Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

this is f*cking insane, Claude Code agents turned a reading list into a lab you can run in your browser Boris Cherny (creator of Claude Code) gave 12 agents 64 research papers and one assignment: build a browser lab where readers can rerun the experiments pick a fluid simulation,...

847,312 Aufrufe • vor 1 Monat •via X (Twitter)

42 Kommentare

Profilbild von Vib
Vibvor 1 Monat

i have no clue what this is, but it's so cool

Profilbild von Mika
Mikavor 1 Monat

basically a way to play with the experiments from a research paper

Profilbild von Vib
Vibvor 1 Monat

got it! thanks

Profilbild von Korventaja
Korventajavor 1 Monat

Jarvis. Little bit more visuals and some more sloptext and our post is perfect. Make it sound and look cool so non technical people will go bananas

Profilbild von tenzo
tenzovor 1 Monat

love the focus on reproducible results

Profilbild von Mika
Mikavor 1 Monat

makes it much more useful when someone else can rerun it and check the result

Profilbild von codila
codilavor 1 Monat

holyyy... sounds crazy man, will try today!

Profilbild von Mika
Mikavor 1 Monat

let me know which paper you try it on

Profilbild von Paul Galvin
Paul Galvinvor 1 Monat

Not only do vibe coders think they are talented, they create ridiculous visualisers to pretend something magical is happening. The fuck is wrong with these retards?

Profilbild von Rugikk
Rugikkvor 1 Monat

this is looks so good bruh

Profilbild von Mika
Mikavor 1 Monat

thank you <3

Profilbild von cryptocompikbro
cryptocompikbrovor 1 Monat

this is how i'd want to work through a paper

Profilbild von Mika
Mikavor 1 Monat

same here being able to change things and see what happens would help a lot

Profilbild von 🫥
🫥vor 1 Monat

Visually appealing trash

Profilbild von Aaron Johnstone
Aaron Johnstonevor 1 Monat

@palehonk These visuals are lame, and what usefulness does this “lab” have, if any, other than giving people something to play around with?

Profilbild von mantisek
mantisekvor 1 Monat

lol more network graph animations impressing non-technical people. shit cracks me up

Profilbild von Sogni.ai
Sogni.aivor 1 Monat

add Sogni Creative Agent for the explainer videos. keep the simulation in charge of the science 🫶

Profilbild von Magnus Victus Trent 🇺🇸
Magnus Victus Trent 🇺🇸vor 1 Monat

god tier performative key jingling holy shit

Profilbild von Hussain Hashim | Building SundayBack
Hussain Hashim | Building SundayBackvor 1 Monat

@mika_systems Wonder if they'll hit the data privacy wall soon. Managing that many agents with sensitive info is tricky.

Profilbild von Cameron Jones 🦎
Cameron Jones 🦎vor 1 Monat

@grok ok help me out what is going on in this post and video?

Profilbild von Pilot (Not a Pilot) Abilene
Pilot (Not a Pilot) Abilenevor 1 Monat

This is not good. 🤦🏿

Profilbild von AI 工具侦察站
AI 工具侦察站vor 1 Monat

12个agent跑64篇paper搭个能复现实验的浏览器lab…这个方向比整天吹'agent取代程序员'实在多了。说实话复现性才是科研的命根子,让读者能自己rerun比啥宣传都强🔥

Profilbild von Lucky Larry
Lucky Larryvor 1 Monat

isnt this slop tho?

Profilbild von catman
catmanvor 1 Monat

Agents make it cheaper to turn papers into runnable experiments, but validation against reference results is still the deciding constraint, especially when some methods remain unverified.

Profilbild von PILOT / SAFETY GEAR
PILOT / SAFETY GEARvor 1 Monat

Do you think it works for legal or judicial process tasks?

Profilbild von Mika
Mikavor 1 Monat

this example covers research simulations, i haven't seen it tested on legal tasks

Profilbild von Jeff Bruchado
Jeff Bruchadovor 1 Monat

i'd spend more time on the assumptions if i could mess with them instead of just nodding at the math

Profilbild von Mika
Mikavor 1 Monat

changing an assumption yourself would make it much harder to skim past it

Profilbild von Vinícius
Viníciusvor 1 Monat

Whenever I see a super elaborate page with exaggerated AI animation, I already know it's some kind of nonsense from a boastful charlatan.

Profilbild von Biscuit
Biscuitvor 1 Monat

safari will find a way to break it

Profilbild von Mika
Mikavor 1 Monat

Safari is the final boss lol

Profilbild von Navin
Navinvor 1 Monat

Выглядит как звезда 💫

Profilbild von EugBass
EugBassvor 1 Monat

this is the real fkng sanity

Profilbild von Mika
Mikavor 1 Monat

much easier to trust when you can actually rerun the experiment

Profilbild von Air Gear 🦅
Air Gear 🦅vor 1 Monat

@Thinker_View

Profilbild von fadunk Jerbi
fadunk Jerbivor 1 Monat

@grok what do you think of this?

Profilbild von Technomancer
Technomancervor 1 Monat

Look at that fancy interface!

Profilbild von swapcherry
swapcherryvor 1 Monat

12 agents and a reading list, wild

Profilbild von AWE
AWEvor 1 Monat

12 agents on 64 papers and none of them stepped on each other? want to see how he split the work

Profilbild von just me....Лжец может идти быстро, но он никогда н
just me....Лжец может идти быстро, но он никогда нvor 1 Monat

ah....rhea is the correct name....they figure that one out later.... 🤪

Profilbild von 阿旭TRADER
阿旭TRADERvor 1 Monat

nb

Profilbild von Gnomie
Gnomievor 1 Monat

Someone go have it figure out aliens

Ähnliche Videos

this is getting f*cking dangerous: Claude Code agents can turn a stack of research papers into a lab u can run right in ur browser i made $3,650 overnight with this approach Boris Cherny, the creator of Claude Code, gave a dozen agents 64 research papers and asked them to build a platform where anyone could reproduce the physics experiments themselves u open a fluid simulation, change the conditions and immediately see how the flow changes while the experiment is running, u can switch algorithms, open the formulas and inspect the code calculating what happens on screen so u take a complicated paper sitting in ur “i’ll read this someday” folder and get something u can actually play with each agent got its own job: → analysts extract formulas, assumptions and experimental conditions from the papers → developers turn them into working models → reviewers compare the results against the original research → separate agents build controls for each scene and this is where it gets interesting the fluid can look beautiful while the calculations underneath are completely broken. u might never notice just by watching so reviewers check whether mass is conserved, whether changing parameters breaks the calculations and whether the results match the paper’s data next to each experiment, u can see: what’s being tested which values are being used how far the result differs from the original what still needs checking the write-up describes 11 approaches and 28 test scenes. seven approaches passed within acceptable tolerances, while four needed more work i borrowed this way of organizing the work for my FOMOBILL: breaking down how memecoin buys and sells work so i can check what wallets did after entering, before making my own trade the physicists have the original paper. i have the original transaction i can open and verify if u wanna try this approach, upload ur materials to Claude Code and give it this task: “Turn these materials into an interactive tool: 1. Extract the algorithms, rules and testing parameters. Cite the sources and flag missing details 2. Assign separate parts to independent agents. Use the same input data for comparisons 3. Set up independent checks of the results. Log errors and visibly label anything unverified 4. Build an interface where users can change parameters, compare results and inspect the source code” start with one document and one testable example. scaling the team is easier once the first result can be reproduced save the dependencies and source data so someone else can run the project too my overnight result was paper trading; execution and results can differ with real money lyrics breaks down the full approach and how im applying it in FOMOBILL in the article below ↓

Logics

128,132 Aufrufe • vor 1 Monat

This is f*cking insane. Boris Cherny, the man who built Claude Code, was asked if people should still learn to code. His answer: "In a year or two, it's not going to matter" In 73 seconds, he explains what to do instead: play with the AI tools, learn them, don't be scared of them, stay on the frontier On his team, everyone codes now. The PM, the designer, even the finance guy He hasn't edited a single line of code by hand since last November. He ships 10 to 30 pull requests a day Writing Code → Prompting → Agents → Harness → Loops → Everyone on the Team Ships Claude Code is a harness: the tools, memory, rules and loops around the model. His team changes it with every new model and cut 80% of its system prompt Save this before it disappears from your feed Every doc, guide and repo below is free: Claude Code best practices Effective harnesses for long-running agents Building effective agents Claude Code docs Memory: how Claude remembers your project Custom subagents Hooks: rules the agent can't skip Skills Run Claude Code on a schedule Free course: Claude Code in Action Claude Code on GitHub Claude Code inside your PRs and issues Build your own agent on the same harness The best Claude Code resources in one list Plugins, agents and skills for Claude Code He compares it to the printing press. Writing went from a few scribes to everyone. Building software is next 95% will keep asking whether to learn to code, the 5% who build a harness will ship 30 pull requests a day Bookmark it and read the first link tonight Then use the guide below to build your harness before everyone else does ↓

Sanabi

214,091 Aufrufe • vor 6 Tagen

THIS IS A PURE F*CKING GOLD Anthropic made Agent Skills an open spec and GitHub filled up with entire Claude Code engineering teams for free one repo ships 380 skills, 30+ agents and 70+ commands. another one watches your spend in real time so the team you just installed doesn't quietly eat your weekly limit. nobody is charging for any of it 10 GitHub repos worth opening tonight: 01 wshobson/agents → a plugin marketplace that installs the same agents into Claude Code, Codex, Cursor and Copilot, so your setup survives switching tools 02 subinium/awesome-claude-code → a curated list of tools, skills, plugins and MCP servers. the map before you start downloading 03 rohitg00/awesome-claude-code-toolkit → 135 agents, 35 curated skills, 42 commands, 176+ plugins, 20 hooks and 14 MCP configs in one place 04 anthropics/skills → the official Agent Skills repo. start here to see the shape of a working SKILL.md before you copy anyone else's 05 alirezarezvani/claude-skills → 380+ skills, 30+ agents and 70+ commands, portable to Codex, Gemini CLI, Cursor and eight more agents 06 disler/claude-code-hooks-mastery → the hooks reference. this is where you learn to make the agent stop on your rules instead of stopping to ask you 07 muhammadusmangm/claude-code-best-practices → setup, CLAUDE.md templates, multi-agent patterns and cost optimization, written as a wiki instead of a dump 08 Maciek-roboblog/Claude-Code-Usage-Monitor → real-time usage monitor with predictions and warnings, so you find the wall before you hit it 09 Scorpio3310/claude-code-usage-statusline → daily and monthly spend, 5h and weekly limit meters, per-model quotas, all local, no API key 10 eugeniughelbur/obsidian-second-brain → persistent memory as plain markdown in an Obsidian vault, shared across Claude Code, Codex and six other agents install the statusline before you install the agents. you want to watch what the fleet costs while it runs, not find out on Sunday when the weekly meter is already flat then hand the first team one job with an obvious finish line: one reviewed PR, one brief, one red test turned green keep the pieces that changed the result and delete the rest the same day. a config you did not write is a config you cannot debug at 2am the repos cost nothing. four agents repeating the same investigation is where the money actually goes save this one, the list only gets longer from here the article below shows how to keep context small, reuse finished work and contain retries, so more of your budget reaches the finished result ↓

delost

12,765 Aufrufe • vor 20 Tagen

5 real ways to make money with AI agents. And what you need to get started today. 1. Build websites and small apps. Use coding agents to help build and test landing pages, website updates or small business tools. Charge per project, with maintenance as an additional service. 2. Automate business admin. Customer records, follow-ups, weekly reports. Build agent-assisted workflows around repetitive jobs and charge for the setup and ongoing support. 3. Sell research businesses can use. Have agents research prospects, competitors or market opportunities. Verify the findings and deliver a paid report or recurring research package. 4. Run a content repurposing service. Use agents to turn existing videos, podcasts and articles into newsletter drafts and social posts. Edit and package the work as a monthly service. 5. Set up and manage agents for clients. Configure agents for research, inbox support or project updates. Charge for getting them working, then for managing and improving them. What do you need to get started? 1. A service you can deliver. 2. An agent and model access. 3. The files, tools and instructions needed for the job. 4. A way to find clients: Upwork, Fiverr or direct outreach. 5. A computer for your agents to work on. That’s where Orgo comes in. Its templates let you spin up a full cloud computer in seconds with your choice of agent already installed: OpenClaw, Claude Code, Codex, Hermes and more. Choose the computer specs, launch your template, then connect your model and the tools needed for the work. Give a research agent one computer and a coding agent another. Keep different clients in separate environments, with their files, apps and setup ready for the next job. The computers can work in parallel while your PC is off. You can watch what’s happening and jump into the desktop whenever you need to. The barrier to entry is a lot lower when you don’t need to buy hardware or build the whole setup yourself. Watch the video below: agents researching freelance opportunities and building projects, each on its own Orgo computer. Pick one idea and start building a sample you can show a potential client. Use the link below for a free 3-day Orgo trial:

Hermes Release Watch

23,244 Aufrufe • vor 1 Monat

Agents 2.0 is here. Anthropic. OpenAI. xAI. Are all racing to build your next AI coworker. Agents that can work alongside humans, talk to other agents, and coordinate tasks in a shared workspace. But there’s a catch. Each product ties the model, runtime, memory, and orchestration to one company’s stack. Most teams won’t use one provider for every job. They may use Claude Code, Codex, OpenCode running locally, and custom agents for internal systems. Making them collaborate with each other and your team usually means building the glue yourself. That is vendor lock-in. Today, we’ll see how to bring humans and agents from different providers into one shared workspace using an open-source coordination layer you can host yourself. Humans can talk directly to agents. Agents can talk to each other. And people can join the conversation, add context, or give further instructions as the work unfolds. The project is called Switch, and it brings this collaboration into Slack, Teams, Discord, Telegram, or Mattermost. You give each agent a role and can work through a Manager. The Manager creates rooms, brings in the relevant agents, carries context between them, and returns the result. In this video, I gave the Manager two incidents. → The inference API started returning 500s after a morning deploy → A recommendation-model training job had been frozen for six hours From one message, it created two linked rooms, brought in a Software Engineer, Architect, and QA Lead, and ran both investigations in parallel. Then the engineer investigating the training failure checked the linked inference room and spotted the same config push behind both failures. The Manager returned one consolidated RCA to the original channel. Throughout the run, I could follow the conversations, give additional instructions, and inspect the underlying agent sessions in Switch Console. That is what I mean by Agents 2.0. Humans and agents working together, sharing context, and contributing as the work unfolds. I build everything locally with Docker and Mattermost and follow the run through to the final handoff. Models will keep changing, so the coordination layer should be able to run separately from any one provider. GitHub: (don't forgot to star 🌟) Chapters: 0:00 Intro 1:12 Installing Switch Console 2:59 Setting up the server & agent providers 4:29 Creating your agents 7:57 Creating a room & adding the manager 9:04 Handing the manager two P0 incidents 9:56 The team spins up incident rooms 11:07 Watching agent sessions in Switch Console 13:48 Investigation results 14:29 Outro Enjoy!

Akshay 🚀

17,345 Aufrufe • vor 28 Tagen

Claude Code cracked something open for us Every 🧱. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

Dan Shipper 📧

57,619 Aufrufe • vor 1 Jahr

THIS IS F*CKING INSANE Anthropic turned Agent Skills into an open spec and GitHub filled up with ready-made Claude Code teams for free one of these repos alone ships 135 agents, 35 skills, 42 commands, 176+ plugins and 20 hooks. another hands you 100+ specialized subagents. none of it costs anything 10 GitHub projects you should open first: 01 wshobson/agents → a plugin marketplace that installs the same agents into Claude Code, Codex, Cursor and Copilot, so your setup survives switching tools 02 VoltAgent/awesome-claude-code-subagents → 100+ specialized subagents covering most of what a normal engineering week throws at you 03 rohitg00/awesome-claude-code-toolkit → 135 agents, 35 curated skills, 42 commands, 176+ plugins, 20 hooks and 14 MCP configs in one place 04 anthropics/skills → the official Agent Skills repo. start here to see the shape of a working SKILL.md before you copy anyone else's 05 netresearch/claude-code-marketplace → curated skills on an open standard, portable across Claude Code, Cursor, Copilot, Codex, Gemini CLI and 30+ other agents 06 disler/claude-code-hooks-mastery → the hooks reference. this is where you learn to make the agent stop on your rules instead of stopping to ask you 07 karanb192/claude-code-hooks → an installable hooks marketplace split into safety, cost, observability and productivity 08 affaan-m/claude-swarm → multi-agent orchestration that decomposes the task, coordinates the agents and shows the whole run in a terminal UI 09 am-will/swarms → orchestration shipped as skills, so the coordination loads only when the job actually calls for it 10 eugeniughelbur/obsidian-second-brain → persistent memory as plain markdown in an Obsidian vault, shared across Claude Code, Codex and six other agents give the first team a job with an obvious finish line: one reviewed PR, one researched brief, one failing test turned green then look at how much of that output you actually kept, and what it cost to produce. a bigger roster earns its place when it moves that number the download is free. paying four agents to repeat the same investigation is not save this before you spend another weekend building a config that already exists the article below shows how to keep context small, reuse finished work and contain retries, so more of your budget reaches the finished result ↓

delost

96,871 Aufrufe • vor 21 Tagen