Offshore
Moon Dev The 7,547% Trading Bot Catch: Why Pro Developers Are Ditching Macs for This $15 Architecture seeing a seven thousand five hundred percent return usually means someone is about to get rugged or they just got incredibly lucky on a meme coin. the catch…
hink that just yesterday i thought i was totally cooked because i couldn't get the whisper voice commands to work on this new profile. it turns out i was just being slow and forgot to install the proper drivers on the remote machine. once that was fixed i could literally just talk to my trading bot while sitting on the couch and have it spin up twenty new backtests without touching the keyboard
if you want to win in this game you have to be willing to iterate to success and embrace the failures as part of the data set. i have been up for three weeks straight instead of blowing up because i stopped over trading and started trusting the automated systems i built. the universe tends to get out of your way when you make a non negotiable contract with yourself to see the process through to the end
we are entering an era where everyone can essentially be their own cto if they just learn how to orchestrate these coding agents properly. tool like vive kanban are launching to help humans and agents collaborate without the terminal logs becoming a total mess of confusion. it allows you to focus on the planning and the quality of the trades instead of getting bogged down in the syntax of the language
i remember shorting bitcoin at ninety six thousand five hundred when everyone else was screaming that it was going to a hundred thousand. they were tearing me apart in the comments but the data showed the exhaustion and the automated system held the line. that single trade would be worth nearly eight hundred thousand dollars right now if i had just let it run which proves that the logic beats the hype every time
staying in a good vibe and focusing on the seven seven seven action is the only way to stay sane in this high pressure environment. i don't need protection from big dogs because i am the data dog and i protect my own systems with the code i write every day. the path to a fully automated life isn't about having the flashiest gear but about having the discipline to keep building when everyone else is distracted by the noise
tweet
if you want to win in this game you have to be willing to iterate to success and embrace the failures as part of the data set. i have been up for three weeks straight instead of blowing up because i stopped over trading and started trusting the automated systems i built. the universe tends to get out of your way when you make a non negotiable contract with yourself to see the process through to the end
we are entering an era where everyone can essentially be their own cto if they just learn how to orchestrate these coding agents properly. tool like vive kanban are launching to help humans and agents collaborate without the terminal logs becoming a total mess of confusion. it allows you to focus on the planning and the quality of the trades instead of getting bogged down in the syntax of the language
i remember shorting bitcoin at ninety six thousand five hundred when everyone else was screaming that it was going to a hundred thousand. they were tearing me apart in the comments but the data showed the exhaustion and the automated system held the line. that single trade would be worth nearly eight hundred thousand dollars right now if i had just let it run which proves that the logic beats the hype every time
staying in a good vibe and focusing on the seven seven seven action is the only way to stay sane in this high pressure environment. i don't need protection from big dogs because i am the data dog and i protect my own systems with the code i write every day. the path to a fully automated life isn't about having the flashiest gear but about having the discipline to keep building when everyone else is distracted by the noise
tweet
Offshore
Video
Moon Dev
openclaw and i now are competing with OpenAi & Anthropic
and we will cook them
Moon Dev AI has officially been launched https://t.co/KESdHxkZcd
tweet
openclaw and i now are competing with OpenAi & Anthropic
and we will cook them
Moon Dev AI has officially been launched https://t.co/KESdHxkZcd
tweet
Offshore
Photo
Fiscal.ai
Why have returns at Roblox been so poor since IPO?
Hours Engaged: +250%
Stock Price: -3.8%
$RBLX https://t.co/QjwlCXuSAt
tweet
Why have returns at Roblox been so poor since IPO?
Hours Engaged: +250%
Stock Price: -3.8%
$RBLX https://t.co/QjwlCXuSAt
tweet
Offshore
Video
God of Prompt
RT @alex_prompter: this is your competition https://t.co/m7XgDebbQW
tweet
RT @alex_prompter: this is your competition https://t.co/m7XgDebbQW
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
Photo
God of Prompt
RT @free_ai_guides: 90% of "business idea" advice is generic garbage.
"Start a SaaS." "Build an agency." "Sell digital products."
That's why I built the Business Creator Mega-Prompt.
It interviews you like a $500/hr consultant:
→ Your skills and experience
→ Your actual budget (not fantasy numbers)
→ Your available time
→ Your risk tolerance
Then builds a complete business plan around YOUR reality.
Comment "Creator" and I'll DM it to you.
tweet
RT @free_ai_guides: 90% of "business idea" advice is generic garbage.
"Start a SaaS." "Build an agency." "Sell digital products."
That's why I built the Business Creator Mega-Prompt.
It interviews you like a $500/hr consultant:
→ Your skills and experience
→ Your actual budget (not fantasy numbers)
→ Your available time
→ Your risk tolerance
Then builds a complete business plan around YOUR reality.
Comment "Creator" and I'll DM it to you.
tweet
Offshore
Video
DAIR.AI
RT @omarsar0: This Composio connect-apps plugin for Claude Code is 🔥
It's the easiest way to instantly connect Claude Code to 500+ apps like Gmail, Slack, GitHub, and Linear.
You really don't need to be setting up MCP servers one by one.
I use it a lot, and it has saved me a ton of time. https://t.co/9D2vPlCLow
tweet
RT @omarsar0: This Composio connect-apps plugin for Claude Code is 🔥
It's the easiest way to instantly connect Claude Code to 500+ apps like Gmail, Slack, GitHub, and Linear.
You really don't need to be setting up MCP servers one by one.
I use it a lot, and it has saved me a ton of time. https://t.co/9D2vPlCLow
tweet
Offshore
Photo
Offshore
Photo
Benjamin Hernandez😎
ong Kong has protested against Panama’s court ruling which struck down the contract granted to Li Ka-shing’s CK Hutchison to operate two ports near the country’s strategic canal https://t.co/0xDbAVJmrO
tweet
ong Kong has protested against Panama’s court ruling which struck down the contract granted to Li Ka-shing’s CK Hutchison to operate two ports near the country’s strategic canal https://t.co/0xDbAVJmrO
tweet
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