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Yes, the rumors are true: The more powerful Notebook experience is now 100% rolled out to ALL Pro users. You might be asking... How exactly do I use it? Here are a few queries to try (ofc dependent on your sources). Hot Tip: bookmark this post! Free users too 😉 For Learning: "Create 5 quizzes of ascending difficulty focusing on a different topic from my sources" "Turn my raw lecture notes into a formatted PDF study guide complete with a 5-question practice quiz." "Add all of the URLs from this class syllabus as sources" For Business: "Calculate revenue growth across each product line and plot the trends" "Read our marketing brief, search the web for competitors, and recommend how we should tune our messaging" For Personal: "I uploaded a bunch of receipts from our kitchen renovation, make a spreadsheet to track all the work and costs" "Help me analyze this property purchase. Look up recent market performance in the neighborhood, predict trends, and make a spreadsheet to model all the data you find"

I usually ignore stuff like this, but I don’t appreciate false narratives and weird rumors being spread about me by parasocials who have nothing better to do than harass women. After SU & my phone number being leaked, I simply cleaned up my following list and unfollowed multiple streamers I didn’t actually end up interacting with. Turning something that simple into a fake storyline about me is seriously disturbing

Ferran Torres’ Explosive Interview Bombshell 🔥 Interviewer: Ferran, there have been endless rumors and agendas circulating about your personal life for a while now. Care to set the record straight? Ferran Torres: Look, I’ve been waiting for the right moment to say this. I’ve always been gay. I honestly don’t know why it took everyone so long to piece it together the signs were there if people were really paying attention. I’ve seen all the deadbeat stories and whispers flying around online, trying to expose me or paint me in some negative light. At first it bothered me, but now? I’m done hiding. I used to wonder if being gay would ruin everything I built in football. But I’ve reached a point where I realize… it doesn’t have to. Yes, I’m gay. I’m a proud member of the LGBT community, and I stand by that 100%. This is who I am, and I’m finally owning it. Interviewer: How are you feeling now that it’s out there? Ferran Torres: Free. For the first time in a long time, I feel free. The game is still my passion, but I’m no longer carrying that weight alone. If my story helps even one young player out there feel less afraid, then it’s worth it.

Devon Witherspoon is rumored to be getting a new contract in the ballpark of $32 MM a year... With Kamari Lassiter eligible for a contract extension next offseason the Texans might have to cough up that sort of money for him In my opinion he's on the same level if not better

🚨 NEW UPDATE on the New Orleans Saints & Chris Olave’s extension! The Saints are expected to get a deal done before week 1 according to Larry Holder of The Athletic. This is consistent with the rumors & reports around Olave’s extension that local media has shared.

The story of AI in the next few years is going to be compute: an essay on the future of AI. K3 in 2 days is already #10# on OpenRouter with ~140B tok/day, and it’s infra is crumbling. Throughput is down from 30tok/s to 13tok/s, E2E latency is up to 72s and time to first token is >20s! It would cost a minimum of $500k to buy the 8 B300s it would take to serve even quantized Kimi K3 and ~$4M for the more recommended GB300 NVL72 rack. I don’t think Moonshot has the compute available to scale to their demand! In fact, even the US based inference providers will likely not be able to scale capacity as much as they’d like even if they were to host it: a 2.8T model is no joke. GPU providers (neoclouds etc) are doing 3yr and I recently hear 5yr commits with an ungodly 30% down, and customers are chomping it up. Prices continue to go to the moon. The two big labs, hyperscaler clouds, Grok and Meta have compute deals locked in prior, and the rest are fighting for scraps. Tier 1 neoclouds (coreweave/nebius etc) are rumored to not even small “smaller” customers. Meta is the biggest wildcard here. With ~7GW of compute by eoy 2026 and no clear big model ties, they either get to frontier on their own or can host the most Kimi K3 capacity (unless they sell it to the labs). Even though the price of models has fallen over time, it’s worth noting that the price of frontier has not. 3yrs ago, GPT-4 released at $60/M, o1 at $60/M, Opus 4 at $75/M, GPT5 at $10/M, Fable at $50/M and now Sol at $30/M and K3 at $15/M. Even if you consider K3 frontier, that’s only a 4-5x flux in 3yrs. In that time, frontier demand has increased at least 3+ ooms and frontier intelligence performance has gone 32x at least by task time by METR. Essentially, so long as a) the demand for frontier intelligence continues to grow to near infinity, b) the frontier continues to grow in performance, even as c) if the price of frontier declines a little, the value accrued to frontier grows significantly! And there’s a tremendous bull case for those who have locked up compute if you’re bitter lesson pilled and believe larger models will always be smarter models.

Chili Pepper Price Trends 2026 Nine years ago there were already rumors that chili pepper futures would be listed. I researched it at length and even wanted to visit the production regions in India, but nothing ever came of it. Here I have to say something about AI hallucination, which increasingly demands vigilance these days. AI swore up and down that the Zhengzhou Commodity Exchange had listed chili pepper futures. Fortunately I trade every day; after a moment of doubt, I confirmed that the Zhengzhou exchange has no such thing. Back to the main story. The top boss among chili producing regions is of course China, now accounting for nearly half of global output, at 22 million metric tons per year against world production of 44 million tons. That output has fully doubled from twenty years ago to today. The growth in chili demand tracks partly with population growth and partly with economic growth: as incomes rise, people dine out more, and chili demand rises with it. Alongside this came the territorial conquest of Sichuan and Hunan cuisine, and the penetration of Mexican tacos into the United States. However, chili flavors differ by variety, and fresh chilis are hard to transport and store, so price correlation between the Americas and Asia is not high. Chinese and Indian chili prices, meanwhile, are increasingly intertwined. This is mainly because India’s S17, a high heat dried chili, occupies an ever larger place in Sichuan and Hunan cooking. People’s palates keep getting heavier: spiciness only ever intensifies, never lightens. India’s actual output is about half of China’s, but because it is dried chili, its nominal volume appears to be only one tenth of China’s. Chili shares two traits with other vegetables. First, the cobweb effect: farmers blindly chase prices, so price and output repeat in cycles, circling in place like a spiderweb, like the succession of ancient dynasties. Second, eating at the mercy of the heavens: climate has an enormous impact on chili output. But climate effects in China, India, and Mexico are not synchronized. If you compare the overlap between apple and chili growing regions, you may find some correlation between Chinese chili and apple prices, but chili has its own characteristics. Cold storage apple prices do not affect new season contracts, whereas chili cold storage inventory can affect market prices directly, with no futures influence. Still, the structural contradiction here is obvious. Last year’s high dried chili inventory and high import volume, stacked on weak food service consumption, formed one side. Meanwhile the small fresh chilis of daily consumption, thanks to the cobweb effect, saw planted acreage fall by 20 percent. So on some wholesale price indexes, you can see Sanyingjiao chili prices rebounding. For those in the growing and wholesale trade, expectation management is critically important. For more on vegetables, fruit, fresh proteins, and meat, visit the fresh commodity world, launching soon.

A few thoughts on the very near future First of all, what had previously been little more than a rumor has now been confirmed: GPT-5.6 had already been fully trained for two months and was available to selected users in early access. The obvious question is why it was not rolled out earlier. I do not think this was because OpenAI feared that the model might be overshadowed by Fable 5 or Mythos 5. Instead, OpenAI likely began working with government and regulatory authorities at a very early stage to ensure that the model could be released at all. Even after it had been previewed and announced, it still took some time before it could be rolled out publicly. That said, OpenAI clearly handled the rollout far better than Anthropic, which apparently did not have the same level of cooperation with government and regulatory authorities. Conversely, however, this also clearly means that future delays and increasingly strict model reviews will probably force us to wait longer for official releases. The next widely discussed rumor is that, within a few weeks, most likely no more than six, we will see either a preview or even the release of GPT-6. (Andrew Curran @AndrewCurran_ is one of the most reliable sources here on X, so I think that's very realistic.) The model has undergone entirely new pretraining, and the pace of releases is accelerating. The numbers are clear: Frontier labs are releasing more and better models at an increasingly rapid pace. Whereas we once had to wait months, quarters, or even half a year for major new releases, they are now arriving almost weekly. The latest frontier models may be more efficient in terms of intelligence per token, but they are also being deployed with much larger reasoning budgets. In practice, models such as Fable 5 and GPT-5.6 often consume considerably more tokens during complex or agentic tasks. This is not necessarily a sign of declining efficiency. Rather, it suggests that improvements in efficiency are being reinvested into deeper reasoning, longer trajectories and more capable agentic behavior. The result is that total compute consumption per task can continue to rise even as the underlying models become more efficient. Fable 5 and GPT 5.6 demonstrate just how intensive token usage has become. Although Sam Altman explicitly stated that GPT-5.6 is 54% more token-efficient (via CNBC), the fact remains that compute demand continues to increase, requiring more powerful and efficient computing infrastructure. Inference chips will probably become even more important as well. In summary, my initial conclusion from the latest releases is that compute demand will not merely continue to grow, but will probably exceed the available supply. This naturally means that energy demand will also increase, and, based on my initial assessment, probably more sharply than previously expected. This is likely to remain the largest bottleneck in the very near future. And this is important to me: there are bottlenecks. Not the training of the models, but besides compute, above all energy. This needs to be taken seriously! The US power grid, for example, is a major bottleneck, and the obvious question is how the necessary expansion can be achieved. Capital expenditure on data centers in the United States continues to rise sharply. This year, it exceeds 800 billion. It is not yet clear what the situation will look like in 2027, but I can hardly imagine investment declining or less CapEx being required. The reason lies precisely in the developments already mentioned: Demand is growing, particularly demand for energy. China clearly has an advantage here, a genuine moat, and I believe the West must be extremely careful not to fall behind because of the energy advantage China already possesses in practice. This could also help explain why, according to a recent Reuters report, China is considering restricting Western access to its frontier models. It may have concluded that it will win the long-term race. Unless there is a genuine breakthrough, whether in small modular nuclear reactors or fusion energy, I expect major problems to emerge over the coming years, for example by 2030. So far, I do not see any viable solutions. We can therefore clearly establish two points: Models are becoming larger, better, and increasingly useful for all users. There is no end to this development in sight. At the same time, the bottleneck appears to be growing increasingly severe, and this is already visible in practice. Regulation, energy demand, and compute demand could mean that, in the very near future, the release cadence will not accelerate as quickly as hoped or desired. This creates a clear contradiction. Thank you for coming to my TED Talk.

Today’s been an absolutely stacked day for AI news. In case you missed it: 1. GPT 5.6 Sol and Sol Ultra dropping tomorrow. Early reviews say it’s not as smart as Fable but very positive. 2. Grok 4.5 launches from Cursor / SpaceX that claims Opus 4.7 quality at 80tps and $2/M in $6/M out price. 3. Bytedance Seedream 5 Pro launches as an image ~#2# and nearly as good as GPT Image 2 at 4x cheaper cost: $0.045-$0.09/image, specializing in edits and infographics. Easily beats Meta’s Muse Image. 4. GPT-Live launches a full duplex non-turn based voice model which allows interactions while you’re talking seamlessly, a huge upgrade in audio AI for consumer Coming soon: 1. Seedance 2.5 Pro expected to launch soon (early July) to further extend Bytedance’s lead in SOTA video gen models. Will use Seedream 5 Pro as the frame generator. 2. Gemini 3.5 Pro launching soon (July 17). Google seems to have fallen quite behind frontier and people eagerly await to see what Google can put out here amidst a string of high profile departures. 3. GPT-6 rumored to launch soon (Polymarket spikes at August 14) with a new larger retrain, taking aim at Fable.