DEV Community: Guoming Fang

NASDAQ最近闪崩2%？背后真正原因一次看懂

Guoming Fang — Mon, 02 Mar 2026 04:42:57 +0000

最近NASDAQ 出现明显回调，引发市场担忧科技股与 AI 主题是否“难唱独角戏”。下面整理出这轮回调的核心驱动因素，以及市场目前的情绪逻辑

软件 & AI 相关股票遭遇恐慌性抛售

本轮回调起点之一是 AI 初创公司 Anthropic 推出新的自动化工具，该工具被部分投资者解读为可能削弱传统软件企业未来营收的威胁，引发软件板块大规模抛售。软件公司、SaaS 企业、法律软件和数据服务类别尤其遭冲击，推动科技板块整体承压。

分析指出，这种抛售更多来自情绪恐慌，而非基本面立刻恶化，类似过去深度求索（DeepSeek）事件引发的短期情绪波动。

芯片股与半导体受业绩前景拖累

AMD 的业绩指引是回调的另一重要原因。尽管 AMD 上季营收和 EPS 均表现强劲，第一季度营收预期（约 98 亿美元）却未达到部分市场对爆发式增长的预期，尤其在 AI 硬件竞争激烈的大环境下，这被视作信号弱于预期。

另一份报道指出，AMD 股价因该指引大幅下跌自2018年最大单日跌幅18%，并带动整个科技股卖压扩散。

与此同时，存储芯片供应与内存短缺等基本面担忧也拖累了 Qualcomm 等芯片股表现，进一步加重了科技板块卖压。

高估值与“更严谨定价”预期

苹果、微软等巨头虽表现相对稳健，但科技股的高估值特征令市场对任何负面消息高度敏感。当估值已经站在远高位置时，市场对未来增长的要求更为严格：
• 不仅要出现增长
• 还要出现持续放量与可验证盈利路径

一旦某家公司（甚至行业）报告的未来节奏未能满足这种“爆炸式成长预期”，资金就可能迅速撤离。

资金风格轮动 & 风险偏好修正

市场还显示出明显的风格轮动特征 —— 资金从高增长科技股流出，转向更传统、估值更低的经济周期股或价值/防御性板块。这种轮动在回调过程中对 QQQ 形成额外下行压力。

情绪 vs 基本面：市场分析视角

Analysts argue that at least部分抛售更多是由恐慌情绪推动，而不是行业基本面突然恶化。主要理据包括：
AI 技术仍处于早期采用阶段
云端资本支出依然强劲增长
软件公司的基本营收与盈利并未出现系统性恶化

不过，市场却在“估值与情绪之间摇摆”，导致股价波动比基本面更剧烈。

EVENTURE 一句话总结

NASDAQ 回调核心不是单一坏消息，而是“高预期下的情绪修正”叠加“业绩指引不及极端预期”导致的科技/AI 板块风险偏好下降。

这一阶段市场正在检验以下几个问题：
• AI / 软件公司的增长是否可持续？
• 芯片及存储供应链是否已经透支未来预期？
• 高估值能否在中期维持？

简单来说：
不是“AI 失效”，而是市场在重新定价未来增长的确定性。

Bitroot: The L1 Built for AI-Native dApps

Guoming Fang — Sat, 21 Feb 2026 05:26:07 +0000

Bitroot's Future Growth Potential and Valuation
Leveraging the Dual Tracks of Public Chains + AI

Three Core Pillars

Technological Breakthroughs (High-performance parallel execution efficiency, AI compatibility)
Ecosystem Scale (Stablecoins, developer data, number of deployed DApps, on-chain transaction volume, high-end resource integration)
Commercial Adoption (RWA collaborations, privacy sector initiatives, progress with major institutional partnerships, regional market compliance)

According to official forecasts, Bitroot's public chain is expected to rank among the top 10 in market capitalization in the public chain sector by 2026, and within the top 3 in three to five years.

Africa’s Most Populous Nation Leading in Cryptocurrency Awareness and Adoption Amid Economic Challenges

Guoming Fang — Mon, 03 Nov 2025 02:04:09 +0000

Nigeria, with approximately 220 million people, is Africa's most populous country and one of its largest economies, with a GDP of approximately US$374.9 billion in 2023. Agriculture is a vital industry, but high inflation (24.7%) and currency instability (Naira devaluation) are major economic challenges.

A Consensys report (2024) shows that 73% of Nigerians are aware of cryptocurrencies.

Nigeria is indeed one of the countries with the highest cryptocurrency adoption rates globally.

🚀 Bitroot – Next-Gen Parallelized AI Blockchain ⚡🌐

Guoming Fang — Sat, 11 Oct 2025 02:42:53 +0000

Bitroot is the world's first parallelized public blockchain for AI infrastructure, dedicated to building the next-generation, high-performance AI computing network. Leveraging its groundbreaking parallelized EVM and BFT consensus mechanism, Bitroot achieves over 100,000 transactions per second (TPS) and an ultra-fast confirmation speed of 0.3 ns, completely overcoming the performance bottlenecks of traditional blockchains. Furthermore, Bitroot natively integrates an AI support layer, providing an efficient, verifiable, and low-cost on-chain operating environment for AI agents, large-scale model training, and GPU computing networks, driving the scaled deployment of decentralized intelligent applications.

🌹🌹🌹 Bitroot's vision is to build a verifiable, decentralized, and inclusive intelligent network, enabling everyone to securely and efficiently access and utilize next-generation AI technologies.

⚡️Bitroot ⚙️ The 3rd Blockchain Revolution | 🚀 AI x Cross-Chain x Speed = Humanity’s Leap Forward

Guoming Fang — Fri, 10 Oct 2025 17:44:39 +0000

Bitroot is the third revolution of blockchain, a product of the development of the times, and a symbol of human technological progress. Its decentralized cross-chain bridge only takes 6-7 seconds, and the AI large model inference is sub-second 0.8 seconds, while OpenAI takes more than 5 seconds. The cost is 10 times lower than Deepseek (Deepseek is more than 10 times cheaper than ChapGPT). Bitroot's block time is the shortest of all public chains at 0.3 seconds. The cost of developing on Bitroot is half that of ETH, twice that of Solnan, and four times that of Sui and Apotes. 4 4. Those who understand will know after testing and experiencing it. This is a blessing for all mankind.

Designing a Cloud-Native Multiplayer Game Platform in Java

Guoming Fang — Thu, 09 Oct 2025 21:03:21 +0000

In today’s fast-moving game industry, developers are searching for tools that can deliver performance, flexibility, and long-term reliability. Java, often seen as a language for enterprise solutions, is quietly regaining attention for game development thanks to its stability, scalability, and powerful ecosystem.

A future-ready game platform must handle large-scale multiplayer environments, real-time data processing, and smooth cross-platform experiences. Java’s virtual machine (JVM) makes it possible to run games on various systems without rewriting core logic, which is crucial for maintaining consistent performance across devices.

Modern frameworks like LibGDX and jMonkeyEngine have proven that Java can compete with C# and C++ in both 2D and 3D development. Combined with tools like Gradle and Maven, developers can automate builds, manage dependencies, and integrate backend systems efficiently. Java’s concurrency features also make it well-suited for managing real-time multiplayer sessions and handling server-side logic for online games.

Security is another major advantage. Java’s robust sandboxing and type safety reduce risks that are common in online gaming, especially when dealing with in-game transactions or user data.

Building a next-generation game platform with Java is not about following trends — it’s about creating a stable foundation that can evolve with new technologies. With its mature ecosystem, scalability, and proven reliability, Java remains a strong candidate for powering the future of interactive entertainment.

The Next Frontier of Decentralized Intelligence

Guoming Fang — Sun, 05 Oct 2025 01:26:19 +0000

Blockchain has already disrupted industries by introducing transparency and decentralization, while Artificial Intelligence (AI) continues to shape automation, data analysis, and predictive modeling. The convergence of these two technologies is now creating an entirely new frontier: decentralized intelligence. This trend could redefine how machines learn, interact, and make decisions—without relying on centralized authorities.

Why Blockchain and AI Need Each Other AI models are only as strong as the data they are trained on, yet data ownership and privacy remain major challenges. Currently, most AI systems are trained using centralized data silos controlled by large corporations. Blockchain offers a solution by decentralizing data ownership, enabling individuals and organizations to share data securely while maintaining control and transparency.

Data Integrity: Blockchain ensures the training data has not been tampered with.
Data Monetization: Users can tokenize and sell their data to AI developers.
Trust in AI Decisions: Blockchain can record the decision-making process of AI models, creating audit trails for explainability.

Decentralized AI Marketplaces Emerging platforms are using blockchain to build decentralized AI marketplaces. Instead of companies hoarding proprietary AI models, developers can publish and license them on-chain. This approach allows anyone to access, test, and even improve AI tools while ensuring fair compensation through smart contracts.

Examples include:

Ocean Protocol: Enables secure data exchange for AI training.
SingularityNET: A marketplace for decentralized AI services, where AI agents collaborate and trade services with one another.

On-Chain Autonomous Agents Imagine an AI system running entirely on a blockchain—an autonomous agent that executes smart contracts, trades digital assets, or even governs decentralized organizations (DAOs). These "on-chain AIs" could act as independent digital entities, accountable to blockchain’s transparency while evolving through continuous data input.

For example:

AI-powered DAOs that adjust governance rules dynamically.
Autonomous trading bots that transparently log all strategies and outcomes on-chain.
Decentralized logistics AIs that optimize supply chains in real time.

Challenges Ahead

Scalability: Blockchain’s limited transaction speed can slow AI training and deployment.
Energy Consumption: Both AI and blockchain are resource-intensive, requiring sustainable solutions.
Regulation and Ethics: Decentralized AI raises questions about accountability, bias, and misuse.

The Road Ahead The fusion of blockchain and AI represents more than just technological progress—it points toward a new socio-economic system where intelligence is decentralized. This could reduce monopolies, empower individuals, and build AI systems that are more transparent, fair, and collaborative.

Conclusion
Blockchain and AI together are not just about efficiency; they are about trust and autonomy. As the two technologies converge, we may be entering an era where intelligence itself is decentralized, democratized, and placed directly into the hands of the people. The result could be a future of autonomous digital ecosystems—transparent, accountable, and self-evolving.

Flutter in 2024: 5 Surprising Use Cases Beyond Mobile Apps

Guoming Fang — Tue, 05 Aug 2025 22:15:09 +0000

Hook: Beyond the Obvious
"Flutter isn’t just for mobile apps anymore. From embedded systems to creative tools, developers are pushing Dart into uncharted territories. Here’s how."
Unexpected Flutter Use Cases
① Desktop Apps That Don’t Compromise

How Superlist (by ex-Uber team) built a macOS/Windows productivity tool with Flutter.

Why Flutter’s hardware-accelerated rendering beats Electron for performance.

② Embedded & IoT Dashboards

Case study: A smart farm using Flutter to control sensors and drones via Raspberry Pi.

Tools: Flutter-Pi and custom platform channels.

③ Gaming Prototypes & Casual Games

Flame engine showcase: A 2D game hitting 60 FPS on mobile and web.

Why indie devs use Flutter for MVP game testing before switching to Unity/Unreal.

④ Interactive Digital Art

Live demo: Generative art app using Flutter + Custom Paint at 120Hz refresh rates.

How artists leverage Hot Reload for real-time creativity.

⑤ Enterprise Kiosks & Touchscreens

McDonald’s self-order terminals (piloted in Asia) – why Flutter won over Java.

Single codebase for maintenance across thousands of devices.

Code Snippets: Flutter’s Flexibility

// Example: Flutter talking to Python via FFI (Foreign Function Interface)

final int result = nativeLibrary.add(10, 20);

print('IoT Device Response: $result');

When Not to Use Flutter
Heavy 3D games (Unreal/Unity still rule).
OS-level hardware access (stick to native Kotlin/Swift).
Legacy systems requiring COBOL/Java EE integrations.
Conclusion: Flutter’s Future
"Flutter’s real power lies in its adaptability. As Google invests in impeller rendering, WASM support, and Dart 3.0, expect even wilder use cases by 2025."

Call-to-Action:
"Tried Flutter for something unconventional? Share your story in the comments!"

🧠 The Future of AI in Software Development: Co-Creation, Not Replacement

Guoming Fang — Tue, 22 Jul 2025 20:39:39 +0000

🚀 Introduction
AI has moved beyond just autocomplete tools and smart IDE suggestions — it's now writing boilerplate code, finding bugs, generating tests, analyzing pull requests, and even designing full software architectures. We're witnessing the early stages of a shift where AI acts as a co-developer, helping us build software faster, smarter, and with fewer errors.

But with great power comes great questions:
Will AI replace developers?
How do we stay relevant in an AI-driven ecosystem?
And where is this all headed?

In this post, we’ll dive deep into how AI is shaping the future of software development — and how you can position yourself at the forefront.

🤖 1. AI as the Developer’s Co-Pilot
Tools like GitHub Copilot, Cursor, CodeWhisperer, and ChatGPT are redefining the coding experience. These tools don’t just assist — they collaborate.

Current capabilities include:

Generating full functions from plain-English prompts

Refactoring large codebases with minimal human input

Recommending optimal libraries or design patterns

Writing tests, documentation, and edge case handling

💡 Reality Check: You still need to know what to build, how to structure it, and how to make engineering decisions. AI helps, but doesn’t lead (yet).

📈 2. AI in the DevOps and QA Pipeline
AI is increasingly useful in non-coding stages of development too:

Automated Testing: AI writes, runs, and even predicts missing tests.

Code Review Assistants: Tools like DeepCode and Codacy analyze PRs in seconds.

CI/CD Optimization: Predict build failures or suggest faster deployment flows.

As pipelines get more complex, AI’s role in ensuring stability and speed becomes invaluable.

🧠 3. AI-Powered Architecture & Planning
LLMs like GPT-4 and Claude can help:

Design microservices from a requirements doc

Translate business rules into scalable architecture

Offer alternatives based on performance, cost, and maintainability

Imagine combining this with prompt-based prototyping using tools like LangChain or CrewAI — developers can literally talk ideas into MVPs.

🌍 4. Human-AI Collaboration Will Define the Next Generation
AI doesn't eliminate creativity — it amplifies it. The best developers of the next decade will:

Know how to orchestrate AI agents and tools

Use AI to handle repetitive work while focusing on high-level problem solving

Stay current with evolving models, APIs, and capabilities

🔧 Pro tip: Learn how to write great prompts, evaluate AI output critically, and integrate it into your stack.

🔮 5. What's Next?
Looking ahead:

Agentic frameworks (like LangGraph, CrewAI) will enable multi-step reasoning and task execution

Model personalization: Developers will fine-tune small LLMs on their own codebases

Regulatory pressure will shape how AI-generated code is reviewed, secured, and licensed

Expect a new role to emerge: AI Software Architect — someone who knows how to manage AI tools in the development lifecycle.

💬 Final Thoughts
AI is not the end of developers — it’s the beginning of a new era where we code smarter, not harder. Whether you're a frontend wizard, backend architect, or DevOps engineer, now is the time to embrace AI as part of your workflow.

Don’t fear the future — build it with AI at your side.
🔗 What’s Your Take?
Are you already using AI in your dev workflow?
What tools are you excited (or cautious) about?

💬 Let me know in the comments — I’d love to hear how you’re navigating the future of coding.