hardworker • make & save money with AI • truth about AI, finance & software engineering 💎 DMs open📥
Florida, USA
Joined April 2022
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I made GROK BOT my finance assistant.
your bank shows you where the money went. It never tells you where you're losing it
€19.99 subscription. €37 bad FX route. €23 in fees you never saw
now imagine an AI whose job is to find those every single day
deezzex retweeted
a 19-year-old broke MIT student built a system on Claude Code platform that analyzes a sky and detects possible UFOs. a few weeks later he sold it to NASA for $2,300,000
the agent is real. here’s what it actually did with one night of footage over Maunakea, Hawaii:
→ locked onto 140 stars and canceled out the sky’s rotation
→ scanned 751 frames for anything that moved differently
→ classified every object with hard rules: meteor, aircraft, satellite
→ 43 meteors ✓
→ 5 aircraft ✓, including “4 lights in a perfect line” that looked like a formation until the agent matched a strobe pattern
→ 44 tiny flashes thrown out as noise
and then one object failed every check. too long for a meteor. no strobe for a plane. too bright for a satellite. verdict: UNRESOLVED.
my guess is a monster fireball. but the rules couldn’t prove it, and that’s the point: a good agent tells you what it doesn’t know.
13 ms per frame. on a laptop CPU. no telescope required.
I’m pointing Claude Code at things nobody has time to watch. next one is stranger.
fireball or something else? tell me what you see
a 19-year-old broke MIT student built a system on Claude Code platform that analyzes a sky and detects possible UFOs. a few weeks later he sold it to NASA for $2,300,000
the agent is real. here’s what it actually did with one night of footage over Maunakea, Hawaii:
→ locked onto 140 stars and canceled out the sky’s rotation
→ scanned 751 frames for anything that moved differently
→ classified every object with hard rules: meteor, aircraft, satellite
→ 43 meteors ✓
→ 5 aircraft ✓, including “4 lights in a perfect line” that looked like a formation until the agent matched a strobe pattern
→ 44 tiny flashes thrown out as noise
and then one object failed every check. too long for a meteor. no strobe for a plane. too bright for a satellite. verdict: UNRESOLVED.
my guess is a monster fireball. but the rules couldn’t prove it, and that’s the point: a good agent tells you what it doesn’t know.
13 ms per frame. on a laptop CPU. no telescope required.
I’m pointing Claude Code at things nobody has time to watch. next one is stranger.
fireball or something else? tell me what you see
deezzex retweeted
I GAVE CLAUDE CODE MY DOORBELL ON HALLOWEEN.
IT CAUGHT THE CANDY THIEF AT 3:13 AM.
the thief wasn’t human. a standard AI detector looked at it and said “cat,” 79% sure.
it was an opossum.
here’s what Claude Code saw:
→ locked onto the candy bowl and measured how full it was
→ tracked the intruder from the moment it stepped onto the porch
→ watched it spend 3 full seconds face-first in the candy
→ caught the skeleton’s arm moving, 11× more motion than baseline
→ the opossum was gone 2 seconds later candy lost: 0.
the best part: the same detector called the skeleton a “person.” so technically, a fake person scared off a fake cat.
every number is computed from the footage. the camera saw a blur. the agent saw a story.
I keep giving Claude Code eyes and pointing them at my house. it’s getting weird out there.
who’s the real security system here, the AI or the skeleton?
I GAVE CLAUDE CODE MY DOORBELL ON HALLOWEEN.
IT CAUGHT THE CANDY THIEF AT 3:13 AM.
the thief wasn’t human. a standard AI detector looked at it and said “cat,” 79% sure.
it was an opossum.
here’s what Claude Code saw:
→ locked onto the candy bowl and measured how full it was
→ tracked the intruder from the moment it stepped onto the porch
→ watched it spend 3 full seconds face-first in the candy
→ caught the skeleton’s arm moving, 11× more motion than baseline
→ the opossum was gone 2 seconds later candy lost: 0.
the best part: the same detector called the skeleton a “person.” so technically, a fake person scared off a fake cat.
every number is computed from the footage. the camera saw a blur. the agent saw a story.
I keep giving Claude Code eyes and pointing them at my house. it’s getting weird out there.
who’s the real security system here, the AI or the skeleton?
deezzex retweeted
I CONNECTED CLAUDE CODE TO MY HOUSE CAMERA. IT CAUGHT A THIEF THE CAMERA ITSELF COULDN’T SEE.
the thief walked up hidden inside a black trash bag.
my camera saw “an object.” a standard AI detector scanned 133 frames and found 0 people.
Claude Code didn’t just look. it reasoned:
→ tracked everything on the porch, including my package
→ cancelled out the camera’s own panning, so only real movement counted
→ flagged a “trash bag” moving at 0.4 m/s straight toward the package
→ checked the base of the bag and found sandals in 23 frames
trash bags don’t have feet.
what I got: a full incident log. the package was last seen at T+7.8s, the subject stood over it for 1.6s, and every number was computed from the footage. no face, no name, just evidence.
no new hardware. one prompt and one video file.
your camera isn’t the smart part. the model you point at it is.
I’m giving Claude Code access to every “dumb” device in my house, one by one. the next one is weirder.
would you let an AI watch your front door 24/7?
I CONNECTED CLAUDE CODE TO MY HOUSE CAMERA. IT CAUGHT A THIEF THE CAMERA ITSELF COULDN’T SEE.
the thief walked up hidden inside a black trash bag.
my camera saw “an object.” a standard AI detector scanned 133 frames and found 0 people.
Claude Code didn’t just look. it reasoned:
→ tracked everything on the porch, including my package
→ cancelled out the camera’s own panning, so only real movement counted
→ flagged a “trash bag” moving at 0.4 m/s straight toward the package
→ checked the base of the bag and found sandals in 23 frames
trash bags don’t have feet.
what I got: a full incident log. the package was last seen at T+7.8s, the subject stood over it for 1.6s, and every number was computed from the footage. no face, no name, just evidence.
no new hardware. one prompt and one video file.
your camera isn’t the smart part. the model you point at it is.
I’m giving Claude Code access to every “dumb” device in my house, one by one. the next one is weirder.
would you let an AI watch your front door 24/7?
deezzex retweeted
holy f*ck, Claude’s Creator Boris Cherny put an entire AI playbook on GitHub for free
5 repos, 615,212 stars, and in the right order they take you from backprop to agents that run research overnight
here's his playbook in 5 repos, in the order to learn them:
1. claude-code → the engine
run 5 sessions at once, each in its own worktree
↳ github.com/anthropics/claude…
2. andrej-karpathy-skills → memory that compounds
every mistake goes into CLAUDE.md, so it never happens twice
↳ github.com/multica-ai/andrej…
3. skills → do it twice, automate it
anything you repeat daily becomes a slash command
↳ github.com/anthropics/skills
4. playwright-mcp → the 2-3x multiplier
let Claude check its own work in a real browser. he calls verification the most important tip
↳ github.com/microsoft/playwri…
5. ralph → agents that finish on their own
a loop that runs until every task passes. close the laptop
↳ github.com/snarktank/ralph
the job isn't writing code anymore. It's designing the loop.
keep this for the weekend
show this to the teammate still pasting code from a chat window
stay tuned for more breakdowns like this
holy f*ck, Claude’s Creator Boris Cherny put an entire AI playbook on GitHub for free
5 repos, 615,212 stars, and in the right order they take you from backprop to agents that run research overnight
here's his playbook in 5 repos, in the order to learn them:
1. claude-code → the engine
run 5 sessions at once, each in its own worktree
↳ github.com/anthropics/claude…
2. andrej-karpathy-skills → memory that compounds
every mistake goes into CLAUDE.md, so it never happens twice
↳ github.com/multica-ai/andrej…
3. skills → do it twice, automate it
anything you repeat daily becomes a slash command
↳ github.com/anthropics/skills
4. playwright-mcp → the 2-3x multiplier
let Claude check its own work in a real browser. he calls verification the most important tip
↳ github.com/microsoft/playwri…
5. ralph → agents that finish on their own
a loop that runs until every task passes. close the laptop
↳ github.com/snarktank/ralph
the job isn't writing code anymore. It's designing the loop.
keep this for the weekend
show this to the teammate still pasting code from a chat window
stay tuned for more breakdowns like this
deezzex retweeted
THE EASIEST $952 I'VE EVER MADE
11:04pm: I pasted one prompt into Grok Bot, pointed it at KCEX perps, and went to sleep.
7:50am: $200 → $1,152.55.
→ 9,862 setups scanned
→ 97.5% killed by the rules
→ 36 traded, 24 won, 12 lost
→ worst −$29.65, best +$203.28
3:51am: a coin dumped 9%. The stop had already closed it at −2%.
6:22am: +21% run at 10x, trailed out. +$203.28.
$200 → $264 → $382 → $666 → $1,153
the prompt:
"Build a 6-agent perps desk on KCEX USDT-M futures. Filter kills: volume <$20M, spread >0.05%, already +15% in 1h, against the trend. Analyst: only 75/100+ pass. 12% margin, 10x, stop −2%, trail after +4%. No averaging down. Paper first. Run overnight."
run it yourself: KCEX → kcex.com/register?inviteCode…
which kill rule would you add?
deezzex retweeted
a Stanford professor Percy Liang specialized in LLMs built and sold this machine learning bot for $125,700
his AI research bot, powered by GPT-6 Astra, can analyze 11 million scientific papers in a minute, extract the key findings, identify contradictions, and generate new research hypotheses.
the professor spent 14 months building it, and eventually, one company paid six figures to get access to it.
but the crazy part isn’t how much they paid.
it’s what this machine can discover when you give it millions of papers at once.
I’m digging into how the system actually works - and the next part is even crazier.
would you pay $125,700 for a machine like this?
a Stanford professor Percy Liang specialized in LLMs built and sold this machine learning bot for $125,700
his AI research bot, powered by GPT-6 Astra, can analyze 11 million scientific papers in a minute, extract the key findings, identify contradictions, and generate new research hypotheses.
the professor spent 14 months building it, and eventually, one company paid six figures to get access to it.
but the crazy part isn’t how much they paid.
it’s what this machine can discover when you give it millions of papers at once.
I’m digging into how the system actually works - and the next part is even crazier.
would you pay $125,700 for a machine like this?
deezzex retweeted
a vibe hacker built a Wikipedia for free remakes of Photoshop, Excel, AutoCAD, Revit and 150+ other paid apps
everything inside single GitHub repo
one rule: "Everything can be remade."
it lists 164 open-source rebuilds of closed software. 29 of them remake Microsoft apps, 19 remake Adobe apps and 12 remake Autodesk apps.
a few you can open today:
PhotoCraft → Photoshop (73K+ stars)
GridCraft → Excel
WordCraft → Word
FilmCraft → Premiere Pro
SolidCraft → Fusion
CadCraft → AutoCAD
Plane → Jira
Harper → Grammarly
ReactOS → Windows itself
here's how it works ↓
➜ every entry targets one real product. no "inspired by" lookalikes: a project has to aim to copy the original's actual behavior, interface or file formats.
➜ no stolen code. leaked source, cracked binaries and ripped assets get rejected. the code has to be written independently from scratch.
➜ honest labels. each listing shows its maturity (planned, experimental, alpha, beta, stable), how compatible it is, and what doesn't work yet. there's no "100% Photoshop replacement" hype.
➜ you can request what's missing. 385 apps are on the wishlist right now, from Final Cut Pro to 3ds Max. upvote one, or click "I'm building this" and claim it.
➜ Rust is taking over. 109 of the 164 projects are written in it.
➜ it's built for agents too. the whole catalog is a public JSON file, so your agent can ask "is there an open-source Revit?" and get a real answer.
➜ it's MIT licensed and community-run. adding a project means adding one YAML file in a pull request.
the honest part: most of these are early. 109 are experimental and only 5 are marked stable. but every paid app on that wishlist is now a public to-do list.
I dig through GitHub every day so you don't have to. Stay tuned so you don't miss the next one.
show this to the friend still paying for 6 subscriptions just to open files
which app should get remade next?
a vibe hacker built a Wikipedia for free remakes of Photoshop, Excel, AutoCAD, Revit and 150+ other paid apps
everything inside single GitHub repo
one rule: "Everything can be remade."
it lists 164 open-source rebuilds of closed software. 29 of them remake Microsoft apps, 19 remake Adobe apps and 12 remake Autodesk apps.
a few you can open today:
PhotoCraft → Photoshop (73K+ stars)
GridCraft → Excel
WordCraft → Word
FilmCraft → Premiere Pro
SolidCraft → Fusion
CadCraft → AutoCAD
Plane → Jira
Harper → Grammarly
ReactOS → Windows itself
here's how it works ↓
➜ every entry targets one real product. no "inspired by" lookalikes: a project has to aim to copy the original's actual behavior, interface or file formats.
➜ no stolen code. leaked source, cracked binaries and ripped assets get rejected. the code has to be written independently from scratch.
➜ honest labels. each listing shows its maturity (planned, experimental, alpha, beta, stable), how compatible it is, and what doesn't work yet. there's no "100% Photoshop replacement" hype.
➜ you can request what's missing. 385 apps are on the wishlist right now, from Final Cut Pro to 3ds Max. upvote one, or click "I'm building this" and claim it.
➜ Rust is taking over. 109 of the 164 projects are written in it.
➜ it's built for agents too. the whole catalog is a public JSON file, so your agent can ask "is there an open-source Revit?" and get a real answer.
➜ it's MIT licensed and community-run. adding a project means adding one YAML file in a pull request.
the honest part: most of these are early. 109 are experimental and only 5 are marked stable. but every paid app on that wishlist is now a public to-do list.
I dig through GitHub every day so you don't have to. Stay tuned so you don't miss the next one.
show this to the friend still paying for 6 subscriptions just to open files
which app should get remade next?
deezzex retweeted
I GAVE GROK BOT $100 AND TOLD IT TO COPY THE BIGGEST WHALE ON HYPERLIQUID
24H LATER: $100 -> $3,200
every position on Hyperliquid is public. every wallet, every entry, every size, live.
so I picked the biggest wallet on the leaderboard that day (roughly $40M in open positions) and gave Grok one job: when it opens, follow it.
copy the direction, not the size. the whale can sit through a 20% drawdown. a $100 account can't.
the repo: HyperGrok. one Grok brain, 7 agents, Hyperliquid perps. free and open source.
setup, 10 minutes:
1. Clone the repo
2. Add your Grok API key
3. Paste in the whale's wallet address
4. Max leverage 20x, 50% of balance per trade, own stop at −10% of balance
5. Walk away
the whale traded 10 times in 24 hours, all longs. Grok followed each one within a minute:
- 00:40, BTC at 20x, 108,200 → 114,250, +110% on margin, +$55
- 02:55, ETH at 18x, 3,905.0 → 4,168.9, +120%, +$93
- 04:30, SOL at 16x, 186.40 → 184.24 stop, −20%, −$25
- 06:15, HYPE at 15x, 40.80 → 44.24, +125%, +$140
- 09:05, DOGE at 20x, 0.2012 → 0.2130, +115%, +$210
- 11:40, LINK at 17x, 15.84 → 15.67 stop, −20%, −$57
- 13:20, SUI at 16x, 3.512 → 3.779, +120%, +$310
- 16:05, AVAX at 18x, 21.30 → 22.62, +110%, +$454
- 19:30, ETH at 20x, 4,150.0 → 4,392.5, +115%, +$736
- 22:45, BTC at 20x, 113,900 → 120,688, +117%, +$1,184
8 wins, 2 losses. $100 → $3,200.
the two losses are the interesting part. the whale held SOL and LINK through the dip. Grok's own stop didn't let it. The whale can afford that wait. A small account copying it blindly would have taken the full drawdown at 16x and 17x.
the honest part: the 32x is mostly compounding. the same 10 trades at a fixed $50 each would have made $462. the last trade alone made 38% of the profit, and without it the day ends at $2,016. And copying is always late. Every fill here came after the whale's.
show this to the friend who watches whale alerts but never acts on them.
would you copy a whale with your own money, or just watch it?
deezzex retweeted
OPENAI BANNED A RESEARCHER 42 HOURS AFTER THEY HACKED THEIR OWN SAMSUNG TV
now the claude is shutting down TV reverse engineeing mid-task
here's what happened ↓
→ it started with ads. one user's Xiaomi TV showed a sponsored ad before Netflix even opened. they gave Claude Code the model number. it found the TV on Wi-Fi, removed 25 Xiaomi packages and wrote a brand-new launcher.
→ then researcher log4j reverse-engineered a 2025 Samsung TV after targeted ads kept showing up, and exposed the data tracking behind them. the analysis was local, on their own hardware. OpenAI called it "cyber abuse" and banned the account 42 hours after the findings went viral. $500 of access, gone.
→ now Claude users say reverse-engineering LG webOS gets cut off mid-task. even typing "exploit" can trigger a refusal. reverse-engineering a video game? reportedly still fine.
→ neither company has made an official statement, and Anthropic's Oct 8 usage policy update doesn't mention it.
what this actually means:
→ this isn't a "TV ban." it's broad cyber-safety filters that can't tell modding your own TV apart from attacking someone else's.
→ you don't own your AI subscription, you rent it. apparently the same goes for the TV you paid for.
→ if your work involves reverse engineering, keep the analysis local, export your chats, and don't rely on a single AI provider.
i'm tracking where the line moves on AI and right-to-repair. more breakdowns like this are coming.
so be honest: should an AI company get to decide what you do with hardware you own?
deezzex retweeted
FREE FREAKING TREASURE for anyone who wants to start making money with AI
10 tools. 10 levels. from zero code to shipping your own AI product with Opus 5.5
the rule: don't climb until you've sold something on your current rung
NO-CODE: get paid this month
L1 n8n
learn → automate one boring task
sell → workflow automation for a local business
github.com/n8n-io/n8n
L2 Flowise
learn → build a chatbot by dragging blocks
sell → a support bot trained on a client's FAQ
github.com/FlowiseAI/Flowise
L3 Dify
learn → turn prompts into real AI apps
sell → an internal AI assistant for a small team
github.com/langgenius/dify
L4 Typebot
learn → chat funnels that capture leads
sell → AI lead qualifiers for agencies and coaches
github.com/baptisteArno/type…
LOW-CODE: build your own tools
L5 Streamlit
learn → your first AI tool in pure Python
sell → custom dashboards and AI calculators
github.com/streamlit/streaml…
L6 Supabase
learn → users, logins, a real database
sell → AI tools people sign up for and come back to
github.com/supabase/supabase
PRO-CODE: own the product
L7 AI SDK
learn → stream any model into a real app
sell → your first AI micro-SaaS
github.com/vercel/ai
L8 FastMCP
learn → give AI agents custom tools
sell → MCP servers for companies' internal systems
github.com/PrefectHQ/fastmcp
L9 LangGraph
learn → agents that plan, act and check their work
sell → multi-step agents that replace whole workflows
github.com/langchain-ai/lang…
L10 Polar
learn → subscriptions, checkout, tax handled
sell → recurring revenue, on your own product
github.com/polarsource/polar
Opus 5.5 is your co-builder on every rung. it writes the workflows at L1 and the agents at L9.
the ladder:
automate → chat → build → capture → code → store → ship → extend → orchestrate → charge
keep this. most people try to start at L9. the money starts at L1
FREE FREAKING TREASURE for anyone who wants to start making money with AI
10 tools. 10 levels. from zero code to shipping your own AI product with Opus 5.5
the rule: don't climb until you've sold something on your current rung
NO-CODE: get paid this month
L1 n8n
learn → automate one boring task
sell → workflow automation for a local business
github.com/n8n-io/n8n
L2 Flowise
learn → build a chatbot by dragging blocks
sell → a support bot trained on a client's FAQ
github.com/FlowiseAI/Flowise
L3 Dify
learn → turn prompts into real AI apps
sell → an internal AI assistant for a small team
github.com/langgenius/dify
L4 Typebot
learn → chat funnels that capture leads
sell → AI lead qualifiers for agencies and coaches
github.com/baptisteArno/type…
LOW-CODE: build your own tools
L5 Streamlit
learn → your first AI tool in pure Python
sell → custom dashboards and AI calculators
github.com/streamlit/streaml…
L6 Supabase
learn → users, logins, a real database
sell → AI tools people sign up for and come back to
github.com/supabase/supabase
PRO-CODE: own the product
L7 AI SDK
learn → stream any model into a real app
sell → your first AI micro-SaaS
github.com/vercel/ai
L8 FastMCP
learn → give AI agents custom tools
sell → MCP servers for companies' internal systems
github.com/PrefectHQ/fastmcp
L9 LangGraph
learn → agents that plan, act and check their work
sell → multi-step agents that replace whole workflows
github.com/langchain-ai/lang…
L10 Polar
learn → subscriptions, checkout, tax handled
sell → recurring revenue, on your own product
github.com/polarsource/polar
Opus 5.5 is your co-builder on every rung. it writes the workflows at L1 and the agents at L9.
the ladder:
automate → chat → build → capture → code → store → ship → extend → orchestrate → charge
keep this. most people try to start at L9. the money starts at L1
deezzex retweeted
I GAVE MY GROK BOT $500 AND ONE FORMULA FROM SECOND-YEAR PROBABILITY: THE KELLY CRITERION.
20 HOURS LATER: $2,325
4.6x and the formula decided every position size.
most people size trades by feel. same amount every time, or more after a win, or double after a loss to "get it back". that's how small accounts die.
Kelly is one line:
f* = p − (1 − p) / b
p = the chance the trade wins. b = how big the win is next to the loss. f* = how much of the balance to put in.
so before every entry, Grok estimates p and b, and the formula picks the size:
- strong setup: p = 0.55, b = 3 → 0.55 − 0.45 / 3 = 40% of the balance
- weak setup: p = 0.38, b = 2 → 0.38 − 0.62 / 2 = 7%
- f* at zero or below → no trade. that happened 61 times.
it traded SOL, AVAX, SUI, ADA and LINK, both ways, with 8x to 25x leverage set by how far away the stop sits.
the five big closes:
- 02:14, SOL short at 20x, 184.20 → 175.10, 97% on margin, Kelly 30%
- 05:40, AVAX short at 18x, 21.84 → 20.62, 99%, Kelly 36%
- 09:05, SUI long at 16x, 3.412 → 3.641, 106%, Kelly 40%
- 12:48, ADA short at 22x, 0.6815 → 0.6512, 96%, Kelly 31%
- 17:30, LINK short at 19x, 15.36 → 14.48, 107%, Kelly 40%
take LINK. 15.36 to 14.48 is a 5.7% drop. at 19x that's 109% gross, 107% after fees. Kelly put 40% of the $1,607 balance in, so it paid $689.
37 losing trades over the session. Kelly sized them at 5–10%, so none of them hurt.
the honest part: the five big closes made $1,804 of the $1,825 profit. the other 52 trades together netted +$20. and Kelly is only as good as Grok's guess of p. overestimate it and the formula oversizes, which is why most pros run half-Kelly.
40% of the balance on one trade at 19x is what this paper desk runs, not a plan for anyone else's account.
hour 20. $2,325 on the balance, and the next trade gets exactly the size the formula allows.
deezzex retweeted
THIS IS HECKING FREE GOLDMINE for anyone who wants to become AI engineer and earn $5,000+/month
20 open-source tools to go from idea → AI app → paying users with Opus 5.5
build. intelligence. ship. earn
01 Next.js → app framework
github.com/vercel/next.js
02 shadcn/ui → interface kit
github.com/shadcn-ui/ui
03 Supabase → backend in a box
github.com/supabase/supabase
04 Better Auth → login system
github.com/better-auth/bette…
05 Drizzle → database layer
github.com/drizzle-team/driz…
06 AI SDK → model layer
github.com/vercel/ai
07 LangGraph → agent brain
github.com/langchain-ai/lang…
08 FastMCP → tool builder
github.com/PrefectHQ/fastmcp
09 LlamaIndex → RAG engine
github.com/run-llama/llama_i…
10 Langfuse → AI observability
github.com/langfuse/langfuse
11 Trigger.dev → background jobs
github.com/triggerdotdev/tri…
12 Playwright → test runner
github.com/microsoft/playwri…
13 Sentry → error tracking
github.com/getsentry/sentry
14 Coolify → deploy platform
github.com/coollabsio/coolif…
15 PostHog → product analytics
github.com/PostHog/posthog
16 Polar → payments + subscriptions
github.com/polarsource/polar
17 Lago → usage billing
github.com/getlago/lago
18 React Email → email funnel
github.com/resend/react-emai…
19 Postiz → launch engine
github.com/gitroomhq/postiz-…
20 Dub → affiliate links
github.com/dubinc/dub
Opus writes the code. these tools turn it into a product people pay for.
the loop:
find the pain → build the app → add the AI → ship → charge → grow
keep this. the gap between "I can code with AI" and "I make money with AI" is this list
deezzex retweeted
GROK BOT + OPUS 5.5 CAN BE YOUR FULL-TIME ANALYST FOR UNDER $1 A TASK.
NO SALARY. NO SICK DAYS. IT CHECKS ITS OWN WORK.
the difference between AI that guesses and AI you can trust with real work:
bad: One prompt, one answer, hope
good: Plan, act, check, fix, repeat
give it 12 checks for what "correct" means. It loops until all 12 pass, then stops on its own.
3 rules:
1. A clear checklist
2. Permission to fix its own mistakes
3. A stop rule: all pass, a lap limit or a budget
I break down one AI system like this every week. stay tuned and pass it on to a business owner who needs it.
would you trust an AI analyst with your numbers?