Month: November 2026

MongoDB公司(MDB-US)截至台北時間03日03:21股價下跌17.74美元，報335.22美元，跌幅5.03%，成交量789,978（股），盤中最高價352.06美元、最低價334.31美元。

美股指數盤中表現

• 道瓊指數：+0.63%
• S&P 500指數：-0.2%
• NASDAQ指數：-0.43%
• 費城半導體指數：-2.58%

MongoDB公司(MDB-US)歷史漲跌幅

• 近 1 週：-4.89%
• 近 1 月：+0.35%
• 近 3 月：+4.59%
• 近 6 月：+33.97%
• 今年以來：-15.9%

更多鉅亨報導

What happened?

A number of stocks fell in the morning session after software stocks extended the selloff from the previous session as investors continued to reprice valuations amid mounting artificial intelligence capital expenditure concerns and decelerating software bookings. 

Oracle filed to raise $20 billion in debt and equity, reigniting concerns over soaring artificial intelligence spending and cash burn. The selloff deepened after quarterly reports from Alphabet and Amazon highlighted soaring data-center outlays that compressed free cash flows, as reported by Bloomberg. That spending concern triggered the tech-heavy Nasdaq’s drop the previous week, cementing its steepest monthly decline in over a year, according to The Wall Street Journal. 

Investors are increasingly demanding clear evidence that massive infrastructure outlays will translate into sustainable profits and robust revenue growth. Without immediate monetization from enterprise adoption, concerns over elevated sector valuations have deepened, leading to broad-based selling pressure across chipmakers, hardware providers, and cloud service operators.

The stock market overreacts to news, and big price drops can present good opportunities to buy high-quality stocks.

Among others, the following stocks were impacted:

Zooming in on JFrog (FROG)

JFrog’s shares are extremely volatile and have had 45 moves greater than 5% over the last year. In that context, today’s move indicates the market considers this news meaningful but not something that would fundamentally change its perception of the business.

The previous big move we wrote about was 3 days ago when the stock dropped 8.8% on the news that underwhelming earnings reports from tech giants Amazon and Intel (after mediocre earnings reports from Alphabet and ServiceNow the previous day) sparked widespread investor anxiety regarding aggressive artificial intelligence spending and sustained cash burn. As reported by CNBC, disappointing quarterly reports from both companies triggered a broad slide across technology equities and hardware suppliers, overshadowing positive momentum in other areas of the market. Investors grew increasingly wary of the sector’s rising capital expenditures—the funds poured into long-term assets such as data centers and computing hardware. With both firms spending heavily to build out artificial intelligence infrastructure, market participants are questioning when these massive investments will translate into meaningful revenue and profit growth per CNBC. The steep drops in Amazon and Intel weighed heavily on market indices, showing how vulnerabilities among dominant technology leaders can rapidly dampen confidence across the broader industry.

JFrog is up 48.7% since the beginning of the year, but at $88.55 per share, it is still trading 14.9% below its 52-week high of $104.03 from August 2026. Investors who bought $1,000 worth of JFrog’s shares 5 years ago would now be looking at an investment worth $2,692.

WHILE YOU’RE HERE: The Next Palantir? One satellite company captures images of every point on Earth. Every single day. The Pentagon wants it. Hedge funds are using it to beat earnings. You’ve probably never heard of it.

This is what the early days of Palantir looked like before it became a giant. Same playbook. Different technology. If you missed Palantir, you need to see this. Claim The Stock Ticker for Free HERE.

Updated on November 2, 2026 · price as of Nov 2 22:00 · figures from Nov 2, 2026 · latest article Oct 23, 2026

Proposed sale
10,000 shares

Common stock listed for sale on November 2, 2026

Aggregate market value
$3,487,942.67

For the 10,000-share block listed for November 2, 2026

Proposed sale
3,000 shares

Common stock listed for sale on November 2, 2026

Aggregate market value
$1,050,505.91

For the 3,000-share block listed for November 2, 2026

Proposed sale
6,275 shares

Common stock listed for sale on November 2, 2026

Aggregate market value
$2,162,887.49

For the 6,275-share block listed for November 2, 2026

Proposed sale
10,000 shares

Common stock listed for sale on November 2, 2026

Aggregate market value
$3,487,942.67

For the 10,000-share block listed for November 2, 2026

Proposed sale
3,000 shares

Common stock listed for sale on November 2, 2026

Aggregate market value
$1,050,505.91

For the 3,000-share block listed for November 2, 2026

Proposed sale
6,275 shares

Common stock listed for sale on November 2, 2026

Aggregate market value
$2,162,887.49

For the 6,275-share block listed for November 2, 2026

TorchTPU is an open-source project developed by Google in collaboration with Meta that provides a native PyTorch backend for Google TPUs. It enables PyTorch workloads to execute on TPU hardware with minimal code modifications, supporting eager and compiled execution, distributed computing, and hardware-specific optimizations.

Google began enabling PyTorch workloads to run on TPUs in 2020 through PyTorch/XLA, which leveraged JAX, XLA, and lazy tensors. According to Google, this approach had significant shortcomings:

Crucially, PyTorch/XLA required intrusive source-code modifications to explicitly define compilable regions, suffered from severe compile-time latency, lacked support for PyTorch’s native imperative (“eager”) execution mode, and lagged in supporting emerging distributed computing primitives within the PyTorch ecosystem.

TorchTPU takes a fundamentally different approach, making eager execution a top priority, and enabling existing PyTorch workloads to run out with minor code modifications.

Specifically, migrating from GPUs to TPUs involves replacing the cuda device with tpu and the nccl distributed backend with tpu_dist. Alternatively, developers can use the new torch.accelerator.current_accelerator API to identify the available accelerator, as shown in the following snippet.

import torch
import torch.distributed as dist

def main():
    device = torch.device("cuda") # -> device = torch.accelerator.current_accelerator
    dist.init_process_group(backend="nccl") # -> dist.init_process_group()
    rank = dist.get_rank()
    word_size = dist.get_world_size()
    ...
    model = model.to(device)
    ...

TorchTPU support three eager execution modes: strict, debug, and DeferAndFuse. The latter is a new hybrid eager/compiled execution mode that Google claims “can be 50% or more faster than Strict Eager while reducing peak HBM memory”.

To test TorchTPU’s ability to run existing PyTorch models with no modifications, Google built an integration pipeline to evaluate about 5,000 Hugging Face models across 500+ architectures in both eager and compiled modes on TPU hardware generations starting from v5e.

We use a robust testing methodology grounded in corpus stratification and balanced architectural selection. Our approach relies on a two-tiered selection process designed to balance real-world adoption with broad architectural diversity, avoiding redundant testing of minor variations while ensuring real production weights are thoroughly evaluated.

To validate TorchTPU’s performance, Google ran a 30-billion parameter Muse Glimmer model implemented in standard PyTorch. On a single TPUv7 chip, inference throughput reached 25.7k token/s at 5120 sequence length and 22.4k token/s at 10,240 sequence length. For training, using four TPUv7 chips, throughput reached 5.6k token/s and 4.6k token/s at 5120 and 10,240 sequence length, respectively.

Google also conducted experiments using Qwen3 MoE 235B for training workloads, scaling from 1,024 to 8,192 TPUv7 chips. For inference, Google reported competitive performance-per-dollar for large-scale MoE workloads using vLLM.

Commenting on the announcement, Hacker News user tingkuanpei observed:

Very interesting work. Google should have done this a long time ago. PyTorch’s ecosystem is just so rich.

Other users pointed out that beyond offering TPUs through GCP, Google sells them to “enterprise client for on premises deployments”, which include Anthropic and in Meta following potentially in the future.

TorchTPU is still at an early stage and therefore in continuous evolution. The project’s roadmap include work on dynamic shapes, compilation performance, operator coverage, reinforcement learning, and quantization. For further details, make sure to read the original announcement, which provides a more comprehensive view of TorchTPU’s architecture and scope.

This week’s Java roundup for October 26th, 2026, features news highlighting: the GA release of TornadoVM 8.0; point releases of JNoSQL, Endive, JBang, JReleaser and LangChain4j; a maintenance release of JobRunr; beta releases of Hibernate ORM and Hibernate Reactive; and introducing the Quarkus Almanac.

JDK 28

Build 22 of the JDK 28 early-access builds was made available this past week featuring updates from Build 21 that include fixes for various issues. More details on this release may be found in the release notes.

For JDK 28, developers are encouraged to report bugs via the Java Bug Database.

TornadoVM

The release of TornadoVM 8.0.0 delivers their new pure-Java compiler, known as the TornadoVM Compiler (TC), that compiles Java kernels to CUDA C++, OpenCL C or Metal Shading Language. This replaces the Graal Compiler and Java-Level JVM Compiler Interface (JVMCI) that have been integral to TornadoVM for the past ten years. Developers won’t need to change their code with TC. Classes such as TaskGraph, TornadoExecutionPlan and KernelContext, and annotations such as @Parallel and @Reduce will work with TC.

TornadoVM now takes advantage of GraalVM Native Image and JEP 516, Ahead-of-Time Object Caching with Any GC, under the auspices of Project Leyden for improved startup time. Further details on this release may be found in the release notes.

Eclipse JNoSQL

The release of Eclipse JNoSQL 1.2.0, a compatible implementation of the Jakarta NoSQL and Jakarta Data specifications, ships with notable changes such as: a new VectorTemplate interface to support vector databases; integrations of the Milvus, Qdrant, and Weaviate vector databases for vector search; and new fluent temporal query operations for working with time-series data. More details on this release may be found in the release notes.

Endive

The release of Endive 1.2.0 provides bug fixes, dependency upgrades and new features such as: support for SIMD instructions, by default, in the interpreter on JDK 11+ without the need to add or configure; improvements to Redline, their experimental, ahead-of-time (AOT) compiler, for more GC-friendly native memory; and continuous improvement on the Endive Component Model, an implementation of the WebAssembly Component Model Design and Specification, to support the Endive runtime. Further details on this release may be found in the release notes.

JBang

The release of JBang 0.143.0 delivers bug fixes, documentation improvements and two new features: replace use of SDKMan with mise-en-place as the former does not support WindowsOS; and a new unified syntax to execute a specific version of an alias. More details on this release may be found in the release notes.

JReleaser

The release of JReleaser 1.27.0 ships with bug fixes, documentation improvements, dependency upgrades and new features such as: the ability for the support for the JReleaser Tool Provider to be invoked and/or loaded using Java Modules; improved generation of the changelog when GitHub issues are linked inside commit messages; and the removal of XPath to parse POM files as there were issues when packaging JReleaser as a native image. Further details on this release may be found in the release notes.

LangChain4j

The formal release (along with the thirty-third beta release) of LangChain4j 1.23.0 provides bug fixes, documentation improvements, dependency upgrades and new features such as: a new OllamaDecisionModel class to support decision models running in Ollama; and a new ConfidenceCascadePlanner class as a confidence cascade decision model-based agentic pattern. More details on this release may be found in the release notes.

Hibernate

The fourth beta release of Hibernate ORM 8.0.0 delivers bug fixes and notable changes such as: the ability to directly declare the @Filter and @SQLRestriction annotations on associations with the Jakarta Persistence @ManyToOne and @OneToOne annotations that include associations mapped using join tables; and a new @GeneratorOptimizer annotation that selects a “standard optimizer for sequence or table identifier generation directly on an identifier field or getter.“

The first and third beta releases of Hibernate Reactive 5.1.0 and 5.0.0, respectively, ship with bug fixes and notable changes such as: a new platform Bill of Materials that manages the versions of Hibernate Reactive, Hibernate ORM and Vert.x SQL clients; and a version check during bootstrap to ensure that Hibernate ORM and Vert.x SQL clients on the classpath aren’t older than the minimum versions it supports. Both of these versions are compatible with Hibernate ORM 8.0.0.Beta4.

JobRunr

The release of JobRunr 9.0.3 provides bug fixes, documentation improvements and dependency upgrades and one new feature that updates the JobHandler class that improves the log messaging from the JobZooKeeper class. Further details on this release may be found in the release notes.

Gradle

The first release candidate of Gradle 9.9.0 delivers notable changes such as: the ability for Kotlin DSL scripts to be incrementally compiled using the Kotlin Build Tools API for improved build performance; new test filters that may be applied to JUnit Platform tests that are not declared as methods; and improvements to the Problems API that provides a predefined hierarchy of problem groups from the Problems interface. More details on this release may be found in the release notes.

Quarkus Almanac

The Quarkus team has introduced the Quarkus Almanac, a utility for developers to compare changes between two releases. These changes include configuration properties, managed dependencies, extensions, and the API of the classes in the Quarkus jars. Developers can go as far back as November 2018 when Quarkus 0.0.1 was released.

MongoDB公司(MDB-US)截至台北時間02日23:01股價下跌17.84美元，報335.13美元，跌幅5.05%，成交量176,509（股），盤中最高價352.06美元、最低價335.00美元。

美股指數盤中表現

• 道瓊指數：+0.97%
• S&P 500指數：+0.59%
• NASDAQ指數：+0.73%
• 費城半導體指數：+0.41%

MongoDB公司(MDB-US)歷史漲跌幅

• 近 1 週：-4.89%
• 近 1 月：+0.35%
• 近 3 月：+4.59%
• 近 6 月：+33.97%
• 今年以來：-15.9%

更多鉅亨報導

What happened?

A number of stocks fell in the morning session after software stocks extended the selloff from the previous session as investors continued to reprice valuations amid mounting artificial intelligence capital expenditure concerns and decelerating software bookings. 

Oracle filed to raise $20 billion in debt and equity, reigniting concerns over soaring artificial intelligence spending and cash burn. The selloff deepened after quarterly reports from Alphabet and Amazon highlighted soaring data-center outlays that compressed free cash flows, as reported by Bloomberg. That spending concern triggered the tech-heavy Nasdaq’s drop the previous week, cementing its steepest monthly decline in over a year, according to The Wall Street Journal. 

Investors are increasingly demanding clear evidence that massive infrastructure outlays will translate into sustainable profits and robust revenue growth. Without immediate monetization from enterprise adoption, concerns over elevated sector valuations have deepened, leading to broad-based selling pressure across chipmakers, hardware providers, and cloud service operators.

The stock market overreacts to news, and big price drops can present good opportunities to buy high-quality stocks.

Among others, the following stocks were impacted:

Zooming in on JFrog (FROG)

JFrog’s shares are extremely volatile and have had 45 moves greater than 5% over the last year. In that context, today’s move indicates the market considers this news meaningful but not something that would fundamentally change its perception of the business.

The previous big move we wrote about was 3 days ago when the stock dropped 8.8% on the news that underwhelming earnings reports from tech giants Amazon and Intel (after mediocre earnings reports from Alphabet and ServiceNow the previous day) sparked widespread investor anxiety regarding aggressive artificial intelligence spending and sustained cash burn. As reported by CNBC, disappointing quarterly reports from both companies triggered a broad slide across technology equities and hardware suppliers, overshadowing positive momentum in other areas of the market. Investors grew increasingly wary of the sector’s rising capital expenditures—the funds poured into long-term assets such as data centers and computing hardware. With both firms spending heavily to build out artificial intelligence infrastructure, market participants are questioning when these massive investments will translate into meaningful revenue and profit growth per CNBC. The steep drops in Amazon and Intel weighed heavily on market indices, showing how vulnerabilities among dominant technology leaders can rapidly dampen confidence across the broader industry.

JFrog is up 48.7% since the beginning of the year, but at $88.55 per share, it is still trading 14.9% below its 52-week high of $104.03 from August 2026. Investors who bought $1,000 worth of JFrog’s shares 5 years ago would now be looking at an investment worth $2,692.

WHILE YOU’RE HERE: The Next Palantir? One satellite company captures images of every point on Earth. Every single day. The Pentagon wants it. Hedge funds are using it to beat earnings. You’ve probably never heard of it.

This is what the early days of Palantir looked like before it became a giant. Same playbook. Different technology. If you missed Palantir, you need to see this. Claim The Stock Ticker for Free HERE.

TorchTPU is an open-source project developed by Google in collaboration with Meta that provides a native PyTorch backend for Google TPUs. It enables PyTorch workloads to execute on TPU hardware with minimal code modifications, supporting eager and compiled execution, distributed computing, and hardware-specific optimizations.

Google began enabling PyTorch workloads to run on TPUs in 2020 through PyTorch/XLA, which leveraged JAX, XLA, and lazy tensors. According to Google, this approach had significant shortcomings:

Crucially, PyTorch/XLA required intrusive source-code modifications to explicitly define compilable regions, suffered from severe compile-time latency, lacked support for PyTorch’s native imperative (“eager”) execution mode, and lagged in supporting emerging distributed computing primitives within the PyTorch ecosystem.

TorchTPU takes a fundamentally different approach, making eager execution a top priority, and enabling existing PyTorch workloads to run out with minor code modifications.

Specifically, migrating from GPUs to TPUs involves replacing the cuda device with tpu and the nccl distributed backend with tpu_dist. Alternatively, developers can use the new torch.accelerator.current_accelerator API to identify the available accelerator, as shown in the following snippet.

import torch
import torch.distributed as dist

def main():
    device = torch.device("cuda") # -> device = torch.accelerator.current_accelerator
    dist.init_process_group(backend="nccl") # -> dist.init_process_group()
    rank = dist.get_rank()
    word_size = dist.get_world_size()
    ...
    model = model.to(device)
    ...

TorchTPU support three eager execution modes: strict, debug, and DeferAndFuse. The latter is a new hybrid eager/compiled execution mode that Google claims “can be 50% or more faster than Strict Eager while reducing peak HBM memory”.

To test TorchTPU’s ability to run existing PyTorch models with no modifications, Google built an integration pipeline to evaluate about 5,000 Hugging Face models across 500+ architectures in both eager and compiled modes on TPU hardware generations starting from v5e.

We use a robust testing methodology grounded in corpus stratification and balanced architectural selection. Our approach relies on a two-tiered selection process designed to balance real-world adoption with broad architectural diversity, avoiding redundant testing of minor variations while ensuring real production weights are thoroughly evaluated.

To validate TorchTPU’s performance, Google ran a 30-billion parameter Muse Glimmer model implemented in standard PyTorch. On a single TPUv7 chip, inference throughput reached 25.7k token/s at 5120 sequence length and 22.4k token/s at 10,240 sequence length. For training, using four TPUv7 chips, throughput reached 5.6k token/s and 4.6k token/s at 5120 and 10,240 sequence length, respectively.

Google also conducted experiments using Qwen3 MoE 235B for training workloads, scaling from 1,024 to 8,192 TPUv7 chips. For inference, Google reported competitive performance-per-dollar for large-scale MoE workloads using vLLM.

Commenting on the announcement, Hacker News user tingkuanpei observed:

Very interesting work. Google should have done this a long time ago. PyTorch’s ecosystem is just so rich.

Other users pointed out that beyond offering TPUs through GCP, Google sells them to “enterprise client for on premises deployments”, which include Anthropic and in Meta following potentially in the future.

TorchTPU is still at an early stage and therefore in continuous evolution. The project’s roadmap include work on dynamic shapes, compilation performance, operator coverage, reinforcement learning, and quantization. For further details, make sure to read the original announcement, which provides a more comprehensive view of TorchTPU’s architecture and scope.