Podcasts about Golden Gate Bridge

Today on The Gist, examining the upcoming criminal verdict for the Golden Gate Bridge climate protesters, breaking down the debate over jail time for high-impact civil disobedience. Then, Sadie Dingfelder returns for another installment of "Is It Bullsh*t?" to investigate the historical and scientific reputation of raccoons and rabies. Then, in the spiel, comparing the foreign policy legacies of Ronald Reagan and Donald Trump and analyzing whether current U.S. intervention strategies serve as a deliberate long game or merely export short-term geopolitical misery abroad. Produced by Corey Wara Video and Social Media by Geoff Craig Do you have questions or comments, or just want to say hello? Email us at ⁠⁠⁠⁠thegist@mikepesca.com For full Pesca content and updates, check out our website at https://www.mikepesca.com/⁠ For ad-free content or to become a Pesca Plus subscriber, check out ⁠⁠⁠⁠https://subscribe.mikepesca.com/ For Mike's daily takes on Substack, subscribe to The Gist List https://mikepesca.substack.com/ Follow us on Social Media:⁠⁠⁠⁠ YouTube https://www.youtube.com/channel/UC4_bh0wHgk2YfpKf4rg40_g⁠⁠⁠⁠ Instagram https://www.instagram.com/pescagist/ X https://x.com/pescami TikTok https://www.tiktok.com/@pescagist To advertise on the show, contact ⁠⁠⁠⁠sales@amplitudemediapartners.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ein Bayer will 1977 nach San Francisco - landet aber 6000 Kilometer entfernt an der US-Ostküste. Trotzdem sucht er tagelang nach der Golden Gate Bridge. Wie wird aus einem Irrtum eine Heldenreise? Und was tun wir, wenn unsere Bilder vom Reiseziel an der Wirklichkeit zerschellen?

Sasha Orloff sits down with Solon Angel, CEO of Remitian, to explore why tax payments remain one of fintech's most overlooked infrastructure problems. They discuss the outdated systems still powering tax compliance, how AI agents are enabling a new payments layer for accountants and taxpayers, and why the convergence of regulatory change, fraud prevention, and agentic AI could transform the $7 trillion tax payment ecosystem into a seamless, deadline-free experience. -- SPONSORS: Notion Boost your startup with Notion—the ultimate connected workspace trusted by thousands worldwide! From engineering specs to onboarding and fundraising, Notion keeps your team organized and efficient. For a limited time, get 6 months of Notion AI FREE to supercharge your workflow. Claim your offer now at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://notion.com/startups/puzzle⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Puzzle

In the latest episode of Rising Tide Ocean Podcast, Natasha Benjamin and Vicki Nichols talk with marathon swimmer Catherine Breed — a record-breaking endurance athlete, former U.S. National Team swimmer, and founder of Sea Dreamers. From world-record swims between the Farallon Islands and the Golden Gate Bridge to crossings of Lake Tahoe and beyond, Catherine shares stories from a life shaped by the ocean and discusses her most ambitious challenge yet: swimming the entire California coast. The conversation explores open-water swimming, adventure, ocean conservation, and of course, everyone's first question — what about the sharks? Tune in for an inspiring and thought-provoking conversation with Catherine Breed.Additional ResourcesSea Dreamers — Opening doors for women to get involved in ocean activities through community, inclusivity, empowerment and education surrounding ocean conservation.Blue Frontier / Substack — Building the solution-based citizen movement needed to protect our ocean, coasts and communities, both human and wild.Inland Ocean Coalition — Building land-to-sea stewardship - the inland voice for ocean protectionFluid Studios — Thinking radically different about the collective good, our planet, & the future.

On this weeks program Chris interviews Tim Dow (Tim Rob Don Dow) and Stephanie Mulder (Yardbird Studio) from Mosey Guide about their slow travel walking and cycling guides for neighbourhoods in Australia, Europe and Asia, including their recent Northside Design Ride: A Leisurely Cycling Tour of Melbourne Design through the inner north organised for Melbourne Design Week. Tim and Stephanie talk about discovering and engaging with local destinations and developing the local guides and their future plans. Guides include A Merri Ride, Footscray, Thornbury and North Melbourne.Second guest is Michael, owner and digital creator of Fuji Roll, we chat about showcasing cycling in Melbourne, Sydney and overseas, documenting experiences, riding as prime method of transport, road conditions, creating instagram reels documenting local events and rides, including the Northside Design Ride, Smith Street, Collingwood, Macarthur Street, East Melbourne, Canning Street, Carlton North, and San Francisco Cycles, Golden Gate Bridge.Local news includes Off Course bike shop fire on 27 May 2026, Critical Mass: Big City Lights tour through Richmond, Abbotsford, Collingwood and Clifton Hill, Dixon Veloway maintenance closure: between Footscray and West Melbourne will be closed overnight on Wednesday 3 and Thursday 4 June from 7pm-5am for planned maintenance. Also we briefly mention Barnaby Joyce's connection to the rise of e-bikes: Why are e-bikes suddenly all over Australia's streets?Program MusicScratching, Malvern StarSparks, (Baby, Baby) Can I Invade Your CountryTrue Love Always, Bicycle RiderHONNE, BIKE

The Page One Podcast, produced and hosted by author Holly Lynn Payne, celebrates the craft that goes into writing the first sentence, first paragraph and first page of your favorite books. The first page is often the most rewritten page of any book because it has to work so hard to do so much—hook the reader. We interview master storytellers on the struggles and stories behind the first page of their books. About the guest author: In addition to being the author of The Way of The Rose,  which she spoke about with her co-author and husband Clark Strand on Ep. 49 of the Page One Podcast, Perdita Finn is the author of several children's books and middle grade novels, including the Time Flyers series for Scholastic Books, My Little Pony Books, among many others and has worked as ghostwriter, book doctor, copy editor and writing teacher.  Perdita Finna also has done extensive study with Zen masters, priests, and healers, and apprenticed with the psychic Susan Saxman, with whom she wrote The Reluctant Psychic. She currently leads popular workshops on Collaborating with the Other Side, in which participants are empowered to activate the magic in their own lives with the help of their ancestors. She lives with her husband in the Catskill Mountains of New York. About the host: Holly Lynn Payne is an award-winning novelist and writing coach, and the former CEO and founder of Booxby, a startup that built an AI book discovery platform with a grant from the National Science Foundation. She is an internationally published author of four historical fiction novels. Her debut, The Virgin's Knot, was a Barnes & Noble Discover Great New Writers book. Her latest book, Rose Girl: A Story of Resilience and Rumi, a medieval, mystical thriller was awarded a Kirkus (starred) review and named Editors Choice from the Historical Novel Society. Holly lives on a houseboat near the Golden Gate Bridge with her daughter and Labrador retriever, and enjoys mountain biking, hiking, swimming and pretending to surf. To learn more about her books and writing coaching services, please visit her at hollylynnpayne.com  and subscribe to her FREE weekly mini-masterclass, Power of Page One, a FREE newsletter on Substack, offering insights on becoming a better storyteller and tips on hooking readers from page one! (And bonus: discover some great new books!) Tune in and reach out: If you're an aspiring writer or a book lover, this episode of Page One offers a treasure trove of inspiration and practical advice. I offer these conversations as a testament to the magic that happens when master storytellers share their secrets and experiences. We hope you are inspired to tune into the full episode for more insights. Keep writing, keep reading, and remember—the world needs your stories.  If I can help you tell your own story, or help improve your first page, please reach out @hollylynnpayne or visithollylynnpayne.com.  You can listen to Page One on Apple podcasts, Spotify, Pandora, Stitcher and all your favorite podcast players. Hear past episodes. If you're interested in getting writing tips and the latest podcast episode updates with the world's beloved master storytellers, please sign up for my very short monthly newsletter at hollylynnpayne.com and follow me @hollylynnpayne on Instagram, Twitter, Goodreads, and Facebook. Your email address is always private and you can always unsubscribe anytime.  The Page One Podcast is created on a houseboat in Sausalito, California, is a labor of love in service to writers and book lovers. My intention is to inspire, educate and celebrate. Thank you for being a part of my creative community!  Be well and keep reading, Holly @hollylynnpayne on IG Thank you for listening to the Page One Podcast! I hope you enjoyed this episode as much as I loved hosting, producing, and editing it. If you liked it too, here are three ways to share the love:Please share it on social and tag @hollylynnpayne.Leave a review on your favorite podcast players. Tell your friends. Please keep in touch by signing up to receive my Substack newsletter with the latest episodes each month. Delivered to your inbox with a smile. You can contact me at @hollylynnpayne on IG or send me a message on my website, hollylynnpayne.com.For the love of books and writers,Holly Lynn Payne@hollylynnpaynehost, author, writing coachwww.hollylynnpayne.com

Travels With Randy Summer Of '26 Episode 1 is here! Up The West Coast On Hwy 1  West Coast Travel and Real Estate Bubba and Randy discussed Randy's recent travels along the West Coast after completing a 17-podcast series about Route 66. They compared weather patterns between different regions, with Randy noting the benefits of spring weather in Washington compared to tornado season in the Midwest. The conversation also covered real estate price increases in California, with Randy sharing how his childhood home in Placentia appreciated from $37,000 in 1967 to $1.3 million currently, and they discussed future housing needs as they approach retirement age. US Road Trip Discussion Bubba and Randy discussed Randy's recent road trip across the United States, including his journey from Route 66 to California and his decision to take the scenic coastal route along Highway 1. They talked about the significant difference in gas prices between California and other states, with Randy noting prices reached $7.50 per gallon in some areas of California. Randy shared interesting facts about Highway 101 being created on the same day as Route 66's centennial, making it one of only a few highways still celebrated from that original group of over 180 numbered highways. Recent Road Trip and Adventures Randy shared details about his recent road trip, including a golf game where he lost by one stroke due to a poor final hole performance. He described visiting Albuquerque and having dinner with his girlfriend Cindy in California for her 60th birthday. The conversation also covered Randy's car brake issues, which required a $1,300 repair after hearing squeaking noises, and his enjoyment of driving along Highway 1 and Highway 101, particularly appreciating the scenic ocean views during early morning drives. Road Trip from Oxnard to Lompoc Randy described a road trip from Oxnard to Lompoc, explaining the route options along highways 1 and 101, and shared details about visiting Channel Islands National Park and Hearst Castle. He noted challenges with morning fog during the trip and recommended allocating time for both Channel Islands and Hearst Castle visits. The conversation ended with a discussion about national parks, particularly questioning how some sites like Gateway Arch and New River Gorge became national parks. National Park Designation Discussion Randy and Bubba discussed the process of national park designations, with Randy expressing disappointment about Indiana Dunes and speculating that Channel Islands became a national park due to favors or political reasons. They discussed the challenges with Route 66, with Randy advocating for it to be managed as a national park or byway to ensure consistent signage and routing. The conversation shifted to Randy's current trip following Highway 1 in California, including his visit to Hearst Castle, and they briefly discussed the location's history and the challenges of accessing Big Sur due to road damage. TV Pilot Script Discussion Randy shared details about a TV pilot script he wrote during an internship at MTM, inspired by the Newhart show and set at Big Sur Inn, which he struggled to get produced despite trying to interest Lindsey Wagner's agent. He reflected on how persistence and timing rather than overnight success determine career outcomes, particularly noting how AI tools would have significantly changed his college film production experience. The conversation concluded with Bubba sharing a personal connection to the Little River Inn in Mendocino, which Randy had previously visited and planned to post photos of later that week. Highway 1 Driving Discussion Bubba and Randy discussed driving on Highway 1, comparing the experience of driving versus being a passenger due to the road's challenging hairpin turns and lack of guardrails. Randy shared a story about encountering aggressive motorcycle riders on the same road, leading to an accident with another motorist. They also discussed the scenic route from San Luis Obispo to San Francisco, including the view of the Golden Gate Bridge and the option to visit Alcatraz Island. Alcatraz and Infrastructure Discussions Bubba and Randy discussed their visits to Alcatraz in San Francisco, sharing memories of the self-guided tours and the historical context of the island. They also talked about the constant maintenance required for the Golden Gate Bridge and compared it to other infrastructure projects. The conversation shifted to challenges in modern construction, particularly the difficulties in building data centers and bullet trains, and they briefly discussed the potential for building data centers in space. Space Data Center Discussion Bubba and Randy discussed the benefits of placing data centers in space, particularly on the moon, due to reduced cooling requirements and other advantages. They reflected on the rapid pace of technological advancement, comparing it to historical innovations like the personal computer and internet, and highlighted how AI technologies like ChatGPT are being adopted quickly and are already integral to daily workflows, as demonstrated by Randy's use of AI in photo processing. They also noted that while technological change can be unsettling, especially regarding job impacts, the current pace of innovation is unprecedented and continues to evolve rapidly. AI Tools for Work Efficiency Randy and Bubba discussed their experiences using AI tools like Gemini and ChatGPT to improve efficiency in their work. Randy shared how he uses Gemini to proofread and fact-check his social media posts, while Bubba described how he leverages AI to manage his book business, including analyzing inventory and making purchasing decisions. They also discussed the challenges some people face when retiring early, noting that staying mentally and socially active is important for overall well-being. Travel Content Planning Discussion Randy and Bubba discussed their upcoming travel content plans, with Randy planning to visit San Francisco and Alcatraz next week before covering northern California up to the Oregon border. They agreed to continue their weekly podcast discussions throughout the summer, focusing on travel topics including the Oregon coast and Northern California's wine regions. Bubba mentioned their Facebook page has grown to 37,000 followers and suggested exploring Hearst Castle during future West Coast trips. SO. MANY. PHOTOS - Come join the conversation on Facebook with our 33,000 friends! https://www.facebook.com/travelswithrandypodcast Have a great idea for the guys?  Want to sponsor us?  Want us to sell something National Park or Route 66 related? Want to be a guest? Want to pay for both of us to go to Alaska? Want me to stop asking questions?   bubba@travelswithrandypodcast.com !!

Die 1280 Meter lange Brücke in der Bucht von San Francisco zählt zu den „Sieben Wundern der modernen Welt“. Etliche Arbeiter haben beim Bau ihr Leben verloren.

On April 15, 2024, dozens of pro-Palestinian protesters blocked the Golden Gate Bridge, in an attempt to pressure the U.S. government into ending military aid for Israel's bombing and invasion of Gaza. Now, seven of those protesters are on trial facing felony charges in San Francisco. If convicted, they could face a maximum sentence of 15 years in prison. Learn more about your ad choices. Visit megaphone.fm/adchoices

Back on this day in 1937, the Golden Gate Bridge opened to the public. At the time of building, the bridge cost $27 million and was considered affordable. 

Steiny & Guru celebrate the 89th Birthday of the Golden Gate Bridge before comparing and contrasting how the city matches up with other west coast hubs.

Today in 1937, the opening of the Golden Gate Bridge in San Francisco - or, more accurately, the beginning of the opening of the Golden Gate Bridge. The city was so pumped, the celebrations went on for days. Plus: starting Friday in Pennsylvania, it's the Johnstown PolkaFest. The 1937 Golden Gate Bridge Opening Was Completely Bananas (KQED)Johnstown PolkaFest Open up our Patreon page and back the show today

What makes America truly great—its mountains, canyons, redwoods, or something far deeper? Today on InGrace, Jim Scudder takes his grandkids on an unforgettable westward adventure from Pikes Peak to the Golden Gate Bridge, revealing the spiritual heritage that crowns our nation's beauty and freedom.

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

Best-selling author, keynote speaker, and Golden Gate Bridge suicide attempt survivor Kevin Hines joins Karena Dawn to talk about bipolar disorder, suicidal ideation, and the daily practices that keep him mentally well. Kevin opens up about the traumatic childhood that shaped his mental health, the instant regret he felt leaving the bridge rail, the sea lion and Coast Guard boat that saved his life, and the 15-year fight to install life-saving nets at the Golden Gate Bridge. He also shares the four words that have kept him alive through 25 years of suicidal ideation, and the science-backed wellness routines that support his brain health every single day. What does it take to survive the unthinkable and spend the rest of your life making sure others never have to? Kevin Hines proves that healing is not a destination but a daily practice, and that the simple words "I need help now" can make all the difference. (02:57) Born Into Crisis From abject poverty and neglect to a loving adoptive home, Kevin's path to the Hines family was anything but linear Why his gut health, brain chemistry, and mental illness were shaped before he could even speak The moment at 17 when his mind began to break, and why no one around him knew (13:11) The Words That Keep Him Here The mantra Kevin returns to every time suicidal thoughts arise, and why it works Four simple words he has taught thousands of people to say in their darkest moments Why sharing your pain with even one person creates real, physiological relief (21:51) Love, Loss, and the Fight for the Bridge Nets The wildly improbable love story that began in a psychiatric ward Why it took nearly two decades to get life-saving nets installed at the Golden Gate Bridge What to say when you see someone in visible pain and don't know how to start (28:58) Building a Life That's Worth Staying For Kevin's morning and evening routine for regulating his nervous system The 23-minute exercise rule backed by University of Georgia research Why his doctor reframed medication as a quality-of-life decision, not a weakness May is Mental Health Awareness Month—Get Involved through Action, not just awareness.  Be a part of change with The Big Silence | Host a benefit with The Big Silence: https://thebigsilence.com/blogs/share-your-silence/the-big-silence-fundraiser-hosting-guide Guest Resources Follow Kevin on Instagram Explore his books Listen to the Hindsights Podcast If this episode moved you, please consider supporting The Big Silence Foundation and exploring our resources: Connect with The Big Silence Community Order: The Big Silence Memoir audiobook Shop The Big Silence Self Love Collection Subscribe on YouTube Donate to The Big Silence Foundation The Big Silence Resource Guide Find exclusive offers from our supporters Show Resources: VISIT THE CHALLENGE PAGE THE BIG SILENCE PODCAST TONE IT DOWN PODCAST Tone It Up App Tone It Up YouTube Tone It Up Instagram Have a message for Karena? She'd love to hear from you and share your comment or question on air! Leave Karena a voicemail: https://www.speakpipe.com/KarenaDawn

My blogs "Black Pine Trees" and A Golden Sunset at the Golden Gate Bridge"Find more at https://timharner.com

"I used the original ethereal recording to run through the entire piece while also finding and recording a few more Golden Gate bridge sounds. These include the fog horns coming in as the melody and a couple others nearby the bridge.:The field recording inspires a warped sense of reality... dreary... a bit crazy. The bridge is a passage for those marching into a city of multiple faces and realities."There was madness in any direction, at any hour. If not across the Bay, then up the Golden Gate or down 101 to Los Altos or La Honda. . . . You could strike sparks anywhere." - Hunter S. Thompson Golden Gate Bridge reimagined by wwjd (Jason Talsma).

The slats in the Golden Gate bridge will resonate at certain wind speeds and directions. the first is a low-pitched + low-frequency tone between 280-700hz created by westward ~22mph winds while the second tone is of a higher pitch + frequency of 1.1khz created by ~27mph winds. This is the singing as heard from Lone Mountain, during a rainy night.Recorded by wwjd (Jason Talsma).

The high school student newspaper, the Redwood Bark, has served Redwood High School in Larkspur, a town a few miles north of the Golden Gate Bridge, since 1958. The reporters there frequently win national awards for their journalism. But this year, students began facing pushback and censorship from the administration for some of their stories. A 1977 landmark California law gives student journalists the autonomy to publish news without interference from principals and other school leaders. But these rights are often violated. Guests: Skye Hammond, reporter and editor, the Redwood Bark Thomas Peele, investigative reporter, EdSource Read more from EdSource: Student journalists' free press rights tested at Marin County high school Cases of student press censorship attempts on the rise in California schools Education Beat is a weekly podcast hosted by EdSource's Zaidee Stavely and produced by Coby McDonald. Subscribe: Apple, Spotify, SoundCloud, YouTube

Nikki and Brie are back together and catching up on two very different but powerful chapters. Brie opens up about her return to WWE, stepping back into the ring as Tag Team Champion with Paige, and finding her rhythm again after WrestleMania. Meanwhile, Nikki shares what life looks like in her recovery era, from pushing through tough rehab days to slowing down and soaking up meaningful moments at home with Matteo. From travel chaos to family time, the twins reflect on how their current seasons are shaping them both individually and together. They also get real about burnout, boundaries, and the simple things that bring them back to center—like nature, long walks, and unplugging from the noise. Plus, they share updates on Bonita Bonita, upcoming appearances, and what's ahead for the Bella Twins. Get into this sister catch up and press play now! Call Nikki & Brie at 833-GARCIA2 and leave a voicemail! Follow Nikki & Brie on Instagram, follow the show on Instagram and TikTok and send Nikki & Brie a message on Threads! Follow Bonita Bonita on Instagram Book a reservation at the Bonita Bonita Speakeasy To watch exclusive videos of this week's episode, follow The Nikki & Brie Show on YouTube, Facebook, and TikTok! You can also catch The Nikki & Brie Show on SiriusXM Stars 109! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Out conversation about the Golden Gate Bridge continues with the opening of the bridge, and a bunch of people who didn't get enough credit. Plus, what the story says about how we need big ideas even in moments when things feel especially dire.Join our America250 newsletter community! Subscribe for free to get the latest news and analysis of how America250 is playing out. Paying subscribers get access to early, ad-free versions of the show. Plus bonus features throughout the year. To support our work and get access to everything, subscribe now.This Day is a proud member of Radiotopia from PRX.Your support helps foster independent, artist-owned podcasts and award-winning stories.If you want to support the show directly, you can do so on our website: ThisDayPod.comGet in touch if you have any ideas for future topics, or just want to say hello. Follow us on social @thisdaypodOur team: Jacob Feldman, Researcher/Producer; Khawla Nakua, Transcripts; music by Teen Daze and Blue Dot Sessions; Audrey Mardavich is our Executive Producer at Radiotopia. Learn about your ad choices: dovetail.prx.org/ad-choices

“If we claim we have no sin, we are only fooling ourselves and not living in the truth.” (1 John 1:8 NLT) Visitors to San Francisco can’t help but be amazed at the architectural marvel that is the Golden Gate Bridge. But its beauty and innovation came at a tremendous cost. During the initial phases of construction, several workers lost their balance and plunged to their deaths in the San Francisco Bay. The builders were concerned about the human tragedy, of course. But they were also concerned about the delays in the schedule because of the deaths. They needed to find a way to keep their workers safe under the most dangerous conditions. The solution they arrived at was something that had never been done before. The builders installed a giant safety net under the construction area. The workers knew that if they fell, the net would catch them. The experience wouldn’t necessarily be pleasant for the unfortunate worker, but at least he would live to tell about it. Thanks to the net, workers could go about their business without the fear of dying. With the threat removed, they were able to move quickly and finish the project. Did you know that God has put a safety net under you? By that I mean, when you slip, when you fall, when you make a mistake, you don’t have to worry that your name will be blotted out of the Book of Life. You don’t have to face the prospect of becoming persona non grata with God. The apostle Paul wrote, “For everyone has sinned; we all fall short of God’s glorious standard. Yet God, in his grace, freely makes us right in his sight. He did this through Christ Jesus when he freed us from the penalty for our sins. For God presented Jesus as the sacrifice for sin. People are made right with God when they believe that Jesus sacrificed his life, shedding his blood” (Romans 3:23–25 NLT). If you believe in Christ, you have a spiritual safety net. You have a barrier against spiritual death. Because Jesus came into your heart, forgave you, and committed Himself to you, He now protects you, seals you, and justifies you because of that commitment. The fact is that we as Christians will sin and fall short. The Scriptures, as well as our own experiences in life, tell us this is true. According to 1 John 1:8, “If we claim we have no sin, we are only fooling ourselves and not living in the truth” (NLT). This isn’t an excuse for ungodly living. Nor is it a license for sin. It’s a simple acknowledgment of reality. Yet Paul wrote, “I am convinced that nothing can ever separate us from God’s love. Neither death nor life, neither angels nor demons, neither our fears for today nor our worries about tomorrow—not even the powers of hell can separate us from God’s love” (Romans 8:38 NLT). Nothing can dismantle our safety net. Reflection question: What does your spiritual safety net mean to you? Discuss Today's Devo in Harvest Discipleship! — The audio production of the podcast "Greg Laurie: Daily Devotions" utilizes Generative AI technology. This allows us to deliver consistent, high-quality content while preserving Harvest's mission to "know God and make Him known." All devotional content is written and owned by Pastor Greg Laurie. Listen to the Greg Laurie Podcast Become a Harvest PartnerSupport the show: https://harvest.org/supportSee omnystudio.com/listener for privacy information.

Structurally the Bay Area's famous landmark, the Golden Gate Bridge, is reported to be incredibly safe. But the financial future of the bridge is a different story. For more KCBS Radio News Anchor Steve Scott spoke with KCBS Insider Phil Matier.

For the seventeeth installment of “50 Weeks That Shaped America” we travel to California in the 1930s, where San Francisco planners have a big idea — build a massive suspension bridge across the Golden Gate strait. We discuss how the project came together despite the Great Depression, the big egos involved, what the story says about how audacious projects can pull a country out of malaise… and why the bridge is the color it is.Join our America250 newsletter community! Subscribe for free to get the latest news and analysis of how America250 is playing out. Paying subscribers get access to early, ad-free versions of the show. Plus bonus features throughout the year. To support our work and get access to everything, subscribe now.This Day is a proud member of Radiotopia from PRX.Your support helps foster independent, artist-owned podcasts and award-winning stories.If you want to support the show directly, you can do so on our website: ThisDayPod.comGet in touch if you have any ideas for future topics, or just want to say hello. Follow us on social @thisdaypodOur team: Jacob Feldman, Researcher/Producer; Khawla Nakua, Transcripts; music by Teen Daze and Blue Dot Sessions; Audrey Mardavich is our Executive Producer at Radiotopia. Learn about your ad choices: dovetail.prx.org/ad-choices

In tonight's dead letter, listener Jeannette shares a deeply atmospheric story from a bike commute across one of the most famous bridges in the world. Enveloped in the freezing, thick San Francisco fog, what starts as a quiet ride she's done a million times turns into an inexplicable encounter with a solitary figure standing near the edge. After a strangely profound exchange of words, she looks back to find the massive walkway entirely empty. She is convinced that he couldn't have had time to have taken his life in that brief moment. It opens up a conversation about crisis apparitions, liminal spaces, and the heavy emotional toll anchored to such a monumental location.REFERENCE LINKSSFGate: Japanese Taxi Drivers Claim Ghost Passengers Hail Cabs at Site of 2011 Tsunami: https://www.sfgate.com/news/article/Japanese-taxi-drivers-claim-ghost-passengers-6806980.phpGolden Gate Bridge Suicide Deterrent Net Project: https://www.goldengate.org/district/district-projects/suicide-deterrent-net/Bridge Rail Foundation: http://www.bridgerail.net/"Unsolved Mysteries"- Tsunami Spirits: https://www.imdb.com/title/tt11107472/KCRW Unfictional Podcast: https://www.kcrw.com/shows/unfictional/latestGhosts of the Tsunami by Richard Lloyd Parry: https://bookshop.org/p/books/ghosts-of-the-tsunami-death-and-life-in-japan-s-disaster-zone-richard-lloyd-parry/4c115c39e1094566?ean=9781250192813&next=tWe're looking for more stories! Send your Dead Letter to deadletteroffice@astonishinglegends.com!

7:05- Mark is learning to speak Spanish through A.I. for $19.99 a month. Mark’s former Spanish teacher was Tim Conway’s (Tim’s dad) Spanish teacher. Queso is cheese, dad. Beware of EWA Learn languages App, 3rd party Paddle app is coming after Mark. They are making it impossible to cancel. Angel knows two words. Conway a great supporter of WOMEN! Fabulous! 7:20- Is rain coming to town? Mark was the ultimate weatherman, the dancing weatherman he started. Inside the mind of a dancing weatherman. Jason Bateman ‘St. Louis DTF’, new show where he is doing karate. Did Mark know if it was going to be a dancing day? 7:35- Are sporting events pricing out the fans?! F’n FIFA ticket prices are too high. Bathroom keys.Most valuable company int he world...Nividia. A bee infiltrates the studio, like working in the jungle. 7:50- LAX people mover, is testing without people. The term “hiccup” when things go wrong, whats the origin? The Golden Gate Bridge cost approximately $35 million to construct (about $1.5 billion in 2019 dollars). Construction began on January 5, 1933, and was completed in 1937 under budget and ahead of schedule, with the final bonds for the project retired in 1971. See omnystudio.com/listener for privacy information.

I went to Taiwan, but the show must go on. While I was away covering the Taipei Cycle Show, Mike Levy, Lisa Charlebois, and Logan Jones-Wilkins had plenty to talk about. Predictably, things got heavily focused on gravel tire pressure, but that wasn't all. This week, the podcast crew debates why roadies need bigger gravel tires, breaks down Specialized's newest tech, and confesses to their strangest cold-weather clothing hacks. In this episode, we cover: Lisa's 300km Mission: A recap of an epic 193-mile ride through Napa and Sonoma, plus a crucial PSA on the protocol for crossing the Golden Gate Bridge late at night. Specialized's New Pathfinder TLR: Logan is currently working his way through a massive pile of test rubber. He explains why pros like Keegan Swenson opt for the slickest options, but argues the more aggressive Terra tread is actually better for the rest of us. Levy's Tire Volume Hot Take: Levy takes a firm stance that most gravel riders are severely under-tired. His advice to roadies hitting the dirt? Stop obsessing over aero, mount the biggest tires your frame clears, and run an insert. New Roval Gravel Wheels: A quick look at the newly launched Roval Terra Aero CLX and Terra CLX3 wheelsets, including a discussion on their 27mm internal width and the decision to use a wide carbon hook. Questionable Winter Kit: Inspired by Jonas Vingegaard's heavily modified, cut-up winter bib shorts, the crew shares their own extreme cold-weather survival tactics—from crotchless long underwear to the merits of baggy mountain bike pants on a drop-bar bike. Give it a listen, and let us know in the comments if you have a favorite tire or if you think Levy is wrong on his hot take. Episode Timestamps: 00:00 - Intro 01:25 - Lisa's 300k 05:04 - Gravel Tires 27:54 - Specialized Wheels 34:28 - Clothing discussion

Amy Appelhans Gubsers (56) is a nurse at UCSF, a mom and grandma, and the first person to swim from the Golden Gate Bridge to the Farallon Islands—nearly 30 miles and roughly 17 hours in cold Pacific water, in what many consider shark territory. This is more than an epic swim. It's a practical conversation about how big goals actually get done: patience over years, calm under pressure, and the ability to keep moving when conditions stop cooperating.In this episode: The long-game reality behind “overnight” achievements  The mental skill that mattered most during 17 hours  Cold-water decision-making + staying calm  Sharks: real risk, smart planning  Why goals like this are never truly solo Takeaway: Massive goals aren't won by hype. They're earned through durable process. From the vault: recorded + released ~1.5 years ago — still one of our clearest blueprints for pursuing a massive goal with real stakes.

Dipsea Generations follows the stories of five young San Francisco Bay area runners who take on the historic Dipsea trail race. The Dipsea is the oldest trail race in the United States, started in 1905, and it covers 7.4 miles of incredible terrain between Mill Valley–just north of the Golden Gate Bridge in Marin County–and Stinson Beach. One interesting twist about this race is it has a handicap format determined by age and gender, so it makes for a more level playing field in those regards.  Nicole grew up in Mill Valley, so she's very familiar with the Dipsea, and she studied cinema, with an emphasis on Documentary and Editing at SF State before going over to England to get her master's degree in documentary filmmaking. Nicole now lives back in the San Francisco Bay Area not far from where she grew up. In addition to being a filmmaker, Nicole is the video producer, editor, and a board member for the Trail Running Film Festival. This means that she sees a lot of documentaries about trail running.   In her own filmmaking Nicole has a strong interest in sharing stories about women runners. That is where she focused her master's thesis documentary, which is titled Finding Her Stride. The documentary follows the stories of several women ultra runners, and weaved throughout, Nicole chronicles her first trail marathon, which at the time was the farthest she'd ever run.  Nicole talks about that experience and her film in this episode, in this story of coming into her own.  From this Episode Nicole Amyx on Instagram: @nixamyx9 Nicole Amyx's website: nicoleamyxfilm.com Finding Her Stride documentary: vimeo.com/393933541?fl=pl&fe=vl Dipsea Generations website: dipseagenerations.com Trail Running Film Festival: trailfilmfest.com More from WRS WRS is on Substack: womensrunningstories.substack.com To support WRS, please rate and review the show iTunes/Apple:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://podcasts.apple.com/us/podcast/womens-running-stories/id1495427631⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Spotify:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://open.spotify.com/show/4F8Hr2RysbV4fdwNhiMAXc?si=1c5e18155b4b44fa⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Music Credits Cormac O'Regan, of⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Playtoh⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Coma-Media⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, via⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Pixabay⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ikoliks, via Pixabay⁠⁠⁠⁠⁠⁠⁠⁠⁠ Music of the Future, via ⁠⁠⁠⁠⁠Pixabay⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠RomanBelov⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, via⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Pixabay⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ways to Connect and Engage with Women's Running Stories WRS Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@womensrunningstories⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ facebook.com/WomensRunningStories⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Website:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ womensrunningstories.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Back in 1933, during construction of the Golden Gate Bridge, unemployed men would stand waiting at the foot of the bridge.You can find a transcript of this video and over 900 more devotions like this one on our website at PeaceDevotions.com.If you find value from these devotions we'd encourage you to support our ministry. You can support us by praying for our pastors, sharing and commenting on our videos, or by donating at https://peacedevotions.com/donateConnect with us on social media, our website, or get these emailed to your inbox.Facebook: https://www.facebook.com/PeaceDevotions/Instagram: https://www.instagram.com/peace_devotions/YouTube: https://www.youtube.com/channel/UC2pFo5lJV46gKmztGwnT3vAWebsite: https://peacedevotions.com/Email List: https://peacedevotions.com/emailYou can also add Peace Devotions to your Flash Briefing on Amazon Echo Devices.https://peacedevotions.com/echo/

Grief and healing after suicidal thoughts and mental health struggles can feel overwhelming, but there is always more to life than your current circumstance. In this episode, we explore mental health awareness, suicide prevention, and healing through storytelling in a deeply personal and honest conversation.I sat down with Kevin Hines, a suicide attempt survivor who jumped from the Golden Gate Bridge and lived, to talk about the reality of what happened in the moments before and after his attempt. Kevin shares the immeasurable emotional pain that led him to the bridge, the instant regret he felt the moment he let go, and the miracle that kept him alive.He reflects on living 26 years beyond the day he thought his life would end and what it means to see life as a gift. Kevin speaks about faith, purpose, and the importance of continuing forward even in the midst of pain. He reminds us to think about the moments, people, and experiences we would miss, and how those possibilities can anchor us in difficult times.We also talk about the power of storytelling and speaking up. Kevin shares how opening up about his experience has impacted others, with many people sharing that his story helped them choose to stay. He emphasizes that giving to others, sharing truth, and creating space for honest conversations can change lives.This episode is a reminder that suicidal thoughts are not the truth, pain does not have to be the end, and with time, effort, and support, things can get better.Attend Kevin's talk: tri-c.edu/kevinhinesConnect with Kevin: https://kevinhinesstory.com/

This is a very special episode of Unpacked by Afar. This week, we hosted Unpacked Live — a live version of the podcast — in partnership with Visit California in Dallas, Texas. The event celebrated California's extraordinary creative landscape, and today's guest has literally shaped the ground beneath many Californian's feet. Roderick Wyllie is an award-winning landscape architect and founding partner of Surfacedesign Inc. A rare San Francisco native, he's helped design some of the Bay Area's most beloved public spaces, including the Lands End Visitor Center above Sutro Baths, a plaza at the Golden Gate Bridge, and Mission Bayfront Park. He also teaches at the Harvard Graduate School of Design. In this episode, Roderick talks about growing up in 1970s San Francisco, what it means to design with rather than against a place, and why he thinks California's greatest creative export might be optimism. On this episode, you'll learn: What it was like to grow up in San Francisco in the 1970s and 80s — and how that "wild frontier" shaped Roderick's creative practice Why Surfacedesign approaches every project like a crime scene investigation, searching for the story embedded in the land How Roderick thinks about water — both as a design tool and as a precious resource in a drought-prone state What he's learning from a current winery rethink at the iconic Robert Mondavi Winery in Napa Where he sends travelers who want to experience California through the lens of landscape and design Travel Recommendations from Roderick: Wineries & Gardens Faust, Napa Valley — A Victorian estate with planting designed to move from light to dark, inspired by the mythology of Faust; beautiful valley views Buena Vista Winery, Sonoma — One of California's most historically significant wine sites, beautifully sited with two landmark historic buildings Scribe, Sonoma — A more informal, less precious winery experience; Roderick especially admires the landscape work by Terramoto Ruth Bancroft Garden, Walnut Creek — A masterwork dry garden celebrating the succulent landscape; Roderick calls it "spectacular" Lotusland, Montecito — A fantasy of a California landscape with a larger-than-life history; the opera singer founder married nine times The Huntington, San Marino/Pasadena — Impeccably maintained, a spectacular garden destination Parks & Natural Spaces Golden Gate Park, San Francisco — "It always feels a little bigger than I think it's going to be" Point Reyes / Inverness — Roderick's favorite stretch of coast, particularly for seeing tule elk in the fog Blunk Space gallery, Point Reyes Station — A small gallery connected to the legacy of California sculptor JB Blunk Restaurants & Markets Valley Bar + Bottle, Sonoma — Informal, locally sourced, creative; Roderick's top pick Zuni Café, San Francisco — A California cuisine institution on Market Street; intimate despite its size Ferry Building Farmers Market, San Francisco — "Incredible to see these purveyors that are focused on peppers only or something like that" Modern Appealing Clothing (MAC), Hayes Valley — A quietly iconic SF clothing store recently named one of the 50 best in the US by the New York Times; Roderick designed a small interior garden inside the space Art & Culture Minnesota Street Project, Dogpatch — A collection of galleries with constantly rotating programming Bay Area Discovery Museum, Sausalito — Roderick and his team designed eucalyptus-inspired play structures; worth a visit even without kids Chapters 00:00:00 Introduction 00:02:00 Growing Up in San Francisco 00:05:00 How Surfacedesign Works 00:08:00 Iconic Bay Area Projects 00:14:00 Water as Design and Resource 00:20:00 Designing Winery Landscapes 00:27:00 The California Creative Mindset 00:35:00 Where to Go in California Resources Surfacedesign Inc. — Roderick's firm Explore the ⁠Afar guide to California Watch the live recording of our Dallas event on YouTube Listen to our other Unpacked Live episodes featuring naturalist Obi Kaufmann and architect Barbara Bestor Be sure to subscribe to the show and sign up for our podcast newsletter, ⁠Behind the Mic⁠, where we share upcoming news and behind-the-scenes details of each episode. And explore our second podcast, ⁠⁠⁠⁠⁠Travel Tales⁠⁠⁠⁠⁠, which celebrates first-person narratives about the way travel changes us, and ⁠⁠⁠⁠View From Afar⁠⁠⁠⁠, where we spotlight the people and ideas shaping the future of travel. Unpacked by Afar is part of ⁠Airwave Media⁠'s podcast network. Please contact ⁠⁠⁠⁠⁠advertising@airwavemedia.com⁠⁠⁠⁠⁠ if you would like to advertise on our podcast. Learn more about your ad choices. Visit megaphone.fm/adchoices

Sacramento doesn't have a Golden Gate Bridge. So they built demand with experiences—and then proved it with room nights. For #NoVacancyNews, I talked with Mike Testa (President & CEO, Visit Sacramento) about how Sacramento positioned itself around farm-to-fork and then stacked events that drive real overnight stays from different customer groups.

Dear Wonderful Reader,The money dwindles in my bank account. My grandma is dying. I have to sell my investment stocks and ETFs to pay for these flights. Hit by a “family emergency” when I have the least wiggle room. I gave up my Soho House membership, which in hindsight seems like an irresponsible and pretentious expense. Today is also my four-year anniversary since I quit my job in New York to pursue this creative life. I've spent 95% of my days since then extremely happy. No regrets. Yet two books published, workshops being taught, a feature in the New York Times, and a mention in Vogue isn't much comfort when my client pipeline is dry.This is a hard moment in my journey. Yet, I still have you, my lovely reader! Thank god you're still here. We're all still here, somehow. Today, I have something special for you. It is one of my favorite images. It is an image that sustains me in these difficult moments of life, and being a human on this planet. I wrote it when I returned to San Francisco from my friends' betrothal. A former tech minion, I have seen the bridge many times. But something about that day was different.Thank you for the opportunity to edit this and give this a little more love and polish. I will keep this in mind when I get on a plane tomorrow, and sit at my grandmother's bedside. Things are hard, but this is keeping me going. This is a gift. This is my gift for you,Love,TashSend this to someone you love

Romans 3:21-31 The Clarity: His Righteousness (vv. 21-26) Revealed apart from the law Redeeming from the law Jesus sacrifice Jesus satisfaction Jesus solution The Conclusion: Humanitys Remedy (vv. 27-31) Justification by faith Resolution of the law More to Consider What kind of God (Father) sends His Son to such a horrific death to satisfy His own sense of justice? First, Jesus voluntarily gave His life (Jn 10:1418). So, this was the eternally agreed-upon plan by Father, Son, and Spirit. Second, the triune Godnot just the Sonis involved in this worlds suffering. The Father Spirit were not detached observers but were intimately involved with the Sons suffering on the cross. Third, consider Gods holiness and sins offensiveness to such a perfect, unsullied, personal Being. Sin elicits His just, wrathful responsethe removal of all traces of both sin and sinner. Fourth, we must grasp Gods limitless love for His human creatures made in His own image. Though He could justly write us off forever, in love God acted to save those who trust in Him. So, while His holiness required the just payment of death for sinners, in love He paid the penalty Himself in the person of His only Son. Ted Cabal et al., The Apologetics Study Bible: Real Questions, Straight Answers, Stronger Faith (Nashville, TN: Holman Bible Publishers, 2007), 1685. During the building of the Golden Gate Bridge over San Francisco Bay, construction fell badly behind schedule because several workers had accidentally fallen from the scaffolding to their deaths. Engineers and administrators could find no solution to the costly delays. Finally, someone suggested a gigantic net be hung under the bridge to catch any who fell. Finally in spite of the enormous cost, the engineers opted for the net. After it was installed, progress was hardly interrupted. A worker or two fell into the net but were saved. Ultimately, all the time lost to fear was regained by replacing fear with faith in the net. As we paid nothing for God's eternal love and nothing for the Son of His love, and nothing for His Spirit and our grace and faith, and nothing for our eternal rest...What an astonishing thought it will be to think of the unmeasurable difference between our deservings and our receivings. O, how free was all this love, and how free is this enjoyed glory...So then let "Deserved" be written on the floor of hell but not on the door of heaven and life. Richard Baxter, The Free Gift.

The time has come to jump into some real shit, folks! Yes, literally. Yes, this episode deals with the scents of public restrooms, and religion as well. Classic combo. This episode deals with hearing issues and fitness as well, classic combo. This episode analyzes Mel Brooks and the Golden Gate Bridge historic nonsense, classic combo. This episode touches on hunters and gatherers and Dwayne The Rock Johnson and Jeopardy and Mormonism and human duality and device addiction and dangerous things we all must stop doing. Time to stream it like y'mean it! So clever. Leave a nice rating or review on iTunes or wherever you listen and have a fabulous life, good people. Logo art by Brandon Lai Music by Micah Julius Shoulder bruise by tetanus shot

As Bob continues his LawNext on Location series – all recorded live in the San Francisco area at locations of each guest's choosing – he sits down with Pablo Arredondo at his home in Tiburon, a quaint Marin County town with a history stretching from Mexican land grants to naval outposts to a southern railway terminus. From Pablo's home office, the view looks out over Richardson Bay towards Sausalito and, if you look carefully, the Golden Gate Bridge can be seen in the distance. It is a setting that is entirely fitting for a conversation with someone who helped shape one of the more remarkable journeys in the annals of legal technology. Pablo was cofounder of Casetext, the once-scrappy startup that spent a decade iterating, pivoting and persisting before striking gold with CoCounsel, the first GPT-4-powered AI legal assistant, unveiled on the nationally televised Morning Joe show on March 1, 2023. Just four months later, Thomson Reuters acquired Casetext for $650 million in cash. Now, 2.5 years later, Pablo recently left TR, where he is, as he puts it, building a Lego Death Star with his daughter and finally paying attention to his well-being after 16 years of nonstop pursuit. In this wide-ranging conversation, Pablo reflects on the long road to CoCounsel – from a failed crowdsourcing experiment to CARA's brief analysis tool to the pivotal moment when Casetext signed a $20,000 innovation license with OpenAI and got early access to GPT-4, 10 weeks before ChatGPT's public launch. He describes the surreal experience of those first 48 hours after CoCounsel's debut, when he and cofounder Jake Heller identified 74 distinct legal use cases the tool could handle – any one of which, he says, "would have been a company in the old world." Pablo and Bob also dig into the bigger questions surrounding legal AI, including whether the field is advancing as fast as he expected; what the foundation models from Anthropic, OpenAI and Google mean for legal-specific AI companies such as Harvey; and why he believes reasoning models and agentic AI represent the next genuinely profound leap beyond GPT-4. Pablo also candidly reflects on the TR acquisition and his work while at TR, and he offers hints on what may lie ahead for him – at least once that Death Star model is done.  It is a conversation that is part memoir, part technology seminar and part meditation on what it means to have built something that changed a profession – and his life – all recorded with a sweeping, albeit cloudy, view of the majesty of San Francisco Bay.    Thank You To Our Sponsors This episode of LawNext is generously made possible by our sponsors. We appreciate their support and hope you will check them out.   Paradigm, home to the practice management platforms PracticePanther, Bill4Time, MerusCase and LollyLaw; the e-payments platform Headnote; and the legal accounting software TrustBooks. Briefpoint, eliminating routine discovery response and request drafting tasks so you can focus on drafting what matters (or just make it home for dinner). Legalweek, March 9-12, North Javits Center, New York City.   If you enjoy listening to LawNext, please leave us a review wherever you listen to podcasts.  

In this powerful and deeply honest conversation, Kevin Hines sits down with global finance leader and venture strategist Erik Sebusch to explore what success really means at the highest levels of capital and leadership.From growing up on the east side of Cleveland to operating in the world of sovereign wealth funds, institutional investors, and venture capital, Erik shares the mindset shifts that shaped his journey. But this conversation goes far beyond money.They discuss the unseen cost of success, burnout in high-performance industries, ethical investing, and why capital without conscience can be dangerous. Erik opens up about work ethic, resilience, risk, integrity, and the responsibility that comes with stewarding billions of dollars.What makes this episode different is the heart behind it. This is not just about finance. It's about leadership. It's about mental health. It's about choosing meaning over ego, legacy over luxury, and purpose over profit.You'll hear:• What top investors really look for in founders• Why the best technology doesn't always win• The pressure young professionals face today• The truth about burnout in finance• How to succeed without losing your soul• Why capital can either widen suffering or help heal itErik also shares why he chose to support suicide prevention efforts and serve as an Executive Producer of the upcoming documentary Death Bridge, a film confronting the suicide crisis at the Golden Gate Bridge.If you're an entrepreneur, investor, leader, or simply someone trying to build a meaningful life - this conversation is for you.Success is not just about accumulation. It's about alignment.You are loved. You are valued. And your life is worth living.

In this one, I talk to journalist Paul Koberstein, whose recent book, “Canopy of Titans,” explores one of the most overlooked ecosystems on Earth: the Pacific Coastal Temperate Rainforest. Stretching roughly 2,500 miles from just north of San Francisco's Golden Gate Bridge to the western Gulf of Alaska, it's the largest temperate rainforest on the planet. Fueled by Pacific storms and cool ocean currents, it supports towering redwoods, Sitka spruce, western hemlock, and cedar — some of the largest and oldest trees in existence. Acre for acre, these forests store more carbon than tropical rainforests like the Amazon, with vast reserves locked in massive trunks, deep soils, roots, and centuries of accumulated woody debris. But even though it's one of the most carbon-dense ecosystems we have, and a critical buffer against climate change, it remains largely overlooked in global climate conversations. Paul pushes back on some of the most common narratives about forests and climate. He points to those industry ads that promise for every tree cut down, three more will be planted. It's an argument that sounds reassuring until you realize a young sapling can take a century to store the amount of carbon held in the massive tree that was felled. Trees are about 50 percent carbon. Through photosynthesis they pull carbon dioxide out of the air, lock that carbon into their trunks and roots, and release the oxygen we breathe. Southeast Alaska's Tongass National Forest alone holds more total carbon than any national forest in the country. That scale of storage is central to Paul's point: the science doesn't say we're powerless. It suggests that we can still influence the climate back toward something more stable. If fossil fuels loaded the atmosphere with excess carbon, then forests, if protected and restored, can help draw it back down. Forests have stabilized the climate for thousands and thousands of years. Whether they continue to do so depends largely on us letting them do their job.

In this one I tell you about: The QUESTION OF THE DAY at Sheetz A show I watched on Netflix that started strong, but turned into a diarrhea shower The ongoing Season 13 of the Curse of Oak Island The amazing engineering that goes into a great pair of underwear Using Artificial Intelligence to track down a super-obscure song The frenzied return of Not Our Kitty I hope you enjoy it. Thanks for listening! Check out expanded show notes at surfreportpod.com Need twice the Surf Report? We've got you covered. Just pop on over to patreon.com/jeffkay, sign up for a $4 (or more) monthly donation, and you'll immediately gain access to the weekly bonus shows. They're each a full-length episode and are only available to supporters at Patreon. Upgrade today! Also, we now have a telephone hotline where you can leave your comments, questions, and suggestions. The number is 570-290-8151. Give us a call and there's a very good chance you'll be part of a future show. It's all voicemail, no actual human will answer. If you're too shy for such shenanigans, email us at surfreportpod@gmail.com

Paul Koberstein is a journalist, whose recent book, “Canopy of Titans,” explores one of the most overlooked ecosystems on Earth: the Pacific Coastal Temperate Rainforest. Stretching roughly 2,500 miles from just north of San Francisco's Golden Gate Bridge to the western Gulf of Alaska, it's the largest temperate rainforest on the planet. Fueled by Pacific storms and cool ocean currents, it supports towering redwoods, Sitka spruce, western hemlock, and cedar — some of the largest and oldest trees in existence. Acre for acre, these forests store more carbon than tropical rainforests like the Amazon, with vast reserves locked in massive trunks, deep soils, roots, and centuries of accumulated woody debris. But even though it's one of the most carbon-dense ecosystems we have, and a critical buffer against climate change, it remains largely overlooked in global climate conversations. Paul pushes back on some of the most common narratives about forests and climate. He points to those industry ads that promise for every tree cut down, three more will be planted. It's an argument that sounds reassuring until you realize a young sapling can take a century to store the amount of carbon held in the massive tree that was felled. Trees are about 50 percent carbon. Through photosynthesis they pull carbon dioxide out of the air, lock that carbon into their trunks and roots, and release the oxygen we breathe. Southeast Alaska's Tongass National Forest alone holds more total carbon than any national forest in the country. That scale of storage is central to Paul's point: the science doesn't say we're powerless. It suggests that we can still influence the climate back toward something more stable. If fossil fuels loaded the atmosphere with excess carbon, then forests, if protected and restored, can help draw it back down. Forests have stabilized the climate for thousands and thousands of years. Whether they continue to do so depends largely on us letting them do their job.

Redesigning one of the world's most-used apps is no small feat, especially when that app is also the second largest search engine in the world: YouTube. Over the last four years, Nate Koechly, UX Director at YouTube, and Matthew Darby, Director of Product Management, have been leading an ambitious effort to balance Google's metrics-driven culture with the subjective challenge of making an app feel “modern.” Visit our Substack for bonus content and more: https://designbetterpodcast.com/p/nate-koechly-and-matthew-darby In our conversation, Nate and Matt share how they developed predictive measurement tools to gauge user perception, why they pair visual updates with quality-of-life features like comment threading and improved video controls, and how their research process has evolved from measuring clicks to understanding satisfied watch time. We also dig into one of YouTube's most complex challenges: the algorithm. As Nate and Matt explain, what users say they want doesn't always match what actually makes them happy on the platform. They also discuss their work exploring ways to give viewers more agency and control, including the possibility of using natural language to tune your feed. Both guests have a genuine passion for how YouTube enables deep expertise and niche interests to find their audiences—from 3D models of the Golden Gate Bridge to forest fire education from Northern California lookouts. Behind the algorithms and design updates is a platform where, as Nate puts it, “when you give people a voice, the things they say are just inspiring.” *** Premium Episodes on Design Better This ad-supported episode is available to everyone. If you'd like to hear it ad-free, upgrade to our premium subscription, where you'll get an additional 2 ad-free episodes per month (4 total). Premium subscribers also get access to the documentary Design Disruptors and our growing library of books: You'll also get access to our monthly AMAs with former guests, ad-free episodes, discounts and early access to workshops, and our monthly newsletter The Brief that compiles salient insights, quotes, readings, and creative processes uncovered in the show. And subscribers at the annual level now get access to the Design Better Toolkit, which gets you major discounts and free access to tools and courses that will help you unlock new skills, make your workflow more efficient, and take your creativity further. Upgrade to paid *** If you're interested in sponsoring the show, please contact us at: sponsors@thecuriositydepartment.com If you'd like to submit a guest idea, please contact us at: contact@thecuriositydepartment.com

Welcome to the Gnar Couch Podshow, where mountain bikes, questionable humor, and barely functioning attention spans all pile onto a metaphorical homeless couch and roll down a metaphorical hill. This week, we're joined by San Francisco legend and urban bike ninja Teddy Hayden, whose riding and viral videos have gotten him more attention from the Forest Service than Rob's last attempt at a show intro (which, let's be real, went about as well as a beer spill in Cheef's lap). We dig in on Teddy's infamous $50,000 fine for shredding cliffs near the Golden Gate Bridge, the ongoing war between mountain bikers and government trail cops (spoiler: none of it could've just been an email), and a surprisingly passionate debate about which bike components we could live without (dropper posts and seats—are you brave enough?). There's also a deep investigation into the true nutritional value of "pussy is low-carb," a couple wiener jokes, and the classic Gnar Couch running gag: Rob forgetting to wrap up the show and the eternal confusion about who's actually supposed to write those episode descriptions. If you're here for serious bike technique or clean comedy, you're outta luck. But if you want stories about human poop on trails, debates about Trader Joe's ravioli, and a group of barely-adult hosts breaking down the finer points of mountain bike culture (with a little self-deprecating banter and bathroom humor), this episode is for you. Strap in, get ready to laugh at our expense, and prepare for at least one beer spill, a few botched intros, and possibly a confession or two that should never see daylight. Guest info: Teddy Hayden Check out our store for sick shirts. Got to our Patreon and give us money. We've added old episodes, downloadable songs, and give you early access to raw, uncut shows for only $4.20/month. We all ride TRP brakes. They're the best. Buy some. Thanks to crankbrothers and Hyland Cyclery for always keeping the bikes running. Get 30% off BLIZ sunglasses and more with the code "sponchesmom".  

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

In this podcast, Greg Voisen sits down with world-renowned scholar and "founding member" of the show, Stephen G. Post, for a profound exploration of his latest work, Pure, Unlimited Love. Imagine a reality where the security and well-being of another is as real to you as your own—a concept so powerful it prompted the 92-year-old Dalai Lama to break his hiatus on writing forewords. From the biology of the "Giver's Glow" to a chilling, prophetic dream involving a stranger on the Golden Gate Bridge, Post bridges the gap between hard science and deep mysticism. He reveals how "carefrontation" can heal toxic workplaces and why the key to solving our global polarization isn't found in politics, but in a spiritual "One Mind." If you have ever wondered if love is more than just a fleeting emotion, this conversation provides the scientific and metaphysical proof that it is the very fabric of our survival.

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All this week, What Next and What Next: TBD are re-airing some of our favorite conversations from throughout the year and checking back with the people in those conversations to see how things have – or haven't – changed. This episode is from August.From the Statue of Liberty to the Golden Gate Bridge, and places in between like Yellowstone and the site of the Battle of Gettysburg, the National Park Service has been a point of American pride since its inception. And with a small budget and actually generating revenue, even fiscal hawks had no reason to complain. So why is the Trump administration cutting their budget? Guests:Jon B. Jarvis,18th director of the National Parks.Kevin Heatley, former superintendent of Crater Lake National Park, Oregon.  If you want to support more of this reporting, in 2026 and beyond, consider signing up for Slate Plus. You'll enjoy ad-free listening across the Slate network, early access to tickets for live events, and you'll never hit the paywall on the site. We're on a mission to get 100 people to join Slate Plus before the new year—and we're even offering a 50-percent-off deal to folks who join us right now. Visit Slate.com/whatnextplus and use the code WHATNEXT50 to get a year of Slate Plus for $59.Podcast production by Ethan Oberman, Elena Schwartz, Paige Osburn, Anna Phillips, Madeline Ducharme, and Rob Gunther.  Hosted on Acast. See acast.com/privacy for more information.

All this week, What Next and What Next: TBD are re-airing some of our favorite conversations from throughout the year and checking back with the people in those conversations to see how things have – or haven't – changed. This episode is from August. From the Statue of Liberty to the Golden Gate Bridge, and places in between like Yellowstone and the site of the Battle of Gettysburg, the National Park Service has been a point of American pride since its inception. And with a small budget and actually generating revenue, even fiscal hawks had no reason to complain.  So why is the Trump administration cutting their budget?  Guests: Jon B. Jarvis,18th director of the National Parks. Kevin Heatley, former superintendent of Crater Lake National Park, Oregon.   If you want to support more of this reporting, in 2026 and beyond, consider signing up for Slate Plus. You'll enjoy ad-free listening across the Slate network, early access to tickets for live events, and you'll never hit the paywall on the site.   We're on a mission to get 100 people to join Slate Plus before the new year—and we're even offering a 50-percent-off deal to folks who join us right now. Visit Slate.com/whatnextplus and use the code WHATNEXT50 to get a year of Slate Plus for $59. Podcast production by Ethan Oberman, Elena Schwartz, Paige Osburn, Anna Phillips, Madeline Ducharme, and Rob Gunther.  Learn more about your ad choices. Visit megaphone.fm/adchoices

All this week, What Next and What Next: TBD are re-airing some of our favorite conversations from throughout the year and checking back with the people in those conversations to see how things have – or haven't – changed. This episode is from August.From the Statue of Liberty to the Golden Gate Bridge, and places in between like Yellowstone and the site of the Battle of Gettysburg, the National Park Service has been a point of American pride since its inception. And with a small budget and actually generating revenue, even fiscal hawks had no reason to complain. So why is the Trump administration cutting their budget? Guests:Jon B. Jarvis,18th director of the National Parks.Kevin Heatley, former superintendent of Crater Lake National Park, Oregon.  If you want to support more of this reporting, in 2026 and beyond, consider signing up for Slate Plus. You'll enjoy ad-free listening across the Slate network, early access to tickets for live events, and you'll never hit the paywall on the site. We're on a mission to get 100 people to join Slate Plus before the new year—and we're even offering a 50-percent-off deal to folks who join us right now. Visit Slate.com/whatnextplus and use the code WHATNEXT50 to get a year of Slate Plus for $59.Podcast production by Ethan Oberman, Elena Schwartz, Paige Osburn, Anna Phillips, Madeline Ducharme, and Rob Gunther.  Hosted on Acast. See acast.com/privacy for more information.