Turing Post

Linda Tong, CEO of Webflow, has seen it up close – and she's redesigning the web to meet them.

In this episode, we talk about what it means to build agent-first websites:

How to talk to bots.

How to let them click buttons.

And how to create experiences that work for humans and AI – without turning the internet into garbage.

We cover:

 – When bot traffic started overtaking humans

 – Why AEO (agentic engine optimization) is the new SEO

 – Why websites need a second language – for LLMs

 – What "agent-ready" structure really means

 – Hybrid UX: visual for humans, semantic for agents

 – Why dynamic, personalized web experiences are overdue

 – Leadership, kindness, and Ender’s Game as a design philosophy

This one's fast, nerdy, real, and fun. Linda’s not afraid to challenge old assumptions – or to break her own product if it means building what’s next.

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Guest:

Linda Tong, CEO @Webflow

https://www.linkedin.com/in/lktong/

https://x.com/yaylt

https://webflow.com/

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*Chapters*

0:00 - Introduction

0:43 - The Rise of Non-Human Traffic

1:54 - When Did the Shift to Bot Traffic Start?

2:24 - Good Bots vs. Bad Bots

3:39 - The Emergence of AEO (AI/Agentic Engine Optimization)

5:18 - Building Websites for Agents

6:43 - What Agents Need from a Website

8:55 - Enabling Agents to Take Action

10:04 - The Future of Websites: Dual Human and Agent Interfaces

12:12 - The Vision for a Conversational Webflow

14:19 - Beyond Creation: The Future of Dynamic Web Experiences

18:42 - Is SEO Dead? The Relationship Between SEO and AEO

22:10 - The Impact of AGI on Web Development

24:19 - The Book That Shaped Linda

27:00 - Final Thoughts: The Need for "Kind AI"

Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live.

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We cover:

- When we'll trust AI to run businesses

- What Microsoft learned from early agent deployments

- How AI makes life easier

- The architecture behind GitHub agents (and why guardrails matter)

- Why developer interviews should include AI tools

- Agentic Web, NLweb, and the new AI-native internet

- Teaching kids (and enterprises) how to use powerful AI safely

- Eric’s take on AGI vs “just really useful tools”

If you’re serious about deploying agents in production, this conversation is a blueprint. Eric blends product realism, philosophical clarity, and just enough dad humor. I loved this one.

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Guest:

Eric Boyd, CVP of AI platform at Microsoft

https://www.linkedin.com/in/emboyd/

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Chapters

0:00 The big question: When will we trust AI to run our businesses?

1:28 From code-completions to autonomous agents – the developer lens

2:15 Agent acts like a real dev and succeeds

3:25 AI taking over tedious work

3:32 Building trustworthy AI vs. convincing stakeholders to trust it

4:46 Copilot in the enterprise: early lessons and the guard-rail mindset

6:17 What is Agentic Web?

7:55 Parenting in the AI age

9:41 What counts as AGI?

11:32 How developer roles are already shifting with AI

12:33 Timeline forecast for 2-5 years re

13:33 Opportunities and concerns

15:57 Enterprise hurdles: identity, governance, and data-leak safeguards

16:48 Books that shaped the guest

Turing Post is a newsletter about AI's past, present, and future. We explore how intelligent systems are built – and how they’re changing how we think, work, and live.

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We get into:

- Why "humans as callable functions" is the wrong metaphor – and what to use instead

- What co-agency really means?

- Why some data tasks now take days, not seconds – and what that says about modern AI

- The biggest bottleneck in human-AI teamwork (and it’s not tech)

- The future of benchmarks, the limits of synthetic data, and why it is important to teach humans to distrust AI

- Why AI agents need humans to teach them when not to trust the plan

If you're building agentic systems or care about scalable human-AI workflows, this conversation is packed with hard-won perspective from someone who’s quietly powering some of the most advanced models in production. Olga brings a systems-level view that few others can – and we even nerd out about Foucault’s Pendulum, the power of text, and the underrated role of human judgment in the age of agents.

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Guest:

 Olga Megorskaya, CEO of Toloka

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Chapters

0:00 – Intro: Humans as Callable Functions?

0:33 – Evolving with ML: From Crowd Labeling to Experts

3:10 – The Rise of Deep Domain Tasks and Foundational Models

5:46 – The Next Phase: Agentic Systems and Complex Human Tasks

7:16 – What Is True Co-Agency?

9:00 – Task Planning: When AI Guides the Human

10:39 – The Critical Skill: Knowing When Not to Trust the Model

13:25 – Engineering Limitations vs. Judgment Gaps

15:19 – What Changed Post-ChatGPT?

18:04 – Role of Synthetic vs. Human Data

21:01 – Is Co-Agency a Path to AGI?

25:08 – How To Ensure Safe AI Deployment

27:04 – Benchmarks: Internal, Leaky, and Community-Led

28:59 – The Power of Text: Umberto Eco and AI

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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If you’d like to keep following

Olga and Toloka:

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https://x.com/TolokaAI

Ksenia and Turing Post:

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https://www.linkedin.com/in/ksenia-se

https://huggingface.co/Kseniase

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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That’s it.

Oh yeap, one more thing: Thank you for watching and sharing this video. We truly appreciate you.

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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https://huggingface.co/Kseniase

Edo explains why a vector database needs to be built from the ground (and then rebuilt many times), that storage – not compute – has become the next hardware frontier, and predicts a near‑term future where ingesting a million documents is table stakes for any serious agent. We also touch the thorny issues of truth, contested data, and whether knowledgeable AI is an inevitable waypoint on the road to AGI.

Whether you wrangle embeddings for a living, scout the next infrastructure wave, or simply wonder how machines will keep their facts straight, this conversation will sharpen your view of “memory” in the age of autonomous agents.

Let’s find out when tomorrow’s AI will finally remember what matters.

(CORRECTION: the opening slide introduces Edo Liberty as a co-founder. We apologize for this error: Edo Liberty is the Founder and CEO of Pinecone.)

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Subscribe to the channel.

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That’s all.

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Guest:

Edo Liberty, CEO and founder at Pinecone

Website: https://www.pinecone.io/

Additional Reading:

https://www.turingpost.com/

Chapters

00:00 Intro & The Big Question – When will we give AI true memory?

01:20 Defining AI Memory and Knowledge

02:50 The Current State of Memory Systems in AI

04:35 What’s Missing for “True Memory”?

06:00 Hardware and Software Scaling Challenges

07:45 Contextual Models and Memory-Aware Retrieval

08:55 Query Understanding as a Task, Not a String

10:00 Pinecone’s Full Stack Approach

11:00 Commoditization of Vector Databases?

13:00 When Scale Breaks Your Architecture

15:00 The Rise of Multi-Tenant & Micro-Indexing

17:25 Dynamically Choosing the Right Indexing Method

19:05 Infrastructure for Agentic Workflows

20:15 The Hard Questions: What is Knowledge?

21:55 Truth vs Frequency in AI

22:45 What is “Knowledgeable AI”?

23:35 Is Memory a Path to AGI?

24:40 A Book That Shaped a CEO – *Endurance* by Shackleton

26:45 What Excites or Worries You About AI’s Future?

29:10 Final Thoughts: Sea Change is Here

In Turing Post we love machine learning and AI so deeply that we cover it extensively from all perspectives: past of it, its present, and our joint-future. We explain what happens the way you will understand.

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We talk about Replit's evolving vision, how software agents are already powering real businesses, and why the next billion-dollar startups might be solo founders augmented by AI. Amjad also shares what still stands in the way of fully autonomous agents, how AGI fits into his long-term view, and why open source still matters in the age of AI.

Whether you're a developer, founder, or just AI-curious, this conversation will make you rethink what it means to “build software” in 2025.

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Subscribe to the channel.

Leave a comment if you have something to say.

Like it if you liked it.

That’s all.

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Guest:

Amjad Masad, CEO and co-founder at Replit

Website: https://replit.com/~

Additional Reading:

https://www.turingpost.com/p/amjad

Chapters

00:00 Why Amjad changed his mind about coding

00:55 From code to agents: the next abstraction layer

02:05 Cognitive dissonance and the birth of Replit agents

03:38 Agent V3: toward fully autonomous software developers

04:51 Engineering platforms for long-running agents

05:30 Do agents actually work in 2025?

05:48 Real-world examples: Replit agents in action

06:36 Is Replit still a coding platform?

07:43 Why code generation beats no-code platforms

08:22 Can AI agents really create billionaires?

10:59 Every startup is now an AI startup

12:31 Solo founders and the rise of one-person AI companies

14:00 What Amjad thinks AGI really is

17:46 Replit as a habitat for AI

19:50 Open source tools vs internal no-code systems

21:02 Replit's evolving community vision

22:19 MCP vs A2A: who’s winning the protocol game

23:48 The books that shaped Amjad’s thinking about AI

25:47 What excites Amjad most about an AI-powered future

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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Replit: https://x.com/replit

Ksenia and Turing Post:

Hugging Face: https://huggingface.co/Kseniase

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Linkedin:

TuringPost: https://www.linkedin.com/company/theturingpost

Ksenia: https://www.linkedin.com/in/ksenia-se

She is also super fun to talk to!

We discussed:

– How to empower developers to understand and work with AI

– Lamini's technical approach to AI hallucinations (it's solvable!)

– Why benchmarks ≠ reality

– A notable industry use case and the importance of focusing on objective outputs: Subjective goals confuse it!

– And one of my favourite moments: Sharon crushes two of the hottest topics – agents and RAG. Turns out researchers don’t understand why there’s all this hype around these two.

– We also talked about open-source and its importance.

– And last but not least, Sharon (who teaches millions on Coursera) shared how to fight the lack of knowledge about AI. Her recipe: lower the barrier to entry, help people level up – plus memes!

Please give this video a watch and tell us what you think!

Likes and subscribing to the channel are hugely appreciated.

00:00 Intro & Sharon Zhou’s Early Days in GenAI

01:25 Maternal Instincts for AI Models

02:42 From Classics to Code: Language, Product, and AI

04:30 The Spark Behind Lamini

07:45 Solving Hallucinations at a Technical Level

09:20 Benchmarks That Matter to Enterprises

11:58 Staying Technical as a Founder

13:27 The Agent & RAG Hype: Industry Misconceptions

18:44 Use Cases: From Colgate to Cancer Research

20:07 The Power of Objective Use Cases

22:28 What Comes After Hallucinations?

23:21 Following AI Research (and When It’s Useful)

26:23 Open Source & Model Ownership Philosophy

28:06 Bringing AI Education to Everyone

32:36 AI Natives & Edutainment for the Next Gen

34:18 Outro

Lamini

Website - https://www.lamini.ai

Twitter - https://x.com/laminiai

Sharon Zhou

LinkedIn - https://www.linkedin.com/in/zhousharon/

Twitter - https://x.com/realSharonZhou/

Turing Post

Website - https://www.turingpost.com/

Twitter - https://x.com/TheTuringPost

Ksenia Se (publisher)

LinkedIn - https://www.linkedin.com/in/ksenia-se

Twitter - https://x.com/kseniase_

What needs to happen for natural, free-flowing multilingual conversations to become reality? Mati says: give it two or three years. Watch to learn more!

Guest:

Mati Staniszewski, co-founder and CEO at ElevenLabs

Website: https://elevenlabs.io/

Additional Reading:

https://www.turingpost.com/p/mati

Chapters

0:00 Real-time voice translation

0:11 Language barriers and AI

0:29 Why ElevenLabs started

0:36 Dubbing in Poland

0:45 Preserving emotion in translation

1:06 Tech challenges in real-time translation

1:17 Ideal device setup

2:32 Speaker diarization and emotional nuance

3:04 Speech-to-text to LLM to TTS pipeline

5:51 Concrete examples: healthcare & customer support

7:05 Real-time AI dubbing use cases

8:02 Lex Fridman podcast dubbing challenge

13:01 Audio model performance & latency

14:44 Conversational AI & multimodal future

16:57 Product vs research focus at ElevenLabs

20:42 Why ElevenLabs didn't open source (yet)

21:28 Strategy: creators, enterprises & brand building

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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