What I learned from the podcasts I listen to (AI engineering, startups, investing), organized by topic, not by episode. Each concept page merges every episode that touched it, keeps disagreements between guests side by side, and links each claim to its source. Maintained by Claude Code; source on GitHub.
Investing — investing
- index-investing: buy-and-hold broad indexes
- compound-interest: slow wealth by starting early
- leverage: borrowed money multiplies losses; margin calls and leveraged ETFs
- concentration-risk: when an index is really 2–3 stocks
- survivorship-bias: you only see the winners
- fomo: others’ gains push you into risk
- market-bubbles: tech-wave bubbles (dot-com, AI); welcome the burst
- cape-ratio: Shiller P/E; high means lower expected returns
- market-timing: why selling before crashes usually fails
AI Engineering — ai-engineering
- ai-sre: agents that investigate and heal production
- ai-rd-rollout: adopting AI across an engineering org
- ai-sdlc: agent teams across the dev lifecycle; start at the edges
- coding-agent-workflow: practical setup: plans, settings, skills, terminals
- multi-agent-orchestration: orchestrator + specialist agents; pitfalls
- harness-engineering: environment that lets agents run autonomously and correctly
- agent-ready-codebase: docs as files, self-updating skills, agent-launchable envs
- test-driven-development: frozen failing tests so agents can’t cheat
- ai-infrastructure: AI factories, GPU networking, power and optics
- agent-architectures: fitting agent structure to the job; shared-brain agents
- ai-red-teaming: adversarial testing of models and agents; RL gyms
- ai-cybersecurity: AI vulnerability finding, model access debate, continuous testing
- agentic-vulnerability-response: agent turns a new CVE into a deployed protection in ~45 min
- image-generation: professional image generation; licensed data, JSON control
- multimodal-llms: vision models in production; hallucinations, injection, visual hints
- hebrew-llms: adapting LLMs to Hebrew; tokenizers, base-model choice
- world-models: JEPA, video and 3D world models, beyond next-token
- continual-learning: models that keep learning after training
- llm-pretraining: how pre-training works; folklore, grokking, context curricula
- llm-reasoning: token-space vs latent reasoning; RL reasoning
- ai-moats: tech as business card; good-enough products; lab economics
- solo-builders: AI builders without VC; shipping, finishing, distribution
- customer-pain-research: mining workaround posts for business ideas; validate by talking
- enterprise-ai-adoption: selling applied AI to enterprises; FDEs; AI OS
- ai-growth-marketing: agentic growth hacking, autonomous outreach, personalization
- agent-ready-products: selling to agents: skills, MCP, marketplaces, CLI auth
- agent-security: assume the agent is compromised; isolate, proxy credentials, approve
- workflow-automation: no-code automation + agents (n8n)
- personal-ai-assistants: OpenClaw-style assistants automating daily work
- skill-engineering: skills as mini software; sharing and versioning
- voice-agents: speech-to-speech vs chained; latency, state machines, audio evals
- small-language-models: fine-tuned small models for narrow tasks
- decision-classifiers: fast calibrated classifiers (e.g. Jev) for bulk decisions
- rag: retrieval-augmented generation; enough for ~99% of cases
- agent-workspaces: humans and agents sharing channels and boards
- proactive-ai: the AI UX paradox; surface capabilities, bring answers to users
- autonomous-agents-outlook: where autonomous agents are heading; open questions
- future-of-software-engineering: coding is dead, engineering isn’t
- llm-pipelines: deterministic LLM pipelines vs agents in production
- ai-engineering-metrics: adoption → productivity → quality of AI coding
- llm-cost-optimization: measuring (FinOps attribution) and reducing LLM spend
- ai-pricing: pricing AI products: credits, per-user buckets, pools, margins
- ai-gateway: central routing, metering, security for LLM calls
- llm-evals: offline + online evals, LLM-as-judge, model A/B tests
- llm-inference: what happens behind an API call and why it costs what it does
- model-selection: small models + narrow tasks + context management
- open-weight-models: cheap competitive open models; weights aren’t code
- ai-hype: wait a month; don’t build what a prompt can
- context-engineering: context makes the difference; keep it lean
- wiki-retrieval: finding the right wiki page; guesses, ranking, RAG
- ai-guardrails: enforce rules technically, not by instruction
- ai-verification: read outputs, demand proof, check sources
- llm-wiki: Karpathy’s raw/wiki/schema pattern (also Knowledge Management)
- open-knowledge-format: universal knowledge-file format (also Knowledge Management)
Knowledge Management — knowledge-management
- second-brain: external brain maintained by an LLM, at personal, work and org scale
- agent-memory: short/long-term agent memory with governance
- human-vs-ai-memory: what neuroscience says about agent memory
- memory-consolidation: nightly “dreaming” jobs that clean memory
- knowledge-graphs: ontologies agents use to understand an org
- knowledge-rot: stale knowledge; routines that check themselves
Unsorted
- biohacking: N=1 self-experiments, sensors, caveats (health)
- personal-genomics: whole-genome sequencing, drug responses (health)
- bayesian-updating: priors, posteriors, test errors (health)
- cost-of-living: competition vs price caps; subsidies captured by sellers (economics)
Shows
- hidden-layers: Hidden Layers
- startup-for-startup: Startup for Startup (monday.com)
- the-startup-ideas-podcast: The Startup Ideas Podcast
- explainable: ExplAInable
- ai-engineering-podcast: AI Engineering (AI אנג׳נירינג)
- ignore-instructions: Ignore the Instructions (תתעלם מההוראות)
- langtalks: LangTalks
- osim-tochna: Osim Tochna (עושים תוכנה)
- lazy-investing: Investing for the Lazy (השקעות לעצלנים)
- developers-outside-the-box: Developers Outside the Box (מפתחים מחוץ לקופסה)
- micha-stocks: Micha Stocks (מיכה סטוקס מגיש: שוק ההון)
People
- amit-bendor: host of Osim Tochna
- mike-erlihson: LLM inference optimizer, ExplAInable co-host
- kfir-schneider: staff DevOps engineer, Riskified
- andrej-karpathy: AI researcher, LLM Wiki pattern
- doron-bleiberg: AWS solutions architect
- dana-maman: AI builder, personal AI OS
- asaf-savich: AI dev lead, Komodor
- avi-lumelsky: production LLM lead, Oligo
- dor-cohen: AI infra lead, monday.com
- elad-granot: AI researcher, Trullion
- eran-hoffman: Chief R&D Officer at Dream
- gavriel-cohen: creator of NanoClaw
- iko-azoulay: EVP Technology, Salt Security
- itamar-friedman: CEO of Qodo
- liad-elidan: CEO of Milestone
- matan-cohen: Head of Slack Israel, Dotti co-founder
- yizhar-gilboa: CTO of Finout
- yonatan-maor: CTO of Clears.ai
- avi-golan: Alice (AI safety, red teaming)
- avishai-zvinsky: security-content group lead at Cato Networks
- ben-mashiach: Director of Data & AI at Claroty
- billy-howell: runs a newsletter with a GrokBot agent team
- gilad-shainer: Nvidia SVP, networking
- gili-kanfo: AI lead at Vega
- gilad-levi: founder of Manifold (continual learning)
- greg-isenberg: host of The Startup Ideas Podcast
- idan-benyon: founder of Build Ship Grow community
- meytar-zemer: neuroscientist (memory)
- micky-haslavsky: founder at enso (agentic marketing)
- misha-feinstein: CTO of Bria
- nadav-erez: CTO of Twine (digital employees)
- nevo-david: solo founder of Postiz
- netanel-abergel: AI Engineering lead at monday.com, podcast host
- tomer-brook: Engineering manager, monday.com agentic developer platform (Atlas)
- roi-zalta: data & AI solutions engineer, Microsoft
- shay-davidson: principal engineer, Lemonade
- shay-shitrit: founder of Lab17 (n8n automations)
- shuki-cohen: VP Data, AI21
- ofri-ziv: co-founder of Tenzai
- pavel-gurevich: founder of Tenzai (AI pentesting)
- ohad-parush: Chief R&D Officer at Gong
- roni-goldshmidt: AI researcher at Nexar
- roy-lazar: Wonderful (enterprise applied AI)
- matan-lach: Leads Taka, monday.com Agent Labs AI social manager
- roy-mann: co-founder and co-CEO of monday.com
- ryan-vogel: OpenCode founding team
- shiran-bareli: VP Research at Cyera
- sharon-dahan: inference architect at Impala
- tamir: host of Investing for the Lazy
- tom-barnea: AI-security detections at Tenable
- uri-eliabayev: host of Hidden Layers
- warren-buffett: American investor
- micha-stocks: host of Micha Stocks
- naftali-bennett: former Israeli PM, ex-tech CEO
- raz-diskin: GTM engineer, monday.com RevAI
- moshe-krispin: tech lead, monday.com RevAI (AgentOS)
Episodes
- startup-ideas—8-github-repos-viral-money: 8 GitHub Repos To Go Viral and Make Money (2026-10-08)
- hidden-layers—cato-networks-avishai-zvinsky: How Do You Handle the New AI-Driven Cyber Threats? (2026-10-08)
- hidden-layers—tenzai-ofri-ziv: Building an AI Security Tester (2026-06-07)
- hidden-layers—qodo-itamar-friedman: How Do You Review AI-Written Code? (2026-03-31)
- hidden-layers—nexar-roni-goldshmidt: AI Models That Understand the World (2026-04-23)
- hidden-layers—twine-nadav-erez: Digital Employees in Cybersecurity (2026-05-03)
- hidden-layers—vega-gili-kanfo: Securing Data Sources in the AI Era (2026-05-18)
- hidden-layers—nanoclaw-gavriel-cohen: Building Secure AI Agents for Organizations (2026-05-21)
- hidden-layers—tenable-tom-barnea: Securing AI Tool Use in the Organization (2026-05-26)
- hidden-layers—cyera-shiran-bareli: Advanced AI Research at a Cyber Company (2026-05-31)
- hidden-layers—claroty-ben-mashiach: AI Protecting the Most Sensitive Facilities (2026-06-02)
- hidden-layers—dream-eran-hoffman: AI Defending Countries From Cyberattacks (2026-06-18)
- hidden-layers—bria-misha-feinstein: Training an Image Foundation Model From Scratch (2026-06-24)
- hidden-layers—alice-avi-golan: Keeping Strong AI Models From Going Rogue (2026-07-01)
- hidden-layers—manifold-gilad-levi: Has a Replacement for Transformers Been Found? (2026-07-06)
- hidden-layers—nvidia-gilad-shainer: Making Sense of Nvidia’s Technology (2026-09-27)
- osim-tochna—gong-ai-for-developers: Gong Rolls Out AI for Developers (2025-11-03)
- osim-tochna—running-llms-at-scale: How Do You Really Run LLMs at Huge Scale? (2026-05-18)
- osim-tochna—ai-in-production-reality-vs-imagination: AI in Production: Reality vs Imagination (2026-02-23)
- startup-for-startup—353-self-updating-team-brain: #353 How We Built a Self-Updating Team Brain (2026-06-09)
- startup-for-startup—354-reliable-agents-lean-context: #354 Reliable Agents Without Overloading Context (2026-06-16)
- startup-for-startup—362-agent-architecture-fit: #362 Matching Agent Architecture to the Problem (2026-08-11)
- startup-for-startup—366-qa-for-agents: How Do You QA the Agents We Build? (2026-09-08)
- startup-for-startup—369-ai-pricing: How Do You Price an AI Product? (2026-09-29)
- startup-for-startup—370-agentos-month-to-day: #370 How We Cut Agent Building From a Month to a Day (2026-10-13)
- startup-ideas—jev-is-here: Jev Is Here (2026-09-18)
- startup-ideas—webmcp-clearly-explained: WebMCP Clearly Explained (2026-08-26)
- startup-ideas—grok-bot-one-person-company: Grok Bot: One-Person Company with Agents (2026-08-21)
- ignore-instructions—26-ai-cyber-pavel-gurevich: #26 When AI Models Get Really Good at Cyber (2026-07-20)
- explainable—152-biohacking-like-a-data-scientist: #152 Biohacking Like a Data Scientist (2026-04-15)
- explainable—157-training-hebatron: #157 Cracking Hebrew: Training Hebatron (2026-06-16)
- explainable—163-hidden-cost-of-agents: #163 A Million Dollars a Month: The Expensive Secret of AI Agents (2026-09-13)
- explainable—164-gilad-levi-continual-learning: #164 Child Prodigy, Transformers and Continual Learning (2026-09-22)
- ignore-instructions—20-idan-benyon-build-ship-grow: #20 The Man Behind the AI Builders Community (2026-03-24)
- ignore-instructions—24-wonderful-road-to-100m: #24 On the Way to $100M Revenue (2026-06-18)
- ignore-instructions—28-ai-marketing-enso: #28 How AI Is Changing Marketing (2026-09-08)
- langtalks—55-context-engineering: #55 Context Engineering (2025-10-11)
- langtalks—56-n8n: #56 n8n (2025-10-25)
- langtalks—57-memory: #57 Memory (2025-11-16)
- langtalks—58-reinvent-predictions: #58 re:Invent 2026 Predictions (2025-12-07)
- langtalks—59-slms: #59 SLMs (2025-12-28)
- langtalks—60-brain-memory: #60 Brain Memory (2026-01-11)
- langtalks—61-voice-agents: #61 Voice Agents (2026-01-24)
- langtalks—62-ai-rd-rollout: #62 AI R&D Rollout (2026-02-07)
- langtalks—63-wake-up: #63 WAKE UP (2026-02-23)
- langtalks—64-ai-coding-metrics: #64 AI Coding Metrics (2026-03-08)
- langtalks—65-ai-sre: #65 AI SRE (2026-03-21)
- langtalks—66-scaling-llmops: #66 Scaling LLMOps (2026-04-12)
- langtalks—67-finops-for-ai: #67 FinOps for AI (2026-04-27)
- langtalks—68-ai-sdlc: #68 AI-SDLC (2026-05-24)
- langtalks—69-marketing-for-agents: #69 Marketing for Agents (2026-06-08)
- langtalks—70-our-claude-code-tips: #70 Our Claude Code Tips (2026-06-28)
- langtalks—71-claw-architectures: #71 Claw Architectures (2026-07-26)
- langtalks—72-personal-assistant-agent: #72 Personal Assistant Agent (2026-08-16)
- langtalks—74-jev: #74 Jev (2026-09-22)
- ai-engineering-podcast—the-ai-ux-paradox: The AI UX Paradox (2026-08-06)
- ai-engineering-podcast—ai-infra-at-scale: AI Infra at Scale (2026-08-17)
- ai-engineering-podcast—atlas-ai-teammate: Atlas: From Agent to AI Teammate (2026-08-27)
- ai-engineering-podcast—company-brain: Company Brain (2026-09-06)
- ai-engineering-podcast—why-your-llm-costs-so-much: Why Your LLM Costs So Much (2026-09-30)
- langtalks—73-harness-engineering: #73 Harness Engineering (2026-09-15)
- osim-tochna—second-brain-and-llm-wiki: Second Brain & LLM Wiki (2026-08-03)
- lazy-investing—108-the-leverage-trap: #108 The Leverage Trap (2026-08-04)
- lazy-investing—111-the-ai-bubble: #111 The AI Bubble (2026-10-11)
- developers-outside-the-box—185-lets-talk-about-our-tools: #185 Let’s Talk About Our Tools (2026-10-10)
- micha-stocks—naftali-bennett-economic-interview: Naftali Bennett Economic Interview (2026-10-11)