Today felt like an emotional roller coaster—starting with anxiety in the early morning, turning into worry later in the morning, followed by a glimmer of progress around noon, sinking into helplessness in the afternoon, and finally ending with relief in the evening. All of this was triggered by a technical presentation I wasn’t prepared for. … I was about to head out but figured I might as well sit in and listen to the CoP meeting to get a feel for its format and vibe. After some quick greetings, the sharing session began. The presenter’s slides were polished and their delivery was smooth. It made me a bit nervous about my own future presentation—my content is still incomplete, some parts borrow other people’s ideas, and my slide design is fairly plain.

Last night, Xiaoyan came home very late because of a flight delay and woke me with the doorbell.

This morning, Hamer anxiously asked whether she needed to rewrite her math assignment, only to learn that she just needed to make corrections—yet she still has to redo it fully this evening.

During my commute, I admired colorful blooms and used ChatGPT for flower identification. It provided a more accurate and detailed result than the now-retired Flower Recognition app.

I spent the day preparing my MCP presentation, generating new flowcharts from the spec and code, and planning to polish and map out the demos and Q&A.

After work, I arrived home early and finalized Hamer’s statistics report, though she insisted that Mom help instead because she finds me “nitpicky.”

Returning to work after the Labour Holiday, I spent the morning helping Hamer decide on a topic for her statistics assignment—she ultimately chose to map sports courts and player numbers. By bedtime, she had sketched out her layout and is ready to colour it tomorrow. I also outlined my upcoming MCP presentation and found more substance than I had expected.

TIL: AI Weekly Newsletter of Week 17

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AI-Assisted Programming

Go Read Peter Naur’s “Programming as Theory Building” and Then Tell Me That LLMs Can Replace Human Programmers

Dave Gauer invokes Peter Naur’s seminal essay “Programming as Theory Building” to argue that LLMs lack the essential “theory” human programmers develop through active engagement with a codebase, and thus cannot truly replace human developers.

To replace human programmers, LLMs would need to be able to build theories by Ryle’s definition or Naur must be wrong about the nature of programming.

I’m betting neither is true.

Andrej Karpathy on AI-Assisted Coding

Andrej Karpathy’s tweet outlines a deliberate, step-by-step “inner loop” for AI-assisted coding—distinguishing serious, production-grade work from casual “vibe coding.” He emphasizes a controlled, incremental, and learning-focused workflow that:

1. Feeds the model just enough context.
2. Requests high-level approaches before code.
3. Manually reviews unfamiliar APIs.
4. Tests and commits changes.

Vibe Coding Is Not an Excuse for Low-Quality Work

Vibe coding—rapid, AI-driven code generation based on conversational prompts—can supercharge productivity, but Addy Osmani warns it must never be an excuse for substandard engineering. He frames AI as “your intern, not your replacement,” and provides a concise field guide for responsible AI-assisted development.

Let’s acknowledge the good: AI-assisted coding can be a game-changer.
…
However, as any seasoned engineer will tell you, speed means nothing if the wheels fall off down the road.
…
Think of these as the new “move fast, but don’t break everything” handbook—a set of guardrails to keep quality high when you’re vibing with the code.

Rules:

1. Always review AI-generated code.
2. Establish coding standards and follow them.
3. Use AI for acceleration, not autopilot.
4. Test, test, test.
5. Iterate and refine.
6. Know when to say no.
7. Document and share knowledge.

New Model Releases

OpenAI: Introducing Our Latest Image Generation Model in the API

OpenAI is bringing the natively multimodal model that powers ChatGPT to the API via gpt-image-1, enabling developers and businesses to integrate professional-grade image generation directly into their own tools and platforms.

Dia – Ultra-Realistic One-Pass TTS

Dia is a 1.6 B-parameter text-to-speech model from Nari Labs, capable of generating ultra-realistic dialogue in a single pass.

Cutting-Edge Vision & Captioning

Describe Anything: Detailed Localized Image and Video Captioning

Describe Anything is a state-of-the-art framework for detailed localized captioning (DLC) that empowers users to obtain rich, context-aware descriptions of specific regions within images or videos—specified via points, boxes, scribbles, or masks. It sets a new standard by combining innovative architecture, scalable data strategies, and robust evaluation.

Developer Productivity Tools

DeepWiki – Automatic Wiki Docs for GitHub Repos

Devin AI has launched DeepWiki, a free tool that generates structured, wiki-style documentation for any GitHub repository. It simplifies understanding unfamiliar codebases by providing a comprehensive overview directly from the repo URL.

Today, I spent a make-up workday testing our MCP server using Cherry Studio and SiliconFlow instead of Claude Desktop, and refactored poorly structured parameters into typed classes. Despite steady progress, I missed my usual excitement when tests didn’t yield expected results.

In the evening, Xiaoyan’s friend joined us for a Brazil-themed dinner, and Hamer dove into her new extracurricular books—she even picked one up on her own, which made me quietly proud.

Overall, I felt tired and a bit unmotivated, but reminded myself it’s okay to take things at a slower pace sometimes.

Hamer started her day by building a tiny notebook and sharpening a pencil to match. After breakfast, we returned to Tianqiao Theater for Into Ballet, arriving early to join the introduction part and waiting for a while at the backstage send-off after the performance.

Following a quick lunch, I headed to a soccer match—playing goalkeeper and leading a thrilling late comeback—while Xiaoyan took Hamer to ballet and a dental appointment. Later, we reunited at a riverside café with live music and helped rescue a mandarin duck from aggressive peers using orange peels.

In the evening, we relaxed for two hours at Liuliu’s dad’s barbershop, where we all got fresh haircuts and enjoyed the lively chatter.

Xiaoyan dropped Hamer at school before leaving for Handan on a business trip.

At work, I completed a major cleanup of our MCP server projects—adding tests, updating pyproject.toml, managing dependencies, and refreshing READMEs—wrapping it up by 8 p.m.

In the evening, I briefly reviewed Hamer’s math homework. She delighted in role-playing as the teacher and reading her Chinese characters aloud. Later, she practiced the Rubik’s cube—her finger movements growing more confident—even as sleepiness soon set in.

Tonight was my first real attempt this year to help Hamer with her homework. She pointed three enhancements after my requesting: no asking her to explain answers, no interrupting her snacks, and she’d prefer Mom help instead.

Her teacher had shared extra exercises and called to discuss her struggles—rushing through problems, getting distracted, or not fully grasping concepts. It made me realize I need to be more involved in her learning going forward.

In the morning, Xiaoyan took Hamer to school. Hamer joked, “Do you even know where my school is?”—a funny moment between them.

At work, I focused on MCP transport types, explored uv, and began organizing the project for long-term maintenance. Slow but satisfying progress.

This morning’s meeting felt dull—low energy all around, and I wasn’t in top form either. I hate feeling that way but haven’t figured out how to fix it yet.

I continued refining the MCP server based on ArcGIS Portal Service, uncovering more subtle issues as I went.

In the afternoon, I had the next stage of my root canal—cavity filling and crown prep. Thankfully, it wasn’t painful.

Evening was spent fixing Xiaoyan’s Line account. After trying and failing to recover it with a temporary number from sms-man, we had to start fresh—meaning re-adding all her contacts.

On the tech front, I signed up for WildCard, activated Anthropic’s API, and tested Claude Code. With Gemini available via Google AI Studio, I’ll hold off for now. Next up: exploring the llm CLI tool with its Gemini and Anthropic plugins. Looking forward to diving deeper.

I woke up early and dove back into the official MCP docs—so well written that re-reading them sparked new insights. It helped me refine some lingering thoughts from yesterday’s discussion, giving me more clarity moving forward.

The morning was filled with meetings. Our usual weekly session felt oddly quiet with few attendees, but the room filled quickly for Zhongzhi’s presentation on supplemental medical insurance—a stark contrast.

Before lunch, there is an update on the UC planning, maybe there still a chance.

In the afternoon, I focused on the Portal Service MCP server using Cursor and Claude 3.7 Sonnet. But this time, the generated results didn’t click. I spent hours debugging with little progress. It made me think: maybe it’s time to go back to basics and handwrite the logic based on the docs for deeper understanding.

After work, I joined a relaxed basketball session. No hard running, just fluid movement. It felt great—no back pain this time.

And yes, the Rubik’s cube craze is still going strong—I solved it in the bathroom, during my ping-pong break, while walking, resting, and again after getting home. Each solve is faster and smoother. It’s become my steady little ritual throughout the day.