韩国人去杠杆差不多了 再跌下去整个国家就完了 这 tm 不是阳谋见了鬼了 在 KOSPI 最高点推出杠杆，总统亲自站台全民炒股 现在两代人积蓄归零，牛马下半生 就像暴涨的最后一段，完全是靠 FOMO + 杠杆 + 惯性 推着股价向上走，和基本面完全没什么关系 现在暴跌的最后一段，是靠 恐慌 + 爆仓 + 惯性，让股价向下，和基本面也没什么关系了 爆仓盘剩的不多了，估值足够低，美联储按兵不动，机构资金是时候入场了 但筑底仍需要时间，10Y 利率太高，流动性很差，不可能一个 V 反弹回去 耐心等待了 现在清掉的性价比太低，我不会清

@BigbirdflyChan 低PE泡沫的Earning的E是虚幻的 E是上游巨额资金“冤大头”涨价冲出来的，等“冤大头”缺货了会忽然断水 “冤大头”不缺货Earning就一直很好，市场天天担心断水不会给太高multiple

Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis. Rationale:   A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers.    Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.   This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.   Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3. 
The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead. Time will tell on both points. And likely fairly quickly. Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.

半导体继续被抛售，存储进入技术性熊市，短期看不到反弹迹象。很可能要等 7 月底的大科技 CAPEX 确认，以及新一季财报季的开启。SK Hynix 也是月底财报。 市场也在 risk off，不光是 AI Trade，也有太空、LPTH 等 high beta。 这种情况下，抄底没有意义，保留子弹、分散投资才是合理的。 BioTech 这段时间一直涨得很不错：我的持仓 $LQDA & 最近新建仓 $KURA 。观察中的 $AKTS ，计划这两天建仓。 $PENG 的财报大超预期，但也只是让股价短暂提升 +10%，随后就回落到 +6% 左右。市场现在不待见存储。我止盈了一点，拿住等下一轮。 说实话现在只要没有 sell the news（好财报 => 股价大跌），我已经很开心了…… PENG 现在是我的第二大持仓。 第一大持仓又回到了 LQDA……我没有加 LQDA，只是其他太拉跨导致的…… 昨天清掉了 $FCEL 真是躲过一雷，增发 $200M 导致股价暴跌。 $AMKR 相较ATH $96.88，两周内已经跌去了 1/3，如果继续跌破支撑就要回到 40 - 50 了 Anyway，这几周的日子不会好过。祝大家 stay safe，熬到月底就是下一轮。

Kimi K3 scores 57 on the Artificial Analysis Intelligence Index. Its intelligence is comparable to Opus 4.8 and GPT-5.5 but remains behind Fable 5 and GPT-5.6 Sol. Moonshot AI has expressed plans to release the 2.8T parameter model's weights, which would make it the leading open weights model Key results: ➤ Strong agentic task performance: @Kimi_Moonshot's Kimi K3 reaches an Elo rating of 1668 on GDPval v2. This is a marked improvement over K2.6’s 1190, surpassing GLM-5.2 (1514), GPT-5.5 (1494), and Claude Opus 4.8 (1600). However, it still lags behind Claude Fable 5 (1760). Kimi K3 also scores an impressive 53% and takes the #1# position on AutomationBench-AA, our implementation of Zapier’s Agentic SaaS workflow evaluation. ➤ Second-highest performance on AA-Briefcase (agentic knowledge work): On our private long-horizon knowledge work evaluation, Kimi K3 reaches an overall Elo of 1547, +732 points from Kimi K2.6 and behind only Claude Fable 5. It is well-rounded: its rubric scoring and analytical quality almost reach Claude Fable 5’s scores, while GPT-5.6 Sol continues to outperform other leading models on presentation quality. ➤ Set to lead open weights models once weights are released: Moonshot AI has not yet released the weights but expressed plans to do so. Once available, Kimi K3 would clearly lead other open weights models including GLM-5.2 (51) and DeepSeek v4 Pro (44). However, at 2.8T parameters, it is significantly larger than its open weights peers (eg. GLM-5.2 at 753B params and DeepSeek V4 Pro at 1.6T), as well as the Kimi K2 to K2.6 models (1T params). ➤ Cost per task ($0.94) is similar to GPT-5.6 Sol ($1.04), ~1/2 the price of Opus 4.8 ($1.80) and higher than open weights peers: Moonshot AI’s pricing for K3 is significantly higher than their K2 pricing (K3’s output token price is $15/1M tokens while K2.6 was $4). This positions the model as cheaper on a cost per task basis than Opus 4.8, similar to GPT-5.6 Sol ($1.04) and more expensive than open weights peers, GLM-5.2 ($0.32) and DeepSeek V4 Pro ($0.04) ➤ Improved token efficiency alongside higher intelligence: Kimi K3’s token usage on the Artificial Analysis Intelligence Index decreased significantly, using 21% fewer output tokens than K2.6. The new model used approximately 132M output tokens to complete all nine evaluations, compared to approximately 166M for K2.6, while achieving higher scores. ➤ Native multimodal capabilities: Kimi K3, like K2.6, is released with native image and text multimodal input. If weights are released, this will position Kimi K3 as one of the leading open weights models with multimodal input capabilities Other model details: Context window: 1M Size: 2.8T total parameters Pricing: The first-party API is priced at $3.00/$15.00 per 1M input/output tokens, with cached input discounted 90% to $0.30 per 1M tokens. Modality: Native multimodal input supports text and images, and the model remains text-only for output. Accessibility: Accessible at launch through Moonshot’s first party API. Model weights are not yet released but Moonshot AI has expressed plans to do so.

$PLTR : 基本面连续 11 个季度加速、股价却近乎腰斩。 与英伟达 $NVDA 的合作打开主权 AI 第二增长曲线 。 此时正逢 AI Capex Trade 降温、软件股开始修复，正逢 PLTR 从超高估值已经几乎腰斩。 这会是我们的建仓机会吗？ 这是我最新的 deep dive 文章，深度讲解 PLTR 的机会和我的想法。