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God of Prompt
RT @godofprompt: R.I.P few-shot prompting.
Meta AI researchers discovered a technique that makes LLMs 94% more accurate without any examples.
It's called "Chain-of-Verification" (CoVe) and it completely destroys everything we thought we knew about prompting.
Here's the breakthrough (and why this changes everything): 👇
tweet
RT @godofprompt: R.I.P few-shot prompting.
Meta AI researchers discovered a technique that makes LLMs 94% more accurate without any examples.
It's called "Chain-of-Verification" (CoVe) and it completely destroys everything we thought we knew about prompting.
Here's the breakthrough (and why this changes everything): 👇
tweet
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Wasteland Capital
$MU now passed the 300%+ return mark. Meanwhile $AEO just hit new 4-year highs.
$LYFT outperforming +55-60% vs $UBER and $DASH (both of those are negative).
Average return now +139%.
Compounding is beautiful. https://t.co/ZerFN5dOf0
tweet
$MU now passed the 300%+ return mark. Meanwhile $AEO just hit new 4-year highs.
$LYFT outperforming +55-60% vs $UBER and $DASH (both of those are negative).
Average return now +139%.
Compounding is beautiful. https://t.co/ZerFN5dOf0
Apart from my earnings reviews, I’ve only tweeted five new cases on here over the last year.
They ended up the #1 Mag7 YTD $GOOG, the #1 Semi YTD $MU & #1 China LargeCap YTD $BABA. Plus 2 🚀 smallcaps, $AEO & $LYFT
Average return +111% currently.
Less is more, as they say. https://t.co/LGr5bglq7t - Wasteland Capitaltweet
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Brady Long
RT @0xgaut: Mom: "how's the job search going?"
you: "Claude, build me a prediction market arbitrage bot" https://t.co/eFnRqOu05Y
tweet
RT @0xgaut: Mom: "how's the job search going?"
you: "Claude, build me a prediction market arbitrage bot" https://t.co/eFnRqOu05Y
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God of Prompt
RT @godofprompt: 🚨 DeepMind discovered that neural networks can train for thousands of epochs without learning anything.
Then suddenly, in a single epoch, they generalize perfectly.
This phenomenon is called "Grokking".
It went from a weird training glitch to a core theory of how models actually learn.
Here’s what changed (and why this matters now):
tweet
RT @godofprompt: 🚨 DeepMind discovered that neural networks can train for thousands of epochs without learning anything.
Then suddenly, in a single epoch, they generalize perfectly.
This phenomenon is called "Grokking".
It went from a weird training glitch to a core theory of how models actually learn.
Here’s what changed (and why this matters now):
tweet
memenodes
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tweet
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X (formerly Twitter)
m (@skitzocat) on X
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Brady Long
If you’re building agents, this matters more than another bigger model launch.
MiroThinker 1.5 is about agentic density: better reasoning per parameter, lower cost, and more controllable behavior.
Explore: https://t.co/mlOCZjARcI
https://t.co/q3P6xXXYhW
tweet
If you’re building agents, this matters more than another bigger model launch.
MiroThinker 1.5 is about agentic density: better reasoning per parameter, lower cost, and more controllable behavior.
Explore: https://t.co/mlOCZjARcI
https://t.co/q3P6xXXYhW
We just flipped the scaling narrative: Agentic Density > Parameter Count.
#MiroThinker 1.5 operationalizes Interactive Scaling—agents that seek evidence, iterate, and revise in real time (with a time-sensitive sandbox to avoid hindsight leakage).
Result: 30B hitting frontier-class agentic search, ~$0.07/query (≈20× cheaper vs 1T-class baselines).
Fully open source, read more: https://t.co/m4HzxiidRX
Try: https://t.co/dwKmu3O9t7
GH:https://t.co/u4VhL8o8Gt
HF: https://t.co/ClqKRrQn6R - MiroMindAItweet