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| Title & Speakers | Event |
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Being a founding engineer at an AI startup
2025-12-03 · 16:28
Gergely Orosz
– host
,
Michelle Lim
– Engineer
@ Warp
Brought to You By: • Statsig — The unified platform for flags, analytics, experiments, and more. • Linear — The system for modern product development. — Michelle Lim joined Warp as engineer number one and is now building her own startup, Flint. She brings a strong product-first mindset shaped by her time at Facebook, Slack, Robinhood, and Warp. Michelle shares why she chose Warp over safer offers, how she evaluates early-stage opportunities, and what she believes distinguishes great founding engineers. Together, we cover how product-first engineers create value, why negotiating equity at early-stage startups requires a different approach, and why asking founders for references is a smart move. Michelle also shares lessons from building consumer and infrastructure products, how she thinks about tech stack choices, and how engineers can increase their impact by taking on work outside their job descriptions. If you want to understand what founders look for in early engineers or how to grow into a founding-engineer role, this episode is full of practical advice backed by real examples — Timestamps (00:00) Intro (01:32) How Michelle got into software engineering (03:30) Michelle’s internships (06:19) Learnings from Slack (08:48) Product learnings at Robinhood (12:47) Joining Warp as engineer #1 (22:01) Negotiating equity (26:04) Asking founders for references (27:36) The top reference questions to ask (32:53) The evolution of Warp’s tech stack (35:38) Product-first engineering vs. code-first (38:27) Hiring product-first engineers (41:49) Different types of founding engineers (44:42) How Flint uses AI tools (45:31) Avoiding getting burned in founder exits (49:26) Hiring top talent (50:15) An overview of Flint (56:08) Advice for aspiring founding engineers (1:01:05) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Thriving as a founding engineer: lessons from the trenches • From software engineer to AI engineer • AI Engineering in the real world • The AI Engineering stack — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe |
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How AI will change software engineering – with Martin Fowler
2025-11-19 · 17:09
Gergely Orosz
– host
,
Martin Fowler
– Chief Scientist
@ Thoughtworks
Brought to You By: • Statsig — The unified platform for flags, analytics, experiments, and more. AI-accelerated development isn’t just about shipping faster: it’s about measuring whether, what you ship, actually delivers value. This is where modern experimentation with Statsig comes in. Check it out. • Linear — The system for modern product development. I had a jaw-dropping experience when I dropped in for the weekly “Quality Wednesdays” meeting at Linear. Every week, every dev fixes at least one quality isse, large or small. Even if it’s one pixel misalignment, like this one. I’ve yet to see a team obsess this much about quality. Read more about how Linear does Quality Wednesdays – it’s fascinating! — Martin Fowler is one of the most influential people within software architecture, and the broader tech industry. He is the Chief Scientist at Thoughtworks and the author of Refactoring and Patterns of Enterprise Application Architecture, and several other books. He has spent decades shaping how engineers think about design, architecture, and process, and regularly publishes on his blog, MartinFowler.com. In this episode, we discuss how AI is changing software development: the shift from deterministic to non-deterministic coding; where generative models help with legacy code; and the narrow but useful cases for vibe coding. Martin explains why LLM output must be tested rigorously, why refactoring is more important than ever, and how combining AI tools with deterministic techniques may be what engineering teams need. We also revisit the origins of the Agile Manifesto and talk about why, despite rapid changes in tooling and workflows, the skills that make a great engineer remain largely unchanged. — Timestamps (00:00) Intro (01:50) How Martin got into software engineering (07:48) Joining Thoughtworks (10:07) The Thoughtworks Technology Radar (16:45) From Assembly to high-level languages (25:08) Non-determinism (33:38) Vibe coding (39:22) StackOverflow vs. coding with AI (43:25) Importance of testing with LLMs (50:45) LLMs for enterprise software (56:38) Why Martin wrote Refactoring (1:02:15) Why refactoring is so relevant today (1:06:10) Using LLMs with deterministic tools (1:07:36) Patterns of Enterprise Application Architecture (1:18:26) The Agile Manifesto (1:28:35) How Martin learns about AI (1:34:58) Advice for junior engineers (1:37:44) The state of the tech industry today (1:42:40) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Vibe coding as a software engineer • The AI Engineering stack • AI Engineering in the real world • What changed in 50 years of computing — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe |
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Python, Go, Rust, TypeScript and AI with Armin Ronacher
2025-10-08 · 15:34
Gergely Orosz
– host
,
Armin Ronacher
– Creator of Flask; former Sentry engineer; startup co-founder
Brought to You By: • Statsig — The unified platform for flags, analytics, experiments, and more. Most teams end up in this situation: ship a feature to 10% of users, wait a week, check three different tools, try to correlate the data, and you’re still unsure if it worked. The problem is that each tool has its own user identification and segmentation logic. Statsig solved this problem by building everything within a unified platform. Check out Statsig. • Linear – The system for modern product development. In the episode, Armin talks about how he uses an army of “AI interns” at his startup. With Linear, you can easily do the same: Linear’s Cursor integration lets you add Cursor as an agent to your workspace. This agent then works alongside you and your team to make code changes or answer questions. You’ve got to try it out: give Linear a spin and see how it integrates with Cursor. — Armin Ronacher is the creator of the Flask framework for Python, was one of the first engineers hired at Sentry, and now the co-founder of a new startup. He has spent his career thinking deeply about how tools shape the way we build software. In this episode of The Pragmatic Engineer Podcast, he joins me to talk about how programming languages compare, why Rust may not be ideal for early-stage startups, and how AI tools are transforming the way engineers work. Armin shares his view on what continues to make certain languages worth learning, and how agentic coding is driving people to work more, sometimes to their own detriment. We also discuss: • Why the Python 2 to 3 migration was more challenging than expected • How Python, Go, Rust, and TypeScript stack up for different kinds of work • How AI tools are changing the need for unified codebases • What Armin learned about error handling from his time at Sentry • And much more Jump to interesting parts: • (06:53) How Python, Go, and Rust stack up and when to use each one • (30:08) Why Armin has changed his mind about AI tools • (50:32) How important are language choices from an error-handling perspective? — Timestamps (00:00) Intro (01:34) Why the Python 2 to 3 migration created so many challenges (06:53) How Python, Go, and Rust stack up and when to use each one (08:35) The friction points that make Rust a bad fit for startups (12:28) How Armin thinks about choosing a language for building a startup (22:33) How AI is impacting the need for unified code bases (24:19) The use cases where AI coding tools excel (30:08) Why Armin has changed his mind about AI tools (38:04) Why different programming languages still matter but may not in an AI-driven future (42:13) Why agentic coding is driving people to work more and why that’s not always good (47:41) Armin’s error-handling takeaways from working at Sentry (50:32) How important is language choice from an error-handling perspective (56:02) Why the current SDLC still doesn’t prioritize error handling (1:04:18) The challenges language designers face (1:05:40) What Armin learned from working in startups and who thrives in that environment (1:11:39) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe |
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Hypergrowth startups: Uber and CloudKitchens with Charles-Axel Dein
2025-09-24 · 16:30
Gergely Orosz
– host
,
Charles-Axel Dein
– engineer
@ Uber
Brought to You By: • Statsig — The unified platform for flags, analytics, experiments, and more. Statsig built a complete set of data tools that allow engineering teams to measure the impact of their work. This toolkit is SO valuable to so many teams, that OpenAI - who was a huge user of Statsig - decided to acquire the company, the news announced last week. Talk about validation! Check out Statsig. • Linear – The system for modern product development. Here’s an interesting story: OpenAI switched to Linear as a way to establish a shared vocabulary between teams. Every project now follows the same lifecycle, uses the same labels, and moves through the same states. Try Linear for yourself. — What does it take to do well at a hyper-growth company? In this episode of The Pragmatic Engineer, I sit down with Charles-Axel Dein, one of the first engineers at Uber, who later hired me there. Since then, he’s gone on to work at CloudKitchens. He’s also been maintaining the popular Professional programming reading list GitHub repo for 15 years, where he collects articles that made him a better programmer. In our conversation, we dig into what it’s really like to work inside companies that grow rapidly in scale and headcount. Charles shares what he’s learned about personal productivity, project management, incidents, interviewing, plus how to build flexible skills that hold up in fast-moving environments. Jump to interesting parts: • 10:41 – the reality of working inside a hyperscale company • 41:10 – the traits of high-performing engineers • 1:03:31 – Charles’ advice for getting hired in today’s job market We also discuss: • How to spot the signs of hypergrowth (and when it’s slowing down) • What sets high-performing engineers apart beyond shipping • Charles’s personal productivity tips, favorite reads, and how he uses reading to uplevel his skills • Strategic tips for building your resume and interviewing • How imposter syndrome is normal, and how leaning into it helps you grow • And much more! If you’re at a fast-growing company, considering joining one, or looking to land your next role, you won’t want to miss this practical advice on hiring, interviewing, productivity, leadership, and career growth. — Timestamps (00:00) Intro (04:04) Early days at Uber as engineer #20 (08:12) CloudKitchens’ similarities with Uber (10:41) The reality of working at a hyperscale company (19:05) Tenancies and how Uber deployed new features (22:14) How CloudKitchens handles incidents (26:57) Hiring during fast-growth (34:09) Avoiding burnout (38:55) The popular Professional programming reading list repo (41:10) The traits of high-performing engineers (53:22) Project management tactics (1:03:31) How to get hired as a software engineer (1:12:26) How AI is changing hiring (1:19:26) Unexpected ways to thrive in fast-paced environments (1:20:45) Dealing with imposter syndrome (1:22:48) Book recommendations (1:27:26) The problem with survival bias (1:32:44) AI’s impact on software development (1:42:28) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Software engineers leading projects • The Platform and Program split at Uber • Inside Uber’s move to the Cloud • How Uber built its observability platform • From Software Engineer to AI Engineer – with Janvi Kalra — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe |
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The state of VC within software and AI startups – with Peter Walker
2025-08-06 · 17:15
Gergely Orosz
– host
,
Peter Walker
– Head of Insights
@ Carta
Brought to You By: • WorkOS — The modern identity platform for B2B SaaS. • Statsig — The unified platform for flags, analytics, experiments, and more. • Sonar — Code quality and code security for ALL code. — In this episode of The Pragmatic Engineer, I sit down with Peter Walker, Head of Insights at Carta, to break down how venture capital and startups themselves are changing. We go deep on the numbers: why fewer companies are getting funded despite record VC investment levels, how hiring has shifted dramatically since 2021, and why solo founders are on the rise even though most VCs still prefer teams. We also unpack the growing emphasis on ARR per FTE, what actually happens in bridge and down rounds, and why the time between fundraising rounds has stretched far beyond the old 18-month cycle. We cover what all this means for engineers: what to ask before joining a startup, how to interpret valuation trends, and what kind of advisor roles startups are actually looking for. If you work at a startup, are considering joining one, or just want a clearer picture of how venture-backed companies operate today, this episode is for you. — Timestamps (00:00) Intro (01:21) How venture capital works and the goal of VC-backed startups (03:10) Venture vs. non-venture backed businesses (05:59) Why venture-backed companies prioritize growth over profitability (09:46) A look at the current health of venture capital (13:19) The hiring slowdown at startups (16:00) ARR per FTE: The new metric VCs care about (21:50) Priced seed rounds vs. SAFEs (24:48) Why some founders are incentivized to raise at high valuations (29:31) What a bridge round is and why they can signal trouble (33:15) Down rounds and how optics can make or break startups (36:47) Why working at startups offers more ownership and learning (37:47) What the data shows about raising money in the summer (41:45) The length of time it takes to close a VC deal (44:29) How AI is reshaping startup formation, team size, and funding trends (48:11) Why VCs don’t like solo founders (50:06) How employee equity (ESOPs) work (53:50) Why acquisition payouts are often smaller than employees expect (55:06) Deep tech vs. software startups: (57:25) Startup advisors: What they do, how much equity they get (1:02:08) Why time between rounds is increasing and what that means (1:03:57) Why it’s getting harder to get from Seed to Series A (1:06:47) A case for quitting (sometimes) (1:11:40) How to evaluate a startup before joining as an engineer (1:13:22) The skills engineers need to thrive in a startup environment (1:16:04) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: — See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe |
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Measuring the impact of AI on software engineering – with Laura Tacho
2025-07-23 · 17:53
Gergely Orosz
– host
,
Laura Tacho
– CTO
@ DX
Supported by Our Partners • Statsig — The unified platform for flags, analytics, experiments, and more. • Graphite — The AI developer productivity platform. — There’s no shortage of bold claims about AI and developer productivity, but how do you separate signal from noise? In this episode of The Pragmatic Engineer, I’m joined by Laura Tacho, CTO at DX, to cut through the hype and share how well (or not) AI tools are actually working inside engineering orgs. Laura shares insights from DX’s research across 180+ companies, including surprising findings about where developers save the most time, why devs don’t use AI at all, and what kinds of rollouts lead to meaningful impact. We also discuss: • The problem with oversimplified AI headlines and how to think more critically about them • An overview of the DX AI Measurement framework • Learnings from Booking.com’s AI tool rollout • Common reasons developers aren’t using AI tools • Why using AI tools sometimes decreases developer satisfaction • Surprising results from DX’s 180+ company study • How AI-generated documentation differs from human-written docs • Why measuring developer experience before rolling out AI is essential • Why Laura thinks roadmaps are on their way out • And much more! — Timestamps (00:00) Intro (01:23) Laura’s take on AI overhyped headlines (10:46) Common questions Laura gets about AI implementation (11:49) How to measure AI’s impact (15:12) Why acceptance rate and lines of code are not sufficient measures of productivity (18:03) The Booking.com case study (20:37) Why some employees are not using AI (24:20) What developers are actually saving time on (29:14) What happens with the time savings (31:10) The surprising results from the DORA report on AI in engineering (33:44) A hypothesis around AI and flow state and the importance of talking to developers (35:59) What’s working in AI architecture (42:22) Learnings from WorkHuman’s adoption of Copilot (47:00) Consumption-based pricing, and the difficulty of allocating resources to AI (52:01) What DX Core 4 measures (55:32) The best outcomes of implementing AI (58:56) Why highly regulated industries are having the best results with AI rollout (1:00:30) Indeed’s structured AI rollout (1:04:22) Why migrations might be a good use case for AI (and a tip for doing it!) (1:07:30) Advice for engineering leads looking to get better at AI tooling and implementation (1:08:49) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • AI Engineering in the real world • Measuring software engineering productivity • The AI Engineering stack • A new way to measure developer productivity – from the creators of DORA and SPACE — See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe |
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Amazon, Google and Vibe Coding with Steve Yegge
2025-07-16 · 17:34
Gergely Orosz
– host
,
Steve Yegge
– Software engineer / technology writer
@ Sourcegraph
Supported by Our Partners • WorkOS — The modern identity platform for B2B SaaS. • Statsig — The unified platform for flags, analytics, experiments, and more. • Sonar — Code quality and code security for ALL code. — Steve Yegge is known for his writing and “rants”, including the famous “Google Platforms Rant” and the evergreen “Get that job at Google” post. He spent 7 years at Amazon and 13 at Google, as well as some time at Grab before briefly retiring from tech. Now out of retirement, he’s building AI developer tools at Sourcegraph—drawn back by the excitement of working with LLMs. He’s currently writing the book Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond. In this episode of The Pragmatic Engineer, I sat down with Steve in Seattle to talk about why Google consistently failed at building platforms, why AI coding feels easy but is hard to master, and why a new role, the AI Fixer, is emerging. We also dig into why he’s so energized by today’s AI tools, and how they’re changing the way software gets built. We also discuss: • The “interview anti-loop” at Google and the problems with interviews • An inside look at how Amazon operated in the early days before microservices • What Steve liked about working at Grab • Reflecting on the Google platforms rant and why Steve thinks Google is still terrible at building platforms • Why Steve came out of retirement • The emerging role of the “AI Fixer” in engineering teams • How AI-assisted coding is deceptively simple, but extremely difficult to steer • Steve’s advice for using AI coding tools and overcoming common challenges • Predictions about the future of developer productivity • A case for AI creating a real meritocracy • And much more! — Timestamps (00:00) Intro (04:55) An explanation of the interview anti-loop at Google and the shortcomings of interviews (07:44) Work trials and why entry-level jobs aren’t posted for big tech companies (09:50) An overview of the difficult process of landing a job as a software engineer (15:48) Steve’s thoughts on Grab and why he loved it (20:22) Insights from the Google platforms rant that was picked up by TechCrunch (27:44) The impact of the Google platforms rant (29:40) What Steve discovered about print ads not working for Google (31:48) What went wrong with Google+ and Wave (35:04) How Amazon has changed and what Google is doing wrong (42:50) Why Steve came out of retirement (45:16) Insights from “the death of the junior developer” and the impact of AI (53:20) The new role Steve predicts will emerge (54:52) Changing business cycles (56:08) Steve’s new book about vibe coding and Gergely’s experience (59:24) Reasons people struggle with AI tools (1:02:36) What will developer productivity look like in the future (1:05:10) The cost of using coding agents (1:07:08) Steve’s advice for vibe coding (1:09:42) How Steve used AI tools to work on his game Wyvern (1:15:00) Why Steve thinks there will actually be more jobs for developers (1:18:29) A comparison between game engines and AI tools (1:21:13) Why you need to learn AI now (1:30:08) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The full circle of developer productivity with Steve Yegge • Inside Amazon’s engineering culture • Vibe coding as a software engineer • AI engineering in the real world • The AI Engineering stack • Inside Sourcegraph’s engineering culture— See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe |
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What is a Principal Engineer at Amazon? With Steve Huynh
2025-07-09 · 18:44
Gergely Orosz
– host
,
Steve Huynh
– Principal Engineer
@ Amazon
Supported by Our Partners • Statsig — The unified platform for flags, analytics, experiments, and more. • Graphite — The AI developer productivity platform. • Augment Code — AI coding assistant that pro engineering teams love. — Steve Huynh spent 17 years at Amazon, including four as a Principal Engineer. In this episode of The Pragmatic Engineer, I join Steve in his studio for a deep dive into what the Principal role actually involves, why the path from Senior to Principal is so tough, and how even strong engineers can get stuck. Not because they’re unqualified, but because the bar is exceptionally high. We discuss what’s expected at the Principal level, the kind of work that matters most, and the trade-offs that come with the title. Steve also shares how Amazon’s internal policies shaped his trajectory, and what made the Principal Engineer community one of the most rewarding parts of his time at the company. We also go into: • Why being promoted from Senior to Principal is one of the hardest jumps in tech • How Amazon’s freedom of movement policy helped Steve work across multiple teams, from Kindle to Prime Video • The scale of Amazon: handling 10k–100k+ requests per second and what that means for engineering • Why latency became a company-wide obsession—and the research that tied it directly to revenue • Why companies should start with a monolith, and what led Amazon to adopt microservices • What makes the Principal Engineering community so special • Amazon’s culture of learning from its mistakes, including COEs (correction of errors) • The pros and cons of the Principal Engineer role • What Steve loves about the leadership principles at Amazon • Amazon’s intense writing culture and 6-pager format • Why Amazon patents software and what that process looks like • And much more! — Timestamps (00:00) Intro (01:11) What Steve worked on at Amazon, including Kindle, Prime Video, and payments (04:38) How Steve was able to work on so many teams at Amazon (09:12) An overview of the scale of Amazon and the dependency chain (16:40) Amazon’s focus on latency and the tradeoffs they make to keep latency low at scale (26:00) Why companies should start with a monolith (26:44) The structure of engineering at Amazon and why Amazon’s Principal is so hard to reach (30:44) The Principal Engineering community at Amazon (36:06) The learning benefits of working for a tech giant (38:44) Five challenges of being a Principal Engineer at Amazon (49:50) The types of managing work you have to do as a Principal Engineer (51:47) The pros and cons of the Principal Engineer role (54:59) What Steve loves about Amazon’s leadership principles (59:15) Amazon’s intense focus on writing (1:01:11) Patents at Amazon (1:07:58) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Inside Amazon’s engineering culture — See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe |
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Meetup #7
2025-07-01 · 17:00
Hi Everyone, We’re thrilled to announce that Meet-Up #7 is now confirmed for Tuesday 1st July 2025! 🎉 Join us at our London HQ, 100 Liverpool Street, for an evening packed full of insightful talks, great conversations and plenty of opportunities to connect with like-minded individuals. Of course, it wouldn’t be a proper meet-up without pizza and refreshments to keep the energy high all night! 🍕🥤 🕕 Doors open for networking at 18:00 🎤 The first talk kicks off at 18:30 🚪 Wrapping up at 20:30 We can’t wait to see you there! #1 Kat Holmes \| Data Director @ Siemens Energy Data Readiness - The Elephant in the AI Room Kat has been transforming organisations through data and technology leadership for the past 20 years – most recently as a data director specialising in strategy and governance. Currently holding a senior leadership role at Siemens Energy, the world's most influential energy technology company, Kat's key responsibilities include driving data readiness for AI throughout the organisation. She has also recently been able to integrate her lifelong commitment to ESG into her data career, through her public talks on diversity, inclusion and belonging in data teams. In 2021, Kat founded MenopauseX, on behalf of Women in Data®. MenopauseX is an initiative whose mission is to help organisations better support their staff through leveraging data and analytics. #2 Muna Tutton, Muna Tutton, Strategic Initiatives Lead, FTSE Russell Engineering, LSEG Lighting the Fire: How Hackathons Ignite Change in Financial Services Muna will be be sharing how a journey from physics to the fast-paced world of financial services taught me that the toughest challenges aren’t just technical — they’re human. From architecture roles to hackathons, I’ll share how embracing ambiguity and empowering people to act can turn ideas into impact. #3 Barbara Correia, Lead Data Scientist @ Mesh-AI & Alejandra Gutierrez, Software Engineer @ Schroders Scaling Equity Research: A Multi-Agent Approach to Analytical Workflows Alejandra is a graduate software engineer at Schroders, where she works in the AI research team. She has a degree in Biomedical engineering from Imperial College. Barbara is a Lead Data Scientist at Mesh-AI and has been working on the Agentic Scope, focusing on the FS industry. |
Meetup #7
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From Software Engineer to AI Engineer – with Janvi Kalra
2025-05-28 · 16:52
Gergely Orosz
– host
,
Janvi Kalra
– AI Engineer
@ OpenAI
Supported by Our Partners • Statsig — The unified platform for flags, analytics, experiments, and more. • Sinch — Connect with customers at every step of their journey. • Cortex — Your Portal to Engineering Excellence. — What does it take to land a job as an AI Engineer—and thrive in the role? In this episode of Pragmatic Engineer, I’m joined by Janvi Kalra, currently an AI Engineer at OpenAI. Janvi shares how she broke into tech with internships at top companies, landed a full-time software engineering role at Coda, and later taught herself the skills to move into AI Engineering: by things like building projects in her free time, joining hackathons, and ultimately proving herself and earning a spot on Coda’s first AI Engineering team. In our conversation, we dive into the world of AI Engineering and discuss three types of AI companies, how to assess them based on profitability and growth, and practical advice for landing your dream job in the field. We also discuss the following: • How Janvi landed internships at Google and Microsoft, and her tips for interview prepping • A framework for evaluating AI startups • An overview of what an AI Engineer does • A mini curriculum for self-learning AI: practical tools that worked for Janvi • The Coda project that impressed CEO Shishir Mehrotra and sparked Coda Brain • Janvi’s role at OpenAI and how the safety team shapes responsible AI • How OpenAI blends startup speed with big tech scale • Why AI Engineers must be ready to scrap their work and start over • Why today’s engineers need to be product-minded, design-aware, full-stack, and focused on driving business outcomes • And much more! — Timestamps (00:00) Intro (02:31) How Janvi got her internships at Google and Microsoft (03:35) How Janvi prepared for her coding interviews (07:11) Janvi’s experience interning at Google (08:59) What Janvi worked on at Microsoft (11:35) Why Janvi chose to work for a startup after college (15:00) How Janvi picked Coda (16:58) Janvi’s criteria for picking a startup now (18:20) How Janvi evaluates ‘customer obsession’ (19:12) Fast—an example of the downside of not doing due diligence (21:38) How Janvi made the jump to Coda’s AI team (25:48) What an AI Engineer does (27:30) How Janvi developed her AI Engineering skills through hackathons (30:34) Janvi’s favorite AI project at Coda: Workspace Q&A (37:40) Learnings from interviewing at 46 companies (40:44) Why Janvi decided to get experience working for a model company (43:17) Questions Janvi asks to determine growth and profitability (45:28) How Janvi got an offer at OpenAI, and an overview of the interview process (49:08) What Janvi does at OpenAI (51:01) What makes OpenAI unique (52:30) The shipping process at OpenAI (55:41) Surprising learnings from AI Engineering (57:50) How AI might impact new graduates (1:02:19) The impact of AI tools on coding—what is changing, and what remains the same (1:07:51) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • AI Engineering in the real world • The AI Engineering stack • Building, launching, and scaling ChatGPT Images — See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe |
The Pragmatic Engineer |
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WARNING -> Sign up on TicketTailor here -> https://www.tickettailor.com/events/opendatamanchestercic/1417909 As Halloween approaches, Open Data Manchester, HER+Data MCR, PyData MCR and Rust Manchester invite you once again to our annual Data Horror Stories event on 30th October! In today's world, algorithms predict our preferences, chatbots engage in surprisingly human-like conversations, and AI systems make crucial decisions affecting millions. But what happens when these technologies don't work as intended? Join us for an evening of real-world cautionary tales and data-driven insights We're calling on data professionals, tech experts, anyone who's encountered tech troubles to share their most unsettling experiences. Has a coding error caused chaos in your organisation? Did a data breach leave you sleepless? Perhaps a digital campaign went horribly wrong? We want to hear about it.
Join us for an evening of eye-opening stories, thoughtful discussion, and practical solutions to address the challenges posed. About HER+Data MCR is a community working to connect, inspire, support and empower the NW UK’s Women in Data. It brings together anyone who identifies as a woman or non-binary and has a connection to data. We talk data science, analytics, research, visualisation, software, applications and experiences women share working in male dominated environments. Follow us on Meetup or Linkedin. PyData MCR is the Manchester chapter of the International PyData Community. For Manchester based data people, to share and learn new things. All open data tooling welcome. Follow PyData MCR on LinkedIn or on Meetup. Location Northcoders, M1 7ED |
Data Horror Stories with Open Data Manchester, Her+Data MCR & Rust Manchester
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dbt Coalesce Watch Party & Conference Recap
2024-10-23 · 15:00
Didn't make it to Vegas for Coalesce this year? Made it to Vegas and want to keep the fire burning? Join the Eastern Canada dbt meetup for a watch party & discussion. Based on YOUR votes, we'll be watching Late-stage transformations: Utilizing dbt Semantic Layer Metrics and, if time permits, we'll check out Don't panic: What to do when your data breaks. Check out the full Coalesce 2024 agenda here. To attend, please read the Health and Safety Policy and Terms of Participation: https://www.getdbt.com/legal/health-and-safety-policy ➡️ Join the dbt Slack community: https://www.getdbt.com/community/ 🤝For the best Meetup experience, make sure to join the #local-eastern-canada channel in dbt Slack (https://slack.getdbt.com/). ---------------------------------- dbt is the standard in data transformation, used by over 40,000 organizations worldwide. Through the application of software engineering best practices like modularity, version control, testing, and documentation, dbt’s analytics engineering workflow helps teams work more efficiently to produce data the entire organization can trust. Learn more: https://www.getdbt.com/ |
dbt Coalesce Watch Party & Conference Recap
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Hair on Fire
2024-08-09 · 19:52
Cris deRitis
– host
,
Mark Zandi
– Chief Economist
@ Moody's Analytics
,
Marisa DiNatale
– Senior Director
@ Moody's Analytics
,
Robin J Brooks
– Senior Fellow
@ Brookings Institution
It has been a hair on fire couple weeks for global investors. Stock, bond, commodity and foreign exchange markets have been buffeted by wild swings. No better person to discuss this with than Robin Brooks, a senior fellow at the Brookings Institution and formerly of the Institute of International Finance, Goldman Sachs and the IMF. Robin weighs in on the reasons for the volatility, including policy missteps by the Bank of Japan, and considers what it all means for monetary policy and the economy. For more on today's guest: Robin J Brooks - Senior Fellow at Global Economy and Development, Brookings Follow Robin on 'X' Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn Questions or Comments, please email us at [email protected]. We would love to hear from you. To stay informed and follow the insights of Moody's Analytics economists, visit Economic View. |
Moody's Talks - Inside Economics |
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#227 DataFramed x Analytics On Fire: Riding the AI Hype Cycle with Mico Yuk, Co-Founder at Data Storytelling Academy
2024-07-18 · 10:00
This special episode of DataFramed was made in collaboration with Analytics on Fire! Nowadays, the hype around generative AI is only the tip of the iceberg. There are so many ideas being touted as the next big thing that it’s difficult to keep up. More importantly, it’s challenging to discern which ideas will become the next ChatGPT and which will end up like the next NFT. How do we cut through the noise? Mico Yuk is the Community Manager at Acryl Data and Co-Founder at Data Storytelling Academy. Mico is also an SAP Mentor Alumni, and the Founder of the popular weblog, Everything Xcelsius and the 'Xcelsius Gurus’ Network. She was named one of the Top 50 Analytics Bloggers to follow, as-well-as a high-regarded BI influencer and sought after global keynote speaker in the Analytics ecosystem. In the episode, Richie and Mico explore AI and productivity at work, the future of work and AI, GenAI and data roles, AI for training and learning, training at scale, decision intelligence, soft skills for data professionals, genAI hype and much more. Links Mentioned in the Show: Analytics on Fire PodcastData Visualization for Dummies by Mico Yuk and Stephanie DiamondConnect with Miko[Skill Track] AI FundamentalsRelated Episode: What to Expect from AI in 2024 with Craig S. Smith, Host of the Eye on A.I PodcastRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business |
DataFramed |
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Leveraging Location Intelligence Data for Data Scientists
2023-09-19 · 16:00
To access this webinar, please register here: https://hubs.li/Q01-lRxv0 Topic: “Leveraging Location Intelligence Data for Data Scientists: Enhancing Analytics and Applications Across Industries” Speaker: Dr. Mohammed Taboun, Principal Data Scientist at Precisely Over the past 15 years, Mohammed has consistently demonstrated exceptional expertise in his field applied to various industries including technology, oil and gas, energy and utilities and telecommunications. With a strong academic background, Mohammed holds a PhD in Mechanical Engineering, specializing in Intelligent Control Systems, as well as a Master of Applied Science (MASc) and a Bachelor of Applied Science (BASc) in Industrial Engineering, focusing on Operations Research. Abstract: The rapid growth of data-driven decision-making has underscored the importance of location intelligence data in modern industries. This presentation aims to provide a comprehensive overview of location data, its challenges and applications, and its value to data scientists in their analytics processes. We begin by exploring the foundational aspects of location data, including addresses, geo-addressing, and data enrichment, followed by examining the challenges data scientists face in managing and analyzing complex datasets in modern networks. We then delve into how location data can enhance analytics, offering richer context and more accurate insights for better decision-making. A telecom fiber planning example will illustrate the practical applications of location data in improving infrastructure development and reducing costs. The presentation highlights the versatility of location data by discussing its applications across various industries, such as retail, transportation, and environmental management. Businesses can optimize processes, boost efficiency, and better understand their target markets by leveraging location intelligence. In addition to location data, we touch upon other types of data enrichment, such as fire, flood, and demographics data, which can be integrated with location data to provide a more comprehensive understanding of the factors affecting industries and communities. By combining these data sources, data scientists can develop more sophisticated models and analyses to inform decision-making. This presentation offers a deep dive into the value of location intelligence data for data scientists, demonstrating its potential in enhancing analytics and applications across a wide range of industries. The audience will learn about the importance of location intelligence data in modern industries, its challenges, and how it can be leveraged to enhance analytics and applications. They will also gain an understanding of the versatility of location data in different industries and the potential for cost reduction and infrastructure development. By integrating location data and other forms of data enrichment, data scientists can unlock new levels of insight and drive more informed, strategic decision-making processes. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01_Yrgb0 • Code of conduct: https://odsc.com/code-of-conduct/ |
Leveraging Location Intelligence Data for Data Scientists
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Leveraging Location Intelligence Data for Data Scientists
2023-09-19 · 16:00
To access this webinar, please register here: https://hubs.li/Q01-lRxv0 Topic: “Leveraging Location Intelligence Data for Data Scientists: Enhancing Analytics and Applications Across Industries” Speaker: Dr. Mohammed Taboun, Principal Data Scientist at Precisely Over the past 15 years, Mohammed has consistently demonstrated exceptional expertise in his field applied to various industries including technology, oil and gas, energy and utilities and telecommunications. With a strong academic background, Mohammed holds a PhD in Mechanical Engineering, specializing in Intelligent Control Systems, as well as a Master of Applied Science (MASc) and a Bachelor of Applied Science (BASc) in Industrial Engineering, focusing on Operations Research. Abstract: The rapid growth of data-driven decision-making has underscored the importance of location intelligence data in modern industries. This presentation aims to provide a comprehensive overview of location data, its challenges and applications, and its value to data scientists in their analytics processes. We begin by exploring the foundational aspects of location data, including addresses, geo-addressing, and data enrichment, followed by examining the challenges data scientists face in managing and analyzing complex datasets in modern networks. We then delve into how location data can enhance analytics, offering richer context and more accurate insights for better decision-making. A telecom fiber planning example will illustrate the practical applications of location data in improving infrastructure development and reducing costs. The presentation highlights the versatility of location data by discussing its applications across various industries, such as retail, transportation, and environmental management. Businesses can optimize processes, boost efficiency, and better understand their target markets by leveraging location intelligence. In addition to location data, we touch upon other types of data enrichment, such as fire, flood, and demographics data, which can be integrated with location data to provide a more comprehensive understanding of the factors affecting industries and communities. By combining these data sources, data scientists can develop more sophisticated models and analyses to inform decision-making. This presentation offers a deep dive into the value of location intelligence data for data scientists, demonstrating its potential in enhancing analytics and applications across a wide range of industries. The audience will learn about the importance of location intelligence data in modern industries, its challenges, and how it can be leveraged to enhance analytics and applications. They will also gain an understanding of the versatility of location data in different industries and the potential for cost reduction and infrastructure development. By integrating location data and other forms of data enrichment, data scientists can unlock new levels of insight and drive more informed, strategic decision-making processes. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01_Yrgb0 • Code of conduct: https://odsc.com/code-of-conduct/ |
Leveraging Location Intelligence Data for Data Scientists
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Leveraging Location Intelligence Data for Data Scientists
2023-09-19 · 16:00
To access this webinar, please register here: https://hubs.li/Q01-lRxv0 Topic: “Leveraging Location Intelligence Data for Data Scientists: Enhancing Analytics and Applications Across Industries” Speaker: Dr. Mohammed Taboun, Principal Data Scientist at Precisely Over the past 15 years, Mohammed has consistently demonstrated exceptional expertise in his field applied to various industries including technology, oil and gas, energy and utilities and telecommunications. With a strong academic background, Mohammed holds a PhD in Mechanical Engineering, specializing in Intelligent Control Systems, as well as a Master of Applied Science (MASc) and a Bachelor of Applied Science (BASc) in Industrial Engineering, focusing on Operations Research. Abstract: The rapid growth of data-driven decision-making has underscored the importance of location intelligence data in modern industries. This presentation aims to provide a comprehensive overview of location data, its challenges and applications, and its value to data scientists in their analytics processes. We begin by exploring the foundational aspects of location data, including addresses, geo-addressing, and data enrichment, followed by examining the challenges data scientists face in managing and analyzing complex datasets in modern networks. We then delve into how location data can enhance analytics, offering richer context and more accurate insights for better decision-making. A telecom fiber planning example will illustrate the practical applications of location data in improving infrastructure development and reducing costs. The presentation highlights the versatility of location data by discussing its applications across various industries, such as retail, transportation, and environmental management. Businesses can optimize processes, boost efficiency, and better understand their target markets by leveraging location intelligence. In addition to location data, we touch upon other types of data enrichment, such as fire, flood, and demographics data, which can be integrated with location data to provide a more comprehensive understanding of the factors affecting industries and communities. By combining these data sources, data scientists can develop more sophisticated models and analyses to inform decision-making. This presentation offers a deep dive into the value of location intelligence data for data scientists, demonstrating its potential in enhancing analytics and applications across a wide range of industries. The audience will learn about the importance of location intelligence data in modern industries, its challenges, and how it can be leveraged to enhance analytics and applications. They will also gain an understanding of the versatility of location data in different industries and the potential for cost reduction and infrastructure development. By integrating location data and other forms of data enrichment, data scientists can unlock new levels of insight and drive more informed, strategic decision-making processes. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01_Yrgb0 • Code of conduct: https://odsc.com/code-of-conduct/ |
Leveraging Location Intelligence Data for Data Scientists
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Leveraging Location Intelligence Data for Data Scientists
2023-09-19 · 16:00
To access this webinar, please register here: https://hubs.li/Q01-lRxv0 Topic: “Leveraging Location Intelligence Data for Data Scientists: Enhancing Analytics and Applications Across Industries” Speaker: Dr. Mohammed Taboun, Principal Data Scientist at Precisely Over the past 15 years, Mohammed has consistently demonstrated exceptional expertise in his field applied to various industries including technology, oil and gas, energy and utilities and telecommunications. With a strong academic background, Mohammed holds a PhD in Mechanical Engineering, specializing in Intelligent Control Systems, as well as a Master of Applied Science (MASc) and a Bachelor of Applied Science (BASc) in Industrial Engineering, focusing on Operations Research. Abstract: The rapid growth of data-driven decision-making has underscored the importance of location intelligence data in modern industries. This presentation aims to provide a comprehensive overview of location data, its challenges and applications, and its value to data scientists in their analytics processes. We begin by exploring the foundational aspects of location data, including addresses, geo-addressing, and data enrichment, followed by examining the challenges data scientists face in managing and analyzing complex datasets in modern networks. We then delve into how location data can enhance analytics, offering richer context and more accurate insights for better decision-making. A telecom fiber planning example will illustrate the practical applications of location data in improving infrastructure development and reducing costs. The presentation highlights the versatility of location data by discussing its applications across various industries, such as retail, transportation, and environmental management. Businesses can optimize processes, boost efficiency, and better understand their target markets by leveraging location intelligence. In addition to location data, we touch upon other types of data enrichment, such as fire, flood, and demographics data, which can be integrated with location data to provide a more comprehensive understanding of the factors affecting industries and communities. By combining these data sources, data scientists can develop more sophisticated models and analyses to inform decision-making. This presentation offers a deep dive into the value of location intelligence data for data scientists, demonstrating its potential in enhancing analytics and applications across a wide range of industries. The audience will learn about the importance of location intelligence data in modern industries, its challenges, and how it can be leveraged to enhance analytics and applications. They will also gain an understanding of the versatility of location data in different industries and the potential for cost reduction and infrastructure development. By integrating location data and other forms of data enrichment, data scientists can unlock new levels of insight and drive more informed, strategic decision-making processes. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01_Yrgb0 • Code of conduct: https://odsc.com/code-of-conduct/ |
Leveraging Location Intelligence Data for Data Scientists
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Leveraging Location Intelligence Data for Data Scientists
2023-09-19 · 16:00
To access this webinar, please register here: https://hubs.li/Q01-lRxv0 Topic: “Leveraging Location Intelligence Data for Data Scientists: Enhancing Analytics and Applications Across Industries” Speaker: Dr. Mohammed Taboun, Principal Data Scientist at Precisely Over the past 15 years, Mohammed has consistently demonstrated exceptional expertise in his field applied to various industries including technology, oil and gas, energy and utilities and telecommunications. With a strong academic background, Mohammed holds a PhD in Mechanical Engineering, specializing in Intelligent Control Systems, as well as a Master of Applied Science (MASc) and a Bachelor of Applied Science (BASc) in Industrial Engineering, focusing on Operations Research. Abstract: The rapid growth of data-driven decision-making has underscored the importance of location intelligence data in modern industries. This presentation aims to provide a comprehensive overview of location data, its challenges and applications, and its value to data scientists in their analytics processes. We begin by exploring the foundational aspects of location data, including addresses, geo-addressing, and data enrichment, followed by examining the challenges data scientists face in managing and analyzing complex datasets in modern networks. We then delve into how location data can enhance analytics, offering richer context and more accurate insights for better decision-making. A telecom fiber planning example will illustrate the practical applications of location data in improving infrastructure development and reducing costs. The presentation highlights the versatility of location data by discussing its applications across various industries, such as retail, transportation, and environmental management. Businesses can optimize processes, boost efficiency, and better understand their target markets by leveraging location intelligence. In addition to location data, we touch upon other types of data enrichment, such as fire, flood, and demographics data, which can be integrated with location data to provide a more comprehensive understanding of the factors affecting industries and communities. By combining these data sources, data scientists can develop more sophisticated models and analyses to inform decision-making. This presentation offers a deep dive into the value of location intelligence data for data scientists, demonstrating its potential in enhancing analytics and applications across a wide range of industries. The audience will learn about the importance of location intelligence data in modern industries, its challenges, and how it can be leveraged to enhance analytics and applications. They will also gain an understanding of the versatility of location data in different industries and the potential for cost reduction and infrastructure development. By integrating location data and other forms of data enrichment, data scientists can unlock new levels of insight and drive more informed, strategic decision-making processes. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01_Yrgb0 • Code of conduct: https://odsc.com/code-of-conduct/ |
Leveraging Location Intelligence Data for Data Scientists
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Leveraging Location Intelligence Data for Data Scientists
2023-09-19 · 16:00
To access this webinar, please register here: https://hubs.li/Q01-lRxv0 Topic: “Leveraging Location Intelligence Data for Data Scientists: Enhancing Analytics and Applications Across Industries” Speaker: Dr. Mohammed Taboun, Principal Data Scientist at Precisely Over the past 15 years, Mohammed has consistently demonstrated exceptional expertise in his field applied to various industries including technology, oil and gas, energy and utilities and telecommunications. With a strong academic background, Mohammed holds a PhD in Mechanical Engineering, specializing in Intelligent Control Systems, as well as a Master of Applied Science (MASc) and a Bachelor of Applied Science (BASc) in Industrial Engineering, focusing on Operations Research. Abstract: The rapid growth of data-driven decision-making has underscored the importance of location intelligence data in modern industries. This presentation aims to provide a comprehensive overview of location data, its challenges and applications, and its value to data scientists in their analytics processes. We begin by exploring the foundational aspects of location data, including addresses, geo-addressing, and data enrichment, followed by examining the challenges data scientists face in managing and analyzing complex datasets in modern networks. We then delve into how location data can enhance analytics, offering richer context and more accurate insights for better decision-making. A telecom fiber planning example will illustrate the practical applications of location data in improving infrastructure development and reducing costs. The presentation highlights the versatility of location data by discussing its applications across various industries, such as retail, transportation, and environmental management. Businesses can optimize processes, boost efficiency, and better understand their target markets by leveraging location intelligence. In addition to location data, we touch upon other types of data enrichment, such as fire, flood, and demographics data, which can be integrated with location data to provide a more comprehensive understanding of the factors affecting industries and communities. By combining these data sources, data scientists can develop more sophisticated models and analyses to inform decision-making. This presentation offers a deep dive into the value of location intelligence data for data scientists, demonstrating its potential in enhancing analytics and applications across a wide range of industries. The audience will learn about the importance of location intelligence data in modern industries, its challenges, and how it can be leveraged to enhance analytics and applications. They will also gain an understanding of the versatility of location data in different industries and the potential for cost reduction and infrastructure development. By integrating location data and other forms of data enrichment, data scientists can unlock new levels of insight and drive more informed, strategic decision-making processes. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01_Yrgb0 • Code of conduct: https://odsc.com/code-of-conduct/ |
Leveraging Location Intelligence Data for Data Scientists
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