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Taste & Craft: A Conversation with Tuomas Artman, CTO Linear & Gergely Orosz, @pragmaticengineer

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AI Engineer· published 2026-04-21· 0:29:17· en· indexed 2026-08-10 23:38

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Scene timeline

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70 shot(s).

keyframes kept every frame deduplicated

What was stored

cues
379
whisperx 379
chunks
51
from 379 cues
keyframes
66
kept of 70 captured
frames with text
66
540 lines read
chapters
11
from the source metadata
keyframe bytes
7.5 MB
word timings on 379 cues

Provenance

Each pipeline stage, its state and the model that produced it
stage state model started took
fetch done — 2026-08-10 23:32 1m 29s
stt done — 2026-08-10 23:34 33s
chunk done — 2026-08-10 23:34 0s
text_embed done — 2026-08-10 23:34 0s
keyframe done — 2026-08-10 23:34 3m 15s
ocr done — 2026-08-10 23:37 16s
frame_embed done — 2026-08-10 23:38 11s

Frames, and what the machine read

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Transcript

379 cues· 5,058 words· 26,911 chars

  1. 0:16 Awesome.
  2. 0:16 So we didn't see it, but hands up if you do use linear.
  3. 0:20 And hands up if you've heard of linear.
  4. 0:22 And hands up if you want to use linear.
  5. 0:25 Awesome.
  6. 0:26 Great to see.
  7. 0:27 So we could be talking about linear, but we're going to be talking about something a bit bigger, which is a bit of a new trend that, with Thomas, we're talking about.
  8. 0:36 Things are trending the wrong way right now.
  9. 0:40 What is trending the wrong way?
  10. 0:46 What happens when agents are capable of doing everything immediately for you?
  11. 0:52 The fact that might be that the pendulum has swung too far into the wrong direction, where if you get a feature request, you might now be in the position to just immediately ship it.
  12. 1:04 And that might be the wrong thing to do.
  13. 1:06 And I reckon that hopefully, half a year from now or a year from now, we'll understand that shipping things without really too much thinking is a bad thing.
  14. 1:16 What will happen is that because you have this enormous power of effectively just shipping every single request that comes in or every single thing that pops into your head, you will effectively ship a software that is not great.
  15. 1:31 Steve Jobs back in the day said that great products come out of saying no to 999 things and yes to one thing.
  16. 1:38 With AI, we might be in a place where it's just too easy to say yes and try things out and ship it and get to a very convoluted place where software actually doesn't work for that customer.
  17. 1:49 nicely anymore, or that the user experience gets confusing.
  18. 1:53 We used to previously have this thing that gated us from doing this, which was the actual engineering used to be hard.
  19. 2:01 So we used to think about these features and these applications that we wanted to build before we actually started engineering, because engineering was such a waste of time, and it took a long time to ship something.
  20. 2:13 So, yeah.
  21. 2:16 But I want to challenge you a little bit on that.
  22. 2:18 Did we not see this happen before, before AI, that some companies were already just like shipping, putting on a bunch of features and stacking?
  23. 2:25 And what are you seeing different right now?
  24. 2:29 Are you actually, are we actually seeing more companies, you know, do more of this like, I don't know, like feature factoring thing?
  25. 2:36 We had a common experience at Uber, where we worked together, where we went through hyper growth.
  26. 2:42 And the thing about Uber was that it was a winner takes it all market.
  27. 2:44 And Uber was going against Lyft back in the day in the US.
  28. 2:48 And you just had to ship immensely and just outpace the competition at all costs.
  29. 2:56 And what I saw at Uber was that hyper growth that I never want to go through again, which was like at all costs, just fighting fires, keeping the infrastructure running, scaling as quickly as possible, trying out everything and trying to come out as a winner in that front.
  30. 3:12 And I see the analogy to AI nowadays because
  31. 3:16 When everybody has the capability of shipping tons of functionalities, you always are in a competition with somebody else.
  32. 3:23 Your competition might be a small team or even one person that is very capable of using AI to ship and build a product that has the same features as you do.
  33. 3:35 And in that world, I think it becomes important to stand out in a way where you build tasteful software.
  34. 3:42 and where you build high quality software and thus maintain some sort of competitive advantage towards your competition.
  35. 3:52 So at Linear, even before AI came out, you were building tasteful software and focusing on those things.
  36. 3:59 But then these tools came out and they became more powerful, specifically since Cloud Code came out.
  37. 4:04 Now we have Opus 4.5.
  38. 4:07 You should be able to ship faster.
  39. 4:09 Your engineering team, your CTO, your engineering team should be able to ship a lot faster.
  40. 4:14 What are you telling them?
  41. 4:16 What should they be doing inside of Linear with this capability?
  42. 4:20 Should they be slowing down?
  43. 4:22 No, right?
  44. 4:23 What's going on inside of Linear?
  45. 4:24 Tell us.
  46. 4:25 Well, yes and no.
  47. 4:26 We still think about every single feature that we put out.
  48. 4:31 We don't go down the route of just trying our prototypes.
  49. 4:34 We want to maintain that design angle that we have and think about the user experience.
  50. 4:40 Still say no to a lot of custom requests.

Chapters

  1. 0:00 Introduction
  2. 0:36 The danger of shipping features too quickly with AI
  3. 3:52 How Linear approaches feature requests and development
  4. 6:43 Thoughts on Anthropic's Claude Code
  5. 7:59 The challenge of measuring software quality
  6. 11:57 Quality Wednesdays at Linear
  7. 16:24 The zero bug policy explained
  8. 19:44 AI agents and the lack of human "taste" in design
  9. 22:21 Building a culture of product-focused engineering
  10. 26:23 The future role of software engineers as "product engineers"
  11. 27:56 Closing advice for aspiring product engineers

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