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TThe Pragmatic Engineer Podcast

The Pragmatic Engineer Podcast sponsors and ad reads

Apple #1905
Host: Gergely Orosz
Publisher: Gergely Orosz

Software engineering at Big Tech and startups, from the inside. Deepdives with experienced engineers and tech professionals who share their hard-earned lessons, interesting stories and advice they have on building software. Especially relevant for software engineers and engineering leaders: useful for those working in tech. newsletter.pragmaticengineer.com

~$1.6K
Estimated 30d gross ad earnings
$263–$11K · 4/4 reads
Jul 13–Aug 11 · episodes published
All tracked~$6.2K
$982–$42K · 18/18 reads
5
Unique Sponsors
54s
Avg promo duration
2%
Ad load (% runtime, last 5 eps)
Aug 4
Most recent

Who sponsors The Pragmatic Engineer Podcast?

5 brands

The Pragmatic Engineer Podcast has 18 quality-qualified public sponsor reads from 5 brands in AdReads, including Antithesis, TurboPuffer, and Sentry. The most recent detected sponsor was TurboPuffer on Aug 4. This is an observed dataset, not a complete advertiser roster.

Sponsor changes over time

Last 3 months
Jun 2026
5 reads
3
sponsors
AAntithesisAntithesis2
SSentrySentry2

Recent reads

Grouped by episode and date · showing 1–18 of 18
+Follow Podcast
BBuildkite
Buildkite
1
Jul 2026
10 reads
5
sponsors
AAntithesisAntithesis5
TTurboPufferTurboPuffer2
BBuildkiteBuildkite1
Aug 2026
3 reads
3
sponsors
AAntithesisAntithesis1
TTurboPufferTurboPuffer1
WWorkOSWorkOS1
Formal methods with Hillel Wayne
Episode date Jul 29
3 reads
AAntithesis
Antithesis1:08-2:13
This is where I need to mention our presenting sponsor, Antisys. Antisysys verifies your system of correctness by running your whole system in hostile simulation and finding bugs. It does this by using an approach called deterministic simulation testing, or DST, which ADW's distinguished engineer Mark Brooker and Ankur Desai have described as lightweight formal methods. Setting aside Intisys for a minute, if you as an engineer want to get more serious in verifying that your system works as intended, your best bet would be to use lightweight formal methods. Now back to Intisys. Intisys turbocharges testing by running your whole system under aggressive fault injection. Imagine Intisysys as hundreds or thousands of versions of the Mario game running, each instance aggressively trying to break the game with increasingly weird input combinations. With Antithesis, you can specify properties at the whole system level, and Antithesis will actively try to disprove them. So you can be confident that if your system holds up in Antithesis, it will hold up in production. There's good reason teams like Janestreet, Fly.io, and the etcd community rely on Antithesis. Head to antithesis.com slash pragmatic to learn more.
mid-rollno codeDetected Aug 4
Est. value ~$450 · range $72–$3K
WWorkOS
WorkOS26:15-26:45
And this is where I need to mention our seasoned sponsor, WorkOS. If you're building any SaaS, especially an AI product, you'll need Auth for apps and agents. This is the layer where close enough is just not good enough. So don't let this layer get improvised by AI. WorkOS gives you the proven implementation, SSO, skim, and fine-grade authorization built for how agents operate and in a way that's easy for them to integrate with an implementation that you can trust. Check it out at workwise.com.
mid-rollno codeDetected Aug 4
Est. value ~$330 · range $54–$2.3K
TTurboPuffer
TurboPuffer26:41-28:02
I also want to talk about our season sponsor, Turbo Puffer. But this time, I don't want to talk about how they are fast, cheap, and extremely scalable search engine built on object storage. Instead, I'd like to talk about their team. I interviewed Simon, the co-founder and CEO on stage at AI Injury World Fair, and also hung out with their team for a few days in person. Here's a few of the interesting things I learned about them. The company is full remote yet feels pretty connected. They have a Slack-first culture. For example, all of their customers have a dedicated Slack channel and engineers are in these channels seeing feedback from these customers, often fixing their bugs. The team gets together for annual summits at least twice a year and campfires form several times a month anytime several remote employees gather in the same city. Simon describes their engineering culture as hardcore and whimsical. They focus on solving difficult problems but also try to have fun. A good example is the Pragmatic Engineering landing page that they built. We agreed to have a custom landing page and then their team decided to build a cool logo that animates on mouse movement. Another interesting thing is their team composition. Pretty much everyone currently working at the company has 10-15 years of experience. For a startup, they are an unusually seasoned team. Finally, I really appreciate how pragmatic their engineering philosophy is. Simon and the team strongly believe in how simplicity scales and this is the reason that object storage is TurboPuffer's only dependency. The team do seemingly silly things like build their job queue in a single file on object storage because they understand their core primitives and they know how they scale. To check out the whimsical animation, or if you're building AI products, head to turbopuffer.com slash pragmatic.
Context engineering with Dex Horthy
Episode date Jul 15
1 read
BBuildkite
Buildkite1:14-2:28
Today's episode is brought to you by Buildkite, the CI orchestration platform trusted by OpenAI, Entropic, Cursor, NVIDIA, Uber, Canva, and more. Today we're talking about pushing the right context into models so that they write better code. Right after that starts working, your agents will write more code, a lot more. Trusting that code avalanche is where many teams face the challenge today. Every change that an agent makes still has to be built, tested, and proven safe before it ships. worked on my machine is not enough, so you obviously need CI. But when agents are pushing 5, 10, or 50 times the commit volume to your pipelines, faster CI runners won't save you. Shaving 30 seconds off a single build is meaningless when a queue is 100 plus jobs deep. What you really want is a CI system that gets faster as the volume grows and CI that offers instant parallelization to give you unlimited concurrency and to intelligently route changes at runtime. This is what BuildKite does and why global software leaders continue to rely on it. The same architecture that observed the scale of Shopify and Uber a decade ago now runs about 1.4 billion job minutes a week across Cursor, Meta, Reddit, and Snowflake. While the rest of the CI world are cracking under the weight of re-architecting their platform, BuildKite continues to reliably grow. Agents running on your infrastructure are BuildKite, Any cloud, any chip, your secrets, your skill. Every artifact and log is captured, so when something fails, either you or your agents have immediate insight for why. As you're engineering the context you'll give to your agents, think about how you'll verify what they hand back. If your system is buckling under the increased volume, head to buildkite.com slash pragmatic. 30-day all-access trial, no credit card, and an actual human engineer on standby. His name's Ola, and he's very helpful.
The Pragmatic Engineer AMA
Episode date Jul 8
2 reads
AAntithesis
Antithesis0:22-0:37
Thanks to Antithesis for being our presenting sponsor. With Antithesis, you can verify your system's correctness without human review or traditional interrogation tests and avoid bugs or outages. With this, let's jump in.
pre-rollno codeDetected Jul 10
Est. value ~$300 · range $49–$2K
How Kent Beck shapes the software engineering industry
Episode date Jul 1
4 reads
AAntithesis
Antithesis0:48-1:03
This episode is presented by Antithesis. If you work with agents, your job is no longer just writing code, it's specifying and testing it. Antithesis is the most effective method of verifying agentic code today.
pre-rollno codeDetected Jul 1
Est. value ~$300 · range $49–$2K
Tech interviews with NeetCode
Episode date Jun 24
3 reads
AAntithesis
Antithesis0:35-0:54
This episode is presented by Antithesis. Antithesis runs your whole system in a hostile simulation and finds every bug before your users do. It sounds like science fiction, but it's actually hardcore engineering. Understand how at antithesis.com/pragmatic.
pre-rollno codeDetected Jul 12
Est. value ~$300 · range $49–$2K
Kubernetes and retiring at the top with Kelsey Hightower
Episode date Jun 3
3 reads
AAntithesis
Antithesis1:08-2:34
pre-rollno codeDetected Jun 5
Est. value ~$220 · range $32–$1.5K
Why Rust is different, with Alice Ryhl
Episode date May 20
2 reads
SSentry
Sentry0:49-1:43
pre-rollno codeDetected Jun 1
Est. value ~$220 · range $32–$1.5K

Sponsor history

Brands with quality-qualified reads detected on The Pragmatic Engineer Podcast
AAntithesis
Antithesis8 reads
SaaS
No offer found
mid-rollno codeLatest Aug 4
TTurboPuffer
TurboPuffer3 reads
Artificial Intelligence
No offer found
mid-rollno codeLatest Aug 4
SSentry
Sentry3 reads
SaaS
No offer found
pre-rollno codeLatest Jul 12
WWorkOS
WorkOS2 reads
SaaS
No offer found
mid-rollno codeLatest Aug 4
BBuildkite
Buildkite2 reads
SaaS
30-day all-access trial, no credit card
mid-rollno codeLatest Jul 18
mid-rollno codeDetected Aug 4
Est. value ~$450 · range $72–$3K
pre-rollno codeDetected Jul 18
Est. value ~$410 · range $65–$2.7K
AAntithesis
Antithesis34:10-35:41
Can you share something about today's presenting sponsor? What? Is this really the question that people are asking? No, this was actually not submitted by anyone, but I still want to talk about it. Now, I admit this was the one question I sneaked in because I really wanted to share something visually interesting about our presenting sponsor, Antithesis. It's how different their UI is. Let me show you with three examples. We already know that Antithesis verifies your system's correctness by running your whole system in hostile simulation and finding bugs. Here's the UI for casualty analysis. You can open a report for a bug and see the probability of a bug occurring throughout the timeline of the simulation. In this case, we can see that at virtual time 25, something happened that makes this bug close to 100% to occur. So we can jump into this point in the virtual timeline simulation to read the logs. This kind of bug probability visualization is one that I've just not seen before. There's also this neat log explorer. You can filter on error messages and then visualize how common or uncommon the error is over time. For example, here we're looking for failing linearization failures, the purple line. And you can understand how rare or common a specific failure was. Again, I've yet to see this kind of error visualization and I really like the innovation on the UI here. And finally, the multiverse debugger. You can go back in time and replay a debug timeline. And you can inject bash commands at any time without affecting the playback of the bug. How cool is that? For example, here we're listing files in a current directory, but as you can imagine, you can debug the whole environment much easier. I really like how the team at Antithesis are pushing what's possible with both debugging and verifying software. Head to antithesis.com/pragmatic to learn more.
mid-rollno codeDetected Jul 10
Est. value ~$450 · range $72–$3K
TTurboPuffer
TurboPuffer1:00-1:59
This episode is brought to you by TurboPuffer. A fun fact about TurboPuffer is how they became the search engine for AI agents. It seems like every week, they add a new use case from one of the leading AI companies. Ramp, Legora, Harvey, Granola, they're all on TurboPuffer. Or as the team likes to say it, they're all Puffin. TurboPuffer offers a full set of search tools, vector search, full text search, attribute filtering, regex, and more in one API. It's built on object storage as a sole stateful dependency, so it's extremely scalable, reliable, and cheap. But it's also very fast thanks to its intelligent caching layer. One cool thing about TurboPuffer is you can have unlimited search indexes, so it's perfect for multi-tenant AI applications. Build an index per tenant, data is isolated by default, and you can scale without thinking about it. Here's the best part: TurboPuffer just announced last week that they've dropped their base price from $64 per month to $16 per month, so there has never been a better time to test it out. Head to turbobuffer.com/pragmatic.
mid-rollno codeDetected Jul 1
Est. value ~$450 · range $72–$3K
TTurboPuffer
TurboPuffer1:00-1:51
This episode is brought to you by TurboPuffer. TurboPuffer is becoming the search engine for AI agents. Every week, they add a new use case from one of the leading AI companies. Ramp, Legora, Harvey, Granola — they're all on TurboPuffer, or as the team likes to say, they're all Puffin. TurboPuffer offers a full set of search tools: vector search, full text search, attribute filtering, regex, and more in one API. It's built on object storage as a sole stateful dependency, so it's extremely scalable, reliable, and cheap — but also very fast thanks to its intelligent caching layer. One cool thing about TurboPuffer is you can have unlimited search indexes, making it perfect for multi-tenant AI applications. Build an index per tenant, data is isolated by default, and you can scale without thinking about it.
pre-rollno codeDetected Jul 1
Est. value ~$410 · range $65–$2.7K
WWorkOS
WorkOS82:50-83:22
Building on solid primitives is exactly what our seasonal sponsor, WorkOS, is all about. If you're building any SaaS, especially an AI product, sooner or later you'll need to build enterprise features — things like auth for apps and agents. WorkOS handles it: SSO, SCIM, fine-grained authorization built for how agents actually operate. The fastest growing AI companies — Anthropic, OpenAI, Cursor, and Perplexity — already trust WorkOS to solve these problems. Check out workos.com.
mid-rollno codeDetected Jul 1
Est. value ~$330 · range $54–$2.3K
SSentry
Sentry0:50-1:49
This episode is brought to you by Sentry. Sentry is a tool I use for application monitoring back when I worked at Uber, and I now use it on all my projects, including the Pragmatic Engineering backend. One new feature I'm really liking about Sentry is their SEER AI agent, which helps investigate production errors. For example, here's an actual error I had in my application. I can just ask here what might be the root cause, and it brings context, and it can also make a plan to fix it, all from the web interface. Oh, and it also works in Slack as well, not just the web. One place that I find even more handy to use Sentry is from codex or clock code using Sentry MCP. After you hook up the MCP server, you can do some very useful things. For example, when an already resolved Sentry is your resurfaces, you can kick off an agent to investigate the regression, read the relevant code, and open a PR with a suggested fix. There's a little work involved to get all this going. You need to connect Sentry to your code repository, add Sentry MCB to a cursor, define the instruction for cursor's agent to investigate, configure the trigger that launches the automation, and test that it all works. But once you have it up and running, you can get regressions fixed faster while still reviewing every and all fixes. I'm not a fan of using AI tools just for the sake of it, but I really like the practical integrations where I can fix errors faster and with more context. Check out Sentry at sentry.io slash pragmatic and start monitoring and fixing regressions today.
pre-rollno codeDetected Jul 12
Est. value ~$410 · range $65–$2.7K
AAntithesis
Antithesis34:15-35:12
I also want to mention our presenting sponsor, Antithesis. Neat talked about memory leaks in a service that he knows about, but just doesn't have the time to chase it down. If you work on distributed systems, you've been there. You know there are deep bugs there, you have no way to root cause them, so you can only hope there's nothing really catastrophic. With antithesis, you no longer have to rely on hope. Antithesis lets you fix and find all sorts of bugs easily and efficiently, so you no longer have to choose between shipping quality and shipping fast. Let me explain how. Antithesis runs your whole system in a hostile simulation. By doing so, it finds every bug before your users do. And because the simulation is fully deterministic, Antithesis doesn't only find bugs, it gives you a perfect reproduction of every issue. I know this sounds closer to science fiction, but it's actually hardcore engineering under the hood. Janestreet, Flydata.io and the etcd community ship agent written code with full confidence because they know it's been verified by antithesis. To see more case studies and details, head to antithesis.com slash pragmatic. That's antithesis.com slash pragmatic.
mid-rollno codeDetected Jul 12
Est. value ~$450 · range $72–$3K
B
Buildkite
Buildkite43:04-44:07
mid-rollno codeDetected Jun 5
Est. value ~$240 · range $36–$1.7K
SSentry
Sentry44:41-45:51
mid-rollno codeDetected Jun 5
Est. value ~$240 · range $36–$1.7K
A
Antithesis
Antithesis15:37-16:35
mid-rollno codeDetected Jun 1
Est. value ~$240 · range $36–$1.7K

What sponsors are featured on similar podcasts?

5 brands

Sponsors detected on similar podcasts but not in the current AdReads record for The Pragmatic Engineer Podcast

GGrainger
Grainger
Detected on Club Shay Shay
VVanta
Vanta
Detected on Invest Like the Best with Patrick O'Shaughnessy
BBoost Mobile
Boost Mobile
Detected on The Herd with Colin Cowherd
MMcDonald's
McDonald's
Detected on The Herd with Colin Cowherd
AAmerican Military University
American Military University
Detected on Club Shay Shay