与「strikers」相关的搜索结果

“But the Nazis weren’t socialists because they crushed labor unions!” It wasn’t until 1980 that the first independent union was legalized in the Soviet bloc. “Solidarity” in Poland reached 10 million members in a year which was absolutely terrible for the commies. So in 1981, the Communist Party declared martial law. They sent their tanks into the streets, shot protesting miners, and jailed tens of thousands just to keep their grip on Poland. Jaruzelski cut the phones, sealed the borders, banned the union, and imprisoned the their leader Lech Wałęsa for 11 months. 3 days after that, riot police stormed the Wujek mine and killed 9 strikers who refused to accept the crackdown. Internments, censorship, and surveillance continued for years afterward.

Cognitive reward shapes in sports and career Sports are amazing environments to learn. When you play a sport for thousands of hours, you start to see the world through that sport. It is a simple fact—your biological neural network is being conditioned to respond to the behavior incentivized by the rules of the sport. The funny thing is that most people choose their sports for accidental reasons such as parents, geography, or school programs. People rarely think about how the particular sport you play influences how your brain thinks more generally. Going a step further, playing the right sport may even benefit your career. My two favorite sports are tennis and soccer. Tennis is one of the best sports for teaching consistency. In tennis, there are hundreds of points in a match, and each point is worth exactly one unit, regardless of whether your opponent made an unforced error or if you constructed the most beautiful point ending with a winner. Tennis is low-variance optimization—you win by reducing unforced errors, playing percentages, and grinding out small advantages. Tennis is also an individual sport, which teaches you to rely on yourself consistently. Tennis has a similar cognitive reward shape to professions like being a surgeon or a pilot. Surgery and aviation require consistency, self-accountability, and deep focus. And similar to how you can only win one point at a time in tennis no matter how spectacular it was, there is no extra credit for the best appendectomy or the smoothest SFO-JFK flight. Your craft is to provide consistency with very low tolerance for error. On the other hand, the tennis mindset transfers relatively little to entrepreneurship. Entrepreneurship is a high-variance, team game where failure is tolerated and occasional creativity gets rewarded exponentially. Minimizing unforced errors in tennis is a totally different mindset from deciding whether to make a moonshot business move that will likely fail but could potentially net a billion dollars. Obviously I am not saying that tennis players cannot be great entrepreneurs, but I do think it is a totally different cognitive reward shape. Being a forward in soccer has a much closer reward shape for entrepreneurship. What a forward in soccer learns is to create many small chances. It is a fact that most of the game, you are not scoring—even if you look at all the times that Mbappe got on the ball in one of his best games, most of those led to nothing! But all that matters is creating enough chances to score once (or a few times) and win the game. If you break down a 90-minute game for a forward, almost all the time is failure or noise, a few minutes will be leverage, and a few seconds will determine the fate of the game. I have not played soccer for thousands of hours, but I can imagine that being a lifetime forward in soccer would teach you to be comfortable with failure and asymmetric returns. In summary, I am claiming that there can be substantial value when the cognitive reward shape of your sport mirrors that of your career. I’ll admit that I’ve done some cherry-picking for illustration purposes—entrepreneurship also requires consistency and error avoidance; and goalies in soccer have reward shapes that are very different from strikers. But I think the point stands. If sports shape how we perceive risk, effort, and reward, then we should choose them wisely.

A TON OF THINGS HAPPENED IN THE STOCK MARKET TODAY. Here's a full recap: 1. The U.S. reportedly offered Iran a deal to halt the siege and lift sanctions in exchange for reopening the Strait of Hormuz and ending proxy attacks, according to Al Arabiya. Axios also reports that Rubio told several foreign counterparts the U.S. does not plan new strikes on Iran for now, with pressure shifting toward the naval blockade and new sanctions campaign instead. Crude Oil fell 4% and the 10-year treasury bond fell from 4.72% to 4.62%. 2. Global physical gold-backed ETFs $GLD attracted $6.4B of inflows last week, their largest weekly intake since January and the 3rd-largest weekly inflow on record. North America led with $4.4B, followed by Europe at $1.7B and Asia at $300M. This marked the 7th straight week of inflows, with global gold ETFs pulling in $16.4B over that stretch. Total AUM in global gold ETFs rose by $33B last week to $615B, the highest level since the second week of May. 3. Intuit $INTU reported Q4’26 revenue of $4.4B, beating estimates of $4.27B and up 14% YoY. Adjusted EPS came in at $4.03 versus $3.58 expected. Global Business Solutions revenue rose 14% YoY to $3.4B, the Online Ecosystem grew 17% YoY to $2.6B, Consumer revenue increased 14% YoY to $930M, and Credit Karma revenue rose 16% YoY to $743M. For FY27, Intuit guided revenue to $23.3B–$23.5B versus $23.72B expected, while adjusted EPS guidance of $22.88–$23.12 came in well below the $27.32 estimate. The company also raised its dividend 15% YoY to $1.38/share, bought back $5.5B of stock, and has $7.9B remaining on its authorization. Management said its strategy is to win as an AI-driven expert platform while staying disciplined on investments and scaling its big bets. 4. President Trump said the U.S. Navy has removed and/or detonated all mines from international waters in the Strait of Hormuz. He said Iran has been notified that any ship or boat placing new mines will be “immediately and systematically destroyed.” Trump added that Space Force is monitoring every square inch of the Strait, along with Pickaxe Mountain and the three previously destroyed nuclear sites, and said a “Zero Tolerance” policy on mine placement is now in full effect. 5. Canada is responding to U.S. tariffs with new tariffs of its own. The country is raising steel tariffs to 50% from 25%, while roughly 700 products will face new tariff rates of 15%, 25%, and 50%. The measures are set to take effect on September 8, marking another escalation in the U.S.–Canada trade dispute. 6. Anthropic is expected to tell IPO investors its total addressable market exceeds $30T, topping SpaceX’s $28.5T estimate, according to WSJ. The figure represents the potential value of work Anthropic believes AI models could eventually perform, not a direct revenue forecast. Anthropic generated $11.6B in Q2 revenue and could seek to raise as much as $100B at roughly a $2T valuation. IPO documents are expected within weeks, potentially setting up a September or early October listing. 7. OpenAI’s data-center head Chris Malone left the company last week, according to WSJ. Malone joined in March 2025 shortly after Stargate was announced and played a key role overseeing OpenAI’s massive data-center buildout. He previously led data-center strategy at Meta and earlier worked on data-center technology at Google. The departure comes just weeks after OpenAI also replaced its chief revenue officer, adding another senior leadership change as the company races to scale infrastructure, revenue, and compute capacity. 8. ClickHouse has surpassed $350M in annual recurring revenue, up 40% since May, as AI agents drive demand for database and observability infrastructure. OpenAI’s usage has reportedly grown roughly 10x over the past year to more than 30 petabytes of data per day, or around 30T events daily. OpenAI has also shifted parts of its log-management workload from Datadog to ClickHouse over the past year. ClickHouse was valued at $15B in January and says gross margins currently range from 50%–70%. Earlier this year, the company acquired Langfuse to expand deeper into monitoring AI applications and agents. Nebius $NBIS owned a 28% stake in ClickHouse as of May 2025, though that stake has likely been diluted by subsequent fundraising. 9. JPMorgan reiterated its Overweight rating on SpaceX $SPCX with a $240 price target, saying the company’s AI ambitions are coming into sharper focus and that it is increasingly positive on Grok. The firm highlighted SpaceX’s completed acquisition of Cursor on 8/14 as an important step in building enterprise AI capabilities. Cursor brings roughly $4B of ARR as of June 2026, with about 75% coming from businesses, which JPMorgan says should help streamline go-to-market and provide valuable model-training data. The firm also said Cursor data is already showing up in Grok’s supplemental training, with tangible improvements in recent model performance. 10. OpenAI says its new Broadcom-built Jalapeno AI chip outperformed Nvidia $NVDA GB300 in both throughput per watt and response latency during internal testing, according to Bloomberg. The chip is built specifically for inference, not training, and runs at roughly 700 watts. OpenAI plans to begin deploying Jalapeno for its models later this year, saying the performance gap widened on larger workloads, including Moonshot’s Kimi model, and that the chip has also performed well on unreleased OpenAI models. The key caveat is that Jalapeno was tested against GB300, not Nvidia’s newer Vera Rubin generation. OpenAI says a second-generation chip is already nearing tape-out, while work on a third generation has begun. 11. The top 10 most active options today by contracts traded were $NVDA with 1.8M contracts, $TSLA with 1.8M contracts, $AAPL with 636K contracts, $SPCX with 548K contracts, $INTC with 540K contracts, $AMZN with 498K contracts, $MU with 483K contracts, $AMD with 403K contracts, $PLTR with 361K contracts, and $SOFI with 359K contracts. 12. Raymond James raised its Nvidia $NVDA price target to $352 from $330 and reiterated a Strong Buy rating. The firm says Nvidia’s CPU opportunity is becoming more important, especially for agentic AI workloads, even though CPUs are only about 3% of sales today. Raymond James expects CPU revenue to reach roughly 5% of total revenue by CY28 and believes Nvidia could potentially become the world leader in CPU revenue within several years. The firm also argued the stock remains inexpensive, trading at less than 15x CY27 GAAP earnings, below the S&P 500 at 18.6x, despite sales and net income growth still expected to exceed 20% in CY28. Its new $352 target is based on a 22x multiple on CY28 estimates, which Raymond James views as conservative given Nvidia’s leadership, CUDA moat, GPU performance, free cash flow, and history of trading at much higher multiples. WALL STREET IS THE GREATEST SHOW ON EARTH.

Today I learned that the U.S. embassy in Kyiv uses my live coverage as one of their main sources for following Russian missile strikes while they are sheltering. I'm not even kidding. Multiple separate people have told me this now.

🚨 Aston Villa to reject formal bid from Al Hilal for Ollie Watkins. Proposal to sign 30yo striker worth €45m package - will be turned down by #AVFC# but #AlHilal# pursuit of England international ongoing. W/ @J_Tanswell @TheAthleticFC after @FabrizioRomano

🚨🔵⚪️ FC Porto are ready to advance and try close Santi Giménez deal soon as first official bid will be sent to AC Milan next. Understand Porto are offering a loan deal with buy option clause. The agreement with Mexican striker on personal terms is already done. 🐉🇲🇽

🔴⚪️🇧🇷 Internacional have agreed deal to sign Tonny Sanabria from Cremonese. Contract until 2029, travel tomorrow and then medical in Porto Alegre for the Paraguayan striker. 🇵🇾

When language models first started using tools well, I was sympathetic to the narrative that instead of scaling up language models, all we needed was a strong enough "cognitive core", say 1B parameters, and anything else could be done with tool use, like browsing the internet or executing code. I think a lot of people were sympathetic to this argument, and indeed it is pretty hard to come up with a meaningful task that cannot be in principle achieved by a 1B model with adequate access to tools. For example, any esoteric fact that a large language model would know can be, in principle, retrieved from the internet and reasoned over by a 1B language model. However I now think this is totally wrong for one simple reason: doing tasks quickly and naturally without tool use matters a lot. The way that I internalized this reason was actually in my personal journey learning badminton this year. In badminton I am very much like a "1B cognitive core". While I can physically do every movement in a badminton shot that my coach teaches me, it requires a lot of work to mentally remember every cue and put it together. In practice I can do a shot almost perfectly, but I struggle to do it across a point and I definitely can't do it consistently in a game. This is obviously different from someone who has practiced a shot ten-thousand times and effortlessly executes it as a natural instinct. In the same way, language models knowing a fact internally, without tool calls, is meaningful. The first reason is that we obviously care about speed; you'd much rather get an answer immediately than have the model think a long time to be sure of its answer or browse the web. A second reason is that there are some things that are simply best learned via backpropagation over lots of data. If you ask about how people generally think of the Shambhala music festival, you'd rather a large language model give you an aggregate opinion based on all the data on the internet, than get a regurgitation of the first three reviews that show up in a web search. A third reason is that having to do a lot of work to find an answer is not as reliable as already knowing the answer. While this does not have to be true in theory, it is probably true in practice, at least for now. If you have to re-look up facts or redo a mathematical derivation all the time there is a higher chance of mistakes, which can compound in a long-horizon task. Once you buy that it is valuable to do things parametrically without tool use, then you must buy the argument that a 1B cognitive core is not sufficient. There is an information limit to how much knowledge can be internalized by a 1B model, and we will surely want AI to know more than that. Even 1T probably won't be enough. We will want the AI to know as much about our world as possible, we will want it to be updated with new information, and our expectations of what AI can do for us will continue to grow. In summary, tool use enables small models to do a lot more, but those who demand the highest quality intelligence will always want larger models. Bitter lesson strikes again.