Built This Week

Jordan Metzner, Samuel Nadler
  • 5.0(4개의 평가)
  • 기업 운영

Built This Week is a weekly podcast where real builders share what they're shipping, the AI tools they're trying, and the tech news that actually matters. Hosted by Sam and Jordan from Ryz Labs, the show offers a raw, inside look at building products in the AI era—no fluff, no performative hype, just honest takes and practical insights from the front lines.

  1. 3일 전

    AI Is Transforming Construction — We’re Entering a Golden Era

    Construction has lagged behind every major industry in technology adoption. Manual data entry. Spreadsheets. Email-based procurement. Slow invoice approvals. Paper delivery tickets. That’s finally changing. In this episode of Built This Week, Sam Nadler and Jordan Metzner sit down with Eldar (Field Materials AI) to break down how AI is automating procurement for commercial and civil contractors — from reading quotes and invoices to verifying pricing, matching delivery tickets, and integrating directly with ERPs. Field Materials builds AI agents that eliminate manual data entry across the procure-to-pay cycle for electrical, mechanical, concrete, drywall, and other commercial subcontractors working on hospitals, data centers, and billion-dollar infrastructure projects. We also explore: • Why construction productivity has barely improved in decades • How AI agents read and process supplier quotes automatically • How foundational model improvements upgrade products overnight • Why procurement automation directly impacts margin • The data center boom forcing construction to modernize • The difference between “adding AI” and building AI-first software • Whether incumbents like SAP and Salesforce are at risk • Why we may be entering a golden era for construction technology This isn’t theoretical AI. This is production AI operating inside large-scale commercial construction projects today. ⏱️ TIMESTAMPS (0:00) Entering the golden era of construction tech (0:24) Welcome to Built This Week (0:43) Introducing Field Materials AI (1:12) What Field Materials actually does (1:41) Scenario modeling demo (BOM shock analysis) (3:51) Pricing intelligence and risk modeling (4:53) How the company started (6:13) Automating quotes, invoices, and delivery tickets (7:23) Who uses Field Materials (commercial subs) (8:49) How procurement actually works today (manual chaos) (10:07) Cutting overhead and scaling without hiring (11:29) Reducing material waste and pricing errors (12:25) Accelerating invoice approval cycles (13:04) AI agents for different document types (14:01) How foundational model upgrades improve the product (15:09) Why construction underinvested in tech (15:52) The data center boom forcing modernization (16:49) AI + robotics + prefabrication (17:31) Anthropic partnerships and enterprise AI integration (18:39) The next wave: AI with “hands” in enterprise systems (19:49) Why incumbents risk building gimmicks (21:07) Salesforce, SAP, and retention vs innovation (24:12) COBOL, modernization, and disruption cycles (26:39) Why building real AI tools is still hard (27:03) Where to find Field Materials 🔗 LINKS Field Materials https://fieldmaterials.ai Built This Week New episodes every Friday 🎙️ HOSTS Jordan Metzner https://linkedin.com/in/jordanmetzner https://x.com/mrjmetz Sam Nadler https://linkedin.com/in/sam-nadler-1881b75 https://x.com/Gravino05

    28분

  2. 2월 21일

    From DNA to Drugs: How AI Is Rewriting Human Biology

    DNA is just another language. In Episode 32 of Built This Week, we sit down with Dov Gertz, founder of Converge Bio, to explore how generative AI is transforming drug discovery. Every human can be represented as 3.2 billion nucleotides built from four letters: A, C, G, and T. If computers run on zeros and ones, we run on biological code. Converge Bio is training frontier foundation models on DNA, RNA, proteins, and small molecules — helping biotech and pharma companies design better drugs, faster and cheaper. We also demo a retro-inspired “Cell Defense Arena” game built for Converge to use at conferences. Then we pivot into AI infrastructure and agent workflows: The GPU bottleneck and pharma’s growing demand for compute Why molecular AI is 5 to 10 years behind text models How AI could reduce drug timelines from 10 years to 6 to 8 Why cancer and autoimmune diseases may benefit first The limits of FDA regulation in shortening approval cycles OpenClaw, multi-agent systems, and infinite AI teams Cloud versus on prem in the era of foundation models The big takeaway: Chatbots are impressive. But AI applied to biology could extend human life. If you work in biotech, pharma, AI research, or frontier infrastructure — this episode is for you. New episodes every Friday. ⏱ TIMESTAMPS (0:00) DNA as code: 3.2 billion nucleotides (0:32) Welcome to Episode 32 (1:00) Meet Dov Gertz and Converge Bio (2:02) Demo: Cell Defense Arena game (3:25) Converge Bio’s $33M raise and mission (4:05) Foundation models for molecular data (5:00) Turning DNA, RNA, and proteins into machine-readable text (6:02) How transformers apply to biology (7:03) 400x more DNA than text on the internet (8:02) Who Converge’s customers are (9:21) Faster, cheaper, better drug discovery (10:39) The three bottlenecks: data, architecture, compute (12:02) The future of personalized medicine (13:02) Which diseases benefit first: cancer, diabetes, autoimmune (14:00) Regulatory realities and clinical trial timelines (16:30) Will AI shorten drug approval cycles? (17:01) NVIDIA, GPUs, and scaling molecular AI (18:30) Pharma as a new AI infrastructure consumer (19:13) Hard pivot: OpenClaw and agentic AI (21:26) Managing teams of AI agents (22:20) Cloud versus on prem debate (25:02) Why developers must adapt weekly (29:26) Closing thoughts and where to find Converge Bio 🔗 LINKS Converge Bio https://converge-bio.com Built This Week New episodes every Friday https://builtthisweek.com Jordan Metzner https://x.com/mrjmetz Sam Nadler https://x.com/Gravino05

    30분

  3. 2월 13일

    How AI Is Replacing 100-Hour Due Diligence (Claude 4.6, Private Equity, and Emblem)

    Private equity due diligence used to take hundreds of hours. Now it takes seconds. In Episode 31 of Built This Week, we sit down with August Kiles, Head of Product at Emblem, to break down how AI is transforming investment funds — from venture capital to growth equity to private equity. Emblem is building what they call the “last platform investors will ever need” — a system that ingests entire data rooms, extracts financials, compares deals, generates reports in Word, Excel, and PowerPoint, and helps funds get to a “no” faster. We also demo a portfolio scenario simulation tool inspired by Emblem — showing how macro events like regulatory pressure or liquidity surges could impact a 30-company portfolio. Then we dive into the latest AI news: Amazon engineers pushing for Claude Code over internal toolsWhy Opus 4.6 is a step-function improvement for codingHow AI is changing software development workflowsElon Musk’s XAI reorg and what it signals about model competitionThe big takeaway: AI is not eliminating analysts. It’s increasing deal throughput and freeing them to focus on alpha. If you work in VC, private equity, family offices, or growth equity — this episode is for you. New episodes every Friday. ⏱ TIMESTAMPS (0:00) Emblem’s mission: the last platform investors will ever need (0:25) Welcome to Episode 31 (0:55) Meet August Kiles from Emblem (1:28) Building a portfolio scenario simulation tool (2:05) Modeling regulatory pressure across a 30-company fund (3:00) Liquidity supernova scenario explained (4:00) What Emblem actually does for investment funds (5:00) AI-powered due diligence and data room indexing (6:00) From 100 hours of analysis to seconds (7:20) The old way vs the AI-powered way (8:30) Will AI reduce analyst headcount? (9:40) Getting to “no” faster in private equity (10:30) Where Emblem shines: seed vs private equity (12:00) Multi-agent model orchestration inside Emblem (13:00) How new models improved financial modeling (15:00) Amazon engineers pushing for Claude Code (17:30) Step-function improvements in Opus 4.6 (19:00) Coding workflows transformed by new models (21:30) Elon Musk’s XAI reorganization (23:00) Why model quality now matters more than IDE (25:00) Final thoughts and wrap-up 🔗 LINKS Emblem https://emblem.pe Built This Week New episodes every Friday https://builtthisweek.com Jordan Metzner https://x.com/mrjmetz Sam Nadler https://x.com/Gravino05

    26분

  4. 2월 7일

    Claude Opus 4.6, Codex 5.3, and the Rise of Multi-Agent AI

    The biggest shift in AI isn’t a new model. It’s agents managing other agents. In Episode 30 of Built This Week, Sam Nadler and Jordan Metzner break down how they’re actually using the latest AI releases — including Claude Opus 4.6 and OpenAI Codex 5.3 — to build real software inside their own workflows. Jordan walks through a private, fully local AI system built with Claude Code that turns raw 23andMe data, blood work, medications, and personal health inputs into a unified health dashboard. The goal isn’t diagnostics — it’s creating a long-term, living record that surfaces insights doctors don’t easily connect. Sam then demos an AI-powered personal trainer built using the new Codex desktop Mac app and high-reasoning models. The system adapts workouts rep-by-rep, adjusts volume in real time, and highlights the tradeoffs between fast iteration tools and slower, deeper reasoning workflows. We close with the biggest AI platform launches of the week: Anthropic’s Opus 4.6 and Agent TeamsOpenAI Frontier and enterprise AI coworkersPerplexity’s Council Mode and LLM swarmsThe era of one chatbot at a time is over. The new skill is learning how to manage AI agents that manage other agents. No hype. No abstractions. Just what actually happens when builders use AI on themselves first. New episodes every Friday. TIMESTAMPS (0:00) The shift from single-agent AI to multi-agent systems (0:21) Welcome to Built This Week Episode 30 (1:00) Agenda and why this week matters (1:38) Why Jordan downloaded his 23andMe data (2:30) Turning unreadable DNA files into usable insights (3:50) Combining genetics, blood work, and medications (5:05) Drug response insights and hereditary signals (6:10) Generating doctor-ready reports for family (7:20) Why this system runs fully local (8:00) Building personal software instead of buying tools (8:40) Sam’s AI personal trainer built with Codex (9:50) Rep-by-rep workout feedback and fatigue detection (10:45) Designing AI interfaces for real-world use (11:40) Codex vs Claude Code: speed vs deep reasoning (12:20) Anthropic Opus 4.6 and Agent Teams (13:00) OpenAI Frontier and AI coworkers (13:25) Perplexity Council Mode and model swarms (14:05) Why multi-agent management is the real inflection (15:15) Becoming a manager of AI managers (16:00) How many agents one human can manage (17:00) AI’s impact on legacy software companies (18:15) Episode 30 wrap-up and what’s next LINKS Built This Week New episodes every Friday https://builtthisweek.com Jordan Metzner https://x.com/mrjmetz Sam Nadler https://x.com/Gravino05

    19분

  5. 2월 1일

    We Built an AI Recruiter Coach in 6 Hours (Plus Claude Cowork in Real Time)

    Can you really build serious internal AI tools in a few hours — and should everyone on your team be doing it? In Episode 29 of Built This Week, Sam Nadler and Jordan Metzner break down an internal AI product they built at Ryz Labs called ScreenEval — a recruiter screen analysis and coaching tool built in under six hours using Claude Code, Supabase, and AWS. We start with a live demo. Sam walks through how ScreenEval ingests recruiter screen transcripts, evaluates candidates, scores recruiter performance, and provides concrete coaching feedback — all without overriding human judgment. The real unlock is turning messy interview transcripts into searchable, structured hiring data across the entire organization. From there, we test Claude Cowork live — Anthropic’s new interface designed to make building accessible to non-technical users — and compare it to running Claude Code directly in the terminal. We discuss where Cowork shines, where terminal-based workflows still win, and why managing multiple AI agents is becoming a core skill. We wrap with AI news, including Anthropic’s massive funding round, pricing changes, and why enterprise-focused AI tooling is pulling spend away from other platforms. No hype. No abstractions. Just what actually happens when you put AI to work inside a real company. New episodes every Friday. ================================================================================ TIMESTAMPS (0:00) Why internal AI tools matter more than external products (0:55) Episode 29 kickoff and overview (1:45) Why Ryz Labs built ScreenEval (3:30) Live demo: recruiter screen transcript analysis (6:15) Candidate evaluation vs recruiter coaching (9:10) What recruiters miss in fast screening calls (11:40) AI feedback that doesn’t override human judgment (14:00) Searching transcripts instead of resumes (17:20) Manager dashboards and recruiter performance analytics (21:10) How long it actually took to build ScreenEval (23:30) The full stack: Claude Code, Supabase, AWS (25:45) Why Anthropic models power everything (27:30) Claude Cowork explained (29:15) Building a new product live with Cowork (32:40) Cowork vs Claude Code in the terminal (36:00) Managing multiple AI agents at once (39:30) Anthropic’s funding round and market momentum (42:15) Why we’re shifting spend away from other AI tools (45:10) AI inside organizations: efficiency without layoffs (48:30) What every team should be building next (50:45) Final thoughts and closing ================================================================================ LINKS SECTION Built This Week New episodes every Friday Jordan Metzner https://x.com/mrjmetz Sam Nadler https://x.com/Gravino05 Built This Week https://builtthisweek.com

    20분

  6. 1월 24일

    Why You Can't Pen Test an Airplane — AI Cybersecurity for Aviation

    Can you really hack an airplane? And if so, how do you test for it without grounding the fleet for a year? In Episode 28 of Built This Week, Sam Nadler and Jordan Metzner sit down with Eero Salih, CTO of Syberian, to explore how AI is transforming cybersecurity for commercial aviation. We start with a live demo — a flight ops cyber radar Sam built to surface real-time security risks across airline operations. Then Eero breaks down what Syberian actually does: building digital twins of aircraft systems to run risk assessments without ever touching the physical plane. This is critical because traditional penetration testing would ground an aircraft for up to a year for recertification. Syberian's AI-powered approach analyzes over 100 technical documents to map every computer system on board — from avionics to entertainment to crew scheduling — and identify vulnerabilities before they become incidents. We also discuss: Why cyber attacks on aviation are now classified as safety threats New 2026 regulations forcing airlines to comply with stricter cybersecurity standards How small teams are replacing developers with AI agent managers The tools Syberian uses: Claude Code, Windsurf, Anthropic, and Gemini Why Google and Anthropic are rejecting ads while OpenAI explores them An ex-Amazon exec who vibe-coded a full CRM replacement in 72 hours No hype.No theory.Just what happens when you put AI in charge of protecting critical infrastructure. New episodes every Friday. ================================================================================ TIMESTAMPS--------------------------------------------------------------------------------(0:00) Why you can't hack-test an airplane(0:45) Episode 28 kickoff and guest introduction(1:30) Live demo: Flight ops cyber radar dashboard(3:00) Analyzing real-time security threats across airline systems(4:30) What Syberian actually does (in plain English)(6:00) Why physical penetration testing grounds planes for a year(7:30) Using AI to build digital twins of aircraft systems(8:15) Hiring managers, not developers — AI agents do the coding(9:30) Tools of the trade: Claude Code, Windsurf, Anthropic, Gemini(10:00) New 2026 aviation cybersecurity regulations explained(11:00) How cyber attacks became classified as safety threats(12:30) The ripple effects: baggage weight, fuel calculations, pilot tablets(13:30) Who are Syberian's customers? Airlines, private jets, and more(14:55) AI News: Google and Anthropic reject ads in chatbots(16:30) Why Anthropic's no-ads stance matters for enterprise customers(17:30) Amazon exec vibe-codes full CRM replacement in 72 hours(18:30) Why vibe coding works for internal tools but not production(19:15) Final thoughts and closing ================================================================================ LINKS SECTION--------------------------------------------------------------------------------Built This WeekNew episodes every Friday Jordan Metznerhttps://x.com/mrjmetz Sam Nadlerhttps://x.com/Gravino05

    20분

  7. 1월 16일

    We Let AI Control Our Data Warehouse — The Results Were SHOCKING

    Can AI actually reduce cloud costs — or does it just create better dashboards? In Episode 27 of Built This Week, Sam Nadler and Jordan Metzner are joined by Ben, CEO of Espresso AI, to break down a real production system that uses machine learning to actively optimize data warehouse compute in real time. We walk through a live demo built specifically to expose hidden inefficiencies inside Snowflake and Databricks environments — from over-refreshing dashboards to duplicated queries and underutilized clusters. Then we go deep on how Espresso AI works under the hood: proxy-based routing, workload-aware ML models, and fine-grained compute orchestration that runs without changing application code. This is not FinOps theater. This is AI actively rewriting how compute is allocated. We also discuss: Why most teams overpay for convenience in the cloudHow real-time query routing beats manual cost controlsWhere AI helps engineers — and where it absolutely does notThe limits of vibe coding for serious infrastructureGemini powering Siri and what it means for voice assistantsMeta’s massive GPU buildout and the future of hyperscalersNo hype. No theory. Just what happens when you put AI in control of real infrastructure. New episodes every Friday. Timestamps (0:00) Why modern AI understands code differently (0:45) Episode 27 kickoff and guest introduction (1:30) Live demo: diagnosing hidden warehouse inefficiencies (3:00) Why dashboards refresh far more than they are viewed (4:30) The real cost of duplicated queries across teams (6:00) What Espresso AI actually does (in plain English) (7:45) Kubernetes for data warehouses, powered by ML (9:30) How real-time query routing works (11:30) Why most companies are not “doing it wrong” (13:00) Transformers and deep code understanding (15:00) Where AI helps engineers today (16:30) Why AI cannot yet run core infrastructure autonomously (18:00) Productivity gains without replacing engineers (19:30) Gemini, Siri, and the next generation of voice assistants (21:00) Meta’s massive GPU investments explained (23:00) Will Meta become a hyperscaler (24:30) Final thoughts and closing Links Section Built This Week New episodes every Friday Jordan Metzner https://x.com/mrjmetz Sam Nadler https://x.com/Gravino05 Espresso AI https://espresso.ai

    25분

  8. 1월 9일

    We Built an AI Trading Bot for Prediction Markets — Here’s What Actually Happened

    Can AI actually beat prediction markets — or does the house always win? In Episode 26 of Built This Week, Sam Nadler and Jordan Metzner kick off 2026 by breaking down a real AI trading bot Jordan built for prediction markets like Kalshi, using live market data, whale detection, coordination signals, and confidence scoring. Jordan walks through the full system — backend, frontend, live alerts, and execution logic — and shares the honest results: a 66 percent win rate that still lost money once fees and market dynamics were factored in. The takeaway is not hype. It is reality. The episode also dives into: Why prediction markets feel like gambling but are regulated differentlyHow insider-like signals emerge from coordination and volume behaviorWhy bots end up trading against botsWhere real alpha might exist (and where it does not)We also cover: Google NotebookLM as a serious education and onboarding toolTurning documents into infographics, slide decks, and audio learningNvidia entering autonomous driving and competing with TeslaNvidia’s new Rubin architecture and why it mattersTesla vs Waymo economics and the future of Full Self DrivingWhy Anthropic and Claude Code are becoming developer defaultsThis is not theory.This is what happens when you actually deploy AI systems into real markets. Timestamps (0:00) Why prediction markets are exploding (1:07) Episode 26 kickoff (2:00) Why build a trading bot at all (4:30) Kalshi vs Polymarket APIs (6:00) Live market signals and whale detection (9:30) Win rate vs profitability (12:00) Why fees destroy returns (14:30) Bots trading against bots (17:00) Where real alpha might exist (18:00) NotebookLM for learning and onboarding (21:00) Nvidia enters autonomous driving (24:00) Tesla vs Waymo economics (27:00) Nvidia Rubin chips explained (28:30) Anthropic and Claude Code momentum (29:30) Final thoughts Links Built This Week New episodes every Friday Jordan Metzner https://x.com/mrjmetz Sam Nadler https://x.com/Gravino05

    30분
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Built This Week is a weekly podcast where real builders share what they're shipping, the AI tools they're trying, and the tech news that actually matters. Hosted by Sam and Jordan from Ryz Labs, the show offers a raw, inside look at building products in the AI era—no fluff, no performative hype, just honest takes and practical insights from the front lines.

정보

  • 제작진
    Jordan Metzner, Samuel Nadler
  • 방송 연도
    2025년 - 2026년
  • 에피소드
    34
  • 등급
    전체 연령 사용가
  • 저작권
    © 2025
  • 웹사이트 보기
    Built This Week