NO WAY. A BIG KICK STREAMER WALKED AWAY FROM $8,614.27 WITH GROK BOT ON. HE CAME BACK TO $131,048.92
he stepped away from the desk. by the time he sat back down, Grok Bot had stacked $122,434.65 on top of his balance. chat was still live, and he was frozen on the trade history, yelling at the screen
he thought he’d see a couple of open positions. what he got was a terminal full of closed trades, logged exits, and an account more than 15x bigger than when he left
here’s how the system divided the job:
→ WATCHLIST tracks a chosen set of wallets and logs every move they make. an alert opens a review, it doesn’t trigger a buy
→ SOURCE CHECK verifies the original trade and confirms the wallet is still holding
→ FILTER looks at liquidity, how concentrated the holders are, token permissions, and how much the price has run since the source bought
→ SIZING works out the order based on free cash, current exposure, and the account’s loss limits
→ EXECUTION sends only the orders that pass, then matches the real fills before updating positions
→ LEDGER stores the source wallet, entry, fees, exit, and outcome in one place
it copied the wallets’ picks, not their bet sizes
what stopped a signal from turning into a trade:
→ the source wallet was already taking profit
→ the pool was too thin for the order size
→ the quoted entry slipped past the allowed limit
→ the same risk budget was already tied up in another position
→ a transaction’s status was unclear, so the bot verified it instead of firing a second order
every skipped entry was logged right next to the trades that went through
starting balance: $8,614.27
session profit: +$122,434.65
ending balance: $131,048.92
he came back to a single dashboard: what closed, what’s still open, and exactly where every dollar came from
the number made him scream. the log showed why
the full course below walks you through building a Grok Bot trading terminal with live market data, controlled execution, and a full record from the first signal to the last exit ↓
THIS IS F*CKING INSANE
the CEOs of Anthropic and Google DeepMind sat on one sofa and argued about when AGI arrives
Dario Amodei: faster than people imagine
Demis Hassabis: more like 5 to 10 years
they disagreed on the date. then they agreed on the scary part:
> Amodei: "half of entry-level white collar jobs could be gone within one to five years"
> Hassabis: this year we'll see the beginnings of it hitting junior jobs
> Amodei on selling AI chips to China: like selling nuclear weapons to North Korea
> his economy scenario: 5-10% GDP growth. and 10% unemployment. at the same time
the two people building it can't agree on when. both say it's coming for the first rung of the career ladder first
worth the 14 minutes. bookmark and watch it today
then read the guide below ↓
Your agent is not autonomous
You are its loop
You check every step because nothing else in the system can
I broke down the 7 levels from babysitting agents to running a fleet ↓ https://t.co/4RacdXA4nE
@f0rgent@VitalikButerin small correction on the Explore one: on a subscription it runs on Opus even if your main model is Sonnet. override with model: haiku in .claude/agents/explore.md
NVIDIA just open-sourced an AI agent tool with one deliberate limitation:
it won't let the agent look at your raw tables
sounds dumb. it's the whole point
ask an agent "what was revenue last quarter?" and it answers in 3 seconds. clean number, nice chart
but your database has revenue_gross, revenue_net, rev_booked_v2 and revenue_recognized
the agent picked one. nobody checks which
Auto Ontology sits between the agent and the database and forces it through business definitions instead:
> reads your metadata, drafts a map of terms like "revenue" or "active customer"
> checks generated SQL against the certified definition
> returns the answer, the SQL and the rows, so you can verify it
> 9 MCP tools, all read-only. no tool to browse raw tables, on purpose
works with Postgres, Snowflake, Databricks, DuckDB. Apache 2.0
what I'd watch before using it:
> no accuracy scores published yet
> db passwords sit in plaintext in Postgres unless you set up Vault
> ~575 stars, no outside contributions accepted
the hard part of AI analytics was never writing SQL. it's agreeing on what the words mean
links below ↓
same prompt. two AIs. completely different personalities
I asked Gemini and Claude to recreate the Pixar intro
Gemini made the lamp
Claude said Pixar is copyrighted and made a toaster
honestly? the toaster has more character
@Klonzu the underrated line here is "point every panel at the timeline, never at its own animation"
that's just single source of truth. same reason most IA-built dashboards lie: every widgets animated on its own clock
THIS IS F*CKING INSANE
Claude Code's head of product on stage at Code w/ Claude:
> one agent across terminal, IDE and desktop
> Code Review that every team at Anthropic uses
> remote agents + CI that fixes itself
> execs are writing code again
meanwhile Anthropic says 80%+ of the code they merge is now written by Claude
10 min. worth it. bookmark ↓