137 Podcast

In this episode, we talk to Boaz Adato (Head of Engineering Guild) about how AI is changing engineering, the uneven impact of AI across new and legacy systems, and how workflows and team autonomy are evolving in practice.

🔍 In the episode:
How AI adoption started (and why many engineers rejected it at first)
What actually changed inside teams,. Workflows, roles, and ownership
Why AI works great in greenfield projects but struggles with large codebases
How non-engineers (finance) started building their own tools
Why key engineering metrics (like PR throughput) haven’t improved as expected
Wild guesses for 2026-2027
🧑‍💼Guest: Boaz Adato
🎙️Hosts: Anton Zhuravsky⁠ & ⁠Dmitry Volokh
📍137 Podcast on X: https://x.com/137_podcast

🙌 This podcast is brought to you by On The Spot Development. We help tech companies build R&D teams in Poland and have open roles for engineers.
Check out the relevant opportunities for AI Engineers: https://onthespotdev.com/careers?*=AI

What changes when you move from API demos to production apps (rate limits, multi-cloud, failover strategies)
Security challenges: prompt injection, MCP as remote code execution, supply chain risks, least-privilege agents
How to measure reliability (confidence in AI responses, cost-of-error, online and offline evals)
Why “code is cheap” now and how that’s reshaping engineering roles
"Trustworthy" UX patterns: transparent agent steps, generative UI, ambient agents
The job shifts already happening: juniors struggling to get hired, first-line support and QA under threat
How AI is lowering the barrier to entry in education and what engineers need to stay relevant

🧑‍💼Guest: Eden Marco
🎙️Hosts: Anton Zhuravsky & Dmitry Volokh

The podcast is brought to you by ⁠⁠On The Spot Development⁠⁠, a software development company which Anton founded. They build R&D teams for startups and tech companies.

How modern search is built and where LLMs fit in.
How semantic search architectures are designed and evaluated
Why BERT is still the baseline in many systems
Fine-tuning strategies, training data, and infrastructure trade-offs
What's possible in multimodal, and what usually isn't worth the effort
Choosing between PostgreSQL and vector databases
What often blocks companies from improving search, from training data to infra

🥐 Hosts - Anton Zhuravsky & Dmitry Volokh
☕️ Guest – Roman Grebennikov , who has been building search at Delivery Hero, and has also built his own search engine – Nixieserach.ai

The podcast is brought to you by ⁠On The Spot Development⁠, a software development company which Anton founded. They build R&D teams for startups and tech companies.

How to build & maintain an engineering culture of experimentation and resilience?
We sit down with Roman, CTO of Flo Health, and discuss how to build a high-performing tech culture, including:
🚀 How to win the "best invention" award as a result of engineering response to a privacy challenge.
🛠️ The hiring playbook that brings in 10 engineers in a day (and actually works).
🔥 "Oh no" moments: Handling media storms and sudden relocations.
📊 The science of running 400 experiments per quarter (without setting everything on fire).
🤖 Future: $200kk in RnD, AI (obviously), wearables, and the fine-tuning of everything.Also there – how to hire top engineers, optimize infrastructure costs, and maintain a data-driven culture.

Hosts - Anton Zhuravsky & Dmitry Volokh,
Guest - Roman Bugaev

The podcast is brought to you by On The Spot Development, a software development company which Anton founded. They build R&D teams for startups and tech companies.

Tune in! Episode release this Thursday, on 20.02 🔥

How to build a team that can win Time's "Best Invention Award" and develop an architecture to run 400 Experiments a Month.

Guest – Roman Bugaev, CTO at Flo Health – on leadership principles and architectural decisions.

🛠️ The hiring playbook that brings in 10 engineers in a day (and actually works).
🔥 Culture of resilience as a response to crises: handling media storms and sudden relocations.
📊 The science of running 400 experiments a month (without setting everything on fire).
🤖 Future: $200kk in RnD, AI (obviously), wearables, and the fine-tuning of everything.