Offshore
Moon Dev How To Build The Billion Dollar OpenClaw Scanner That Front Runs Wall Street while wall street dinosaurs are busy reading quarterly reports that are already three months old, there is a billion dollar gold mine hidden in plain sight on your phone…
ling to iterate to success. it is not about being a genius, it is about being a killer who is willing to test one hundred ideas just to find the one that actually works
the research phase is the most important part of algorithmic trading because ideas are the only thing that cannot be easily replicated. once your infrastructure is thick and your systems are running, your only job is to come up with better questions to ask the data
if you are twenty two years old and you think it is too late to start, you are already losing the war before it has even begun. i did not start learning to code until i was thirty, and now i have fully automated systems trading for me while most people are still stuck in the cycle of getting liquidated
the code i use is relatively low volume because the heavy lifting is done by the ai browser control. this allows the system to extract visible videos, check engagement metrics, and store everything in a database without me ever having to lift a finger
we are living in a time where the tools of the elite have been handed to the masses, but most people are too distracted to use them. i choose to share everything because i know that if i give value, it comes back ten thousand times over in the form of new ideas and better strategies
trading is war and every other trader is trying to take money from your pocket, so you better have a weapon that works while you are sleeping. the goal is to reach a level of automation where you are no longer the bottleneck in your own business, allowing the machines to do the grunt work while you focus on the vision
the secret to winning is not in finding a perfect bot that prints money forever, but in building a system that allows you to test your own theories at scale. when you have an ai that is ticktocking for you while everyone else is just scrolling, you have an unfair advantage that wall street cannot touch
this version of the social arbitrage bot is just the beginning, and the real alpha comes from how you choose to expand on the keywords and the scoring logic. code really is the great equalizer because it gives you the power to create your own world and build the life you want through pure logic and iteration
tweet
the research phase is the most important part of algorithmic trading because ideas are the only thing that cannot be easily replicated. once your infrastructure is thick and your systems are running, your only job is to come up with better questions to ask the data
if you are twenty two years old and you think it is too late to start, you are already losing the war before it has even begun. i did not start learning to code until i was thirty, and now i have fully automated systems trading for me while most people are still stuck in the cycle of getting liquidated
the code i use is relatively low volume because the heavy lifting is done by the ai browser control. this allows the system to extract visible videos, check engagement metrics, and store everything in a database without me ever having to lift a finger
we are living in a time where the tools of the elite have been handed to the masses, but most people are too distracted to use them. i choose to share everything because i know that if i give value, it comes back ten thousand times over in the form of new ideas and better strategies
trading is war and every other trader is trying to take money from your pocket, so you better have a weapon that works while you are sleeping. the goal is to reach a level of automation where you are no longer the bottleneck in your own business, allowing the machines to do the grunt work while you focus on the vision
the secret to winning is not in finding a perfect bot that prints money forever, but in building a system that allows you to test your own theories at scale. when you have an ai that is ticktocking for you while everyone else is just scrolling, you have an unfair advantage that wall street cannot touch
this version of the social arbitrage bot is just the beginning, and the real alpha comes from how you choose to expand on the keywords and the scoring logic. code really is the great equalizer because it gives you the power to create your own world and build the life you want through pure logic and iteration
tweet
Offshore
Video
Dimitry Nakhla | Babylon Capital®
The new stock that Bill Ackman added:
$META
Source: Pershing Square 2026 Annual Investor Presentation
https://t.co/k50JBqdLHD https://t.co/uNTjfQQIIf
tweet
The new stock that Bill Ackman added:
$META
Source: Pershing Square 2026 Annual Investor Presentation
https://t.co/k50JBqdLHD https://t.co/uNTjfQQIIf
Bill Ackman was on Fox Business this week saying “very high-quality businesses are showing up at very attractive levels”
He added that Pershing Square is approaching 15% cash & is “finishing due diligence on a company we’ve really wanted to own for years — now available at a bargain price”
Over the last several weeks, I’ve shared that many quality compounders are trading at the lower end of their 3-year valuation ranges and look attractive relative to their growth, durability, & moats
Before going any further I want to be clear: 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐩𝐨𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐰𝐡𝐢𝐜𝐡 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐁𝐢𝐥𝐥 𝐜𝐨𝐮𝐥𝐝 𝐛𝐞 𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐢𝐧𝐠 𝐢𝐬 𝐩𝐮𝐫𝐞𝐥𝐲 𝐬𝐩𝐞𝐜𝐮𝐥𝐚𝐭𝐢𝐯𝐞
I simply enjoy analyzing great investors and their frameworks, & @BillAckman has been one I’ve respected for years
Now lets guess 🤔
I believe the company is potentially Mastercard $MA & here’s why:
@KoyfinCharts recently shared Bill’s investment principles & $MA checks off every box
𝟏. 𝐊𝐞𝐲 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐜𝐡𝐚𝐫𝐚𝐜𝐭𝐞𝐫𝐢𝐬𝐭𝐢𝐜𝐬
✅Simple predictable FCF generative business
• $MA runs a toll-road-like payments network along with value added services & solutions & maintains >50% FCF margins
✅Formiddable barriers to entry
• $MA operates in a duopoly — a new competitor would need global merchant onboarding, bank integrations, regulators’ approval, & brand trust, among other things
✅Limited exposure to extrinsic factors that we cannot control
• $MA revenue is very stable especially over long periods & the company does not lend money, so it has no direct credit or balance-sheet risk
✅Generally low financial leverage levels
• $MA uses modest conservative leverage with strong interest-coverage ratios & stable cash generation
✅Minimal capital markets dependency
• Given its predictable recurring-like FCF, $MA is a self-funded business
✅Typically highly liquid mid & large cap companies
• $MA has a $488B market cap
𝟐. 𝐀𝐭𝐭𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧
✅Fair price as is but a substantial discount to optimized value
• $MA trades for 29x (lower end of its 3 year range & a PEG <2.00)tweet
Offshore
Photo
Benjamin Hernandez😎
The "Consumer" Speculation
Recommendation: $MB ~$4.85
MasterBeef Group is a tiny micro-cap up +10.23%. Consumer services rotation is hitting the smaller names.
Reason calling it: At $83M market cap, it only takes a little volume to send this to $6.50. https://t.co/9Ag2RE1zi5
tweet
The "Consumer" Speculation
Recommendation: $MB ~$4.85
MasterBeef Group is a tiny micro-cap up +10.23%. Consumer services rotation is hitting the smaller names.
Reason calling it: At $83M market cap, it only takes a little volume to send this to $6.50. https://t.co/9Ag2RE1zi5
tweet
Offshore
Photo
DAIR.AI
// Scaling Generalist Agent Intelligence //
This new research introduces AgentSkiller, a fully automated framework for synthesizing multi-turn interaction data across realistic, semantically linked domains. The architecture uses a Directed Acyclic Graph to ensure determinism and recoverability throughout the generation pipeline.
The pipeline follows five phases.
* First, it builds domain ontologies and Person-Centric Entity Graphs to structure data around realistic scenarios.
* Second, it defines Service Blueprints for MCP servers.
* Third, it populates environments with constraint-satisfying databases and strict domain policies.
* Fourth, a cross-domain fusion mechanism links services to simulate complex multi-turn tasks.
* Finally, execution-based validation filters out any trajectories that fail runtime checks.
The Dual-Model Architecture decouples semantic reasoning from code implementation. A Textual LLM handles domain expansion and policy formulation, while a Coding LLM handles SQL generation and Python implementation.
With approximately 11K synthesized interaction trajectories, AgentSkiller-14B achieves 79.1% on tau2-bench, outperforming GPT-o3 at 68.4% and Claude Sonnet 4 at 56.8%. On ACEBench-Agent, it scores 78.0%, surpassing Gemini-2.5-pro by 14.6 points.
AgentSkiller-4B at just 4 billion parameters scores 66.0% on tau2-bench, outperforming xLAM-2-70B at 41.0% and Qwen3-Thinking-235B at 58.6%. Cross-domain data is critical: adding it to single-domain training boosts tau-bench from 45.4% to 67.2%.
High-quality synthetic data with deterministic environment construction lets compact open-source models match or exceed proprietary systems on complex tool-use tasks.
Paper: https://t.co/6d92LkXBGm
Learn to build effective AI Agents in our academy: https://t.co/LRnpZN7L4c
tweet
// Scaling Generalist Agent Intelligence //
This new research introduces AgentSkiller, a fully automated framework for synthesizing multi-turn interaction data across realistic, semantically linked domains. The architecture uses a Directed Acyclic Graph to ensure determinism and recoverability throughout the generation pipeline.
The pipeline follows five phases.
* First, it builds domain ontologies and Person-Centric Entity Graphs to structure data around realistic scenarios.
* Second, it defines Service Blueprints for MCP servers.
* Third, it populates environments with constraint-satisfying databases and strict domain policies.
* Fourth, a cross-domain fusion mechanism links services to simulate complex multi-turn tasks.
* Finally, execution-based validation filters out any trajectories that fail runtime checks.
The Dual-Model Architecture decouples semantic reasoning from code implementation. A Textual LLM handles domain expansion and policy formulation, while a Coding LLM handles SQL generation and Python implementation.
With approximately 11K synthesized interaction trajectories, AgentSkiller-14B achieves 79.1% on tau2-bench, outperforming GPT-o3 at 68.4% and Claude Sonnet 4 at 56.8%. On ACEBench-Agent, it scores 78.0%, surpassing Gemini-2.5-pro by 14.6 points.
AgentSkiller-4B at just 4 billion parameters scores 66.0% on tau2-bench, outperforming xLAM-2-70B at 41.0% and Qwen3-Thinking-235B at 58.6%. Cross-domain data is critical: adding it to single-domain training boosts tau-bench from 45.4% to 67.2%.
High-quality synthetic data with deterministic environment construction lets compact open-source models match or exceed proprietary systems on complex tool-use tasks.
Paper: https://t.co/6d92LkXBGm
Learn to build effective AI Agents in our academy: https://t.co/LRnpZN7L4c
tweet
Offshore
Photo
NecoKronos
At this point, if you're still ignoring stacked imbalances after seeing my posts, you're actively choosing to make trading harder
It is literally the market showing you exactly where the unfinished business is.
If you have the correct template, these levels scream at you.
Stop ignoring the clearest alpha on your screen.
Wake up
#BTC
tweet
At this point, if you're still ignoring stacked imbalances after seeing my posts, you're actively choosing to make trading harder
It is literally the market showing you exactly where the unfinished business is.
If you have the correct template, these levels scream at you.
Stop ignoring the clearest alpha on your screen.
Wake up
#BTC
Stacked imbalances = The ultimate market magnets. 🧲
When aggressive orders stack up, they leave a trail of "unfinished business" that price is destined to return to.
In simple terms, an order flow imbalance happens when aggressive buyers or sellers completely overwhelm the other side at a specific price.
Stop guessing and start following the flow!
#BTC tweet
Offshore
Video
The Transcript
$SHOP:
tweet
$SHOP:
Shopify’s Q4 2025 earnings are out.
The headlines:
→ We now power more than 14% of the US ecommerce market
→ GMV is up 29% to $378B
→ Revenue is up 30% to $11.5B
→ Free cash flow exceeded $2B
→ Q4 was our first quarter above $3B in revenue
But 2025 wasn’t just about growth, it was about building for what’s next.
AI is changing how people shop. Merchants are racing to meet it. And we plan to keep powering them.
Watch the video for more. tweet
Offshore
Video
The Transcript
RT @tobi: it's going ok
tweet
RT @tobi: it's going ok
Shopify’s Q4 2025 earnings are out.
The headlines:
→ We now power more than 14% of the US ecommerce market
→ GMV is up 29% to $378B
→ Revenue is up 30% to $11.5B
→ Free cash flow exceeded $2B
→ Q4 was our first quarter above $3B in revenue
But 2025 wasn’t just about growth, it was about building for what’s next.
AI is changing how people shop. Merchants are racing to meet it. And we plan to keep powering them.
Watch the video for more. tweet