Offshore
Photo
DAIR.AI
Memory is the bottleneck for LLM agents.
Fixed memory pipelines waste compute on irrelevant information while potentially discarding what a specific query actually needs.
This new research introduces BudgetMem, a runtime agent memory framework that extracts memory on-demand with explicit, controllable performance-cost trade-offs.
As agents scale to longer interactions and more complex tasks, memory cost becomes a first-class concern. BudgetMem provides a systematic framework for explicit performance-cost control in runtime agent memory.
Instead of treating memory as a monolithic pipeline, BudgetMem structures extraction into modular stages, each offered in three budget tiers (Low/Mid/High).
A lightweight neural router, trained with reinforcement learning, selects the right tier per module based on the current query and intermediate context.
They study three complementary strategies for realizing budget tiers: implementation tiering (varying method complexity), reasoning tiering (varying inference behavior like direct vs. reflection), and capacity tiering (varying model size).
On LongMemEval with LLaMA-3.3-70B, BudgetMem-CAP achieves a Judge score of 60.50, surpassing the strongest baseline LightMem (48.51) by a wide margin. On HotpotQA with Qwen3-Next-80B, BudgetMem-CAP scores 72.08 at just $0.22 cost, while BudgetMem-REA reaches 70.83 at an even lower $0.17. The trained router also transfers across model backbones without retraining.
The analysis reveals that implementation and capacity tiering span broader cost ranges for exploring budget extremes, while reasoning tiering acts as a fine-grained quality knob within a tighter cost band.
Paper: https://t.co/qkKmawVNrk
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7deE
tweet
Memory is the bottleneck for LLM agents.
Fixed memory pipelines waste compute on irrelevant information while potentially discarding what a specific query actually needs.
This new research introduces BudgetMem, a runtime agent memory framework that extracts memory on-demand with explicit, controllable performance-cost trade-offs.
As agents scale to longer interactions and more complex tasks, memory cost becomes a first-class concern. BudgetMem provides a systematic framework for explicit performance-cost control in runtime agent memory.
Instead of treating memory as a monolithic pipeline, BudgetMem structures extraction into modular stages, each offered in three budget tiers (Low/Mid/High).
A lightweight neural router, trained with reinforcement learning, selects the right tier per module based on the current query and intermediate context.
They study three complementary strategies for realizing budget tiers: implementation tiering (varying method complexity), reasoning tiering (varying inference behavior like direct vs. reflection), and capacity tiering (varying model size).
On LongMemEval with LLaMA-3.3-70B, BudgetMem-CAP achieves a Judge score of 60.50, surpassing the strongest baseline LightMem (48.51) by a wide margin. On HotpotQA with Qwen3-Next-80B, BudgetMem-CAP scores 72.08 at just $0.22 cost, while BudgetMem-REA reaches 70.83 at an even lower $0.17. The trained router also transfers across model backbones without retraining.
The analysis reveals that implementation and capacity tiering span broader cost ranges for exploring budget extremes, while reasoning tiering acts as a fine-grained quality knob within a tighter cost band.
Paper: https://t.co/qkKmawVNrk
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7deE
tweet
Offshore
Photo
The Transcript
RT @TheTranscript_: Blue Owl only sees pristine credit metrics in tech even as markets flash warnings:
"“Tech lending has worked, continues to work...We don’t have red flags. In point of fact, we don’t have yellow flags. We actually have largely green flags."
$OWL https://t.co/sHtqAPpuEp
tweet
RT @TheTranscript_: Blue Owl only sees pristine credit metrics in tech even as markets flash warnings:
"“Tech lending has worked, continues to work...We don’t have red flags. In point of fact, we don’t have yellow flags. We actually have largely green flags."
$OWL https://t.co/sHtqAPpuEp
tweet
Offshore
Photo
DAIR.AI
RT @omarsar0: I think one of the most underappreciated findings in AI engineering is what this paper calls the "Grep Tax."
First, they ran nearly 10,000 experiments testing how agents handle structured data, and the headline result is that format barely matters.
But here's the weird finding: a compact, token-saving format they tested (TOON) actually consumed *up to 740% more tokens* at scale because models didn't recognize the syntax and kept cycling through search patterns from formats they already knew.
It's one of the reasons my preferred formats are XML and Markdown. LLMs know those really well.
The models have preferences baked into their training data, and fighting those preferences doesn't save you money. It costs you.
The other finding worth sitting with: the same agentic architecture that improves frontier model performance actively *hurts* open-source models. It seems that the universal best-practices guide for AI engineering may not exist.
tweet
RT @omarsar0: I think one of the most underappreciated findings in AI engineering is what this paper calls the "Grep Tax."
First, they ran nearly 10,000 experiments testing how agents handle structured data, and the headline result is that format barely matters.
But here's the weird finding: a compact, token-saving format they tested (TOON) actually consumed *up to 740% more tokens* at scale because models didn't recognize the syntax and kept cycling through search patterns from formats they already knew.
It's one of the reasons my preferred formats are XML and Markdown. LLMs know those really well.
The models have preferences baked into their training data, and fighting those preferences doesn't save you money. It costs you.
The other finding worth sitting with: the same agentic architecture that improves frontier model performance actively *hurts* open-source models. It seems that the universal best-practices guide for AI engineering may not exist.
tweet
Offshore
Photo
Pristine Capital
RT @realpristinecap: 1-Week Index ETF Performance via Pristine Capital 📊
$ARKK -5.96% (Innovation)
$QQQ -1.97% (Nasdaq)
$SPY -.20% (S&P 500)
$TLT +.86% (20+ Yr Treasuries)
$IWM +2.07% (Russell 2000)
$DIA +2.45% (Dow Jones) https://t.co/y2iiwQKA80
tweet
RT @realpristinecap: 1-Week Index ETF Performance via Pristine Capital 📊
$ARKK -5.96% (Innovation)
$QQQ -1.97% (Nasdaq)
$SPY -.20% (S&P 500)
$TLT +.86% (20+ Yr Treasuries)
$IWM +2.07% (Russell 2000)
$DIA +2.45% (Dow Jones) https://t.co/y2iiwQKA80
tweet
Offshore
Photo
Fiscal.ai
Eli Lilly v. Novo Nordisk
$LLY Mounjaro: $7.4B, +110% YoY
$NVO Ozempic: $5B, +7% YoY
$LLY Zepbound: $4.3B, +123% YoY
$NVO Wegovy: $3.4B, +25% YoY
Eli Lilly is pulling ahead in the weight-loss drug category. https://t.co/pqOIWkp2s5
tweet
Eli Lilly v. Novo Nordisk
$LLY Mounjaro: $7.4B, +110% YoY
$NVO Ozempic: $5B, +7% YoY
$LLY Zepbound: $4.3B, +123% YoY
$NVO Wegovy: $3.4B, +25% YoY
Eli Lilly is pulling ahead in the weight-loss drug category. https://t.co/pqOIWkp2s5
tweet
Benjamin Hernandez😎
A powerful finish to a profitable week for all our members.
We locked in +89.58% on $SMX. The wins continued with $SLNG, $CNI, $WHLR, $FLXS, $BNAI, $DRCT, and $LIF.
Momentum is on our side for next week.
Relax this Saturday.
tweet
A powerful finish to a profitable week for all our members.
We locked in +89.58% on $SMX. The wins continued with $SLNG, $CNI, $WHLR, $FLXS, $BNAI, $DRCT, and $LIF.
Momentum is on our side for next week.
Relax this Saturday.
Most losses come from being late.
By the time a tweet is seen, the move is often gone. I share explosive stocks and real-time breakout alerts on WhatsApp while momentum is still building
Stop chasing✅ https://t.co/71FIJIdBXe
Being early changes the game
$PLTR $SOFI $AMD $OPEN tweet
X (formerly Twitter)
Benjamin Hernandez😎 (@HayesStocks) on X
Most losses come from being late.
By the time a tweet is seen, the move is often gone. I share explosive stocks and real-time breakout alerts on WhatsApp while momentum is still building
Stop chasing✅ https://t.co/71FIJIdBXe
Being early changes the game…
By the time a tweet is seen, the move is often gone. I share explosive stocks and real-time breakout alerts on WhatsApp while momentum is still building
Stop chasing✅ https://t.co/71FIJIdBXe
Being early changes the game…
Offshore
Photo
Javier Blas
RT @JavierBlas: COLUMN: Buyers of Russian and Iranian oil are switching (to a point) to non-sanctioned alternatives. That has big implications for the price of crude.
@Opinion
https://t.co/pBuo6AA3WP
tweet
RT @JavierBlas: COLUMN: Buyers of Russian and Iranian oil are switching (to a point) to non-sanctioned alternatives. That has big implications for the price of crude.
@Opinion
https://t.co/pBuo6AA3WP
tweet
Offshore
Video
Moon Dev
Information Arbitrage: Cracking PolyMarket With A Consensus-Driven AI Agent Swarm
the code to dominating polymarket has finally been cracked and it doesn't involve being a genius or having a crystal ball. the real secret lies in building a machine that never sleeps and never gets tired of scanning thousands of prediction contracts while the rest of the world is busy sleeping. if you are still trying to find a market edge by manually clicking through the website you are effectively volunteering to be the exit liquidity for the bots that were there ten minutes earlier
prediction markets are the ultimate battlefield for information and most people walk into that fight with nothing but a blunt knife and some twitter rumors. there is a massive amount of noise generated every single hour by new contracts that range from global politics to obscure sports results. most humans can only keep track of maybe five or ten markets at a time before their brain starts to melt into a puddle of indecision
i realized early on that the only way to survive this game was to stop playing by the rules that the platform wants you to follow. i spent way too much money in the past hiring developers to build apps for me because i was terrified of the syntax of a python script. once i saw my capital disappearing into liquidations because of over trading and emotional mistakes i knew that automation was my only hope of staying in the game
this realization led me to build a swarm of ai agents that act like a specialized research department inside a major hedge fund. instead of asking one ai for its opinion i have six different models from grok to claude and deepseek all looking at the same data simultaneously. they work in parallel to analyze the market and if they don't reach a consensus i simply ignore the noise and move on to the next opportunity
the power of a swarm is that it cancels out the individual hallucinations or biases that a single model might have when looking at a complex trade. imagine having five experts in a room all staring at a massive bet that just hit the tape and waiting for them to all nod their heads in agreement. when a majority of these agents tell me that a market is inefficient i know i have found a signal that is worth my attention
i have always believed that code is the great equalizer because it allows a solo trader to have the same research depth as a firm on wall street. the big institutions have entire floors of people dedicated to finding these anomalies but they will never share their tools with the public. by using open source agents you are taking the power back and putting it into your own hands where it belongs
most people think they need a massive server to run this but you can actually do it for free using something like ollama for local processing. the technology has progressed so fast that the cost of an api call is basically approaching zero while the intelligence is skyrocketing. this creates an environment where the only real limit to your success is how fast you can iterate on your own ideas
tracking whale movements is one of the most consistent ways to find where the smart money is moving before the crowd catches on. my system uses a websocket to watch every single trade that happens on polymarket in real time without any lag or delays. when i see a trade over two thousand dollars i immediately flag it for the ai swarm to tear apart and analyze for any signs of an insider edge
if a big trader is dumping ten thousand dollars into an obscure election in another country they probably know something that the average retail trader doesnt. my bots filter out the emotional crypto and sports bets because those are often driven by fan bias rather than cold hard facts. we are looking for the markets where information is being priced in slowly and the ai can spot the gap between reality and the current odds
i learned to code in public on youtube because i wanted people to see that you dont need a ph d to build a life chan[...]
Information Arbitrage: Cracking PolyMarket With A Consensus-Driven AI Agent Swarm
the code to dominating polymarket has finally been cracked and it doesn't involve being a genius or having a crystal ball. the real secret lies in building a machine that never sleeps and never gets tired of scanning thousands of prediction contracts while the rest of the world is busy sleeping. if you are still trying to find a market edge by manually clicking through the website you are effectively volunteering to be the exit liquidity for the bots that were there ten minutes earlier
prediction markets are the ultimate battlefield for information and most people walk into that fight with nothing but a blunt knife and some twitter rumors. there is a massive amount of noise generated every single hour by new contracts that range from global politics to obscure sports results. most humans can only keep track of maybe five or ten markets at a time before their brain starts to melt into a puddle of indecision
i realized early on that the only way to survive this game was to stop playing by the rules that the platform wants you to follow. i spent way too much money in the past hiring developers to build apps for me because i was terrified of the syntax of a python script. once i saw my capital disappearing into liquidations because of over trading and emotional mistakes i knew that automation was my only hope of staying in the game
this realization led me to build a swarm of ai agents that act like a specialized research department inside a major hedge fund. instead of asking one ai for its opinion i have six different models from grok to claude and deepseek all looking at the same data simultaneously. they work in parallel to analyze the market and if they don't reach a consensus i simply ignore the noise and move on to the next opportunity
the power of a swarm is that it cancels out the individual hallucinations or biases that a single model might have when looking at a complex trade. imagine having five experts in a room all staring at a massive bet that just hit the tape and waiting for them to all nod their heads in agreement. when a majority of these agents tell me that a market is inefficient i know i have found a signal that is worth my attention
i have always believed that code is the great equalizer because it allows a solo trader to have the same research depth as a firm on wall street. the big institutions have entire floors of people dedicated to finding these anomalies but they will never share their tools with the public. by using open source agents you are taking the power back and putting it into your own hands where it belongs
most people think they need a massive server to run this but you can actually do it for free using something like ollama for local processing. the technology has progressed so fast that the cost of an api call is basically approaching zero while the intelligence is skyrocketing. this creates an environment where the only real limit to your success is how fast you can iterate on your own ideas
tracking whale movements is one of the most consistent ways to find where the smart money is moving before the crowd catches on. my system uses a websocket to watch every single trade that happens on polymarket in real time without any lag or delays. when i see a trade over two thousand dollars i immediately flag it for the ai swarm to tear apart and analyze for any signs of an insider edge
if a big trader is dumping ten thousand dollars into an obscure election in another country they probably know something that the average retail trader doesnt. my bots filter out the emotional crypto and sports bets because those are often driven by fan bias rather than cold hard facts. we are looking for the markets where information is being priced in slowly and the ai can spot the gap between reality and the current odds
i learned to code in public on youtube because i wanted people to see that you dont need a ph d to build a life chan[...]
Offshore
Moon Dev Information Arbitrage: Cracking PolyMarket With A Consensus-Driven AI Agent Swarm the code to dominating polymarket has finally been cracked and it doesn't involve being a genius or having a crystal ball. the real secret lies in building a machine…
ging system. iterate to success is a motto that has saved me from giving up when a bot failed or a strategy turned out to be a dud. every failure is just a data point that helps you refine the next version of the machine until it finally clicks
the system prompts i use are designed to make the ai think like a professional market analyst who is focused on deep discovery. you can customize these prompts to look for very specific things like historical polling data or legislative nuances that a human might overlook. this level of customization is what turns a generic tool into a specialized weapon for your own specific trading style
it is important to remember that these systems are not a magic button that prints money without any human oversight or logic. they are tools for market discovery and for finding the anomalies that jim simons used to build his legendary empire at renaissance technologies. you still have to put your own mind on top of the machine and decide which signals are worth the risk of your own capital
most traders are afraid of the blockchain because they think it is too technical but for us it is a transparent gold mine of data. every trade and every market is recorded in a way that allows a bot to scan for patterns that have happened before. by saving all these predictions into a local database you can look back after a few months and see exactly where the ai was right and where it was wrong
this historical record becomes your own personal library of alpha that no one else can buy or steal from you. the markets are constantly evolving and what worked last month might not work today which is why the iteration process never truly ends. the people who win in the long run are the ones who are willing to keep building even when the market is quiet
the wall street firms will eventually catch up and start using these same agent swarms for their prediction market departments. however because we are using open source code we are already three steps ahead of the curve and we can adapt much faster than a slow corporation. the agility of a solo coder with a powerful swarm of ai is the most dangerous thing in the financial world right now
i want to make sure that people understand this isnt just about making one good trade today but about building a system that works for you forever. the peace of mind that comes from knowing a machine is watching your back while you sleep is something that manual trading can never provide. the end of the liquidation era starts when you finally decide to let the machines do the heavy lifting for you
the future of trading is going to be dominated by these types of autonomous agents and the people who start now will have the biggest advantage. there is still so much low hanging fruit in these new prediction markets that even a simple system can find massive inefficiencies. i invite you to grab the code and start making it your own because the only way to fail is to never start at all
tweet
the system prompts i use are designed to make the ai think like a professional market analyst who is focused on deep discovery. you can customize these prompts to look for very specific things like historical polling data or legislative nuances that a human might overlook. this level of customization is what turns a generic tool into a specialized weapon for your own specific trading style
it is important to remember that these systems are not a magic button that prints money without any human oversight or logic. they are tools for market discovery and for finding the anomalies that jim simons used to build his legendary empire at renaissance technologies. you still have to put your own mind on top of the machine and decide which signals are worth the risk of your own capital
most traders are afraid of the blockchain because they think it is too technical but for us it is a transparent gold mine of data. every trade and every market is recorded in a way that allows a bot to scan for patterns that have happened before. by saving all these predictions into a local database you can look back after a few months and see exactly where the ai was right and where it was wrong
this historical record becomes your own personal library of alpha that no one else can buy or steal from you. the markets are constantly evolving and what worked last month might not work today which is why the iteration process never truly ends. the people who win in the long run are the ones who are willing to keep building even when the market is quiet
the wall street firms will eventually catch up and start using these same agent swarms for their prediction market departments. however because we are using open source code we are already three steps ahead of the curve and we can adapt much faster than a slow corporation. the agility of a solo coder with a powerful swarm of ai is the most dangerous thing in the financial world right now
i want to make sure that people understand this isnt just about making one good trade today but about building a system that works for you forever. the peace of mind that comes from knowing a machine is watching your back while you sleep is something that manual trading can never provide. the end of the liquidation era starts when you finally decide to let the machines do the heavy lifting for you
the future of trading is going to be dominated by these types of autonomous agents and the people who start now will have the biggest advantage. there is still so much low hanging fruit in these new prediction markets that even a simple system can find massive inefficiencies. i invite you to grab the code and start making it your own because the only way to fail is to never start at all
tweet
Benjamin Hernandez😎
The tech sell-off is finally over. Support levels held firm and the rebound was violent. Reset your mind this weekend and trade with peace on Monday.
Trade easier: 👉 https://t.co/71FIJId47G
Message “CALM” to join a group that trades easy. $ORCL $AMD $INTC $NVDA $TSM $TCNNF
tweet
The tech sell-off is finally over. Support levels held firm and the rebound was violent. Reset your mind this weekend and trade with peace on Monday.
Trade easier: 👉 https://t.co/71FIJId47G
Message “CALM” to join a group that trades easy. $ORCL $AMD $INTC $NVDA $TSM $TCNNF
A powerful finish to a profitable week for all our members.
We locked in +89.58% on $SMX. The wins continued with $SLNG, $CNI, $WHLR, $FLXS, $BNAI, $DRCT, and $LIF.
Momentum is on our side for next week.
Relax this Saturday. tweet
X (formerly Twitter)
Benjamin Hernandez😎 (@HayesStocks) on X
A powerful finish to a profitable week for all our members.
We locked in +89.58% on $SMX. The wins continued with $SLNG, $CNI, $WHLR, $FLXS, $BNAI, $DRCT, and $LIF.
Momentum is on our side for next week.
Relax this Saturday.
We locked in +89.58% on $SMX. The wins continued with $SLNG, $CNI, $WHLR, $FLXS, $BNAI, $DRCT, and $LIF.
Momentum is on our side for next week.
Relax this Saturday.
Offshore
Video
Moon Dev
dont buy an expensive computer to host these llms for clawdbot or whatever
your time is worth so much more than that https://t.co/l5Bif9xFSi
tweet
dont buy an expensive computer to host these llms for clawdbot or whatever
your time is worth so much more than that https://t.co/l5Bif9xFSi
tweet