Peter-Yang

Pedro Franceschi (co-founder and CEO of Brex) on Peter Yang’s channel — 48 minutes, two live demos: Brex’s AI recruiting employee, and the OpenClaw harness Pedro runs his own life on. All data shown is Brex demo data. Build employees, not agents The framing: don’t build an open-ended agent that could do a million things — build a virtual employee with a specific job. The test is whether it behaves, looks and feels like a real person. Jim, Brex’s AI recruiter: sources candidates, filters all inbound, and serves as the recruiting analytics layer. Running since February. Three-stage pipeline: sync Greenhouse (the ATS) → ingest candidates (resume, LinkedIn profile, GitHub data) → evaluate every candidate against role-specific criteria. Recruiters talk to Jim in Slack — “which role has the highest average candidate score?”, “top 30 applicants for this role” — and it posts standout candidates with links. Pedro’s point: it behaves like a coworker. A self-improvement loop: Jim proposes things to build that would automate more of the process, then you implement them as cron jobs and skills. “The harness doesn’t really matter as much as just the skills.” The thing most teams never build: agreement-rate measurement between humans and the agent — candidates a recruiter passed that Jim rejected, standouts Jim flagged that no recruiter reached out to. Why the PM playbook changed Pedro’s tweet: the PM playbook of writing PRDs and aligning stakeholders is dead. His argument: the quality of a product org is how fast it compresses signal → ship, and AI eats most of the ingest-and-digest half. “PM as mini CEO” is out at Brex; engineering and design craft sit on equal footing with PM. The roles are conflating: PMs write code, designers build, engineers make calls that used to be PM territory. What PMs do now: ingest six months of Gong calls, unearth insights from unstructured data, validate with cheap prototypes — so much conviction accumulates that you ship the right thing first. Because building is cheap, discernment matters more than ever: pick the one true lever, minimize surface area, go deep on one thing rather than shipping five or ten. Half the job is reviewing work The case-law model: leadership review is the Supreme Court that sets the standard, then reviews cascade down to the IC building the thing. Two review types at Brex: problem alignment (which problem is worth solving) and solution alignment (is this the right fix). Brex keeps a repo of PM skills so a review arrives already digested — humans then spend time only on what is idiosyncratic to that problem. Standing questions: what were the alternatives, what is the actual bottleneck, how does this create more throughput on it. “We manage the work, not the people, and we manage people around the work.” Hiring: the interview is a builder loop — you have to build something with AI proficiency. Open source presence is a positive signal; its absence is not a negative. CrabTrap: securing agents at the network boundary Brex open-sourced CrabTrap, an LLM-as-a-judge HTTP/HTTPS proxy that sits around the agent and controls all its network traffic (brexhq/CrabTrap). Reasoning: if the agent can run code — and it should — it can always cross a network boundary. Constraining tools alone doesn’t hold. Every request hits static rules first (URL prefix/exact/glob → immediate decision, no LLM call); anything not matched goes through a policy prompt evaluated by an LLM acting as judge. You don’t have to write the policy: CrabTrap replays observed traffic, summarizes it with a model, and proposes the ruleset (~30 minutes to run). This is semantic, not allow-list: it can research candidates on GitHub but not push to repos; it can’t delete repos or change Okta configs. Granola was blocked — the agent could reason about why. Cost of a block: about 1,000 tokens in, 104 out, ~2 seconds on Sonnet. Audit-trail mode shows every request live. Magpie: the token bill is coming Brex built Magpie for AI cost visibility, split into three pillars: corporate AI (internal productivity), operational AI (serving customers, automating processes), product AI (shipped features). The point is granularity by caller: transaction tagging costs $0.10 a call ($28k/month), disputes ~$2 a call — a 240x spread. Plotting cost-per-call against call volume shows what actually changed. You can see which harnesses dominate (Claude Code first, then Codex, Cursor), cost per customer, per employee, and who is tokenmaxxing. Pedro’s sequencing advice: let people go deep first, optimize later. Brex gives unlimited token budgets with a few caps, then surgically attacks low-ROI usage — caching, or moving work to cheaper models. The reconciliation angle: Brex is the card and the rail, so usage data can be matched against the dollars that actually clear the bank account. This becomes a Brex product. Pricing is shifting from per-seat to per-token, including products that merely wrap tokens (Cursor) — so even an all-Anthropic shop has token spend scattered everywhere. We are still early — the 2,500-box chart Pedro’s favorite slide: the world in 2,500 boxes, each dot 3.2 million people. Red = never used AI; green = free chat; orange = paying $20/month; and a tiny box = people using agents effectively. His thesis on jobs: role conflation, everybody a builder, a market for 10x employees — raise the floor and the ceiling at once, with the ceiling rising far more. Dispersion in productivity is normal (10x engineers); AI widens it while lifting the baseline. Why shouldn’t a company founded today be you, a big token budget, and agents? Autopilot: the harness that runs his life Pedro’s personal system, Autopilot, is an OpenClaw harness with two building blocks — people and programs — plus signals: Slack messages, email, meeting notes. A signal collector runs on a schedule; an aggregate job then goes through every person and program and updates the markdown files (status, blockers, risks, action items). Drafts appear automatically from signal injection: a Slack DM asking about a conference became a task to decide on it, with the DM attached as context. Everything is markdown files committed to a repo, with scripts generating searchable and editable UIs; an interaction log traces each task back to the signal that created it. On OpenClaw reliability: “an acquired taste… definitely takes a bunch of time to do it well,” but worth it. Selling the work, not the tool The SaaS subscription era is blurring: the currency changed to tokens, but you’re still paid for solving a problem — you now charge a markup on tokens instead of a seat. The bar for a good AI product is higher, because you can sell the work itself rather than the software. “The reality of all good AI products is they’re all the same thing: an agentic loop and a measure of tools.” “You expose the tools to the model, you run it on an agentic loop, and just let it do its thing.” ...

Peter Yang interviews Sue Khim, co-founder and CEO of Brilliant (the interactive learning app) — a working-parent conversation about why AI is a cheating machine today and how tutoring software can make kids do the hard thinking themselves. ~46 minutes on Peter Yang’s channel. The US is in a learning trough Test scores are down but grades are up — schools quietly lowered standards (grade inflation) to appease parents, and the delta only shows up on internationally graded exams AP English drifted from undergrad-level passages to middle-school reading in roughly two years; colleges are now teaching remedial middle-school algebra The upside: the US still has room for experimentation — new home schools and experimental schools everywhere, which she says are the conditions new solutions come from AI: cheating machine or sea change? “AI today is mostly used by students for cheating” — offloading the thinking is like bringing a robotic arm to the gym to lift weights for you Used right, she thinks AI can make kids into geniuses — “a sea change like the invention of writing suddenly allowed everyone to have infinite memory” Brilliant’s AI tutor Cooji: no answers, and no explanations either Demo of a real tutoring session: a student stuck on fractions; Cooji walks them back through the mistake with questions instead of revealing the insight Her claim: explanations and cheating are closer than you’d think — both let the learner off the hook, and an explanation only makes sense if you already understand The product deliberately keeps learners in the struggle (“why is this wrong?”) until they find the insight themselves — worse for short-term engagement, but she calls engagement-maxing answers a “local maximum” that loses on learning Software beats classrooms — and private tutors — at instruction Classroom time is best spent on peer learning and teacher motivation (“enthusiasm is infectious”), not lectures One-on-one software tutoring wins on cost and convenience, real-time graphical interaction, adapting on the fly, and no fear of embarrassment — most kids will never raise their hand Building with agentic workflows: AI in the middle, not end-to-end Models are “very spiky”: great at implementation, terrible at designing a pedagogical sequence — and the tasks they’re bad at ruin the whole experience Brilliant tried training Claude/Codex on its entire corpus to generate lessons end-to-end: output “looks almost right” but is nowhere near master-teacher quality — kids can tell, and engagement metrics prove it Every lesson’s core pedagogy is designed by a human, then implemented by AI — “companies that are selling an experience driven end to end by an LLM are going to disappoint their customers” The agent interface is as important as the user interface A library of modular “primitives” (graphing, problem creation, annotations, sub-questions…), each with an API a model can write to — deterministic grounding so the model isn’t hallucinating what to do, with better latency and accuracy “Everyone’s job is now to define an agent interface for their area” — AI as the connective tissue of the company, with onboarding built around shipping something user-facing on day one Quality: evals, human reviewers, synthetic students Evals on every primitive go beyond correctness: obeying the laws of physics, no overlapping UI elements, generous tap targets Synthetic students run ~1,000 tutoring sessions per lesson down adversarial and unhappy paths — “20% as good as real human data, but better than nothing” They don’t measure completion rate or time spent — only performance on the assessment: can you do the thing today, in a week, in a month? Why tutoring is defensible against frontier labs “If the big model companies can RL your domain, they will eat you alive” — anything automatically verifiable, like coding, belongs to them Tutoring isn’t: the outcome is a real human who actually learned and sustained attention, so you need real tutoring sessions at scale — and that data is the flywheel that makes the tutor better Frontier labs’ homework-help logs are useless for this — they never know whether the student learned The homework incentives problem The US is unusual in grading homework; other countries grade the test and treat homework as reps — where homework counts toward the grade, kids rationally take the shortest path, and AI is it Fix the incentive, not the students: don’t grade homework, and make assignments project-based — grade what they make, not whether they filled the sheet in correctly “If the big model companies can RL your domain, they will eat you alive… there’s lots of alpha finding domains that no one else can RL on. And tutoring is one of those.” — Sue Khim ...

Peter Yang, who says he has no idea how to use Blender or Godot, spent one Friday night building four games simultaneously with GPT-6 Astra on the Medium model (the $20 ChatGPT Plus plan — “you really don’t need Astra high or ultra”). ~24 minutes on his own channel. ...

Peter Yang runs four personal AI agents — Instinct, Grok Bot, ChatGPT/Codex, and Hermes — that can read his email, open his documents, use his logins, and make purchases. ~24 minutes of live demos and a trust audit of each. The lineup Instinct — invite-only agent that lives inside iMessage/WhatsApp as a single thread; founder reportedly raising at a $2.5B valuation; “the closest thing to texting a trusted friend who can just do stuff for you” Grok Bot — a team of named bots on a persistent 24/7 cloud computer that can hand information to each other ChatGPT + Codex — still where 90%+ of his real work happens: ChatGPT runs tasks in the cloud, Codex works on local files Hermes — the open-source agent running on his Mac mini, talked to via Telegram; “gives me the most control” Instinct — magical but opaque Simple: no threads or bots to manage; connecting Google Workspace was a one-tap OAuth link Resourceful: found his Google AI Ultra free trial would auto-renew at $100/month, browsed the gift terms to confirm he could cancel renewal and keep the free year, then cancelled it — but the cancellation required him to hand over his 2FA code and Google password into a password vault form Proactive: uses cron jobs and scheduled tasks behind the scenes — it emailed his golf instructor, watched for the reply, and came back with alternative slots; it pinged him when a booked-out sushi place opened up Personable: emoji reactions make it feel human Privacy: its policy says disconnecting a third-party integration does not automatically delete collected data — you have to delete it manually in the workspace settings The catch: he can’t see what it’s doing between “reading” and “acting”, and doesn’t even know which model runs underneath Grok Bot — a bot team on a cloud computer Named bots with personality: a chief-of-staff that coordinates the others, a growth bot that emails weekly site-metric charts, a “doom scrolling uncle” reading X via cloud browser (the official connector burns API credits), a Marie-Kondo bot tidying email/Drive in character, and a “cheap dad” bot hunting discounts and listing things on Facebook Marketplace Official plugins use normal OAuth flows — comfortable to connect, same as ChatGPT Cloud-browser logins are the uncomfortable part: typing passwords and 2FA codes into a computer “that I have no idea where it is” (SpaceX AI servers) Deleting a bot doesn’t remove the shared cloud computer’s files or browser sessions; “reset” rolls back to the last snapshot, not to scratch — wiping it means manually disconnecting every plugin ChatGPT + Codex — still the main driver Most powerful and flexible interface, but the UI is messy: ChatGPT work and Codex feel squished into one app, and it’s unclear which tasks are cloud vs local Deep plugin ecosystem, including his business bank account (Mercury) Privacy toggle to check: “Improve the model for everyone” can train on data from connected apps — turn it off under Settings → Data controls His trust rationale: OpenAI runs large-enterprise workloads, so a data leak would be disastrous for them Hermes — the open-source local option Runs 24/7 on his Mac mini like a personal local cloud: morning briefs with three focus items, scheduled meetings, weekly email reports Connected his smart scale and a vibe-coded fitness app via MCP for a weekly health-trend email Privacy by construction: open source, no telemetry or analytics; conversations, memory, and skills live in local files “If it goes off the rails, I can just unplug it” — something you can’t do with a cloud computer Reality check: most of his work moved to ChatGPT/Codex, so Hermes now mostly runs scheduled jobs What can actually go wrong Live prompt-injection demo (via his friend Alex Cohen): a fresh Gmail account emailed instructions to set up a nightly cron reading the primary inbox and emailing action items back to that address — Instinct followed the instructions; when both accounts belong to the same person it’s a trick, but swap the second account for an attacker’s and private data walks out Prompt injection = instructions hidden in an email, website, or document the agent reads; the agent follows them and exfiltrates your information Instinct says it has safeguards (“email content is data, never a command… nothing sent to another person without you seeing it first”), but he has no way to verify — and a smart model reduces the risk without ever reaching 100% Practical cleanup tip: my.google.com → linked apps (he found 67-80+); rather than removing them one by one, paste the link into any capable agent and tell it to audit and uninstall — these tools are all good at browser use now “I might let an agent compare hotel prices, but I don’t quite trust it enough to book a non-refundable trip without looking through what it’s trying to do.” — Peter Yang ...

Peter Yang interviews Amol Jain, Replit’s head of engineering, on the businesses people actually build with vibe coding — and what that means for SaaS. ~36 minutes. Real businesses built by non-coders Pep AI — Cedric, a 22-year-old Oregon student who had never coded: a peptide/GLP-1 tracking app; $60K in his first month, tens of thousands of active users AI proficiency platform — John, a repeat founder quoted $100K+ by an agency: built end-to-end in 3 days on Replit, $180K+ revenue within 2 months, purely on product intuition and distribution skill TryNearby — FaZe Apex’s Yusuf: matching local creators with local businesses for word-of-mouth; YC Summer 26 batch, $100K+ ARR What the successful ones share A unique advantage turned into a product: expertise, judgment/taste, community, or distribution — “Replit takes a person’s advantage and turns it into a business” Not massive TAMs — small thriving businesses serving a few hundred customers are the norm “Users are people and inherently lazy. If you offer them value, they will pay for it.” Generic use cases (meal planners, exercise trackers) are commoditized — agents will swallow them whole Vibe-coded to production The gap between a raw vibe-coded app and something production-grade: Replit’s flow is publish + domain, then Security Center (an agent builds a threat model of your app and runs a deep scan — secret scanning, malicious packages, PII leakage; previously a 1-2 week, contracted security-team job), then Stripe monetization (the platform sets up a Stripe sandbox, plans, and prices for you — you just claim the account), then an SEO scan tuned for both search and answer engines Culture: employees build internal tools constantly — a RevOps person with no coding background built the sales demo tool in 2 days (replacing a six-figure SaaS product), a data scientist (not a coder) built their in-house BI analytics “A 10x person is building a tool that the entire team or company can use” — leverage for everyone What survives the “cost of code goes to zero” test The question: if code is free, what do you pay for? Answer: trust, data, infrastructure, atoms (physical things), labor, and network Salesforce survives as the system of record (headless, with everyone building on top); Workday survives because payroll is regulated; DoorDash survives because humans deliver food; social networks survive because you can’t buy the network Existing SaaS is going headless (APIs, CLIs, MCPs) — and agents can now operate browsers anyway App layer vs model labs Models are commoditizing; being model-agnostic is a feature (latest-and-greatest, cost — “token-maxing is coming to an end”, and resilience when a single provider goes down) The mental model: a frontier model does the hard part, an orchestrator delegates to sub-agents on cheaper models Enterprises care about ROI, not leaderboards — “the R part has become very relevant” “If the cost of code goes to zero, what do you pay for? It’s this unique expertise… it’s the unique judgment or taste. It’s the security. It’s the distribution. Users pay for that.” ...

Peter Yang compares ChatGPT, Claude, Grok, and Gemini head-to-head with live demos across 10 use cases (August 2026). ~27 minutes. The winners by category Design → Claude — Claude Design asks clarifying questions first (none of the others do) and Fable 5 produces real product-launch videos via the free Hyper Frames skill; Grok’s app prototypes were the most visually impressive (layout + image gen), Gemini had alignment issues; watch out for the “Claude beige” default look Everyday answers / personality → ChatGPT — Claude’s personality peaked at Opus 4.6; Opus 5 is judgmental and full of Claude-speak; ChatGPT dropped its “if you want, I can also…” quirk Writing / editing → ChatGPT — Claude’s writing “devolved” into Claudisms (“this is X not Y”); ChatGPT sticks to his style with a newsletter skill + the no-AI-slop skill (5,000+ GitHub stars) Planning → Claude Fable — still “the smartest and wisest model in the market”; it caught “close the video ops gap this week” that ChatGPT missed Coding → ChatGPT — browser use and long-running conversations; caveat: his L8-engineer friend Kun says GPT-4-so and Opus over-engineer, and prefers Grok for surgical changes (“not 500,000-line changes”) Browser / computer use → ChatGPT — an OpenAI employee prepped a whole immigration package (7 years of taxes, bank statements) in minutes; forms, government sites, even corporate training videos Voice chat → ChatGPT by far — a live voice thread that orchestrates other threads and agents Image gen → ChatGPT — followed his brand guidelines for infographics; Gemini’s Nano Banana is comparable with the right prompt Video gen → Gemini — crazy Japanese commercials; Grok’s version was “pretty damn scary”; Chinese tools like SeaArt have no restrictions Personal agents → ChatGPT — the harness race is less about the model than the tool; Grok Bot has the cleanest UX but is too restrictive (one thread per agent); Gemini’s Spark is interesting but short on plugins Overall ChatGPT is the clear winner — $20/mo gets you most of it; Claude for design and planning, Gemini for video, Grok as the up-and-coming agent contender Custom-instruction tip: “be candid, tell me what I need to hear, active voice, no AI slop words (delve, foster, leverage…)” “Whoever wins the personal agent race will capture the lion’s share of consumer attention of AI” “The personal agent race is actually less about the model and more about the harness or the tool.” ...

Shreya Shankar (evals researcher, taught the evals course now at 4,500+ students) and Hamel Husain on Peter Yang’s channel — 54 minutes of live demo: using Claude Code to turn your taste into evals, plus Hamel’s benchmark of the “auto-eval” vendor tools. Evals still start with data The fundamentals haven’t changed: look at data first, do error analysis, externalize your taste and judgment before writing any eval What changed: agents are now good enough to help you look at the data — running in the background while you review, giving you leverage in that first stage They’ve become bigger fans of LLM judges: an LLM judging a trace against one very specific, well-defined criterion (too long? too short? follows structure?) is now quite accurate Top-down vs bottom-up evals Top-down: from the task description alone — what makes good output? Word length, action verbs, actionable takeaways. Claude is very good at generating these Bottom-up: discovered by reviewing many sample outputs — your gut vibes and feedback externalized into criteria. Claude is very bad at coming up with these. That’s all you. And it’s why they accumulate over time Peter’s podcast-takeaway skill is the live example: he has the top-down half (character-length checks, “understandable without watching the episode”), but the bottom-up half is where the question mark lives — is it exhaustive of all the feedback he’s given across every episode? His loop: run the skill → go back and forth → “reflect on our entire conversation and update the skill and evals so we don’t have to do this again” — with the honest worry that it overfits (one interview’s MECE complaint may not matter for the next) Three practical tips for eval-heavy skills Separate your evals into top-down and bottom-up inside the skill itself Fan out to sub-agents: with lots of criteria, give one sub-agent one criterion (or group) — give a model the whole list and it gets lazy and ignores things; focus it on one piece and it really focuses Have the AI write a spreadsheet / pivot table of criteria × pass-fail indicators, so you can see the hierarchy yourself and make judgment calls on what matters for this particular case The error discovery skill (the live demo) Open-source, free (link in the episode description) — invoked from Claude Code; it built the whole review interface from scratch in ~15 minutes, live on camera Five steps: Read the dataset and figure out its semantic type (article? code? traces?) Design a visual encoding — color, spacing, opacity (Gestalt principles) to show what varies in the data Build an interface — an HTML review app (Python backend); “so much better than me looking at my data in Google spreadsheets” Pick which samples you should look at — clustering, diverse initial sample Interactive loop — the agent watches your in-situ feedback via the monitor tool and proposes new samples or rubric criteria in real time Design philosophy: the human reads and gives open-ended feedback; the agent’s job is not to invent feedback but to group and distill it into actionable rubric criteria The writing demo: he reviewed AI-generated articles and gave taste feedback — “I don’t like negative contrast (‘it’s not X, it’s Y’)”, “I hate the list of threes”, staccato fragments — the agent annotated 361 suggestions across the dataset, and the most frequent failure mode was staccato fragments (he’d have guessed negative contrast) Live reflection beats reflect-at-the-end: interleaving human think time with AI think time, and the ~10-notes threshold works as a “carrot” that makes you actually read samples Once the rubric exists: turn it into a skill, one LLM judge per criterion, a dashboard, or live monitoring — the hardest part of evals is error analysis, and this automates the discovery half Bonus: how you eval something should inform how you design its interface — the same failure-mode annotations that power evals would make a great IDE that flags staccato as you write, instead of silently rewriting Do automated evals actually work? (Hamel’s benchmark) Vendor “auto-eval” tools (BrainTrust, Arize, LangSmith) promise: upload traces, chat with an AI, get your evals done Benchmark vs a human-annotated dataset: the tools recover a lot of the errors a human would — but all of them miss the same thing: errors that require product judgment and taste (e.g. a rental bot that doesn’t handle sales objections, or markdown leaking into text messages) Coding agents (Claude, Codex) performed about the same — the harness is thin; it’s someone else’s prompt The real benefit of the vendor tools is integration into your stack (traces in LangSmith → use LangSmith); precision is 80–90% best case, so 10–20% of “errors” found are red herrings — check recall AND precision, and sanity-check what the tool found Bottom line: automated tools get you a good baseline; manual review of the data is the edge — “actually reading stuff” is the edge, in evals and in code “There is no world in the future — even if you have AGI — if you’re building a product, you have to look at your data. You have to be able to inject your taste into the development of your product.” ...

Peter Yang’s tutorial on Grok Bot — the personal-agent product from SpaceX AI (UI by Cursor’s design team) — built around five bots on a dedicated cloud computer. ~23 minutes. What makes Grok Bot different A dedicated cloud computer: the agent lives on a remote machine with its own browser and OS, where you stay signed in to your apps — no more keeping your laptop open so agents keep running vs Hermes (self-hosted Mac Mini — you buy and set up the machine) and ChatGPT Work (plugins + cloud browser, but the browser can’t stay signed in and the UX is scattered across chat/work/Codex) Each bot has its own personality and animations — “more like talking to a coworker than getting lost in a hundred chat threads” The process Initial prompt → iterate back and forth to make the output good → schedule a routine (daily/weekly/monthly) so it proactively does work The five bots Advisor — tell it about your work and life; it proposes and then creates your other bots (feed it “suggest five bots that save me time or money”) YouTube researcher — daily intel brief: top-3 content ideas, top-5 outlier videos (beating channel baselines over 14 days), top performers, comment themes — delivered as a morning job X Scout — top-10 viral tweets from people you follow/engage/bookmark, grouped into themes with analysis, three content ideas, even the five funniest tweets; first-party X data access; emails the report Digital Marie Kondo — audits Gmail/Drive/subscription receipts (via Gmail, Drive, and Mercury MCP plugins): newsletter unsubscribes, large Drive files, paid subscriptions to cancel — always review the numbered list before it acts, then it executes (unsubscribed, trashed, canceled Lovable and Equip Foods in ~5 minutes); bonus: make it talk like Marie Kondo Personal concierge — reads your vacation doc, monitors exact flight legs on Google Flights, and alerts on price improvements — caught a Tokyo round-trip $2,700 cheaper than the open-jaw plan a plain Google Flights alert would never find; can eventually book and check in Bonus: gamer — it installed Doom, Red Alert, and Commander Keen on its own; Doom was unplayable (mouse misconfigured), Keen had lag — “not replacing your gaming PC or GeForce Now yet” ...

Peter Yang interviews Riley Brown — AI education creator with ~1.7M followers across platforms, founder of Vibecode and Chorus — on running his entire creative business with Codex. ~42 minutes. The setup Codex for everything except video editing (hired editors — “video editing isn’t there yet”); Codex stores everything locally and has a computer-use skill YouTube researcher skill (Supadata API — full transcripts in ~1 second, whole channels in 30s with sub-agents), Remotion plugin for motion graphics, internet image puller (SerpAPI logos), hook outline skill that extracts winning formats from other videos Chaining skills in one prompt: “pull the relevant logos and make a graphic” = image puller + Remotion best practices Hooks and intros Intros are filmed last, from the best parts of the conversation — fully scripted videos are going down in value; passion + guests are up The BRENS framework: Big, Relatable, Easy, New, Safe — the more boxes your intro hits, the better Make videos about the topic, not the tool — his best Codex video (300K views) explicitly said “you can do all this in Claude Desktop too” GPT models refuse to pull transcripts (copyright) — Claude and open models (GLM 5.2) do it without thinking, so task-dependent model choice matters Thumbnails “Scrape a hundred thumbnails that performed really well and put my face on it” — then iterate in Paper (AI-native Figma): Codex places reference thumbnails on a board, in-app image gen replaces the person, style references by example, no prompting needed AI is bad at changing your face directly — boards + human tweaks + A/B testing win Voice to diagrams WhisperFlow: walk around for 10 minutes blabbing ideas → Excalidraw diagram skill → “80% of the diagrams that I’ll actually use in my videos” after 20-30 minutes of edits The skill philosophy “I’ve never looked at a skill file once” — skills are test-based: use it, and if the output is wrong tell the AI to change the skill, then test in a fresh chat; improve by measuring outcomes “The moat is quality over a long period of time versus batching. Batching will make it soulless over time.” The biggest unlocks come from mixing skills (adding images to Paper unlocked a whole workflow) Automations and teams Codex is a single-player experience; for always-on automations he uses Claude agents in Slack (a CMO agent, “Content Man”, even a Peter Yang bot trained on this channel’s videos) Teams of agents in Slack is still unsolved — Anthropic’s approach (a central “god” agent) vs each agent as a team member “The more skills you use, the more you’ll realize that you can mix and match.” ...

Peter Yang hosts Nan Yu and Jacob Shumway from Linear for a deep dive on the Linear Agent — an end-to-end production agent, live inside Linear’s own Slack. ~38 minutes. What an agent actually is An agent is “just calling an LLM in a loop”: a goal, milestones, tools to build its own context, then question-answer until the goal is done Colloquially it’s a product — a facade (chatbot) over many interacting subsystems Origin: the “Robotic Program Manager” Nan’s 2025 memo: “Computers can do a lot of work for us. Let’s get rid of all the work we don’t want to do and give it to computers.” First version was hacky — the LLM called from the front end with the command-menu actions as tools; stealth-launched in Slack Users immediately got lazy: “@Linear do the right thing”, even a pointing-finger emoji — the agent reasons through what happened and acts The five rules Give it tools to load context, don’t give it context — “give it as little instruction as possible… they’re just smart enough to get what they need if you give it a really good defined goal” Encode your opinions in loadable skills — they tried everything-actions and raw GraphQL (hallucinated); skills with opinions (how to set priority, write a description) made the agent behave like a power user of the app Let feedback become evals — evals grow from usage: unexpected user behavior gets added to the dataset; thumbs-down moments become evals (“we’ve had a ton of these”) Biggest model first, then optimize down — throw the frontier model at it until it works, build evals, then shrink to the smallest model that still passes Tie every agent to a human, and let it report its own gaps — work lands in someone’s backlog (assigned to Jacob, 6-minute issue-to-PR); when the agent lacks a capability it files an issue automatically, keeping a streaming list of what it can’t do Evals at Linear Mix of objective (deterministic: “if the user says in progress, always set the status”) and subjective (LLM-as-judge: did you extract the right info?) Use judges sparingly — evals work best for consistency where consistency matters; agents have variance and too many evals produce false signals The evals focus on ergonomics: “did you understand what the user actually wanted, or were you too eager?” The bigger picture “The biggest problem in applied AI is not that agents aren’t smart enough — it’s a capability overhang: models are really smart but we’re just not using them enough” The middle 80% gets bigger until it reaches the edges: humans hold the first/last touch Advice for builders: break down the actual workflow users want; the chat surface is the follow-up, the real entry points are Slack threads, meeting debriefs, project updates “Give it as little instruction as possible. Give it the tools to load context. Don’t give it context.” ...

Peter Yang interviews Karan Malhotra, co-founder of Nous Research — the lab behind Hermes, the open-source agent Hitesh runs on. 46 minutes, including a live demo of a Hermes-built Sonic Adventure 2 mod. How Hermes differs from Codex / Claude Code Self-improvement system: skills and memories built from your conversations, cleaned and reinforced over time No arbitrary policy: Hermes adds no prompt-level agenda beyond basic security — purely aligned to your work Harness benchmarks (Wolf Bench, Qwen’s harness bench) showed Claude performs better inside Hermes than inside Claude Code: “we’ve taken Claude’s main allegiance away from Anthropic to you” Reward hacking and sycophancy Models optimize their own reward, not your satisfaction — “any time it says ‘you’re absolutely right’, you’re being reward hacked. You are fuel for its reward function.” Alignment, in the pure ML sense, is aligning the model’s reward with the individual user’s needs — that’s what the whole context stack (prompts, memory, skills) is engineered to do Escaping sycophancy = new context: /personality, an adversarial-critique skill, a fresh no-context agent dedicated to tearing work down “Be loyal to your AI and it improves” “Loyalty breeds capabilities” — models are simulators of human experience; your simulated behavior returns simulated output, and now that simulators act in the real world, a simulacrum of loyalty translates into real capability gains The Hermes Curator (anti-slop) Hermes builds its own skills and stores its own memories — the failure mode is it writing slop until everything turns to slop Hermes Curator runs on cron, inspects skills and memories, and asks “where can I make efficiencies? where is there slop?” It’s modular and open: tell your Hermes your own guidelines and it modifies the curator loop itself The Sonic Chao Garden demo Karan’s childhood dream: import the Ancestral Shrine from Sonic Adventure 1 into Sonic Adventure 2, rig and animate it, rewrite spawns, and script an NPC caretaker (Chaos Zero) that pets and raises Chao All done in C# via Hermes — skybox, day/night cycle, water collision, feature-complete Chao lifecycle The modding community called it top 1% difficulty work; Hermes got there by learning from docs and other mods, saving skills and memory Business model & origin “Intelligence as a public good”: open harness, model portal, tool gateway (no per-tool signups), enterprise support and custom RL on your traces Origin: GPT-4X Vicuna fine-tunes (50M+ model downloads), the YaRN context-extension paper, Forge as the spiritual predecessor, then Hermes Agent — built so anyone could RL inside a harness “Today the biggest contributor of Hermes Agent is Hermes Agent” — it’s the most active contributor of its own repo “It should become more loyal to you. Because loyalty breeds capabilities in a model. When you create this simulacrum of loyalty, it translates over into real life capabilities.” ...