this before in the past. Then you're googling it. So you probably want these relevant links or this relevant Google Doc that you have, something like that. How did it evolve since then?

Samiur Rahman:

Yeah, so a lot of the product surface area in the beginning was simply we're indexing all your public browsing. So it's a browser extension that indexes all the readings that you're doing, videos that you're watching. It also integrates with your email, your Google calendar, slack notion, all that stuff, places where important content exists. And then the main way is to surface that while you're doing your normal browsing. So when you're searching in Google, show up with other helpful search results. When you're looking at an article, can we show you other potentially related articles? Maybe we know information about the author. Like you've read other things by that author. Can we show you that? Can we show you? Have, has someone mentioned this to you in some other channel like in slack or Gmail? So we can show like this was shared to you by Demetrius two weeks ago and you just forgot about it.

Samiur Rahman:

So that was like the initial kind of state of the product. But over time we evolved that to do more with AI. So we allowed people to organize things automatically into topics so they could tell us like, hey, I'm interested in machine learning. As I'm reading new things about machine learning, I can kind of build like an ever growing knowledge base by saying every day we suggest like, hey, here's like five things you read about machine learning yesterday. Do you want to save them way into your machine learning topic, and so it makes it easier for you to keep your topics growing. But now we wanted to use that back end to serve coaches. And the way we've been helping them is two big ways. One, how do we make it so that they can focus on their superpower, which is having insightful conversations with their clients and not do all the other stuff around it.

Samiur Rahman:

So they usually have to take a lot of notes, they have to prepare ahead of the session, and then they have to review their notes to be like, well, what was that client working on? And they usually have like ten to 15 clients. It's hard to keep all that in memory, so they're usually juggling a bunch of stuff. They frequently recommend documents or articles or things for the client to read and track, action items and things like that for that client. And so we just built an AI that can both understand all the conversations that someone's having with their client because we have an email integration or slack or the documents that are being shared between that client. And we added a Zoom integration, which as part of that pipeline, that's where we use Deepgram, where we transcribe the audio from recordings of a client session and then we extract the most meaningful insights from it with the context of the client overall. So previous sessions, like all the notes that the coach may have taken, the email threads between. So we understand, hey, here are the things that are important to this client. So if you have like a 20 minutes conversation about vacation to start the conversation, that's not the most important thing to pull out as an insight, right? So that's been super well received by coaches because they now can go use heyday to instead not take notes during sessions, be really present with their client, and then not spend only like five minutes correcting the auto generated notes from heyday and sending it off to their clients.

Samiur Rahman:

But then it becomes like part of their system that they get prompted to review right before their next session by Heyday. It's like, heyday is like, oh, hey, here's the stuff from the last session. Here's like general themes. This is probably all you need to review to be prepped for your next session. So that's been super helpful. And then the other piece is, coaches are generating lots of useful insights. They're having these thought provoking conversations, but they're like solo entrepreneurs. They have to grow their brand and their business to get more clients, or they want to turn all those insightful things that they're talking about into writing, even if they don't want to grow their brand, maybe they want to create like a framework and share it with their customers.

Samiur Rahman:

And so we've also built an AI content writing assistant, which this is very general, I would say to lots of people, because we help essentially be like an outliner plus first draft writer using only the content either you've written yourself or from your own conversations over email or Zoom or whatever, or if you want to source information from other folks. If you've been doing a lot of research about llms but you haven't written much, you can also, because of the browser extension, we automatically know everything you've read about llms. You can say, no, actually, I want to use all the readings that I've been doing on my research about llms, too. And so it's like not just Chat GPT style bullshit being drawn out. Our goal is to help people share original, authentic insights. But for folks who are not like natural writers, help them get a first draft out, but only from the content that is actually their own.

Demetrios:

Yeah. So it goes much deeper. And it's not just that surface level. I can see that. And I know just in the times that I've had coaches interact with me and I've had coaching sessions, right. One of the biggest things that you do is you write down a lot of these learnings. Me as the person that is not the coach, the coach tells me to go write this stuff down, and they come back the week or two weeks later and they say, so, all right, were you able to do that? Show me what you learned from it. But that is exactly like what you're saying.

Demetrios:

They have to keep it in memory and they have to know, review their notes. Like, yeah, we talked about this, and maybe they're not picking up everything because they're being more present than writing notes. And so I see the value in this.

Samiur Rahman:

I love it.

Demetrios:

One last question for you before we jump. What have been some challenges when building with AI?

Samiur Rahman:

I mean, I've been working on this for forever, across many products since Amazon days. So when you have very specific problems, right, like you're predicting, all you want to do is predict the right product to add to the accessory. And it's very simple. That's like easy AI stuff. Back then it was a little harder, but even then it was easy to beat humans at that. The next big thing I'd worked on was I was working at a company called Mattermark as head of machine learning, and we were a private company database, and people wanted to search for companies by descriptions. Our customers were like vcs and salespeople. So if someone searched, hey, I want to find all the ride sharing companies in the Bay area or in California or all the gig economy companies in the Bay Area, we wanted to be able to, at search time, do kind of what is done.

Samiur Rahman:

Now, the semantic search, we had a vector database that was like the inspiration for like, wait, there's a better way to do search. We can do embeddings, we can do similarity, and we could do this across a lot of content. So that was like a directed problem we were solving. We're helping salespeople and vcs descriptively find the companies that they were looking for. I made the leap to make it super general too quickly, and I think I've struggled for years based on that. And so when you see things like rewind, which is really cool, but again, I think they're just like, maybe they're going to execute much better than we did. But my problem with it is it's a hammer looking for an ale, even though it's cool that you can index all this stuff, but what am I going to use it for? Very few companies figure that out, like Google. Google is probably one of the only ones that's like, all it is is a search box for the entire world, and people will figure out how to use it.

Samiur Rahman:

A lot of times I think that starting with defined problems and then finding the people who really need it solved, building something for them, and then realizing, oh, it could be generalized to a bunch of other things. Let's do it again. Let's roll forward and figure out what are the actual needs and then automating those things. And the other thing is, I think anytime you try to automate away humans from something, it's going to seem really cool at first, but generally speaking, it's going to be like 80% good enough, and that's fine for a lot of things, right? I might say all the SEO content that gets put out was all garbage in the first place. And so AI written SEO content is probably good enough, but you don't really want Chat GPT spat out content about how to build startups in AI, right? Should me and you be replaced by Chat GPT just like splatting out stuff? No, not yet, at least.

Demetrios:

Yeah. Words of wisdom. Samir, I appreciate you coming on here and getting to talk to us about what you are doing, what you are building. And I'm going to take a lot of this to heart because I feel like it resonates a ton with me right now. This idea, especially that you just mentioned on finding people who have that problem. First defining a problem and then finding people who resonate with that problem is huge for me.

Samiur Rahman:

You could go the other way, too. You could easily just go talk to people and then find the problem by talking to them. I guess there's like a high level thing. You can't just show up with nothing. But we showed up with, hey, we're going to be an AI knowledge base for everything for you. What would you use that to automate? That was, like, a pretty general question, and we learned a lot of cool stuff from that that we had no idea about.

Demetrios:

Well, this has been great, dude. Thank you so much for coming on here.

Samiur Rahman:

Of course, man. Thank you for having me.