HIMA's Stelato S9T has been on the mkt for 1 yr. S9 & S9T have accumulated over 60k in sales. It's currently battling Denza Z9 series as the top selling NEV sedan > 300k mkt. A very small but lucrative mkt as demand shifts to SUVs. This is also eating into BBA's 56E dominance.

A room with a view: The aurora today as seen from the SpaceX Dragon window. Space station is moving foward and the window looks out in the direction of travel. You can make out the window framing. Z9 1/4s 15mm ISO 8000 MP4.

While AI Safety debate is ranging, let me change the topic slightly and share something phenomenal I discovered today. 1) Importance of WebSearch If you replace DeepSeek Harness' default search with @ExaAILabs , your performance of DS V4.1 Flash model will be consistently at Astra High/Fable level. @ExaAILabs rocks! Before this, I did not realize Search API can make so much difference to quality of answers and analysis. I thought it to be an undifferentiated product. Earlier during my testing at least 10% of the model used to answer dumb, while other 90% of times it used to be good. But, now it is consistently great. At its max setting, the model is at Astra High level - which is very astonishing. @teortaxesTex @WilliamBryk 2) Desire to blow your mind: I was asking a complex tech question to DS V4.1 Flash and I was disagreeing with the model (it was about Huawei's 7.2T NPO Engine specification). It held its ground and it provided this diagram to me. It was pretty good for the answer. I though how amazing Exa is (which it absolutely is), then I realised this diagram was made by DS V4.1 Flash by cutting and pasting diagrams from different sources and adding the red coloured markers. It was f*cking precise!!! This model has so much potential for greatness, on top of being exceedingly fast. These experiences have changed my viewpoint towards flash models. May be flash is all you need! @zephyr_z9 @jukan05 @vikramskr @iamfabian @austinsemis 3) Fast Fast Deep Deep Research: I really stopped ChatGPT and Claude's Deep Research because - although it was excellent - it used to take lot of time. With DS V4.1 Flash running nearly at 300+ tokens per second and fast Exa search, I can use DeepSeek Harness's PTC mode (programmatic tool calling) or Agent Team mode (launches subagents) and get very well done research that is very fast (although later is a bit flaky). Astra Max is something that is industry leading in my opinion, but DS V4.1 Flash with DS Harness is exceptional (cost and speed wise wrt to quality ration is best possible)

The deadliest mountain in the world is K2 in Pakistan. But how deadly will the new K3 model by Moonshot be? In my view, this is not another "DeepSeek" moment. Kimi K3 is net-positive for the AI trade and compute demand, while being a net-negative for Western fronteir labs. After reading many reviews on X, my understanding is that Moonshot are just a few months behind the fronteir, rather than a full generation behind. I will caveat that I am far from being an AI "expert". However, I believe I understand the commercials at a high enough level to comment on and summarize. So from a commercial point of view, K3 comes out at roughly half the cost of Opus 4.8 for a slightly better model overall. Moonshot is charging ~$3 per million input tokens and ~$15 per million output tokens, the same rate card as Anthropic's mid-tier Sonnet and nearly 4x its own previous model. In my opinion, these pricing dynamics are what makes the DeepSeek comparisons I've seen on X to be very lazy. DeepSeek in Jan 2025 was a deflation event where they had near frontier capability built very cheaply and effectively given away at such low prices which implied that Western AI capex was excessive. Moonshot is doing the opposite. It's charging Western prices because it believes it is selling capability, not cheapness, most likely because Chinese labs are starving for compute and actually need the revenue to continue building and serving demand. The business model for fronteir labs like OpenAI and Anthropic is essentially the monetisation gap between their own leading models and everyone elses. Both of those companies are raising money at valuations that assume that gap stays wide and highly monetisable for years to come (at growing rates). However, many AI experts on X say that the Kimi K3 gap can now be measured in months, and that the tier just below the frontier, where the bulk of real commercial workloads actually run, is potentially about to be served up at very low commodity prices. As a result, the labs' defensible ground shrinks to the true frontier, to enterprise trust and security, and to distribution. Revenue can keep growing while pricing power erodes, and in my view, it is the pricing assumption (not the demand assumption) that carries Anthropic's and OpenAI's lofty valuations. Using SoftBank as a proxy for OpenAI, we saw today that their stock price fell a huge 9% after Kimi K3 was released. I believe, now, that this valuation-digest was and is warranted. I know many of my followers will care about the AI trade in upstream names though. However, I do not view the latest Kimi K3 model as having a fundamentally negative impact on those companies. Every K3 token will still consume chips, memory and power that remain effectively sold out into next year. Moonshot will certainly need to secure additional compute to facilitate growing demand for their models. As of right now, it does seem like exposure to model-layer margins, lab valuations and their listed proxies looks worse. Conversely, exposure to the infra in upstream supply chain names that serves tokens regardless of whose model wins looks cheaper, all things considered, for no fundamental reason. However....to be slightly more contrarian - you could argue that if cheaper Chinese models come to market at a rate of knots and leads to margin compression for the likes of OpenAI and Anthropic ---> will that not have a negative effect on their spend in the long-term? As a result, a negative effect on the upstream AI supply chain? I am yet to think through this in much detail, but it certainly deserves exploring. I want to finally mention some of the technical experts I have enjoyed reading since Kimi K3 came out, as a non-technical person: - @zephyr_z9 - @scaling01 - @FundaAI If you want to be someone who is constantly learning about AI, these guys are must-follows in my opinion. Of course, as always, I would encourage anyone to correct me or debate me if I am wrong in any area.