Offshore
Video
God of Prompt
RT @alex_prompter: OpenClaw broke the internet
But you DON'T need to setup any servers to use it
Here's the easiest way to run OpenClaw on a website
No Mac Minis required https://t.co/6xspOtHaxT
tweet
RT @alex_prompter: OpenClaw broke the internet
But you DON'T need to setup any servers to use it
Here's the easiest way to run OpenClaw on a website
No Mac Minis required https://t.co/6xspOtHaxT
tweet
Offshore
Video
Moon Dev
Stop Being Exit Liquidity: Deploying AI Agents to Capture Market Deviations 24/7
the market is a cold machine designed to harvest human emotion and if you aren't trading with code then you are likely the one being harvested. most people think they can beat the range by staring at a one day chart but the reality is that the only winners in these choppy waters are the systems that never sleep
my name is moon dev i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself. w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated
building a trading bot used to be reserved for the math geniuses and wall street elites with million dollar budgets. now we have access to tools like chatgpt that can churn out complex algorithms in seconds but there is a hidden catch that most people ignore until their account hits zero
we are focusing on a mean reversion strategy that looks for a specific deviation from the average price to find high probability entries. the logic is simple enough for a machine to understand but humans often hesitate because they think the trend will keep going forever or they get scared of a small pullback
the bot is set with a five percent profit target and a three percent stop loss to ensure the risk to reward ratio stays in our favor. by using five times leverage we can amplify these moves while maintaining a safety net that pulls us out of the market before things get ugly
while the ai can write the skeleton of these entry rules it often struggles with the complex math of real time profit calculation. if you blindly trust the code spit out by an assistant you might find yourself stuck in a trade with no way to exit even after your targets are hit
when prompting the ai for specific exit logic you often run into errors that require a refresh or a deeper understanding of the exchange api. it might write code that calculates profit based on current price minus position cost but in the world of crypto leverage that math is rarely as straightforward as it seems
the position cost variable is notoriously tricky to fetch correctly from exchanges like phemex using libraries like ccxt. if your bot doesn't know exactly what your entry price was then it has no way of knowing if it is up five percent or down ten percent until it is too late
i found that the ai code for profit loss was actually quite janky and would have led to a system failure if i hadn't intervened. there is a secret way to integrate custom functions that act as a safety override for when the machine gets confused by the numbers
by importing a specialized functions file we can call a pre built routine to handle the profit and loss monitoring with surgical precision. this allows the bot to focus on the signal while our proven math handles the heavy lifting of managing the open position size and exit timing
if you are in position the bot needs to constantly check the thresholds every few seconds without crashing the entire script. we use a time sleep function to give the api a breather while ensuring we are still looking at the current bid and ask prices to find the exit
once the exit logic is locked in the real challenge begins which is scaling this single strategy into a full portfolio of automated agents. most traders get stuck on one method but the real wealth is built by running multiple different types of algorithms simultaneously to capture every market condition
the plan involves building out mean reversion and trending algorithms alongside breakout and statistical arbitrage models. adding a market maker and machine learning models into the mix creates a diversified system that can profit whether the market is going sideways or exploding[...]
Stop Being Exit Liquidity: Deploying AI Agents to Capture Market Deviations 24/7
the market is a cold machine designed to harvest human emotion and if you aren't trading with code then you are likely the one being harvested. most people think they can beat the range by staring at a one day chart but the reality is that the only winners in these choppy waters are the systems that never sleep
my name is moon dev i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself. w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated
building a trading bot used to be reserved for the math geniuses and wall street elites with million dollar budgets. now we have access to tools like chatgpt that can churn out complex algorithms in seconds but there is a hidden catch that most people ignore until their account hits zero
we are focusing on a mean reversion strategy that looks for a specific deviation from the average price to find high probability entries. the logic is simple enough for a machine to understand but humans often hesitate because they think the trend will keep going forever or they get scared of a small pullback
the bot is set with a five percent profit target and a three percent stop loss to ensure the risk to reward ratio stays in our favor. by using five times leverage we can amplify these moves while maintaining a safety net that pulls us out of the market before things get ugly
while the ai can write the skeleton of these entry rules it often struggles with the complex math of real time profit calculation. if you blindly trust the code spit out by an assistant you might find yourself stuck in a trade with no way to exit even after your targets are hit
when prompting the ai for specific exit logic you often run into errors that require a refresh or a deeper understanding of the exchange api. it might write code that calculates profit based on current price minus position cost but in the world of crypto leverage that math is rarely as straightforward as it seems
the position cost variable is notoriously tricky to fetch correctly from exchanges like phemex using libraries like ccxt. if your bot doesn't know exactly what your entry price was then it has no way of knowing if it is up five percent or down ten percent until it is too late
i found that the ai code for profit loss was actually quite janky and would have led to a system failure if i hadn't intervened. there is a secret way to integrate custom functions that act as a safety override for when the machine gets confused by the numbers
by importing a specialized functions file we can call a pre built routine to handle the profit and loss monitoring with surgical precision. this allows the bot to focus on the signal while our proven math handles the heavy lifting of managing the open position size and exit timing
if you are in position the bot needs to constantly check the thresholds every few seconds without crashing the entire script. we use a time sleep function to give the api a breather while ensuring we are still looking at the current bid and ask prices to find the exit
once the exit logic is locked in the real challenge begins which is scaling this single strategy into a full portfolio of automated agents. most traders get stuck on one method but the real wealth is built by running multiple different types of algorithms simultaneously to capture every market condition
the plan involves building out mean reversion and trending algorithms alongside breakout and statistical arbitrage models. adding a market maker and machine learning models into the mix creates a diversified system that can profit whether the market is going sideways or exploding[...]
Offshore
Moon Dev Stop Being Exit Liquidity: Deploying AI Agents to Capture Market Deviations 24/7 the market is a cold machine designed to harvest human emotion and if you aren't trading with code then you are likely the one being harvested. most people think they…
in one direction
by the time we reach the end of the roadmap these models will be enhanced with machine learning to adapt to changing market regimes. the goal is to have all of these working in harmony so that the human element is completely removed from the decision making process
the secret to surviving the next year of volatility isn't finding one magic indicator but learning how to iterate through the failures of your code. people often ask if ai can really replace human traders and the answer depends entirely on your ability to fix the mistakes the ai makes during the build process
you have to be willing to throw away janky code and replace it with something more robust even if it means writing a few lines yourself. the ai is a powerful assistant but you are still the pilot who has to guide the system toward a successful execution every single day
the mean reversion algorithm works by identifying when the price has moved too far away from its simple moving average. we look for a two percent deviation which acts as a rubber band effect where the price is likely to snap back to its fair value
most human traders fail here because they see the price dropping two percent and assume the world is ending so they panic sell at the bottom. the bot simply sees a mathematical opportunity to enter a long position and wait for the reversion to the mean to pay out its five percent profit target
if you are lost in the sea of variables and exchange params it helps to see the full algorithms explained step by step. getting access to proven code saves you thousands of hours of trial and error and prevents the liquidations that come from small syntax errors in your trading logic
code is the ultimate bridge between where you are now and where you want to be in the financial markets. by automating every step from entry to exit you finally stop being the exit liquidity and start being the one who controls the range
my journey from spending thousands on devs to building my own live on stream proves that anyone can do this with enough persistence. keep iterating and keep building because the bots are already winning and it is time you joined their ranks
tweet
by the time we reach the end of the roadmap these models will be enhanced with machine learning to adapt to changing market regimes. the goal is to have all of these working in harmony so that the human element is completely removed from the decision making process
the secret to surviving the next year of volatility isn't finding one magic indicator but learning how to iterate through the failures of your code. people often ask if ai can really replace human traders and the answer depends entirely on your ability to fix the mistakes the ai makes during the build process
you have to be willing to throw away janky code and replace it with something more robust even if it means writing a few lines yourself. the ai is a powerful assistant but you are still the pilot who has to guide the system toward a successful execution every single day
the mean reversion algorithm works by identifying when the price has moved too far away from its simple moving average. we look for a two percent deviation which acts as a rubber band effect where the price is likely to snap back to its fair value
most human traders fail here because they see the price dropping two percent and assume the world is ending so they panic sell at the bottom. the bot simply sees a mathematical opportunity to enter a long position and wait for the reversion to the mean to pay out its five percent profit target
if you are lost in the sea of variables and exchange params it helps to see the full algorithms explained step by step. getting access to proven code saves you thousands of hours of trial and error and prevents the liquidations that come from small syntax errors in your trading logic
code is the ultimate bridge between where you are now and where you want to be in the financial markets. by automating every step from entry to exit you finally stop being the exit liquidity and start being the one who controls the range
my journey from spending thousands on devs to building my own live on stream proves that anyone can do this with enough persistence. keep iterating and keep building because the bots are already winning and it is time you joined their ranks
tweet
Offshore
Photo
Moon Dev
opus 4.6 for trading
today in the private zoom we are using opus 4.6 and applying it directly to trading
everything ive heard about opus 4.6 so far is that it is revolutionary
lets get our hands on it together in a private zoom only
check to see if theres a ticket left here
dont miss this https://t.co/Aw7dcEvv2n
moondev
tweet
opus 4.6 for trading
today in the private zoom we are using opus 4.6 and applying it directly to trading
everything ive heard about opus 4.6 so far is that it is revolutionary
lets get our hands on it together in a private zoom only
check to see if theres a ticket left here
dont miss this https://t.co/Aw7dcEvv2n
moondev
tweet
Offshore
Video
Startup Archive
Palmer Luckey: If your boss says they don’t care about money, you should be worried
Shaan Puri, host of the My First Million podcast, jokes that “the Silicon Valley thing is to pretend you don’t like money.”
Anduril and Oculus founder Palmer Luckey comments:
“I actually tell our employees during onboarding that if you work at a place where your boss is saying that [money is not the real objective], you should be worried. It’s one thing to say that to the press or your marketing materials, but if your employees don’t believe that at the end of the day you’re trying to make their job a fiscally responsible decision — if you’re effectively telling them that they could be making more money elsewhere and your financial success is not my priority — you should be concerned.”
That’s not to say that money should be the only objective though. Palmer comments:
“When I started Oculus, it was not because I thought it would be the thing that made the most money. There had never been a successful VR company in history, to be clear. I did it because it was something I was really passionate about. That said, one of the things that I’m most proud of in my whole career is that everyone who worked at Oculus achieved financial independence because we were able to build something incredible. I feel great that everyone who was part of that mission and supported me early on was able to make a bunch of money, and a lot of them have gone on to do incredible things.”
Video source: @myfirstmilpod (2022)
tweet
Palmer Luckey: If your boss says they don’t care about money, you should be worried
Shaan Puri, host of the My First Million podcast, jokes that “the Silicon Valley thing is to pretend you don’t like money.”
Anduril and Oculus founder Palmer Luckey comments:
“I actually tell our employees during onboarding that if you work at a place where your boss is saying that [money is not the real objective], you should be worried. It’s one thing to say that to the press or your marketing materials, but if your employees don’t believe that at the end of the day you’re trying to make their job a fiscally responsible decision — if you’re effectively telling them that they could be making more money elsewhere and your financial success is not my priority — you should be concerned.”
That’s not to say that money should be the only objective though. Palmer comments:
“When I started Oculus, it was not because I thought it would be the thing that made the most money. There had never been a successful VR company in history, to be clear. I did it because it was something I was really passionate about. That said, one of the things that I’m most proud of in my whole career is that everyone who worked at Oculus achieved financial independence because we were able to build something incredible. I feel great that everyone who was part of that mission and supported me early on was able to make a bunch of money, and a lot of them have gone on to do incredible things.”
Video source: @myfirstmilpod (2022)
tweet
Offshore
Photo
Benjamin Hernandez😎
Banco De Sabadell Appoints TSB’s Marc Armengol as Next CEO https://t.co/RRttygBQcM
tweet
Banco De Sabadell Appoints TSB’s Marc Armengol as Next CEO https://t.co/RRttygBQcM
tweet
Offshore
Photo
DAIR.AI
RT @omarsar0: NEW research on improving memory for AI Agents.
(bookmark it)
As context windows scale to millions of tokens, the bottleneck shifts from raw capacity to cognitive control. Knowing what you know, knowing what's missing, and knowing when to stop matters more than processing every token.
Longer context windows don't guarantee better reasoning. This is largely because the way devs handle ultra-long documents today remains expanding the context window or compressing everything into a single pass.
But when decisive evidence is sparse and scattered across a million tokens, passive memory strategies silently discard the bridging facts needed for multi-hop reasoning.
This new research introduces InfMem, a bounded-memory agent that applies System-2-style cognitive control to long-document question answering through a structured PRETHINK–RETRIEVE–WRITE protocol.
Instead of passively compressing each segment as it streams through, InfMem actively monitors whether its memory is sufficient to answer the question. Is the current evidence enough? What's missing? Where in the document should I look?
PRETHINK acts as a cognitive controller, deciding whether to stop or retrieve more evidence. When evidence gaps exist, it synthesizes a targeted retrieval query and fetches relevant passages from anywhere in the document, including earlier sections it already passed. WRITE then performs joint compression, integrating retrieved evidence with the current segment into a bounded memory under a fixed budget.
The training recipe uses an SFT warmup to teach protocol mechanics through distillation from Qwen3-32B, then reinforcement learning aligns retrieval, writing, and stopping decisions with end-task correctness using outcome-based rewards and early-stop shaping.
On ultra-long QA benchmarks from 32k to 1M tokens, InfMem outperforms MemAgent by +10.17, +11.84, and +8.23 average absolute accuracy points on Qwen3-1.7B, Qwen3-4B, and Qwen2.5-7B, respectively.
A 4B parameter InfMem agent maintains consistent accuracy up to 1M tokens, where standard baselines like YaRN collapse to single-digit performance. Inference latency drops by 3.9x on average (up to 5.1x) via adaptive early stopping.
These gains also transfer to LongBench QA, where InfMem+RL achieves up to +31.38 absolute improvement on individual tasks over the YaRN baseline.
Paper: https://t.co/4wxeCua7a7
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
tweet
RT @omarsar0: NEW research on improving memory for AI Agents.
(bookmark it)
As context windows scale to millions of tokens, the bottleneck shifts from raw capacity to cognitive control. Knowing what you know, knowing what's missing, and knowing when to stop matters more than processing every token.
Longer context windows don't guarantee better reasoning. This is largely because the way devs handle ultra-long documents today remains expanding the context window or compressing everything into a single pass.
But when decisive evidence is sparse and scattered across a million tokens, passive memory strategies silently discard the bridging facts needed for multi-hop reasoning.
This new research introduces InfMem, a bounded-memory agent that applies System-2-style cognitive control to long-document question answering through a structured PRETHINK–RETRIEVE–WRITE protocol.
Instead of passively compressing each segment as it streams through, InfMem actively monitors whether its memory is sufficient to answer the question. Is the current evidence enough? What's missing? Where in the document should I look?
PRETHINK acts as a cognitive controller, deciding whether to stop or retrieve more evidence. When evidence gaps exist, it synthesizes a targeted retrieval query and fetches relevant passages from anywhere in the document, including earlier sections it already passed. WRITE then performs joint compression, integrating retrieved evidence with the current segment into a bounded memory under a fixed budget.
The training recipe uses an SFT warmup to teach protocol mechanics through distillation from Qwen3-32B, then reinforcement learning aligns retrieval, writing, and stopping decisions with end-task correctness using outcome-based rewards and early-stop shaping.
On ultra-long QA benchmarks from 32k to 1M tokens, InfMem outperforms MemAgent by +10.17, +11.84, and +8.23 average absolute accuracy points on Qwen3-1.7B, Qwen3-4B, and Qwen2.5-7B, respectively.
A 4B parameter InfMem agent maintains consistent accuracy up to 1M tokens, where standard baselines like YaRN collapse to single-digit performance. Inference latency drops by 3.9x on average (up to 5.1x) via adaptive early stopping.
These gains also transfer to LongBench QA, where InfMem+RL achieves up to +31.38 absolute improvement on individual tasks over the YaRN baseline.
Paper: https://t.co/4wxeCua7a7
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
tweet
Offshore
Photo
Bourbon Capital
$AMZN Operating cash flow vs Free cash flow https://t.co/9KdPK1iGk2
tweet
$AMZN Operating cash flow vs Free cash flow https://t.co/9KdPK1iGk2
$AMZN Total debt vs Operating cash flow https://t.co/TKPOFvFic4 tweet
Javier Blas
RT @BarakRavid: U.S.-Iran talks in Oman have ended for today. Another round of talks to take place in the coming days, per source with knowledge
tweet
RT @BarakRavid: U.S.-Iran talks in Oman have ended for today. Another round of talks to take place in the coming days, per source with knowledge
tweet
Offshore
Photo
Wasteland Capital
Absurd guide down at health insurer $MOH, seems borderline criminal. Last Q they guided for ~$14 in ‘26, now they say $3.20 ($5 adjusted)!
I told you last summer to stay the f**k away from health insurers. Know what you own & don’t own sectors under attack.
Stock down -30%. https://t.co/uafPZcQf4o
tweet
Absurd guide down at health insurer $MOH, seems borderline criminal. Last Q they guided for ~$14 in ‘26, now they say $3.20 ($5 adjusted)!
I told you last summer to stay the f**k away from health insurers. Know what you own & don’t own sectors under attack.
Stock down -30%. https://t.co/uafPZcQf4o
tweet