Does AI save time at work? Oksana and Konstantin compare repeatable tasks with code that still needs engineering, and ask why faster tools can leave them working longer.

1. 00:00 The promise of more time
2. 00:59 What AI can actually replace
3. 04:45 The engineering cost of generated code
4. 10:49 The repeatability test
5. 12:02 How to spot the bubble
6. 20:28 The cost of keeping up
7. 23:19 Why faster tools can make us busier

Watch the episode · YouTube · 26 min

Oksana tells the story of starting ANODA, splitting responsibilities with a business partner, growing the team and learning when a founder needs to set boundaries.

1. 00:00 The introvert behind ANODA
2. 00:19 Why this conversation matters
3. 00:40 Writing code at five
4. 02:48 The first clients and companies
5. 09:16 The cab-driver wake-up call
6. 12:19 From CEO to employee
7. 17:39 How ANODA began
8. 21:32 Shipmate and the unexpected spotlight
9. 26:04 Splitting the founding roles
10. 29:58 Growing the team and surviving lockdown
11. 32:53 Partners, boundaries and the business prenup
12. 41:08 Clients, product design and measurable results
13. 44:59 AI still needs your judgment
14. 46:30 What comes next

Watch the episode · YouTube · 47 min

Which workflows are worth automating? Oksana and Konstantin discuss process ownership, reports nobody reads and how to tell whether automation improves a result.

1. 00:00 Paying for results, not time
2. 00:18 Who owns the process?
3. 02:21 Years of experience versus seniority
4. 05:14 Building without a clear product need
5. 06:34 Automation from the developer’s side
6. 07:05 The automation joke: goals and tradeoffs
7. 09:11 Management, responsibility and risk
8. 13:14 Could voice replace interfaces?
9. 17:11 Changing team workflows with AI
10. 20:10 Reports nobody reads
11. 21:17 Initiative and useful feedback
12. 24:26 Reliable delivery with little initiative
13. 26:29 Management and communication
14. 27:43 What does value mean?
15. 28:09 Final thoughts

Watch the episode · YouTube · 29 min

Oksana and Konstantin ask what a design team should deliver when AI can produce average work, from client judgment and useful feedback to taking responsibility for a result.

1. 00:00 Can you beat average AI output?
2. 00:16 AI, teams and results
3. 01:29 Rethinking the design team
4. 06:39 Value beyond the task list
5. 07:59 AI as industrialization
6. 11:08 Average output and prompt limits
7. 14:07 Clients, delivery and human skills
8. 16:06 Vibe coding and the pull of automation
9. 20:38 Scrum needs responsibility
10. 24:03 Companies pay for results
11. 27:48 Interview skills versus real work
12. 29:57 Laziness and automation

Watch the episode · YouTube · 32 min

What does it cost to build an AI product beyond the prompts? The hosts discuss token budgets, running models locally and the work of turning a prototype into a product people can trust.

1. 00:00 Meet Oksana & Kostya
2. 00:31 The real AI bill
3. 02:45 A week's AI tokens, gone in a day
4. 03:18 Are local AI models actually cheaper?
5. 07:11 The AI slop problem
6. 10:26 AI content: facts vs repetition
7. 11:58 LinkedIn: bots talking to bots
8. 16:51 MVP vs a real product
9. 22:27 Your prototype is going to production
10. 24:05 Quality, budgets & earning trust
11. 26:26 Building the product is only the beginning
12. 32:26 Final thoughts & subscribe

Watch the episode · YouTube · 33 min

When a coding agent changes login while fixing padding, who catches it? Oksana and Konstantin discuss scope, human oversight and accountability for generated code.

1. 00:00 Meet Oksana & Kostya
2. 00:31 Good code vs a product that works
3. 05:12 AI mistakes: data leaks & broken systems
4. 06:25 Human oversight still matters
5. 09:51 Why local AI feels disappointing
6. 12:32 Context, instructions & wrong expectations
7. 17:02 Asked for padding—AI hacked the login
8. 17:42 Who pays for AI mistakes?
9. 23:15 "Legacy" code written five minutes ago
10. 26:43 Are engineers becoming managers?
11. 31:16 She thought GitHub was angry
12. 32:35 Final thoughts & subscribe

Watch the episode · YouTube · 33 min