8s
If you are struggling financially in life
There's a higher chance you have been thinking small, playing it safe
THINK BIG
You are designed for the LARGER
π 12 waitlists you can join right now
Takes only 5 minutes
Check the listπ
π· The Sandbox Studio ( by @TheSandboxGame )
Sandbox Studio is the AI game engine built for the next generation of creators
Raised: $120.95M
π https://t.co/Goej6RwrLQ
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π· Barter ( @BarterDeFi )
Barter is a DeFi infrastructure firm dedicated to improving liquidity and capital efficiency across blockchain networks
Raised: $3M from Maven 11 Capital
π https://t.co/dzWiDJftP6
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π· ShopAgentic ( @ShopAgentic )
ShopAgentic is an AI-powered shopping system where different agents handle different tasks
Raised: $2.19M from Greenfield Capital and others
π https://t.co/KK4oeueWBR
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π· Flip ( @fliptexts )
Flip is an AI financial assistant that lives inside iMessage
Raised: $1.4M from a16z and others
π https://t.co/g4YN71sUOp
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π· Pear ( @tradeonpear )
Pear is a social trading platform that lets users trade different markets from one account
Supported by @solana and @0xPolygon
π https://t.co/6yvnXbCged
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π· Unimarkets ( @unimarketsdotio )
UniMarkets is the next-gen Prediction Markets Aggregator allowing users to access multiple prediction markets, execute trades and arbitrage just from a single Platform
π https://t.co/SCr1ED7LQQ
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π· MNX ( @MNX_fi )
MNX is a decentralized futures exchange for trading AI related assets and prediction markets
Raised $6.4M
π https://t.co/Wr4mfVdcIx
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π· HyperSignals ( @HyperSignals_ai )
HyperSignals is a social analytics platform on Hyperliquid for tracking top traders, generating AI-powered signals and copy trading perps
π https://t.co/i6Q2GB79J9
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π· Talise ( @taliseio )
Talise is a cross-border money app built on @SuiNetwork
π https://t.co/aA2MnB5QdU
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π· continuum ( @c8ntinuum )
c8ntinuum is a cross-chain solution that connects different blockchain ecosystems
π https://t.co/xYJKw1FyMV
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π· Horizon ( @horizon_trade_x )
Horizon is an AI platform for building, backtesting, and automating trading strategies in stocks and crypto with no coding required
π https://t.co/rjaj2bSKU4
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π· Astroverse: SOVA ( @astroversewtf )
Astroverse: SOVA is a Web3 game on Avalanche
π https://t.co/62HXXcsqXB
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π Don't miss any updates π
https://t.co/4c8ls8kFtw
The smartest quants at Jane Street aren't smarter than you.
They just know how to use the Bloomberg Terminal the way institutional desks use it to extract real signal, this 1 hour lecture teaches you exactly that from scratch. Bookmark now.
The most important strategy for catching the $BTC bottom.
One article. That's all I've ever created, and likely all I ever will.
It explains how I will be using mathematics and historical drawdowns to catch the bottom with leverage and trade alongside the hedge funds.
Study.
Iβve written a new Masterclass that will change the way you trade.
It includes 6 different strategies that:
- Improve your take profits
- Cut losers early
- Compound on winners
+ Several trade examples...
Iβve also included a cheatsheet
β
COMPLETE VOLUME PROFILE TUTORIAL
0:30 - foundations
0:55 - tick size
2:14 - profile components
2:42 - volume gaps
2:57 - high volume nodes
4:12 - visible range tool
4:57 - fixed range tool
5:38 - trending moves
6:53 - reading the profile
8:49 - ranges
9:20 - selecting a range
10:38 - reading a range
11:51 - line in the sand levels
12:43 - balance vs imbalance
13:39 - prior areas of balance
14:52 - high conviction plays
16:33 - other tools
A self-taught Quant just published the exact technique that separates real trading edge from data mining - permutation tests on backtested strategies in Python.
Quant Twitter quietly knows about him. Quants juniors send each other his videos in DMs.
Bookmark it tonight before the algorithm pushes him mainstream. Then read the article, I built the AI quant system that runs thousands of these tests per week.
The BEST 7 Trading Indicators
0:00 - RSI & SMA
1:17 - Trend Trader
2:36 - Smart Divergences
3:52 - RSI & Envelope Band
4:54 - Buy & Sell Signals
5:49 - Trailing Stop Loss
6:47 - Liquidity
Most quant desks run a 12-step methodology before placing a single position
Every single step is in the article below with the exact code.
Pass it to your AI coding agent and you have a working system by tonight
Most people spend years before reaching step 3
The full breakdown is below
Stop wasting hours trying to learn AI.
I have already done it for you.
With one list. Zero confusion. And no fluff.
πΉ Videos:
1. LLM Introduction: https://t.co/sfqLeUwf3W
2. LLMs from Scratch: https://t.co/GbnKbfvhcg
3. Agentic AI Overview (Stanford): https://t.co/EnqB4YMpeY
4. Building and Evaluating Agents: https://t.co/vp8RCDEoZP
5. Building Effective Agents: https://t.co/mngwlvMHna
6. Building Agents with MCP: https://t.co/TVk18pOf6Z
7. Building an Agent from Scratch: https://t.co/bfnRYfrFjd
8. Philo Agents: https://t.co/SQcGLseeM1
ποΈ Repos
1. GenAI Agents: https://t.co/cXJNVqPZqv
2. Microsoft's AI Agents for Beginners: https://t.co/WHiolowRZi
3. Prompt Engineering Guide: https://t.co/rVMK9vZfBJ
4. Hands-On Large Language Models: https://t.co/zpmaATDtdr
5. AI Agents for Beginners: https://t.co/WHiolowRZi
6. GenAI Agents: https://t.co/s9uA1N24PV
7. Made with ML: https://t.co/AKffs9HkUz
8. Hands-On AI Engineering: https://t.co/h9OVhJ3tWn
9. Awesome Generative AI Guide: https://t.co/lV1YMGL52R
10. Designing Machine Learning Systems: https://t.co/IUXQzlY97i
11. Machine Learning for Beginners from Microsoft: https://t.co/KrSHxdZMju
12. LLM Course: https://t.co/6U4Vww6Uyk
πΊοΈ Guides
1. Google's Agent Whitepaper: https://t.co/5Wpf7xvQqz
2. Google's Agent Companion: https://t.co/bVmjIK8Xam
3. Building Effective Agents by Anthropic: https://t.co/7SsNu6xr6Y
4. Claude Code Best Agentic Coding practices: https://t.co/X22UJOHlbC
5. OpenAI's Practical Guide to Building Agents: https://t.co/Bn5SYDT9KR
π Books:
1. Understanding Deep Learning: https://t.co/csAFkaw3Qp
2. Building an LLM from Scratch: https://t.co/72W4q5QV4z
3. The LLM Engineering Handbook: https://t.co/WgHM7dn8xq
4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/2vXzCQXEqg
5. Building Applications with AI Agents - Michael Albada: https://t.co/MQAwMPbzQZ
6. AI Agents with MCP - Kyle Stratis: https://t.co/CcaNk01utK
7. AI Engineering: https://t.co/GD45IogK63
π Papers
1. ReAct: https://t.co/Nk77rLspmX
2. Generative Agents: https://t.co/CJEokZcGSw
3. Toolformer: https://t.co/GVKiIt2pj3
4. Chain-of-Thought Prompting: https://t.co/YyoEidCGMi
π§π« Courses:
1. HuggingFace's Agent Course: https://t.co/288ifz8r9R
2. MCP with Anthropic: https://t.co/F07zf0lfXi
3. Building Vector Databases with Pinecone: https://t.co/6MFjlpTHab
4. Vector Databases from Embeddings to Apps: https://t.co/ngGDY3Rc7r
5. Agent Memory: https://t.co/BnlgGadL7o
Follow @iansh04_ for more!!
π Comment βAIβ for more resources
Repost for your network β»οΈ
Bookmark for future.
BLUEPRINT: How to build a fully automated solo-business with AI agents (step-by-step)
this guy runs 40+ businesses with zero employees using Claude Code and AI agents
I've been building the same way. here's the exact playbook:
Step 1: pick one painful problem people will pay to solve
don't start with the tool. start with the pain
find a problem β validate that people are already paying for bad solutions β build yours with AI
Step 2: build the product with Claude Code
no team. no freelancers. just you and the terminal
- scaffold the entire app with one detailed task doc
- let Claude handle frontend, backend, database, auth
- ship the MVP in days, not months
Step 3: deploy your agent army
this is where it gets crazy. build agents for every function:
support agent β handles tickets, escalates bugs to dev agent
dev agent β reads the issue, fixes the code, merges the PR while you sleep
marketing agent β manages ads, creates creatives, runs A/B tests, sets budgets
sales agent β handles inbound, qualifies leads, follows up automatically
each agent is a full-time employee that costs $0/mo in salary
Step 4: connect everything to one dashboard
use Harbor, OpenClaw, or build your own Streamlit command center
one place to see:
- all agents and their status
- support tickets being handled
- bugs being fixed
- ads being optimized
- revenue coming in
you run the business from your phone
Step 5: let the loop compound
the agents learn. the system improves. you wake up to:
- bugs fixed and PRs merged before breakfast
- customers replied to in seconds
- ad spend optimized overnight
- revenue growing while you were sleeping
the honest truth: 50% of your time will be debugging. 30% improving the setup. only 20% is pure output
but that 20% is so powerful it replaces an entire team
we're 3-6 months from AI running basic businesses end-to-end
the founders who start building this way NOW will own markets before everyone else figures it out
if this hits 500+ Likes β€οΈ I'll drop a full article breaking down how to build each agent with real examples and prompts you can copy
full breakdown in the VIDEO β