Inference by Turing Post
Inference by Turing Post
Podcast Description
Inference is Turing Post’s way of asking the big questions about AI — and refusing easy answers. Each episode starts with a simple prompt: “When will we…?” – and follows it wherever it leads.Host Ksenia Se sits down with the people shaping the future firsthand: researchers, founders, engineers, and entrepreneurs. The conversations are candid, sharp, and sometimes surprising – less about polished visions, more about the real work happening behind the scenes.It’s called Inference for a reason: opinions are great, but we want to connect the dots – between research breakthroughs, business moves, technical hurdles, and shifting ambitions.If you’re tired of vague futurism and ready for real conversations about what’s coming (and what’s not), this is your feed. Join us – and draw your own inference.
Podcast Insights
Content Themes
The podcast explores themes centered on AI development, coding philosophy, and AI challenges. Key episodes include discussions on the future of coding with Amjad Masad reflecting on AI agents and their role in software development, Sharon Zhou dissecting AI hallucinations and the importance of grounding benchmarks in reality, and Mati Staniszewski tackling real-time language translation and maintaining emotional nuance in AI voice synthesis, illustrating a commitment to thoughtful, nuanced explorations of AI's impact on society.
Inference is Turing Post’s way of asking the big questions about AI — and refusing easy answers. Each episode starts with a simple prompt: “When will we…?” – and follows it wherever it leads.
Host Ksenia Se sits down with the people shaping the future firsthand: researchers, founders, engineers, and entrepreneurs. The conversations are candid, sharp, and sometimes surprising – less about polished visions, more about the real work happening behind the scenes.
It’s called Inference for a reason: opinions are great, but we want to connect the dots – between research breakthroughs, business moves, technical hurdles, and shifting ambitions.
If you’re tired of vague futurism and ready for real conversations about what’s coming (and what’s not), this is your feed. Join us – and draw your own inference.
What it actually takes to build models that improve over time. In this episode, I sit down with Devvret Rishi, CEO and co-founder of Predibase, to talk about the shift from static models to continuous learning loops, the rise of reinforcement fine-tuning (RFT), and why the real future of enterprise AI isn’t chatty generalists – it’s focused, specialized agents that get the job done.
We cover:
The real meaning behind “train once, learn forever”
How RFT works (and why it might replace traditional fine-tuning)
What makes inference so hard in production
Open-source model gaps—and why evaluation is still mostly vibes
Dev’s take on agentic workflows, intelligent inference, and the road ahead
If you’re building with LLMs, this conversation is packed with hard-earned insights from someone who’s doing the work – and shipping real systems. Dev is super structural! I really enjoyed this conversation.
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Guest:
Devvret Rishi, co-founder and CEO at Predibase
https://predibase.com/
If you don’t see a transcript, subscribe to receive our edited conversation as a newsletter: https://www.turingpost.com/subscribe
Chapters:
00:00 – Intro
00:07 – When Will We Train Once and Learn Forever?
01:04 – Reinforcement Fine-Tuning (RFT): What It Is and Why It Matters
03:37 – Continuous Feedback Loops in Production
04:38 – What’s Blocking Companies From Adopting Feedback Loops?
05:40 – Upcoming Features at Predibase
06:11 – Agentic Workflows: Definition and Challenges
08:08 – Lessons From Google Assistant and Agent Design
08:27 – Balancing Product and Research in a Fast-Moving Space
10:18 – Pivoting After the ChatGPT Moment
12:53 – The Rise of Narrow AI Use Cases
14:53 – Strategic Planning in a Shifting Landscape
16:51 – Why Inference Gets Hard at Scale
20:06 – Intelligent Inference: The Next Evolution
20:41 – Gaps in the Open Source AI Stack
22:06 – How Companies Actually Evaluate LLMs
23:48 – Open Source vs. Closed Source Reasoning
25:03 – Dev’s Perspective on AGI
26:55 – Hype vs. Real Value in AI
30:25 – How Startups Are Redefining AI Development
30:39 – Book That Shaped Dev’s Thinking
31:53 – Is Predibase a Happy Organization?
32:25 – Closing Thoughts
Turing Post is a newsletter about AI’s past, present, and future. Publisher Ksenia Semenova explores how intelligent systems are built – and how they’re changing how we think, work, and live.
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Devvret and Predibase:
https://devinthedetail.substack.com/
https://www.linkedin.com/company/predibase/
Ksenia and Turing Post:
https://x.com/TheTuringPost
https://www.linkedin.com/in/ksenia-se
https://huggingface.co/Kseniase
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