Tuesday, July 28, 2026

AI Certifications from Anthropic

 Focused on "enterprise customers" Anthropic is now also offering certifications.

And as usual, there are good courses on Udemy for preparation


Practice Exams Claude Certified Architect Professional CCARP

Practice Exams Claude Certified Architect Foundations CCAR-F

Practice Exams Claude Certified Developer Foundations CCDV-F

Practice Exams Claude Certified Associate Foundations CCAO-F

Search Claude Certified | Udemy Business
Course: Claude Certified Architect (CCA-F, CCAR-F) - 2026 Exam Prep | Udemy Business
Claude Certified Architect - Professional Practice Exam Pack | Udemy Business
Claude Certified Developer - Foundations Practice Exam Pack | Udemy Business
Practice Exams Claude Certified Architect Professional CCARP | Udemy Business S.M.

Course: Claude Code - The Practical Guide | Udemy Business Frank K.
Claude Code: Building Faster with AI, from Prototype to Prod | Udemy Business Max.S.



Claude Certification Program | AI, Machine Learning & LLM Development


The Claude certification exams: an honest review | LinkedIn




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AMD Ryzen AI Halo vs NVIDIA DGX Spark

The AMD Ryzen AI Halo and NVIDIA DGX Spark are compact desktop developer workstations featuring 128GB of unified memory. The primary differences lie in pricing ($3,999 vs $4,699), operating system support (Windows and Linux vs Linux-only), and networking capabilities. [1, 2, 3]

Hardware and Performance

  • Memory & Bandwidth: Both use LPDDR5X unified memory (256 GB/s on AMD vs 273 GB/s on NVIDIA). Token generation speeds for single-node text inference are largely comparable.
  • Raw Compute: The DGX Spark has an advantage in heavy prompt processing and prefill-heavy workloads (running 25–50% faster in specific tasks) due to its specialized Blackwell architecture and higher bandwidth.
  • General Tasks: The AMD platform handles general x86 CPU duties (like compiling or data compression) faster and serves as a more versatile general-purpose PC. [3, 4, 5]
Software and Flexibility
  • Operating Systems: The AMD Ryzen AI Halo boots native Windows 11 as well as Linux, whereas the DGX Spark is restricted to NVIDIA's specialized Linux-based DGX OS.
  • Ecosystem: NVIDIA relies on the mature CUDA ecosystem, which is ideal for standard PyTorch/vLLM enterprise pipelines. AMD utilizes ROCm and Vulkan-based stacks, providing open flexibility that bridges standard PC use and local model serving.
  • Networking & Scaling: The DGX Spark includes built-in dual 200GbE QSFP and RDMA fabric support for native multi-node clustering, while the AMD unit features a single 10GbE port tailored for single-box scale-up tasks. [3, 6]
Pricing and Hardware Design
  • Price: The AMD base model starts at $3,999 with a 2TB SSD, undercutting the NVIDIA DGX Spark Founders Edition priced at $4,699.
  • Storage Upgrades: The AMD system accepts standard M.2 2280 NVMe drives for easy, high-capacity aftermarket upgrades, compared to more restrictive form factors on the Spark. [2, 5]

[1] https://www.youtube.com/watch?v=Rkl4X689g0E&vl=en-US


Manufacturer Product Pages

Official overview pages from AMD and NVIDIA provide full developer documentation, architectural overviews, and hardware specifications:

Where to Purchase

Both devices are sold through major electronic retailers, component vendors, and the manufacturers' official market hubs:

AMD Ryzen AI Halo

NVIDIA DGX Spark

  • NVIDIA Marketplace: Check stock, request quotes, and buy directly through the manufacturer at the official NVIDIA Enterprise Marketplace.
  • Corsair: You can order fully built ecosystem platforms directly through the Corsair Online Webstore.
  • Viperatech: For specialized tech integration and worldwide deployment, check standard catalog availability at Viperatech Systems. [7]






What's Under the Hood

AMD Ryzen AI Halo Developer Platform with Ryzen AI Max+ 395 processor
 with full ROCm software support.

128GB LPDDR5x Unified Memory

60 FP16 TFLOPS  GPU Performance

Windows or Linux Operating System

50 TOPS NPU Performance

 AMD Built the DGX Spark Rival I Predicted… But There's a Catch - YouTube

Alex Ziskind evaluates a new $4,000 developer workstation powered by AMD's Ryzen AI / Strix Halo APU architecture, positioning it as a direct competitor to compact AI hardware setups like NVIDIA's DGX/Spark systems.

Key Takeaways & Findings

  • Unified Memory & VRAM: The system leverages AMD's high-bandwidth unified memory architecture, allowing large portions of system RAM to be allocated directly as VRAM for running large language models (LLMs) locally.

  • Performance Comparisons:

    • Compares inference speed, token-processing rates, and video/image generation benchmarks against NVIDIA's desktop ecosystem and Apple Silicon Workstations.

    • Highlights software backend performance differences (e.g., standard Vulkan backend vs. ROCm / llama.cpp backends), noting significant prompt processing speed boosts when using optimized ROCm builds.

  • The "Catch":

    • Price-to-Performance Ratio: At around $4,000, the high price tag makes it a hard sell for casual tinkerers or developers who can achieve similar or better token speeds on multi-GPU setups or Apple Silicon alternatives.

    • Software Ecosystem Maturity: While the hardware performance is strong, AMD's software stack for local AI inference still requires tinkering compared to NVIDIA's turnkey CUDA environment.

  • Future Potential (Multi-Node Stacking): Teases running multiple units together to scale VRAM capacity and run massive models (such as ~400B parameter models) across bridged systems.

Summary: The Ryzen AI Strix Halo workstation is a capable, compact local AI development box with impressive unified memory advantages, but its $4,000 price point and software setup requirements mean it is strictly targeted at serious developers rather than budget enthusiasts.


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(Sad) business story: GE

 How Just One Man Destroyed America's Greatest Industrial Empire - YouTube

This video examines the decline of General Electric (GE), once a titan of American industry, arguing that its collapse was driven by the leadership philosophy of former CEO Jack Welch.

Key takeaways from the video:

  • The Foundation (1892 - 1980): GE was built on a culture of craftsmanship and long-term investment in technology, symbolized by the creation of the first industrial research lab (3:58) and the generational loyalty of its workforce.
  • The "Shareholder Value" Shift: When Jack Welch became CEO in 1981, he pivoted the company's focus from product excellence to maximizing share price for investors (9:41). This led to massive layoffs—earning him the nickname "Neutron Jack"—and a shift toward outsourcing manufacturing (10:35 - 12:40).
  • The Rise of GE Capital: Under Welch, the company transformed from an industrial manufacturer into a finance-heavy entity. By 2001, GE Capital was responsible for nearly half of the company's profits (17:06), masking the stagnation of its industrial divisions.
  • Pension Fund Manipulation: The video highlights how GE used the surplus in its employee pension fund to book "paper gains" as operating income for decades, rather than investing in the company's future (18:55 - 19:48). When the financial crisis hit in 2008, the pension fund swung into a massive deficit (19:54).
  • The Aftermath (2001 - 2024): Successors like Jeff Immelt struggled to pivot back to manufacturing, often making high-risk bets (like the Alstom acquisition) and utilizing stock buybacks to prop up the share price (26:50 - 27:50). Eventually, the company was removed from the Dow Jones Industrial Average (29:14) and, under Larry Culp, was broken into three separate companies in 2024 (29:40).

Conclusion: The video posits that GE didn't fail due to market forces, but because it abandoned its identity as an industrial home for its workers in favor of becoming a short-term financial instrument for shareholders (32:15 - 32:39).


General Electric - Wikipedia



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