Podcasts about Tangent

Hello friends! Today's tale of pentest pwnage isn't a start-to-finish march to DA – it's me finally emptying out the backlog of "gosh, I've got to share this next time" internal network tips that have been rattling around in my head. Here's what we get into: Don't skip the boring stuff. Even when I'm testing the same network for the third or fourth time, I've got an ever-growing list of things I check every single time – because config drift has a nasty habit of quietly reintroducing problems that were fixed years ago. Get a second opinion on your tools. Lately I've had BloodHound tell me a network is squeaky clean, and then gone and checked manually only to find the exact opposite sprawled all over the place. I don't know how to account for it, but it's changed how I work. (If you know the source of truth here, please write in!) Ghost machines. That innocent little checkbox in Active Directory that turns a computer object into a gift-wrapped present for an attacker. We keep finding these in environments that had zero of them last year – and I share the two-pass trick that shakes even more of them loose. The weekend freebie. Why I like to get my box lit up on a Friday even when the test doesn't officially start until Monday, and what tends to come wandering into my capture over 48 quiet hours. SNMP sweeps. I've never been caught doing one, and yet they'll happily hand over the make, model and firmware of some firewalls, switches and storage systems in the building. I think this finding deserves way more attention than it gets. (There are a few little commandlets waiting for you over at 7MinSec.wiki.) Be a consultant, not a Terminator 1000. Why I run certain checks even when I'm 99% sure I'll find nothing, why "you don't have this thing at all" belongs in the accolades section, and how that one habit has led to some of the most appreciated conversations we've had in report delivery meetings. Tangent department: the dumb-but-glorious AI project that gave me the giggidies – a fully automated lobby bot for a Steam game that is absolutely, positively not for the kiddos. Also: the one line I won't cross with it, no matter how much my buddy eggs me on. Got a tip of your own I should be adding to the "always check this" list? I'd love to hear it! 7MinSec.com for security services and show notes | 7MinSec.club for our Substack and weekly TuesdayTOOLSdays | 7MinSec.wiki for pentesting tips, scripts and cheat sheets

A Corporate Time with Tom and Dan — Episode Notes Episode: "Best Of the Week — August 17–21" Hosts: Tom and Dan | Guest: Ross McCoy, John Graham, Billy (Claremont Knights), Marlon Wayans (via Zoom) Sponsors / Ad Reads Central Homes Roofing — Family-owned Orlando roofer of 30+ years, voted #1 roofer three years running by Orlando Weekly. Free inspections/estimates, hurricane warranties, in-house (non-1099) crews, will handle small repairs other roofers skip, 4.8-star Google rating, same-day estimates, and $500 off a re-roof for Tom and Dan listeners (centralhomesroofing.com). Core Flooring — Sponsor of the Weird Job Wednesday segment; contact Corey at Core Flooring in Winter Park for flooring work. Segment Rundown Cold open: Snack Scout naming bit — Tom and Dan welcome in-studio comedian Ross McCoy and John Graham (thatguyontv.com) for the monthly food segment, riffing on rebranding John's recurring "garbage food" bit (Snack Scout, Commissioner of Junk Food, Garbage Man) before diving into product tastings. 7 Up's secret reformulation — John reveals 7 Up is quietly changing its formula (not just adding a flavor) and flipping "lemon lime" to "lime lemon" on cans, sparking nostalgia about the "7 Up Yours" campaign, the Spot mascot's Sega Genesis game, and childhood freebies like McDonald's records and Domino's Noid toys. Protein Pop-Tarts and cup noodle taste test — Dan trashes protein Pop-Tarts his wife bought; the group samples Texas Barbecue and Kansas City Barbecue cup noodles, arguing Texas vs. Carolina-style barbecue, sodium/fat content, and confusing FDA daily-value percentage labeling. Pork brains and gas station oddities — Tangent on canned pork brains in milk gravy sold at Kentucky/Indiana convenience stores, its sky-high cholesterol content, and old 7-Eleven chili-cheese cup-noodle toppings. Tombstone gluten-free potato-crust pizza — The crew tries a Tombstone loaded bacon cheddar pizza with a potato-starch gluten-free crust, spinning into a debate about how many people falsely claim gluten intolerance versus real conditions like Crohn's, plus stories of self-diagnosed sensitivities that turned out to be lifestyle-related (alcohol, shellfish). Papa John's "garlic flavored sauce" gotcha — Ross spots the fine print on a grocery-store Papa John's garlic dipping sauce revealing it's "garlic flavored," not real garlic, printed nearly invisibly on the label; leads into margarine vs. butter and the "ultra-processed food" trend, including Dan's story of overdoing it on Slim Jims as a kid. Coors Light Cheez-Its taste test — A Target-exclusive Coors Light beer-cheese Cheez-It is sampled; talk turns to favorite Cheez-It varieties (white cheddar, with burned/"extra toasty" as the house favorite) versus Goldfish, and kids outgrowing "baby food" snacks like Goldfish and Uncrustables. Homemade Caesar salad lunches — Ross describes his daughter's daily self-made Cardini's Caesar salad lunch ritual and her brand loyalty, which detours into a chat about Worcestershire sauce containing anchovies. Chips Ahoy mystery-flavor contest — The group tastes a soft-baked "mystery flavor" Chips Ahoy tied to a $25,000 guessing contest, guessing vanilla/maple/birthday cake, and debates soft-baked vs. crispy cookies and whether the campaign will flop. Peach Moon Pies and "location foods" — Peach mini Moon Pies are taste-tested, leading to a debate about foods that only belong in specific settings (s'mores only while camping, hot dogs only at cookouts, peanuts/sunflower seeds only at ballgames or while driving). Ultra-processed rebranding and vegan pranks — Talk of brands (Kellogg's dropping dyes, Utz's "three ingredients" marketing) trying to seem less processed, plus a story about a vegan family member's protein powder that turns out to just be ground peanuts. Tootsie Pop flavor-swap taste test — The crew tries new fruit-filled Tootsie Pops (blue raspberry/cherry, strawberry/lemon, green apple/watermelon), jokes about why they weren't renamed "Gordon Pops" after the family that owns them (versus the Reese family drama), and a tangent on the trucking-industry origin of the word "Gaylord" for pallet boxes. Hard candy nostalgia — Discussion of why older generations favor hard candy (fading taste buds craving sweet/salty extremes), fond memories of the "crunchy center" moment on Tootsie Pops and Blow Pops, and whether candy left out ever truly spoils. Segment wrap and plugs — John plugs thatguyontv.com and workshops a new nickname (Snack Hunter); a riff on graham crackers' inventor Sylvester Graham believing they cured alcoholism and "chronic masturbation"; plug for the Moe Comedy Jam at Orlando Funny Bone. Weird Job Wednesday: meet Billy — Segment sponsored by Core Flooring; Tom and Dan welcome Billy, a former Marine and commissioner/coach for the Claremont Knights Youth Football League, discussing his crushing handshake and his family's move from Staten Island to Clermont, Florida. Coaching rules then vs. now — Billy explains Pop Warner's shift from weight classes to an age cutoff (July 15), banned drills like the "nutcracker" and Oklahoma drill, and new safety gear like guardian caps, contrasted with the no-water, no-sympathy coaching Tom and Ross remember from their own 1980s youth football days. Finding the "dog" in young players — Billy discusses helping kids get past the fear of contact, spotting natural talent early (including a 12-year-old opponent who stood 6'8"), and the competitive instinct every kid has but must learn to control. Work ethic and parent commitment — Talk of the grueling practice schedule (up to five days a week), year-round training across football, baseball, and conditioning, Billy's daily 100-pushup rule for his son, and how he handles parent complaints about playing time (tabling them until "Monday," never game day). Fundraising for stadium lights — Billy details the Claremont Knights' $120,000 campaign to install lights at Eastridge Middle School so teams can practice later into the fall, plus the league's quiet scholarship program for kids who can't afford to play, directing listeners to claremontknights.com to donate. Refs, replay, and closing thoughts — Discussion of how paid, tested Pop Warner referees are held to a higher standard than assumed, Billy's own concussion-era playing memories versus modern protocols, and a closing reflection on coaching as a volunteer's "return on investment" through watching his kids succeed. Marlon Wayans on Scary Movie's return — Marlon Wayans joins via Zoom to discuss the new Scary Movie's $230+ million global box office and its September 5 Paramount+ streaming release, explaining why he and his siblings sat out installments 3–5 before reclaiming the franchise. Football training for the role — Marlon describes training for football scenes opposite Tyreek Hill, getting pulled off actual throwing duties by the director in favor of a body double, and getting private quarterback coaching from "Jordan Palmer's brother." Funny Bone marathon and work philosophy — Talk of Marlon's seven-show Funny Bone run (two Friday, three Saturday, two Sunday), his philosophy of constant stagecraft and studying audience reactions, and his upcoming stand-up special and touring set. Liga Tridente cigars — Marlon discusses his Honduran-made cigar brand, expanding from five to roughly 100 (soon possibly 1,000) retail locations, and how he got into the business through his ex-girlfriend's now-business-partner Victor. Family life and the Wayans clan — Marlon talks about his three-and-a-half-year-old daughter, turning 54, hosting family gatherings at his house, and why the family has always turned down reality-show offers. Post-interview reflections on hustle — After the call, Tom and Dan compare Marlon's estimated net worth to his other ventures (likening him to Shaquille O'Neal's diversified business empire), debate what "trying your hardest" really means, and recall the "collard greens" bit with their producer Butler that cemented their relationship with Marlon. Key Dates / Plugs Mentioned This Friday — A BDM member is visiting the show/community from England. This weekend (Fri–Sun) — Marlon Wayans' Orlando Funny Bone run: two shows Friday, three Saturday, two Sunday. September 5 — The new Scary Movie begins streaming on Paramount+. Tickets on sale in September; event October 17 — The Moe Comedy Jam at the Orlando Funny Bone. Ongoing — Sign up for BDM membership at tomanddan.com registration for extra shows, private events, and the Facebook group. Ongoing — Donate to the Claremont Knights' stadium-lights fundraiser at claremontknights.com. Notable Guests/Callers Ross McCoy — In-studio comedian, part of the food segment and Weird Job Wednesday discussions. John Graham (thatguyontv.com) — Monthly food-segment guest bringing in unusual snack products. Billy — Weird Job Wednesday guest; commissioner and coach for the Claremont Knights Youth Football League, former U.S. Marine. Marlon Wayans — Actor/comedian, joined via Zoom to discuss Scary Movie and his Orlando Funny Bone stand-up run. ### Website: https://tomanddan.com/ App: https://tomanddan.com/app Become a BDM: https://tomanddan.com/registration Merch: https://tomanddan.myshopify.com/ YouTube: https://www.youtube.com/@TomandDanLive Twitch: https://www.twitch.tv/tomanddanlive Facebook: https://www.facebook.com/AMediocreTime Instagram: https://www.instagram.com/tomanddanlive/ X: https://x.com/TomAndDanLive TikTok: https://tiktok.com/@tomanddanshow Reddit: https://reddit.com/r/tomanddan ACT RSS: https://feeds.libsyn.com/61976/rss AMT RSS: https://feeds.libsyn.com/18904/rss

We welcome you back to another episode of Upstairs Neighbors with special surprise guests, Micky and Tanner! They crash the pod and talk about Disney parties, the issue with 67, and discover that Micky is Scottish. Enjoy! Calling all LA Neighbors - let's watch the Love Island Reunion together! RSVP here - https://handstamp.com/e/upstairs-islanders-live Offers: Smirnoff Ice: Available at local retailers near you. Please drink responsibly, 21+ Cash App: Download Cash App Today: https://click.cash.app/ui6m/v1g7rxgc #CashAppPod. New Cash App customers can earn $10 if they use the code CASHAPP10 in their profile at signup and send $5 to a friend within 14 days. Terms apply. *Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. Cash App Visa® Debit Flex Cards issued by Sutton Bank, Member FDIC, and The Bancorp Bank, N.A., pursuant to a license from Visa U.S.A. Inc. See terms and conditions for the Sutton prepaid card, Sutton debit flex card, and Bancorp debit flex card. Cash App Green features, Savings, Direct deposit, Round ups, Overdraft coverage and Discounts provided by Cash App, a Block, Inc. brand. Visit cash.app/legal/podcast for full disclosures. Talkiatry: Head to Talkiatry.com/UPSTAIRS to complete the short assessment and get matched with an in‑network psychiatrist in just a few minutes. Squarespace: Head to Squarespace.com/UPSTAIRS for a free trial, and when you're ready to launch, use OFFER CODE: UPSTAIRS to save 10% off your first purchase of a website or domain. Seat Geek: Use our code for 10% off your next SeatGeek order*: https://seatgeek.onelink.me/RrnK/UPSTAIRSNEIGHBORS10 Sponsored by SeatGeek. *Restrictions apply. Max $20 discount Podcast Socials: IG: https://www.instagram.com/upstairsneighborspod/ Tiktok: https://www.tiktok.com/@upstairsneighborspod Follow our Hosts + Guests: Maya IG: https://www.instagram.com/mayamoto_/ Maya TikTok: https://www.tiktok.com/@mayahasatiktok Dom IG: https://www.instagram.com/domrobxrts/ Dom TikTok: https://www.tiktok.com/@domnotateenmom Micky IG: https://www.instagram.com/mickyygordon/ Micky TikTok: https://www.tiktok.com/@mickycashflow Tanner IG: https://www.instagram.com/tannertan36/ Tanner TikTok: https://www.tiktok.com/@tannertan36Tangent w/ Tanner IG: https://www.instagram.com/tangentwithtanner/Tangent w/ Tanner TikTok: https://www.tiktok.com/@tangentpodtanner Learn more about your ad choices. Visit megaphone.fm/adchoices

Smylie Kaufman is back home after broadcasting the U.S. Amateur at Merion, and he and Charlie Hulme spend the front half of the show breaking down the course — its unusual routing, the brutal 14-18 closing stretch, the debate over how much green-complex slope can survive modern speeds, and why it's such a compelling preview for the 2030 U.S. Open there. Jack Whaley beat Jay Leng Jr. for the title, with Ryder Cowan and Carter Loflin standing out as names to know.From there, the guys run their favorite trees in golf — a loose, hilarious countdown through Merion, Pebble Beach, The Renaissance Club, Cypress Point, TPC Sawgrass, Panmure (where Ben Hogan prepped for the 1953 Open), Prairie Dunes, Seminole, Monterey Peninsula, and the tree at Augusta National.Plus: the case for Scottie Scheffler as the clear Player of the Year after his 8-shot win at TPC Southwind, what the PGA Tour's new playoff points structure means for players fighting to stay inside the top 50, and the fallout from Jon Rahm's reported LIV Golf exit — including what it means for the DP World Tour, the Challenger Series, and the sport's shifting global landscape heading into 2027.Don't forget to like, comment, subscribe, and follow us on socials @thesmylieshow!Chapters0:00 Intro & catch-up: fresh off the U.S. Amateur at Merion2:59 Merion's unique routing and standout holes7:47 Tangent: the Bobby Jones cinematic universe11:33 Merion's brutal closing stretch and the 17th green debate12:49 Setting up Merion for a 2030 U.S. Open18:46 The story behind Merion's wicker-basket logo22:27 Merion trivia: director-of-golf tenure & clubhouse details27:05 Best trees in golf: the debate begins (Merion's 8th)29:03 Pebble Beach's 18th hole cypress31:58 The Renaissance Club's 11th hole33:16 Cypress Point: Walker Cup picnics & the 17th tree36:47 TPC Sawgrass's tree-lined holes38:29 Panmure: where Hogan prepped for the 1953 Open40:08 Prairie Dunes' "The Shoot" at 1542:15 Seminole's 16th hole palms43:42 Monterey Peninsula's 11th hole45:54 The Tree at Augusta National & honorable mentions48:10 U.S. Amateur standouts: Ryder Cowan & Carter Loflin50:26 The case for Scottie Scheffler as Player of the Year59:07 PGA Tour playoff points shakeup & the scramble for top 501:04:41 Jon Rahm, LIV Golf, and the DP World Tour's shifting landscape1:19:11 Sign-off: Bob McIntyre preview, Ryder Cup to the South & JT's Firestone flashback#golf #pgatour #smylieshow #scottiescheffler #Merion

A Mediocre Time with Tom and Dan — Episode Notes Episode: 888 - "Nuttin's Gon' Change, No One Does S***" (Friday free show) Hosts: Dan, Tom | Guest: comedian Ross McCoy Sponsors / Ad Reads My Eternal Vitality (Dr. Powers) — opening segment, interview with Andrea Dennis about her weight loss (down ~30 of a 40-lb goal), hormone replacement therapy, and microdosing GLP-1s since March; plans to stop in Sept/Oct. Jeff's Bagel Run — plugging the new limited-edition cucumber dill cream cheese; free bagel for downloading the app. Hollerbach's German Restaurant (Sanford) — pitched as a venue for fantasy football drafts ($30/person: beer, pretzels, charcuterie, dedicated draft TV); also books their "Night's Hall" for parties/events. Bearcat (promo code BDM420) — cannabis product ad. Segment Rundown Cold open / ad — Andrea Dennis interview on HRT and GLP-1 microdosing (see above). Show open — Ross McCoy intro'd as guest; bit about Ross shaving/mowing the lawn in the heat. Business/announcements — Studio "pinball meet and greet" event coming up (very limited tickets, announced last-minute); Andrew (the "pinball dude") adding more machines; Pizza Bruno sponsoring food, White Claw sponsoring drinks; framed as a fundraiser for a new app/website they're building. Tangent: lambskin condoms — Riff sparked by mishearing "chic" as a brand name; potted history of the "Sheik" condom brand (named after the 1921 Valentino film, marketed for disease prevention since birth-control marketing was illegal), plus a long bit about what lambskin condoms are actually made from. Voicemail: cousin Gary (from Texas) — Dale's older brother; called in after hearing Dale had called the show. Reminisced about Dan's late father and uncle Doug ("the funniest fuckers ever"), said Dan looks/sounds more like Uncle Doug than his own dad. Dan reflects on losing touch with Virginia family after his dad died, and on reconnecting now. Gary left a callback number and invited a visit/get-together. Health aside — Dan discusses recent heart workup: calcium score of zero, clean echo, no chamber enlargement — good news after some health scares that have him "rethinking a lot of relationships." News: Amelia Earhart — Discussion of the Nikumaroro Island "castaway theory" (a freckle-cream jar and heat-damaged glass found near an old campfire site); Purdue University's Legacy Institute sending an expedition to the island October 7, chasing a satellite anomaly thought to be her Lockheed Electra. Tangent: unsolved mysteries — MH370, giant rich-guy experimental planes that vanish, Alcatraz escapees (the "Escape from Alcatraz" pair), Billy the Kid death theories, ancient/pyramid conspiracy stuff, DB Cooper, and a long detour into Jeffrey Epstein death conspiracy theories — including the viral story about an Epstein-linked email tied to a Fortnite account ("Little St. Jeff1") that resurfaced in released files. Coal-mining oddities — Time-Life "Mysteries of the Unknown" book bit (frog found inside a lump of coal, petrified tree stump found underground); Centralia, PA mine fire mentioned as a real, "solved" phenomenon (not a mystery, just still burning). News: cemetery "niche" robberies — Three Central Florida suspects at large for breaking into cemetery niches (small mausoleum lockers holding cremains) and stealing ~$30,000 in jewelry/valuables. Long bit ranking this on a "scale of crimes" (near the bottom, close to victimless). Reddit story tangent — A woman discovered her late mother's belongings (rare rock-photography prints of Skynyrd, Tom Petty, etc.) being resold at Orlando's Thrift Con after her storage unit was auctioned off; she's trying to track down the buyer. Break plug — Ross McCoy's comedy show, "What Do You Want From Us" Moe Comedy Jam at the Funny Bone, October 17; tickets on sale "early next month." Caller: "Concrete Mike" — Grave-robbing story from his younger days selling magazines with a traveling group; friends back home (while he was on the road) robbed cemetery mausoleums for jewelry to fund LSD, made national news, several went to prison. Segues into a long string of acid-trip stories (Twister movie premiere, Walmart at 2:30am, etc.). Discussion: leaving Florida — Sparked by a BDM (fan community) thread; framed around Jordan Foley's farewell show that night at Will's Pub (moving to a small town near Nashville). Debate over whether people who don't actually use Florida's beaches/outdoors amenities should just move somewhere cheaper; examples cited include Dan's niece Daisy moving to Georgia, and general "normal American" suburban living not being tied to location once remote work is factored in. House-demolition story — Dan describes watching a neighboring house get fully demolished by an excavator crew in 58 minutes; tangent into the "McMansion" trend replacing old Florida houses on his street, and permit rules requiring a percentage of original walls to remain standing. Wrap-up / plugs — Teased "big" announcements (a possible at-sea event and a land-based event) to be revealed on Monday's BDM show (fan membership show, sign up at tomandan.com). Sign-off. Key Dates / Plugs Mentioned Tonight: Jordan Foley's farewell show, Will's Pub (~6:30–7:30pm), before his move near Nashville, TN. Tomorrow: Real Radio poker tournament (Dan playing). October 7: Purdue-led expedition departs for Nikumaroro Island (Amelia Earhart search). October 17: Ross McCoy's "What Do You Want From Us" comedy show at the Funny Bone (tickets on sale early Sept). Monday: BDM (fan club) show — event announcements plus continued "Mysteries of the Unknown" book bit. Upcoming ("next week" as of recording): Studio pinball meet-and-greet, sponsored by Pizza Bruno and White Claw. Notable Guests/Callers Andrea Dennis — on-couch interview guest (weight loss/HRT segment). Ross McCoy — in-studio guest, Orlando comedian. Gary (cousin from Texas, Dale's older brother) — voicemail. "Concrete Mike" — phone-in caller with the grave-robbing/acid story. ### Website: https://tomanddan.com/ App: https://tomanddan.com/app Become a BDM: https://tomanddan.com/registration Merch: https://tomanddan.myshopify.com/ YouTube: https://www.youtube.com/@TomandDanLive Twitch: https://www.twitch.tv/tomanddanlive Facebook: https://www.facebook.com/AMediocreTime Instagram: https://www.instagram.com/tomanddanlive/ X: https://x.com/TomAndDanLive TikTok: https://tiktok.com/@tomanddanlive Reddit: https://reddit.com/r/tomanddan ACT RSS: https://feeds.libsyn.com/61976/rss AMT RSS: https://feeds.libsyn.com/18904/rss

Link Up w/The Morning Sickness Digitally All Over:Instagram: @hms_98_official, @bosskupd, @bretvesely, @dickToledoX/Twitter: @HMSon98, @DickToledo, @bretveselyFacebook: @HMSKUPDYouTube: @hmspodcast9320, @98kupdRequest/Call in/Wakeup Song line:(IN AZ) 602.585.9800More HMS: www.holmbergpodcast.com, www.98kupd.comEmail: dtoledo@98kupd.com, bvesely@98kupd.com, bbogen@98kupd.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Link Up w/The Morning Sickness Digitally All Over:Instagram: @hms_98_official, @bosskupd, @bretvesely, @dickToledoX/Twitter: @HMSon98, @DickToledo, @bretveselyFacebook: @HMSKUPDYouTube: @hmspodcast9320, @98kupdRequest/Call in/Wakeup Song line:(IN AZ) 602.585.9800More HMS: www.holmbergpodcast.com, www.98kupd.comEmail: dtoledo@98kupd.com, bvesely@98kupd.com, bbogen@98kupd.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Whiz-Bang and MirthMaker riff through a scattershot episode of Tangent Station, bouncing from bizarre international laws (no chewing gum in Singapore, camo banned in the Caribbean, underwear required in Thailand) to personal anecdotes about sleep habits and marriage. They riff on sneaky modern annoyances—smart appliances that judge you, parking apps and convenience fees—and nostalgic complaints about product fads like "extreme" snacks and Shake Weights. The banter mixes absurdity with relatable pet peeves, memories of cheap candy and CDs, and plenty of toilet humor; listeners get a comic, off‑the‑rails meditation on how life keeps getting stranger and more inconvenient.

This episode is a freewheeling mash-up of offbeat banter, nostalgia and a running litany of terrible inventions. Our hosts riff on everything from Crystal Pepsi, Jolt Cola and Segways to Google Glasses and QR-code menus — skewering the logic (or lack of it) behind each idea. They weave in personal asides about tattoos, childhood heroes, road-trip listening, and the perils of self-checkout. Expect toilet humor, product deep-dives like bacon- and pickle-flavored toothpastes, and a recurring “joke grenade” that lands later. It's messy, irreverent, and intentionally all over the tracks — in the best Tangent Station tradition.

This episode is a freewheeling, laugh-out-loud ramble about the weird little things adulthood never prepared us for. Our hosts bounce from complaining about sticker shock, checking the weather obsessively, and why you always lose the thing you just held, to body quirks like neuropathy and random joint noises. Interspersed are pop-culture detours (Seinfeld and Silo), awkward dad-humor, and a few too many anecdotes about brownies, gummies, and napkins. It's affectionate, messy, and familiar — the kind of conversational tangents that make ordinary life feel entertaining and absurd all at once.

Rowland Hobbs is CEO and co-founder of Stake, a fintech platform that rewards renters with cash back, working to make renting financially rewarding. Before Stake, he led design and innovation at Teneo and served as head of product design for Accenture North America, and he founded Post+Beam, an innovation design firm, and Linea, a computer vision driven photo sharing app. Rowland is based in Dallas, TX.(04:10) - Why Rent Was Left Out of Loyalty(06:00) - Loyalty Programs Go Multifamily(08:30) - Financial Amenities vs. Flashy Perks(12:20) - Cash back for Delinquency, Retention & Vacancy(14:40) - Rewarding Renters Instead of Punishing Them(15:50) - Bilt Rewards(24:50) - Stake's Cash back Business Model(27:10) - Cash back by Property Type(28:50) - UMoveFree Acquisition in Texas(31:10) - Vertical Integration in Multifamily(32:20) - Rising Housing Costs & Renter Churn(36:50) - Renter Loyalty's Next 25 Years(38:50) - Collaboration Superpower: Barack Obama & Loyalty Program's Inventor

Hosts Kevin (the mirth maker) and Willis (the self-styled whiz bang) tumble through a chaotic, hilarious episode of Tangent Station that feels like listening in on two old friends at a diner. They riff on everything from Joe Rogan and David Lee Roth impressions to deodorizing microphones and the smell of underwear. Along the way they veer into nostalgia — video stores, clubbing days, and local Cincinnati haunts — while sharing oddball stories about mosquitoes, 23andMe surprises, and a radio tease about a man who fathered hundreds of children. The tone is loose, brash, and affectionate: conversational comedy with a surprising heart when Willis talks about his wedding.

Our Out on a Tangent podcast had upgraded to video format! But don't fret - we still have our audio only formats on our regular streaming platforms. Join the Youth Services team on a journey through teen literature, fandoms, and more! Sometimes they'll stay on topic, but usually, they'll go out on a tangent.This episode, Erin and Becca talk about Greek mythology! Origins, gods & goddesses, transformations, retellings, and more!Find us on our socials: Facebook @ Merrimack Public Library; Tiktok, Twitter, and Instagram @Merrimacklibnh

Every time one of these comes around it feels like the first time in a long time. You all put up with our distracted antics each episode, and now, finally, we are bringing you an episode of nothing but distractions. We talk devastating TV shows, the days shenanigans, and dreams. So many dreams. Some scary, some not so scary, some disturbing in multiple senses of the word. Either way, strap in and get ready to never know what's going to come out of our mouths next.   Check out our affiliates: Javvycoffee.com Use code ORSO77605 to get 15% off every order. Venomscent.com Use code ORSO28248 to get 10% off every order. Donate monthly here: https://www.patreon.com/orsotheysaypod Or a once off here: https://www.paypal.com/donate/?hosted_button_id=T22PHA8NAUTPN  And don't forget to swing by here: https://www.redbubble.com/people/orsotheysaypod/shop

Clayton Callander is the founder of FTL (Field Technologies), a diagnostic platform built to give commercial buildings something like a check-engine light, closing the service gap for the roughly 95% of properties that enterprise vendors like Honeywell and Siemens can't economically reach. Before founding FTL, Clayton spent seven years maintaining nuclear systems aboard a US Navy submarine, worked at SpaceX, and earned his MBA from Harvard Business School. He built FTL for the people fixing America's commercial buildings because he used to be one of them, and the tool he needed didn't exist yet. Clayton is based in San Francisco, CA.(00:00) - Intro: Why Six Million Commercial Buildings Have No Real-Time Data (01:10) - Meet Clayton Callander, Founder of Field Technologies(02:40) - The Telephone Game: What Happens When an HVAC Unit Breaks (05:50) - Zach's Take: Preventative Maintenance's 20-Year Elusive Goal (07:10) - It's an Access Problem, Not a Technology Problem (09:40) - Why Building Controls Stay Complicated and Expensive (13:10) - What FTL Does (16:50) - Pricing FTL at a Fraction of Traditional Controls (20:20) - Why It's Called Facilities Triage (22:20) - Which Asset Classes Are Ready for FTL (25:00) - What 60 Site Deployments Taught FTL (26:30) - Unlocking the Other Six Million Buildings (29:30) - LoRaWAN Explained: Why Not Wi-Fi (32:30) - Proprietary vs. Open Systems: A VC's View (36:00) - Collaboration Superpower: Immanuel Kant (Wikipedia) and Ding Huan (Wikipedia)

Eoin Sheahan, Mick McCarthy and Arthur O'Dea bring back the most sought after segment on Irish radio; A Slight Tangent!The week of the All Ireland football final, Eoin's Kerry yerra-ism is back in full swing, while the lads discuss everything that is just tangential to the sporting world!

Justin Segal is President of Boxer Property, where he oversees operations, leasing, technology, and marketing across a national portfolio of office, retail, and hotel assets. He co-founded Brava Systems, an AI-enabled platform originally built for Boxer's own operations and now deployed across the commercial real estate industry, and Relay Human Cloud, a global staffing and team augmentation company. Justin is based in Houston.(02:50) - Building the Data Foundation for AI (05:10) - The Data You Have vs. What You're Missing (06:30) - Why Data Cleanup Isn't a Massive IT Project (09:00) - Why Most CRE Companies Aren't Data-Ready (11:10) - The Chief Innovation Officer's Mixed Track Record (14:10) - Avoiding Agent Sprawl and Vendor Lock-In (17:20) - Vibe Coding: Great for Interfaces, Dangerous for Logic (20:20) - Rethinking How We Measure AI ROI (22:40) - What Boxer Actually Spends on Tokens and Compute (24:30) - Putting a Number on AI-Driven Deal Flow (27:30) - Balancing Employee Access to AI Tools With Governance (29:00) - Treating Tokens and Compute as a Trackable Line Item (31:50) - Where AI Adoption Creates the Need for More Humans (34:20) - Freeing People From Being "Professional Rememberers" (36:30) - Collaboration Superpower: Thomas Davenport (Wikipedia)

The weekly podcast from The Lynch & Taco Morning Show on 101one WJRR in OrlandoSee omnystudio.com/listener for privacy information.

Dan Mosher is the CEO of DealGround, an AI-native platform helping commercial real estate brokers and investors find deals faster by organizing property data, identifying opportunities, and connecting directly with property owners. A veteran Silicon Valley entrepreneur, operator, and investor, Dan previously built and led the Merchant team at Postmates through its acquisition by Uber, served as President of Presto, and helped scale BrightRoll tenfold before its acquisition by Yahoo. Live from ICSC+Proptech in Las Vegas.(01:09) AI Survey with FirstAmerican (03:07) How DealGround Helps Brokers Find More Deals (06:08) Why AI Adoption Outpaces Trust (10:14) MCPs, Integrations & Fitting Into Broker Workflows

Noah Walters is the co-founder of Tower, a custom-built AI platform for every stage of the due diligence process. He previously practiced at Dentons Canada, where he focused on tech regulatory compliance, private M&A, and venture financings as a founding member of the firm's FinTech and AI industry teams. Noah is based in Canada. Recorded live at ICSC+Proptech in Las Vegas.(01:34) Due Diligence Bottleneck(03:44) Execution Risk & Trust(04:54) Tower's Approach (07:31) Beyond Document Review(09:39) Change Management for AI Adoption(15:11) Future of AI Deal Rooms(18:02) Collaboration Superpower: Kendall Jenner & Dario Amodei

Ryan Botwinick is the founder and CEO of Fyxt, an AI operating system for commercial real estate that unifies workflows, data, and automation across enterprise portfolios. Ryan came to the problem from both sides, as a real estate broker and through building his own property management company, before founding Fyxt in 2017 to bridge the operational disconnect between owners, managers, tenants, and service providers. Fyxt has particular depth in net-lease and mixed commercial properties, with clients including Delta, Realty Income, STAG Industrial, Kite Realty, Global Net Lease, and Friedman Real Estate. Ryan is based in Los Angeles. Episode recorded live at ICSC+Proptech in Las Vegas.(01:20) How Ops Get Complex(02:56) Today's NOI Pressure(05:06) Where Ops Break Down(07:52) What Fyxt Actually Fixes(09:28) Operating System vs Point Tools(14:20) AI Impact on CRE Ops(15:28) Role of the PM of the future(17:20) AI & Automation Misconceptions(19:07) Collaboration Superpower: the Government

We're back! The fellas review a movie they've both seen! They talk Supergirl, which leads to thoughts on that Digger reel, Christopher Nolan, and a handful of other things. Join us! 

This week on Without A Country, Corinne Fisher breaks down the political shakeups coming out of New York City's primary elections and what Zohran Mamdani's growing influence could mean for the future of the Democratic Party heading into 2028. She examines surprise victories from DSA-backed candidates, the role of endorsements in modern politics, and the broader battle for the direction of the American left. Corinne and Mike also dive into Jimmy Fallon's controversial decision to host Conor McGregor despite the fighter's recent civil seggsual assault ruling. Then, in a deep-dive exposé, Corinne unpacks the bombshell Washington Post investigation into Tulsi Gabbard, her upbringing in the Science of Identity Foundation, allegations of cult-like influence, and the mysterious network of advisers who may have helped shape her political career. Plus: the new report shared by the UN about children in Gaza, the Trump administration's foreign policy moves, Israel and Gaza, international election interference concerns, developments in Cuba, unrest in Bolivia, and staggering rising domestic violence statistics in one US state.0:00 Intro1:20 Welcome to Without a Country2:41 Patreon & show support3:40 NYC's closed primaries explained4:49 On political endorsements 6:28 Brad Lander's win & his political persona9:20 Mamdani's endorsement slate & DSA momentum11:30 Darializa Avila Chevalier's stunning upset15:35 Enemy of the State: Jimmy Fallon20:08 Fallon hosting Conor McGregor26:43 Conor McGregor's r@pe case, explained32:45 McGregor's comeback fight vs. Max Holloway34:54 Deep dive: The Washington Post's Tulsi Gabbard exposé38:08 Tulsi Gabbard's upbringing inside the Science of Identity Foundation42:22 The Informant51:56 Tangent: who actually runs the DSA?59:54 Chris Butler's political origins in Hawaii1:06:31 The 1990s anti-gay-marriage ad featuring young Tulsi1:09:57 On-the-ground investigation in Hawaii1:17:19 Scripted tweets and talking points1:23:59 Attempts to Discredit Informant 1:29:55 Running the memos through Claude AI1:36:12 Gabbard's abrupt departure as DNI1:39:59 Gabbard's parting shots at Fauci over Covid origins1:43:59 The White House's "media offenders" list1:46:54 The UFC 250 White House attack plot1:57:44 So who actually runs the DSA?1:59:20 Why political "vessels" need star power2:04:13 Politico: "Mamdani emerges as kingmaker"2:08:56 Brad Lander's district & the Jewish vote2:21:31 NYT profile: Who is Darializa Avila Chevalier?2:31:57 Could Be Worse: Gaza coverage fatigue & media consolidation2:35:12 UN report: Israel targeting Palestinian children2:39:09 Cuties Corner: the ant slave rebellion2:43:01 Bolivia's state of emergency2:43:29 Colombia's election & Trump's meddling2:44:45 Cuba's economic reforms under US pressure2:46:08 One state's rise in domestic violence2:47:24 Outro & how to support the showSUBSCRIBE TO THE PATREON:https://patreon.com/WithoutACountry?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLinkFOLLOW WITHOUT A COUNTRY ON IG: https://www.instagram.com/withoutacountrypodcast/FOLLOW CORINNE ON IG: https://www.instagram.com/philanthropygalFOLLOW MIKE ON IG: https://www.instagram.com/themharrington/FOLLOW ALONG:ENEMY OF THE STATE: JIMMY FALLONhttps://sports.yahoo.com/article/conor-mcgregors-rape-case-explained-210139281.htmlMAIN STORIESTulsi Gabbard exposéhttps://www.washingtonpost.com/investigations/2026/06/21/tulsi-gabbard-her-guru-mysterious-messages-that-helped-shape-her-political-career/Hawaii anti-same sex marriage ad: https://www.youtube.com/watch?v=tXPH_b_ATioScience of Identity Foundationhttps://en.wikipedia.org/wiki/Science_of_Identity_FoundationWHITE HOUSE GREAT LAWN UFC ATTACK PLOThttps://www.justice.gov/usao-sdoh/pr/five-men-arrested-charged-plot-attack-kill-government-officials-others-attending&https://www.npr.org/2026/06/23/nx-s1-5867278/authorities-arrest-suspects-attack-ufc-showMUNICIPALMamdani the Kingmaker https://www.washingtonpost.com/politics/2026/06/25/mamdani-emerges-tuesday-primaries-big-winner-other-takeaways/Darializa Chevalierhttps://www.nytimes.com/2026/06/23/nyregion/who-is-darializa-avila-chevalier.htmlCOULD BE WORSE/GUUUURLGaza Gen0cide Continueshttps://news.un.org/en/story/2026/06/1167790Boliviahttps://www.bbc.com/news/articles/cr47wn92zdgoTrump & Columbiahttps://www.theguardian.com/world/2026/jun/24/colombia-presidential-election-abelardo-de-la-espriellaCuba Economic Liberalization After Pressure from UShttps://www.democracynow.org/2026/6/22/headlines/under_intense_us_pressure_cuban_lawmakers_approve_sweeping_economic_changesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Tangent city in this weeks edition of Talkback. The boys cover the best feeds to eat whilst on the turps, how Dad's rocking baby carriers are actually elite and translating different barks from dogs (which gets pretty rogue). Enjoy the rest of your week legends and have a ripper weekend!Got a yarn for Talkback? Email it to carryon@alphablokes.com.auWant Poo to review your Tinder profile? Email the big fella with your intel to possibly get on to Poo's Reviews: poobandit@alphablokes.com.auEver wanted to watch the Podcast? Check out full visual, uncut and ad-free versions on our Patreon. Only $5 a week plus access to all of our exclusive vlogs. The full Alphafest Documentary has just dropped: patreon.com/alphablokespodcastBetter Beer: Jog in a can, win in a tin, the athletes choice. Try their new Halfy's at any bottle-o near you: https://www.betterbeer.com.au/Neds: Whatever you bet on, take it to the neds level: https://www.neds.com.au/SP Tools: Schmicker tools for an even schmicker price, use code "ALPHA" at checkout for 10% off and check out their brand new catalogue: sptools.comPortwest: Tough workwear for tough jobs. Check out their vast variety of PPE for the jobsite here: https://www.portwest.com/market/Papa Macros: ready made unreal meals if you're too flat out to meal prep Sunday arvo. Use the code "ALPHA" for $30 off your first order or "ALPHA10" for any reoccuring order for 10% off at papamacros.com.au OR simply use the links below:$30 off your first order: https://www.papamacros.com.au/?coupon-code=ALPHA&sc-page=shop10% off: https://www.papamacros.com.au/?coupon-code=Alpha10&sc-page=shop0:00 - Intro4:00 - Carry Ons1:19:00 - Winning Call / Outro Hosted on Acast. See acast.com/privacy for more information.

Celebrate a decade of stargazing with Astrophiz!

Yaakov Zar is the founder and CEO of Lev, a software platform built to modernize the workflow of commercial real estate professionals. Yaakov started Lev after experiencing firsthand how broken the CRE financing process was, watching a $4 million loan take six months to close. What began as a tech-enabled brokerage has evolved into a purpose-built agentic workflow platform helping lenders, brokers, and investors manage deals, ingest unstructured data, and move faster. Yaakov is based in New York City.(02:26) Bottom Up vs Top Down(04:31) Slack Origin Tangent(05:59) MetaProp Skills Library(09:43) What Is Defensible AI(11:12) MCP & Rapid Change(12:41) Pilots Everywhere & Demo Fatigue(17:34) Same Workflow, Turbocharged(19:34) Real Estate's Move 37 Moment(22:04) Why Winning Is Hard to Define(26:07) Lev Agentic Workflows(29:14) Leapfrogging Past Salesforce(31:43) Data Quality Pushback(33:49) Ingesting Email Into CRM(35:54) Selling Software to CRE(39:06) Overhyped AI and Security Risks(42:50) Collaboration Superpower: Steve Jobs

It’s time for another tangent cut out of the main show. Here we talk about the Absolute DC books and Charlie getting back into Hitman! [ Additional Show Notes ] Music by Alfred Etheridge-Nunn. Read Miles's blog or Charlie's blog. [ Support this show on Ko-fi ] Subscribe to this Podcast: Apple PodcastsSpotifyAndroidRSSThe post Casual Tangent – Absolute Hitman first appeared on Nerd & Tie Network.

Ben and Trevor take a significant detour from their usual routine this week, embarking on an unusually large and lengthy exploration of culinary possibilities. They spend a considerable amount of time debating the merits of "fusion" foods like meatloaf Swiss rolls with relish, various international-style burritos (including spaghetti, pizza and HSP varieties), and the precise technical requirements for the perfect piece of toast involving Vegemite and cheese.Despite the heavy detour into food science, they still manage to play a few rounds of Click Pitch to develop some game designs:A tactile investigation into document forgery and provenanceA competitive county fair game involving the production and judging of prize-winning sausagesA high-stakes logistics simulation for packing grocery bags under specific environmental conditionsAn investigative game where you must find out why solar systems are being wiped out in a section of the milky way, and we are headed to that spot.

Leave your morals at the door and join Mike and Jeremy as they discuss some of their favorite horror movie killers.Thank you for listening!

Andreas Rotenberg is Co-founder and COO of Pulley, an AI-powered permitting platform helping developers and operators move projects through approvals faster. Before Pulley, he was part of the team at Honest Buildings through its acquisition, then served as Chief of Staff at Procore through its IPO. Pulley has supported over $15 billion in projects approved across the U.S. Live from ICSC+Proptech in Las Vegas.(0:00) - First ever ICSC+Proptech live podcast(1:47) - Why Permitting Is a Growing Bottleneck(2:41) - What's Happening During Permitting Timelines(4:13) - Jurisdictional Complexity Across the U.S.(5:08) - What CRE Teams Underestimate About Permitting(7:35) - Why Pulley(8:18) - The Origin Story(10:53) - Combining Technology with Local Expertise(14:26) - Where AI Creates Real Value in Permitting(17:36) - Trust, Hallucinations & Accuracy(19:07) - Municipalities & Public Sector Modernization(20:40) - Second & Third Order Effects of Faster Permitting(22:41) - Collaboration Superpower: Vaclav Smil

Remixed off the Endless Tangent's new EP, Special Source. https://endlesstangent.bandcamp.com/album/special-source

James Harris built his real estate career from scratch after relocating from the UK to Los Angeles. His latest venture is Breezy, an AI tool for real estate agents designed to reduce the administrative load that eats into most agents' days. Breezy recently raised a $10 million pre-seed round led by Ribbit Capital with participation from Fifth Wall, DST Global, and others. Over the past decade he's closed more than $6 billion in luxury residential sales across Beverly Hills and the Platinum Triangle, and appeared on Bravo's Million Dollar Listing LA for seven seasons spanning over nine years before stepping away to focus on his next chapter. He also hosts Rise Above the Ranks, where he talks through the practical side of the business: negotiation, discipline, and how to build something that lasts.(2:11) - What Proptech Gets Wrong About Real Estate Agents(6:49) - Realtor Painpoints(8:54) - What is Breezy(13:32) - Why Now(16:26) - Real Estate Agent & Founder Resiliency(18:31) - Brand, Distribution & Early Traction(21:42) - AI Agents Enhance and/or Replace Real Estate Agents(24:20) - First Mover Advantage(26:36) - What Parts of Real Estate Will Be Human-driven in 3-5 years(28:24) - The Power of Data & Workflow Platforms(29:16) - What Changes in Real Estate Over the Next 2-3 Years?(30:49) - Collaboration Superpower: The Founder Entrepreneur

Here you'll meet Dustin Raschein from Farmland Tractor and Supply based in Tangent, Oregon and a place we'll be visiting in July during the National Tractor Parts Dealer Association Summer Meeting.  In this conversation we talk about the beginnings of his business, his work with the NTPDA and what the company does in offering both new and remanufactured (reman) tractor parts, with over 30 acres of inventory on-site and a huge database of parts to find what folks need. They have parts for tractors, combines and industrial machines including Allis Chalmers, Case, David Brown, Ford New Holland, International Harvester, John Deere, Kubota, Massey Ferguson and Oliver.  Here's Dustin... Thanks for listening! The award winning Insight on Business the News Hour with Michael Libbie is the only weekday business news podcast in the Midwest. The national, regional and some local business news along with long-form business interviews can be heard Monday - Friday. You can subscribe on  PlayerFM, Podbean, iTunes, Spotify, Stitcher or TuneIn Radio. And you can catch The Business News Hour Week in Review each Sunday Noon Central on News/Talk 1540 KXEL. The Business News Hour is a production of Insight Advertising, Marketing & Communications. You can follow us on Twitter @IoB_NewsHour...and on Threads @Insight_On_Business.

What's going on in Ariana Grande's new music video? Let's play a game: Did these celebrities get replaced because they were fired, quit, or died? Did you know Chris Farley was supposed to be Shrek? Sarah's telling us about the most expensive celebrity's baby photo ever. Dang, People Magazine has some dough. The generations are at war. It's Pride month! The Giants still suck. Californians are filing bankruptcies. Vinnie's got great gifts for the high school graduates in your life.

Dillon Okner is the Founder & Partner of SiteRise, a retail development and construction platform helping brands streamline site selection, store development, and portfolio expansion. With more than 15 years of experience in retail construction and operations, Dillon previously helped support the expansion of major brands including Apple and Tesla. Today, he focuses on helping retailers, restaurants, and franchise operators eliminate development bottlenecks and open locations faster through better data, workflow management, and portfolio visibility.(01:13) - How Retail Openings Break(03:36) - Retail Innovation Edge (05:00) - Store development with SiteRise(07:38) - Apple & Tesla Lessons (09:26) - Proprietary Data (14:07) - Construction Influence on Deals(15:27) - Working with Brokers(16:33) - SiteRise's Clients(17:44) - Getting Buy-in From Stakeholders(20:50) - Collaboration superpower: Dillon's father and grandfather

Will Parrish is the Co-Founder and Chief Customer Officer of Lula, a Kansas City-based proptech platform built to streamline property maintenance for property managers and their residents. Will co-founded Lula alongside CEO Bo Lais with a mission to make property maintenance smarter — pivoting the business during the pandemic to focus on property managers in the single-family rental space, a move that fueled rapid growth. Lula recently closed a $28 million Series A round and is expanding from 42 markets to 60, with heavy investment in AI and automation. Before co-founding Lula, Will spent nearly two decades in enterprise sales and business development, including a long tenure at Thomson Reuters. (00:53) - How Lula Started(02:34) - Trading Corporate for Startup Life(03:29) - Is Maintenance Archaic(05:49) - Where Work Orders Fail(07:30) - Scaling 100K Work Orders(12:28) - Building Vendor Trust & Quality(13:19) - Expanding Markets(16:16) - Flat Rate Pricing Playbook(19:15) - Ideal Rental Customers(21:54) - Integrations(25:47) - AI In Maintenance(30:21) - Future of Lula(32:14) - ROI for Property Owners & Operators(35:49) - Hardware play ahead?(39:12) - Collaboration Superpower: MacGyver

Django and Roman are spouting their comic knowledge everywhere this week, since Jeff isn't here to keep them reined in. And yet, we still talk about mechs, hidden kaijus, hidden demonic ingredients, nude nuns, and so much more. Let's just say, things go off the rails without Jeff to keep us sane! So hit play already.0:03:14 - Well Welcome Wellmer!0:07:20 - Absolute Batman #200:18:36 - Hidden Springs #10:24:11 - Innards #10:29:38 - Excommunicated #10:33:41 - Spectacular Spider-Man: Brand New Day #10:37:07 - Django's Tangent about Punisher: One Last Kill & a bit about Daredevil: Born Again0:40:05 - Barbara Gordon: Breakout #10:49:27 - Fury of Firestorm #20:55:10 - Ultimate Endgame #4SPOILERS! Tread carefully dear listener, because we're going to talk about what happened in these books. So definitely pause this, read your comics, and come back. We'll still be here!And an enormous thank you, as always, to Andrew Carlson for editing this mess into something listenable.Subscribe to us on Apple, Spotify or wherever you like to get your podcasts.Email us at jeff@thecomicsplace.com! We love hearing from you and there's a good chance we will read it on air!Cover art by Nil VendrellVisit us at The Comics Place next time you're in Bellingham, Washington!Comics Place Book Club - second Thursday of every month. Check the shop for details!

Theme Party: The Podcast features a collection of clever creatives that come up with an inventive, original idea for an episode based on the theme of the season. Season One's theme is “Family Traditions". Each creator will feature the results of their hard work in our podcast feed, as well as appear in an interview episode where we celebrate their achievement, talk about their process, and get an anecdote or two about their experience with parties (themed or not). In Theme Party: The Podcast's first episode by creator Lindsay Fawn, a rebellious young octopus races to finish a mech suit to honor her late mother and protect her kind from the surface world. Website: https://www.lengthytangent.com/ Binge on all of our audio shows at atlantafringe.org/fringe-audio or wherever you enjoy podcasts.  

Don Tepman, known as StripMallGuy, is the founder of TownCentre Capital, a private equity firm focused on buying neighborhood strip centers across the United States. With over 20 years of retail real estate investing, he has completed over 45 acquisitions and raised more than $150M in LP capital, and has built one of commercial real estate's most followed voices on X, LinkedIn, and Instagram with over half a million followers.This episode was recorded live on the red carpet at the 4th Annual Real Estate Gala in New York City, co-hosted by Don Tepman and Bob Knakal.(00:00) – Tangent Joins Commercial Observer(01:40) – ICSC Vegas Panel & Live Podcasts (03:41) – Social Media & In-person Events as a Business Tool(03:59) – StripMallGuy's AI AHA Moment(04:18) – Why PropTech Is Hard(05:02) – Death of the Cold Call(5:33) - Last Tech Solution SMG Adopted(6:45) - Collaboration Superpower: Charlie Munger(07:31) – Rise of Tech Founders with CRE Backgrounds

Al and Greg talk about Tomodachi Life: Living The Dream. Timings 00:00:00: Theme Tune 00:00:30: Intro 00:04:13: What Have We Been Up To 00:21:57: Game News 00:57:42: Tomodachi Life: Living The Dream 01:49:37: Outro Links Outbound Un-Delay Farm To Table Early Access Release Date Farm To Table Un-Delay Tales of Seikyu 1.0 Release Horticular Switch Release Starsand Island “New Connections” Update Hello Kitty Island Adventure “Month of Meh” Animal Crossing x Sealife Expansion Contact Al on Mastodon: https://mastodon.scot/@TheScotBot Email Us: https://harvestseason.club/contact/

Leroy is back and Tobin is happy that he wasn't kidnapped! The guys go through the many eliminations and second round playoff matchups! The Maple Leafs come under fire during their press conference introducing new GM John Chayka, the guys discuss the decision. Tobin shares a funny nugget from his coverage of boxing this weekend which spirals into more boxing talk from the guys!

In this special episode of Tangent Proptech, Edward Cohen is on the red carpet at one of the most exclusive commercial real estate events of the year: the Real Estate Gala in New York City. This episode features rapid-fire conversations with founders, investors, brokers, developers, and operators across the proptech and commercial real estate ecosystem. A big focus of the evening was on AI. Namely, this question: how is AI being used in real estate right now? And possibly more front-and-center: what's hype and what's here to stay? From leasing and marketing to underwriting and financial modeling, this episode explores where artificial intelligence is already driving value in real estate, where it's falling short, and how we can close the gap.(00:00) - Welcome to the Real Estate Gala Red Carpet Interviews (02:30) - Cyrus Claffey (ButterflyMX): AI Across Product, Marketing, & Operations (06:00) - Zach Molzer (Molzer Development) & Madi Bremer (CBRE): Networking & AI in Leasing (08:30) - Gabe Einhorn (VryfID): Content, Consistency, and AI Efficiency (10:00) - Kaylan Knitowski (Franklin Street): AI Workflows and Competing with Experience (13:30) - David Auerbach (Hoya Capital): Driving Tech Adoption in Real Estate (14:45) - Adam Steiner (Rick, Steiner, Fell, and Benowitz): Document Automation & Bridging Tech and Business (16:45) - Humberto Lopes (HL Dynasty, Gotham Housing Alliance): A Human-First Real Estate Perspective (19:15) - Jovian Lopes (Gotham Housing Alliance): AI for Research vs Human Relationships (21:00) - Lauren O'Breza (Foresite CRE): AI in Brokerage & Underwriting (24:30) - Pablo Barreiro (Fortec): Simplifying Tech Adoption & the Future of Financing (26:00) - Shanti Ryle (Crexi): AI Data Enrichment & Storytelling Advantage (30:30) - Rameen Inayat (Ryan): AI for Admin & Property Tax Insights (32:00) - Collaboration Superpower: Priya Parker 

Dave and the crew are back from hiatus and catching up on everything from luxury party snacks and properly aged gummy bears to bitter lion's mane mysteries, banana ripening, homemade tofu, chicken consommé, and the eternal problem of the soggy bottom half-inch of banana bread. Plus, on-off steak cooking, peanut butter whiskey, milk punch theory, and a ruling on whether shabu-sliced beef smashed into a bun counts as a burger. Hosted on Acast. See acast.com/privacy for more information.

A brief tangent into memories of the Big Jim Camper, but I brought it back to discussion of news, questions and lots of talk about recent comics.

Here it is, folks, a true tangent for the books. Lord knows we always have some asides throughout our episodes, so we decided to let loose, and see if those unscripted asides would give us a full episode. And to no ones surprise, we sure did it. So sit back and enjoy over an hour of us just seeing where the wind takes us, and then changing course anyway.   Check out our affiliates: Javvycoffee.com Use code ORSO77605 to get 15% off every order. Venomscent.com Use code ORSO28248 to get 10% off every order. Donate monthly here: https://www.patreon.com/orsotheysaypod Or a once off here: https://www.paypal.com/donate/?hosted_button_id=T22PHA8NAUTPN  And don't forget to swing by here: https://www.redbubble.com/people/orsotheysaypod/shop

Ido Genosar is the CEO and Founder of Verobotics, the pioneering robotics solution for building façade maintenance, inspection, and upkeep. Prior to this, he led innovation at Aluminium Construction, Israel's biggest façade constructor. His diverse background in building exteriors, technology and global business development is at the core of his mission to solve deep-rooted inefficiencies with breakthrough innovation.(00:50) - “Miracle” Robotics(02:50) - What the Façade Robot Does(03:11) - Humanoids vs. Task-Specific Robots(04:28) - Why Robotics Demos Fail in the Real World(06:05) - VC Lens on Robotics(09:13) - Funding & Adoption Reality(12:48) - Façades as a Data Blind Spot(15:11) - Construction Signoff & Compliance(16:00) - Unions, Scaffolding & Safety(18:07) - New Data-driven Decisions(20:52) - Where's the Long-Term Value (29:33) - Humans in the Loop(31:48) - Best-Fit Buildings Today(33:03) - Robots in 5 to 10 Years(36:43) - Collaboration Superpower: Sir James Dyson, Leonardo Torres

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

Zach Schofel is the Co-Founder and CEO of Cosign, a data-driven guarantor platform helping property owners and managers boost economic occupancy while expanding housing options for renters. Cosign aims to redefine renter underwriting and help multifamily owners convert more demand into leases. He is also a Principal at Eastman Residential, the largest buyers of distress student housing in the country. He leads a portfolio of 3,000+ multifamily units across the US, with a focus on student housing and value-add multifamily strategies. (01:34) - The Eastman Residential Story (02:29) - Cosign Origin (03:39) - Limits of Credit Scores (06:46) - Fraud Screening Landscape (08:05) - Scale of the Problem (11:54) - Underwriting Signals (13:25) - Value creation in Multifamily tech (15:49) - How VC Underwrites Insurtech (18:06) - Feature: Blueprint: The Future of Real Estate 2026 in Vegas on Sep. 22-24 (20:35) - Cosign's Differentiation (24:17) - Mark Cuban's Investment (26:21) - AI in Operations(28:18) - Collaboration Superpower: Jared Kushner & Philip Hubert

Well... we meant to discuss various outstanding mysteries from Isles of the Emberdark, but it somehow devolved into numerous tangents on Hoid in this book. We're sure we'll get to those other things eventually! We have Eric (Chaos), David (Windrunner), Evgeni (Argent), and Bonnie (Cosmeregirl)! Thumbnail art is from Isles of the Emberdark, by Esther Hi'ilani Candari. 00:00:00 Introductions 00:01:14 When did Isles of the Emberdark Come Out? 00:05:29 Hoid's Wife and Kids 00:15:04 Identity of Hoid's Wife 00:28:18 A Tangent on Wind and Truth Epigraphs 00:42:09 Hoid's Wife Resurrected? 00:53:57 An Alternative Resurrection Target 01:01:44 Another Tangent, on the Shattering this time 01:04:56 Candidates for the Identity of Hoid's Wife 01:27:24 The Twins (Hoid's Kids) 01:35:16 The Vault and Hoid's Entrapment 01:59:23 Accepting this Episode's Fate 02:03:02 Who's That Cosmere Character If you like our content, support us on Patreon: https://www.patreon.com/17thshard Purchase merch here! https://store.17thshard.com/ For discussion, theories, games, and news, come to https://www.17thshard.com Come talk with us and the community on the 17th Shard Discord: https://discord.gg/17thshard Want to learn more about the cosmere and more? The Coppermind Wiki is where it's at: https://coppermind.net Read all Words of Brandon on Arcanum: https://wob.coppermind.net Subscribe to Shardcast: http://feeds.soundcloud.com/users/soundcloud:users:102123174/sounds.rss Send your Who's That Cosmere Characters to wtcc@17thshard.com

Tangent hive, THIS is your episode. We've got a rare in-person recording on our hands and hoooo boy do these two cover a RANGE of topics. Rats. Bloomin onions. The Purge. Davidoff Cool Water. Auditions. Tongue twisters. The New Girl episode too, of course. But it wouldn't be The Mess Around without a healthy dose of metaphorical roaming around, ya know?Follow @TheMessAroundPod on Instagram, TikTok and YouTube.Rate The Mess Around on Apple Podcasts and Spotify!This is a Headgum podcast. Follow Headgum on Twitter, Instagram, and Tiktok.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.