Beyond Coding

Amp ships to production around 50 times a day with AI agents, but the change that makes their team ship faster isn't AI at all. Dario Hamidi, AI engineer at Amp, makes the case that pull requests are holding your team back, and explains what replaces them.
In this video, we cover:
Why Amp works without pull requests or feature branches, and how to try it with your own team
Agent permissions, approval fatigue, and building environments where you can trust agents
Orbs: remote agent execution, parallelism, and multiplayer sessions
Short-lived credentials and workload identity federation for cloud agents
Triaging 60+ bug reports a day with agents without frying your brain
For engineers and engineering leads who want to ship faster with AI agents and are ready to question the workflows they've always used.
Timestamps:
00:00:00 - Is It Time to Kill the Pull Request?
00:00:45 - What Happens If Every PR Gets Auto-Closed Tomorrow
00:05:04 - Pull Requests Were Built for Strangers, Not Teammates
00:07:20 - Does Dropping PRs Hurt Quality at Scale?
00:08:53 - Agent Permissions: Why No Default Makes Everyone Happy
00:12:42 - Don't Juggle Knives: Building Environments You Can Trust
00:14:56 - The Hidden Hard Parts of Cloud Agents
00:16:39 - What's an Orb? Remote Agent Execution Explained
00:19:10 - How to Give Agents Credentials Without Leaking Secrets
00:22:51 - Why Orbs Live Forever (and Multiplayer Orbs)
00:25:49 - The State Management Problem Nobody Talks About
00:29:22 - When Will Everyone Have Remote Dev Environments?
00:32:23 - Orbs That Spawn Orbs: Orchestrating Agents at Scale
00:34:42 - How Dario Triages 60 Bug Reports a Day With Agents
00:39:23 - Avoiding Burnout When Agents Remove the Ceiling
#AIAgents #SoftwareEngineering #PullRequests

How do top engineers still get hired in 2026 when most applicants get ghosted, 120,000 people have been laid off this year, and hiring managers say they can't find talent? A recruiter, a hiring lead, a career coach and an open source engineer explain why your resume is dead on arrival when every CV looks the same, and what actually gets you in the room instead.
In this episode, we cover:
Why 120,000 tech layoffs and 60,000 open engineering roles exist at the same time
Why only 20% of LinkedIn messages get a reply, and how to write the ones that do
Why one hiring team bans AI tools in interviews and is building an agentic coding session instead
Adaptability and resilience: the two soft skills that keep you in the room
Getting hired through GitHub, referrals and open source when your CV can't stand out
Whether junior engineers still have a path, and which engineering cohort is at risk in 3 to 5 years
Titles vs scope: how to grow when your title never changes
For software engineers, students about to graduate, and anyone in tech who wants to know what recruiters and hiring managers are actually filtering on right now.
Timestamps:
00:00:00 - Intro: 1,000 Layoffs a Day
00:00:47 - How Bad Is the Tech Job Market in 2026?
00:02:05 - Why 120K Layoffs and 60K Open Jobs Don't Add Up
00:03:40 - Why AI Tools Are Banned From Interviews
00:06:28 - Hard Skills Get You In, Soft Skills Keep You There
00:09:20 - "I'm a University Dropout": How GitHub Got Me Hired
00:10:18 - CVs Are Too Good Now: Why Referrals Win
00:13:04 - How to Get Your Open Source PR Merged
00:15:02 - Why 80% of Your LinkedIn Messages Get Ghosted
00:16:51 - How Open Source Led to a HashiCorp Job Offer
00:19:15 - Is There Still a Place for Junior Engineers?
00:22:00 - The Engineers Who'll Be Obsolete in 3 to 5 Years
00:24:20 - Should You Contribute to Open Source at All?
00:26:05 - AI Skills Required, Algorithms Still Tested
00:27:54 - Stop Chasing Titles: Scope, Impact and Owning Your Career
#softwareengineering #techjobs #careeradvice

Jordan Tigani helped create Google BigQuery, then got fired as Chief Product Officer on a Friday morning. He planned to hack on DuckDB to learn Rust. Investors offered to fund it before he'd decided to start a company. That company became MotherDuck
In this episode, we cover:
The DuckDB Labs partnership: why MotherDuck gave the open-source creators a co-founder share instead of going open-core
From alpha to paid product: 11 founders, 3 to 4 months to alpha, two years to something people would pay for
AI on top of the data warehouse: vibe-coded dashboards (Dives), pipelines (Flights), a context layer (Guides), and why business users catch mistakes analysts miss
Are dashboards dead? Jordan wrote "Big Data Is Dead"; his answer on dashboards is different
Career advice: why you shouldn't want to work on the query optimizer, and the skill Jordan says matters more
For engineers curious about database and infrastructure companies, open-source business models, and how AI is changing who gets to ask questions of data.
Timestamps:
00:00:00 - Intro
00:00:32 - Fired on a Friday: how MotherDuck accidentally started
00:04:50 - Giving DuckDB Labs a co-founder share of the company
00:07:43 - Why most open-source SaaS products are just "managed"
00:09:31 - Why VCs said yes: Snowflake, DuckDB, and BigQuery credibility
00:10:50 - The 11-person founding team that skipped the wrong designs
00:12:20 - Alpha in 4 months, beta in a year, paid in two
00:15:20 - Vibe-coded BI: Dives, Flights, Guides, and an agent harness
00:20:10 - The questions business users ask that analysts never do
00:24:14 - Are dashboards dead in the age of agents?
00:27:57 - Everybody wants to work on the optimizer (don't)
00:31:23 - The engineer superpower most engineers look down on
00:33:20 - Writing: the one skill Jordan would learn (and still hates)
00:36:05 - Does contributing to DuckDB get you hired at MotherDuck?
00:37:20 - Why a database company leaned into the duck
#MotherDuck #DuckDB #SoftwareEngineering

Niantic Spatial CTO Brian McClendon on how the best engineers solve problems most give up on — from a 4D model of the world to shipping research in months. He built Google Earth and ran Google Maps for over a decade, and he's been there, done that. What he's building now is harder, and it's already live for customers. Along the way: who makes it on his team and who doesn't, and the one piece of advice he'd give every engineer using AI.

In this video, we cover:
- The 4D model of the world: visual positioning, change detection, and treating a pile of photos like a database
- Gaussian splats and real-to-sim: capturing a room and loading it into Nvidia Isaac to train robots
- Turning a research idea into a production service in six months
- What Google Maps taught him about building for robots instead of humans
- Designing problems AI can self-check, and why token maxing is a waste

For engineers and engineering leaders who want to work on problems that don't have a known answer yet — and who want to know what a CTO who's built the definitive product in his field looks for in the people he hires.
Recorded at the AI4 conference 2026.

Timestamps:
00:00:00 - Google Earth? Been There, Done That
00:00:46 - Turning a Research Idea Into Production in 6 Months
00:02:37 - How Any Photo Gets Located Within Half a Meter
00:06:37 - The Long-Term Goal: A 4D Model of the World
00:08:37 - Treating a Pile of Photos Like a Database
00:11:58 - What Google Maps Taught Him About Training Robots
00:15:22 - Gaussian Splats Explained in Plain Terms
00:17:42 - The Unsolved Problem: Scale and Semantic Change
00:20:34 - Why Google Earth Is Good Enough
00:22:09 - Who Makes It on His Team and Who Doesn't
00:23:38 - Designing Problems AI Can Self-Check
00:26:03 - The Insights Hidden in the Physical World
00:28:18 - Digital Twins, Cities, and Ready Player One
00:31:35 - Visual Positioning When GPS Gets Spoofed
00:33:24 - Token Maxing Is Bullshit: Advice for Engineers

Guest: Brian McClendon, CTO at Niantic Spatial. The engineer behind Google Earth; ran Google Maps for over a decade.
https://www.linkedin.com/in/brianmcclendon

#NianticSpatial #GoogleEarth #SoftwareEngineering

How do new staff engineers build judgment without the years of experience that used to come with the role? Mallika Rao, engineering leader in big tech, explains why the data-structures-and-algorithms foundation everyone was trained on is no longer enough on its own, and where the complexity has actually shifted now that AI writes the implementation.
In this video, we cover:
Why "how does AI affect engineers" is the wrong question, and what to ask instead
Rehearsing multiple futures: what judgment looks like in a staff engineer
The case method: building judgment from incident reports and system design history instead of waiting years for it
Cognitive coordination, code review load, and the surprise ask for more meetings at staff level
Tiger teams vs scaled teams, trust as architecture, and building evals from a spreadsheet
Splitting planning from execution so engineers stop falling behind with agents
Taste vs judgment, and how to build both outside of software
If you've just made staff, or you're about to, this conversation gives you a frame for what the level actually demands now and how to grow into it faster than the old apprenticeship allowed.
Recorded at the AI4 conference 2026.
Timestamps:
00:00:00 - How AI Is Changing Senior Engineering Careers
00:00:41 - Why "How Does AI Affect Engineers" Is the Wrong Question
00:03:26 - What Judgment Actually Is: Rehearsing Multiple Futures
00:05:24 - Why Data Structures and Algorithms Are No Longer Enough
00:07:22 - Learning Judgment From Incident Reports Like the 2017 S3 Outage
00:11:13 - The New Staff Engineer's Core Challenge: Cognitive Coordination
00:14:48 - What Managers, Universities, and Shakespeare Each Owe You
00:17:55 - Code Review Load, Meeting Notes, and the Surprise Ask for More Meetings
00:23:59 - Trust as Architecture: Why Evals Started as a Spreadsheet
00:27:09 - Tiger Teams vs Big Teams: Product Managers Reviewing Code
00:32:39 - Why Some Engineers Can't Keep Up With Agents
00:35:46 - Local AI Champions and Splitting Planning From Execution
00:38:38 - Go Deep or Go Broad? Search in a World of Agents
00:44:12 - Taste vs Judgment: Thinking in 50 Layers

Guest: Mallika Rao, engineering leader in big tech.
Rehearshing the Future framework
 If by Rudyard Kipling