Mega IPOs and massive capex between AI and supply chain reshoring all competing with bonds driving rates and inflation up and liquidity down?
Seems like a recipe people are starting to panic about
$URNM will be up ~150% from here around this time next year
☢️#SPUT: Sprott Physical #Uranium Trust
Third largest discount to NAV since inception in Jul '21; a metric that tends to coincide with lows in the sector...
The stocks are also historically undervalued relative to spot, presenting an asymmetric setup for contrarian speculators...
xitter.cf/uraniuminsider/status/…
It is absolutely criminal that this has such low OI and volume
Make trading vol great!
Would really like to see $BVIV on @HyperliquidX get more liquidity and volume.
Where the MM's at??
Issh retweeted
If you are mid to long term bullish, but derisking short term
You are expressing the opinion that you will beat the market twice in the short term
First time that you will derisk high enough and second time you will be able to buy back in low enough
That's two additional short term trades you need to nail on top of your long term view
Good riddance to the concept of the IoT
No more stupid internet connected smart devices spying constantly
There is about to be a massive booming market for "dumb" appliances
Also the TV's and shit spying needs to end as well, though that wasn't a back, just shady
Whenever you're trying to decide where to position, a helpful exercise is to chart the tickers against each other rather than vs USD/BTC
For example I'm quite bullish ENA and WLD and had to look at ENA/WLD as a pair to give a better sense of how to position and sizing etc
Would really like to see $BVIV on @HyperliquidX get more liquidity and volume.
Where the MM's at??
Belief has a profound influence on our lives. It can shape every facet of the choices we make and paths we take.
Most live passively with beliefs a byproduct of whatever experiences they found themselves a part of.
Lots can change when we realize we have the option to steer.
One of the best takes I've seen on the current capability of frontier models.
"opus-5.5 is a coding superintelligence on par with world class experts and bad code is a user's skill issue at this point" is an increasingly popular take being echoed around. so, what are we still doing here?
i've been coding through various robotics ML projects for the past ~3 years. everything ranging from throwaway r&d codebases during early conceptualization to stable and optimized codebases during later productionization. i'm by admittedly by no means a "world class expert" in coding, and since gpt-4 was released just as i was getting interned in what is my first full-time engineering job, i've consistently leveraged the capabilities of these models and evolved alongside them every step of the way from the humble days of gpt-4 to the undeniably cracked opus-5.5 and on.
now the interesting question that comes to mind is that since i was born too late to independently develop deep practical expertise in any engineering subfield that i can then capitalize on to confidently supervise and steer those models, what do i even contribute to any of my AI-assisted projects? am i really just a middle-manager between upstream management and downstream coding agents? well, i don't think so, or at least not yet.
while modern agents definitely don't lack engineering know-how and a versatile technical skillset, i've always found, and do continue to find, that they do lack the kinds of natural commonsense that we humans take for granted. models remain unbelievably shortsighted, inefficient, and autistically oblivious to various unverbalized expectations around outcomes. despite superhuman coding skills, even the legendary opus-5.5 finds it impossible to hold back from diving head-on into unsustainably hacky solutions, paranoid defensive coding patterns, confused design choices with utter disregard to obvious long-term trade-offs, and above all, painfully bad taste decisions around high-level project directions. i am yet to take my hands off the steering wheel and not find myself frustratingly interrupting to re-steer soon after.
the thing about this particular set of cognitive blindspots and shortcomings that i find especially unique is how orthogonal they seem to be relative to all other predictable improvements in capability. since the early days of gpt-4, i don't find it to be the case that models have improved much on minimalism-by-default, on reframing problems instead of stubbornly hacking through them, or on effectively leveraging theory of mind to satisfy general expectations in regards to writing style be it code, documentation, or anything involving external readers. the degree to which i find myself putting in constant effort to verbalize obvious human intuitions into explicit and persistent notes and skills to auto-steer agents away from their alien (not the intelligent kind) basins leaves me disillusioned with their independent judgement to this day.
i think our reward functions are incentivizing the wrong priorities when attempting long-running agentic work. i think our modeling algorithms are failing to reliably internalize fundamental human biases, such as the deep desire for efficiency in all aspects of life (aka "working smarter, not harder"). i think the capacity to generate beautiful original ideas and reason about next steps at a high level remains largely underoptimized across the current frontier. i think we don't yet have AGI in the sense that we can expect from it the basic level of sane, well-calibrated intuitions we'd expect from a competent junior engineer, let alone a senior domain expert, despite obviously superhuman technical knowledge and skills.
tl;dr: don't leave the driver's seat just yet. impressive lane assist and auto-parking is where we're currently at.
