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This video will make you an expert loop engineer in 21 minutes. Prompting is dead. You need to be running loops. 0:51 Understanding LLMs 3:41 wtf is a loop? 6:17 Two kinds of loops 8:54 /goal versus /loop 10:44 Website audit demo 13:27 Real loop workflow ideas 19:11 Scheduling loops

Miles Deutscher

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164,149 次观看 • 5 天前 •via X (Twitter)

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This video will make you a Claude Fable 5 expert in 20 minutes. 0:00 Intro 2:43 wtf is a loop? 5:30 Loop Engineering 101 *demo included* 8:12 Fable 5 x Claude Skills 10:10 3 ways to build effective Fable 5 Skills 12:12 Visual capabilities 14:04 Building a local memory system 18:17 How I'm personally using Fable now

This video will make you a Claude Fable 5 expert in 20 minutes. 0:00 Intro 2:43 wtf is a loop? 5:30 Loop Engineering 101 demo included 8:12 Fable 5 x Claude Skills 10:10 3 ways to build effective Fable 5 Skills 12:12 Visual capabilities 14:04 Building a local memory system 18:17 How I'm personally using Fable now

Miles Deutscher

314,897 次观看 • 28 天前

Anthropic engineer: "At Antropic, >80% of our engineers are building with self-improving loops. in 3-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of $$$." in 30-minute podcast, an Antropic engineer shows how to build the right spec for an agentic loop. Loop + plan + PRDs + spec + markdown - that’s the secret stack. Worth more than a $500 agentic course on the internet. Watch it now, then read how quants are building trading loops from scratch.

Anthropic engineer: "At Antropic, >80% of our engineers are building with self-improving loops. in 3-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of $$$." in 30-minute podcast, an Antropic engineer shows how to build the right spec for an agentic loop. Loop + plan + PRDs + spec + markdown - that’s the secret stack. Worth more than a $500 agentic course on the internet. Watch it now, then read how quants are building trading loops from scratch.

Movez

280,895 次观看 • 1 个月前

Claude Code creator Boris Cherny: "Verification is the single most important part of closed-loop development" "Companies underinvest in it, and that is the biggest mistake" In a 26-minute talk, Boris breaks down real AI coding loops with Spotify's Chief Engineer: 00:00 - Closed-loop development 08:33 - Why one-shot code generation fails 10:02 - Agentic verifier and PR success rate 12:00 - Verification loops without humans in the loop 14:00 - Tests, skills, CLAUDE.md, and MCP as the loop infrastructure 20:11 - Why consistent codebases make agents work better This one video is worth more than 100 Claude coding setup tutorials Bookmark and watch it today Then read the full guide on loop engineering below

Claude Code creator Boris Cherny: "Verification is the single most important part of closed-loop development" "Companies underinvest in it, and that is the biggest mistake" In a 26-minute talk, Boris breaks down real AI coding loops with Spotify's Chief Engineer: 00:00 - Closed-loop development 08:33 - Why one-shot code generation fails 10:02 - Agentic verifier and PR success rate 12:00 - Verification loops without humans in the loop 14:00 - Tests, skills, CLAUDE.md, and MCP as the loop infrastructure 20:11 - Why consistent codebases make agents work better This one video is worth more than 100 Claude coding setup tutorials Bookmark and watch it today Then read the full guide on loop engineering below

rari

72,802 次观看 • 13 天前

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Codez

26,868 次观看 • 29 天前

The creator of Claude Code deleted his IDE in November 2024. In the last 30 days, 100% of his contributions were written by Claude Code. 259 PRs. His job is not prompting anymore. His job is writing loops that prompt Claude for him. Here is what that progression actually looks like: → stage 1: write code by hand with autocomplete → stage 2: run 5-10 claude sessions in parallel, prompt each one yourself → stage 3: write loops that prompt claude — you become the author, the model becomes a subroutine → the ralph loop (2025): bash one-liner, fixed anchor files, built a programming language for $297 → the 2026 loop: loops supervising loops, scheduling on infrastructure time, git-backed durable state → five building blocks: automations, durable state, validator split, loop orchestration, crash recovery Most developers are still in stage 2, prompting sessions by hand. The ones at stage 3 are writing the loops that do the prompting for them. Read this article to be ahead of the curve! Follow NeilXbt for more AI engineering intelligence that shows you exactly where the leverage moved and what building at that level actually looks like.

The creator of Claude Code deleted his IDE in November 2024. In the last 30 days, 100% of his contributions were written by Claude Code. 259 PRs. His job is not prompting anymore. His job is writing loops that prompt Claude for him. Here is what that progression actually looks like: → stage 1: write code by hand with autocomplete → stage 2: run 5-10 claude sessions in parallel, prompt each one yourself → stage 3: write loops that prompt claude — you become the author, the model becomes a subroutine → the ralph loop (2025): bash one-liner, fixed anchor files, built a programming language for $297 → the 2026 loop: loops supervising loops, scheduling on infrastructure time, git-backed durable state → five building blocks: automations, durable state, validator split, loop orchestration, crash recovery Most developers are still in stage 2, prompting sessions by hand. The ones at stage 3 are writing the loops that do the prompting for them. Read this article to be ahead of the curve! Follow NeilXbt for more AI engineering intelligence that shows you exactly where the leverage moved and what building at that level actually looks like.

NeilXbt

11,556 次观看 • 1 个月前

NVIDIA CEO, Jensen Huang: "Nobody writes prompts anymore. The new job is to design and manage loops. Self-learning agentic systems powered by loops are what you should be learning right now" in 30-minute talk, 6 greatest minds in AI discuss the skills you need to stay competitive in the AI era. Watch it, then read about Band’s real loop setup in the post below.

NVIDIA CEO, Jensen Huang: "Nobody writes prompts anymore. The new job is to design and manage loops. Self-learning agentic systems powered by loops are what you should be learning right now" in 30-minute talk, 6 greatest minds in AI discuss the skills you need to stay competitive in the AI era. Watch it, then read about Band’s real loop setup in the post below.

Codez

13,865 次观看 • 1 个月前

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch

GREG ISENBERG

82,271 次观看 • 17 天前

$Boris Cherny, head of Claude Code at Anthropic: "I don't prompt Claude anymore. I have loops that are running; they are the ones prompting Claude and figuring out what to do. My job is to write loops." He's right. Karpathy and Steinberger arrived at the same conclusion from different angles. The skill has a new name. Loop engineering. Cherny's token bill is zero. He works at Anthropic. Steinberger burned $1.3 million in OpenAI credits in 30 days. He works at OpenAI. Loops are the right architecture. But the people evangelizing them aren't the ones paying. I wrote a breakdown of five plays that land most of what a loop gives you at a fraction of the cost, plus the one case where a loop is worth paying for.$

Boris Cherny, head of Claude Code at Anthropic: "I don't prompt Claude anymore. I have loops that are running; they are the ones prompting Claude and figuring out what to do. My job is to write loops." He's right. Karpathy and Steinberger arrived at the same conclusion from different angles. The skill has a new name. Loop engineering. Cherny's token bill is zero. He works at Anthropic. Steinberger burned $1.3 million in OpenAI credits in 30 days. He works at OpenAI. Loops are the right architecture. But the people evangelizing them aren't the ones paying. I wrote a breakdown of five plays that land most of what a loop gives you at a fraction of the cost, plus the one case where a loop is worth paying for.

Alex Prompter

15,166 次观看 • 1 个月前

Boris Cherny, Head of Claude Code at Anthropic: "I do not prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops." The person who built the most used AI coding tool stopped prompting entirely. Most people using it every day are still typing one request at a time. This Github repo changes that. ↳ Seven production loop patterns with real token cost estimates ↳ CLI that scaffolds your entire loop in five minutes ↳ Starters for Grok, Claude Code, Codex, and Opencode The repo gives you the framework. My article covers the mental shift that makes it actually work.

Boris Cherny, Head of Claude Code at Anthropic: "I do not prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops." The person who built the most used AI coding tool stopped prompting entirely. Most people using it every day are still typing one request at a time. This Github repo changes that. ↳ Seven production loop patterns with real token cost estimates ↳ CLI that scaffolds your entire loop in five minutes ↳ Starters for Grok, Claude Code, Codex, and Opencode The repo gives you the framework. My article covers the mental shift that makes it actually work.

Kanika

20,148 次观看 • 5 天前

The future of vibecoding is looping. ( my recent app cnvs has done $5.7k in 5 days and was made primarily with design looping ) you can easily break free from ai slop and create designs that feel artisan and worth paying for. im begging you to bookmark this 10 min beginner guide and start setting up loops, it will change everything for you. Timestamps : 0:27 – The ultra-simple looping diagram explained 1:18 – The best loop to start with: the design loop 1:38 – Using Codex + GPT Image Gen for the mockup 3:57 – Handing the mockup to Claude Code 4:45 – Loop starts (screenshot → compare → iterate) 7:31 – Loop finished : reviewing the result

The future of vibecoding is looping. ( my recent app cnvs has done $5.7k in 5 days and was made primarily with design looping ) you can easily break free from ai slop and create designs that feel artisan and worth paying for. im begging you to bookmark this 10 min beginner guide and start setting up loops, it will change everything for you. Timestamps : 0:27 – The ultra-simple looping diagram explained 1:18 – The best loop to start with: the design loop 1:38 – Using Codex + GPT Image Gen for the mockup 3:57 – Handing the mockup to Claude Code 4:45 – Loop starts (screenshot → compare → iterate) 7:31 – Loop finished : reviewing the result

Max Blade

21,767 次观看 • 1 个月前

THIS 11-PAGE CLAUDE LOOP ENGINEERING PLAYBOOK IS THE CHEAT CODE FOR SENIOR DEV PROMOTIONS typing prompts line by line won't get you hired. you need to know how to design self-prompting loops: > Discovery: agent finds its own work from logs, failing tests, and commits > Handoff: tasks execute in isolated git worktrees to prevent parallel collisions > Verification: a second reviewer agent audits the builder's code to catch blind spots > Persistence and Scheduling: code writes to disk and loops run 24/7 on a timer the shift is simple: stop prompting the agent, build the system that prompts it read the full setup blueprint 👇

THIS 11-PAGE CLAUDE LOOP ENGINEERING PLAYBOOK IS THE CHEAT CODE FOR SENIOR DEV PROMOTIONS typing prompts line by line won't get you hired. you need to know how to design self-prompting loops: > Discovery: agent finds its own work from logs, failing tests, and commits > Handoff: tasks execute in isolated git worktrees to prevent parallel collisions > Verification: a second reviewer agent audits the builder's code to catch blind spots > Persistence and Scheduling: code writes to disk and loops run 24/7 on a timer the shift is simple: stop prompting the agent, build the system that prompts it read the full setup blueprint 👇

Mr. Buzzoni

44,524 次观看 • 1 个月前

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

153,379 次观看 • 25 天前

Spotify Chief Architect, Niklas Gustavsson: "Once we implemented loops in our workflow, our agent success rate went from 20-30% to 80%." 26 minutes with Claude Code creator Boris Cherny on how Spotify runs AI agents across 20 million lines of code. Today 73% of their code is written by AI, most merged without a human ever seeing it. The model matters less than the loop you build around it. Watch it, then read the full guide on building loops below.

Spotify Chief Architect, Niklas Gustavsson: "Once we implemented loops in our workflow, our agent success rate went from 20-30% to 80%." 26 minutes with Claude Code creator Boris Cherny on how Spotify runs AI agents across 20 million lines of code. Today 73% of their code is written by AI, most merged without a human ever seeing it. The model matters less than the loop you build around it. Watch it, then read the full guide on building loops below.

Anatoli Kopadze

840,673 次观看 • 1 个月前

Boris Cherny: "My job is to write loops." 5-day version you can copy for Claude Code Day 1: repo memory - write CLAUDE.md - include stack, test commands, code style, release rules, gotchas - add .claude/settings.json with the shell commands Claude can run Day 2: verification skill - pick 1 flow Claude keeps breaking - put the browser/API test in .claude/skills/ /scripts/ - make it return: pass/fail, failing step, screenshot/log path Day 3: commands Add these files: .claude/commands/babysit.md - checks your PRs - reads CI - handles obvious review nits - surfaces design questions .claude/commands/triage-issues.md - labels new issues - dedupes against existing ones - assigns owners .claude/commands/deploy-watch.md - checks the live app - reports regressions - avoids touching production Day 4: loops > /loop 5m /babysit > /loop 15m /triage-issues > /loop 5m /deploy-watch Day 5: overnight work - schedule /morning-report - schedule /deep-audit - write results to .claude/inbox/ - let your morning loop read from that folder The rule: every code-writing loop gets a separate verifier. Builder makes the change. Verifier runs the real app. You read the diff. Skip that and you wake up to 14 broken PRs with very confident summaries. Video: "Reflecting on a year of Claude Code" with Boris Cherny & Cat Wu

Boris Cherny: "My job is to write loops." 5-day version you can copy for Claude Code Day 1: repo memory - write CLAUDE.md - include stack, test commands, code style, release rules, gotchas - add .claude/settings.json with the shell commands Claude can run Day 2: verification skill - pick 1 flow Claude keeps breaking - put the browser/API test in .claude/skills/ /scripts/ - make it return: pass/fail, failing step, screenshot/log path Day 3: commands Add these files: .claude/commands/babysit.md - checks your PRs - reads CI - handles obvious review nits - surfaces design questions .claude/commands/triage-issues.md - labels new issues - dedupes against existing ones - assigns owners .claude/commands/deploy-watch.md - checks the live app - reports regressions - avoids touching production Day 4: loops > /loop 5m /babysit > /loop 15m /triage-issues > /loop 5m /deploy-watch Day 5: overnight work - schedule /morning-report - schedule /deep-audit - write results to .claude/inbox/ - let your morning loop read from that folder The rule: every code-writing loop gets a separate verifier. Builder makes the change. Verifier runs the real app. You read the diff. Skip that and you wake up to 14 broken PRs with very confident summaries. Video: "Reflecting on a year of Claude Code" with Boris Cherny & Cat Wu

Alpha Batcher

141,110 次观看 • 1 个月前

since everyone woke up talking about loops, this article talks about domain chips, which i think are super underrated compared to loops.. a chip is a small specialist that's only good at one job QA, trading, content, research whatever ... and not one giant brain trying to do everything.. you wrap it in three things: >a workflow (same shape every time, so it's stable) >the tools that job needs >a benchmark that says what good looks what makes this special is the benchmark; without it, a loop just changes things randomly, when with it, the chip can score every pass and keep only what actually wins.. the problem with loops is they can spin 1000× and still not improve when a domain chip not only focuses on the domain you delegate to them, but they actually improve themselves with every spin. you can start with domain chips here this repo will help you build your first chip, the workflow, the benchmark pack, and the autoloop policy, with guardrails so it can't fool itself: below you can read the article fully + i put a good example of the difference between having a normal loop and a domain chip..

since everyone woke up talking about loops, this article talks about domain chips, which i think are super underrated compared to loops.. a chip is a small specialist that's only good at one job QA, trading, content, research whatever ... and not one giant brain trying to do everything.. you wrap it in three things: >a workflow (same shape every time, so it's stable) >the tools that job needs >a benchmark that says what good looks what makes this special is the benchmark; without it, a loop just changes things randomly, when with it, the chip can score every pass and keep only what actually wins.. the problem with loops is they can spin 1000× and still not improve when a domain chip not only focuses on the domain you delegate to them, but they actually improve themselves with every spin. you can start with domain chips here this repo will help you build your first chip, the workflow, the benchmark pack, and the autoloop policy, with guardrails so it can't fool itself: below you can read the article fully + i put a good example of the difference between having a normal loop and a domain chip..

JUMPERZ

36,515 次观看 • 1 个月前

Anthropic Engineer: "Fable 5 is back, and most people will use it wrong" "You can set up Sonnet 5 and Fable 5 properly in one afternoon and stop overpaying every single day" In 29 minutes, he breaks down each Claude model, why setup matters, and where Fable 5 fits Not every task needs the strongest model Some jobs need speed. Some need reasoning. Some need long agent loops Fable 5 is where the loop work gets interesting: planning, checking, fixing, and running again Most people will upgrade and keep the same messy workflow Power users will route the right task to the right model and save money daily Bookmark and watch this before everyone starts wasting Fable 5

Anthropic Engineer: "Fable 5 is back, and most people will use it wrong" "You can set up Sonnet 5 and Fable 5 properly in one afternoon and stop overpaying every single day" In 29 minutes, he breaks down each Claude model, why setup matters, and where Fable 5 fits Not every task needs the strongest model Some jobs need speed. Some need reasoning. Some need long agent loops Fable 5 is where the loop work gets interesting: planning, checking, fixing, and running again Most people will upgrade and keep the same messy workflow Power users will route the right task to the right model and save money daily Bookmark and watch this before everyone starts wasting Fable 5

rari

20,772 次观看 • 29 天前

Anthropic engineer: “70% of our engineers are using self‑improving loops. Now everyone is building agentic Graphs" in 3-6 months, we’ll all be building graphs to orchestrate self‑improving agents. No more prompting.” in 30-minute lecture, Anthropic engineer shows how to build a self-improving agentic workflow from scratch. Worth more than a $500 agentic course. Watch this video, then read the article below on how to become a graph architect.

Anthropic engineer: “70% of our engineers are using self‑improving loops. Now everyone is building agentic Graphs" in 3-6 months, we’ll all be building graphs to orchestrate self‑improving agents. No more prompting.” in 30-minute lecture, Anthropic engineer shows how to build a self-improving agentic workflow from scratch. Worth more than a $500 agentic course. Watch this video, then read the article below on how to become a graph architect.

Movez

617,255 次观看 • 5 天前

The creator of Pydantic explained how to make live agents smarter after they ship in 1 hour 21 minutes - better than $2500 agent evals bootcamps. collect traces from your live agent -> score them with an eval -> run GEPA to auto-optimize the prompt -> deploy the winner -> repeat forever. That loop is how Pydantic AI agents self-improve while running in production. Pydantic AI + Logfire tracing + GEPA prompt optimizer + evals + feedback loops - that's the stack. Watch and save it, then wire GEPA into your live agent this week.

The creator of Pydantic explained how to make live agents smarter after they ship in 1 hour 21 minutes - better than $2500 agent evals bootcamps. collect traces from your live agent -> score them with an eval -> run GEPA to auto-optimize the prompt -> deploy the winner -> repeat forever. That loop is how Pydantic AI agents self-improve while running in production. Pydantic AI + Logfire tracing + GEPA prompt optimizer + evals + feedback loops - that's the stack. Watch and save it, then wire GEPA into your live agent this week.

h100envy

49,466 次观看 • 12 天前

What exactly are AI agents, and how will they change the way we work? Aaron Levie, martin_casado, Steven Sinofsky, Erik Torenberg discuss: (0:36) What Is an Agent? Agency, Autonomy, and Feedback Loops (4:27) Task Specialization and Division of Labor in AI (6:04) Anthropomorphizing AI and Its Economic Impact (9:10) Predictions, Progress, and Recursive Self-Improvement (13:13) Hallucinations, Verification, and Expert Productivity (16:16) Tool Adoption, Experts, and Changing Workflows (45:55) Division of Labor, Verticalization, and New Roles in Applied AI (54:44) Platform Competition and the Application Layer

What exactly are AI agents, and how will they change the way we work? Aaron Levie, martin_casado, Steven Sinofsky, Erik Torenberg discuss: (0:36) What Is an Agent? Agency, Autonomy, and Feedback Loops (4:27) Task Specialization and Division of Labor in AI (6:04) Anthropomorphizing AI and Its Economic Impact (9:10) Predictions, Progress, and Recursive Self-Improvement (13:13) Hallucinations, Verification, and Expert Productivity (16:16) Tool Adoption, Experts, and Changing Workflows (45:55) Division of Labor, Verticalization, and New Roles in Applied AI (54:44) Platform Competition and the Application Layer

a16z

89,137 次观看 • 11 个月前

In a recent batch talk, YC General Partner Tom Blomfield broke down how to build a self-improving, AI-native company. He walks through how to create recursive, self-improving AI loops, and why founders who get this right will run companies that improve while they sleep. 00:00 — Companies Are Roman Legions 00:54 — Copilots Are the Wrong Mental Model 01:55 — Extract the Domain Knowledge 02:24 — The Recursive Self-Improving Loop 04:12 — The Holy Shit Moment at YC 05:50 — Self-Optimizing Product and Support Loops 06:29 — Burn Tokens, Not Headcount 07:23 — Middle Management Is Over 08:05 — Make Everything Legible to AI 09:40 — Regenerating the YC User Manual 11:19 — Software Is Ephemeral, Context Is Valuable 12:18 — Where Humans Still Matter

In a recent batch talk, YC General Partner Tom Blomfield broke down how to build a self-improving, AI-native company. He walks through how to create recursive, self-improving AI loops, and why founders who get this right will run companies that improve while they sleep. 00:00 — Companies Are Roman Legions 00:54 — Copilots Are the Wrong Mental Model 01:55 — Extract the Domain Knowledge 02:24 — The Recursive Self-Improving Loop 04:12 — The Holy Shit Moment at YC 05:50 — Self-Optimizing Product and Support Loops 06:29 — Burn Tokens, Not Headcount 07:23 — Middle Management Is Over 08:05 — Make Everything Legible to AI 09:40 — Regenerating the YC User Manual 11:19 — Software Is Ephemeral, Context Is Valuable 12:18 — Where Humans Still Matter

Y Combinator

592,788 次观看 • 2 个月前