auto_frank

随便玩玩，记录一下生活

[auto_frank @DogFrankW]

@elonmusk @wholemars 中国只是给美国打工而已，不然美国哪能买到这么便宜的东西？美国巨头才是最赚钱的

[auto_frank @DogFrankW]

@SVScholar @ke65954174 没想到啊，但是我也觉得回调20-30％就差不多了，没想到啊。感觉这是一辆失控的汽车

[auto_frank @DogFrankW]

@xiaomustock 只有看懂相信才能拿住，不然没人拿的住

[auto_frank @DogFrankW]

@SVScholar 哈哈，a股也在跌

[auto_frank @DogFrankW]

看来形式很严峻啊，老黄开通x第一条就发请愿贴啊。也印证了今天半导体大跌的原因。

[Jensen Huang @JensenHuang]

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.

AI will transform every industry, power every company, and be built by every country.

Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.

The world needs both frontier closed models and frontier open models.

https://t.co/AUKzoQ5Ikb

[auto_frank @DogFrankW]

持续看好

[Wall St Engine @wallstengine]

INTEL $INTC Q2’26 EARNINGS HIGHLIGHTS

🔹 Revenue: $16.1B (Est. $14.50B) 🟢; +25% YoY
🔹 Adj. EPS: $0.42 (Est. $0.22) 🟢
🔹 Adj Gross Margin: 41.8% (Est. 39%) 🟢; +1,210bps YoY
🔹Raises FY26 Capex $20B (prior $18B)

Q3 Guide:
🔹 Revenue: $15.8B-$16.8B (Est. $15.2B) 🟢
🔹 Non-GAAP EPS: $0.38 (Est. $0.28) 🟢
🔹 GAAP EPS: $0.31
🔹 GAAP Gross Margin: 41.0%
🔹 Non-GAAP Gross Margin: 42.0%

Segment Net Revenue:
🔹 CCPG: $8.9B; +13% YoY
🔹 DCAI: $6.3B; +59% YoY
🔹 Total Intel Products: $15.1B; +28% YoY
🔹 Intel Foundry: $5.8B; +31% YoY
🔹 All Other: $0.7B; -33% YoY

Other Q2 Metrics:
🔹 Non-GAAP Operating Margin: 17.2% (Est. 10.7%) 🟢; +21.1 ppts YoY
🔹 Operating Cash Flow: $7.0B (Est. $5.23B) 🟢
🔹 Non-GAAP Net Income: $2.2B (Est. $1.15B) 🟢
🔹 GAAP EPS: -$2.16; includes a non-cash mark-to-market charge on Escrowed Shares tied to the U.S. government agreement

Comments:
🔸 “Our Q2 results represent our strongest revenue growth in more than fifteen years, enabled by greater speed, accountability, and customer focus.”

🔸 “We delivered a strong second quarter, exceeding our financial guidance on robust demand and improved execution, including volume upside driven by higher factory yields and improved cycle times.”

[auto_frank @DogFrankW]

@xiaomustock 韩国杠杆应该去完了，半导体也该反弹了。

[auto_frank @DogFrankW]

@WallStreetMav maybe allow more indian people

[auto_frank @DogFrankW]

@FrankLeo888 现在市场分歧还不小，到我认为如果看1-2年的话，问题不大。

[auto_frank @DogFrankW]

@dee_bosa you need learn what is distillation.

[auto_frank @DogFrankW]

@alex_shenwenai @bakkermichiel 哈哈，下次把时间调回去，把原来吃的亏都找补回来

[auto_frank @DogFrankW]

The brain's a great thing, but some people just don't have one. So why haven't other countries managed to pull off distillation? No competitive models coming out of them? What about Europe, or other Asian countries? You should be thanking China for open-sourcing stuff, otherwise you'd be paying way more in stupid tax. And honestly, the US-China rivalry is what's pushing tech forward — where else would the new economic growth come from?

[auto_frank @DogFrankW]

The brain's a great thing, but some people just don't have one. So why haven't other countries managed to pull off distillation? No competitive models coming out of them? What about Europe, or other Asian countries? You should be thanking China for open-sourcing stuff, otherwise you'd be paying way more in stupid tax. And honestly, the US-China rivalry is what's pushing tech forward — where else would the new economic growth come from?

[auto_frank @DogFrankW]

@xiaomustock 大佬这杠杆666

[auto_frank @DogFrankW]

@zephyr_z9 some peoples are not smart

[auto_frank @DogFrankW]

@quxiaoyin 如果简单蒸馏就能做出来，那请问欧洲，日本为啥没有做来？美国其它公司呢？简单蒸馏就能搞出来，那大模型有护城河吗？

[auto_frank @DogFrankW]

@TheAhmadOsman anthropic need buy more gpu from nvidia.

[auto_frank @DogFrankW]

anthropic和openai：我们需要更多的算力和存储，不然中国可能要超过我们了。要是真被中国开源模型超过我真不知道你们的价值是什么？

[Kimi.ai @Kimi_Moonshot]

Introducing Kimi K3: Open Frontier Intelligence

🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows

Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.

🔗 API: https://t.co/XCrgjXAqMw
🔗 Tech blog: https://t.co/YTfiMSNM1f

[auto_frank @DogFrankW]

@OmedVibeCodes lack computing power

[auto_frank @DogFrankW]

留给anthropic和openai时间不多了，再不上市就越来越不值钱了

[Artificial Analysis @ArtificialAnlys]

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.