SaaStr 858: Feature Differentiation Is Dead. Here's What Actually Wins Now with Lovable's Elena Verna
Feature Differentiation Is Dead. Here's What Actually Wins Now.
When AI writes 80-plus percent of your code, the feature advantage you spent years building can be replicated in a day. Elena knows this better than most - she spent 15 years running growth at Dropbox, Miro, SurveyMonkey, and Amplitude, then joined Lovable and watched the old playbook stop working in real time. At Lovable, $400M ARR and 200 people, no titles, shipping multiple times a day, the rules are different. In this session, she breaks down what replaced feature moats, why she fired herself from her own VP job to go back to being an IC, and what it actually looks like to run a company at this velocity.
You'll learn:
Which moats still hold - network effects, data, brand, security and compliance - and why hardware is harder to copy than software ever was
Why freemium is now a marketing budget line item, not a cost problem, and how Lovable's LinkedIn Premium partnership is converting at double digits
What "no titles, everyone ships" looks like in practice, including a 20-year-old engineer pushing back on a VP's pricing page PR
Why the next career flex isn't climbing to VP - it's becoming a high-power IC who builds what used to take a team of dozens
How to build context for your AI so it actually replicates your thinking instead of producing average output for everyone
This is for you if:
You're a founder or growth leader trying to figure out what your actual moat is when feature differentiation keeps evaporating
You're in management and quietly wondering if you'd be better off getting your hands dirty again
You're trying to understand how an AI-native company actually operates day to day, not just in theory
Agentic Architecture: Why Files Aren't Always Enough
<p>What are the limitations of using a file-based agent workflow? Why do massive context windows tend to collapse? This week on the show, Mikiko Bazeley from MongoDB joins us to discuss agentic architecture and context engineering.</p>
<p>Mikiko is an applied AI engineer. She helps developers and organizations build AI and ML applications using MongoDB. We dig into the debate of files versus a database. What are some of the limitations of building an agent with just a folder of files? </p>
<p>We explore the surprising limitations of massive context windows and strategies for fixing them. Mikiko also shares advice and resources to help you get up to speed on building your own agent skills. Our conversation touches on multiple topics in the current development landscape.</p>
<p>This episode is sponsored by <a href="https://serpapi.com/?utm_source=realpython&utm_medium=podcast&utm_campaign=q12">SerpApi</a>.</p>
<div class="alert alert-primary" role="alert">
<p><strong>Video Course Spotlight:</strong> <a href="https://realpython.com/courses/building-type-safe-llm-agents-with-pydantic-ai/">Building Type-Safe LLM Agents With Pydantic AI</a></p>
<p>Build type-safe LLM agents in Python with Pydantic AI using structured outputs, function calling, and dependency injection.</p>
</div>
<p>Topics:</p>
<ul>
<li>00:00:00 – Introduction</li>
<li>00:02:31 – Catching up with MongoDB</li>
<li>00:07:02 – Are the files all you need?</li>
<li>00:15:14 – What is a workflow agent?</li>
<li>00:24:43 – Sponsor: SerpApi</li>
<li>00:25:45 – Model vs harness</li>
<li>00:29:57 – Context rot and tool loadouts</li>
<li>00:41:07 – Sharing state and coordination of agents</li>
<li>00:47:27 – Video Course Spotlight</li>
<li>00:49:16 – What do dataflows look like</li>
<li>01:00:38 – The human-in-the-loop & coding agents</li>
<li>01:10:30 – Resources to explore</li>
<li>01:17:49 – What are you excited about in the world of Python?</li>
<li>01:18:38 – What do you want to learn next?</li>
<li>01:22:54 – Thanks and goodbye</li>
</ul>
<p>Show Links:</p>
<ul>
<li><a href="https://thenewstack.io/ai-agent-memory-architecture/">The “files are all you need” debate misses what’s actually happening in agent memory architecture - The New Stack</a></li>
<li><a href="https://www.mongodb.com/">MongoDB: The World’s Leading Modern Data Platform</a></li>
<li><a href="https://venturebeat.com/data/karpathy-shares-llm-knowledge-base-architecture-that-bypasses-rag-with-an">Karpathy shares ‘LLM Knowledge Base’ architecture that bypasses RAG with an evolving markdown library maintained by AI - VentureBeat</a></li>
<li><a href="https://www.llamaindex.ai/blog/files-are-all-you-need">Files Are All You Need: Context, Search, Skills Guide | LlamaIndex</a></li>
<li><a href="https://www.mongodb.com/company/blog/technical/converged-datastore-for-agentic-ai">Converged Datastore For Agentic AI - MongoDB</a></li>
<li><a href="https://thenewstack.io/why-developers-need-vector-search/">Why Developers Need Vector Search - The New Stack</a></li>
<li><a href="https://www.oreilly.com/radar/why-multi-agent-systems-need-memory-engineering/">Why Multi-Agent Systems Need Memory Engineering – O’Reilly</a></li>
<li><a href="https://www.philschmid.de/context-engineering">The New Skill in AI is Not Prompting, It’s Context Engineering - Phil Schmid</a></li>
<li><a href="https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html">How Long Contexts Fail - dbreunig.com</a></li>
<li><a href="https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html">How to Fix Your Context - dbreunig.com</a></li>
<li><a href="https://arxiv.org/abs/2601.11653">AI Agents Need Memory Control Over More Context - arxiv.org</a></li>
<li><a href="https://www.latent.space/p/ainews-is-harness-engineering-real">AINews - Is Harness Engineering real? - Latent.Space</a></li>
<li><a href="https://adambaitch.substack.com/p/the-model-vs-the-harness-which-actually-matters-more-59dd3116bb31">The Model vs. the Harness: Which Actually Matters More?</a></li>
<li><a href="https://realpython.com/chromadb-vector-database/">Embeddings and Vector Databases With ChromaDB – Real Python</a></li>
<li><a href="https://github.com/humanlayer/12-factor-agents">12-factor-agents: What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?</a></li>
<li><a href="https://12factor.net/">The Twelve-Factor App</a></li>
<li><a href="https://learn.mongodb.com/">MongoDB Courses and Trainings - MongoDB University</a></li>
<li><a href="https://github.com/mongodb-js/mongodb-mcp-server">mongodb-mcp-server: A Model Context Protocol server to connect to MongoDB databases and MongoDB Atlas Clusters.</a></li>
<li><a href="https://www.mongodb.com/docs/mcp-server/">What is the MongoDB MCP Server? - MongoDB Docs</a></li>
<li><a href="https://github.com/mongodb/mongo-python-driver">mongo-python-driver: PyMongo - the Official MongoDB Python driver</a></li>
<li><a href="https://github.com/mongodb/agent-skills">agent-skills: Use the official MongoDB Skills with your favorite coding agent to build faster.</a></li>
<li><a href="https://reachymini.net/">Reachy Mini - Open-Source Desktop Humanoid Robot</a></li>
<li><a href="https://www.linkedin.com/in/mikikobazeley/">👩🏻💻 Mikiko B. - LinkedIn</a></li>
<li><a href="https://mikikobazeley.substack.com/">Building AI Products From Scratch - Mikiko Bazeley - Substack</a></li>
</ul>
<p>Level up your Python skills with our expert-led courses:</p>
<ul>
<li><a href="https://realpython.com/courses/getting-started-claude-code/">Getting Started With Claude Code</a></li>
<li><a href="https://realpython.com/courses/building-type-safe-llm-agents-with-pydantic-ai/">Building Type-Safe LLM Agents With Pydantic AI</a></li>
<li><a href="https://realpython.com/courses/pydantic-simplify-data-validation/">Using Pydantic to Simplify Python Data Validation</a></li>
</ul> <p><a rel="payment" href="https://realpython.com/join">Support the podcast & join our community of Pythonistas</a></p>
988: In Case You Missed It in April 2026
In this month’s episode of In Case You Missed It, Jon Krohn talks to guests about memory and education, and how artificial intelligence is continuing to help lower the barriers to access. Hear from Matt Glickman, Traci Walker-Griffith, Richmond Alake, and Linda Haviv, discussing the foundations of AI agent memory, how engineers can develop at scale, and why they believe AI could be your child’s perfect tutor in the classroom.
Additional materials: www.superdatascience.com/988
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
985: The Four Types of Memory Every AI Agent Needs, with Richmond Alake
Oracle’s Director of AI Developer Experience Richmond Alake returns to the show to talk to Jon Krohn about agent memory; the network of systems, models, databases and LLMs that enable AI agents to learn and adapt over time. Listen to the episode to hear about Richmond’s “100 Days of Agent Memory” initiative, retrieval-augmented generation’s (RAG) limitations with AI agents, the layers of the AI agent stack, and what makes the Oracle AI database so useful to developers.
Additional materials: www.superdatascience.com/985
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
(03:15) What agent memory is and why it’s important
(28:28) RAG’s limitations for AI agents
(35:19) What matters in the AI agent stack beyond memory
(41:34) Why memory was undervalued in the AI agent stack
20Growth: Inside Lovable's $400M ARR Growth Machine | How Lovable Does Product Launches | How Lovable Hacks Social To Make Posts Go Viral | How Lovable Makes Every Employee a Brand with Elena Verna
Elena Verna is the Head of Growth at Lovable, one of the fastest growing companies in the world having hit $400M in ARR in just 18 months. Prior to Lovable, Elena was Head of Growth at both Dropbox and Miro.
AGENDA:
00:00 – Why "Growth Is Now a Trust Problem" (Not a Marketing Problem)
06:10 – Is SEO Dying Because of AI Search?
07:00 – Did Lovable's Growth Come From the Founder's Personal Brand?
08:30 – Why Every Founder Should Push Employees to Be Marketers?
13:10 – Why Every Employee at Lovable Ships Code (Even Marketing)
21:20 – Why Paid Marketing in Year One Is a "Death Trap"
31:50 – Why Annual Subscriptions Are the Wrong Monetization Model for AI
37:00 – If Elena Had an Unlimited Marketing Budget, What Would She Do?
48:00 – How Lovable Does Product Launches
From Legacy to AI-Ready: How MongoDB AMP Accelerates Modernization
Summary
In this episode, Shilpa Kolhar, SVP of Product and Engineering at MongoDB, discusses using MongoDB as a unified foundation for AI-driven and agentic applications. She explains how the Application Modernization Platform (AMP) accelerates the transition from legacy relational systems to a document-first architecture, driven by the need for AI-readiness and speed of change. Shilpa highlights MongoDB's features, such as its native JSON document model, Atlas Vector Search, auto-embeddings, and integrated search, which help eliminate drift and latency across operational data, indexing, and vectors, emphasizing the importance of keeping context, transactions, and embeddings together for real-time AI use cases. She shares best practices for re-architecting legacy systems, including schema validation and versioning patterns to tame schema drift, aggregation pipelines for consistent reads, and pragmatic standardization across services, while also detailing AMP's approach to scoping large estates and the balance of LLM-powered automation with human-in-the-loop governance.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
If you lead a data team, you know this pain: Every department needs dashboards, reports, custom views, and they all come to you. So you're either the bottleneck slowing everyone down, or you're spending all your time building one-off tools instead of doing actual data work. Retool gives you a way to break that cycle. Their platform lets people build custom apps on your company data—while keeping it all secure. Type a prompt like 'Build me a self-service reporting tool that lets teams query customer metrics from Databricks—and they get a production-ready app with the permissions and governance built in. They can self-serve, and you get your time back. It's data democratization without the chaos. Check out Retool at dataengineeringpodcast.com/retool today and see how other data teams are scaling self-service. Because let's be honest—we all need to Retool how we handle data requests.
Your host is Tobias Macey and today I'm interviewing Shilpa Kolhar about using MongoDB as the foundation for AI-driven applications
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what MongoDB is and the core primitives that it offers?
The MongoDB engine has gone through substantial evolution since it was first introduced over 20 years ago. What are some of the most notable features that have been added in recent years?
You recently launched the MongoDB Application Modernization Platform (AMP). What are the key elements of modernization that it is focused on?
How do the core primitives of the MongoDB engine align with modernization objectives?
There is a lot of attention being paid now to AI applications where data is the most critical element for success. What are the features of MongoDB that lend itself to being the context store for generative AI services?
Besides the data used for context and grounding, AI applications also want to track user interactions and form short and long term memory to improve the system over time. How can MongoDB assist in that work as well?
While the lack of schema enforcement on write can be beneficial to rapid evolution of software, it can also be a detriment if not managed well. How can MongoDB help in avoiding schema drift over time that leads to old data being incompatible with current code?
What are the most interesting, innovative, or unexpected ways that you have seen MongoDB used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on MongoDB and application modernization?
When is MongoDB/AMP the wrong choice?
What do you have planned for the future of AMP?
Contact Info
LinkedIn
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.
Links
MongoDB
MongoDB AMP
Google Gemini
Voyage AI
Qdrant
ChromaDB
Weaviate
Pinecone
MongoDB Autoembedding
Retool
ODM == Object Document Mapper
RAG == Retrieval Augmented Generation
Agentic Memory
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Production-Grade AI Systems with Fred Roma
Engineering teams around the world are building AI-focused applications or integrating AI features into existing products. The AI development ecosystem is maturing, which is accelerating how quickly these applications can be prototyped. However, taking AI applications to production remains a notoriously complex process. Modern AI stacks demand LLMs, embeddings, vector search, observability, new caching layers, and constant adaptation as the landscape shifts week to week. Increasingly, the data layer has become both the foundation and the bottleneck to AI app productionization.
MongoDB has been expanding beyond its core document database into a full AI-ready database platform with integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval. The company also recently acquired Voyage AI to provide accurate and cost-effective embedding models and rerankers to its users.
Fred Roma is a veteran engineer and is currently the SVP of Product and Engineering at MongoDB. He joins the show with Kevin Ball to talk about the state of AI application development, the role of vector search and reranking, schema evolution in the LLM era, the Voyage AI acquisition, how data platforms must evolve to keep up with AI’s breakneck pace, and more.
Full Disclosure: This episode is sponsored by MongoDB.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Production-Grade AI Systems with Fred Roma appeared first on Software Engineering Daily.
No Priors Live: Building Durable Software in the AI Age with MongoDB President & CEO CJ Desai
Why are there only a handful of companies in the world with over $10 billion in pure-play software revenue? CJ Desai believes the reason is that products are replaceable, but platforms are forever. For No Priors’ very first live from MongoDB.local SF, Sarah Guo is joined by CJ Desai, CEO and President of software developer MongoDB, to discuss the shifting landscape of enterprise software. CJ discusses whether AI will erode the value of software, and what truly constitutes a “moat” in the age of generative AI. CJ also talks about why AI adoption with Fortune 500-sized companies is still lagging, the importance of customer relationships, and why the “bear thesis” on SaaS may be overblown.
Sign up for new podcasts every week. Email feedback to show@no-priors.com
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @cj_mongodb | @MongoDB
Chapters:
00:00 – Cold Open
00:58 – CJ Desai Introduction
01:38 – The AI Stack and the Future of Software
04:18 – Why Platforms, Not Products, Are Sticky
09:59 – Vibe Coding and the Threat of On-Demand Apps
12:15 – Paths to Success for Software Vendor Incumbents
14:24 – How CJ Chose MongoDB
18:55 – Debunking the SaaS Bear Thesis
22:07 – Fortune 500 Perspectives on AI Value
24:24 – Can AI Native Startups Replace Systems of Record?
28:10 – The Importance of Customer Relationships
31:46 – Managing Through Massive Technology Transitions
36:37 – Conclusion
You need quality engineers to turn AI into ROI
SPONSORED BY MONGODB
Pete Johnson, Field CTO, Artificial Intelligence at MongoDB, joins the podcast to talk about a recent OpenAI paper on the impact that AI will have on jobs and overall GDP. Pete, who reads the papers (and datasets) so you don’t have to, says that looking at AI’s impact as a job killer is a flawed metric. Instead, he and Ryan talk about how AI will be a collaborator for actual human workers, how embeddings and vectorization will move the productivity needle, and the five decisions you need to make to realize ROI on AI.
Episode notes:
If you’re curious, read the OpenAI blog post and paper yourself.
For those of you looking for inspiration, check out Werner Vogel’s keynote from re:Invent 2025.
MongoDB provides a flexible and dynamic database that excels with AI data.
Connect with Pete on LinkedIn.
Congrats to Populist badge winner Scheff's Cat for dropping a banger of an answer on error: non-const static data member must be initialized out of line.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The new AI growth playbook for 2026: How Lovable hit $200M ARR in one year | Elena Verna (Head of Growth)
Elena Verna is the head of growth at Lovable, the leading AI-powered app builder that hit $200 million in annual recurring revenue in under a year with just 100 employees. In this record fourth appearance on the podcast, Elena shares how the traditional growth playbook has been completely rewritten for AI companies. She explains why Lovable focuses on innovation over optimization, how they’ve shifted from activation to building new features, and why giving away their product for free has become their most powerful growth strategy.
We discuss:
1. Why 60% to 70% of traditional growth tactics no longer apply in AI
2. Why you have to re-find product-market fit every 3 months
3. The specific growth tactics driving Lovable’s unprecedented growth
4. Why giving away product is a growth strategy that beats paid ads
5. “Minimum lovable product” as the new standard (not minimum viable product)
6. Why activation now belongs to product teams, not growth teams
7. Whether you should join an AI startup (honest tradeoffs)
—
Brought to you by:
WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs
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Persona—A global leader in digital identity verification
—
Transcript: https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna
—
My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/181207556/my-biggest-takeaways-from-this-conversation
—
Where to find Elena Verna:
• X: https://x.com/elenaverna
• LinkedIn: https://www.linkedin.com/in/elenaverna
• Newsletter: https://www.elenaverna.com
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Introduction to Elena Verna
(05:19) The scale and growth of Lovable
(08:55) Confidence in Lovable as a business
(12:17) Retention at Lovable
(15:02) Lovable’s unique growth levers
(28:13) The role of marketing in Lovable’s success
(38:09) Launching new features
(40:59) Hiring and team dynamics
(43:17) The value of vibe coding
(49:46) The importance of community
(51:47) Giving away your product for free
(56:26) Tripling their company size
(01:00:23) Product-market-fit challenges
(01:08:50) Advice for joining AI companies
(01:12:00) Work-life balance
(01:15:20) What it’s like to work at Lovable
(01:19:45) Women in tech
(01:25:29) Final thoughts and lightning round
—
Referenced:
• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company
• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led
• 10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey): https://www.lennysnewsletter.com/p/10-growth-tactics-that-never-work-elena-verna
• Lovable: https://lovable.dev
• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika
• Stripe: https://stripe.com
• What differentiates the highest-performing product teams | John Cutler (Amplitude, The Beautiful Mess): https://www.lennysnewsletter.com/p/what-differentiates-the-highest-performing
• How to win in the AI era: Ship a feature every week, embrace technical debt, ruthlessly cut scope, and create magic your competitors can’t copy | Gaurav Misra (CEO and co-founder of Captions): https://www.lennysnewsletter.com/p/how-to-win-in-the-ai-era-gaurav-misra
• “Dumbest idea I’ve heard” to $100M ARR: Inside the rise of Gamma | Grant Lee (CEO): https://www.lennysnewsletter.com/p/how-50-people-built-a-profitable-ai-unicorn
• Eric Ries on LinkedIn: https://www.linkedin.com/in/eries
• Elena’s post on LinkedIn about Lovable Missions: https://www.linkedin.com/posts/elenaverna_everythingispossible-lovableway-activity-7401627519646474242-hn6e
• SheBuilds: https://shebuilds.lovable.app
• Shopify + Lovable: https://lovable.dev/shopify
• The Product-Market Fit Treadmill: Why every AI company is sprinting just to stay in place: https://www.elenaverna.com/p/the-product-market-fit-treadmill
• Cursor: https://cursor.com
• The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell
• Unorthodox frameworks for growing your product, career, and impact | Bangaly Kaba (YouTube, Instagram, Facebook, Instacart): https://www.lennysnewsletter.com/p/frameworks-for-growing-your-career-bangaly-kaba
• The adjacent user: https://brianbalfour.com/quick-takes/the-adjacent-user
• Granola: https://www.granola.ai
• Wispr Flow: https://wisprflow.ai
• I’m worried about women in tech: https://www.elenaverna.com/p/im-worried-about-women-in-tech
• Slack founder: Mental models for building products people love ft. Stewart Butterfield: https://www.lennysnewsletter.com/p/slack-founder-stewart-butterfield
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.
To hear more, visit www.lennysnewsletter.com
20Sales: John McMahon on How to Hire, Train & Retain the Best Sales Reps | How Sales Changes in a World of AI | Sales Lessons from Snowflake and MongoDB | How to Create and Drive a Sales Process with Urgency
John McMahon is widely regarded as one of the greatest enterprise-software sales leaders of all time. He's the only person to have served as Chief Revenue Officer at five public software companies: PTC, GeoTel, Ariba, BladeLogic and BMC Software. He helped scale BladeLogic from a startup into a public company — ultimately leading to its ~$880M sale to BMC — and drove GeoTel into a multi-billion dollar acquisition. Today he sits on the boards of top names such as Snowflake and MongoDB, while also mentoring and influencing a who's-who of modern SaaS sales leaders.
AGENDA:
03:33 The Art and Science of Sales: Insights from a Veteran
04:29 Adapting Sales Strategies in the Age of AI and PLG
07:47 The Ultimate Framework to do Deal Qualification
14:13 How to Drive Urgency and Maintain Sales Process
20:06 How to Hire the Best Sales Reps
25:11 Step-by-Step Guide to Training Sales Reps
45:22 The Mindset of the Best Sales Reps
54:55 Single Most Important Skill to Win in Sales
871: NoSQL Is Ideal for AI Applications, with MongoDB’s Richmond Alake
Agentic AI, AI success strategies, and why flexibility will be so important to keep up with the AI market: Jon Krohn talks to Richmond Alake about the NoSQL database MongoDB, including why it’s a great addition to your toolkit for developing (agentic) AI applications, with a look under the hood at its native vector database. Richmond also talks about why he expects multi-agent AI architectures to go mainstream in 2025.
Additional materials: www.superdatascience.com/871
This episode is brought to you by the Dell AI Factory with NVIDIA and by ODSC, the Open Data Science Conference.
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
(04:10) How Richmond became a Staff Developer Advocate
(07:40) How NoSQL database differs from a relational database
(16:50) The advantages of working with the cloud-based MongoDB Atlas
(32:26) Richmond’s predictions for agentic AI
(40:38) How to create an effective AI strategy
MongoDB’s Sahir Azam: Vector Databases and the Data Structure of AI
MongoDB product leader Sahir Azam explains how vector databases have evolved from semantic search to become the essential memory and state layer for AI applications. He describes his view of how AI is transforming software development generally, and how combining vectors, graphs and traditional data structures enables high-quality retrieval needed for mission-critical enterprise AI use cases. Drawing from MongoDB's successful cloud transformation, Azam shares his vision for democratizing AI development by making sophisticated capabilities accessible to mainstream developers through integrated tools and abstractions.
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Mentioned in this episode:
Introducing ambient agents: Blog post by Langchain on a new UX pattern where AI agents can listen to an event stream and act on it
Google Gemini Deep Research: Sahir enjoys its amazing product experience
Perplexity: AI search app that Sahir admires for its product craft
Snipd: AI powered podcast app Sahir likes
10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey)
Elena Verna is one of Silicon Valley’s most sought-after growth advisors and operators. She previously led growth at companies like Amplitude, Miro, Dropbox, and SurveyMonkey and is currently doing full-time advising for high-growth tech companies. In our conversation, Elena and I discuss:
• 10 growth tactics that never work
• Her 3 favorite growth frameworks
• How to increase your career optionality
—
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Find the transcript at: https://www.lennysnewsletter.com/p/10-growth-tactics-that-never-work-elena-verna
—
Where to find Elena Verna:
• Newsletter: https://www.elenaverna.com/
• X: https://x.com/elenaverna
• LinkedIn: https://www.linkedin.com/in/elenaverna
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Welcome back, Elena!
(06:02) Common mistakes growth teams make
(08:31) #1: Hiring for growth roles too soon
(15:09) #2: Hiring a head of growth to fix your problems
(19:20) #3: Doing a rebrand to drive growth
(25:11) #4: Obsessing over your competition
(34:00) #5: Believing that your problems are unique
(42:32) #6: Prioritizing other growth channels above earned channels
(50:55) #7: Failing to evolve your growth model
(01:01:06) #8: Not hiring advisors
(01:05:55) #9: Over-experimenting
(01:10:44) #10: Color optimizations, third-party signups, one-email wonders, and removing friction
(01:15:00) Elena’s favorite growth frameworks
(01:18:50) Contrarian corner: full-time jobs
(01:26:05) Lightning round and final thoughts
—
Referenced:
• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company
• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led
• Six rules of hiring for growth: https://www.lennysnewsletter.com/p/hiring-growth
• Figma: https://www.figma.com/
• Miro: https://www.figma.com/
• Notion: https://www.figma.com/
• Carol Wong on LinkedIn: https://www.linkedin.com/in/carol-wong-14133927/
• Dropbox: https://www.dropbox.com/
• The Law of Shitty Clickthroughs: https://andrewchen.com/the-law-of-shitty-clickthroughs/
• Miroverse: https://miro.com/miroverse/
• GitHub: https://github.com/
• My 9 Favorite Growth Frameworks: https://www.elenaverna.com/p/my-9-favorite-growth-frameworks
• Growth Loops are the New Funnels: https://www.reforge.com/blog/growth-loops
• Racecar Growth Framework: https://www.reforge.com/blog/racecar-growth-framework
• The Adjacent User: https://andrewchen.com/the-adjacent-user-theory/
• Unorthodox frameworks for growing your product, career, and impact | Bangaly Kaba (YouTube, Instagram, Facebook, Instacart): https://www.lennysnewsletter.com/p/frameworks-for-growing-your-career-bangaly-kaba
• Why I’m Unquitting Full-Time Roles: https://www.elenaverna.com/p/why-im-unquitting-full-time-roles
• Noah Smith’s newsletter: https://www.noahpinion.blog/
• Beef on Netflix: https://www.netflix.com/title/81447461
• Veep on Max: https://www.max.com/shows/veep/37cb4217-c710-4166-8e9f-352a61f2cd3a
• The Last of Us on Max: https://www.max.com/shows/last-of-us/93ba22b1-833e-47ba-ae94-8ee7b9eefa9a
• Heated boots: https://www.amazon.com/heated-boots/s?k=heated+boots
• Airpods Max: https://www.apple.com/airpods-max/
• Memes by Elena: https://www.elenaverna.com/p/ten-funniest-growth-memes
—
Recommended books:
• Project Hail Mary: https://www.amazon.com/Project-Hail-Mary-Andy-Weir/dp/0593135229/
• The Martian: https://www.amazon.com/Martian-Andy-Weir/dp/0553418025
• We Are Legion (Bobiverse #1): https://www.amazon.com/We-Are-Legion-Bob-Bobiverse/dp/1680680587
• Fire Upon the Deep: https://www.amazon.com/Fire-Upon-Deep-Zones-Thought/dp/0812515285
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe
The Art of Database Selection and Evolution
Summary
In this episode of the Data Engineering Podcast Sam Kleinman talks about the pivotal role of databases in software engineering. Sam shares his journey into the world of data and discusses the complexities of database selection, highlighting the trade-offs between different database architectures and how these choices affect system design, query performance, and the need for ETL processes. He emphasizes the importance of understanding specific requirements to choose the right database engine and warns against over-engineering solutions that can lead to increased complexity. Sam also touches on the tendency of engineers to move logic to the application layer due to skepticism about database longevity and advises teams to leverage database capabilities instead. Finally, he identifies a significant gap in data management tooling: the lack of easy-to-use testing tools for database interactions, highlighting the need for better testing paradigms to ensure reliability and reduce bugs in data-driven applications.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
It’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity.
Your host is Tobias Macey and today I'm interviewing Sam Kleinman about database tradeoffs across operating environments and axes of scale
Interview
Introduction
How did you get involved in the area of data management?
The database engine you use has a substantial impact on how you architect your overall system. When starting a greenfield project, what do you see as the most important factor to consider when selecting a database?
points of friction introduced by database capabilities
embedded databases (e.g. SQLite, DuckDB, LanceDB), when to use and when do they become a bottleneck
single-node database engines (e.g. Postgres, MySQL), when are they legitimately a problem
distributed databases (e.g. CockroachDB, PlanetScale, MongoDB)
polyglot storage vs. general-purpose/multimodal databases
federated queries, benefits and limitations ease of integration vs. variability of performance and access control
Contact Info
LinkedIn
GitHub
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.
Links
MongoDB
NeonPodcast Episode
GlareDB
NoSQL
S3 Conditional Write
Event driven architecture
CockroachDB
Couchbase
Cassandra
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Dev Ittycheria - The Database Evolution - [Invest Like the Best, EP.373]
My guest today is Dev Ittycheria. Dev is the CEO of MongoDB, the developer data platform with tens of thousands of customers in 100 different countries. He joined the company as CEO in 2014, taking it public in 2017, and is now approaching a decade of leading MongoDB to become a go-to choice for the most sophisticated organizations around the world. We discuss Dev’s philosophy for constructing an exceptional enterprise sales organization, why he feels a leader must be incredibly judgemental to drive excellence, and how he plans to guide MongoDB through another technological transition. Please enjoy this conversation with Dev Ittycheria.
Listen to Founders Podcast
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
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-----
Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.
Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com).
Show Notes:
(00:00:00) Welcome to Invest Like the Best
(00:03:39) A CEO's Perspective Of The AI Revolution
(00:05:50) The Evolution of Apps From Trivial to Transformative
(00:08:12) MongoDB's Journey From Startup to AI Era
(00:10:03) Building a Modern Database Company: MongoDB's Story
(00:13:19) The Long-Term Vision for MongoDB
(00:15:51) Dev’s Formative Experiences as a Tech CEO
(00:19:18) The Art of Enterprise Sales
(00:25:28) The Development of Dev as a Leader
(00:29:01) Getting the Most Out of Your Talent
(00:33:17) Managing a Multi-Product, Multi-Channel Enterprise
(00:37:29) Dev’s Recruiting Philosophy
(00:43:12) The Role of Leadership and Mentorship in Career Growth
(00:46:08) Dev’s Deepest Worry With MongoDB
(00:49:35) Personal Investment Philosophy and Identifying Potential
(00:53:52) The Art of Leadership: Accountability and Development
(00:57:50) Learning from Legends: Andy Grove's Management Insights
(01:02:54) The Power in MongoDB’s Business
(01:06:13) Up Next for Dev and MongoDB
(01:08:34) The Kindest Thing Anyone Has Ever Done For Dev
#176 CEO MongoDB, Dev Ittycheria: Edge
Guest: Dev Ittycheria, CEO and President of MongoDB
When you think about who you were and the decisions you made two, or four, or eight years ago ... how do you feel? Dev Ittycheria, the President and CEO of MongoDB, says he’s embarrassed about certain things he did — and that’s a good thing. “If you’re not [embarrassed], that means you’re not really growing that fast,” he says. He recalled one of his mentors, former BladeLogic chairman Steve Walske, explaining that everyone has an overinflated opinion of themselves, and the great leaders keep the gap between that opinion and reality narrow. One of the hallmarks of such a leader, Dev says, is that they have the intellectually honesty to recognize their own strengths and weaknesses, which others perceive.
In this episode, Dev and Joubin discuss looking for bad news, chips on your shoulder, Ivy League schools, being an outsider, highly educated parents, “aging out,” Bruce Springsteen, Chief People Officers, Frank Slootman and John McMahon, passive aggression, vulnerability as strength, imposter syndrome, open-source licenses, introverts, and time management.
In this episode, we cover:
Shlomo Kramer and the “burden of persona” (00:59)
Why BladeLogic started in Boston (04:30)
The psychological edge (07:08)
Dev’s family and education (08:56)
“Am I good enough?” (13:11)
“Do not squander this opportunity” (16:22)
Dev’s wife (19:32)
Fear of irrelevance (21:23)
Relevance after retirement (26:06)
Why CEO is a lonely job (28:14)
Trusting your team (31:43)
The meaning of life (35:16)
Judgment and introspection (38:16)
Taking people to the woodshed (40:54)
What matters to other people (44:39)
Taking risks at MongoDB (51:08)
Founder-led businesses (53:26)
What type of company is MongoDB? (57:39)
Work-life harmony (01:00:20)
Who MongoDB is hiring (01:03:17)
Links:
Connect with DevTwitter
LinkedIn
Connect with JoubinTwitter
LinkedIn
Email: grit@kleinerperkins.com
Learn more about Kleiner Perkins
This episode was edited by Eric Johnson from LightningPod.fm
#173 Author of “The Qualified Sales Leader,” John McMahon: The Five-Time CRO
Guest: John McMahon, author of The Qualified Sales Leader: Proven Lessons from a Five Time CRO
A hell of a lot of people work in sales. But until recently, says five-time CRO and The Qualified Sales Leader author John McMahon, it was rare for colleges and universities to offer a sales degree. Salespeople had to learn on the job from experienced coaches, and adapt. And their bosses, John explains, had to themselves as agents of transformation. “If somebody’s really smart, they’re going to pick up the knowledge,” he says. “If they have what I call a PHD — persistence, heart, and desire — they’re going to learn the skills ... You’re going to have to do thousands and thousands of repetitions before you’re going to get good.”
In this episode, John and Joubin discuss lazy LinkedIn cold calls, Tom Brady’s retirement, being “married to your job,” Carl Eschenbach, crying, sales as a calling, corporate culture vs. coaching culture, adaptable workers, opportunity vs. title, Bob Muglia, transactional leaders, sad rich people, cookie-cutter advice, handshake evaluations, and David Cancel.
In this episode, we cover:
CRO to CEO? (02:21)
Ego and relevance (04:25)
Escaping the 90-day grind (06:25)
Persistence and physical discipline (09:05)
Daily habits and positive energy (14:12)
Why John quit BMC (17:09)
Poor communication (21:17)
Was there another way? (24:37)
Identifying sales talent (28:36)
Showing that you care (32:58)
Sales leaders as hockey coaches (39:46)
Firing people (44:25)
Interviewing the right type of salesperson (49:14)
Snowflake and Chris Degnan (51:22)
“What’s the book on you?” (56:03)
Managing from a position of power (58:01)
The three “whys” (01:00:31)
Why John never went VC (01:04:33)
Is he really done? (01:07:17)
Shlomo Kramer (01:10:20)
Having impact (01:13:11)
Bad advice (01:16:19)
Working with marketing (01:19:32)
Sizing people up (01:21:26)
Can CEOs give up? (01:26:51)
Coaching sales “artists” (01:28:29)
What “grit” means to John (01:30:48)
Links:
Connect with JohnLinkedIn
Buy The Qualified Sales Leader: Proven Lessons from a Five Time CRO
Connect with JoubinTwitter
LinkedIn
Email: grit@kleinerperkins.com
Learn more about Kleiner Perkins
This episode was edited by Eric Johnson from LightningPod.fm
How MongoDB is Paving The Way for Frictionless Innovation with Peder Ulander
Peder Ulander, Chief Marketing & Strategy Officer at MongoDB, joins Corey on Screaming in the Cloud to discuss how MongoDB is paving the way for innovation. Corey and Peder discuss how Peder made the decision to go from working at Amazon to MongoDB, and Peder explains how MongoDB is seeking to differentiate itself by making it easier for developers to innovate without friction. Peder also describes why he feels databases are more ubiquitous than people realize, and what it truly takes to win the hearts and minds of developers.
About Peder
Peder Ulander, the maestro of marketing mayhem at MongoDB, juggles strategies like a tech wizard on caffeine. As the Chief Marketing & Strategy Officer, he battles buzzwords, slays jargon dragons, and tends to developers with a wink. From pioneering Amazon's cloud heyday as Director of Enterprise and Developer Solutions Marketing to leading the brand behind cloud.com's insurgency, Peder's built a legacy as the swashbuckler of software, leaving a trail of market disruptions one vibrant outfit at a time. Peder is the Scarlett Johansson of tech marketing — always looking forward, always picking the edgy roles that drive what's next in technology.
Links Referenced:
MongoDB: https://mongodb.com
EP 84: Dev Ittycheria’s (CEO, MongoDB) Leadership Lessons From Scaling MongoDB to $25B
Dev Ittycheria is the president and CEO of MongoDB, his third public company as CEO. In this episode, Dev discusses his take on AI, the importance of hiring, how to build an A-plus culture, and many other leadership lessons from scaling MongoDB to $25B. Overall, a really fun conversation with one of the absolute best operators in tech.
(0:00) Intro
(0:38) Taking the CEO job at MongoDB
(2:34) First things Dev changed at MongoDB
(5:53) When unicorns were actually rare
(7:50) Overcoming Monetization Challenges of Open Source
(9:54) MongoDB Atlas and the license change?
(19:18) What is the job of the CEO?
(22:49) Vulnerability is a strength
(27:03) The power of self-awareness as a Leader
(29:37) Building an A+ culture
(32:43) Holding people accountable
(35:04) Keeping feedback loops tight
(36:22) How hybrid work helps MongoDB thrive
(38:09) RIFs
(40:20) 3 steps for holding people accountable
(42:03) Why you should always be recruiting
(43:55) Dev’s unique recruiting tactics
(45:56) Favorite interview questions
(46:53) Hiring internally vs externally
(50:53) Finding passion for sales
(52:58) The perfect job doesn’t exist
(55:00) Running BladeLogic
(57:18) Ben Horowitz, Mark Andreessen, and John McMahon
(1:02:36) How does AI compare to past tech trends?
(1:05:47) Conventional Silicon Valley wisdom Dev disagrees with
Mixed and edited: Justin Hrabovsky
Produced: Rashad Assir
Executive Producer: Josh Machiz
Music: Griff Lawson
🎙 Listen to the show
Apple Podcasts: https://podcasts.apple.com/us/podcast/the-logan-bartlett-show/id1606770839
Spotify: https://open.spotify.com/show/5WqBqDb4br3LlyVrdqOYYb?si=3076e6c1b5c94d63&nd=1
Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9zb0hJZkhWbg
🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1
Follow on Socials
📸 Instagram - https://www.instagram.com/theloganbartlettshow
🐦 Twitter - https://twitter.com/loganbartshow
🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow
About the Show
Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode, Logan goes behind the scenes with world-class entrepreneurs and investors. If you're interested in the real inside baseball of tech, entrepreneurship, and start-up investing, tune in every Friday for new episodes.
Executive Producer: Rashad Assir
Producer: Leah Clapper
Mixing and editing: Justin Hrabovsky
Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1
Follow on Socials
📸 Instagram - https://www.instagram.com/theloganbartlettshow
📱 X - https://twitter.com/loganbartshow
🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow
About the Show
Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode of The Logan Bartlett Show, we sit down with the people behind today’s most important startups and extract the tactics, lessons, and frameworks they’ve learned the hard way. Conversations span hiring to GTM, product, growth, fundraising and everything in between - collectively forming the ultimate playbook to make you a better CEO, investor or board member.
Tap follow and enable notifications to stay ahead of the game.
EP 76: John McMahon (5x CRO and Enterprise Sales Expert) on The Startup Sales Playbook Every Founder Needs
John McMahon has served on the board of MongoDB and Snowflake and is best known as a five-time CRO who has built the sales processes that power enterprise sales in Silicon Valley. In this episode, John talked through a ton of practical topics around sales, including the biggest sales mistakes that early-stage startups make, how to hire a sales team to supercharge your company, and more.
(0:00) Intro
(1:20) The Qualified Sales Leader
(9:57) Sales basics in building an efficient and scalable sales org
(16:53) Carlo Carelli - Greatest Salesperson in the World
(20:55) Product market fit
(26:07) The medic qualification process
(36:35) Difference between a champion and a coach
(42:51) Best interview questions when hiring
(53:49) The hardest part of transitioning to sales management
(1:03:06) On firing
(1:10:06) The process of letting someone go
(1:11:30) Accidental sales leader
(1:17:25) John McMahon as a sales rep early on
(1:21:24) Blade Logic and Opsware
(1:26:37) The most common misconception about sales
Mixed and edited: Justin Hrabovsky
Produced: Rashad Assir
Executive Producer: Josh Machiz
Music: Griff Lawson
🎙 Listen to the show
Apple Podcasts: https://podcasts.apple.com/us/podcast/three-cartoon-avatars/id1606770839
Spotify: https://open.spotify.com/show/5WqBqDb4br3LlyVrdqOYYb?si=3076e6c1b5c94d63&nd=1
Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9zb0hJZkhWbg
🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1
Follow on Socials
📸 Instagram - https://www.instagram.com/theloganbartlettshow
🐦 Twitter - https://twitter.com/loganbartshow
🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow
About the Show
Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode, Logan goes behind the scenes with world-class entrepreneurs and investors. If you're interested in the real inside baseball of tech, entrepreneurship, and start-up investing, tune in every Friday for new episodes.
Executive Producer: Rashad Assir
Producer: Leah Clapper
Mixing and editing: Justin Hrabovsky
Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1
Follow on Socials
📸 Instagram - https://www.instagram.com/theloganbartlettshow
📱 X - https://twitter.com/loganbartshow
🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow
About the Show
Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode of The Logan Bartlett Show, we sit down with the people behind today’s most important startups and extract the tactics, lessons, and frameworks they’ve learned the hard way. Conversations span hiring to GTM, product, growth, fundraising and everything in between - collectively forming the ultimate playbook to make you a better CEO, investor or board member.
Tap follow and enable notifications to stay ahead of the game.
MongoDB CEO Dev Ittycheria on great leadership, building winning teams, and more | E1783
This Week in Startups is brought to you by…
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Roots is a real estate investment platform for all investors. There are no entry fees, and you can start with as little as $100. Head to investwithroots.com/TWIST to sign up and start investing today!
*
Today’s show:
MongoDB CEO Dev Ittycheria joins Jason to discuss recent tech trends (10:58), the move towards remote work (17:58), his strategy for building winning teams (37:17), and much more!
*
Time stamps:
(00:00) MongoDB CEO Dev Ittycheria joins Jason
(2:32) Dev’s background and how MongoDB operates
(9:28) OpenPhone - Get 20% off your first six months at https://openphone.com/twist
(10:58) The last 9 months in tech and the pace of AI
(13:57) RIFs and staffing a modern-day tech company
(17:58) Remote, Hybrid, or back to the office
(22:25) Embroker - Use code TWIST to get an extra 10% off insurance at https://Embroker.com/twist
(25:33) Hiring and inspiring the next generation
(28:47) The trust but verify operating philosophy
(31:32) Entitlement in the tech industry
(35:52) Roots - Head to investwithroots.com/TWIST to sign up and start investing today!
(37:17) Great leadership and managing a team
(41:14) Bad management and the feedback cycle
(53:30) Recruiting talent in the U.S.
*
Check out MongoDB: https://www.mongodb.com
Follow Dev: https://twitter.com/dittycheria
*
Read LAUNCH Fund 4 Deal Memo: https://www.launch.co/four
Apply for Funding: https://www.launch.co/apply
Buy ANGEL: https://www.angelthebook.com
Great recent interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland, PrayingForExits, Jenny Lefcourt
Check out Jason’s suite of newsletters: https://substack.com/@calacanis
*
Follow Jason:
Twitter: https://twitter.com/jason
Instagram: https://www.instagram.com/jason
LinkedIn: https://www.linkedin.com/in/jasoncalacanis
*
Follow TWiST:
Substack: https://twistartups.substack.com
Twitter: https://twitter.com/TWiStartups
YouTube: https://www.youtube.com/thisweekin
*
Subscribe to the Founder University Podcast: https://www.founder.university/podcast
685: Tools for Building Real-Time Machine Learning Applications, with Richmond Alake
Richmond Alake, a Machine Learning Architect at Slalom Build, sits down with Jon to share real-time ML insights, tools and career experiences for a high-energy and high impact episode. From his work at Slalom Build to his two AI startups, discover the software choices, ML tools, and front-end development techniques used by a leader in the field.
This episode is brought to you by Posit, the open-source data science company, by AWS Inferentia, and by WithFeeling.ai, the company bringing humanity into AI. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.
In this episode you will learn:
• What is a Machine Learning Architect? [03:09]
• Richmond's startups [12:07]
• Why Richmond started a podcast [29:51]
• Richmond's new course on feature stores [38:05]
• Why Richmond produces data science content [43:25]
• Why All Data Scientists Should Write [51:30]
Additional materials: www.superdatascience.com/685
The ultimate guide to product-led sales | Elena Verna
Brought to you by Linear—The new standard for modern software development | Braintrust—For when you needed talent, yesterday | Rows—The spreadsheet where data comes to life
—
Elena Verna is a leading growth expert with over 15 years of experience in tech. She was SVP of Growth at SurveyMonkey and interim CMO at Miro, where she built high-performing teams that drove significant growth. She recently served as interim Head of Growth at Amplitude and currently advises and is a board member for early-stage startups. In today’s podcast, we discuss:
• What product-led sales is
• How product-led sales differs from product-led growth
• Unpacking common acronyms: PQAs, PQs, PQLs, and MQLs
• When and how to consider investing in PLS
• Metrics for identifying qualified accounts
• The team, data, and tooling required for implementing PLS
• Common pitfalls to avoid when adding PLS
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Find the full transcript at: https://www.lennyspodcast.com/the-ultimate-guide-to-product-led-sales-elena-verna/#transcript
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Where to find Elena Verna:
• LinkedIn: https://www.linkedin.com/in/elenaverna
• Twitter: https://twitter.com/elenaverna
• Newsletter: https://elenaverna.substack.com/
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Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
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In this episode, we cover:
(00:00) Elena’s background and what she’s doing now
(07:13) Product-led sales (PLS) vs. product-led growth (PLG)
(12:47) How sales solutions can be applied to enterprise-level problems
(15:06) Defining enterprise-level problems
(17:51) How product-led companies start with PLS
(20:30) When to add sales
(22:36) Two ways to get to PLS
(24:27) Why every sales-led-growth company needs to add PLG
(26:50) Two ways you can own revenue
(28:37) PQAs, PQs, PQLs, and MQLs
(37:17) How to get started adding PLS
(42:01) Metrics to identify PQAs
(47:00) Why sales should be carefully applied
(49:07) Systems, infrastructure, and tooling
(50:59) The people and resources required for PLS
(53:42) Why you should have a clear ROI for every new hire
(55:05) Why product needs to be accountable for monetization with PLS
(59:57) Revenue-based goals product teams should have
(1:06:28) Common pitfalls startups run into when implementing PLS
(1:09:15) Benchmarks and the amount of time needed for implementing enterprise solutions
(1:12:04) Using onboarding to profile users
(1:13:08) Will AI be the next sales movement?
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Referenced:
• Elena’s previous episode on Lenny’s Podcast: https://www.lennyspodcast.com/elena-verna-on-how-b2b-growth-is-changing-product-led-growth-product-led-sales-why-you-should-go-freemium-not-trial-what-features-to-make-free-and-much-more/
• Miro: https://miro.com/
• Figma: https://www.figma.com/
• Elena’s PLS funnel diagram: https://www.linkedin.com/posts/elenaverna_b2b-product-led-sales-guide-activity-7052664130763206658-yxLK/?utm_source=share&utm_medium=member_desktop
• Elena’s memes: https://www.elenaverna.com/memes
• Mixpanel Signal reports: https://mixpanel.com/blog/mixpanel-signal-launch/
• Amplitude Compass chart: https://help.amplitude.com/hc/en-us/articles/235147347-The-Compass-chart-discover-your-users-a-ha-moments
• Looker: https://www.looker.com/
• Tableau: https://www.tableau.com/
• Salesforce: https://www.salesforce.com/
• HubSpot: https://www.hubspot.com/
• Marketo: https://nation.marketo.com/
• Waitlist for PLG course on Reforge: https://www.reforge.com/programs/product-led-growth
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
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Lenny may be an investor in the companies discussed.
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe
Moving up a level of abstraction with serverless on MongoDB Atlas and AWS
The history of computing has been a story of moving up levels of abstraction: from hard-coding algorithms and directly manipulating memory addresses with assembly languages to using more natural language constructs in high-level general purpose languages to abstracting the hardware of the computer in cloud compute. Now serverless functions take that abstraction even further. We’ve made the algorithms that process data simple and natural; MongoDB wants to do the same for how we persist data.
On this sponsored episode of the podcast, we chat with Andrew Davidson, SVP Products at MongoDB, about how they’re turning a database into a fully-managed service that developers can use in a more natural way. Along the way, we discuss how the cost bottleneck has moved from the storage media to developers’ minds, how greater abstractions can enable developers, and how to get insights from production data faster.
Episode notes
Try MongoDB Atlas on AWS for free.
You can get started with MongoDB Atlas directly from the AWS Marketplace.
If you’re at a startup, you can take advantage of their special offer for startups.
The community edition of their classic database is available to download as well.
If you’re looking to learn a thing or two before diving in, check out MongoDB University.
Our thanks to Great Question badge winner Derek 朕會功夫 for asking How can I reverse an array in JavaScript without using libraries? You know the rarest kung fu of all: asking great questions.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Shorten the distance between production data and insight
Modern networked applications generate a lot of data, and every business wants to make the most of that data. Most of the time, that means moving production data through some transformation process to get it ready for the analytics process. But what if you could have in-app analytics? What if you could generate insights directly from production data?
On this sponsored episode of the podcast, we talk with Stanimira Vlaeva, Developer Advocate at MongoDB, and Fredric Favelin, Technical Director, Partner Presales at MongoDB, about how a serverless database can minimize the distance between producing data and understanding it.
Episode notes:
Stanimira talked a lot about using BigQuery with MongoDB Atlas on Google Cloud Run. If you need to skill up on these three tools, check out this tutorial.
Once you’ve got the hang of it, get your data connected with Confluent Connetors.
With Atlas, you can transform your data in JavaScript.
Connect with Stanimira on LinkedIn and Twitter.
Connect with Fredric on LinkedIn.
Congrats to Stellar Question winner SubniC for Get name of current script in Python.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
#125 CRO Starburst, Javier Molina: Reading Cues
Guest: Javier Molina, CRO of Starburst
Starburst CRO Javier Molina’s peers, former colleagues, and even his wife often tell him the same thing: He’s difficult to read. That doesn’t mean he’s not listening, though. In fact, he’s focusing on many different things such as speech patterns, the words being used, and the priority of those words while simultaneously keeping a pulse on social cues as well. This uncontrolled habit he describes as both a superpower and his achilles heel. “It allows me to interview really well and assess talent,” says Javier, who describes himself as a social introvert. “It allows me to read situations … understand room dynamics… It helps me understand my customers [but] I think a lot of people like extroverts because of how they’re so expressive and flashy ... and that’s not me.”
In this episode, Javier and Joubin discuss Austin culture, making eye contact, social introverts, living in the future, self-awareness, betting on yourself, workhorse culture, reverse job interviews, short-term wins, in-car WiFi, great partners, and world-class interviewing.
In this episode, we cover:
San Francisco vs. Austin and the flood of techies moving to Texas (01:08)
The “movie that you can’t turn off” and assessing people quickly (05:49)
Patience, focus, and being present (14:10)
“What is a common misconception of you?” (19:53)
Self-awareness as a proxy for potential, and feeling different from the crowd (25:04)
Buying houses, and betting on yourself (32:00)
Being hired as an executive, and the culture of teams at bootstrapped companies (39:00)
What Starburst does and turning the tables on CEO Justin Borgman (45:44)
Being intentional, celebrating wins, and “enjoying the climb” (50:31)
Getting away from work, and the strength of entrepreneurs’ relationships (57:22)
The little things in interviews, and why “a problem well stated is half solved” (01:02:50)
How to screen for grit (01:07:41)
Links:
Connect with JavierTwitter
LinkedIn
Connect with JoubinTwitter
LinkedIn
Email: grit@kleinerperkins.com
Learn more about Kleiner Perkins
This episode was edited by Eric Johnson from LightningPod.fm
20VC: When to Make Your First Growth Hire? Senior or Junior? How To Onboard Them? How To Monitor Their Progress? from Growth Leaders @ Facebook, Instagram, Lyft, Instacart, Miro and more
Casey Winters is the Chief Product Officer at Eventbrite. Prior to Eventbrite, Casey led the growth product team at Pinterest. Before Pinterest, Casey started the marketing team at Grubhub and scaled Grubhub's demand-side acquisition and retention strategies.
Elena Verna is the Interim Head of Growth at Amplitude. Former exec @ Miro, Netlify, SurveyMonkey. Growth Advisor to companies including Krisp, MongoDB, Ledgy, Builder.io and SimilarWeb.
Kieran Flanagan is SVP Marketing at HubSpot, where he has helped the business grow internationally, move to a product-led business, quadrupled its marketing demand, and built out its media team, including the acquisition of 'The Hustle.'
Andy Johns career started in growth at Facebook when the company scaled from 100M-500M active users. Since he has worked in some of the leading growth orgs at companies like Twitter, Quora and more recently at Wealthfront as Head of Growth and President.
Bangaly Kaba is the Director of Product Management @ Youtube. Prior to Youtube, Bangaly led the product growth and consumer product orgs at Instacart and before Instacart was Head of Growth @ Instagram, helping grow Instagram from 440M to > 1B monthly actives in 2.5yrs.
Ed Baker is a growth advisor to various startups including Lime, Zwift, Whoop, Crimson Education, GoPeer, and Playbook. Ed was the VP of Product and Growth at Uber from 2013-2017. Prior to Uber, Ed was the Head of International Growth at Facebook.
Adam Fishman was the Chief Product and Growth Offer @ Imperfect Foods. Before Imperfect, Adam was VP of Product and Growth @ Patreon, Before Patreon, Adam was the Head of Growth @ Lyft, Adam was the first growth and marketing employee hired and grew the team to 18 people.
In Today's Discussion on When To Hire a Head of Growth:
1.) When is the right time to hear your first growth hire?
2.) Is this hire a senior growth leader or a more junior growth engineer?
3.) What can early-stage startups do to entice senior growth leaders to their early-stage company?
4.) What data infrastructure should be in place prior to hiring your first growth hire?
5.) What does the optimal onboarding process look like for all growth hires?
6.) What can founders and CEOs do to set their growth hires up for success?
The robots are coming… but when?
Despite our hope for the power of robotics, the technology is still far from mainstream. That’s because the amount of effort needed to get hardware to do useful things at scale is…well…hard.
When Eliot started Viam, his goal was to address this challenge by creating software that supports a range of hardware builds right out of the box. As the company explains - “we’re addressing these issues by building a novel robotics platform that relies on standardized building blocks rather than custom code to create, configure and control robots intuitively and quickly. We’re empowering engineers – aspiring and experienced – across industries to solve complicated automation problems with our innovative software tools.” The company announced the opening of its public beta earlier this week.
While Eliot elaborates on his vision for Viam, Ben reflects on his time covering drones for The Verge and working on robotics at DJI.
Inquisitive badge winner, Neeta, gets props for asking well-received questions on 30 separate days.
Follow Ben and Eliot on Twitter.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
599: MLOps: Machine Learning Operations
This week, Mikiko Bazeley, Senior Software Engineer at Mailchimp joins the podcast to share her in-depth knowledge of MLOps: Machine Learning Operations. Tune in to hear her discuss what it entails, why it's so critical for the efficiency of any data science team, and the most important tools you need to master for career success in this field.
In this episode you will learn:
• What MLOps is [11:40]
• Mikiko’s role at Mailchimp and why MLOps is critical for the efficiency of any data science team [27:11]
• The three most important MLOps tools [32:15]
• The six most essential MLOps skills for data scientists [47:01]
• The key factors Mikiko looks when hiring engineers [1:07:31]
• Mikiko’s productivity tricks for balancing software engineering, content creation, and her athletic pursuits [1:13:20]
Additional materials: www.superdatascience.com/599
