Balu

Web3 Enthusiast - Memecoin Hunter - On-Chain Alpha | Early Gems 💎

[Himanshu Kumar @codewithimanshu]

A 33-year-old developer turned $1,000 into $946,207 trading BTC with a stolen weather forecasting trick.

No trading background. No quant team. Just a method meteorologists use every single day that traders completely ignore.

The method: weather models never predict storms with one forecast. They run 31 simulations and take the consensus. He copied that exact framework for Bitcoin.

Built a Claude agent that monitors every 5-minute BTC candle and feeds it into MiroFish simulator running 31 parallel prediction paths.

Trade only fires when 28 out of 31 models converge.

Below 26 votes? Trade dies instantly.

The agent moves faster than any human trading desk:

→ Collects market data 24/7 without breaks
→ Runs continuous simulations inside MiroFish engine
→ Operates fully autonomous with zero manual input
→ Every trade executes only when consensus hits threshold
→ Every dollar captured is pure market inefficiency exploit

That is the entire edge.
Not prediction. Consensus.

Position sizing follows Kelly criterion. Signal fires or it does not. Most signals fail the vote count, so the system stays flat most days.

Took him years to accept certainty does not exist and agreement is the only edge that matters.

You only need Claude + device + 1 hour per day.

Giving this free for 24 hours.

To get it:

1. Comment the word "Claude"
2. Like and retweet this
3. Follow me @codewithimanshu so I can DM you

Save this post. Build the consensus system this week. Start with $200. Scale on evidence.

[Tommi Pedruzzi @TommiPedruzzi]

R.I.P Real Estate.

A 90-page AI ebook can make you more money than a $200,000 property.

They’re not as sexy as owning a home.

But if you start today, you can hit $3,000–$5,000/month by the end of July.

I usually charge $199 for my exact system but today, it’s free.

Like this post + comment 'Start' and I’ll DM you the entire strategy for free.

(Must be following, or I can’t message.)

⏳ Taking this down in 24 hours.

[Khairallah AL-Awady @eng_khairallah1]

Anthropic pays $750,000+ a year for engineers who can build LLMs from scratch.

Not how to prompt them.
Not how to fine-tune them.
Not how to build RAG pipelines.

But how to build them from scratch.

This 2-hour Stanford lecture teaches you everything.

Scaling laws.
Data collection.
Architecture design.
Post-training alignment.

Free. From Stanford.

Watch first. Then read this.

The lecture is the theory.

full guide in the article below

[Harris @HarrisDecodes]

Delete almost every prompt you've ever saved.

I did it.

For years, I collected prompts like they were assets.

→ Writing prompts.
→ Research prompts.
→ Marketing prompts.
→ Coding prompts.

Eventually I had hundreds & thousands of them.

And one day I realized something:

I was spending more time searching for prompts than actually using AI.

So I built a giant master prompt instead.

That worked...

Until every new chat forced me to explain myself all over again.

→ My style
→ My preferences
→ My workflow
→ My rules

The AI forgot everything.

I kept repeating myself.

Again. And again. And again.

That's when I stopped optimizing prompts.

And started optimizing memory.

Today I maintain just 3 markdown files.

And they've completely replaced my prompt library.

1️⃣

Don't write the file yourself.
Let Claude interview you.
Open a fresh Claude Cowork session and paste:

"You are building my about-me.md file.

Interview me with 20 questions, one at a time using AskUserQuestion.

Push back on vague answers.

Compile everything into a clean about-me.md under 2000 words."

Spend 20 minutes answering honestly.

The magic isn't the output.

It's the questions.

Claude will uncover patterns about how you think that you've never properly documented before.

By the end, you'll have a surprisingly accurate personal operating manual.

2️⃣

Keep it short.

My first version was over 20,000 words.

Terrible idea.

→ More tokens
→ More noise
→ Slower outputs

I cut it to under 2,000 words.

The quality improved immediately.

✔️ Smaller
✔️ Sharper
✔️ More useful

3️⃣

Create three files.

1. about-me.md

→ Who you are
→ How you think
→ Goals
→ Preferences

2. my-voice.md
→ Writing style
→ Favorite phrases
→ Phrases you avoid
→ Real writing samples
3. my-rules.md

→ Show a plan first
→ Ask before executing
→ Never delete without approval
→ Follow my workflow

Three files.

That's it.

4️⃣

Put them inside a single folder.

Claude Cowork
└── About Me

Then tell Claude:

"I'm Harris. Always read the files in my About Me folder before every task. Use my voice and follow my rules."

✔️ Done.

Now every task starts with context.

My prompts became dramatically shorter.

Sometimes one sentence. Sometimes one line.
But the outputs became:
• More accurate
• More consistent
• More useful
• More me

The biggest lesson?

People don't need more prompts.
They need a system that helps AI remember who they are.

Three markdown files solved a problem that thousands of saved prompts never could.

[Atrahasis @atrahasisdev]

Atrahasis API Client and Development Platform
https://t.co/uclOs5ZBmZ

[Khusboo Tayal @KhusbooT14835]

The engineer who built Claude Code just dropped a 28-minute video on how to write prompts that actually work

I've seen $300 courses that don't cover what he shows in the first 10 minutes

CLAUDE.md files, memory shortcuts, parallel sessions, prompting patterns

all in one video and completely free works whether you're a developer, a beginner, or someone who's been using Claude for months.

[Ridark @ridark_eth]

https://t.co/DemragJVoX

[Codez @0xCodez]

https://t.co/nkojcYI4rS

[MoonPay 🟣 @moonpay]

sending $100 in Bitcoin to 5 people that like this post

[John @johnvirality]

claude fable 5 just made it possible to post 100 AI UGC videos per day across 4 platforms in JUST 30 minutes of production

everything else runs 100% autonomously

clippers might be cooked 😭

this mythos model watches raw video footage and finds viral moments transcripts would miss

it scrolls tiktok while you sleep and builds trend reports, generating 100 production packages and renders them through higgsfield MCP without you touching another tab.

so i documented the ENTIRE machine with every prompt, every setup instruction, and every workflow step

here's what's inside:

→ the overnight market research system that runs while you sleep

(cowork scrolls 5 platforms, analyzes 60-80 pieces of content each, returns a trend intelligence report with 10 specific content ideas by the time you wake up)

→ raw-pixel clip identification that finds moments human clippers miss

(facial expression shifts, product reveals, body language peaks, visual incongruities. all timestamped and ranked by predicted virality.)

→ batch script and asset generation: 20 complete production packages per prompt, run 5x for 100 total

(6-shot scripts, character prompts, product frame prompts, voice direction, platform captions. 15-25 minutes total.)

→ the higgsfield MCP pipeline that renders all 100 videos automatically

(fable 5 calls seedance 2.0 directly. character reference locked. lip sync aligned. zero human involvement.)

→ the virality predictor that filters your top 20 candidates before posting

(hook score, hold rate, brain region activation. bottom 5 get diagnosed and revised automatically.)

→ the CPM math: 400 platform-posts/day × 3,000 avg views = 36M views/mo = $180k/mo at $5 CPM

→ all 6 copy-paste prompts that run the entire machine

all from my personal experience in looking behind the scenes on how Rizz App + Looksmax AI + Memix scaled past 7-figures with this method

like + comment "100" and i'll send you the ENTIRE system

(must be following + RT for priority access)

will stop sending these out in 24h...

[Daniro @Dan1ro0]

This trader used Claude to build a Quant Bot and made +$123,803 on Polymarket

Since Mar 26, this wallet has been averaging about $1,608 in profit per day while doing almost 17 trades per hour

31,209 predictions with a 56% win rate in 77 days

This guy started with about $19.7K in deposits and is now up roughly +629.5% ROI

This trader’s Polymarket account:https://t.co/TxO4fvoqFX

The bot strategy is simple:

>It keeps scanning for prices that are still behind the move

>Gets in before the market fully catches up
then runs the same setup again and again across a huge number of entries

>The edge on each trade is small, but repetition is what turns it into a large result

Most profitable trades:
$883 → $1,833 (+$949, +107.47%)
$638 → $1,405 (+$766, +120.12%)
$482 → $1,193 (+$711, +147.56%)

It is a repeatable short-window process that keeps exploiting delayed pricing until the gap closes

[CyrilXBT @cyrilXBT]

Left column: what you do manually and how long it takes.

Right column: what the system handles automatically.

Morning brief: 45 min manual vs 0 min automated.

Weekly review: 2 hours manual vs 0 min automated.

Meeting processing: 30 min manual vs 0 min automated.

Total: X hours saved per week.

Numbers stop scrolls. This one has strong share potential.

[Elvis @elvissun]

https://t.co/q0JG6Tir16

[Balu @shdx779]

fucxing scamers out there

[Punisher @0x_Punisher]

🚨 STUDY POLYMARKET ARBITRAGE

This wallet made $20,000+ profit in just 2 months.

While biggest single win is only $1,535.

Read that again.

Because the gap between those two numbers is the whole lesson.

If your biggest win is $1,500 but your total profit is twenty grand, you didn't get lucky once.

You won small, over and over and over.

That is a pair-sum arb machine running on the simplest concept.

Enter, resolve, exit, repeat.
Almost 1,900 times.

The PnL chart tells the rest of the story.

Flat for months, then a clean takeoff.

This guy was grinding and building until the system worked, then letting it run.

His profile: [https://t.co/yP4A1Srvbn]

This is what small-edge trading actually looks like on Polymarket.

Just a tiny repeatable edge captured thousands of times until the law of large numbers does the heavy lifting.

This is exactly the shape of arbitrage and sweeper strategies.

Buy both sides when they misprice below a dollar.

Sit in the queue and collect the last fraction of a cent before resolution.

Each trade barely moves the needle.
Stacked at volume, they print.

Most people chase the one big win.

They want the 50x longshot, the perfect call and a nice screenshot.

The wallets that actually compound do the opposite.

They find an edge worth a few cents and run it until it becomes twenty thousand dollars.

The dev behind this wallet published a full public guide on how the infrastructure works.

Leaving it quoted below so you can understand the logic.

Good luck.

[Rahul @sairahul1]

Anthropic and OpenAI are both telling engineers to write loops.

Not prompts.
Not agents.
Loops.

That is not a coincidence.

When the two most important AI labs on the planet independently converge on the same pattern — that is a signal worth paying attention to.

Most engineers are still thinking in terms of single calls.

Input → model → output.

The engineers winning in 2026 think in cycles.

Output becomes input. The model evaluates its own work. The loop runs until the result is right.

This is the complete breakdown of what loops are, why they matter, and how to build them ↓

[Harman @itsharmanjot]

A TEAM OF AI RESEARCHERS JUST OPEN-SOURCED THE BLOOMBERG TERMINAL FOR QUANT FINANCE.

A Bloomberg Terminal costs $25,000 per year per seat. Banks pay for thousands of them.

This thing reads every quant paper, every financial blog, every SEC filing, every arXiv preprint, and turns it into a searchable knowledge base. For free.

It's called QuantMind.

It just got accepted to the NeurIPS 2025 GenAI in Finance Workshop.

Here's what it actually does:

→ Ingests arXiv quant papers, financial news, blogs, and reports automatically
→ Parses PDFs, HTML, tables, and figures into structured knowledge
→ Tags every paper by research area and topic
→ Builds a semantic knowledge graph you can query in plain English
→ Plugs into DeepResearch, RAG, and MCP for multi-hop reasoning
→ Two-stage architecture: extract once, retrieve forever

Here's the wildest part:

The financial research industry publishes around 500 new papers and reports every single day.

Hedge funds pay six-figure salaries to junior analysts whose entire job is reading them.

QuantMind reads all of it. Tags it. Embeds it. Lets you ask it questions.

154 stars. 22 forks. 173 commits. MIT license. Python.

One honest note: this is a framework, not a magic alpha machine. You still need to know what to ask. But the "I haven't read that paper yet" excuse is officially dead.

The thing Wall Street charges $25,000 a year for is sitting on GitHub. Free.

Link in the comments.

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

Holy shit, brothers, there are really a ton of ridiculously free projects on GitHub.

Many of them can straight-up replace the software you're paying monthly for.

1. TradingAgents
AI multi-agent quantitative trading framework
https://t.co/xunNFf8ZxT

2. LibreChat
An interface that connects to multiple models like ChatGPT, Claude, Gemini, etc.
https://t.co/MJbeSPkVA1

3. HyperFrames
HeyGen's open-source video generation engine
https://t.co/BZX6g7tLga

4. Fincept Terminal
Open-source version of Bloomberg Terminal
https://t.co/CZvl5bAd63

5. MoneyPrinterTurbo
AI one-click short video generator
https://t.co/7Bgikd2DsQ

6. Agentic Inbox
Cloudflare's open-source AI email assistant
https://t.co/LLRoslkkCL

7. VoxCPM
AI voice cloning tool
https://t.co/UzExPt8eFd

8. Flowsint
Open-source OSINT intelligence analysis tool
https://t.co/kBlOzgRKAs

9. agent-skills
Claude Code skills library
https://t.co/Pxwwo42xnN

10. Nango
Open-source API integration platform
https://t.co/hQNCgSJtlg

Brothers, these really aren't toy projects.

A lot of the software you're still paying monthly fees for already has open-source alternatives made by someone on GitHub.

One sentence:

Stop just bookmarking AI tool websites.

The really killer stuff is mostly hidden on GitHub.