I GAVE 7 AI BOTS A JOB. THEN MADE THEM WORK TOGETHER.
One finds ideas. Another judges them. Others build, test, and review.
I broke down the entire system, including the handoffs that make it work.
STOP BUILDING YOUR CLAUDE SECOND BRAIN BY HAND
Obsidian’s CEO already open-sourced the skills and four more repos finished the rest.
Most people still copy broken tutorials from last month.
The play:
Clone kepano/obsidian-skills.
Point Claude Code at your vault.
Drop any file into /raw and type ingest.
The map writes the page, links it to five existing ones and updates the rules with how you actually think.
After 90 days the search bar cites your own notes with dates.
Google cites strangers.
Your vault cites you.
The people running this don’t open ChatGPT anymore.
They open their own search.
The catch: without a source date on every ingest the vault starts inventing.
One missing date and it fills the gap with whatever sounds right.
One 5-minute setup.
Runs forever on the laptop in front of you.
Compounds for years.
Bookmark this before you open another empty chat.
YOUR AGENT FORGETS EVERYTHING WHEN YOU SWITCH TOOLS
Living Brain keeps one persistent memory across ChatGPT, Claude, Cursor, Codex and your own agents. Write in one, retrieve in any other.
the play:
→ connect via plugin or MCP
→ save a decision or note in one tool
→ open the next tool and ask for it
→ it pulls the memory in ~464ms
> the catch
the memory lives outside the model. check what gets stored and who can access it
> what not to do
don't dump every chat into it on day one. start with decisions and project context only
one brain that travels with you beats rewriting the same context every time you switch tools
bookmark this
HOLY SH*T. YOU’VE BEEN GOOGLING STRANGERS FOR 20 YEARS
Karpathy’s 5 minute second brain setup crossed 16m views. one folder, one Claude.md every article, podcast, meeting and note you already consume becomes searchable by you.
Install Obsidian, open Claude Code in the vault, drop anything into /raw and type ingest. it writes the wiki page, links it to 5 existing ones and updates the rules with how you actually think.
After 90 days the search bar cites your own past notes with dates. Google cites strangers. your vault cites you.
The people running it don’t open ChatGPT anymore. they open their own search.
One extra rule i added: every ingest must cite the original source date. without it the vault starts inventing. with it, after month 3 it drafts in your voice better than you do at 2am.
Your coworker started this on a saturday in february. by august his tweets landed 4x your engagement on the same topics. by october his first draft of any doc was already better than your third.
Compounds for 10 years. after year 3 your vault knows more about what you believe than you do.
5 minutes to wire. runs forever on the laptop in front of you.
Bookmark this before you open another empty chat
NO F*CKING WAY
SPACEXAI HANDED CODERS 345 TOOLS FOR FREE
17 official Grok Build plugins that turn one agent into a full stack team. all of them already on github
people drop $19 on a single skill. here you get 150 skills, 5 agents, 59 browser tools and 131 web guides. roughly $3k in skills, zero to you
already sitting at 380k stars combined. late, but not too late
your agent writes code. it still can't open your figma, hit the database or read the error killing signups
17 plugins worth installing first:
1. superpowers → 15 skills. plan first, test first, debug properly. 304k stars
github.com/obra/superpowers
2. pstack → 58 skills + 2 agents from cursor. investigate, design, review, verify
github.com/cursor/plugins
3. modern-web-guidance → 131 how-tos from google so it stops writing old web code
github.com/GoogleChrome/mode…
4. figma → reads real designs and turns them into code
github.com/figma/mcp-server-…
5. chrome-devtools → 59 tools. live chrome for the agent. clicks, network, performance
github.com/ChromeDevTools/ch…
6. browser-use → agent clicks through the app in a real browser and qa’s it
github.com/browser-use/plugi…
7. supabase → database, auth, storage straight from the agent
github.com/supabase-communit…
8. mongodb → 8 skills for schema, queries, search, slow-query fixes
github.com/mongodb/agent-ski…
9. stripe → official payment best practices + mcp server
github.com/stripe/ai
10. firecrawl → any site into clean llm-ready markdown
github.com/firecrawl/firecra…
11. exa → real-time web search with sources
github.com/exa-labs/exa-grok…
12. tavily → search, extract, crawl, deep research
github.com/tavily-ai/tavily-…
13. vercel → 37 skills, 3 agents, 4 commands for deploys and logs
github.com/vercel/vercel-plu…
14. cloudflare → 14 skills for workers, durable objects, wrangler
github.com/cloudflare/skills
15. netlify → 12 skills for functions, edge, blobs, deploys
github.com/netlify/context-a…
16. sentry → real prod errors and stack traces inside the agent
github.com/getsentry/plugin-…
17. datadog → logs, metrics, traces in plain english
github.com/datadog-labs/grok…
pick 3 today. one that thinks, one that ships, one that catches bugs
test each on one real task. if the output isn’t better, uninstall it
--trust lets a plugin run code on your machine. two minutes on the repo saves a bad week
bookmark this. the tool you were about to build is probably already here
Kodzilla retweeted
How LongCat-2.5 compared with Gemini, GPT and GLM on the same SVG task
One shot. Same prompt:
an animated SVG of a pelican riding a bicycle.
LongCat-2.5 · $0.25
The most complete scene. Fish in the pouch, a ringing bell, a passing seagull, parallax background. Legs follow the pedals.
Gemini 4 Argon · $0.37
Best colors and a bold, clean pelican. The background is simpler.
GLM-5.3 Flash · $0.24
Clean and accurate drawing, a calm seaside scene. Fewer details and less motion.
GPT-6.1 Terra · $0.05
Cheapest by far. Correct pelican and bike, but almost no scene around them.
Prices = estimated cost of this task at each model's official API rates.
🎁 Giving away 10 LongCat-2.5 codes with 10M free tokens each.
Reply "LONGCAT" + what you'd build 👇
Introducing LongCat-2.5, our latest natively multimodal agentic model with 1.6T total parameters and ~48B active.
Built for long-horizon agentic tasks, it brings stronger visual understanding and agentic coding to work ranging from complex coding projects to visual coding such as creative design and 3D tasks.
Learn more about LongCat-2.5: longcat.ai/blog/longcat-2.5/
HOW TO RUN SONNET AS LEAD HAIKU AS SWARM AND OPUS AS ADVISOR ON CALL
Anthropic just shipped the cleanest multi-model split in Claude Code. one command, three models, way fewer tokens.
the play: sonnet 5.5 drives the session > haiku 5.5 swarms search/grep/audit in parallel > opus 5.5 sits on call and only reviews the final diff
steps:
1. open terminal and run
claude --model sonnet --advisor opus
code.claude.com/docs/en/sub-…
2. create three haiku 5.5 subagents in .claude/agents:
- search (maps the repo)
- grep (finds call sites)
- audit (checks the diff in a throwaway worktree)
3. add one rule to CLAUDE.md:
send every repo search and test run to the haiku 5.5 subagents so the main context stays small
4. route fast deterministic forks (which file, which tool, retry or stop) through JEV so the big models never see them
5. paste this prompt so Claude rebuilds the tree and shows every change as a diff first:
Rebuild my Claude Code setup around this tree:
1. Run the main session on Sonnet 5.5 with Opus 5.5 as advisor.
2. Create three Haiku 5.5 subagents in .claude/agents: search (maps the repo), grep (finds call sites), audit (checks the diff).
3. Give audit isolation: worktree so its checks never touch my working branch.
4. Add one rule to CLAUDE.md: send every repo search and test run to the Haiku 5.5 subagents so the main context stays small.
5. Route fast deterministic forks (tool choice, path selection, retries) through JEV.
Show me every change as a diff first.
Nothing gets edited until I say go.
> the catch
opus still burns tokens if you call it on every small decision. keep it strictly on-call.
> what not to do
do not run the whole session on opus or let haiku write code. wrong model = wasted context.
sonnet builds. haiku swarms. opus reviews.
bookmark this and reply with your current claude setup if you want the exact agents folder structure.
HOW TO BUILD A 200+ AGENT RESEARCH TEAM IN GROK
20 analysts, one portfolio manager, one risk desk. Running from a single interface.
the play: portfolio manager → 20 analysts → risk desk → parallel agents
> first steps
- open Grok and create a portfolio manager agent
- spin up 20 analyst agents with clear research scopes
- add a risk desk agent that challenges findings
- wire the handoffs so they run in parallel
> the catch
200 wallets is a starting point, not proof. High profit numbers can be noise, luck, or something else. The agents flag patterns. You still have to verify.
> what not to do
- don’t treat the output as confirmed insider trading
- don’t skip the risk desk. Unchecked agents just amplify noise
- don’t scale the team before the handoffs actually work
one command, 20 analysts, zero manual wallet checks.
reply with the first research task you’d give the team
bookmark this before you build the next agent
Kodzilla retweeted
this DeepSeek paper is f*cking insane
they gave an agent a subagent tool and ~20 real tasks. it used it 0 times out of 5.
same ~300 messages, 4 agent setups, 5 runs each:
→ one session, told to "use a subagent when appropriate". delegated 0 times. did everything itself, kept getting interrupted. worst case 29.5 min
→ same session, told to "delegate first". still decided one task was "small" and did it itself
→ router + workers: 12.4 min median, 15.7 worst case
so a better prompt won't fix this. deepseek's router doesn't even get bash, it can only talk to you and hand out work.
in Claude Code that's 2 files:
> router.md: model haiku, tools: Agent, Read
> worker.md: model opus, full tools, one task each
> claude --agent router
costs 2-4x more tokens. i'd pay it.
paper below ↓ bookmark it
THIS SHOULDN'T EVEN WORK
I CUT OFF GROK'S HANDS. IT CAN'T BUY. CAN'T SELL. CAN'T TOUCH A STOP. A DUMB BOT CLICKS FOR IT. $333 BECAME $1,587 IN 28 HOURS.
i didn't teach Grok to trade. i gave it a trading bot to manage.
SCOUT reads news, funding, OI and X. CHART writes entry, stop, target. DESK MANAGER runs the loop. all three are Grok.
Jev writes nothing. it only picks: accept, verify_more or reject.
Freqtrade is the only thing that can send an order. 24x, 3 slots, stop at 20% of stake, withdrawals off.
7 days in dry_run, then live. UTC:
Nov 1 04:00 $ZRO long 1.87, stopped 1.854, -$25
Nov 1 04:00 $M short 0.899 → 16:00 0.837, +$181
Nov 1 08:00 $LIT long 3.13 → 12:00 3.26, +$100
Nov 1 09:40 $GTC long 0.2270 → 10:40 0.2500, +$247
Nov 1 16:00 $M long 0.837 → Nov 2 00:00 0.850, +$97
Nov 1 16:00 $ZEC long 1,238 → Nov 2 04:00 1,278, +$209
Nov 1 16:00 $ENA short 0.185 → Nov 2 08:00 0.178, +$247
Nov 2 00:00 $NEAR short 4.29 → 08:00 4.11, +$306
Nov 2 04:00 $MORPHO long 2.24, stopped 2.221, -$85
fees and funding, -$23
$333 → $308 → $655 → $836 → $1,143 → $1,587
both reds got verify_more first. got through on pass two and died. the clean accepts never lost.
no fill, no done. every close checked against real fills.
$BTC never left $77k-$78k. the limits live in a config no agent can edit.
the full 12-step desk is in the last article. every prompt, every gate ↓
412 km up. 7.69 km/s. 3 engine bells, 4 landing legs, 8 hull sections that come apart in orbit and dock back together. meet the Halcyon HX-7, a new orbital freighter prototype that only exists as a simulator
I built it with Elvar's teardown method. one free Claude Code skill, the whole ship in one offline HTML page
the whole mission is one timeline. 9 phases: approach, hold, gear down, cargo open, mast up, optics scan, teardown, reassembly, ascent burn. every panel reads that timeline. the main drive sits at 86 kN on approach, drops to 13 kN while the hull is apart and climbs to 89 kN on the ascent burn. the moment the sections split, all 8 load bars flip to DETACHED. the gauges follow the model, so they can't show a number the ship isn't living through
here is the gist:
→ type /teardown, name the vehicle, attach one real reference. without it you get a toy
→ write the mission as a timeline before any part gets a shape: 9 phases, each with a start time
→ split the hull into sections you can pull out alone: drive, aft, mid, fwd, nose, pod, mast, gear
→ give every phase real numbers: thrust in kN, altitude, velocity, which subsystem is live
→ point every panel at the timeline, never at its own animation
→ blow the hull apart mid-mission and check that every bar says DETACHED
→ dock it back and fire the burn. if one section comes home late, the numbers show it before your eyes do
a ship you can open, cut and fly says more than a render deck
the 9 prompts are in the quoted article
nose iris opening in orbit or 4 legs coming down at 7.69 km/s, which part of your own ship would you cut open first?
↳ concept simulation, method from the article
Seven hourly bots wake up 1,176 times a week, even on a quiet day.
That is 7 bots x 24 hours x 7 days. Arithmetic, not a measured bill: whether a wakeup counts as a billable run depends on how the platform counts runs.
The leaner setup in this article: one weekly scan, and the other six bots run only when an event or a handoff calls them. One ledger records what changed.
The part worth stealing: a line in a file does not wake the next bot. If a handoff has no real trigger, you have a checklist, not a team.
Test it before you trust it:
> disable the next trigger after the first bot writes its output
> the system must report the blocked handoff
> it must not pretend the next bot ran
For scale: one hourly routine is 168 runs a week (my count). Seven of them is 1,176.
How many of your wakeups find nothing?
The full workflow is in the article below ↓
I BUILT A 20-REPO AI WORKFORCE WHILE ASLEEP
Cloned the bots. Wired the handoffs. Turned them into one system.
The whole thing ran on its own.
Video of the full run from start to "1 system"
This is the 7-bot setup from the article.
Scout finds the repos, builder clones, judge verifies, chief coordinates.
No manual steps. Just the output while I was offline.
F*CK. GROK BOT JUST BUILT A LIVING KNOWLEDGE BASE THAT CATCHES ITS OWN CONFLICTS
3,061 records. 12,906 links. One graph.
It ingests raw articles, patches the wiki, then runs a nightly linter that flags conflicts, orphans and stale pages.
In the video it finds a pricing contradiction ($49 vs $39), marks an orphan idea, and waits for a human call instead of overwriting.
This is what a second brain actually looks like when the agent is allowed to maintain it.
THIS IS F**KING ADDICTIVE
7 GROK AGENTS PASS EVERY TRADE DOWN THE LINE. ONE SAYS NO, IT'S DEAD. $117 BECAME $1,212 ON HALLOWEEN.
midnight UTC, Oct 31. BTC sat at $77,221 and barely moved all day. the desk didn't care
every stop sits 0.87% away. one loss costs 8% of the account. never more
→ 412 setups flagged
→ 263 killed as noise
→ 101 killed on funding and book
→ 36 killed by the Skeptic
→ 12 traded. 11 green, 1 red
ZRO long 1.71, stopped, -$9. back in at 1.70, out 1.85, +$117
LIT 2.71 → 3.07, +$211
MORPHO 2.15 → 2.38, +$202
BTW 1.14 → 1.18, +$38. short 1.19 → 1.08, +$174
STX 0.301 → 0.328, +$143
shorts: TRX 0.329 → 0.307 +$72, M 0.957 → 0.902 +$62, PEARL 1.48 → 1.45 +$32, UNI 7.16 → 6.97 +$28
SP, the only meme: 0.0097 → 0.0178, +$25
$117 → $108 → $174 → $895 → $1,187 → $1,212
the prompt:
You are a kill-first desk. 7 agents, one Grok brain. Any agent can set stop=true and the trade is dead.
LISTENER: find coins X suddenly talks about.
BOT HUNTER: kill paid shills and bots.
CHAIN CHECKER: kill if funding is crowded or the book is thin.
SKEPTIC: find one reason not to trade.
SIZER: never risk over 8%.
TRADER: stop before entry. never move it.
DIARY: log every kill.
If nothing passes, do nothing.
10x on a day BTC moved 0.3%
i broke down all 7 agents in the last article. every rule, every kill reason ↓
Kodzilla retweeted
IN CHESS, A HUMAN PAIRED WITH A MACHINE WAS THE STRONGEST FOR THIRTY YEARS. IN WORK, THAT WINDOW MAY CLOSE MUCH FASTER.
That's the view of Sholto Douglas and Nick Marwell, lead researchers on reinforcement learning at Anthropic.
They said it in a conversation with Joe Lonsdale.
And it's the thought that won't leave me alone.
Because we are in a pleasant phase right now.
AI helps people work faster, so there is more work, not less.
Companies are hiring, not firing.
And this pleasant phase hides what is actually happening.
When a technology makes every worker more valuable, it feels like it will always be that way.
But chess showed something else.
At first a human and a machine together beat everyone.
Then the human in the pair became an unnecessary link.
And nobody announced that moment in advance.
That's what worries me most.
Because full replacement doesn't arrive with a sign on it.
It arrives disguised as a temporary boost.
So I'd put the main question differently.
Not whether work will remain.
But what remains for the human once the pair with the model is no longer needed.
I think the answer isn't execution.
The machine will take execution.
What remains is choice.
What is worth doing.
Who needs it.
Who answers if something goes wrong.
Look at chess today.
Machines have long been stronger than any grandmaster.
And we still watch matches between people.
Because we don't care about the best move.
We care how a person arrived at it.
So maybe value is simply moving.
From the result to the meaning that someone alive puts into it.
But there is one more question, and it's sharper.
The researchers say that if AI really makes the world far more productive, the main problem won't be creating abundance.
It will be how to share it.
So that profits don't flow only to those who already had capital.
Imagine two worlds.
In both, AI has made everything cheap.
In the first, everyone has access to it.
In the second, a few hands hold the key.
The technology is identical.
The only difference is what we managed to decide while the window was open.
I think this is the most important thing.
Not learning to work faster with AI.
That will be useful, but it isn't enough.
We must not miss the moment when we can still influence the rules.
Because once the pair of human and machine stops mattering, the rules will already be written.
And the only question is by whom.
Kodzilla retweeted
OPENAI SENT 3 MODELS. GOOGLE ONLY NEEDED GEMINI 4 CARBON 🥶
Same prompt: build a tiny living city.
· Gemini 4 Carbon: 49m · ≈ $6.2
· GPT-6 Astra: 18m · $3.98
· GPT-6.1 Sol: 17m · $0.76
· GPT-6.1 Terra: 6.5m · $0.23
Carbon took the crown: the most alive city by far.
Terra built a whole city in 6 minutes for 23 cents.
Sol came really close to Astra for 5× less.
Is Astra worth the extra?
Waiting for Carbon on aimlapi.com
GEMINI 4 CARBON BURIED OPUS AND ASTRA THIS HALLOWEEN 💀
Same prompt: a zombie hand claws out of a grave and rots down to the bone.
· Gemini 4 Carbon: 1h 23m · $10.2
· Opus 5.5: 1h 6m · $8.1
· GPT-6 Astra: 24m · $5.7
Carbon went full horror movie: candlelit graveyard, labeled anatomy at every stage.
Opus nailed the rot but lost on the scene.
Astra finished first and built a landing page with a hand on it.
Halloween test via aimlapi.com
10 USE CASES. 7 ROLES. ONE BIG PROBLEM: HANDOFFS.
This is a good look at how versatile Grok Bot can be.
The video covers 10 practical use cases:
→ A second brain for saving and researching content
→ Bots that build product features
→ Agent swarms managing 10+ agents
→ Automated morning briefs and recurring workflows
→ Content repurposing
→ Token spend and subscription tracking
→ Group chat extraction
→ Qualified lead generation
→ AI competitor research
→ Managing X replies and draft ideas
Each use case solves a different problem. But there’s a bigger opportunity when you connect specialized agents into one coordinated workflow.
That’s the idea behind my 7-bot Grok system.
Scout finds opportunities. Pain Miner gathers evidence. Judge challenges weak ideas. Builder creates the product. Pitch prepares the offer. Chief coordinates the operation. Inbox handles incoming responses.
Each agent has a defined role, clear handoffs, and specific checkpoints.
I broke down the full architecture, prompts, and launch workflow in my latest article.
THIS IS WHAT A GROK BOT FLEET LOOKS LIKE.
One account. Six specialized agents. One independent judge.
Here’s how the workflow is structured:
Scout scans for new tokens and unusual wallet activity.
Filter checks contracts for honeypots, suspicious permissions, and liquidity risks.
Analyst compares wallet behavior, volume, and trading signals.
Executor prepares orders only after the checks pass.
Risk enforces position limits, stop-losses, and exposure rules.
Logger records every decision, including rejected trades.
Then comes the 7th agent.
Jew acts as an independent judge. It evaluates the evidence and can block a trade before execution.
The useful part? You can apply the same architecture beyond crypto.
Give each agent one job. Separate analysis from execution. Require independent approval before irreversible actions. Log every rejection so you can measure whether your safeguards actually work.
That’s how you turn a collection of AI bots into a system you can audit.
The video visualizes the workflow. Full breakdown in the quoted article.
Kodzilla retweeted
holy f*ck, web designers have no idea what's coming
the ones who learn these tools now will be fine. the ones who don't will be searching job in 2027.
7 design repos you need to know to survive AI, try this asap:
1. UI UX Pro Max
gives Claude real design taste: 192 color palettes, 74 font pairings, rules for every industry. your fintech app stops looking like a crypto casino
github.com/nextlevelbuild…
2. Impeccable
catches "AI slop" design the moment it's written. tell it to critique, polish or make it bolder, and it actually does
github.com/pbakaus/impecc…
3. Framelink Figma MCP
paste a Figma link into Claude Code and get that exact layout in code. no more eyeballing screenshots
github.com/GLips/Figma-Co…
4. React Bits
215+ free animated components and backgrounds. the difference between "nice site" and "wait, how did they do that"
github.com/DavidHDev/reac…
5. GSAP Skills
the official skills that teach your AI proper GSAP: timelines, scroll animations, text effects. and GSAP is fully free now, every plugin included
github.com/greensock/gsap…
6. HyperFrames
write HTML, get a finished video. your AI can make product promos and motion graphics without After Effects
github.com/heygen-com/hyp…
7. MCP for Blender
this one's wild. Claude gets hands inside Blender and builds real 3D scenes, models and materials from a text prompt
github.com/ahujasid/mcp-f…
the cell in this video?
I built it myself with Claude Code and a few of these 3D and motion tools. no studio, no team.
want to make something like this?
like + save this post, comment "CELL" or DM me and I'll send you the exact prompt.































