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https://x.com/i/lists/1669153613199835138?t=R0mCicxs7zfJE_yOAek4gQ&s=09
Moon Dev
building trending bots for hip 3 gold and oil markets https://t.co/O1ahr2kAc0
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Moon Dev
patching hyperliquid h3 price rounding for limit orders https://t.co/VtaLuWWEPf
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Moon Dev
The AI Agent Swarm: How To Automate 19 Quant Strategies In Under 18 Minutes
trading is a game of psychology where the house always wins because humans are hardwired to make the worst decisions at the most expensive times. most people approach the charts with a gut feeling and a prayer but i realized early on that my emotions were the only thing standing between me and a consistent equity curve. if you have ever moved your stop loss or stayed up all night staring at a 1 minute candle then you already know that you are your own worst enemy. the reality is that a stanford professor once told me that we literally cannot think logically when we are in an emotional state. that is why i decided to turn my entire process over to the machines and build a system that never sleeps and never feels fear. but there is a specific three step framework i use called rbi that allows me to build 19 different strategies in less than twenty minutes while most traders are still trying to find their first indicator. i am going to show you exactly how this works because once you see the speed of ai agents you will never look at a manual trade the same way again
the first step is research and most people get this completely wrong by looking for the holy grail in some paid discord group. i prefer to hunt for alpha in plain sight on places like google scholar or twitter where the real quants are dropping gems for free. i recently found a thread about adx indicators where a professional breakout specialist claimed that stability beats optimization every single time. this sparked a thought loop in my head because if i could find universal parameters that work across entire sectors i would have a massive edge. most traders find one idea and spend weeks trying to make it work but i want a bucket of ideas that i can test in parallel. the secret is that i don't even have to understand the math behind every indicator anymore because i have a team of ai agents that can translate a simple tweet into a fully functional backtest. this is where the rbi system really starts to pull away from the pack and i want to show you how i scaled my testing from four hours per strategy down to just a few seconds
backtesting is the second pillar and it is where most dreams go to die because coding is hard and slow for most people. i spent hundreds of thousands of dollars on developers in the past because i thought i was too dumb to code myself. eventually i realized that code is the great equalizer and if i could just learn enough to guide the ai then i could outwork entire hedge funds from my bedroom. i built an agent swarm that takes a text file of ideas and starts coding and testing them all at once without me lifting a finger. you can see it working in real time as it creates folders and outputs data on things like sharpe ratios and maximum drawdowns. the goal isn't to find one perfect strategy but to find the outliers that have a quantifiable edge in the past data. if a strategy worked for ten years it is much more likely to work tomorrow than a random guess based on how you feel after your morning coffee. but there is a specific metric i look at called exposure time that most people ignore and it is the secret to staying alive in these volatile markets
exposure time is basically how long your money is actually at risk in the market and i want that number as low as possible. i found a strategy today that had a high return with only point twelve percent exposure time which is absolutely insane. most traders are sitting in positions for days hoping for a move while their capital is stuck and vulnerable to black swan events. my bots are looking for those tiny windows of opportunity where the probability is heavily skewed in our favor and then they get out. i use claude to help me refine these codes and fix bugs because even the best ai agents make mistakes sometimes. it is a continuous loop of iteration where i am the architect and the machines are the builders. the beautiful thing about this is t[...]
The Transcript
$LYFT CEO: Lyft Ads scaled from concept to $100M exit run-rate in just two years.
“Lyft Ads, 2 years ago, when we were doing Investor Day, it was an idea. It was an early concept. Now we've done exactly what we said we wanted to do, which is reach $100 million run rate exit rate from Q4.”
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Javier Blas
IEA executive director Fatih Birol proposes focusing the agency's work in three areas during the next few years:
1) energy security -- "first and foremost" mission
2) new energy uptake (wind, solar, geothermal, nuclear)
3) afordability of energy
"IEA 3.0" may well be over.
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Moon Dev
solana trenches were just waiting for all the incels to pivot to ai https://t.co/rEsH8C2Hbm
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Startup Archive
Sam Altman on the advice he wish he received when he enrolled in YC in 2005
Y Combinator CEO Garry Tan asks OpenAI founder Sam Altman what he wish knew when he was going through YC back in 2005.
Sam responds:
“I wish someone had taught me the importance of conviction and resilience over a long period of time. People don’t really talk about how hard that is. It’s easy for a little while, but your reserves kind of wear down on it.”
He continues:
“Also just trust that it’s eventually going to work out. Obviously my first startup [Loopt] didn’t work that well. A lot of people give up after one failed startup, but startups don’t workout all the time. Learning how to keep working through that is really important. So is developing trust in your own instincts and increasing that trust as you refine your decision-making instincts over time. Courage to work on stuff that is out of fashion but is what you believe in and care about is also really important.”
Sam recently had a kid and reflects on how everyone will tell you that it’s “the best thing you will ever do, but also the hardest thing you will ever do.” He believes startups are similar:
“The good parts are really great — better than you think. And the hard parts are shockingly much harder than anyone can express in a way that makes any sense to you, and you have to just keep going.”
Video source: @ycombinator (2025)
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NecoKronos
RT @anthdm: We are soon rolling out historical data for trades. Both on MMT and API.
This means you can have sub 1 minute backfills for your tools.
We also rolling out Hyperliquid Full data through our API's.
- full orderbook depth
- positions
- liquidations
The hole shebang.
Might be a good time to start using MMT.
We all love you, you know that.
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Moon Dev
If you actually want to use openclaw
I made all the mistakes so you don’t have to https://t.co/T4nrspt93T
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The Transcript
$NOW ServiceNow CEO: "Our pipelines have never been better. Let me be clear, never been better...So you should feel really good about ServiceNow."
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Benjamin Hernandez😎
$WING 14% EPS COMPOUNDER!
Wingstop +14.55% hitting $288.41. EPS growth is +79.0% YoY. Buy ratings across the board. The fundamental moat is widening.
Buy on any shallow pullback, the trend is unstoppable.
DM to get the specific Wave 3 entry points.
$SOC $BMNR $BYND $NB $PULM https://t.co/zSGxt9jfZ9
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he game when you are trading these degenerate meme coins. i like to throw ten dollars at a bunch of different tokens and if one hits a thousand percent return it covers all the small losses from the rugs. the bot trades emotionally free which is impossible for a human who is watching their life savings bounce around on a chart
you can use api endpoints to see the last trade time and ensure that the token still has active buyers in the last hour. if a token has zero trades in sixty minutes we drop it immediately because it is already a dead ship. it be like that sometimes in the wild west of solana but as long as we have the tools to filter the noise we will find the signal
building a market maker is the next level after you master the sniper bot because it allows you to create your own volume and profit from the spread. we are constantly coming up with new strategies and back testing them to see if they worked in the past before we ever risk a single cent. this is the path from being a gambler to being a quantitative trader who actually understands the math behind the moves
there is so much opportunity in this space right now that it is almost overwhelming but you have to take action to get separation from the crowd. most traders will just keep chasing pumps and losing money because they are too lazy to learn how to code their own edge. i am going to keep building these crazy bots and showing you every single step because i want us all to win together
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Javier Blas
U.S. shale is the gift that keeps giving:
“I've been wrong," said Kaes Van't Hof, chief executive of Permian producer Diamondback Energy. "I thought we'd be down by now."
https://t.co/e3JCHMqxMz
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Brady Long
companies are gonna post record profits while eliminating the only job available in some towns
PolyAI has raised $200M from Nvidia, Khosla Ventures, and multiple top VCs.
We're one of the fastest-growing companies in the UK, and we handle 500M+ calls for:
• Marriott
• PG&E
• Gordon Ramsay's restaurants
• And 3,000 more real deployments
Which means that if you've ever called them, chances are you've talked to our voice agents.
Every restaurant we onboard books thousands in revenue within 30 days.
But how?
Because PolyAI works 24/7, answering every call in <2
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AkhenOsiris
RT @MRatable: If i was a seat based developer tool and I wanted to make my stock go up, I wouldn’t announce a hiring freeze.
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Moon Dev
we might be able to take on wall street https://t.co/uXtomC1g2A
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hat i can test nineteen different versions of a strategy in the time it takes you to eat lunch. this volume of data gives me the confidence to move to the final step which is implementation without the typical fear of losing it all
implementation is the final eye in rbi and it is where you turn your backtest into a living breathing trading bot. i always start with tiny size because a backtest is just a map and the real market is the terrain. if the live results don't match the historical data then i know something is wrong and i can kill the bot before it does any real damage. i spent years getting liquidated and losing money because i was over trading and using too much leverage on a whim. now i have fully automated systems that sit one tick in front of the biggest orders in the book using high frequency logic. i am building this all live on youtube because i want to show that anyone can do this if they are willing to iterate to success. wall street wants you to think this is a secret club for geniuses but they are just using the same logic and faster computers. i am giving away the roadmap and the code because i believe that when we all have access to these tools the playing field finally becomes level
the final piece of the puzzle is understanding that this is a numbers game and you have to be willing to fail fast and often. i might run a hundred backtests today and only find one or two that are worth putting a single dollar behind. the reason i am successful now is because i stopped trying to be right and started trying to be logical. my ai agents are currently scanning the markets and running tests while i am talking to you and that is the power of automation. if you are still clicking buttons manually you are competing against machines that don't get tired and don't care about their bank account balance. i decided to stop being the liquidity for the big players and started building the tools to join them. you have to decide if you want to keep guessing or if you want to start building a system that can actually scale. the code is out there and the tools are free so the only thing left is for you to decide to stop being the emotional human and start being the rational architect of your own financial future
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Moon Dev
correcting the math on hyperliquid hourly funding rates https://t.co/6aFopg7l9E
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Fiscal.ai
Can any company disrupt this business?
$MCO https://t.co/pywBHPVZL5
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Quiver Quantitative
Senators Josh Hawley and Richard Blumenthal have introduced a bipartisan bill that would force AI data centers to build out their own power supply.
What do you think about this proposal? https://t.co/qfU0Ynr5d0
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Fiscal.ai
Wingstop just crossed 3,000 restaurants globally.
They've grown locations at a 14% CAGR since 2019.
Why wouldn't this continue?
$WING https://t.co/dSbJZa7ukj
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The Transcript
RT @TheTranscript_: $OWL CEO: Hyperscale data center lease financings now reaching unprecedented $30B–$50B transaction sizes
"Our biggest deal is just under $30B for Meta. We're working on deals. I am not exaggerating. This is just for the shell and maybe some GPUs in there, $50B. I mean, in our lifetime, I never thought I'd be financing deals of that size, but I never thought I'd come across CapEx projects of that magnitude."
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App Economy Insights
RT @EconomyApp: 💰 13F filings just dropped!
What was Wall Street buying in Q4?
🔎 A lot more $GOOGL
⚡️ AI power/infrastructure
🌏 Global e-commerce players
But we have surprises (not AI-related).
https://t.co/Oqm4jrfCTb
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Dimitry Nakhla | Babylon Capital®
$MCO CEO was just asked about AI threats & the durability of its moat. His response:
“First of all, a lot of the data simply isn’t available to the public… built on decades of commercial agreements & IP rights… legal & regulatory constraints… semantic complexity… entity resolution… historical depth… governance…
Every bank I talk to tells me, ‘Good enough is not good enough for our institution.’ What they want from us, they want to move, in many cases, to fewer trusted providers…
“We’ve never had seat-based licenses…thinking as we speak and trailing different pricing models to be able to capture some of that upside.”
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Brady Long
RT @thisdudelikesAI: the last time something this big happened to an industry this fast was kodak. and kodak had a warning...
PolyAI has raised $200M from Nvidia, Khosla Ventures, and multiple top VCs.
We're one of the fastest-growing companies in the UK, and we handle 500M+ calls for:
• Marriott
• PG&E
• Gordon Ramsay's restaurants
• And 3,000 more real deployments
Which means that if you've ever called them, chances are you've talked to our voice agents.
Every restaurant we onboard books thousands in revenue within 30 days.
But how?
Because PolyAI works 24/7, answering every call in <2
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God of Prompt
RT @godofprompt: 🚨 Holy shit… Stanford just published a paper that questions whether we even need humans to study humans.
The title sounds like a joke:
“This human study did not involve human subjects.”
But it’s dead serious.
The researchers are asking a controversial question:
Can LLM simulations count as behavioral evidence?
Here’s the core idea.
Instead of recruiting thousands of participants, running surveys, and waiting weeks for results, they simulate people using large language models.
Not generic prompts.
But structured simulations where the model is assigned demographic traits, preferences, beliefs, and contextual constraints.
Then they test whether the simulated responses statistically match real-world human data.
And disturbingly… they often do.
Across multiple behavioral tasks, the LLM-generated “participants” reproduced known human patterns:
• Established psychological biases
• Preference distributions
• Decision-making trends
• Even demographic splits
Not perfectly. Not universally.
But far closer than most people would expect.
The key contribution of the paper isn’t “LLMs are human.”
It’s validation.
They systematically compare simulated outputs to ground-truth human datasets and evaluate alignment using statistical benchmarks.
When the distributions match, the simulation isn’t just storytelling.
It becomes empirical evidence.
That’s the uncomfortable shift.
If a sufficiently constrained LLM simulation reproduces real behavioral patterns, does it become a legitimate experimental proxy?
Because if the answer is yes, this changes everything:
• Behavioral economics
• Political science
• Market research
• Policy testing
• UX experimentation
You could prototype social interventions before deploying them in the real world.
You could stress-test messaging strategies across simulated demographics.
You could explore rare edge-case populations without recruitment bottlenecks.
But here’s where Stanford is careful.
The models don’t “understand” humans.
They reflect training data patterns.
They can amplify biases.
They can collapse under distribution shift.
And they can simulate plausibility without causality.
So the paper doesn’t claim replacement.
It argues for calibration.
LLM simulations can be useful behavioral instruments if validated against real data and bounded within known limits.
That’s the distinction.
Not synthetic humans.
Synthetic behavioral priors.
The wild part?
This paper forces academia to confront something bigger:
If large models encode large-scale behavioral regularities from the internet, they become compressed maps of human tendencies.
Not minds.
Maps.
And maps can be useful.
We’re moving from “AI as text generator” to “AI as behavioral simulator.”
The ethics, methodology, and epistemology implications are massive.
Because once simulation becomes statistically reliable, the bottleneck in social science shifts from data collection to model alignment.
And that might be the real revolution hidden in this paper.
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Moon Dev
Building the Ultimate Solana Sniper: How to Filter 1,000x Gems Before They Ever Hit Birdeye
the ultimate beginners guide to solana trading bots is actually a blueprint for financial freedom because the market is a casino where the house always wins unless you are the one running the code. there is a specific way to see every single token launched on solana before it even reaches a website like birdeye and i am going to show you how to build that filter. i believe that code is the great equalizer because i used to spend hundreds of thousands on developers thinking i could not do it myself but now i live on the edge of the blockchain
the secret to finding 1000x tokens before they hit the trending page starts with the birdeye api which is the most important tool in our arsenal. most people look for gems by staring at a screen all day drinking coffee and hoping for luck but we use computers to do the heavy lifting for us. getting these contract addresses straight from the rpc is the first step but the real magic happens in the filtering logic that removes ninety nine percent of the trash tokens launched every hour
most people look at volume but there is a secret metric called unique wallets per hour that tells you if the volume is real or just one guy washing his own bags. if the unique wallets do not match the trade frequency you know it is a ghost town and you save your money instead of getting liquidated. we pull all data from the api including the total value locked and the twenty four hour trade count to decide which tokens are actually worth our time
if the unique wallets do not match the trade frequency you know it is a ghost town and you save your money. i once spent hundreds of thousands on developers because i thought i could not code but then i discovered the great equalizer that allows an average joe to beat wall street. when you put real money on the line you realize that you need to learn this yourself because nobody else is going to build a bot with your specific edge built into it
now i build my own systems and the iterates to success happen in minutes instead of months. there is a rug pull indicator hidden in the price change history that shows up exactly ten minutes before the floor collapses. we built a filter that looks at the last two hundred and fifty orders to see if the sell pressure is building up to a dangerous level
when you see a ninety percent drop in a five minute candle it is already too late but our code sees the sell pressure building at seventy percent and pulls us out. it is crazy that wall street would never show you this information but that is why i share everything here for the squad. you have to adapt your strategies based on market cap and participant behavior because the regime models are always changing in crypto
the final piece of the puzzle is the execution through jupiter where we can automate the buy and sell orders while we sleep. this sniper bot is designed to find those tiny micro caps under twenty thousand dollars and get in before the massive pump happens. with the bot submitting orders based on cold hard math you finally stop being the exit liquidity for the big players
i believe that anyone can learn this because i was not a coder before i started this journey and i was scared just like you might be. once you realize that coding is just repetition and logic it becomes much easier than learning a second language like spanish. you can take a little risk to learn how to automate your trading and the worst case scenario is that you end up with a high paying job skill
the filtering system we built takes fifteen thousand tokens and drops them down to the best fifteen shots of the day. it uses regex to clean up the symbol names and pandas to organize the data so we can see exactly which tokens have the most potential. we even integrated functions to check if the minting authority is revoked so we do not get caught in a honey pot trap
risk management is the only thing that keeps you in t[...]
Benjamin Hernandez😎
Market breadth is decent for today
$AZN $MTB-H $MLEC $TCMD
$IBRX $JELD $WING $SNSE
$TSLA $META $MSFT $GOOGL
$IBRX is a high beta play for day traders. $GOOGL gives us a tech anchor. Monitoring for confluence signals before open!
Join the VIP group today!!
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Wasteland Capital
95% chance that the S&P500 just simply goes back up to the top of this bouncy range as we see earnings-driven buying and short covering, and some BTFDOMO on top, in many of the Saaspocalypse victims. https://t.co/ZwZtuPkwkC
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AkhenOsiris
Morgan Stanley's Nowak on $AMZN
AWS growth can be 30% for quite some time
The second catalyst is agentic commerce. Nowak said Amazon’s growing last-mile inventory, expanding infrastructure and technology investments position it to lead in both vertical and horizontal agentic shopping.
The company’s platform-specific agent Rufus is already contributing 140 basis points to fourth-quarter 2025 gross merchandise value (GMV) growth, he notes.
Amazon has acknowledged the need to “collectively figure out a better customer experience” with horizontal AI agents, and added that “we continue to have a number of conversations,” signaling potential partnerships ahead.
“We look for AMZN horizontal agentic partnerships to emerge, which will make investors feel more confident in AMZN’s long-term positioning,” Nowak wrote.
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