(Photo taken during my visit to Amazon Spheres, with The Westin Building visible on the far left. Image source: Ernest.)

✳️ tl;dr

• I’ve passed by this building countless times while attending meetings or exploring coffee shops and restaurants in downtown Seattle, never realizing its significance.
• The Westin Building Exchange is a major telecommunications hub facility located in downtown Seattle, Washington. ¹
• The building was constructed in 1981 (around the same era as me XDD),
• Originally named The Westin Building, it served as the headquarters for the Seattle-based Westin Hotel chain.
• It also houses the Seattle Internet Exchange (SIX) and the Pacific Northwest Gigapop Pacific Wave Exchange.

• Since 2019 or earlier, heat generated by the building’s data center has been piped to Amazon’s Doppler building next door, providing heating for Doppler and several other Amazon buildings.
• Amazon estimates that over the system’s expected 25-year lifespan, it will save 80 million kilowatt-hours of electricity, equivalent to 65 million pounds of coal.

• The trigger: I came across The Verge’s report on Microsoft’s plan to migrate GitHub entirely to Azure, which made me curious about its infrastructure before the acquisition. ²
• GitHub was acquired by Microsoft in 2018. So I went back to check the situation in 2017.
• GitHub’s blog once described: “Those facilities don’t store customer data, rather they’re focused on internet and backbone connectivity as well as direct connect and private network interfaces to Amazon Web Services.” ³
• GitHub is now (2025) undergoing what may be its largest infrastructure migration ever, planning to move the entire platform from self-owned data centers to Azure within 24 months.
• The core driver of this migration is the explosive growth of AI workloads: GitHub Copilot generates millions of code suggestions daily, consuming massive computing capacity, and the existing data centers have reached their physical expansion limits. (There’s always an official narrative for the public—take it with a grain of salt) (There’s a kind of cold where grandma thinks you’re cold. There’s a kind of migration where ____ tells you to migrate?!)
• Migration strategy: Most work needs to be completed within 12 months (because new and old systems need to run in parallel for at least 6 months), GitHub has asked teams to delay feature development and prioritize infrastructure migration.

• In 2024, GitHub experienced 119 incidents, including 26 major outages, with an average resolution time of about 106 minutes. ⁴
• Curious to see how it performs after the move.
• For those of you tech managers using GitHub, I’m curious—what’s your take? Will this trigger any preparations on your end?

^{5 6}

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(Image source: Arduino UNO Q.)

✳️ tl;dr

• Qualcomm acquired Arduino, marking the third strategic acquisition following Edge Impulse and Foundries.io, demonstrating Qualcomm’s determination (or strategic positioning?) to build a fullstack edge AI platform ^{1 2}
• Arduino boasts over 33 million active community members, a developer foundation that competitors like Raspberry Pi don’t have.

• The newly launched Arduino UNO Q adopts a “dual-brain architecture”: Qualcomm Dragonwing QRB2210 quad-core processor (Quad-core Arm Cortex-A53 @ 2GHz) paired with STM32U585 real-time microcontroller (Arm Cortex-M33 @ 160MHz), simultaneously handling high-performance computing and real-time control.
• Wireless connectivity = dual-band Wi-Fi 5 + Bluetooth 5.1
• Edge Impulse integration provides AutoML capabilities, enabling even non-AI experts to deploy machine learning models.
• The TinyML market is projected to reach $200 billion by 2030, and Qualcomm is positioning itself to capture this high-growth segment?!
• UNO Q is priced at $44 (2GB RAM/16GB eMMC) to $59 (4GB/32GB), benchmarking against Raspberry Pi 5 at about $7 less. (But RPi 5 now offers 8GB RAM and 16GB RAM options, worth tracking going forward.)

• Software is licensed under GPL 3.0 or Mozilla Public License, hardware design under CC BY-SA 4.0, meaning you can legally create and sell derivative versions

^{3 4 5}

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(Photo by Rikin Katyal on Unsplash)

✳️ tl;dr

• AWS introduces ECS Managed Instances, achieving the optimal balance between operational simplicity and flexibility by offloading infrastructure management to AWS while maintaining full EC2 control ¹
• Runs on Bottlerocket OS, which maintains only ~100 package definitions compared to general-purpose operating systems with 50,000 packages, significantly reducing attack surface and management complexity ²
• Protects the root filesystem with dm-verity and SELinux enforcing mode, making it difficult to persist attacks even after container escape, with automatic system restart upon tampering detection ³⁴

• The container orchestration market is projected to grow from $10.8 billion in 2025 to $76.5 billion by 2034, with a CAGR of 24.16%, demonstrating strong demand for managed container services ⁵
• Research shows that heterogeneous task allocation strategies can reduce container orchestration costs by 23% to 32%, with ECS Managed Instances’ automatic workload consolidation being key to achieving this goal ⁶

• Bottlerocket’s atomic update model reduces the time to patch critical vulnerabilities from days or weeks to hours, potentially cutting update-related downtime by 80% compared to traditional systems ⁷⁴
• The service is currently available in six AWS regions, including US East (North Virginia), US West (Oregon), Europe (Ireland), Africa (Cape Town), Asia Pacific (Singapore), and Asia Pacific (Tokyo), with plans to expand to more regions to support global deployment needs
• Supports deployment through AWS Management Console, CLI, CDK, and CloudFormation, seamlessly integrating with existing DevOps toolchains to lower adoption barriers
• In 2022, Ernest shared “Running Laravel/PHP on AWS” at AWS Builders Day Taiwan, comparing various Amazon ECS Launch Types. Looks like it’s time to update those slides. ⁸

⁹¹⁰¹¹

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(Photo by Brett Jordan on Unsplash)

✳️ tl;dr

• Claude Code introduces Checkpoints feature, enabling real-time progress saving and rollback to previous states, solving pain points in long-term development ¹²
• Native VS Code extension released, supporting real-time inline diffs display of Claude Code’s code changes
• Claude Sonnet 4.5 achieves 77.2% on SWE-bench Verified (full 500 problems), using simple bash and file editing tools ¹³

• High-compute configuration with parallel testing reaches 82.0%, using rejection sampling and internal scoring models to select best candidates ¹
• API adds memory tools and automatic context management, allowing agents to maintain context across long-running tasks ¹⁴
• Pricing remains the same as Claude Sonnet 4: $3 per million input tokens, $15 per million output tokens, with prompt caching saving up to 90% costs ⁵

• Cursor CEO states Claude Sonnet 4.5 demonstrates state-of-the-art programming performance on long-horizon tasks, making it the top choice for developers solving complex problems ¹

• Achieves 61.4% on OSWorld benchmark, leading all competitors; Sonnet 4 was only 42.2% four months ago
• Implements AI Safety Level 3 (ASL-3) protection framework, CBRN threat detection false positive rate reduced tenfold compared to initial description
• Makes progress in prompt injection attack defense, one of the most serious security risks facing agentic AI systems
• First inclusion of “Interpretability” technical assessment in System Card, improving model transparency and credibility ⁶

⁷

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(Photo by Jonny Gios on Unsplash)

✳️ tl;dr

• New toy Claude for Chrome ^{1 2}
• Signed up and joined the waitlist a long time ago
• Received the invitation email yesterday
• Found some time to play with it today
• Need to continue working on my tasks
• Wait, shouldn’t I ask it to help me with my tasks?!

• Looks like I’m in the second batch of Max plan users getting Claude for Chrome research preview access
• Tried a few tasks with Claude for Chrome during the holiday
• It requests permission for a specific domain in the URL first, and you can approve step by step

• If I were to read the documentation and operate manually by myself, especially when unfamiliar (or doing it for the first time), it would take about 2~3 minutes (reading while operating on dual screens) to complete the task. But it requires mental processing and finding things on the screen (or using cmd+f to find action links or buttons to proceed).
• In comparison, Claude for Chrome takes about 10~15 minutes, but it did complete the tasks (for example, helping me set up GTM, helping me dig through Quickbooks to find the transaction history of a specific Account). This is because it needs to operate step by step - each step requires a screenshot, interpreting the screen and deciding where to click next, some screens require typing text into input fields, and even after clicking a text box it takes another screenshot to confirm the focus is on the text box (I almost impulsively wanted to help it click several times, but held back to avoid disrupting the flow).

• I’m currently using it with Edge (because I need vertical tabs and multiple isolated profiles), and it seems I encountered situations where Claude for Chrome changed my original tab group names, closed other tabs in the original tab group without asking me, and Claude for Chrome opened new tabs (e.g., GA4) in the tab group and started operating on its own, while I was still on the GTM tab on my screen.
• From an experience perspective, of course at this stage it’s not efficient.
• But from a results perspective, Claude for Chrome did complete the tasks, which means the reasoning for breaking down steps and the knowledge base are sufficient. We just need to wait for next-generation models or possible future local infrastructure to reduce the round-trip time, and efficiency will directly improve.

• Are you also thinking about how to introduce AI Agents or Agentic Workflows into your team or workflow? Feel free to chat.

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Problem Description

Recently, while playing with Kiro (Agentic IDE) and experimenting with multiple-role agents and hooks, I encountered a quite frustrating problem: within the same CLI session, Kiro couldn't retrieve the return values from the second command.

The problem scenario was as follows:

• When I asked Kiro to execute the first command, everything worked perfectly - the command executed successfully and could retrieve the output correctly.
• However, when executing the second command within the same shell session, Kiro would be unable to get the return value from that command.
• So Kiro would just hang there waiting for the response, unable to get the vibe going.

This problem significantly impacted my workflow:

• Frequent session restarts:
  • After executing the first command, I had to manually close the CLI session each time. If I need to intervene this frequently as a human, that’s not the AI agent I want.
• Manual output copying:
  • Or I needed to manually copy the return values from the second command to Kiro Chat.
• Reduced work efficiency:
  • The originally smooth agentic coding workflow became fragmented. While I can relax when it’s time to relax, for work tasks, I want to improve efficiency.

Problem scenario video record (some key information in the video has been regenerated, please don’t panic):

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(Photo by Felix Eka Putra Kuntjoro on Unsplash)

Background

I just thought of switching the post-processing for MacWhisper Dictation from the original google/gemma-3-12b to using openai/gpt-oss-20b in LM Studio, but I kept encountering an issue where the gpt-oss model would return the reasoning process as part of the dictation output results. Here’s the problematic output:

We need to correct punctuation: use full-width. 
Input: "嗨,我們明天去兒童樂園玩好嗎?" 
We replace comma with ，, question mark with ？. 
Also add period at end? 
The sentence ends with question mark already. 
So output: "嗨，我們明天去兒童樂園玩好嗎？"
嗨，我們明天去兒童樂園玩好嗎？

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(Illustration: By decompositioning and reintegrating the world using vectors, we may be able to rediscover a new world. Image source: Photo by James Wainscoat on Unsplash)

✳️ tl;dr for Technical Managers

• Amazon S3 Vectors integrates native vector search functionality directly into S3 object storage, aiming to simplify architecture and reduce costs.¹
• For RAG, semantic search and other AI applications, AWS claims up to 90% savings in vector storage and query costs.
• This means we no longer need to maintain a separate, expensive vector database for certain AI scenarios, significantly reducing operational complexity (TCO).²
• S3 Vectors provides sub-second query performance, suitable for large-scale applications with non-real-time latency requirements.³

• Core Advantage: Achieves “Storage-Compute Separation” for vector data, maximizing the cost-effectiveness of long-term storage.⁴
• Through integration with Amazon OpenSearch, enables “hot-cold” data tiering strategies that balance cost with high-performance query requirements.⁵
• Seamless integration with Amazon Bedrock Knowledge Bases makes building and scaling RAG applications unprecedentedly simple.⁶
• The emergence of S3 Vectors may force existing vector database vendors (like Pinecone) to rethink their market positioning and pricing strategies.

• Technical teams now need to reassess existing AI technology stacks to determine which workloads can migrate to S3 Vectors for cost optimization.
• The feature introduces new Vector Buckets, making vector management as simple as managing regular S3 objects.
• Speculation: S3 Vectors may further enhance its query capabilities in the future, such as supporting hybrid search to adapt to more complex scenarios.

✳️ tl;dr for Engineers & Developers

• Amazon S3 now natively supports vector storage and search with the new S3 Vectors feature.¹
• You can directly create Vector Buckets and Vector Indexes in S3, then use APIs to store and query embeddings.
• For many RAG applications, you can skip the step of deploying and managing a separate Vector DB.⁷
• APIs support k-NN similarity search with sub-second response times, supporting distance metrics like Cosine and Euclidean.

• Development Highlight: Queries can use metadata filters, such as (category = 'ernest-pkm' AND year > 2023), which is very practical.³
• Seamless integration with Amazon Bedrock Knowledge Bases - once you set up S3 as a data source, Bedrock automatically handles embedding and synchronization.⁶
• If your application needs lower latency or more complex searches (like hybrid search), you can export hot vectors to Amazon OpenSearch Service.⁵

• For developers already using AWS, the learning curve is low - basically just learning a few more S3 API calls.
• The entire service is serverless, meaning you don’t need to worry about scaling, provisioning, or aRPU - just focus on customer application scenarios.
• You can more economically build a massive long-term memory repository for your AI agents, with all interaction records or knowledge vectorized and stored in S3.
• For massive documents or images stored in S3 Data Lake,⁴ you can now directly build indexes in-place and perform semantic search without ETL to another system. (Well… if your massive files aren’t in S3 yet… just make a few API calls XD)

✳️ tl;dr for Marketing & Product People

• Imagine all your company’s documents, customer service conversations, product images, and even videos being searchable using natural language. Amazon S3 Vectors is making this cheaper and simpler.¹
• Previously, only resource-rich companies could afford to build large-scale vector search systems. Now, AWS has built this capability directly into S3, dramatically lowering the technical barriers and costs.
• Use Cases: Media companies can quickly find relevant clips in PB-scale video libraries; healthcare institutions can identify similar cases among millions of medical images.
• For e-commerce, this can build more precise semantic search engines that understand what users “want,” not just what they “type.”

• Business Value: This is not just a technical upgrade, but a key to unlocking the value of enterprise “unstructured data”.⁴
• S3 Vectors makes RAG (Retrieval-Augmented Generation) technology more accessible, meaning your chatbots or AI customer service can provide relatively accurate, well-grounded responses.⁶
• Market Trend: Vector search is evolving from a niche technology to part of cloud storage infrastructure.

• This innovation will accelerate AI adoption across industries by solving the fundamental “data preparation” and “knowledge storage” cost problems.
• For product managers, this means you can be bolder when planning AI features that require massive knowledge bases. (Think of S3 as the master key, like the keymaker in The Matrix.)
• S3 Vectors’ integration with Amazon Bedrock Knowledge Bases provides a one-stop knowledge base solution.⁶
• Speculation: More third-party SaaS applications based on S3 Vectors will emerge, focusing on industry-specific knowledge management and semantic search. This speculation is based on the development patterns of ISVs in the AWS ecosystem.²
• Enterprises should now consider: What dormant data can be “vectorized” to create new business value? Don’t worry about data formats initially - if you think it’s “data,” try it out, starting with small datasets.

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(Illustration: Claude Code performing agentic coding. Image source: Anthropic.)

✳️ tl;dr

• Anthropic recently shared real-world use cases of how their internal teams use Claude Code ^{1 2}, giving me a glimpse of the evolution from simple code completion to “agentic Software Development Life Cycle (agentic SDLC)”.
• Data Infrastructure teams let Claude Code use OCR to read error screenshots, diagnose Kubernetes IP exhaustion, and provide fix commands
• Non-technical finance staff can simply describe requirements in natural language, and Claude Code automatically generates queries and outputs Excel reports
• Product Development teams in auto-accept mode let Claude Code autonomously write 70% of Vim mode code
• Security Engineering uses Claude Code to quickly parse Terraform plans, complete security reviews, and reduce development bottlenecks
• Inference teams rely on Claude Code to generate unit tests covering edge cases, reducing research and development time by 80%
• DS/ML teams use Claude Code to build 5,000-line TypeScript dashboards, transitioning from one-time analysis to long-term reusable tools

• MCP (Model Context Protocol) ³ allows Claude to access precise configurations and data in secure environments
• Claude Code leverages “self-verification loops”: write code → run tests/CI → automatically fix errors, advancing agentic SDLC
• Third-generation AI coding tools are integrating into end-to-end development processes, from requirements to deployment with full automation
• Anthropic uses RLAIF and Constitutional AI training methods to enable Claude to demonstrate industry-leading self-correction capabilities in code generation

• Have you ever wondered what degree AI coding tools can achieve within your organization or for your own workflows?
• In May this year, I shared “Reinventing Programming: How AI Transforms Enterprise Software Development” at AWS Summit Hong Kong 2025, breaking it down from SDLC. Those interested can read along with the slides.

⁴⁵⁶⁷⁸

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(Illustration: Hydrographic survey storage area. Each tube contains a registered hydrographicsurvey. Image source: Photo by NOAA on Unsplash.)

✳️ tl;dr

• The US government released America's AI Action Plan in July 2025, aiming to reshape America’s global AI dominance
• Three strategic pillars: Accelerate AI Innovation, Build AI Infrastructure, Lead International AI Diplomacy & Security
• Revoked previous administration’s AI Executive Order 14110, removing “red tape and overregulation”

• Emphasizes protecting free speech and American values, ensuring AI systems objectively pursue truth
• Encourages open-source and open-weight AI models development to promote innovation and commercial adoption
• Establishes streamlined permitting for data centers, semiconductor manufacturing, and energy infrastructure, realizing the “Build, Baby, Build” vision

• Revitalizes US semiconductor manufacturing through CHIPS Program Office
• Builds military-grade high-security data centers to resist nation-state attack threats
• Exports complete AI technology stack to partners

• Establishes regulatory sandboxes and AI excellence centers, enabling research institutions, startups, and enterprises to rapidly deploy and test AI tools
• Creates AI Workforce Research Center to continuously assess AI’s impact on labor markets and provide policy recommendations
• Invests in automated cloud laboratories covering engineering, materials science, chemistry, biology, and other scientific fields

• Includes full text: America's AI Action Plan (PDF to Markdown) ¹

^2314567

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