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
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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
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Offshore
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Fiscal.ai
Why have returns at Roblox been so poor since IPO?
Hours Engaged: +250%
Stock Price: -3.8%
$RBLX https://t.co/QjwlCXuSAt
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Why have returns at Roblox been so poor since IPO?
Hours Engaged: +250%
Stock Price: -3.8%
$RBLX https://t.co/QjwlCXuSAt
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Offshore
Video
God of Prompt
RT @alex_prompter: this is your competition https://t.co/m7XgDebbQW
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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
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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.
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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
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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
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Offshore
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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
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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
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Offshore
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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
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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
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