Built This Week

Marketing teams are about to change forever.

Instead of hiring designers, copywriters, analysts, SEO specialists, and performance marketers… companies are starting to run AI marketing agents that handle everything.

From planning campaigns to creating content, analyzing performance data, generating ads, and optimizing strategy automatically.

In this episode of Built This Week, Sam Nadler and Jordan Metzner sit down with Iliya Valchanov, CEO of Juma, to explore how AI agents are transforming modern marketing workflows.

Juma is building an AI marketing super-agent that can autonomously plan, execute, and optimize marketing campaigns across channels like social media, ads, analytics, and SEO.

During the episode, Ilia demos how a single prompt can generate a complete social media strategy, content calendar, and visual assets in minutes.

Even more surprising — Ilia explains how their own company replaced a 7-person go-to-market team with just one person using AI agents.

We also dive into the future of AI agents, how developers are working with coding agents like Claude Code and Codex, and why the next wave of AI tools may turn websites into constantly evolving, self-optimizing systems.

In this episode we discuss:

• How AI agents automate entire marketing workflows
 • Turning a single prompt into a full social media calendar
 • Why marketing teams are early adopters of AI
 • How AI agents connect to tools like Google Analytics, HubSpot, and Meta Ads
 • Why saving time isn’t the real benefit of AI
 • How AI increases marketing quality and experimentation
 • How agencies measure ROI and billable hour savings from AI
 • Why companies need a dedicated “AI transformation leader”
 • Claude Code vs Codex vs Cursor for AI coding workflows
 • Andrej Karpathy’s new auto-research AI experiments

This episode is a glimpse at how AI agents may reshape marketing, coding, and digital products over the next decade.

⏱️ TIMESTAMPS

(0:00) Welcome to Built This Week
(0:31) Introducing Ilia from Juma
(0:46) What Juma is building
(1:26) Live demo: AI marketing agent
(2:10) Generating a social media calendar with one prompt
(3:02) Researching competitors automatically
(3:52) Building a full content strategy
(4:30) Creating Instagram carousels with AI
(5:21) Integrations with Google Analytics, HubSpot, and Ads
(6:03) Can AI learn which content performs best?
(6:49) Who is using Juma today
(7:40) Marketing teams vs marketing agencies
(8:05) Replacing a 7-person marketing team with AI
(8:58) Publishing blog posts in 3 minutes
(9:40) Why AI unlocks new marketing opportunities
(10:27) Measuring ROI and billable hours saved
(11:06) How AI removes the need for specialized marketing roles
(12:00) Why ad optimization is the biggest AI opportunity
(12:27) Biggest lessons from running AI agents in marketing
(13:04) Why companies need an AI transformation leader
(14:02) Claude Code vs Codex vs Cursor
(16:00) The future of AI coding agents
(18:56) Why developers are reading less code
(20:45) How programming may change in the AI era
(22:06) Andrej Karpathy’s new auto-research AI tools
(24:38) AI experiments and self-optimizing websites
(27:10) Final thoughts on the future of AI agents
(28:06) Where to find Juma

🔗 LINKS

Juma
 https://juma.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

Clinical trials are one of the slowest and most expensive processes in modern medicine.

It can take 10–15 years and up to $3 billion to bring a new drug to market — and many trials fail simply because they can’t enroll enough patients.

In this episode of Built This Week, Sam Nadler and Jordan Metzner sit down with Dr. Chadi Nabhan, Chief Medical Officer at RyghtAI, to explore how AI-powered digital twins of clinical trial sites can dramatically improve the speed and success of clinical trials.

RyghtAI has built a platform that creates digital twins of thousands of clinical trial sites worldwide, allowing pharmaceutical companies to instantly identify the best locations and investigators for any given trial.

Instead of relying on manual site selection or reputation-based decisions, AI analyzes historical trial performance, patient demographics, biomarker capabilities, and infrastructure to determine which sites are most likely to enroll patients successfully.

The result: faster trials, better patient representation, and potentially life-saving therapies reaching the market sooner.

In this episode we discuss:

• Why 80% of clinical trials fall behind schedule
• Why half of clinical trial sites enroll 0–1 patients
• How AI parses 200-page trial protocols in seconds
• The role of digital twins in predicting trial success
• How AI improves patient diversity in clinical trials
• Why biomarker data is becoming essential in modern medicine
• How AI agents infer site capabilities from historical trial data
• Why informed patients using AI tools may actually improve healthcare outcomes

If AI can dramatically improve the speed and efficiency of clinical trials, it could reshape how quickly new treatments reach patients worldwide.

⏱️ TIMESTAMPS

(0:00) Welcome to Built This Week
 (0:37) Introducing Dr. Chadi Nabhan from Ryght AI
 (1:12) What RyghtAI is building
 (2:14) The problem with clinical trial site selection
 (3:07) Digital twins for clinical trial sites
 (4:01) Manual vs AI-driven trial strategy simulation
 (5:15) Why clinical trials fail
 (6:03) The massive cost and time of drug development
 (6:51) How AI identifies the best trial sites
 (8:00) Ranking clinical trial sites using AI scoring
 (9:03) Diversity challenges in clinical trials
 (10:02) Using census data to improve patient representation
 (10:35) Biomarkers and genomic trial requirements
 (11:48) Predicting future trial success from past data
 (12:14) How AI accelerates trial matching
 (13:04) AI agents reading clinical trial protocols
 (14:20) Parsing 200-page protocols in seconds
 (15:00) AI identifying investigators and site contacts
 (15:57) Helping overlooked clinical sites get discovered
 (17:47) AI’s expanding role in healthcare innovation
 (18:00) Eight Sleep raises $50M at a $1.5B valuation
 (21:09) Apple releases a $599 MacBook
 (23:00) Dr. Nabhan’s upcoming book: AI and Cancer Care
(23:33) Will AI replace Google for patient research?
(25:30) The future of personalized AI healthcare
(26:10) Final thoughts and wrap-up

🔗 LINKS

Ryght AI
 https://ryght.ai

Dr. Chadi Nabhan
 https://chadinabhan.com

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

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

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

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 tools
• Why Opus 4.6 is a step-function improvement for coding
• How AI is changing software development workflows
• Elon Musk’s XAI reorg and what it signals about model competition

The 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

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 Teams
• OpenAI Frontier and enterprise AI coworkers
• Perplexity’s Council Mode and LLM swarms

The 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

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

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 Week
New episodes every Friday

Jordan Metzner
https://x.com/mrjmetz

Sam Nadler
https://x.com/Gravino05

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 cloud
• How real-time query routing beats manual cost controls
• Where AI helps engineers — and where it absolutely does not
• The limits of vibe coding for serious infrastructure
• Gemini powering Siri and what it means for voice assistants
• Meta’s massive GPU buildout and the future of hyperscalers

No 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

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 differently
• How insider-like signals emerge from coordination and volume behavior
• Why bots end up trading against bots
• Where real alpha might exist (and where it does not)

We also cover:

• Google NotebookLM as a serious education and onboarding tool
• Turning documents into infographics, slide decks, and audio learning
• Nvidia entering autonomous driving and competing with Tesla
• Nvidia’s new Rubin architecture and why it matters
• Tesla vs Waymo economics and the future of Full Self Driving
• Why Anthropic and Claude Code are becoming developer defaults

This 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