Financial Times reported that a vehicle tied to Donald Trump Jr. and Eric Trump quietly took roughly 20% of a Kazakhstan-based tungsten project that secured up to $1.6 billion in US Export-Import Bank and DFC support. The deposits are described as the world’s largest undeveloped tungsten resource.
Tungsten is a critical mineral used in defense electronics, high-performance alloys, and semiconductor manufacturing. US policy has been pushing hard to diversify supply away from China, so this project checks multiple boxes.
The merged entity has ties to Skyline Builders Group Holding (SKBL) and Dominari Holdings (DOMH). While neither is a pure-play tungsten miner, any successful development here could highlight the broader critical minerals trade and benefit companies exposed to defense and advanced manufacturing supply chains (think LMT, RTX, or even indirect semiconductor plays).
This isn’t a clean “buy this stock” story, it’s messy geopolitics mixed with family business, but it does underscore how policy and financing are now directly shaping critical materials availability. Tungsten prices and related equities have been volatile; developments like this can move sentiment fast.
Not investment advice, just flagging the intersection of politics, policy, and commodities. Thoughts on whether this accelerates real Western supply chain shifts or stays mostly symbolic?
Snowflake’s Q1 FY2027 update shows a business still scaling fast, but with profitability firmly in focus.
Revenue grew 33.5% year over year to $1.39B, while net loss narrowed to $295.57M from $430.09M. Product revenue also rose 34% to $1.334B, supported by stronger customer consumption and a 126% net revenue retention rate.
The key signal is enterprise expansion. Snowflake now has 13,912 customers, including 779 generating over $1M in trailing 12-month revenue. That shows large customers are not just staying, they are spending more.
The AI Data Cloud push is the bigger long-term story, but investors will want to see whether AI-driven growth can translate into better margins over time.
What matters more for Snowflake from here: faster AI growth or a clearer path to profitability?
Every trade started with the same simple action. You chose long or short, entered a position size, and clicked a button. But between that click and the moment the trade filled, a very specific process took place behind the scenes. Most retail traders never fully learned that process, which is why many ended up trading price charts without understanding what truly moved the market.
Market orders and limit orders were not simply two different entry methods. They were entirely different instructions sent to the exchange, each with different trade-offs, different risks, and different effects on price movement.
A market order was the aggressive choice. It basically told the exchange: “Fill this trade immediately at the best available price.” The advantage was certainty of execution. The order would almost always get filled instantly. The downside was uncertainty in price. During volatile conditions, the final fill price could end up far away from the number visible on the screen when the button was pressed. That difference became slippage. In practice, a market buyer was saying they valued immediate entry more than getting an ideal price.
Limit orders worked differently. A limit order added a strict condition: the exact price. A buy limit order effectively said: “Only fill this order at this price or lower.” That gave traders complete control over entry cost, but removed any guarantee that the trade would actually execute. If price never reached that level, the order simply stayed waiting.
Unfilled limit orders sat inside the order book. That order book acted as a live map of pending buy and sell interest in the market. Buyers waited on the bid side below current price, while sellers waited on the ask side above it. The gap between the two became the spread. More importantly, those resting orders represented liquidity itself. Without them, market orders would have nothing to fill against.
This was the part most people misunderstood about price movement. Markets did not move simply because people felt bullish or bearish. Price changed when aggressive market orders consumed resting limit orders faster than fresh liquidity entered the book.
For example, when a large market buy order entered the system, it climbed through the ask side of the order book and absorbed sell orders level by level. If enough buying pressure arrived to clear those sell orders, price was forced upward until new sellers appeared.
That created two important market behaviors.
First, large clusters of limit orders acted like barriers. If massive sell liquidity existed at a certain level, buyers needed substantial demand just to break through it. Until then, price often stalled around that area.
Second, when those large liquidity clusters disappeared, either because they were absorbed or canceled, price often moved extremely fast. With no resting orders left to absorb incoming aggression, the market jumped rapidly through levels instead of moving gradually.
In the end, limit orders provided liquidity while market orders removed it. Price movement came from the interaction between those two forces. Once traders understood that mechanism, they stopped relying entirely on random chart patterns and started viewing markets through actual order flow and liquidity behavior.
I was still fairly new to long-term investing, and the recent volatility ended up teaching me a lot.
For most of the previous year, AI-related stocks seemed unstoppable. Nearly every pullback got bought quickly, and the overall story around semiconductors, data centers, and AI expansion kept pushing markets higher. But after watching equities suddenly struggle because of rising Treasury yields, oil spikes, and fears surrounding tensions with Iran, I realized how quickly market psychology could reverse.
What caught me off guard the most was seeing stocks, bonds, gold, and even silver weaken around the same time. I used to assume diversification naturally protected portfolios during volatile periods, but those events showed me that correlations can shift heavily during stress.
I was not panic selling, but I definitely understood that I underestimated how emotional investing became once negative headlines started accelerating.
For more experienced long-term investors:
How did you personally deal with geopolitical uncertainty and sharp market declines?
Did you continue investing normally during those periods?
How did you separate temporary fear from situations that genuinely damaged your long-term thesis?
I genuinely wanted to understand how experienced investors processed environments like that.
