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Weekly Reading #26

2026-06-29T00:00:00+00:00

Outside of Work

After taking a job at the Conference Board, a business research organization, he rummaged through libraries to unearth measures of railcar freight loading and trends in short staple cotton prices since the Civil War. “Instead of reading ‘Gone with the Wind,’ I was happy to immerse myself in ‘Copper Ore Deposits in Chile,’” he wrote.

Rand assigned Greenspan a nickname, “the Undertaker,” given his serious manner and because he always wore a dark suit and tie. Greenspan prided himself on his reasoning ability and thought he could beat anyone in an intellectual debate. Arguing with Rand “was like starting a game of chess thinking I was good, and suddenly finding myself in checkmate,” he wrote. “I was intellectually limited until I met her.” They remained friends until her death in 1982.

Walters and Greenspan remained friends but didn’t marry, in part because she didn’t love music in the same way that he did. “That was a filter which separated how I looked at women,” Greenspan told Mallaby.

Years later, Bush blamed Greenspan for his 1992 election loss. “I reappointed him, and he disappointed me,” Bush said coldly. To Greenspan, the whole episode was pitiful. The U.S. economy was “really moving up in 1992, and if he couldn’t make it work, it wasn’t my fault,” Greenspan said in a 2007 interview with the Sunday Times of London.

A few years later, Greenspan earned plaudits again for resisting pressure from colleagues to raise rates aggressively as the unemployment rate tested new 30-year lows. Conventional forecasts relied on something known as the Phillips curve, which predicts that wages and prices accelerate when the unemployment rate falls below some natural, sustainable level that can’t be directly observed. But Greenspan was studying corporate profit margins. He marshaled data to suggest a productivity boom thanks to personal computing was under way. Strong productivity translated to declining or stable unit labor costs, he argued, which would allow the economy to tolerate more growth without higher inflation.

Isn’t this potentially where we are now?

Greenspan believed financial institutions had a self interest in not taking risks that would wipe out shareholders; similarly, he thought they had natural incentives to behave ethically to preserve the value of their reputations. Under Greenspan’s watch, the Fed didn’t blow the whistle on riskier lending practices in which Wall Street used financial alchemy to turn dodgy mortgages into highly rated securities.

Why China Got Rich and India Didn’t

In the five decades since the death of Mao Zedong, China has grown much faster than its fellow Asian giant. China has become a manufacturing superpower and the single fastest-growing economy of the last 50 years; its per capita GDP, on par with India’s in 1976, is now about 2.5 times higher. And so Chinese people have become much better-off than their Indian counterparts. In 1987, median purchasing-power adjusted income in China was $1.88 per day, compared to $2.94 per day in India. Chinese median wages surpassed Indian ones in the early 2000s; and by 2022, China recorded a median income of $13.36, against $5.54 in India. In the 35 years between 1987 and 2022, Chinese median income rose 611 percent, while Indian median income rose only 88 percent.

And in its place, the Chinese state forged a new nation along its preferred ideological lines. Economic development always eluded Mao; but human development—mass education and mass health—proved more attainable. Literacy campaigns and mass education helped raise the literacy rate from roughly 20 percent in 1949 to almost 70 percent by 1982. These gains were concentrated among women: Chinese women went from “virtual complete illiteracy” to a literacy rate of about 50 percent during the same period. The progress in health was similarly rapid: child mortality fell by 80 percent between the early 1950s and the late 1970s. Even with all the horrors of Maoist rule—including, it should be remembered, the largest famine in history—between 1949 and 1976 China recorded one of the largest sustained increases in life expectancy of any country in history, rising from about 41 years in 1949 to 61 by 1976. And for the first time in history, Chinese women were meaningfully included in public life: by the late 1970s, China had a female labor force participation rate exceeding that of many rich countries.

When analysts from the World Bank visited China in the early 1980s, they reported that its low-income groups were “far better off in terms of basic needs than their counterparts in most other poor countries”; if China’s “immense wealth of human talent, effort and discipline can be combined with policies that increase the efficiency of resource use,” their report said, “China will be able, within a generation or so, to achieve a substantial increase in the living standards of its people.” And that’s exactly what happened.

Tbh, in the past, I thought of the period from 1950 to 1976 as brutal for Chinese people. Many old traditions that had been part of China for thousands of years were forcefully eliminated within a decade. But this “modernization” perspective is quite refreshing.

In 1950, 27 percent of Indian children died before the age of five, compared to 32 percent of Chinese children; by 1980 it was 17 percent in India, against 6.3 percent in China.

All of this meant that the Indian state was never able to achieve the social modernization that the Chinese state accomplished. The dense web of kinship obligations and customary authority that governed social life remained intact. Caste panchayats still adjudicated disputes; joint families still pooled and redistributed income; and women remained bound by all the strictures of traditional life.

In the end, it’s human capital that matters. China had the labour force ready for opening up at the end of the 1970s, while India did not.

Weekly Reading #25

2026-06-21T00:00:00+00:00

Outside of Work

Switzerland’s Radical Proposal on Immigration: Cap the Population

When immigration happens too fast, it usually does not end well.

‘Toy Story 5’ Director on Pixar’s Secret to Making Adults Cry

Andrew Stanton has been a hero of mine for a long time. Toy Story 5 is a must-watch!

达尔文之后，我们该怎样重新理解进化？

变异是进化的基础，生物个体之间天然存在差异。这种差异来自两个来源：基因突变，以及父母双方基因在繁殖过程中的重新组合。正是这种无处不在的变异，为自然选择提供了原材料。

自然选择是进化的动力，环境在变，能够更好适应当下环境的个体，留下更多后代，其特征得以延续；反之则逐渐消失。

Weekly Reading #24

2026-06-13T00:00:00+00:00

The next chapter in flood resilience: Open-sourcing Google’s hydrology framework

Interesting…Google is open-sourcing something not related to the search, web, distributed computing, ai but a hydrology model.

The Shift to Distributed LLM Inference

Explore distributed inference and training with MLX - WWDC26

I’m learning distributed LLM inference.

Outside of Work

Are Memories Transferable — or Edible?

While planarians have proven uncooperative, modern neuroscience is validating McConnell’s underlying thesis that molecular mechanisms (such as RNA) can transfer behavioral conditioning.

Yishan Wong on IPO pricing

When the bankers price the IPO for a modest pop, that’s the default compromise between these interests: the company makes a little bit less than it would get from the public, the big buyers take a risk and get a profitable flip sometimes, and the bankers lend their market expertise and get paid their fees. When it skews in either direction, one or more of those parties takes a hit - but the others benefit.

Interesting triangle here.

Weekly Reading #23

2026-06-06T00:00:00+00:00

Mem0 AI Memory Layer

It’s still very early. I’m also wondering how different memory designs work with different models. Should there be any protocol in between? If so, what should that protocol look like?

Outside of Work

Bill Gates Spent Years Crafting His Image. Now It’s Cracking.

A styling group stores troves of neutral tone crew and V-neck sweaters, button-down shirts, slacks and extra pairs of the Silver Lining Opticians “Carbon” glasses at an off-site building, current and former employees said. Once options are selected for public-facing engagements, employees usually send three options for approval by senior staff. The goal: to depict someone calm and approachable, like Mister Rogers.

Some people familiar with the matter said they heard about his admission to staff with disbelief: In his divorce proceedings, allegations related to more than 20 affairs had come up.

UC Faculty Urge Reinstatement of Standardized Testing for STEM Majors

More than 1,100 University of California math and science professors are urging UC regents to reinstate college-entrance exams, saying that unprepared students are lowering academic standards and draining teaching resources. The request, delivered in a two-page letter last week, cites a sharp decline in readiness among students studying science, technology, engineering and math. Nearly one-third of students taking first-semester calculus at UC Berkeley “displayed severe preparation deficits,” the letter said.

This is quite unthinkable from a Chinese perspective, where prestigious schools always require students to have the best academic records.

Carsick Cars and Its Era, Pt. 1

It was recommended by Michael Pettis.

何伟（Peter Hessler）的文章

When Peter Hessler writes, you read.

How to register a company in Japan as a foreigner

2026-06-03T00:00:00+00:00

Starting a company in Japan is not simple. Somehow, it is both traditional and modernised. This post is a summary of my journey doing it in Japan. Hopefully, you find it useful.

You can, of course, outsource this to a lawyer. But as you know, that can be quite expensive and I was unemployed at the time. :P

Eligibility

The first thing you need to do is check your visa type. Most foreigners come to Japan for work. In these cases, they hold a standard work visa. If you check your Residence Card (在留カード), you should see this: 技術・人文知識・国際業務 (Engineer/Specialist in Humanities/International Services). The company hiring you is the sponsor of your visa. Under this status, while you are legally allowed to register and own a company, you cannot work full-time for your own company or receive executive remuneration from it.

You may ask (I actually asked this question): Can I work for my own company as a second job (副業) while continuing my main employment? The answer is, it depends. Some employers strictly disallow side jobs in their company regulations. You must check your company’s rules before proceeding.

Some people hold a Highly Skilled Professional visa (高度専門職). If you hold a 高度専門職 II visa, you are allowed to register a company and work for it, but since you still have a sponsoring organization, you must ensure your main employer permits side businesses. If you hold a 高度専門職 I visa, you can engage in business management, but only if the business is directly related to your primary sponsored activities.

If you are a Permanent Resident (永住者), you have no legal restrictions regarding employment or business ownership. However, you still need to check whether your primary employer allows side jobs.

[!WARNING] Crucial Immigration Update for 2026: If you do not have Permanent Residency or a Highly Skilled Professional visa and plan to apply for or switch to a Business Manager Visa (経営・管理) to run your company, be aware that Japanese immigration laws changed drastically on October 16, 2025.

The minimum capital requirement has been raised from 5 million JPY to 30 million JPY.

You must employ at least one full-time resident of Japan (Japanese citizen, PR, or spouse of a citizen/PR).

You must secure a physical, independent office space; virtual offices are generally no longer accepted for new Business Manager Visa approvals.

If you are simply registering a company as a side business under an existing HSP II or PR status (where you don’t need a Business Manager Visa), the virtual office and low capital approach described below still works perfectly fine.

Things you must have

Once you have confirmed you can legally proceed, you need to ensure you have the following items. Having these ready will make your application much smoother.

A Japanese phone number

Needless to say, there are countless forms and applications where a valid, local phone number is mandatory.

A personal Japanese bank account

This is required to verify your startup capital. While the legal minimum capital can be as low as 1 JPY, you must prove you have that capital in a personal bank account before the corporate account is established.

A personal stamp certificate (印鑑証明書)

For anyone who has lived in Japan for a while, you know how crucial personal seals are when you are involved any formal process. If you don’t have a registered stamp and certificate, you can obtain them at your local city hall (区役所).

Things that are highly recommended

A My Number Card

While technically optional, the process would be incredibly difficult without one. During registration, you will need to authenticate your identity digitally multiple times.

If you have all of the above, congratulations! The rest is just following the process.

Step 0: Company name, type, and address

There are only two hard things in Computer Science: cache invalidation and naming things.

And Japan certainly makes naming things even harder. You must choose how to position your company type in the name:

株式会社xyz or xyz株式会社 (for a KK). Both are valid. Interestingly, having the type at the front (前株 - maekabu) is very common in Japan. You can mix Kanji, Katakana, Hiragana, English letters, and numbers.

Once you have a name, you need to decide on the company type: KK (株式会社 - Kabushiki-Kaisha) or GK (合同会社 - Godo-Kaisha).

I won’t cover all the differences here, but if you choose a KK, your corporate details must be published. This means you need a public company address.

If you plan to rent a physical office, that’s straightforward. But if you aren’t, you need an alternative address. If you live in an apartment building (マンション or アパート), your lease agreement likely prohibits using the residential address for corporate registration. If you live in a house (一戸建て), you can use it, but doing so exposes your private residence address on the public registry.

This is where a virtual office (バーチャルオフィス) comes to the rescue. These services rent out physical addresses to multiple corporations and forward any received mail to the representative’s actual residence. Note that if you use a virtual office, you must choose a bank that permits virtual office addresses for corporate accounts.

I personally used DMM for my virtual office.

Step 1: Full DIY or use a SaaS platform

Once your company structure is decided, you must choose whether to register completely on your own or use a helper SaaS platform.

My thinking was simple: I was opening a KK, and I am not a Japanese legal expert. Going full DIY was too much risk and effort. As a result, I chose freee会社設立.

Step 2: Creating the Articles of Incorporation (定款)

The English term for 定款 (Teikan) is Articles of Incorporation. It contains all the structural and operational details of your company. One of the most critical parts of the 定款 is deciding the business categories (事業目的) your company will operate in. While you are free to add many, it is recommended to focus on the core activities related to your business. It is also highly recommended to include this catch-all clause at the end of your list:

前各号に附帯又は関連する一切の業務
(Any and all businesses incidental or related to the preceding items)

If you need to add or change business categories later, you have to file an amendment with the Legal Affairs Bureau (法務局), which costs a registration tax fee.

If you use Freee, they will introduce a partnering judicial scrivener or notary. Once you input your company details, address, and business categories into Freee, they will generate the 定款 for you.

To proceed with this, you will need to provide:

Your personal stamp certificate (印鑑証明書)
A valid ID (My Number Card, passport, etc.)

Step 3: Capital verification

You must prove that you actually possess the capital claimed in your 定款. This is verified using your personal bank account. You need to show:

The exact capital amount specified in the 定款 has been transferred or deposited into your personal account on or after the date the 定款 was created.

You can simply take screenshots of your online bank account app showing the transaction and account details, then upload them to Freee. The platform provides detailed, step-by-step guides on exactly how to capture these screenshots.

Step 4: Submit your application and get Articles of Incorporation certification (定款認証)

[!NOTE] This step is only required if you are establishing a KK (株式会社). GK (合同会社) establishments do not require notary certification of the 定款.

This step requires a basic level of Japanese or someone to help you because an interview with a notary public is required. Fortunately, the interview can be conducted online.

To do this online, you must have a My Number Card because this formal legal process requires digital signatures, which you can complete using your card reader or a smartphone that supports My Number Card authentication.

The process can be quite demanding. Essentially, you must:

Submit your application to the Legal Affairs Bureau at least 24 hours before your 定款認証 interview.
Complete the online interview with the notary public (��証役場) and wait for certification.

If you use Freee, it will guide you through submitting the application to the bureau. Your documents will be reviewed by officials first. If anything is incorrect, the application will be sent back for corrections. In my experience, I had to resubmit a few materials before the application was approved and the interview was scheduled. Once approved, the interview itself is mostly a formality.

After this step, you can take a deep breath—the hardest part is over. You will receive the certified 定款 from the notary, and shortly after, your corporate number (法人番号).

Step 5: Retrieving your company’s legal documents

While some of these processes can technically be completed online, a system glitch at the Legal Affairs Bureau prevented me from doing so. I went to the physical office and picked up:

Certificate of Registered Matters (登記事項証明書 - Tokibo Tohon)
Corporate Seal Certificate (印鑑証明書)

These physical documents are required when applying for a corporate bank account.

While you are at the Legal Affairs Bureau, it is highly recommended to apply for your Corporate Seal Card (印鑑カード) at the same time. Freee provides all the necessary guidance for this. The registration date on these certificates is officially your company’s establishment date.

Step 6: Notify the Pension Office and Tax Offices

If you plan to pay any salaries (including to yourself), you must notify the Pension Office (年金事務所).

[!IMPORTANT] Under Japanese law, enrolling in social insurance (社会保険 - health insurance and pension) is legally mandatory for corporate entities (both KK and GK) from the day of establishment, even if you are the sole director/employee.

You must also file corporate registration notifications with your local national and municipal tax offices within 2 months of establishment. When doing so, make sure to submit the 青色申告の承認申請 (Application for Approval of Blue Tax Return) as it offers substantial tax benefits and deductions.

Step 7: Opening a corporate bank account

We have made it so far! This is the final major step.

Unfortunately, opening a corporate bank account in Japan is again, very hard.

From what I heard getting approval at major megabanks (like MUFG, SMBC, or Mizuho) is highly unlikely for new startups. Therefore, I recommend going directly to online-focused banks like GMO Aozora Net Bank or Rakuten Bank. After researching, I chose GMO.

Even with online banks, you must fill out a big form, submit your company’s official documents, verify your identity, and wait for approval.

Getting the approval was not easy in my case.

I run an online service and use Paddle as my Merchant of Record/payment processor. The bank reviewers had a very hard time understanding how money flowed between my users, Paddle, and my company’s bank account. Because Paddle operates primarily in English and does not issue standard Japanese invoices or receipts, the bank reviewers repeatedly sent back my applications.

My application was rejected multiple times because the reviewers couldn’t clearly identify my business intent. Customer support couldn’t give me direct reasons but dropped subtle hints. After a month of back-and-forth rejections, I realized I needed to hold their hands through the process.

I put together a comprehensive, illustrated guide in Japanese: showing exactly how a user signs up for my service, selects a plan, pays via Paddle, and how the payouts are recorded on the Paddle dashboard before being transferred to my bank account. I submitted this visual walkthrough, and finally, I was approved!

Some key rules of thumb for this stage:

If you are using a virtual office, make sure your provider supports receiving and forwarding 転送不要の簡易書留 (non-forwardable simple registered mail). Banks often send physical verification mail this way, and if your virtual office can’t accept it, your account application will be automatically rejected.
Confirm beforehand that your chosen bank accepts virtual office addresses.
Provide a clear, visual explanation of your business model, cash flow, and payment processors (especially if using foreign platforms like Paddle or Stripe). Making it easy for the reviewers to understand your business is the key to a smooth approval.

Weekly Reading #22

2026-05-30T00:00:00+00:00

Last week I was quite busy… So only a short list this week.

The Best Companies Will Stop Making Software

Hmm, I feel like this is already happening. Many companies are outsourcing the code development to other countries.

An Engineer’s Guide to Harness Orchestration

While I find the topics in the article interesting, why do they need to keep inventing new concepts?

Outside of Work

The Remarkable Proliferation of Cancer Immunotherapies

The ramp up of cancer immunotherapy is remarkable. Now we’re seeing vaccines achieve some cures or remissions in the most refractory cancers: pancreatic, melanoma, glioblastoma, renal, triple-negative breast cancer.

Weekly Reading #21

2026-05-23T00:00:00+00:00

The Four Levels of Agentic Software Development in the Enterprise

Agentic development from a platform engineering point of view.

Content for Content’s Sake

From my reading, I also find “slot,” “harness,” and “guardrails” to be annoying.

Will we be able to notice these differences in the future?

Inference Engineering

I think Inference Engineering will be the future (or is it already here everywhere?). There is a lot of work in this area: managing memory, both short-term and long-term (can the agent remember what I did previously?), agent performance evaluation (does the agent finish the task as requested?), etc.

Outside of Work

Xi’s Forever Purge

But he also seems frustrated with self-revolution’s results thus far. He has complained, with unusual frankness, that “if I do not write comments on reports, no work gets done.” Among the millions who staff the party-state, he sees too many officials with “inadequate understanding,” “old-fashioned concepts,” and “insufficient ability.”

If true, it’s pretty remarkable.

The wager is that the party does not need elections or rule of law to remain effective. Internal discipline and self-correction, Xi believes, can generate enough accountability, legitimacy, and success to sustain its rule.

现代人体能量危机，正在拖慢我们的思考 (Chinese)

Actually, any exercise that keeps your heart rate in Zone 2 can significantly improve mitochondria production.

The Water Use of Data Center Workloads

Water usage in data centers is not as bad as people might think.

Why Japanese Companies Do So Many Different Things

And this means that Japanese companies strive to avoid financial pressure from outsiders. Relationships with suppliers are longstanding and entrenched: many Japanese companies have been working with the same suppliers for 50 years or longer. Outside investors seeking to interfere in this happy picture will find few avenues for influence. A standard Japanese firm’s board of directors is composed almost exclusively of the firm’s own senior managers; a large fraction of the firm’s equity is held not by outside investors but cross-held by other Japanese firms; and most of the firm’s financing comes from a single “main bank” that provides loans and monitors performance.

And as a result, Japanese companies don’t really try too hard to return profits to shareholders. Earnings are mostly reinvested, and investor dividends are kept low. For a long time, Japanese firms would spend as much entertaining the managers of other firms as they would on dividends to shareholders.

But in order to understand each other’s jobs, workers cannot be specialized: they have to rotate across different workplace functions to the point where they’re familiar with much of the plant’s operations. In order to rotate across different workplace functions, they need broad training; and it makes no sense to train them broadly if you don’t keep them for a very long time. And if you have generalist workers who are around for a long time, you can’t reward them based on how they do in one role, because then they’d have no desire to leave that role for another role where they might do worse. Instead you have to pay them based on company performance, and promote them based on seniority. And you also have to give them an ironclad commitment not to fire them if economic conditions worsen: if they can get laid off at any moment, why would they invest years of effort in learning all the idiosyncratic things that your firm does.

And that basic impulse toward survival is why Japanese companies are so insistent on diversification. If you’ve made a commitment to keep people employed for life, then you need to create jobs for them if their current jobs stop making sense.

And this system, as it turned out, was really good at particular things. Aoki’s key insight was that the J-mode had a comparative advantage in environments of moderate volatility: situations where conditions changed frequently enough that rigid central plans would be outdated before they were executed, but not so radically that only top-down strategic intervention could cope. In an environment of stable, predictable demand, the H-firm did fine; in an environment of extreme disruption, where the whole product line had to be rethought, centralized authority was indispensable, and the H-firm also did fine. But in between—where the challenge was to make constant small adjustments in a changing but recognizable paradigm—the J-firm excelled.

I heard from a friend who worked at Fujifilm a long time ago that they were pivoting toward being a cosmetics company because film was dying.

While large Japanese companies do so many different things, many small companies (restaurants are a good example) only do one thing.

Weekly Reading #20

2026-05-16T00:00:00+00:00

LLM-based Hospital Course Summaries and Physician Burnout

In this study, an LLM-based agentic workflow produced hospital course summaries that were frequently used with minimal risk of harm identified. The intervention was associated with a reduction in physician burnout, supporting the viability of AI summarization to mitigate documentation burden.

Everyone needs healthier doctors.

AI and Coder Employment: Compiling the Evidence

We find robust evidence that coder employment growth fell after that release. After controlling for industry-level shocks we find that coder employment growth has been 3 percent lower since the introduction of ChatGPT.

Keep in mind that some of the largest employers have increased their AI expenditures by 300%.

Pushing Local Models with Focus and Polish

I can’t wait to see more local models. What if we could also “borrow” or “combine” the compute power of family and friends to run models for the clan?

A Few Words on DS4

DS4 is a big deal, in my opinion.

Outside of Work

Argus: AI Anomaly Intelligence Original reddit url: I analyzed 79,621 declassified UFO reports with AI

This is a great use of LLMs.

Why Gen Z is Abandoning Beijing for Greener Fields Elsewhere in China

The number of Beijing permanent residents in that age group has nearly halved over the past decade, plummeting from 4.62 million in 2015 to 2.49 million in 2024.

Beta Moms and TikTok Influencers

In 1975, for example, women spent an average of 14 minutes a week helping their kids with their homework. By 2018, the most recent year for which the modern data is harmonized with historical, that amount had nearly quintupled (to an hour and nine minutes).

It looks like the Chinese are not unique in this regard.

Nike’s Competition in China’s Running Shoe Market

I doubt Nike can reverse course. It has no branding advantage over its competitors, and its products fall far short of Chinese brands.

David Attenborough’s 100th Birthday Card from King Charles III

David Attenborough’s 100th birthday card from King Charles III. I’ll never forget the first time I watched Planet Earth.

Weekly Reading #17

2026-04-25T00:00:00+00:00

Japan’s mighty carmakers are in serious trouble

If you look at Honda’s new car, prelude. You should be able to know why they are not doing well.

Every company is now a media company, and every boss a star

Fighting for people’s attention and time.

Critical Atlantic current significantly more likely to collapse than thought

Same author published a similar article last August: Collapse of critical Atlantic current AMOC no longer low likelihood study warns

New research is showing the situation gets worse.

Community Letter from Tim

Tim makes Apple better.

What will be scarce?

Rich households, in other words, do not just buy more stuff. They shift their spending toward goods and services where the human element, the experience, or the social meaning matters more.

It reminds of this article: The Ultrarich Are Spending a Fortune to Live in Extreme Privacy

You need to be the person whose involvement makes the product feel like it was made for someone, by someone. The economics of structural change tells us that when technology makes one type of production cheap, the economy doesn’t collapse. It transforms. It shifts toward the things that technology can’t make cheap. For AI, those things are exactly the ones where human involvement carries inherent, irreplaceable value.

Mimetic desire pushes against this scenario because this aspect of demand is not quickly satiated. As highlighted above, the fact that the preference for status and exclusivity is comparative means that people will keep reallocating spending toward goods that satisfy it as incomes rise

Weekly Reading #16

2026-04-19T00:00:00+00:00

China’s History Introduction - MIT

An introduction to China’s history from MIT.

Security with AI Agents

Security with AI agents: new problems keep emerging.

Can a country get too rich?

Since 1991 the government has amassed a sovereign-wealth fund worth $2.2trn, or $400,000 for every one of Norway’s 5.6m people. Proceeds sustain one of the world’s most generous welfare states.

more than 70% of unskilled service workers (think baristas and call-centre staff) born in Norway have masters degrees.

These are some truly astonishing facts.

从细胞到自我意识：生命的层层阶梯共同构成思想这种东西 (Chinese)

Beautifully and abstractly written.

Why Everyone Hates the Ivy League

Speaking of living in your own bubble—this is also a classic example of bureaucracy, where a committee is needed to conclude a well-known fact.

Jim Whittaker, First American to Summit Everest, Dies at 95

first as a climbing guide and rescue leader, and later as the first full-time employee of Recreational Equipment, or REI

With her and their two sons, Whittaker lived on a sailboat for four years in the late 1990s, traveling around the Pacific with stops in Tahiti, Fiji and Australia.

What a life he had.

Also, during the 1963 American Everest expedition, Tom Hornbein reached the summit via a non-conventional southwest route. The large couloir on the west side of Everest’s north face is named after him (the Hornbein Couloir).

Weekly Reading #15

2026-04-11T00:00:00+00:00

Tim Cook, the iPhone, and the Secretive Archives That Help Run Apple

We shipped products that weren’t successful. We just got up the next day and put our head down and went on to the next thing.

Fascinating. I think people have forgotten that the iPhone was not an instant hit. Apple’s stock price only really started to climb around 2009, even though the iPhone was launched in 2007.

An Ugly House in a Spectacular Spot Gets a Modern Redo

Cool.

李录与比亚迪的故事 (Li Lu and the Story of BYD)

Fascinating story of how Li Lu invested in BYD and persuaded Warren Buffett to invest in 2008.

Why women, more than men, are abandoning rural Japan

Many smaller places have now established strategies to tackle gender discrimination

I was wondering why this was happening when I saw the title, and I was right. Gender equality is a major advancement in modernization.

The Ivy Plus Education

…that the most important thing a student gets from an Ivy Plus education isn’t instruction or prestige or even connections. It’s the opportunity to learn how to succeed in an environment filled with the world’s most talented and ambitious people.

This series of articles is worth a read:

Who got into the Ivy Plus schools?

We first show you a simple fact – applicants from families in the top 1% of the income distribution are more than twice as likely to attend Ivy-Plus colleges, even among applicants with the same SAT or ACT scores

The Future of Highly Selective Colleges

Tests + lottery.

Bill Ackman’s Pershing Square Offers to Buy Universal Music Group

As a music lover, I don’t want Bill Ackman to take over Universal Music.

PropagandaScope

This is an interesting site. Consumption (消费) is the most popular keyword in Chinese state media.

Mount Everest scam: Sherpas ‘poisoned’ trekkers in insurance fraud

Investigators say the scam involved multiple actors across the trekking ecosystem, including sherpas, trekking company owners, helicopter operators and hospital executives.

Wait, what? There are always surprises during the Everest season.

YouTube Is the World’s Largest Media Company

YouTube’s estimated $62 billion in 2025 will have allowed it to pass The Walt Disney Co.’s media business, which generated $60.9 billion last year (excluding Disney’s lucrative experiences division).

The firm, which declared YouTube the “new king of all media” last year, is now valued at between $500 billion-$560 billion, far above any traditional media competitors. The closest would be Netflix, which has a market cap of about $409 billion as of writing.

Alphabet’s current market cap is $3.8 trillion. According to this estimate, YouTube accounts for about 15% of Alphabet’s total value.

A Wild Bluesky Thread

This is pretty wild. The first reply in the thread cracked me up.

How Modern and Antique Technologies Reveal a Dynamic Cosmos

There are still many analog pictures of the cosmos waiting to be analyzed.

Extensive reading: The Biggest Ever Digital Camera Is This Cosmologist’s Magnum Opus

An array of 189 CCDs totaling 3.2 billion pixels, the Rubin Observatory’s Legacy Survey of Space and Time (LSST) camera is the largest digital camera ever built. Over the next 10 years, it will repeatedly photograph approximately 20 billion galaxies.

抽象：数学的美感 (Abstraction: The Beauty of Mathematics)

Abstraction is truly beautiful. Reading this article reminds me of another one: How Writing Changes Mathematical Thought.

Why the U.S. Fertility Rate Has Hit a Record Low

In 2025, birthrates for women in their late 30s exceeded those for women in their early 20s for the first time.

The US fertility rate has also hit a record low. I wonder how many women in their late 30s are using IVF.

In Japan: Many Japanese Teens Giving Up on Getting Married and Having Children

Inside Trump’s Secret Plans for Iran

Did the NYT install a camera and a microphone in the White House?

Weekly Reading #14

2026-04-05T00:00:00+00:00

Operation Epic Fury Should Make China Very Afraid

Really? It’s quite funny that while the US is attacking Iran with massive failure, someone in a “think tank” is still thinking about China. This seems like a serious obsession. It feels like there is also a Propaganda Department (宣传部) in the US now.

England has shown the world how to replace farm subsidies

Really? I don’t think Jeremy Clarkson would agree.

阿波罗计划的遗产

综上所述，地球之所以能够成为生命的摇篮，月球起到了关键的作用。问题在于，宇宙中两颗大质量行星发生撞击的概率是很低的，目前天文学家虽然已经在银河系中发现了数千颗行星，但没有任何迹象表明这种事情曾经发生过。如果月球真的是由两颗行星碰撞而来的，那么我们几乎可以肯定地说，生命的出现是一个极小概率事件，我们人类很可能是银河系中的一个孤独的存在。

Weekly Reading #13

2026-03-28T00:00:00+00:00

The Jony Ive Design for Christie’s Rostrum

Of course, we know the auctioneer won’t understand how the mechanism was designed and how it functions, but I do believe they will sense care.

Jony Ive and his design team even designed the new Christie’s Rostrum. I only learned what a Rostrum was after watching this video. He articulates so well.

Running Agents on Kubernetes with Agent Sandbox

Kubernetes joins the agent orchestration game.

How Many Wealthy Americans Are There?

There are about 430,000 U.S. households worth $30 million or more, according to an analysis of Federal Reserve data by Zidar. Within that, there are about 74,000 worth $100 million or more. Over the past few decades, the growth in the number of very rich households has surpassed general population growth.

Many of them are boomers, which shows the power of compound interest.

Drinking 2-3 Cups of Coffee a Day Tied to Lower Dementia Risk

Evidence from a study of more than 130,000 people suggests that two to three cups of coffee a day can reduce dementia risk and slow cognitive decline.

Tea with caffeine also helps.

The Death of Spotify: Why Streaming Is Obsolete

What if Jimmy is right? If the DSPs are “minutes away from obsolete,” what replaces them? Well, I’m not sure the DSPs are going to disappear overnight, but if you’re an artist or a manager trying to sustain yourself in this evolving music economy, the answer is direct ownership.

A Shopify for music artists? Honestly, I LOVE the experience of being able to listen to any song I can think of within 10 seconds.

Why the ATM Didn’t Kill the Bank Teller

And I suggested that given the vast number of frictions and bottlenecks that exist in any human domain—domains that are, after all, defined around human labor in all its warts and eccentricities, with workflows designed around humans in mind—we should expect to see a serious gap between the incredible power of the technology and its impacts on economic life.

First, by reducing the cost of operating a bank branch, ATMs indirectly increased the demand for tellers: the number of tellers per branch fell by more than a third between 1988 and 2004, but the number of urban bank branches (also encouraged by a wave of bank deregulation allowing more branches) rose by more than 40 percent. Second, as the routine cash-handling tasks of bank tellers receded, information technology also enabled a broader range of bank personnel to become involved in “relationship banking.” Increasingly, banks recognized the value of tellers enabled by information technology, not primarily as checkout clerks, but as salespersons, forging relationships with customers and introducing them to additional bank services like credit cards, loans, and investment products.

But by talking about why ATMs didn’t displace bank tellers but iPhones did, I want to highlight an important corollary, which is that the true force of a technology is felt not with the substitution of tasks, but the invention of new paradigms.

Fantastic writing. I also learned about the Jevons paradox.

AI Can Do Science Too

AI can do science too, of course. There is also AI doing AI: PostTrainBench.

Weekly Reading #12

2026-03-20T00:00:00+00:00

Trump’s Plan to Squeeze Iran Faces a Major Obstacle: The Strait of Hormuz

“They had no plan to address the crisis in the strait,” said Sen. Chris Murphy (D., Conn.), who joined a classified briefing Tuesday with administration officials about the operation. “The fact that these guys didn’t have a plan ahead of time, and a week into the war still didn’t have a plan, was pretty shocking.”

Typically, war preparations include weeks or months of classified deliberations, written planning documents, the airing of dissenting views from diplomats and intelligence officials, and National Security Council meetings with cabinet members to make the most informed decision. Only a small group was looped into the preparations for Iran—including Vice President JD Vance, Secretary of State Marco Rubio and Hegseth. That narrowed the advice, information and ideas available to the president, who had to balance the many downsides of an attack.

Trump faces two difficult choices, analysts say: End hostilities and leave a wounded regime likely to rebuild its arsenal and terrorize regional allies. Or continue bombing at the risk of broader instability, more casualties and a political backlash among voters who believed Trump would end U.S. involvement in foreign wars, especially in the Middle East.

Dubai’s crazy rich Chinese

I read this article about a week before the US and Israel attacked Iran. Now that Dubai is in chaos, I can’t stop thinking about it.

Hong Kong’s property market has turned

Over 300,000 mainlanders have received “talent” visas of various kinds over the past six years, on top of 155,000 others who settled in the city via family ties

Over the next five years the government aims to add 189,000 units, an almost mainlandish pace of construction

Distinct AI Models Seem to Converge on How They Encode Reality

Models think alike. Maybe that means they also have common sense.

A Single, ‘Naked’ Black Hole Rewrites the History of the Universe

We still know very little about black holes. They challenge our understanding of the evolution of the universe. I believe the JWST could be one of the most important scientific projects in human history.

There is also a related video explaining the evolution of black holes (in Chinese): https://www.youtube.com/watch?v=oltrRIhmgmE

Women’s midlife: the front line of Alzheimer prevention

Women have a higher risk of dementia.

Parity — the number of times a woman has given birth — generally shows a nonlinear relationship with AD risk (10, 11). Several studies report an inverted U-shape association, where having 1–4 children is linked with lower dementia risk (106, 113, 114), while grand multiparity (≥5 children) is associated with increased risk (106, 115, 116). A meta-analysis found that both nulliparous women (RR 1.11, 95% CI 1.06–1.16) and those with 5 or more children (RR 1.28, 95% CI 1.15–1.44) had increased dementia risk, with each additional childbirth increasing risk by 3% (92). Some studies also suggest associations between longer cumulative time spent pregnant or breastfeeding and reduced AD risk (103, 114, 117), although others report the opposite (102). Since estrogen levels are suppressed during lactation, these effects may involve different mechanisms.

Impact of Minimum Pay Rules on Gig Delivery Drivers

A minimum pay law for gig delivery workers in Seattle doubled base pay per task but resulted in reduced tips, fewer available tasks, and no net increase in monthly earnings.

A classic Economics 101 conclusion.

Loud & Clear

This year, more than 13,800 artists generated at least $100,000 on Spotify.

Introducing Reqfleet

2026-03-17T00:00:00+00:00

I left my previous employer about a year ago and started my own business: Reqfleet.

It’s a product I’ve been working on for a while. It helps enterprises and individual developers conduct load tests. Please give it a try! You can use the invitation code BLOG when registering.

If you are interested in buying an enterprise plan, please feel free to send me an email. kejun#reqfleet.com. Please note if you read it from my blog. :P

In this blog post, I want to share my thoughts behind such a big change.

Why I started my own business

The field does not matter (much)

It took me a while to finally make the decision. One thing that made me hesitant to start working on Reqfleet was that the expertise required to build it was vastly different from what I had been working on. I didn’t want to waste the experience I’ve gained over the years. But gradually, my mind changed and I started to realise the field doesn’t matter that much. I just want to build products, ship products, and do it in a way that I’m passionate about.

Can I do it for 10 years or longer?

The next question I asked myself was: if I chose to work on a product, could I commit myself for 10 years or longer?

The answer became clear once I knew what I wanted to do:

The product I’ll be working on should be technically challenging and have potential to evolve.
I want to build a company that I myself would want to work in. It should be product-centric and customer-centric. It should be able to generate a healthy profit by serving customers well.
If possible, I want to build a team that I’ll enjoy working with.

That’s much more attractive than barely surviving in a complex corporate world. If the company I describe can survive for 10 years, that will be one of the best achievements of my life.

Why load testing?

I’ve always been interested in performance-related topics. Much of my major work in the past was focused on making web applications faster—from constructing a cache layer to developing a novel API gateway. Around 8 years ago, I started to develop an open-source solution to help internal teams conduct load tests more easily. Since then, I’ve helped countless teams run load tests and detect performance issues. However, I didn’t spend my full time working on it over the last 8 years. So, while I had many ideas along the way, I wasn’t able to make them happen. Starting Reqfleet allows me to turn those ideas into reality.

Let me use a phrase from Toy Story to end this blog:

To infinity and beyond!

Weekly Reading #11

2026-03-14T00:00:00+00:00

GNU and the AI reimplementations

Redis author (Antirez)’s take on AI reimplementation.

Harness engineering: leveraging Codex in an agent-first world

A big agent.md cannot work for the agent.
Boring tech is easy for an agent to work on.
Need to centralize the knowledge for the agent to understand.

This resembles leading a large engineering platform organization: enforce boundaries centrally, allow autonomy locally.

This really feels like working with a human. The limits of the agent are very clear.

What’s become clear: building software still demands discipline, but the discipline shows up more in the scaffolding rather than the code. The tooling, abstractions, and feedback loops that keep the codebase coherent are increasingly important.

Is Iran on the Brink of Another Revolution?

An Iranian commentator providing an Iranian point of view.

Russ Cox on AI in Go

Russ Cox has written extensively about how to integrate AI commits into Go:

AI tools have seduced many people into a false belief that these fundamentals no longer apply. People brag about codebases of hundreds of thousands of lines that have never been viewed by people, churned out in record time. On closer inspection, these codebases inevitably turn out to be more like dancing elephants than useful engineering artifacts.

Can an elephant dance? (It cannot). Ref: BBC - Can Elephants Dance?

It’s even more interesting to read together with OpenAI’s harness engineering article.

Tony Hoare (1934-2026)

Tony Hoare has passed away. He was the inventor of Quicksort and had a big impact on Go design (CSP).

Interview with Mitchell Hashimoto

I really enjoyed this interview. I was a Vagrant user back in 2013 and a Consul user starting in 2017, and I even met Hashimoto once in a meeting. What he did in the infra/DevOps world was pretty amazing.

What Private-Credit Investors Need to Know About the Industry’s Turmoil

Quite fascinating. Why US investors would invest in such funds when they could simply buy S&P 500?

We are living in a new era

2026-02-14T00:00:00+00:00

Back in October, I mentioned that I don’t write TypeScript anymore. In other words, at the time, I still wrote programs in languages I knew well, like Go or Python. However, after a short and memorable winter break, when I started programming again, I found out I didn’t need to write those either.

Suddenly, I realized that last December was probably the last time I hand-wrote Go code.

How I worked before

Before the AI boom, this is how I worked: I wrote down the idea for something I wanted to build in a Markdown file. If it was just an initial idea, I’d try to articulate it well so that either the idea was killed after careful thought, or it was established enough to move to the next stage. Then I would lay out the detailed requirements. Once the document was written, I knew well enough how to build the project. The next step was converting those requirements into multiple cards in Trello, where each card might contain a checklist. Each item in the checklist was one small feature of the project, and I translated those items into code. Item by item, card by card, the project was built.

Of course, you’ll find this experience very similar to working in a team.

How I work now

I still write the idea down. I kill the fragile ideas and write down the requirements when an idea is solid. Then I tell an agent to read the doc I wrote and ask it (it still feels weird to call it it) to finish the development.

The agent (could be Gemini or GPT-5.2) picks up the requirements.md and, even without asking too many questions, the work is done a couple of minutes later.

Even after months of working with agents, I’m still astonished by the process and the results.

I was the bottleneck

Continuing with the previous example: usually, after such speedy work, you would question the quality. I did some tests and noticed some issues. I reported them back to the agent; the issues got fixed, but then another issue came up. Oops—we were in a vicious, endless loop. I gave up, sent the pull request anyway, and expected the PR review agent to provide some clues.

And guess what? It did. It turned out my original design was not sound. The design caused the seesaw issue. I changed my design, the agent fixed the issue, and it was done.

So, my coding agent did exactly as I told it to. The review agent pointed out the flaw in the design. I fixed the design, and the coding agent updated it accordingly.

I was the stupid one.

A new era of software

Just about three months ago, I still hadn’t felt a clear productivity boost; sometimes I still had to wrestle with the agent to achieve a goal. However, now, given their capabilities, building a sophisticated tool takes less time than a single trip to the toilet. No wonder the value of SaaS companies has vanished by more than $1 trillion (link). People are asking: is SaaS dead?

Complexities aren’t gone

Let’s use an example. Writing a TODO app was the canary test for JavaScript frameworks back in the day. A TODO app is a classic example that looks simple at the beginning but soon becomes quite complicated when you want to polish it:

I want to add sub-items to a main item so I can create a shopping list.
I have repeated tasks that I want the app to manage automatically.
I want to include a collaborator.
- I don’t want the collaborator to be able to edit the item.
I want the app to remind me at a specific time with a notification.
etc.

The list could go on forever. Complexity grows exponentially as you add more items. As the designer/developer, it’s your responsibility to manage that complexity at both the product and technical levels. Software can become bloated if you just keep adding features. When it’s bloated, it’s also brittle—it could break at any time.

Building SaaS: the hard part

While the sales and non-technical parts of SaaS are intimidating, we aren’t talking about those here.

From my observation, the hardest part of building a SaaS is the conflicting interest between the unique requirements of a few customers and the nature of any SaaS product: standardization. This applies not only to public SaaS but also to internal services serving as platform software for internal teams.

For example, if a team is building a container-based app deployment platform, they’ll usually choose Kubernetes as the orchestrator and develop some APIs on top of the Kubernetes API. It all looks pretty cool. The team expects users to use their APIs to run containers and serve production traffic. However, after a few production incidents—big or small—new requirements from customers start coming in:

I want the load balancer to be isolated (dedicated).
I want my app containers to have affinity control automatically.
I want my app running on specific hardware.
etc.

These requirements are still relatively simple and can be considered healthy improvements to the platform. But experienced developers know that any feature requirement should be abstracted into a concrete new feature that can later benefit the majority of customers. In other words, you should NOT build a single feature for a single customer or a single scenario.

After some iterations, the product looks mature and stable. But new requirements keep coming:

I want alerts sent to our private Slack channel.
I want interconnection between your platform and my VM in another network environment.
I want traffic shadowing for my app.
etc.

Unlike the initial requests, these requirements are truly sparse. In other words, 100 customers might provide 80 different requirements. It’s really hard for platform developers to decide what is valuable to most people. In the end, it comes down to experience. Only people who have been in the field long enough know how to build a product that serves the most common interests. Usually, these people are deep in one vertical field, often in platform teams (or SaaS companies).

Similar to the previous example, just continuing to add features to the platform will surely make it unstable and eventually unusable. Experienced people know when and how to say no.

So, will SaaS die? I don’t think so. Complexity doesn’t go away. What’s worse, many complexities are hidden. You only discover them once you start building.

Building SaaS: the new era

AI surely has enabled us engineers to build in ways we could only dream of in the past. Features that took us weeks or even months in the past now probably take less than an hour. There is no excuse anymore. That also means we have more tests to run, more documentation to understand, and the software becomes much more complex than before. Considering much of the code will not be written directly by us, understanding the software and maintaining it will become much more difficult.

But this is what engineers do. We tame the complexity and enjoy the process.

Some random thoughts on AI and its future

2025-10-08T00:00:00+00:00

Paradigm shift

A while back I read a quote from Armin, and I’ve kept thinking about it:

Do I program any faster? Not really. But it feels like I’ve gained 30% more time in my day because the machine is doing the work.

This is the same feeling. While sometimes AI can implement a feature in less than five minutes (although I feel like with Codex it often takes longer now), to make it work and to make it less verbose still requires plenty of iterations. It certainly does not make me program faster either.

However, I realised it has gradually changed the way I program.

I started programming very early. I learned BASIC when I was in primary school. In middle school I programmed in Pascal on a blue screen with white text. Later, at university and at work, it was Java, Lisp, C++, Python, Lua, JavaScript, etc. All these languages are so-called high-level programming languages: they sit between human and machine. They translate real-world abstractions into bits and bytes for computers. Still, they are languages with limited vocabularies and expressions. They must balance the expressiveness of natural language with the precision of machine instructions. So, in order to master a programming language, the programmer usually needs to fully grasp its keywords, vocabulary, and expressions.

With AI, when I’m writing TypeScript I shamelessly admit that I no longer know the language as well as I know Python or Go, because I don’t write TypeScript directly anymore. I interact with the machine using natural language. In the past, compilers (interpreters) translated high-level languages into machine code. Now, LLMs translate natural language into TypeScript, which is then translated into machine code.

One of the biggest benefits of high-level programming languages is that they lower the bar to becoming a programmer. With AI, the bar is lowered even further. Is this a good thing? Of course. Look at how humanity progressed when mass literacy became a reality.

Thought experiment on an AI-only software world

Before we start the thought experiment, we should define what software and a program are. There is a big difference between a program and software. A program is usually a piece of code solving a specific small problem—for example, a program to find a specific user in the database or draw a rectangle on the screen. Software is a combination of programs, and this is usually where the complexity lies.

Today, when we develop software we follow some well-recognised principles:

KISS — Keep it simple, stupid
DRY — Don’t repeat yourself
Abstraction and encapsulation
Low coupling and high cohesion

All these principles emerged because the capacity of the human brain is limited. We can quickly be overwhelmed by complexity as programs accumulate in software. When software needs to communicate with other systems, we design succinct communication protocols and data formats in plain text so humans can understand them.

All these design decisions sacrifice efficiency at the machine level in exchange for readability and maintainability at the human level.

But what if in the future humans no longer need to program? When AI does all the heavy lifting, given the capabilities they have, will they still need to follow the principles I mentioned above?

In other words, if the computer architecture remains the same and we let AI design the programming languages, tooling, network protocols, and data exchange formats, what would it be like? What levels of complexity would be acceptable for AI?

Implicitly, the principles I mentioned above are collective consensus built over years of development. Each AI model will surely have its own level of capability. Will they try to find common ground via social activities as humans do? More importantly, all the current knowledge sources for AI are from humans. How and when can they start to realise they are different from humans and explore their own complexity boundaries?

At this stage it’s difficult to answer. After all, today AI is instructed by humans. Kids could never grow up if they’re never let go by their parents.

Let’s assume somehow they can make their own decisions. What kind of decisions could they make? Before answering that, maybe we should first discuss what capabilities AI will have that humans do not. The biggest difference I see today is an almost unlimited capacity for text processing and pattern matching. A good example is debugging. If I’m given a log message to debug an issue in a project, it might take me a while to find the related repository, search the code, read it, reason about it, and figure out a potential cause. For AI, this might only take less than five minutes.

Given its superpower in text processing and pattern matching, one thing I’m pretty sure of is that data exchange formats such as XML and YAML will be replaced by much more machine-friendly binary protocols.

Writing well-abstracted and encapsulated code? I’m not so sure. One thing humans learn from years of development is the importance of good APIs. Exposing too many details to the outside world is considered a drawback because it can impose unnecessary cognitive load on humans. But this seems less of an issue for AI. Writing good code for others to understand is a challenging task for both AI and humans. If the readers have superpowers, writing spaghetti code may well be acceptable.

If the above two things happen, what will humans do? Will we even notice? On broader terms, what will the transition be like?

The good thing is that, as long as humans remain in control or involved, accumulated wisdom will still be valued.

How I use AI tools

2025-07-05T00:00:00+00:00

In the fast-evolving AI world, this article might soon become obsolete. So taking a snapshot now can help us glimpse back from the future.

There are several areas where I’m heavily using AI tools:

Travel route planning
- e.g. Could you help me find a mountain to trek that is within 2 hours’ drive and suitable for kids
Document polishing (including this article)
General knowledge query
Programming
Financial report analysis
- Here is the latest financial report of Google. Could you list the main revenue sources and where the main capital expenses are? (Gemini is best for this task)

I’ll go into the details of programming.

Main tools I’m using

I was an Emacs user before switching to VS Code about 7 or 8 years ago. Even after switching to VS Code, I still use Emacs keybindings in Code.

VS Code comes with some free Copilot credits. The tab-to-complete feature is pretty amazing. I almost use up all the credits just after a couple of days each month. Cursor is a natural replacement given the good reviews.

However, one thing I am horrified by is forking an upstream application. That usually requires deep knowledge of the application and years of dedication. So when I heard Cursor forked VS Code, I could not pull the trigger. Adopting an editor that’s central to my professional life from a startup with an unknown future is a big risk.

So I started paying for GitHub Copilot. One immediate additional benefit is Copilot Pull Review. This is very helpful since I’ve been working on a project by myself. Having an additional eye is a big plus.

Here are the areas where I’ve found AI excels, as well as those where it still has room for improvement.

lib code

In one of my projects, I needed to write client code to implement a broker based on Redis. In the past, code like this would probably cost me 30 minutes with test cases. With Copilot’s help, this was done within 1 minute with well-covered test cases. The produced code did not even need any editing. It’s written and ready to be used.

test cases

Writing test cases manually is a pain. Designing a complete test suite is even more difficult. However, with Copilot, this has become a joy. All you need to do is:

/tests please create a test case for the newly created func in the xxx package.

After several seconds, a comprehensive test case is created and can be run immediately. This has helped me find many edge cases that previous implementations had not considered.

Frontend work

I also started working on a GUI project with Next.js and TypeScript. The experience so far has been amazing. I haven’t written a single line of code for a week since I started working on it. All I do is cmd + ctrl + i to trigger the agent mode in GitHub Copilot and provide a prompt like this:

I need you to add an XX feature to the page. The state management is here: xxx.ts. You can check how the YY feature is implemented and I want to have the same UX.

It can finish the feature within a couple of minutes and it usually does not require additional tweaks.

I also notice that Claude Code Sonnet 4 performs marginally better than others when doing frontend work.

Finding repeated pattern

More often than not, projects have repeated patterns for certain logic. A well locally indexed Copilot is sharp at picking up the pattern and suggesting the edit. This has saved me a tremendous amount of time. For example, since I’m not a fan of ORM, the price I pay is writing a lot of repeated code for getting a DB connection, preparing the statement, and executing the statement. Without AI in the past, this was definitely not pleasant. However, with Copilot, once I finish the function name, it has already filled in the implementation.

Of course, there are certain areas Copilot does not do well.

Complex scaffolding

Copilot agent was published a while back. When it was announced, I decided to give it a go with a big feature at hand. I wanted to check whether it could at least build some foundation work. So I gave the requirements to the agent and it started to work.

Putting the detailed implementation aside, the main problem with the solution is the lack of package design. Everything was put under the same package, and much logic was cluttered into the same file. While you can provide feedback to the agent, this certainly takes more time.

Working too much

A lot of the time, I know what I am doing and I don’t need any suggestions, or maybe just simple suggestions to finish typing. But Copilot is often suggesting big chunks of code that are never needed.

Gemini-cli

Since Gemini-cli (Claude code) came out, I’ve been wanting to give it a try. I recently started a new experimental project and began to use Gemini-cli. Clearly, it requires guidance and iteration. The first implementation was not ideal.

Another piece of work it has done is helping me resolve issues found by staticcheck. I have a legacy project that has many places failing the Go staticcheck.

foo.go:1248:4: unnecessary use of fmt.Sprintf (S1039)

I simply copy all the errors like above into Gemini-cli and ask it to resolve them. It successfully resolved 75 of them in one go. This again saved me at least 1 hour of work.

Final words

In our day-to-day professional life, much of the repeated work no longer needs manual effort—the copy and paste days are gone. However, it requires us engineers to be able to:

Explain the problem clearly to AI. It’s like product managers writing requirements to engineers in the past.
Establish a quick feedback loop to either understand the solution AI provides or validate the correctness of the solution. (Unit testing, CI are even more valuable)

The productivity boost is real. I can fully expect many new things to be created in the future and create more opportunities for everyone. At the same time, working closely with AI for the last half year also makes me think about the value of human engineers: if AI can work out your idea within minutes, that certainly means your idea is no longer novel. If it struggles, it might mean the idea is worth exploring and may indicate where the innovation lies.

Kernel vs. User-Level Networking: Don’t Throw Out the Stack with the Interrupts

2025-06-09T00:00:00+00:00

I found this paper here: https://www.sigmetrics.org/sigmetrics2024/accepted_papers.html

However, instead of reading through the whole paper by myself, I decided to get some help from Gemini. What I did was uploading the PDF and ask him(her) to summarise the paper. And this is the summary:

Of course. Here is a summary of the paper "Kernel vs. User-Level Networking: Don't Throw Out the Stack with the Interrupts" by Peter Cai and Martin Karsten.

1. Core Problem & Motivation
The paper addresses the widely-held belief that user-level networking and kernel-bypass stacks (like DPDK-based solutions) are inherently superior to the traditional kernel network stack for high-performance applications. While these user-level approaches show significant performance gains, the authors argue that the specific reasons for these gains are not well-quantified or understood. The paper aims to identify the true source of this performance gap and determine if the benefits of user-level networking can be achieved without completely abandoning the mature, feature-rich, and general-purpose Linux kernel stack.

2. Main Hypothesis
The authors' central hypothesis is that a major source of overhead in traditional kernel networking is not the stack's processing logic itself, but the direct and indirect costs of asynchronous hardware interrupt requests (IRQs).

IRQs disrupt the CPU's processing pipeline, causing context switches, cache pollution, and other micro-architectural inefficiencies. This leads to a lower Instructions Per Cycle (IPC), which throttles overall performance. User-level stacks avoid this problem by using a continuous polling model, which creates better temporal and spatial alignment between network processing and application logic.

3. Methodology
To prove their hypothesis, the authors use an indirect measurement approach:

Performance Model: They use a simple model where throughput is a function of Instructions Per Cycle (IPC) and Instructions Per Query (IPQ). This allows them to analyze whether performance changes are due to more efficient code (lower IPQ) or better CPU utilization (higher IPC).
Analytical Setups: They first evaluate two "impractical" kernel configurations to isolate the effect of IRQs:
IRQ Packing: All network interrupts are routed to a small, dedicated set of cores, separating them from application cores.
IRQ Suppression: Uses NIC hardware features (interrupt coalescing) to drastically reduce the rate of interrupts.
These setups confirm that reducing IRQs significantly improves performance, primarily by increasing IPC.
4. The Proposed Solution: "Kernel Polling"
Based on their findings, the authors propose a practical, minimally invasive solution called Kernel Polling. It requires a small modification (~30 lines of code) to the Linux kernel.

How it works:

It modifies the behavior of system calls like epoll_wait().
When an application is busy and has data to process, the kernel keeps the hardware interrupts for the corresponding network receive (RX) queue disabled.
Instead of waiting for an interrupt, the kernel polls the network card for new packets when the application is ready for more work (i.e., calls epoll_wait()).
Interrupts are only re-enabled as a fallback mechanism when the application becomes idle (i.e., there is no data to process), preventing wasted CPU cycles from continuous polling on an idle system.
This approach effectively mimics the synchronous, efficient processing of a user-level stack but does so within the existing kernel framework, retaining its benefits.

5. Key Results and Findings
The experimental evaluation, primarily using the Memcached key-value store, demonstrates the success of Kernel Polling:

Significant Throughput Gain: Kernel Polling improves throughput by up to 30-31% in a standard (UMA) setup and up to 45% in a multi-socket (NUMA) configuration compared to a vanilla Linux kernel.
Excellent Tail Latency: Unlike crude IRQ suppression, Kernel Polling achieves this throughput gain without compromising or degrading 99th-percentile tail latency.
Comparable to User-Level Stacks: With this modification, the kernel's performance becomes comparable to that of a comprehensive user-level stack (F-Stack), proving that abandoning the kernel is not necessary to achieve high performance.
IPC is Key: The performance gains are shown to come almost entirely from an increase in IPC, confirming the authors' initial hypothesis that avoiding interrupt-driven disruption is the critical factor.
Locality Matters in NUMA: The performance benefit is even greater across NUMA nodes because Kernel Polling ensures that network processing happens on the same core as the application thread, improving data locality.
6. Conclusion and Implications
The paper's title, "Don't Throw Out the Stack with the Interrupts," perfectly encapsulates its conclusion. The primary bottleneck in kernel networking is not the stack's design but its reliance on asynchronous interrupts.

The key takeaways are:

A minor, targeted change to the kernel can yield a significant portion of the performance benefits attributed to user-level networking.
Kernel Polling offers a practical path to boosting performance without the complexity, resource dedication (pinned cores), and feature loss of kernel-bypass solutions.
For researchers and practitioners, it is crucial to use a competitively configured baseline (with proper IRQ handling) when comparing new systems against the Linux kernel to avoid drawing misleading conclusions.

Then I started to ask a question to myself: the idea looks naive and it seems pretty straightforward to provide such a feature in the kernel. Why the kernel does not provide it?

Then I could not stop myself pasting the thinking back to Germini again and this is the answer he(she) provides:

That is an excellent and insightful question. The paper makes a compelling case for Kernel Polling, so it's natural to wonder why it's not a standard feature.

The short answer is that the Linux kernel is designed to be a general-purpose operating system, and a feature like Kernel Polling represents a trade-off that prioritizes one specific use case (high-throughput, low-latency servers) over the kernel's core principles of fairness, robustness, and universality.

Here are the key reasons why a feature like Kernel Polling isn't implemented by default and faces a high bar for inclusion:

1. The General-Purpose vs. Special-Purpose Trade-off
Kernel's Goal: The Linux kernel has to work well everywhere—on your laptop, your phone, a web server, a supercomputer, and an embedded device. Its default behavior must be a safe, fair, and reasonable compromise for all these scenarios.
Kernel Polling's Goal: Kernel Polling is an optimization specifically for applications that are I/O-bound and process data in a tight loop. For a desktop system running a web browser and a word processor, this model is not only unnecessary but could be detrimental. The default interrupt-driven model is far more efficient for handling a mix of sporadic events from different sources (mouse, keyboard, network, disk).
2. Risk of Misbehaving or Malicious Applications
This is the most critical technical barrier. Kernel Polling hands over control of when interrupts are re-enabled to a user-space application.

The "Hostage" Scenario: A poorly written or malicious application could call epoll_wait(), receive data, process it, but then never call epoll_wait() again. It might get stuck in a long computation or an infinite loop.
The Consequence: If this happens, the interrupts for that network queue would remain masked indefinitely. No new packets could ever be delivered to that queue, effectively starving the application and any other services that might rely on it. The network interface would appear to be "stuck" for that application.
The Kernel's Philosophy: The kernel's design principle is to never fully trust user-space. It must always be able to preempt or manage resources regardless of what an application does. Kernel Polling, in its simplest form, violates this principle.
3. Existing (But Imperfect) Mechanisms
The kernel already has mechanisms that try to address this problem, which shows that developers are aware of the issue. These include:

NAPI (New API): As the paper mentions, NAPI is a hybrid model that polls for a short time after an interrupt arrives. This is the kernel's first line of defense against interrupt storms.
SO_BUSY_POLL: This socket option allows the kernel to poll for a very short, fixed duration before putting a task to sleep. However, as the paper points out, it's not a complete solution because interrupts are immediately re-enabled after the system call returns, allowing new IRQs to disrupt the application while it's processing the data it just received.
XDP and AF_XDP: For extreme performance needs, the kernel community has invested heavily in the eXpress Data Path (XDP), which allows processing packets before they even enter the main network stack. This is the "official" path for kernel-bypass-like performance.
From the perspective of a kernel developer, the combination of NAPI, busy polling, and XDP already provides a spectrum of options. A new mechanism needs to prove it's significantly better and safer than the existing tools.

4. The Path to Adoption is Not Impossible
The authors of the paper are aware of these challenges. They explicitly state that their proof-of-concept would need enhancements to be accepted into the mainline kernel. The necessary changes would include:

Security/Permissions: The ability to use Kernel Polling would likely be restricted to privileged processes (e.g., requiring the CAP_NET_ADMIN capability), so a random application can't disrupt the system.
Safety Timeouts: The kernel would need a watchdog timer. If an application masks interrupts and doesn't call epoll_wait() again within a certain timeout (e.g., a few milliseconds), the kernel would forcibly re-enable the interrupts to prevent the queue from being starved.
With these safeguards, a patch for Kernel Polling would have a much better chance of being accepted, as it would balance the performance gains with the kernel's need for robustness and security. The paper's contribution is showing that this path is worth pursuing.

The answer is pretty impressive I would say.

nowhere null

Weekly Reading #26

Outside of Work

Weekly Reading #25

Outside of Work

Weekly Reading #24

Work Related

Outside of Work

Weekly Reading #23

Work Related

Outside of Work

How to register a company in Japan as a foreigner

Eligibility

Things you must have

A Japanese phone number

A personal Japanese bank account

A personal stamp certificate (印鑑証明書)

Things that are highly recommended

A My Number Card

Step 0: Company name, type, and address

Step 1: Full DIY or use a SaaS platform

Step 2: Creating the Articles of Incorporation (定款)

Step 3: Capital verification

Step 4: Submit your application and get Articles of Incorporation certification (定款認証)

Step 5: Retrieving your company’s legal documents

Step 6: Notify the Pension Office and Tax Offices

Step 7: Opening a corporate bank account

Weekly Reading #22

Work Related

Outside of Work

Weekly Reading #21

Work Related

Outside of Work

Weekly Reading #20

Work Related

Outside of Work

Weekly Reading #17

Weekly Reading #16

Weekly Reading #15

Weekly Reading #14

Weekly Reading #13

Weekly Reading #12

Introducing Reqfleet

Why I started my own business

The field does not matter (much)

Can I do it for 10 years or longer?

Why load testing?

Weekly Reading #11

We are living in a new era

How I worked before

How I work now

I was the bottleneck

A new era of software

Complexities aren’t gone

Building SaaS: the hard part

Building SaaS: the new era

Some random thoughts on AI and its future

Paradigm shift

Thought experiment on an AI-only software world

How I use AI tools

Main tools I’m using

lib code

test cases

Frontend work

Finding repeated pattern

Complex scaffolding

Working too much

Gemini-cli

Final words

Kernel vs. User-Level Networking: Don’t Throw Out the Stack with the Interrupts