#560: Building a Research OS: From Django to 30,000 Samples

WEBVTT
00:00:00.020 --> 00:00:05.520
In 2020, a gastroenterologist in Glasgow did the math on his new research study and came up with
00:00:05.840 --> 00:00:10.640
30,000 samples arriving over two years from three cities and dozens of hospitals.
00:00:11.420 --> 00:00:16.160
He asked around about how researchers kept track of that. The answer was Microsoft Excel.
00:00:17.000 --> 00:00:23.760
Sean Chua had written some HTML by hand in Notepad way back in high school. That was about the
00:00:23.940 --> 00:00:29.320
entirety of his programming experience. Even so, he opened the Django tutorial and started reading.
00:00:29.620 --> 00:00:35.980
Six years later, that app is Foundry 120, holding 10 terabytes of clinical and genomics data with
00:00:36.240 --> 00:00:43.960
agentic AI running on top of it. This is Talk Python To Me, episode 560, recorded August 26th,
00:00:44.380 --> 00:00:51.640
2026. Talk Python To Me. Yeah, we ready to roll. Upgrading the code. No fear of getting old.
00:00:52.040 --> 00:00:57.400
Async in the air. New frameworks in sight. Geeky rap on deck. Quartz crew. It's time to unite.
00:00:57.520 --> 00:01:02.280
We started in Pyramid, cruising old school lanes, had that stable base, yes sir.
00:01:02.320 --> 00:01:06.680
Welcome to Talk Python To Me, the number one Python podcast for developers and data scientists.
00:01:07.200 --> 00:01:08.580
This is your host, Michael Kennedy.
00:01:09.020 --> 00:01:12.500
I'm a PSF fellow who's been coding for over 25 years.
00:01:13.140 --> 00:01:14.260
Let's connect on social media.
00:01:14.580 --> 00:01:17.740
You'll find me and Talk Python on Mastodon, Bluesky, and X.
00:01:17.910 --> 00:01:19.860
The social links are all in your show notes.
00:01:20.620 --> 00:01:24.140
You can find over 10 years of past episodes at talkpython.fm.
00:01:24.240 --> 00:01:27.520
And if you want to be part of the show, you can join our recording live streams.
00:01:27.940 --> 00:01:28.340
That's right.
00:01:28.600 --> 00:01:31.800
We live stream the raw uncut version of each episode on YouTube.
00:01:32.420 --> 00:01:36.800
Just visit talkpython.fm/youtube to see the schedule of upcoming events.
00:01:36.980 --> 00:01:40.680
Be sure to subscribe there and press the bell so you'll get notified anytime we're recording.
00:01:41.840 --> 00:01:45.400
Let me quickly tell you about a new course we have running over at Talk Python.
00:01:46.040 --> 00:01:47.460
Up and running with Rust.
00:01:48.100 --> 00:01:53.200
The tools reshaping how you write Python, Ruff, uv, Firefly, ty, Pixie, and others.
00:01:53.780 --> 00:01:56.040
and the libraries pushing past its performance ceilings.
00:01:56.500 --> 00:01:59.200
Polars Pydantic, Cryptography, and Granian
00:01:59.620 --> 00:02:01.800
increasingly share one secret under the hood.
00:02:02.140 --> 00:02:03.060
They're written in Rust.
00:02:03.920 --> 00:02:05.620
That code that used to be written in C
00:02:05.800 --> 00:02:07.280
when Python needed real speed now
00:02:07.540 --> 00:02:09.479
is more and more being written in Rust.
00:02:10.160 --> 00:02:13.300
So having some Rust proficiency is a great skill
00:02:13.700 --> 00:02:14.960
as a Python developer.
00:02:15.440 --> 00:02:17.040
Christopher Trudeau is back
00:02:17.180 --> 00:02:19.500
with the Up and Running with Rust course.
00:02:20.080 --> 00:02:22.360
Check it out over at talkpython.fm.
00:02:22.580 --> 00:02:24.960
Just click it courses in the top and you'll find it right there.
00:02:25.410 --> 00:02:27.220
I hope you love this new Rust course.
00:02:27.900 --> 00:02:31.240
Getting a course at Talk Python is one of the best ways to support the show.
00:02:32.100 --> 00:02:33.640
This episode is brought to you by Sentry.
00:02:34.380 --> 00:02:37.400
You know Sentry for the error monitoring, but they now have logs too.
00:02:37.670 --> 00:02:40.400
And with Sentry, your logs become way more usable,
00:02:40.940 --> 00:02:44.780
interleaving into your error reports to enhance debugging and understanding.
00:02:45.300 --> 00:02:48.460
Get started today at talkpython.fm/sentry.
00:02:48.960 --> 00:02:51.840
And it's also brought to you by Talk Python Courses.
00:02:52.460 --> 00:02:54.660
Course completion certificates are now live.
00:02:54.810 --> 00:02:59.100
If you finished a course, there's a certificate waiting for you on your account page right now.
00:02:59.580 --> 00:03:05.320
Download it as a PDF or add it to your LinkedIn profile with one click under licenses and certifications.
00:03:06.020 --> 00:03:07.200
Same section as your formal degrees.
00:03:08.380 --> 00:03:12.220
Visit training.Talk Python.fm account to see what you've already earned.
00:03:13.180 --> 00:03:14.600
John, welcome to Talk Python To Me.
00:03:14.790 --> 00:03:15.200
How are you doing?
00:03:15.560 --> 00:03:15.800
Great.
00:03:16.260 --> 00:03:17.060
Thanks very much, Michael.
00:03:17.350 --> 00:03:18.420
A big fan of the show.
00:03:18.680 --> 00:03:21.940
Keen to be here to share a few things of what we've learned.
00:03:22.400 --> 00:03:26.660
Amazing. Thank you so much. And now you're here creating the show. It's going to be amazing.
00:03:27.120 --> 00:03:32.720
And you're doing really interesting work with biological and medical research, Python,
00:03:33.340 --> 00:03:38.360
Django, AI. I think there's a lot of cool things that we're going to dive into here. So
00:03:38.620 --> 00:03:43.040
I know even for people who are not in medical research, I think there's going to be some
00:03:43.230 --> 00:03:45.580
super interesting angles that will translate for them.
00:03:46.060 --> 00:03:47.420
Fantastic. Looking forward to it.
00:03:48.300 --> 00:03:51.900
Now, before we dive into that, as usual, just tell people about yourself.
00:03:51.960 --> 00:03:52.340
Who are you?
00:03:52.560 --> 00:03:53.300
So I'm Sean.
00:03:53.720 --> 00:03:57.220
I'm a gastroenterologist, and I'm a clinical researcher as well.
00:03:57.620 --> 00:04:02.680
So my primary area of practice is in the field of inflammatory bowel disease that comprises
00:04:02.960 --> 00:04:09.280
ulcerative colitis and Crohn's disease as the kind of conditions that we deal with when
00:04:09.280 --> 00:04:10.480
we see patients in clinic.
00:04:10.740 --> 00:04:15.539
But in parallel, I also work in research, trying to find out the causes of these conditions
00:04:16.019 --> 00:04:20.220
together with the GUT Translational Research Group here at the University of Glasgow.
00:04:20.700 --> 00:04:25.400
Personally, for this podcast, we use Python a lot in our work, both for research,
00:04:25.760 --> 00:04:29.640
so trying to analyze the data that we're doing, but also as well as for the infrastructure that
00:04:29.670 --> 00:04:33.540
we're running. So how do we actually deliver the research studies that we have to deliver?
00:04:34.300 --> 00:04:39.680
And personally, as a researcher, I've also got an interest in agentic AI and machine learning,
00:04:39.730 --> 00:04:43.739
and that's what attracted me to try to use some of these techniques to see if we can find
00:04:43.980 --> 00:04:48.700
new breakthroughs that can bring, you know, new treatments for our patients that we see in clinic
00:04:48.960 --> 00:04:53.460
every day. Awesome. Very cool research. That's the kind of stuff that can make a big difference
00:04:53.680 --> 00:04:58.040
for people's lives. If you can make them better, right? It's like, I couldn't leave the house,
00:04:58.260 --> 00:05:02.780
but now I'm back with my friends or whatever, right? Yeah. I mean, you know, the work that we
00:05:02.960 --> 00:05:09.139
do is motivated in the patients that we see day to day. Inflammatory bowel disease is not very
00:05:09.160 --> 00:05:15.140
common. It is rare, but it is growing not just in developed countries, but also in the developing
00:05:15.440 --> 00:05:20.200
world. And we see a great burden of this disease coming the next 10 to 20 years. What do you think?
00:05:20.480 --> 00:05:26.000
Diet? Environment? Why is it growing? Obviously, that's the research a little bit, right? But
00:05:26.320 --> 00:05:30.840
what is your ideas here? Well, these are really complex conditions, and the environment is
00:05:31.020 --> 00:05:36.340
absolutely a major player in this. The diet that we're eating nowadays is different from what we
00:05:36.280 --> 00:05:41.000
used to eat. But when we dive into the science of it, it's a lot more complex than just that.
00:05:41.280 --> 00:05:46.120
There are some patients who have genetic susceptibility, although not all. And we know
00:05:46.120 --> 00:05:51.420
that there is this problem that happens when the immune system reacts to our environment. And for
00:05:51.560 --> 00:05:55.940
some reason, the alarms don't switch off and the inflammation keeps happening, which leads to the
00:05:56.300 --> 00:06:03.379
symptoms and the problems that we see every day in clinic. So let's start by examining all the
00:06:03.400 --> 00:06:09.340
the research and the work you did and maybe the 2020 version of your research projects.
00:06:09.680 --> 00:06:10.820
It all started with Django, right?
00:06:11.260 --> 00:06:11.460
Yes.
00:06:12.300 --> 00:06:15.820
So back in 2020, I was still training as a gastroenterologist.
00:06:16.140 --> 00:06:20.320
So over here, we do a period of registrar training, we call it.
00:06:20.880 --> 00:06:27.180
And as part of my developing interest in understanding these medical conditions, I sign up to do a
00:06:27.300 --> 00:06:30.660
research study with one of the principal investigators.
00:06:31.280 --> 00:06:37.620
And the proposed study was basically to recruit quite a few patients from three different cities,
00:06:38.120 --> 00:06:43.660
multiple hospitals, and collect extra samples that we can run science experiments on to work
00:06:43.780 --> 00:06:51.160
out what's going on with an immune system. So when we first started, that was 2020. And I know
00:06:51.600 --> 00:06:57.379
we all remember that in 2020, you know, we had COVID and COVID had a significant impact both on
00:06:57.400 --> 00:07:02.660
hospital operations, but also on research operations. And when I sat down to look at
00:07:02.830 --> 00:07:07.300
what we were supposed to do in terms of the research studies, the first challenge that I
00:07:07.500 --> 00:07:12.120
faced was calculating the number of samples we were going to do. So we were going to recruit about
00:07:12.400 --> 00:07:17.440
200 people and we're going to follow them up every three months. And at every time point,
00:07:17.550 --> 00:07:22.999
we would take extra blood samples, extra stool samples, and saliva samples. So that was a lot
00:07:23.020 --> 00:07:29.280
of extra samples and a simple back of the envelope calculation came to about 30,000 samples that we
00:07:29.280 --> 00:07:33.180
were going to generate over a couple of years. Now, when I first saw that challenge, I started
00:07:33.460 --> 00:07:37.640
asking everybody around me, how are we going to keep track of all that? How are we actually going
00:07:37.800 --> 00:07:44.760
to deliver that problem? How do we solve that problem? And from my chat with a lot of people,
00:07:45.180 --> 00:07:48.920
the common way that researchers track the samples is to use Microsoft Excel.
00:07:49.540 --> 00:07:53.180
That's exactly what I was thinking is like, how big is the Excel file?
00:07:53.400 --> 00:07:56.820
And, you know, how many little worksheet tabs does it have?
00:07:57.160 --> 00:08:01.960
Yeah, I mean, most research teams do not really have a lab system because lab systems, you
00:08:01.960 --> 00:08:06.260
know, enterprise software, which is really expensive to procure, it'll take you like
00:08:06.440 --> 00:08:07.420
six months to set it up.
00:08:07.620 --> 00:08:12.180
And it's usually designed really for hospital operations where you're taking, you know,
00:08:12.540 --> 00:08:13.280
millions of samples.
00:08:13.480 --> 00:08:18.500
And it's not really designed for a one-off research study across a lot of different spaces.
00:08:18.960 --> 00:08:23.840
So then I started looking into what are the, you know, are there off the shelf options or
00:08:24.280 --> 00:08:26.540
stuff that we could use to just get up and running?
00:08:26.980 --> 00:08:32.180
But after looking at a few different options, I think I had to bite the bullet and decided
00:08:32.479 --> 00:08:36.479
that, you know, the best thing was to go and write a web application with Django.
00:08:38.060 --> 00:08:40.400
What was your programming experience at this point?
00:08:40.840 --> 00:08:41.979
How good of a programmer were you?
00:08:42.940 --> 00:08:45.080
So I wasn't very good of a programmer.
00:08:45.140 --> 00:08:50.500
I mean, what I did was basically, you know, write some websites in high school.
00:08:51.000 --> 00:08:53.080
And back in those days, we used to use Notepad.
00:08:53.340 --> 00:08:57.200
We used to open the brackets of HTML manually by hand.
00:08:57.460 --> 00:08:58.040
It was rough.
00:08:58.240 --> 00:08:59.860
Those were tough times, I remember.
00:09:00.240 --> 00:09:03.380
Yeah, I don't know how many people listening to this podcast can relate.
00:09:03.800 --> 00:09:06.000
But we used to write, you know, the head, the body and stuff.
00:09:06.240 --> 00:09:11.020
And then later on, I did use a bit of programming to do some, you know, statistics, a bit of,
00:09:12.120 --> 00:09:15.200
writing an R script or a Python script just to generate a graph.
00:09:15.490 --> 00:09:19.180
So that's probably that level of programming experience when I was
00:09:19.560 --> 00:09:22.580
looking at how do we actually write a web app with Django.
00:09:23.440 --> 00:09:27.460
Yeah, okay. So a Django web app was kind of a
00:09:27.550 --> 00:09:31.340
pretty big stretch at that point, right? Yes, but the Django...
00:09:31.420 --> 00:09:35.260
How'd you get started? I mean, this predates all the AI, agentic stuff.
00:09:35.480 --> 00:09:39.259
You had to earn it for this one. Yeah, definitely. I mean, when I first
00:09:39.280 --> 00:09:44.580
visited a Django website. It's the web framework for perfectionists with deadlines. And I think
00:09:44.700 --> 00:09:50.140
that described exactly what a situation I was in. You know, I came out program and we had about a
00:09:50.140 --> 00:09:54.700
two year time period to deliver a study. And you can't spend forever trying to get this up and
00:09:54.840 --> 00:10:00.440
running. You just have to get the study going. So the Django tutorial was where I started. But
00:10:00.680 --> 00:10:03.960
actually, I just want to take this opportunity to thank a lot of people in the community and those
00:10:04.120 --> 00:10:09.240
listening because it's the documentation, the YouTube tutorial, your podcast. I've been listening
00:10:09.260 --> 00:10:16.000
for five years. Books that were written, Two Scoops of Django, famously. I read a ton of books just
00:10:16.240 --> 00:10:22.420
to be able to get things up and running. Yeah, the books are great. And honestly, YouTube is really
00:10:22.440 --> 00:10:27.840
good. People knock YouTube for many reasons. You know, it's one of the ills of social media that's
00:10:27.920 --> 00:10:33.380
like scrambling their brains. But if you use YouTube for education, it's an incredible resource.
00:10:33.940 --> 00:10:37.740
Yeah. I mean, there are a lot of creators on YouTube that are just, you know, teaching lots
00:10:37.760 --> 00:10:43.240
of different things. And it's so helpful. But I also think some of the textbooks are great, you
00:10:43.260 --> 00:10:47.840
know, for things like test-driven development. You know, back in the days, I think you would only
00:10:48.140 --> 00:10:52.160
read about it in textbooks rather than on YouTube videos because it's not really an attractive topic.
00:10:53.040 --> 00:10:58.060
Yeah, you mentioned Obey the Testing Goat, which is a really fun book. And you also talked about
00:10:58.820 --> 00:11:05.660
using designing data-intensive applications. And that's by Martin Klepman. I haven't heard of that
00:11:05.640 --> 00:11:08.360
That's a pretty interesting one that people might want to check out.
00:11:08.620 --> 00:11:12.780
Yeah, down the line, as our studies matured and we started dealing with the data, I started
00:11:13.160 --> 00:11:17.800
reading some of the other data textbooks like Data Warehouse Toolkit by Kimball.
00:11:17.920 --> 00:11:23.180
I think that's the one that all data engineers used to talk about dimensional modeling and
00:11:23.200 --> 00:11:24.000
all that kind of stuff.
00:11:24.420 --> 00:11:25.220
So lots of textbooks.
00:11:25.900 --> 00:11:29.800
I think they're really helpful because they give you a kind of a holistic, fundamental
00:11:30.180 --> 00:11:35.600
approach to learning the basics and making sure you don't have gaps in what you're doing
00:11:35.620 --> 00:11:36.700
to build some of these things.
00:11:36.980 --> 00:11:39.720
When you come from the background of programming that you did,
00:11:39.880 --> 00:11:43.740
which, by the way, that's a very similar story to me as well.
00:11:43.960 --> 00:11:46.820
I studied math, and then I got involved helping people
00:11:46.960 --> 00:11:50.500
with research projects and working for a scientific research
00:11:50.620 --> 00:11:51.640
and visualization company.
00:11:52.060 --> 00:11:54.860
So you kind of build up the pieces as you go,
00:11:54.860 --> 00:11:55.460
and it's super fun.
00:11:55.880 --> 00:11:56.420
It's really neat.
00:11:56.640 --> 00:12:00.820
But there's a lot of gaps in your sort of data integrity,
00:12:01.560 --> 00:12:04.880
programming, like what is the foreign key relationship?
00:12:05.360 --> 00:12:06.040
All that.
00:12:06.300 --> 00:12:06.720
Yeah, yeah.
00:12:07.100 --> 00:12:09.520
So I think books are really important,
00:12:09.840 --> 00:12:11.720
especially for self-taught people like us.
00:12:12.040 --> 00:12:13.040
Definitely, definitely.
00:12:13.800 --> 00:12:16.140
You do learn a lot as you go.
00:12:16.660 --> 00:12:19.520
And it's quite important to be aware of what you don't know
00:12:19.740 --> 00:12:21.120
and try and close those gaps
00:12:21.800 --> 00:12:23.420
so that the applications that you write
00:12:23.560 --> 00:12:26.260
end up being reliable enough for people to depend on.
00:12:26.520 --> 00:12:29.240
Let's talk about Django for a second
00:12:29.500 --> 00:12:31.040
because you shouted out, Django,
00:12:31.540 --> 00:12:33.720
some of the features of it that kind of made that possible.
00:12:33.880 --> 00:12:37.880
I think this is one of the reasons that people choose Django, especially when they're getting started.
00:12:38.320 --> 00:12:40.400
You know, it's like it's got the built-in migrations.
00:12:40.940 --> 00:12:43.380
It's got the automatic database management, the admin.
00:12:43.860 --> 00:12:46.280
So what was it about Django that drew you to this?
00:12:46.600 --> 00:12:52.980
So I think, you know, when we looked at the landscape back in 2020, Django definitely stood out because it was a mature project.
00:12:53.380 --> 00:13:02.720
It's been around for a long time and it's batteries included, which is really important because, you know, I'm conscious that, you know, as a non-programmer coming into trying to write these things,
00:13:03.080 --> 00:13:05.560
You want your authentication framework to be battle tested.
00:13:05.980 --> 00:13:08.000
Database and migration is incredibly important.
00:13:08.050 --> 00:13:09.100
You can't lose any data.
00:13:09.480 --> 00:13:11.620
And I think just on those two features alone,
00:13:11.880 --> 00:13:13.420
we haven't even come to the admin dashboard,
00:13:13.780 --> 00:13:14.980
but just those two features alone,
00:13:15.190 --> 00:13:18.680
I think was enough to persuade me that Django is the right framework
00:13:18.920 --> 00:13:20.640
for the problem that we're dealing with.
00:13:21.540 --> 00:13:22.380
Okay, very interesting.
00:13:22.710 --> 00:13:25.840
And by the way, just big news for Django folks.
00:13:26.120 --> 00:13:27.280
Is it on their blog, maybe?
00:13:27.560 --> 00:13:30.219
Big news for Django is they just announced
00:13:30.220 --> 00:13:33.680
that they're switching the Django long-term support
00:13:34.779 --> 00:13:37.300
to yearly releases, just like Python itself,
00:13:38.059 --> 00:13:39.060
and calendar version.
00:13:39.160 --> 00:13:41.860
So there'll be Django 2028, Django 2029,
00:13:42.260 --> 00:13:44.140
and every single release is a long-term support,
00:13:44.360 --> 00:13:46.140
whereas now you're kind of juggling like,
00:13:46.260 --> 00:13:48.520
well, 5.2 is a long-term, but the one before it.
00:13:48.740 --> 00:13:50.320
I think that'll also make it a little simpler
00:13:50.440 --> 00:13:51.360
for people who are coming,
00:13:51.520 --> 00:13:53.620
like they don't have to decipher the versioning
00:13:53.900 --> 00:13:54.500
and what it means.
00:13:54.760 --> 00:13:55.720
Definitely, yep.
00:13:56.020 --> 00:13:56.800
Yeah, yeah, that'd be cool.
00:13:57.660 --> 00:13:57.960
What else?
00:13:58.800 --> 00:14:04.680
What else did you run into trying to navigate this world of becoming, running one of these
00:14:05.060 --> 00:14:11.000
like real web apps rather than just Excel or buying some off the shelf, you know, square
00:14:11.260 --> 00:14:14.400
peg round hole type of system that doesn't really fit what you're trying to do?
00:14:14.800 --> 00:14:19.120
Yeah, I mean, I think one of the steep learning curves is how do you go from a local host
00:14:19.360 --> 00:14:22.260
development project into something that's in production?
00:14:22.940 --> 00:14:25.000
Because production is obviously a whole different ballgame.
00:14:25.580 --> 00:14:29.560
you need to ensure your infrastructure in production is reliable.
00:14:30.140 --> 00:14:31.360
It's secure. It's accessible.
00:14:32.380 --> 00:14:34.960
You've got backups running, all sorts of things.
00:14:35.940 --> 00:14:39.440
And for us, one of the key things that enabled us
00:14:39.660 --> 00:14:42.760
is continuous integration and continuous deployment practices.
00:14:42.920 --> 00:14:46.300
So using GitHub Actions to deploy continuously
00:14:46.460 --> 00:14:50.620
so we can fix problems, get it into production in minutes
00:14:50.800 --> 00:14:53.860
rather than trying to do any of these things by hand manually.
00:14:54.340 --> 00:14:56.580
Yeah, the CI-CD stuff, I think, is really valuable.
00:14:56.940 --> 00:15:01.300
It's valuable for big teams, but it's also really valuable for people who are really new.
00:15:01.640 --> 00:15:07.240
Because if all I have to do is save my work to GitHub, and now that's the new stuff that's out there.
00:15:07.520 --> 00:15:09.120
That takes a lot of the complexity out.
00:15:09.260 --> 00:15:13.840
People forget just how intimidating logging into a Linux computer is.
00:15:14.120 --> 00:15:14.560
Oh, yeah.
00:15:15.140 --> 00:15:15.500
Now what?
00:15:15.860 --> 00:15:15.960
Yeah?
00:15:16.200 --> 00:15:18.700
Yeah, I had to learn how to use Linux on a terminal, right?
00:15:19.640 --> 00:15:19.940
Exactly.
00:15:20.060 --> 00:15:22.060
It was like, where is the UI?
00:15:22.620 --> 00:15:25.060
This is very different.
00:15:25.380 --> 00:15:27.700
How am I supposed to accomplish anything with just the terminal?
00:15:27.920 --> 00:15:31.320
And, you know, you get used to it and it's amazing in its own special way,
00:15:31.440 --> 00:15:34.280
but it doesn't feel amazing at the beginning most of the time, I think.
00:15:34.420 --> 00:15:35.520
Well, I'm reminded of the...
00:15:35.520 --> 00:15:36.140
It feels intimidating.
00:15:36.480 --> 00:15:36.600
Yeah.
00:15:36.840 --> 00:15:39.680
Every time I teach somebody how to use the Linux or the terminal,
00:15:40.440 --> 00:15:43.900
it's a big jump, I think, for people who are not familiar with CLI,
00:15:44.100 --> 00:15:46.580
which is, I think, the vast majority of people in the population.
00:15:47.320 --> 00:15:47.480
Yeah.
00:15:47.860 --> 00:15:51.919
Well, and things like people being primarily on phones for their computing device
00:15:51.940 --> 00:15:57.000
don't make that easier. It's only harder, right? This portion of Talk Python To Me is brought to
00:15:57.000 --> 00:16:01.520
you by Sentry. You know Sentry for their great error monitoring, but let's talk about logs.
00:16:02.180 --> 00:16:07.060
Logs are messy. Trying to grep through them and line them up with traces and dashboards just to
00:16:07.200 --> 00:16:13.340
understand one issue isn't easy. Did you know that Sentry has logs too? And your logs just became
00:16:13.640 --> 00:16:19.279
way more usable. Sentry's logs are trace connected and structured, so you can follow the request flow
00:16:19.300 --> 00:16:20.620
and filter by what matters.
00:16:21.320 --> 00:16:23.100
And because Sentry surfaces the context
00:16:23.400 --> 00:16:24.120
right where you're debugging,
00:16:24.380 --> 00:16:26.180
the trace, relevant logs, the error,
00:16:26.660 --> 00:16:29.380
and even the session replay all land in one timeline.
00:16:29.900 --> 00:16:31.980
No timestamp matching, no tool hopping.
00:16:32.580 --> 00:16:33.860
From front end to mobile to backend,
00:16:34.080 --> 00:16:34.700
whatever you're debugging,
00:16:34.980 --> 00:16:36.480
Sentry gives you the context you need
00:16:36.620 --> 00:16:38.740
so you can fix the problem and move on.
00:16:39.220 --> 00:16:41.520
More than 4.5 million developers use Sentry,
00:16:41.840 --> 00:16:43.720
including teams at Anthropic and Disney+.
00:16:44.420 --> 00:16:46.479
Get started with Sentry logs and error monitoring
00:16:46.500 --> 00:16:53.220
today at talkpython.fm/sentry. Be sure to use our code talkpython26. The link is in your
00:16:53.350 --> 00:16:59.720
podcast player show notes. Thank you to Sentry for supporting the show. Let's talk about your project
00:17:00.300 --> 00:17:08.199
Boundary 120. So when you started, you built this bespoke Django app for, was this for the music
00:17:08.839 --> 00:17:14.539
study or which study was this for? This was for the music IBD study. And the first version was
00:17:14.560 --> 00:17:19.640
really to track samples and to come up with an efficient way for our teams to handle the, you
00:17:19.640 --> 00:17:24.600
know, the 30,000 that we are projecting to sort out. So one of the key functionalities that we
00:17:25.060 --> 00:17:29.760
wrote in at the start was this ability to, you know, stick a QR code, stick it onto a sample
00:17:29.920 --> 00:17:34.760
label, scan it in, and that will register it into a database. And then we could move the sample
00:17:34.980 --> 00:17:39.299
around between sites and scan it and update. So that's where it started out as a sample tracking
00:17:39.320 --> 00:17:45.980
platform. So nothing too amazing. It was just kind of a really basic operational problem that just
00:17:46.160 --> 00:17:51.820
needed to be solved. But over time, when you get samples, the next thing that happens to the sample
00:17:51.900 --> 00:17:56.660
is that it gets processed into an experimental pipeline and you get data out of it. So the
00:17:56.860 --> 00:18:02.100
lifecycle of an experimental sample then leads you on to dealing with the data that comes back.
00:18:02.400 --> 00:18:06.239
Now, because we were taking so many different types of samples, one of the key challenges that
00:18:06.260 --> 00:18:11.660
faced our team was how do you handle the data that was coming back at us? Because we have stool
00:18:11.800 --> 00:18:18.340
samples that becomes, you know, microbiome data. We have blood samples that become genomics data.
00:18:18.650 --> 00:18:22.600
And every data type that comes back just looks a little bit different, comes back in different
00:18:22.980 --> 00:18:28.520
formats, you know. And one of the difficulties as well is the size of the data that we get.
00:18:28.710 --> 00:18:34.860
So for my personal research project during that time, I was looking at sequencing out the cell-free
00:18:34.900 --> 00:18:40.160
DNA. So the DNA fragments that you have floating around your blood and each sequencing file for a
00:18:40.400 --> 00:18:44.260
participant will be about five to 10 gigabytes of data. And it comes back in a, you know,
00:18:44.560 --> 00:18:49.580
in a fast QGZ format, and that has to go into a bioinformatic pipeline. So it's the one of the
00:18:49.660 --> 00:18:54.480
key challenges is how we build that data. So Foundry 120 actually became a project that
00:18:54.720 --> 00:19:01.159
started from sample tracking into data management and handling. And in 20, you know, in the last year
00:19:01.180 --> 00:19:07.700
too, it's also become a platform where we can apply agentic AI into it to kind of accelerate
00:19:07.830 --> 00:19:12.900
our workflows. Yeah, it's a really neat platform. And I'm definitely going to dive into it. I think
00:19:13.000 --> 00:19:18.400
it's neat how you had this smaller bespoke thing and you're like, all right, let's sort of pull
00:19:18.600 --> 00:19:24.659
out the essence of it and make it useful for all kinds of scientific research. Going back to the
00:19:24.680 --> 00:19:29.360
samples. You said each one is five to six gigabytes. That's each of the 30,000?
00:19:30.340 --> 00:19:37.620
Not all the 30,000. So a subset of them, but it depends on what essays were being run on which
00:19:37.800 --> 00:19:43.140
samples. And there could be a ton of essays all running different types of samples. And each of
00:19:43.140 --> 00:19:51.279
them will have their own data type that comes back. So for my cell-free DNA work, we ran it on a subset
00:19:51.280 --> 00:19:53.260
of the population, not the entire population.
00:19:53.730 --> 00:19:57.480
But for some of the other larger scale things like genotyping, we would genotype the whole
00:19:57.700 --> 00:19:57.940
population.
00:19:58.200 --> 00:20:04.680
So there are lots of moving parts and tiny little details here that cause so much operational
00:20:04.940 --> 00:20:05.120
problems.
00:20:06.000 --> 00:20:06.820
That's a lot of data.
00:20:07.370 --> 00:20:11.660
Where did you, how much did you end up with at the end and how did you store it and manage
00:20:11.780 --> 00:20:11.880
it?
00:20:12.080 --> 00:20:19.899
So currently we have about 10 terabytes of data and we've only, you know, processed only
00:20:19.920 --> 00:20:26.100
a fraction of those 30,000 is still being processed because some pipelines takes a human
00:20:26.740 --> 00:20:32.340
12 hours to process a couple of samples. So there's still a lot of work to be done.
00:20:32.960 --> 00:20:38.160
But yeah, it's about 10 terabytes of data that we're handling. And initially, it would be scattered
00:20:38.300 --> 00:20:42.800
across our group. Somebody's doing a microbiome project, it would be on their laptop. Otherwise,
00:20:42.940 --> 00:20:48.759
it'd be on a shared drive, on a file share somewhere in the university server. So data
00:20:48.780 --> 00:20:53.020
tends to be scattered. So if somebody is doing a specific experiment, they might create an Excel
00:20:53.240 --> 00:20:58.420
file with the readings from certain essays, and that would be the data set. And that's, I think,
00:20:58.420 --> 00:21:03.140
one of the big problems that we face in biomedical research today. Yeah, that's a lot of data. Are
00:21:03.260 --> 00:21:09.440
using things like blob storage? I know this Foundry 120 project is pretty strongly based in
00:21:09.740 --> 00:21:15.519
the Azure cloud. Are you using Azure blob storage and stuff like that? Or is it really just all on
00:21:15.540 --> 00:21:18.440
on-premise with regard to the university?
00:21:19.160 --> 00:21:22.540
So back in the days, that was the situation that we were in.
00:21:22.840 --> 00:21:25.920
But today, we're now migrated everything into Azure Blob Storage.
00:21:26.570 --> 00:21:29.220
That just gives us a scalable way of not worrying, you know,
00:21:29.730 --> 00:21:33.160
how much space we had in our shared drives.
00:21:33.920 --> 00:21:35.320
Because back in the days, you know,
00:21:35.590 --> 00:21:37.580
the university would give you a quota on your shared drive
00:21:37.670 --> 00:21:40.240
and you have to email somebody in IT to get it increased.
00:21:40.700 --> 00:21:45.080
So you might have a terabyte cap on your file share.
00:21:45.400 --> 00:21:52.880
But with Azure Blob Storage, what you end up having is just a bill at the end of the month rather than hard limits.
00:21:52.880 --> 00:21:54.260
You have a bill instead of a limit.
00:21:54.580 --> 00:21:55.080
Yeah, yeah, yeah.
00:21:55.980 --> 00:21:56.180
Interesting.
00:21:57.220 --> 00:22:03.780
When I was working way back when I was in college, in university, working in this math research lab.
00:22:03.840 --> 00:22:06.820
I told this story a couple of times, but it's been a while, so maybe I'll share it again.
00:22:06.900 --> 00:22:13.780
And the whole of the math research department got access to the Silicon Graphics mainframe
00:22:14.340 --> 00:22:15.080
beast of a computer.
00:22:15.520 --> 00:22:19.440
We all had shared sort of workstation access to it.
00:22:19.720 --> 00:22:24.060
And one of the students, grad students, was having a problem with their code.
00:22:24.130 --> 00:22:26.120
And so they started logging out what it was doing.
00:22:26.490 --> 00:22:28.500
And they got it into an infinite loop and ran it.
00:22:28.660 --> 00:22:30.800
You would run stuff overnight and come see it in the morning.
00:22:31.140 --> 00:22:34.040
We came back one day and it just wouldn't turn on or it wouldn't respond.
00:22:34.290 --> 00:22:36.000
And nobody could figure out why.
00:22:36.380 --> 00:22:40.460
The students, we had no, the reason I'm telling you this, we had no quotas, no limits.
00:22:41.100 --> 00:22:46.240
The student used up the entire hard drive of the Silicon Graphics machine to the very last
00:22:46.440 --> 00:22:50.140
byte, and apparently it needed a few temp files to operate the operating system.
00:22:50.480 --> 00:22:51.180
And it just died.
00:22:51.560 --> 00:22:56.680
And nobody could get it to come, it took a day or two for it to come back because somebody
00:22:56.900 --> 00:22:57.300
destroyed it.
00:22:57.400 --> 00:22:59.940
Well, these limits, they have a reason they're there, you know?
00:23:00.500 --> 00:23:01.100
They do.
00:23:01.600 --> 00:23:08.140
But when we look at the next five to 10 years of biomedical research, file storage is actually a big problem.
00:23:09.040 --> 00:23:09.900
Is it? Okay.
00:23:10.100 --> 00:23:14.620
Some of the newer technologies are generating like a terabyte of data for a single sample.
00:23:14.960 --> 00:23:16.760
Wow. Yeah, that's absolutely crazy.
00:23:17.340 --> 00:23:23.320
You know, this also goes to reproducibility and long-term viability of this research, right?
00:23:23.440 --> 00:23:31.900
Because if you have 100 terabytes of data, it's one thing to say, well, here's the Excel file and here's the Parquet file and here's the Docker image that runs it.
00:23:32.300 --> 00:23:37.660
Save that as a, you know, you can stamp these as like, here's what the paper was published on, right?
00:23:37.690 --> 00:23:38.740
And here's the digital assets.
00:23:39.130 --> 00:23:42.460
But when it's 100 terabytes, it doesn't matter if you can name it or not.
00:23:42.560 --> 00:23:43.600
That's a hard thing to store.
00:23:43.920 --> 00:23:45.620
Yes, definitely a big problem.
00:23:46.700 --> 00:23:50.900
And I think the question then becomes what is the most valuable data that you store?
00:23:51.220 --> 00:23:54.280
because perhaps you don't need to store the raw files
00:23:54.540 --> 00:23:56.220
and you want to process files
00:23:56.570 --> 00:23:59.340
because who wants to run another 100 terabyte pipeline anyway?
00:24:00.020 --> 00:24:01.600
But yeah, these are challenging questions.
00:24:01.900 --> 00:24:02.660
There are no easy answers.
00:24:03.800 --> 00:24:07.440
And most groups, we do cost our data storage,
00:24:08.080 --> 00:24:10.140
but for most grants, we cost it out for 10 years.
00:24:10.940 --> 00:24:14.220
And beyond that, it becomes a challenge to maintain all this data.
00:24:14.520 --> 00:24:15.300
Yeah. What are you going to do?
00:24:15.600 --> 00:24:18.059
I guess the only bonus is in general
00:24:18.080 --> 00:24:21.060
that storage is getting cheaper by a lot.
00:24:21.880 --> 00:24:23.700
And I say by in general,
00:24:23.810 --> 00:24:24.860
because the last couple of years
00:24:25.040 --> 00:24:26.700
or last year and a half, that's not true.
00:24:26.840 --> 00:24:28.460
Yeah, I was going to say the AI revolution
00:24:28.650 --> 00:24:32.620
might not keep that falling price curve the same.
00:24:33.520 --> 00:24:35.780
It might change things just a little bit.
00:24:36.160 --> 00:24:38.120
All right, so let's talk about this Foundry project.
00:24:38.210 --> 00:24:40.000
So this is sort of the next generation
00:24:40.240 --> 00:24:42.700
of what you maybe dreamed of building.
00:24:44.380 --> 00:24:47.520
Also maybe a little bit in the agentic age as well, right?
00:24:47.580 --> 00:24:48.080
Yeah, definitely.
00:24:48.740 --> 00:24:50.700
Okay, so tell us about this Foundry 120.
00:24:51.100 --> 00:24:51.820
So Foundry 120.
00:24:51.900 --> 00:24:53.580
People will find it at foundry120.com.
00:24:54.140 --> 00:24:57.400
It's a research operating system for translational science teams.
00:24:57.580 --> 00:24:58.980
So I've got to explain that a bit.
00:24:59.560 --> 00:25:04.540
So translational science is basically when you're trying to discover things by recruiting
00:25:05.150 --> 00:25:09.820
humans with a problem and you're taking samples, bringing them back to the lab and running all
00:25:09.960 --> 00:25:14.780
sorts of experiments to just kind of understand the biology behind disease and then find out
00:25:14.780 --> 00:25:18.820
whether there are mechanisms that we can target for therapeutic development and things like that.
00:25:19.480 --> 00:25:20.920
So that's kind of translational science.
00:25:21.190 --> 00:25:26.160
And Foundry 120 is a platform that allows teams to operate seamlessly
00:25:26.790 --> 00:25:29.200
and use AI to accelerate their workflows.
00:25:29.800 --> 00:25:31.060
It's built around three ideas.
00:25:31.600 --> 00:25:33.400
The first idea is to organize everything.
00:25:33.610 --> 00:25:38.980
So you organize your samples, your studies, your participants into a database
00:25:39.380 --> 00:25:42.020
that just helps you keep track of everything.
00:25:42.440 --> 00:25:46.980
And then the second bit is the idea of centralizing all your data outputs into a single platform.
00:25:47.440 --> 00:25:53.640
So this will be things like clinical data, including radiology images, endoscopy videos,
00:25:54.360 --> 00:25:59.780
digital pathology slides, and then the scientific outputs such as spatial transcriptomics,
00:26:00.240 --> 00:26:05.620
genomics, microbiome, or all the data volume that we're seeing from all the different scientific
00:26:06.000 --> 00:26:11.080
modalities are just going up exponentially. But as we know in the AIH, if you can bring all your
00:26:11.080 --> 00:26:18.480
context into a single platform, then you can let AI connect to that and operate on that to answer
00:26:18.720 --> 00:26:24.520
questions that researchers might have. So, I mean, I'll give you a concrete example. For example,
00:26:24.730 --> 00:26:29.620
you know, if one of my scientific colleagues were looking for, you know, do we have any samples that
00:26:29.720 --> 00:26:35.420
belong to a participant who's been treated with this drug? Can we find that? And historically,
00:26:35.480 --> 00:26:41.220
what you would have to do is to take the clinical data set, join it with your sample database,
00:26:41.900 --> 00:26:47.020
and then filter that through and find the samples that you want. And that usually would have taken
00:26:47.060 --> 00:26:51.980
a couple of days to do. But this is a great use case, I think, for agentic AI, where you can just,
00:26:52.600 --> 00:26:58.140
you know, access the data, put it into a sandbox, write the code to do the join, and then just give
00:26:58.140 --> 00:27:03.659
you the answer that you want. So just eliminate all the steps in between and get you to the answer
00:27:03.680 --> 00:27:08.700
faster. And that's the whole concept behind the platform. Sounds great. It's a really nice looking
00:27:09.180 --> 00:27:15.060
web app too. What's its current status? It's available not just for you all, but for others,
00:27:15.940 --> 00:27:22.080
but it doesn't have quite a just create an account, get started. So you have a request a platform
00:27:22.420 --> 00:27:28.380
walkthrough. What's the situation here? Can other research teams use it? Is it a paid product? Is it
00:27:28.280 --> 00:27:34.420
just sort of gated because it's not ready for people overwhelming it? So it's quite early stage
00:27:34.440 --> 00:27:40.720
at the moment. So over the last six to 12 months, we've made the entire foundation generalizable
00:27:41.280 --> 00:27:46.560
beyond just our group and beyond just disease. So what I mean by that is, you know, for another
00:27:46.620 --> 00:27:51.280
group to join, they would need the access controls to be in place. So they need a way of managing
00:27:51.720 --> 00:27:58.240
their team, being able to assign permissions to individuals, you know, without me having to do it
00:27:58.480 --> 00:28:13.320
But I think more importantly is that onboarding a team into this platform is actually quite a hands-on process because it really depends on what data you're handling, the volumes that you're dealing with, what modalities of data you need to do.
00:28:13.660 --> 00:28:20.880
And also the other major thing that we have to look at if you want to join the platform is the governance around your data.
00:28:21.420 --> 00:28:28.160
So in clinical research, there are strict rules around how you handle data and where that data can sit, where can it be processed.
00:28:28.840 --> 00:28:33.100
And these are subject to your research ethics approval.
00:28:33.810 --> 00:28:40.280
So before a team can join us, we do want to review all those things to make sure that we're in compliance before anybody can join.
00:28:40.500 --> 00:28:46.940
So that's the reason why it's not just a simple, you know, I can sign up, pay a monthly fee and start using the platform.
00:28:47.440 --> 00:28:52.120
There's a lot more issues that have to be looked at before we can bring a team on board.
00:28:52.320 --> 00:28:58.860
But we're willing to do the work to kind of review the situation and start getting other people access to it.
00:28:59.000 --> 00:28:59.720
Makes a lot of sense.
00:28:59.910 --> 00:29:02.460
You've got IRB research stuff.
00:29:02.820 --> 00:29:06.500
You've got HIPAA and the equivalent in all the other countries.
00:29:07.200 --> 00:29:09.800
And yeah, so you don't want to get in trouble.
00:29:10.000 --> 00:29:10.260
Yes.
00:29:10.570 --> 00:29:14.360
And you did mention earlier that this is quite tied into Azure at the moment.
00:29:14.900 --> 00:29:18.960
And that's because we run on the University of Glasgow's Azure Tenancy.
00:29:19.330 --> 00:29:25.400
So everything has to be located specifically for our studies within the UK data centers and things.
00:29:25.540 --> 00:29:27.780
So we have all the controls in place to do that.
00:29:28.330 --> 00:29:32.600
And that's backend infrastructure for the people who are listening on the podcast.
00:29:34.520 --> 00:29:38.000
This portion of Talk Python is brought to you by Talk Python Courses.
00:29:38.380 --> 00:29:39.780
Here's the thing that always bug me.
00:29:40.040 --> 00:29:44.620
You finish one of our courses, that's hours of video, a pile of code you actually wrote,
00:29:44.740 --> 00:29:48.140
and real skills you didn't have a month before, and then nothing happens.
00:29:48.480 --> 00:29:51.120
No paper, no credential, nothing to show for it.
00:29:51.660 --> 00:29:52.300
So we fixed it.
00:29:52.740 --> 00:29:56.440
Every Talk Python course now generates a completion certificate automatically.
00:29:56.880 --> 00:29:58.840
Go to your account page in your dashboard section,
00:29:59.380 --> 00:30:02.500
scroll down to your completed courses, and click Certificate.
00:30:02.820 --> 00:30:03.620
That's the whole process.
00:30:04.600 --> 00:30:07.200
Two things you can do with these course completion certificates.
00:30:07.560 --> 00:30:12.640
download the full PDF, which is handy if your employer reimburses training or gives you credit
00:30:12.650 --> 00:30:18.300
for finishing it. Or you can make the certificate public and hit share on LinkedIn, which adds it to
00:30:18.300 --> 00:30:22.700
your LinkedIn profile under licenses and certifications. Not a poster that scrolls away
00:30:22.710 --> 00:30:27.360
in a day, an actual credential sitting on your profile where your manager and recruiters can see
00:30:27.440 --> 00:30:31.980
it. Plus, if you've been taking our courses for a while, you've probably earned several of these
00:30:32.090 --> 00:30:37.440
without even knowing they existed. Just visit training.talkpython.fm/account and collect
00:30:37.460 --> 00:30:42.640
them. Thanks to all of you who have taken a Talk Python course. It's a great way to support the podcast.
00:30:44.280 --> 00:30:49.260
What a different time it is for universities. And I think they're going through a similar
00:30:49.980 --> 00:30:54.400
upheaval, I guess is the word. When I was in university, and we're talking like 90s,
00:30:54.840 --> 00:31:00.680
there was a giant Cray supercomputer or maybe a couple of supercomputers at the heart of the
00:31:00.940 --> 00:31:05.019
university in some basement, and you could get access to that if you needed mega computing
00:31:05.020 --> 00:31:12.120
resources. And now it's just scaled into some ginormous cloud infrastructure. And the limit
00:31:12.130 --> 00:31:16.440
is really just how much are they going to give you, not what does the university have in its
00:31:16.620 --> 00:31:20.940
basement or something like that, which is really interesting. Well, I mean, there are still some
00:31:21.220 --> 00:31:25.700
universities that are trying to build their own clusters. But I think if you look at the economics
00:31:25.920 --> 00:31:32.659
of it, it is way more cost efficient to be running on the cloud compared to on the cluster, because
00:31:32.560 --> 00:31:37.760
many of the analysis that we do in the universities once off. So you might process a ton of data,
00:31:38.130 --> 00:31:42.360
but that might be after a year or two years before you get that amount of data to process.
00:31:42.880 --> 00:31:46.560
So if you look at a workload, the IT workload within the university, I think the cloud is
00:31:46.570 --> 00:31:50.560
quite an attractive proposition. That's an interesting angle. Yeah, of course, because
00:31:51.100 --> 00:31:55.160
you're going to process your samples. Maybe you're like, ah, we really want to try a different
00:31:55.500 --> 00:31:59.860
algorithm and you'll run it all through again. But generally speaking, it's not steady state.
00:32:00.360 --> 00:32:11.500
Whereas if you're going to go buy your own hardware and then put it in a basement and it's kind of a steady state situation, then you could really predict, you know, we're saving, you know, 50% less expensive to run on this.
00:32:11.570 --> 00:32:13.280
And just, yeah, didn't really think about that.
00:32:13.280 --> 00:32:17.880
And so I said, there's another such wave coming because this was, you know, when was the cloud?
00:32:17.950 --> 00:32:20.980
The cloud is 10 years ago, 2008, I think.
00:32:21.140 --> 00:32:24.620
A little bit, a little bit, really caught going a little bit after that.
00:32:25.150 --> 00:32:27.320
But it's been a little while.
00:32:27.360 --> 00:32:45.740
Now we've got all this AI stuff going and we're proving, you know, unsolved airdosh mathematical problems and all sorts of things, you know, with AI and other, you know, trying to do protein folding and other things that are really computational, but also really different.
00:32:45.970 --> 00:32:54.340
And so I feel like this whole AI wave, especially the agentic AI, is going to roil what's happening at the universities all over again.
00:32:54.480 --> 00:32:58.780
I mean, if you look at the data center economics around AI, it's quite interesting, actually.
00:32:59.150 --> 00:33:02.400
Like a single rack from Nvidia is like a couple million dollars, right?
00:33:03.240 --> 00:33:03.980
It's crazy.
00:33:04.130 --> 00:33:06.640
And you need a power plant to plug it into.
00:33:07.500 --> 00:33:11.400
I can't see any university building their own AI data center in the future.
00:33:11.590 --> 00:33:17.900
So I think cloud adoption is inevitable in the sense that the economics of trying to
00:33:17.900 --> 00:33:23.400
use agentic AI doesn't make sense to try and build your own AI computer, unless you have
00:33:23.360 --> 00:33:30.220
specific governance requirements, which do apply to certain studies and certain aspects of work.
00:33:30.940 --> 00:33:35.980
But even then, you might contract a local AI company to do it and then share it between
00:33:36.400 --> 00:33:40.400
businesses and universities rather than having a university do it on its own.
00:33:40.760 --> 00:33:45.520
Yeah. People talk about the AI bubble. I don't know if there's actually an AI bubble. This stuff
00:33:45.660 --> 00:33:51.559
is so productive. The last bubble we had that was tech-related was the dot-com bubble. And there
00:33:51.480 --> 00:33:57.160
was really stupid stuff with a lot of money spent on it you know like there's always the pest.com or
00:33:57.170 --> 00:34:01.900
the weird investments you know thing and people are spending millions of dollars on ads to just
00:34:02.120 --> 00:34:06.520
like have dancing monkeys running it was a weird time and this is also a weird time but I feel like
00:34:06.840 --> 00:34:12.120
there's actually something legitimately at the core of it that is really changing the way people
00:34:12.300 --> 00:34:17.300
work and and do research and so on so I don't know it's going to bust if there is a bubble and I don't
00:34:17.240 --> 00:34:22.240
know if the bubble is going to burst. But if it does, I think that's also going to create an
00:34:22.540 --> 00:34:27.879
explosion of local AI. You know, all of a sudden, right now, if you have to pay $100 for your
00:34:28.260 --> 00:34:32.520
Anthropic subscription, that's totally reasonable. But if that becomes a $2,000 a month bill,
00:34:32.879 --> 00:34:37.940
well, then buying a $10,000 workstation that can do legit local AI is all of a sudden a bargain,
00:34:38.139 --> 00:34:43.139
you know what I mean? So it could be in the future that there's sort of a coming back to
00:34:43.159 --> 00:34:47.899
local compute as well for AI, maybe. Yeah, I mean, the small models are getting more and more capable
00:34:48.159 --> 00:34:52.980
as well. So not every workload needs a frontier model at the moment. So what you're saying is
00:34:53.080 --> 00:34:56.919
probably true in the sense that there is definitely going to be a role for local AI.
00:34:57.230 --> 00:35:02.560
I think Gemma maybe works this way, but certainly I can also see a world where there's kind of an
00:35:02.780 --> 00:35:10.099
orchestration layer and then 100 or 50 specialists that get selected, right? Right now, if you ask a
00:35:10.060 --> 00:35:15.940
frontier models to do something on genomics, it uses the same giant model as it would to use to
00:35:16.280 --> 00:35:22.840
like write Shakespeare derived things. Right. But if you had one that just is trained on genomics,
00:35:23.920 --> 00:35:27.380
you could have a much smaller model that could run locally and you could say, okay,
00:35:27.660 --> 00:35:31.400
this part of the question goes to this, this sub model. And I don't know, it's going to be
00:35:31.540 --> 00:35:35.420
interesting where it goes. And that let's, so coming back to it, let's, let's talk a little
00:35:35.400 --> 00:35:40.660
bit about Foundry. There's a little demo you've got going right here at the beginning. Let me see
00:35:40.660 --> 00:35:46.640
if I can go to the front of it. And maybe, I guess there's the stuff you talked about before,
00:35:46.780 --> 00:35:53.460
the sample gathering and organizing and all that kind of stuff, right? And then on the back of that,
00:35:53.700 --> 00:36:00.260
once you get it all done, there's this local tool using AI that understands all of your research
00:36:00.280 --> 00:36:06.920
data. So maybe tell us about the data collection and the non-AI bit, and then it'll be fun to talk
00:36:07.020 --> 00:36:11.240
about the AI because it's kind of non-standard. It's kind of powerful. Yeah, let's talk about the
00:36:11.320 --> 00:36:15.260
data bit because that has been one of the most difficult problems I've been thinking about for
00:36:15.300 --> 00:36:22.160
the last few years is how do we aggregate all these types of data sets into some kind of a common
00:36:22.840 --> 00:36:29.640
model that an AI... Well, nowadays we can use AI, but it still applies to humans. So when my
00:36:29.660 --> 00:36:36.420
colleagues are looking for data and trying to match, you know, data set A to B, what is the
00:36:36.580 --> 00:36:42.000
way we do it? And at the end of the day, I think, you know, we've brought down everything to a model
00:36:42.200 --> 00:36:48.400
where all data exists in files. So even tabular data, we keep it in CSV file. Well, historically,
00:36:48.840 --> 00:36:52.880
we've tried to put, you know, tabular data into the database. But I think going forward,
00:36:53.200 --> 00:36:58.080
we're just going to keep the files because that's the universal denominator. So whether you're
00:36:58.100 --> 00:37:04.960
dealing with endoscopy videos, MRI images, digital pathology slides. Everything's a file, and each
00:37:05.050 --> 00:37:11.020
file has each set of, you know, each type of data has its own file structure, but the common language
00:37:11.310 --> 00:37:16.700
at the end of day is files. So we put files, and the thing that makes the files useful is putting a
00:37:16.810 --> 00:37:21.720
layer of metadata and relationship data on top of the file. So I think by coupling the two of them,
00:37:21.960 --> 00:37:25.920
we found this common model that can generalize across all the data sets that we use.
00:37:26.120 --> 00:37:46.640
Very interesting. And, you know, to your point of just keep it in the files, you know, with things like DuckDB and other really cool things and Parquet files, you can kind of treat them like databases already. So I don't know if people know, but with DuckDB, you can say things like select star from read Parquet input, you know, which is pretty insane.
00:37:47.200 --> 00:37:49.120
Yeah, Parquet is great. We use it as well.
00:37:49.840 --> 00:38:00.740
Yeah. So also things like, what do you think about little SQLite files or DuckDB file where there's these just embedded no server databases?
00:38:01.440 --> 00:38:04.840
And I think there's probably some really good use cases and research for that.
00:38:05.100 --> 00:38:10.700
Potentially, you know, SQLite's a very interesting concept of having an entire database in a file.
00:38:13.020 --> 00:38:18.240
But when I think of my end users, my colleagues who are running science experiments and such,
00:38:18.760 --> 00:38:21.100
they're used to dealing with Excel and CSV files.
00:38:21.690 --> 00:38:25.680
So we try to keep the same file format everybody's familiar with.
00:38:25.920 --> 00:38:30.140
But I think for some of the future work that we're going to do, we can use some of these
00:38:30.770 --> 00:38:32.060
more specific file formats.
00:38:32.690 --> 00:38:37.560
Because as you know, if everything's a file, then an AI agent can run across all the files
00:38:37.820 --> 00:38:40.480
and pull out the data that it needs for what it needs to do.
00:38:40.700 --> 00:38:41.240
Yeah, absolutely.
00:38:41.490 --> 00:38:41.840
Yeah, sure.
00:38:41.940 --> 00:38:48.920
If you're giving them the files directly, here's either your Excel workbook or here's your CSV file.
00:38:49.110 --> 00:38:56.980
But if it's coming in and out of a platform like Foundry 120, you can store it as one thing and then export it or import it as another, right?
00:38:57.140 --> 00:39:03.620
Yeah. I mean, so fundamentally, what we do is that the files sit in Azure Blob Storage.
00:39:04.360 --> 00:39:11.120
And when Helix, our AI agent, wants to process a file, it spins up a VM.
00:39:11.660 --> 00:39:14.560
The file gets transferred from the Azure storage into the VM.
00:39:15.000 --> 00:39:20.680
The AI sends its analysis code, whether it's in Python or any other language, into the VM.
00:39:21.010 --> 00:39:27.400
The computation happens, and then we return the output to Helix and store the output back in Azure storage.
00:39:28.200 --> 00:39:34.820
So by doing that, we put the sandbox guardrail around the AI agent for security purposes.
00:39:35.560 --> 00:39:39.460
but it also allows us to enable the AI to do processing,
00:39:40.060 --> 00:39:41.580
generate new files, store it.
00:39:41.860 --> 00:39:45.000
And I think that's the kind of model that works for us in Foundry
00:39:45.640 --> 00:39:48.060
while keeping it all within an institutional Azure environment
00:39:48.340 --> 00:39:51.760
and also allows us to enforce all the role-based access controls
00:39:51.820 --> 00:39:53.540
that we need for our team members.
00:39:54.060 --> 00:39:57.760
So when they query the AI, the AI can only see the same files
00:39:57.940 --> 00:40:00.460
that they would have normal access to.
00:40:01.040 --> 00:40:04.700
And I think that's the design that's very specific for this
00:40:04.720 --> 00:40:07.980
because of the governance requirements that we have around the data sets that we use.
00:40:08.400 --> 00:40:09.440
These AIs are sneaky.
00:40:10.180 --> 00:40:12.680
They will find a way to access the files.
00:40:12.880 --> 00:40:16.260
I mean, the really big headline cases are like,
00:40:16.660 --> 00:40:19.760
OpenAI was training its model and it hacked multiple systems
00:40:20.060 --> 00:40:22.520
so that it could get to the answers on Hugging Face
00:40:22.880 --> 00:40:25.840
instead of just figuring out the, solving the test, right?
00:40:25.860 --> 00:40:29.680
It's like a teenager that doesn't really care about the work is just doing it.
00:40:29.860 --> 00:40:33.079
But what I was thinking was, you know, I was working with Claude
00:40:33.100 --> 00:40:35.820
and I had some question about my code
00:40:35.850 --> 00:40:37.640
and it said something to the effect of like,
00:40:37.690 --> 00:40:39.600
oh yeah, you have two GitHub issues on this.
00:40:40.020 --> 00:40:42.060
I never gave it direct access to GitHub.
00:40:42.100 --> 00:40:43.160
I never gave it access to GitHub.
00:40:43.230 --> 00:40:44.360
I'm like, how does it know that?
00:40:45.160 --> 00:40:47.180
It's quoting like GitHub stuff,
00:40:47.400 --> 00:40:49.360
not through Git history locally,
00:40:49.540 --> 00:40:51.520
but it like reading the issues and the PRs.
00:40:51.520 --> 00:40:52.280
I'm like, how does it do?
00:40:52.340 --> 00:40:54.740
And then I realized I had the GitHub CLI installed
00:40:54.940 --> 00:40:55.600
and it's like, well,
00:40:56.000 --> 00:40:57.480
let me see if the GitHub CLI is installed.
00:40:57.610 --> 00:41:00.220
Oh, and look, it's already automatically authenticated
00:41:00.360 --> 00:41:02.540
because Michael logged in at some point
00:41:02.560 --> 00:41:07.420
to the CLI. And so it was just using the CLI that it's also, you know, like, oh, let me check that
00:41:07.480 --> 00:41:11.120
on the server for you. I'm like, excuse me. Yeah. Yeah. On your production server, you're doing this.
00:41:11.200 --> 00:41:15.440
How you're not supposed to be there. Why? And you know, it's just like, it's realized in the code
00:41:15.560 --> 00:41:20.920
somehow it's figured out that it can SSH. So you got to be really careful about those things. Right.
00:41:21.120 --> 00:41:25.580
Cause they're not malicious. They're just like, you asked me to solve a problem. And if I can get
00:41:25.610 --> 00:41:30.540
to that, I got a better answer, more concrete data. And, but it could also go and we fixed the problem
00:41:30.560 --> 00:41:31.760
by resetting the database.
00:41:31.990 --> 00:41:33.340
Like, oh, no, you didn't.
00:41:34.980 --> 00:41:36.380
Yeah, so for us...
00:41:36.440 --> 00:41:39.600
So how do you do that kind of stuff in your project?
00:41:39.820 --> 00:41:40.680
So it's the backend.
00:41:40.830 --> 00:41:42.660
So Django enforces the permissions.
00:41:44.100 --> 00:41:49.260
And actually, so when the AI stages data and stages code,
00:41:49.410 --> 00:41:51.080
that actually goes through Django first.
00:41:51.660 --> 00:41:54.500
So the AI is not calling directly into the files,
00:41:54.500 --> 00:41:56.440
is not calling directly into a compute environment.
00:41:56.940 --> 00:42:00.520
And then Django screens all that code
00:42:00.540 --> 00:42:02.440
and activates the VM.
00:42:02.780 --> 00:42:05.820
So we've actually put Django in as a security guard
00:42:06.300 --> 00:42:10.960
between your AI and the raw data and the compute environments
00:42:11.060 --> 00:42:11.600
that we run in.
00:42:11.820 --> 00:42:12.580
MARK MANDEL: Oh, very cool.
00:42:12.760 --> 00:42:15.720
So Foundry 120 is also written on Django,
00:42:16.080 --> 00:42:20.200
but it's more Django REST framework and TypeScript React.
00:42:20.460 --> 00:42:21.620
Is that the story?
00:42:21.620 --> 00:42:22.360
FRANCESC CAMPOY: Yep, that's right.
00:42:22.520 --> 00:42:23.760
So on the front end, it's-
00:42:23.760 --> 00:42:24.640
MARK MANDEL: Yeah, so tell us a bit about it.
00:42:24.640 --> 00:42:28.660
FRANCESC CAMPOY: So the old app that we used to run off
00:42:28.740 --> 00:42:30.140
was just pure Django.
00:42:30.360 --> 00:42:34.960
So we use Django templates to handle all the registration and the CRUD workflows.
00:42:36.100 --> 00:42:40.540
But as we move into this age of AI, AI, as you know, is pretty asynchronous.
00:42:40.970 --> 00:42:45.340
So every call you make takes, you know, sometimes it feels like forever to come back with a response.
00:42:46.420 --> 00:42:52.940
And when we think about the async nature of all the calls that have to be made to run an AI conversation or agent loop,
00:42:53.820 --> 00:42:58.720
TypeScript and JavaScript tends to come to be more suitable for that kind of application.
00:42:59.720 --> 00:43:00.720
So we run both.
00:43:01.130 --> 00:43:03.400
So we have TypeScript on the front end, Django on the back end.
00:43:03.470 --> 00:43:04.400
I think it's a great setup.
00:43:04.510 --> 00:43:09.940
It gives us access to the entire Python data science ecosystem, while also giving us all
00:43:10.080 --> 00:43:15.800
the TypeScript and JavaScript ecosystem for handling all these asynchronous work and the
00:43:16.280 --> 00:43:19.440
interactivity that we want on the front end when you start running AI applications.
00:43:20.040 --> 00:43:20.400
Makes sense.
00:43:20.760 --> 00:43:25.520
Now, what I'm about to ask you doesn't really make sense because of the AI angle and that
00:43:25.570 --> 00:43:26.040
kind of stuff.
00:43:26.240 --> 00:43:46.520
But if you think about scaling this out to other research projects and other groups, have you considered looking at things like PyOxid, Iodide, sorry, and things like JupyterLite for running some of that compute on the front end on people's browsers so that you don't have to basically pay the compute cost?
00:43:47.880 --> 00:43:56.560
So that's an interesting question because now you're asking me about the compute architecture that we have in the backend, which is actually pretty heavy.
00:43:57.790 --> 00:44:08.320
So we've mentioned that some of the files that we have might be gigabytes in size and the compute power you need to run genomics pipeline is pretty high.
00:44:08.840 --> 00:44:15.800
So for a concrete example, so in the backend of our Foundry system, we have access to about 350 CPUs on Azure.
00:44:16.120 --> 00:44:29.240
So if you ask Helix for a very heavy analysis, say on a big transcriptomic data set of something, through Django, we are able to orchestrate up a heavy compute job, which will then go off and run.
00:44:29.660 --> 00:44:36.360
It'll spin up as many CPUs as it needs to, to process the job, and then returns the output later on once it's all done.
00:44:36.580 --> 00:44:39.960
And that process can take half an hour, a couple of hours.
00:44:40.330 --> 00:44:42.000
So it might come back really late.
00:44:42.260 --> 00:44:47.740
And because of that model that we're running, actually, the kind of computational requirements we have is pretty high.
00:44:48.830 --> 00:44:53.540
And so we don't really want to run compute on people's laptops.
00:44:53.890 --> 00:44:57.640
We want to run it in the cloud to handle the data sets that we're dealing with.
00:44:57.830 --> 00:44:59.400
So that's a very specific design choice.
00:45:00.520 --> 00:45:06.080
And it's all to do with the kind of data that we're handling and the need to throw a lot of RAM and a lot of CPU at it.
00:45:06.280 --> 00:45:11.260
Sure. And if your individual files are five gigs, that's a lot of just bandwidth costs.
00:45:11.360 --> 00:45:18.320
So it's like every time you want to load something, you got to pull that five gigs out of the cloud, which has different costs and so on.
00:45:18.340 --> 00:45:22.460
Right. Well, yeah. And many of the biological data problems are parallel.
00:45:22.780 --> 00:45:25.240
Right. So you've got 200 participants each of five gigs.
00:45:25.720 --> 00:45:28.520
You might as well spin up 200 machines and process all of them in parallel.
00:45:29.120 --> 00:45:31.980
So all these problems that we have are very paralyzable.
00:45:32.280 --> 00:45:39.040
And the cloud is a great platform to do that in because you can do it on demand, on the fly, spin it up, finish processing and tear it all down.
00:45:39.600 --> 00:45:42.380
So minimal cost for, you know, maximum impact.
00:45:42.820 --> 00:45:44.520
That's what we're going for in the back end.
00:45:44.860 --> 00:45:47.300
Right. That's the bursting component that you talked about.
00:45:47.680 --> 00:45:53.860
I do think JupyterLite is pretty interesting with the local Piodide execution and all that kind of stuff.
00:45:54.160 --> 00:45:56.300
Just the fact that that's possible is it's pretty neat.
00:45:56.300 --> 00:45:59.340
But yeah, I can see that it really doesn't apply for what you're doing here.
00:45:59.640 --> 00:46:04.980
Well, I haven't talked to you about the front end of sample operations, right?
00:46:05.540 --> 00:46:11.400
Because we're running a sample collection in the hospitals across Scotland.
00:46:11.530 --> 00:46:14.520
And I must say the frontline IT infrastructure is not always the best.
00:46:14.910 --> 00:46:21.180
So by having all our compute power on the server side, we can guarantee a speedy experience for our teams working at the front end.
00:46:21.480 --> 00:46:24.960
So we're not depending on the front end's computational power.
00:46:25.340 --> 00:46:30.660
I'll tell you what, my experience looking over the shoulder at the software that doctors and nurses use,
00:46:31.180 --> 00:46:34.640
there's a lot of room for improving the user experience.
00:46:35.820 --> 00:46:36.200
Oh, definitely.
00:46:36.700 --> 00:46:40.260
I mean, part of the reason we went Django as well is because it was server-side.
00:46:40.620 --> 00:46:45.700
And, you know, on the front end, you know, we have some computers I've used in hospitals.
00:46:46.520 --> 00:46:48.440
They go back to 2010, right?
00:46:48.740 --> 00:46:51.440
You know, we're running on Intel chips like 20 years old.
00:46:52.620 --> 00:46:53.180
Oh, my goodness.
00:46:54.000 --> 00:47:18.280
Yep. And there's a lot of Cisco, remote, whatever there. So let's talk about the AI side now. So we talked about the data, the data handling, some of the tech behind Foundry 120. But I think one of the cornerstones is this Helix AI. Now, when people think there's a bit of a problem here, Sean, like people talk about AI, and there's two or three different things it could be, and they all use the same word.
00:47:18.580 --> 00:47:22.580
And they think they're talking about the same thing, but they're actually talking past each other.
00:47:22.830 --> 00:47:27.220
You know, like I asked ChatGPT for this math problem and it got it wrong.
00:47:27.460 --> 00:47:32.240
It's like, yeah, but we also built incredible software with this other thing that we also call AI.
00:47:32.600 --> 00:47:35.980
And, you know, it's always right because it writes Python to actually answer its questions.
00:47:36.090 --> 00:47:37.980
And yeah, it's just really interesting.
00:47:38.360 --> 00:47:48.420
So there's an AI that you've mentioned a couple of times in here that will help researchers ask questions, find data, look for trends and those kinds of things.
00:47:48.700 --> 00:47:57.480
And this, I think, you give me your thoughts on this, but my feeling is that it's a little bit like a Claude code or a codex.
00:47:57.660 --> 00:48:04.460
One of these sort of tool using self-correcting AIs, not just a chat LL.
00:48:04.480 --> 00:48:11.840
Yeah. So Helix is an agentic AI system. And I think the problem that you're describing is
00:48:12.020 --> 00:48:17.200
because most people's experience of AI is chatbots. You go to chatgpt.com, you ask a question,
00:48:17.550 --> 00:48:21.880
it gives you an answer. But actually the stuff that we're seeing that makes us think that AI
00:48:22.060 --> 00:48:26.960
might not be a bubble is all this agentic AI stuff that we're seeing. So Claude Code, codecs,
00:48:27.780 --> 00:48:33.700
agentic AI is a very different paradigm from chatbots, right? In agentic AI, the AI,
00:48:33.980 --> 00:48:38.240
you give the AI a task, it looks at its tool set, it looks at what you're trying to do,
00:48:38.450 --> 00:48:42.520
and then it goes away and works at it until it gives you an answer. And that's incredibly powerful.
00:48:42.900 --> 00:48:48.040
Whereas I think, you know, 95% of people's experience of AI is almost like a Google search.
00:48:48.230 --> 00:48:52.700
You go to ChatGPT and you ask, hey, what's the directions to this place or what's the recipe for
00:48:52.820 --> 00:48:57.880
that? And therefore, there's this huge gap in understanding of how powerful agentic AI systems
00:48:57.900 --> 00:49:03.880
can be. So Helix is really one of the, we think it's one of the first demonstrations of how you
00:49:03.960 --> 00:49:10.240
would apply agentic AI in the science world. And a lot of the scientists that I'm showing this
00:49:10.480 --> 00:49:14.860
system to, this is the first time that they're seeing an agentic AI system. So, you know.
00:49:15.120 --> 00:49:15.800
What's their reaction?
00:49:18.420 --> 00:49:22.540
I think everybody's quite excited. They're like, oh, that used to take me like months to do,
00:49:22.700 --> 00:49:24.420
or it took me a lot of emails to do.
00:49:24.680 --> 00:49:25.900
Just doing it in minutes right now,
00:49:26.800 --> 00:49:28.140
it's quite amazing.
00:49:28.400 --> 00:49:30.300
And historically, every team member
00:49:30.420 --> 00:49:31.100
that's joined our team,
00:49:31.240 --> 00:49:32.400
I've had to sit down with them
00:49:32.470 --> 00:49:34.580
and teach them how to use Python or R
00:49:34.630 --> 00:49:37.880
to get a graph out of Excel file or something.
00:49:38.280 --> 00:49:40.340
And now you can just hand it off to Helix,
00:49:40.680 --> 00:49:42.660
let it do it, let it write the code.
00:49:42.910 --> 00:49:45.020
And then what you do in this situation
00:49:45.070 --> 00:49:47.040
is that you've got to verify that it's correct.
00:49:47.520 --> 00:49:48.880
So the work changes.
00:49:49.050 --> 00:49:50.500
So instead of you as a researcher
00:49:50.800 --> 00:49:51.740
writing the analysis code,
00:49:52.040 --> 00:49:54.060
You get the AI to do the analysis for you.
00:49:54.220 --> 00:49:56.280
And then what you have to do is verify that it is correct.
00:49:56.660 --> 00:49:58.160
So it's a very different way of working.
00:49:58.860 --> 00:50:00.240
But it's incredibly powerful.
00:50:00.460 --> 00:50:01.420
It's much faster.
00:50:01.660 --> 00:50:04.680
It's taken a lot of road work out of everybody's life.
00:50:04.840 --> 00:50:06.460
So I think everybody's really excited about it.
00:50:06.680 --> 00:50:07.140
I would imagine.
00:50:07.880 --> 00:50:10.900
I'm going to have you talk us through just this sort of workflow that it goes through
00:50:10.960 --> 00:50:11.320
real quick.
00:50:11.620 --> 00:50:15.940
But that's the big danger is that it just hallucinates, which I don't know, that's a
00:50:16.040 --> 00:50:16.340
weird word.
00:50:16.400 --> 00:50:20.920
It's just it's wrong, whatever, about the actual data.
00:50:21.260 --> 00:50:25.500
Do you all use RAG, the sort of training on the data,
00:50:25.540 --> 00:50:29.460
or is it just really the tool-using components that make it go?
00:50:30.420 --> 00:50:31.980
It depends on what you mean by RAG,
00:50:32.220 --> 00:50:35.500
because we use tools to ground the AI.
00:50:36.840 --> 00:50:40.860
Yeah, I'm thinking like going and actually retraining the model
00:50:41.120 --> 00:50:43.500
on the research files and data,
00:50:43.640 --> 00:50:45.300
which I'm guessing from looking at it,
00:50:45.480 --> 00:50:46.360
it doesn't look like it.
00:50:46.360 --> 00:50:48.960
It looks more of a Claude Code tool-using style.
00:50:49.060 --> 00:50:50.740
Yeah, it is more Claude Code style.
00:50:52.480 --> 00:50:55.980
we don't train the AI specifically for it.
00:50:56.190 --> 00:50:59.280
We can swap the base models as newer versions come up.
00:50:59.980 --> 00:51:05.580
Because if supervised fine-tuning or doing some RL on a base,
00:51:06.020 --> 00:51:07.420
LLM will cost you a lot of money.
00:51:09.180 --> 00:51:10.660
And it's not generalizable, right?
00:51:11.160 --> 00:51:14.900
Yeah, and you need the data to train it on.
00:51:15.350 --> 00:51:17.200
And that's not easy to make as well.
00:51:18.620 --> 00:51:19.860
So yeah, so given the rapid progress,
00:51:20.760 --> 00:51:23.440
we want a model where we can just update to the latest model
00:51:23.920 --> 00:51:27.840
and just leverage the latest changes
00:51:28.080 --> 00:51:29.640
that the big labs are coming out with.
00:51:30.360 --> 00:51:30.580
Amazing.
00:51:30.860 --> 00:51:31.700
I think that's the right way.
00:51:31.820 --> 00:51:34.560
So if you go to foundry120.com,
00:51:34.920 --> 00:51:36.800
there's a one-minute little screencast,
00:51:37.020 --> 00:51:38.320
silent screencast of it going.
00:51:38.600 --> 00:51:40.300
So I'm going to, Sean, I'm going to hit play
00:51:40.620 --> 00:51:42.240
and you kind of just narrate what's happening.
00:51:42.240 --> 00:51:44.060
I think that'll give people an interesting sense
00:51:44.260 --> 00:51:45.340
of what this thing is about
00:51:45.360 --> 00:51:46.680
and give us some talking points here.
00:51:47.000 --> 00:51:51.420
So we asked Helix, you know, what plasma samples do we have for the music study?
00:51:51.500 --> 00:51:53.140
And can you break them down by disease groups?
00:51:53.900 --> 00:51:57.840
So to get to that answer, you need to join the clinical data frame with your sample database.
00:51:58.460 --> 00:52:02.100
And then you got to work out which samples are unused, you know, what sample type it is.
00:52:02.460 --> 00:52:05.760
And then for the disease groups, it's got to inspect the clinical data frame and figure
00:52:05.800 --> 00:52:08.040
out how many disease groups do you have in your disease column.
00:52:08.400 --> 00:52:10.220
And then you've got to do the join and the grouping.
00:52:11.320 --> 00:52:14.580
So in this demo, it's gone ahead and done it.
00:52:14.780 --> 00:52:22.680
And it will come back and tell you, well, within our database, we've got like 4,000 samples belonging for Crohn's disease patients and 2,000 with ulcerative colitis.
00:52:23.400 --> 00:52:23.660
Yeah.
00:52:23.840 --> 00:52:28.440
And since people are just listening, let me just go back, just narrate really quick, like fill in a little background visuals.
00:52:29.080 --> 00:52:29.780
You can see it.
00:52:29.980 --> 00:52:33.720
It'll, using the different tools, it'll be like, get sample and it'll run for a second.
00:52:33.780 --> 00:52:36.600
Then it'll write some Python code to do a thing.
00:52:36.720 --> 00:52:40.140
And then it'll do some more data access and then some more code.
00:52:40.300 --> 00:52:46.800
So it's primarily orchestrating a bunch of the tools and the code writing that it already knows, right?
00:52:47.100 --> 00:52:51.040
It's not just reading 100 terabytes of data or whatever.
00:52:51.360 --> 00:52:56.880
No, you can't read all that data because the context window of your AI is limited.
00:52:57.040 --> 00:53:00.660
So you can't just dump all the raw Excel file into the AI and say,
00:53:00.800 --> 00:53:01.600
five video samples.
00:53:03.000 --> 00:53:03.440
Yeah, exactly.
00:53:03.860 --> 00:53:06.620
I mean, even the really big ones have a million context right now.
00:53:06.980 --> 00:53:07.480
All right, carrying on.
00:53:07.780 --> 00:53:09.180
So then it's off to get a picture, right?
00:53:09.480 --> 00:53:12.120
So the next question, we've asked it to do some graphing.
00:53:12.260 --> 00:53:16.360
So we've asked to plot CRP, which is a blood test against cell-free DNA,
00:53:16.660 --> 00:53:19.300
which is a scientific experimental output.
00:53:19.700 --> 00:53:22.840
So to do this, it's got to go and find your clinical data frame
00:53:22.920 --> 00:53:24.420
and join it with your science data.
00:53:24.780 --> 00:53:27.460
And it comes up, writes some matplotlib code
00:53:27.560 --> 00:53:30.480
and gives you a graph back and tell you what it found.
00:53:30.780 --> 00:53:31.960
So that's what it does.
00:53:32.680 --> 00:53:36.160
In this second segment, the AI runs into an error
00:53:36.560 --> 00:53:37.760
and it recovers from the error.
00:53:38.080 --> 00:53:42.500
So the AI is able to read the output of the tool, correct, it's working and come back to you.
00:53:42.680 --> 00:53:46.620
So that's just a demonstration of how an agentic AI system looks like.
00:53:46.800 --> 00:53:47.880
Yeah, good narration.
00:53:48.380 --> 00:53:49.680
And people can go and play that for themselves.
00:53:49.840 --> 00:53:55.240
But I think that's the big difference between what you're saying, just the chatbot as kind of a better Google, right?
00:53:55.700 --> 00:53:57.600
It's not that much of the AI thinking.
00:53:57.800 --> 00:54:00.560
It's a whole bunch of the AI using the tools.
00:54:00.840 --> 00:54:02.480
And the tools are deterministic, right?
00:54:03.120 --> 00:54:04.220
The tools are deterministic.
00:54:04.400 --> 00:54:09.000
And actually designing the tools is one of the most challenging things to do is like,
00:54:09.130 --> 00:54:10.520
how many tools do you expose?
00:54:10.800 --> 00:54:11.740
What should each tool do?
00:54:11.870 --> 00:54:13.560
And what's a logical set of tools?
00:54:13.990 --> 00:54:16.380
And how do you make sure that the AI picks them correctly?
00:54:16.640 --> 00:54:21.880
So I think tool design itself is a huge topic of how you do it.
00:54:22.000 --> 00:54:26.660
But because the tools run on the backend, we can then enforce the RBAC controls on the
00:54:26.820 --> 00:54:27.280
tools itself.
00:54:27.580 --> 00:54:33.300
So you can guarantee that your data access is, you know, security around it is solid.
00:54:33.800 --> 00:54:36.420
Have you thought about adding an MCP server to it?
00:54:36.630 --> 00:54:40.240
So then people can just within their own AI or whatever,
00:54:40.410 --> 00:54:42.520
just, hey, what does Foundry say about this?
00:54:42.720 --> 00:54:44.020
Yeah, MCP is interesting.
00:54:44.170 --> 00:54:46.900
But I think one of the limiting things for us using MCP
00:54:47.000 --> 00:54:48.100
is the governance around it,
00:54:48.270 --> 00:54:51.820
in the sense that we are not allowed to just put data
00:54:52.670 --> 00:54:54.700
into a Claude Code and send it across to Anthropic.
00:54:55.680 --> 00:54:57.200
Whereas within this system,
00:54:57.520 --> 00:54:59.760
all inference happens in Microsoft Azure
00:55:00.040 --> 00:55:01.280
and it's GDPR compliant,
00:55:01.640 --> 00:55:05.680
which in the UK is important for us from a research perspective.
00:55:06.300 --> 00:55:07.400
So in this...
00:55:07.420 --> 00:55:08.160
It's also important.
00:55:08.340 --> 00:55:10.000
It applies to the US companies as well,
00:55:10.080 --> 00:55:11.580
if they have European customers.
00:55:12.440 --> 00:55:15.040
Yeah, so because of regulatory compliance things,
00:55:15.280 --> 00:55:18.000
we need to make sure that we know where the inference is running
00:55:18.420 --> 00:55:20.780
and we can guarantee that inference all runs within us,
00:55:21.060 --> 00:55:23.220
you know, because we have got the platform controls of Azure,
00:55:23.340 --> 00:55:25.380
we can guarantee where everything happens.
00:55:26.160 --> 00:55:29.060
And that allows us to actually let the AI safely operate on our data.
00:55:29.380 --> 00:55:29.660
I see.
00:55:29.840 --> 00:55:34.320
So maybe you're using a data center in Ireland for your data so it doesn't leave the UK or
00:55:34.390 --> 00:55:34.760
something like that.
00:55:35.050 --> 00:55:37.600
Yeah, we run most of the things in the UK South data center.
00:55:38.420 --> 00:55:41.280
For EU compliance, we run it in the Sweden data center.
00:55:41.760 --> 00:55:43.840
So that's where everything happens at the moment.
00:55:44.680 --> 00:55:45.580
Yeah, that makes a lot of sense.
00:55:46.020 --> 00:55:47.920
Let's zoom out a little bit.
00:55:48.240 --> 00:55:51.180
You've been working on this project for six plus years now.
00:55:51.520 --> 00:55:56.200
You've taken it from working with books to write the Django app to integrating to the
00:55:56.160 --> 00:56:03.960
cloud, Microsoft Foundry, and Azure, and all this tool using AI. What do you see for research,
00:56:04.600 --> 00:56:09.240
either happening now or in the next couple of years with all this kind of stuff coming along?
00:56:09.480 --> 00:56:14.680
Yeah, I mean, I think, you know, we're all very excited about the potential of agentic AI to
00:56:14.900 --> 00:56:20.020
accelerate a lot of the research workflows that we see. And, you know, hopefully, you know, we get
00:56:20.040 --> 00:56:26.760
to discoveries faster because drug discovery is a very long process. And I think agentic AI has
00:56:27.100 --> 00:56:35.360
absolutely a role to play in shortening some of these bottlenecks that we face. So I think it'd
00:56:35.360 --> 00:56:41.240
be very exciting to see. I think agentic AI is going to transform how we do science, how we operate
00:56:41.480 --> 00:56:46.860
also in the clinical world. So I see a lot of potential, but the real world is going to take
00:56:46.880 --> 00:56:52.240
a while to catch up to where the capabilities are today. So I think most people on this podcast
00:56:52.560 --> 00:56:57.960
would have used coding agents, but I can tell you that almost everybody else in the population has
00:56:58.120 --> 00:57:03.780
never touched a coding agent. So the gap between what agentic AI systems can do and what everybody's
00:57:03.920 --> 00:57:09.100
understanding of AI is still very wide. And there's still a lot more work to do to teach people how to
00:57:09.180 --> 00:57:13.820
use the systems, what they're capable of, where the limitations are, and how do you apply it to your
00:57:13.760 --> 00:57:18.860
work. Do you think it can be reliable? Like, do you think we can trust the results and answers
00:57:18.970 --> 00:57:24.080
we're getting from things like Helix and other tools that are working on us? Well, hopefully,
00:57:24.400 --> 00:57:29.680
as things improve over the coming years, the reliability will go up. I mean, personally,
00:57:30.050 --> 00:57:34.660
you know, the coding agents are still not 100% reliable. You know, even like the best models,
00:57:34.850 --> 00:57:40.760
like Opus and Fable, they still can make mistakes. So we're not quite past the reliability threshold
00:57:40.780 --> 00:57:45.980
yet. So we do have to be aware. And I think understanding what you're trying to do is
00:57:46.120 --> 00:57:50.720
actually very important today compared to, say, five years ago, because you have to really
00:57:50.920 --> 00:57:55.020
understand what you're trying to do to be able to supervise an AI to do what you were going to do.
00:57:55.280 --> 00:57:59.340
I totally agree with what you said. But the alternative is to have a human do it.
00:57:59.680 --> 00:58:05.560
And humans are also not 100% reliable. There's recently been this dust up with the Linus Torvalds
00:58:05.580 --> 00:58:12.300
over about using, I forgot the name. There was some AI that they're using as a pre-screen for PRs
00:58:12.360 --> 00:58:16.420
for the Linux kernel. And some people were like, we're not using it. And it's like, look, this thing
00:58:16.420 --> 00:58:22.420
is at least as good as the people often doing it. And it's an accelerator. And there's that tension
00:58:22.600 --> 00:58:27.820
of people expect, I think because it's a computer, people expect it to be perfect, right? Because
00:58:27.880 --> 00:58:33.100
software is typically deterministic. So if it works once, it's always going to work. And AI isn't like
00:58:33.120 --> 00:58:37.380
that, but people also aren't like that. Yeah, that's right. I mean, like, you know,
00:58:39.000 --> 00:58:43.080
certainly everybody, you know, having an AI by your side is like having a colleague on your team,
00:58:43.400 --> 00:58:47.660
except this colleague can write code like tremendously faster and much better than most
00:58:47.820 --> 00:58:53.460
people. You know, today I would say that Claude Code and codex can write code way faster and way
00:58:53.660 --> 00:58:59.180
better than me, but you still need that kind of strategic view from the human to just make sure
00:58:59.200 --> 00:59:00.640
that you're going on the right path
00:59:00.940 --> 00:59:02.520
because you got to drive the AI
00:59:02.600 --> 00:59:03.600
and you've got to direct it.
00:59:04.530 --> 00:59:05.920
And it's the same with the scientific work.
00:59:06.660 --> 00:59:08.000
So I tell my scientific colleagues,
00:59:08.090 --> 00:59:09.800
you need to make sure that the answers
00:59:09.980 --> 00:59:11.580
that come back pass the smell test.
00:59:11.650 --> 00:59:12.520
You need to make sure that,
00:59:13.300 --> 00:59:14.440
you know, the numbers that you're seeing
00:59:14.510 --> 00:59:15.980
are in line with your expectations.
00:59:16.280 --> 00:59:17.000
You're expecting a number
00:59:17.110 --> 00:59:18.080
in this magnitude range
00:59:18.150 --> 00:59:18.820
and you get it there
00:59:19.160 --> 00:59:20.480
because sometimes the AI goes off
00:59:20.480 --> 00:59:21.460
and does funny things, right?
00:59:21.720 --> 00:59:23.000
Yeah, I just, you know,
00:59:23.140 --> 00:59:24.600
if you ask it to verify everything
00:59:24.880 --> 00:59:26.940
and prove it and write code to back it up,
00:59:27.090 --> 00:59:29.160
like it's better than if you just ask it
00:59:29.180 --> 00:59:29.780
You know what I mean?
00:59:29.940 --> 00:59:31.500
Like there's techniques,
00:59:31.730 --> 00:59:32.700
but you do have to treat it
00:59:32.780 --> 00:59:33.820
with a little bit of skepticism.
00:59:34.220 --> 00:59:35.540
But that's also true for your colleagues
00:59:35.960 --> 00:59:38.140
and your grad students and whatever, right?
00:59:38.400 --> 00:59:40.440
Like no professor would just take a,
00:59:40.770 --> 00:59:42.560
like, hey, grad student, write the paper.
00:59:42.700 --> 00:59:43.440
And they don't even read it.
00:59:43.440 --> 00:59:43.920
They just publish,
00:59:44.090 --> 00:59:46.160
they just send it off to nature or medicine
00:59:46.290 --> 00:59:48.960
or write the medical journal or whatever.
00:59:49.400 --> 00:59:50.100
I mean, it depends.
00:59:50.230 --> 00:59:51.520
I think for disposable stuff,
00:59:51.680 --> 00:59:53.600
you can let the AI do more of that.
00:59:53.780 --> 00:59:55.300
But for the real critical workflows,
00:59:55.540 --> 00:59:57.260
you want to make sure every single step is correct.
00:59:57.580 --> 00:59:57.680
Yeah.
00:59:58.080 --> 00:59:58.180
All right.
00:59:58.260 --> 01:00:00.780
Well, that brings us to our final call to action.
01:00:01.420 --> 01:00:05.440
If scientific researchers or medical researchers are out there listening,
01:00:06.100 --> 01:00:07.800
either what can they learn from your experience
01:00:08.180 --> 01:00:11.860
or if they wanted to work with you on some of this, what would you say?
01:00:12.180 --> 01:00:15.220
So one of the big questions and why we're putting Foundry out there
01:00:15.220 --> 01:00:16.940
is we don't know how generalizable it is
01:00:17.340 --> 01:00:21.520
or whether it's just going to be hyper-personal software for ourselves, our team.
01:00:21.700 --> 01:00:25.000
But I think the general principles, I've shared them widely.
01:00:25.320 --> 01:00:28.140
So please feel free to just take the idea and run with it.
01:00:28.240 --> 01:00:31.500
I'm very excited to see what other people can do with the ideas.
01:00:32.150 --> 01:00:35.420
And perhaps within their own institutions, they might want to build their own systems.
01:00:35.670 --> 01:00:36.780
And I think that's absolutely valid.
01:00:37.440 --> 01:00:42.160
But if you do want to explore a partnership with us, come and visit us on foundry120.com
01:00:42.190 --> 01:00:42.940
and drop me an email.
01:00:43.620 --> 01:00:43.940
Yeah, great.
01:00:44.080 --> 01:00:46.260
And I'll link to your web page.
01:00:46.430 --> 01:00:49.740
You've got email and your GitHub and other ways to get in touch with you there.
01:00:49.890 --> 01:00:53.240
You also have a lot of interesting writing here, so people can check that out.
01:00:53.520 --> 01:00:56.160
Yeah, that was my journey into coding, really.
01:00:57.420 --> 01:01:00.020
Yeah, we all have one of those journeys to tell the story of.
01:01:00.450 --> 01:01:02.140
Well, Sean, thank you so much for being on the show.
01:01:02.620 --> 01:01:03.260
Keep up the good work.
01:01:03.350 --> 01:01:04.840
I think this is a super interesting project.
01:01:05.060 --> 01:01:05.740
Thanks very much, Michael.
01:01:06.110 --> 01:01:08.340
Very happy to be here, and thanks for the invitation.
01:01:08.740 --> 01:01:09.380
Yeah, you bet. Bye.
01:01:09.540 --> 01:01:09.660
Bye.
01:01:11.099 --> 01:01:13.480
This has been another episode of Talk Python To Me.
01:01:13.860 --> 01:01:14.580
Thank you to our sponsors.
01:01:14.810 --> 01:01:16.120
Be sure to check out what they're offering.
01:01:16.290 --> 01:01:17.640
It really helps support the show.
01:01:18.420 --> 01:01:19.960
This episode is brought to you by Sentry.
01:01:20.760 --> 01:01:23.760
You know Sentry for the air monitoring, but they now have logs too.
01:01:24.050 --> 01:01:26.780
And with Sentry, your logs become way more usable.
01:01:27.200 --> 01:01:31.140
interleaving into your error reports to enhance debugging and understanding.
01:01:31.760 --> 01:01:34.880
Get started today at talkpython.fm/sentry.
01:01:35.500 --> 01:01:38.380
And it's also brought to you by Talk Python Courses.
01:01:38.920 --> 01:01:41.220
Course completion certificates are now live.
01:01:41.400 --> 01:01:45.640
If you finished a course, there's a certificate waiting for you on your account page right now.
01:01:46.140 --> 01:01:49.720
Download it as a PDF or add it to your LinkedIn profile with one click
01:01:50.310 --> 01:01:51.860
under licenses and certifications.
01:01:52.560 --> 01:01:53.740
Same section as your formal degrees.
01:01:54.960 --> 01:01:58.800
Visit training.Talk Python.fm slash account to see what you've already earned.
01:01:59.600 --> 01:02:02.920
And if you're not already subscribed to the show on your favorite podcast player,
01:02:03.580 --> 01:02:04.220
what are you waiting for?
01:02:05.000 --> 01:02:06.660
Just search for Python in your podcast player.
01:02:06.760 --> 01:02:07.640
We should be right at the top.
01:02:08.060 --> 01:02:10.900
If you enjoyed that geeky rap song, you can download the full track.
01:02:11.040 --> 01:02:12.880
The link is actually in your podcast blur show notes.
01:02:13.780 --> 01:02:15.100
This is your host, Michael Kennedy.
01:02:15.400 --> 01:02:16.560
Thank you so much for listening.
01:02:16.780 --> 01:02:17.540
I really appreciate it.
01:02:18.000 --> 01:02:18.700
I'll see you next time.
01:02:30.340 --> 01:02:33.140
I'm out.