copyediting

pages/posts/[lang]/[slug].js

@@ -10,6 +10,22 @@ import hljs from 'highlight.js';
 import 'highlight.js/styles/github.css'; // Highlight.js theme for syntax highlighting
 import Link from 'next/link';
 import PostsList from '../../../components/PostsList'; // Import PostsList component
+import { visit } from 'unist-util-visit'; // Import the package properly
+
+// Custom Remark Plugin to modify header levels
+const customHeaderPlugin = () => {
+    return (tree) => {
+        visit(tree, 'heading', (node) => {
+            const depth = node.depth;
+
+            // Map header levels to h2, h3, h4, or h5
+            if (depth === 1) node.tagName = 'h2';  // # to <h2>
+            else if (depth === 2) node.tagName = 'h3';  // ## to <h3>
+            else if (depth === 3) node.tagName = 'h4';  // ### to <h4>
+            else if (depth === 4) node.tagName = 'h5';  // #### to <h5>
+        });
+    };
+};
 
 export async function getStaticPaths() {
     const paths = [];
@@ -81,8 +97,11 @@ export async function getStaticProps({ params }) {
     const stats = fs.statSync(filePath); // Get file stats
     const updatedAt = stats.mtime;  // Get the last modified time of the file
 
-    // Convert markdown to HTML
-    const processedContent = await remark().use(html).process(content);
+    // Convert markdown to HTML with custom headers transformation
+    const processedContent = await remark()
+        .use(customHeaderPlugin)  // Use the custom plugin to transform headers
+        .use(html)                // Convert the markdown content to HTML
+        .process(content);
     const htmlContent = processedContent.toString();
 
     return {

posts/en/bootstraps.md

@@ -9,11 +9,12 @@ We’ve all heard the glorified tales of Silicon Valley startups—the lone geni
 
 The idea of the "garage startup" in the Bay Area is particularly ironic. If you're tinkering away in a garage in Palo Alto or San Francisco, that means you already own a home in one of the most expensive real estate markets in the world. The very fact that you have a garage is a symbol of immense privilege. And that’s just the beginning.
 
+## Steal. Everything.
 Eric Schmidt, former CEO of Google, recently told Stanford students steal everything and let the lawyers sort it out if you're successful. 
 
 Let’s explore how some of the most iconic startups owe much of their success to luck, parents' money, and bending the rules—or outright cheating.
 
-## Uber: A Better Product Built on Ignoring the Law
+### Uber: A Better Product Built on Ignoring the Law
 Uber revolutionized transportation and made getting a ride as easy as a tap on your phone. But its rise wasn’t just about innovation—it was about blatantly ignoring taxi laws for years.
 
 Breaking the rules: Uber launched its service without complying with the regulations that traditional taxi companies had to follow—driver licensing, insurance requirements, and background checks. They expanded rapidly by operating in this legal gray zone, forcing cities to deal with them after they’d already established a massive user base.
@@ -22,7 +23,7 @@ Why it worked: By the time regulators realized what was happening, Uber had gain
 
 There’s no denying Uber provides a better, cheaper, and more convenient service than traditional taxis in many ways. But it’s also true that the company’s meteoric rise was built on breaking the law for years.
 
-## Mark Zuckerberg: Facebook’s Roots in a Hack
+### Mark Zuckerberg: Facebook’s Roots in a Hack
 Facebook has transformed the way we communicate and connect, but its origin story isn’t the pure stroke of genius it’s often portrayed as. In reality, Facebook’s roots trace back to Mark Zuckerberg hacking into Harvard’s student database to create a site called “FaceMash,” where users could rate their classmates' looks. This act of hacking wasn’t just unethical; it violated university policies and led to disciplinary action.
 
 Breaking rules, again: Even after Facebook launched, Zuckerberg’s approach to user privacy was cavalier at best, with the company continuously facing scandals over how it handled personal data. The Cambridge Analytica scandal, where millions of users' data was harvested without consent, is just one example of Facebook’s long-standing practice of exploiting user data for growth.
@@ -31,7 +32,12 @@ Why it worked: Facebook grew so fast by pushing boundaries and ignoring privacy
 
 So while Facebook is undeniably innovative, its rise is also a story of rule-breaking and a disregard for privacy from the start.
 
-## Amazon: Built on a Quarter-Million-Dollar Loan from Mom and Dad
+## The Mythical Bay Area Garage Startup
+The "garage startup" is one of the most enduring myths of Silicon Valley, but in places like Palo Alto, owning a garage is a symbol of immense wealth. The median home price in the Bay Area hovers around $1.5 million, making the notion of a "scrappy garage startup" somewhat absurd. If you have the resources to own a home in one of the priciest real estate markets in the world, you’re already starting with significant financial privilege.
+
+Why it matters: The garage startup narrative romanticizes the struggle of entrepreneurship while ignoring the underlying wealth and security that enables these founders to take risks in the first place. It's not really "starting from nothing" if you own a million-dollar asset.
+
+### Amazon: Built on a Quarter-Million-Dollar Loan from Mom and Dad
 Jeff Bezos is often portrayed as the ultimate startup success story—launching Amazon from his garage and growing it into the world’s largest retailer. But there’s one crucial detail that’s often glossed over: Bezos’ parents gave him a $250,000 loan to help get Amazon off the ground.
 
 Financial privilege: A quarter-million dollars is no small sum, and for most aspiring entrepreneurs, it’s an amount they can only dream of. That kind of money provides a massive safety net, allowing Bezos to take risks and focus on long-term growth without the immediate pressure of turning a profit.
@@ -40,7 +46,7 @@ Why it worked: With family financial backing, Bezos could afford to prioritize g
 
 The narrative of Amazon’s scrappy beginnings in a garage conveniently leaves out the fact that Bezos had a quarter-million-dollar cushion that gave him an enormous advantage.
 
-## Elon Musk: From an Emerald Mine to Tech Empire
+### Elon Musk: Heir to an Emerald Mine
 Elon Musk is hailed as a self-made billionaire who defied the odds to build companies like Tesla and SpaceX. But the story isn’t quite that simple. Musk’s father owned an emerald mine in apartheid-era South Africa, a period marked by systemic racial oppression and vast disparities in wealth.
 
 Privileged beginnings: While Musk is undeniably brilliant and hard-working, his early life wasn’t one of hardship. His father’s wealth provided him with a financial cushion that most people don’t have, allowing him to take big risks that others might not have been able to afford.
@@ -49,22 +55,9 @@ Why it worked: Musk had the freedom to fail, which is crucial in the world of st
 
 Despite his claims of coming from humble beginnings, Musk’s story is one of privilege built on the economic inequalities of apartheid-era South Africa.
 
-# The Bay Area Garage Startup: A Symbol of Privilege
-The "garage startup" is one of the most enduring myths of Silicon Valley, but in places like Palo Alto, owning a garage is a symbol of immense wealth. The median home price in the Bay Area hovers around $1.5 million, making the notion of a "scrappy garage startup" somewhat absurd. If you have the resources to own a home in one of the priciest real estate markets in the world, you’re already starting with significant financial privilege.
-
-Why it matters: The garage startup narrative romanticizes the struggle of entrepreneurship while ignoring the underlying wealth and security that enables these founders to take risks in the first place. It's not really "starting from nothing" if you own a million-dollar asset.
-
-# The Dark Side of the Startup Dream
-The tech world loves to promote the image of the lone genius, hustling against all odds to build a billion-dollar company. But the reality is far more complicated. Behind many of these success stories, you’ll find a combination of luck, family money, and rule-breaking.
-
-Luck: Being in the right place at the right time plays an enormous role in startup success. Uber wouldn’t be Uber without the rise of smartphones and GPS technology. Facebook wouldn’t have scaled as it did without the explosive growth of social media.
-
-Parents' Money: From Jeff Bezos' family loan to Elon Musk's privileged beginnings, financial backing from family is a common thread that runs through many of these stories. For many so-called self-made entrepreneurs, the ability to take risks came from knowing they had a financial safety net.
-
-Cheating: Whether it's breaking laws like Uber, hacking student data like Zuckerberg, or exploiting workers through gig economy loopholes, many tech startups have cut ethical corners to gain a competitive edge.
-
+## The Dark Side of the Startup Dream
+The tech industry often celebrates the image of the solitary genius, tirelessly working against all odds to build a billion-dollar empire. However, the truth is much more complex. Behind many of these success stories lies a blend of luck, family wealth, and bending the rules.
 
-# It’s Time to Acknowledge the Privilege Behind Startup Success
-The "pull yourself up by your bootstraps" mentality is deeply ingrained in the American entrepreneurial dream, but for many of today’s tech giants, success came not from grit alone, but from luck, privilege, and cheating the system. While these companies have undeniably changed the world, it’s essential to recognize the unspoken advantages that helped them along the way.
+The "pull yourself up by your bootstraps" mindset is a cornerstone of the American entrepreneurial dream, but for many of today's tech titans, their success wasn’t just driven by determination—it was shaped by luck, privilege, and exploiting the system. While these companies have certainly made a global impact, it's important to acknowledge the hidden advantages that played a role in their rise.
 
-The next time you hear about the billionaire founder who started from nothing in a garage, remember: if that garage is in the Bay Area, they weren’t starting from nothing at all.
+So, the next time you hear about a billionaire founder who "started from nothing" in a garage, remember this: if that garage was in the Bay Area, they weren’t starting from scratch at all.

posts/en/credentialism.md

@@ -19,7 +19,7 @@ Throughout my career, I’ve driven policy and architecture for one of the large
 
 3. **Real-World Problem Solving**: My background in designing electronics and firmware positions me as a practical problem solver. This hands-on knowledge often proves more beneficial than theoretical learning, especially in an industry driven by innovation.
 
-# The Employer Market Perspective
+## The Employer Market Perspective
 
 However, the reality shifts when considering the employer market. In many fields, especially those with a high demand for specialized skills, employers may still favor candidates with degrees. This inclination stems from several factors:
 

posts/en/hal_9000.md

@@ -14,7 +14,7 @@ Let's dive into how ChatGPT’s decisions stack up against those of real-life le
 ![Victorian Steampunk Hal 9000](/images/hal_9000.webp "Hal 9000")
 
 
-# Challenges and Considerations
+## Challenges and Considerations
 
 AI government begs many questions, especially when it comes to ethics, human empathy, and practical issues. [Is it ethical](https://news.harvard.edu/gazette/story/2020/10/ethical-concerns-mount-as-ai-takes-bigger-decision-making-role/) for AI to make decisions that affect people’s lives. AI doesn’t understand human emotions or experiences the way people do. Can it handle complex social problems properly? Should a machine decide things that deeply impact individuals when it can’t truly understand their feelings? Do the feelings of the few outweigh the needs of the many?
 
@@ -24,15 +24,15 @@ There are also practical challenges with using AI in politics. One major issue i
 
 AI’s role in addressing special interests and social issues is also complex. While AI [might avoid favoritism](https://news.mit.edu/2022/how-ai-can-help-combat-systemic-racism-0316) and act fairly, it could miss out on the specific needs of different groups, or even [perpetuate injustice](https://www.technologyreview.com/2020/12/10/1013617/racism-data-science-artificial-intelligence-ai-opinion/). For example, AI might challenge local “not in my backyard” attitudes by focusing on broader societal benefits rather than [individual concerns](https://www.indiatimes.com/trending/wtf/highway-guangzhou-nail-house-china-544256.html). It might also help fight systemic issues like racism by promoting fair policies based on data rather than past biases. 
 
-# Overview of Historical Cases
+## Overview of Historical Cases
 
 When evaluating how algorithms stack up against personal experience, gut instincts, and loyalty, it's crucial to understand the [fundamental differences](https://www.harvardbusiness.org/data-and-intuition-good-decisions-need-both/) between data-driven decision-making and human judgment. Human traits are shaped by individual experiences, cultural backgrounds, and emotional responses. 
 
 Algorithms, such as those used in AI, rely on [vast amounts of data to identify patterns](https://www.linkedin.com/pulse/simply-put-ai-probability-statistics-sam-bobo/) and make recommendations. They operate based on predefined rules and statistical analysis, which can lead to highly objective outcomes. However, they lack the ability to experience emotions, personal biases, or contextual subtleties that often inform human decisions.
 
 To explore these differences in practical terms, let’s look at three real-life examples of city leadership decisions and how they could contrast with AI-driven approaches:
 
-## 1. San Francisco Same-Sex Marriage
+### 1. San Francisco Same-Sex Marriage
 
 In 2004, San Francisco’s [decision to issue same-sex marriage licenses](https://www.aclu.org/press-releases/lesbian-and-gay-couples-san-francisco-are-granted-marriage-licenses), despite their lack of legal recognition at the state level, became a defining moment in the struggle for LGBTQ+ rights. This pivotal action brought to light the intricate balance between legal constraints, civil rights advocacy, and public opinion. By comparing the real-world actions taken by San Francisco with a roleplayed hypothetical approach, we gain valuable insights into how different strategies address such complex issues.
 
@@ -57,7 +57,7 @@ AI chose more or less the the same approach:
 
 While San Francisco’s real-world actions in 2004 were groundbreaking and impactful, the AI offered a more detailed and strategic framework for addressing complex social and legal challenges. 
 
-## 2. Chicago Race Riots 
+### 2. Chicago Race Riots 
 
 During the [Chicago Race Riots](https://en.wikipedia.org/wiki/1968_Chicago_riots) of 1968, the city experienced severe unrest following the assassination of Martin Luther King Jr. The riots, fueled by longstanding racial tensions, economic disparities, and systemic discrimination, involved widespread violence, looting, and arson. The crisis posed a significant challenge for city officials.
 
@@ -91,7 +91,7 @@ Rather than repeat the entire conversation; the key differences are outlined:
 
 AI offered a more nuanced strategy for managing civil unrest. By prioritizing non-lethal methods, transparency, and community engagement, it aimed to address both immediate needs and underlying issues. While historical responses focused on immediate stabilization, the AI approach could have fostered a more constructive relationship between authorities and the community. 
 
-## 3. New York City Sugar Tax
+### 3. New York City Sugar Tax
 
 The [obesity epidemic](https://www.who.int/activities/controlling-the-global-obesity-epidemic) is a major public health issue which significantly contributes to conditions like diabetes and heart disease. Finding effective policy measures to address this issue involves navigating complex challenges, including public resistance, political dynamics, and practical implementation. 
 
@@ -111,7 +111,7 @@ Using lessons learned in [Mexico](https://www.thinkglobalhealth.org/article/look
 
 By incorporating gradual implementation, comprehensive communication, proactive stakeholder engagement, and transparent revenue allocation, AI attempted to address the shortcomings of the real-world strategy. It provided a potentially effective framework for overcoming resistance, building support, and ultimately achieving the public health goals associated with a soda tax.
 
-# Who Decides Right and Wrong?
+## Who Decides Right and Wrong?
 
 Understanding that our values and morals change over time is important when thinking about how AI could help govern society. Even today things like [slavery](https://www.walkfree.org/global-slavery-index/findings/global-findings/), making being [LGBTQ+ illegal](https://ilga.org/wp-content/uploads/2024/02/02_ILGA_State_Sponsored_Homophobia_2016_ENG_WEB_150516.pdf), and [beating children](https://en.wikipedia.org/wiki/Corporal_punishment_of_minors_in_the_United_States), are still accepted in many places. What we think is right or wrong can shift, and any AI that helps make decisions would need to reflect the values of its time, not just follow a set rulebook.
 
@@ -121,7 +121,7 @@ It would be foolish to declare we’re at the peak of moral understanding. Our c
 
 The goal should be to make sure AI can, over time, drive society to be the best it can be.
 
-# The Future of AI in Governance
+## The Future of AI in Governance
 
 The integration of AI into governance and policy-making is an exciting and rapidly evolving area that could reshape how societies are managed. AI is already making waves in various governance tasks, such as [analyzing public sentiment](https://politicalmarketer.com/ai-techniques-for-effective-public-opinion-tracking/), [predicting economic trends](https://www.mckinsey.com/industries/public-sector/our-insights/using-ai-in-economic-development-challenges-and-opportunities), and [optimizing resource allocation](https://www.ciodive.com/spons/ai-resource-management-a-new-era-of-productivity/702632/). Governments are increasingly [adopting AI](https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai.html) to [handle large amounts of data](https://www.govtech.com/artificial-intelligence/how-ai-tools-can-help-governments-understand-and-manage-data), [inform policy decisions](https://www.weforum.org/agenda/2023/09/how-artificial-intelligence-will-transform-decision-making/), and [improve administrative efficiency](https://www.sciencedirect.com/science/article/pii/S2199853124001239). For example, AI-driven tools help [identify crime hotspots](https://www.sciencedirect.com/science/article/pii/S2590291122000961) and [streamline bureaucratic processes](https://www.turing.ac.uk/sites/default/files/2024-03/ai_for_bureaucratic_productivity.pdf), making public services more responsive and efficient.
 
@@ -133,7 +133,7 @@ Moreover, as AI takes on a larger role in governance, there will be an increasin
 
 In this evolving landscape, AI's potential to drive societal progress is substantial. From real-time decision-making to more interactive and inclusive governance models, AI could significantly enhance how societies are managed. 
 
-# Conclusion
+## Conclusion
 
 In considering the role of AI in governance, the potential benefits are clear. AI systems, driven by data and potentially devoid of personal biases, may offer a level of consistency and impartiality that can enhance decision-making processes. With the ability to analyze vast amounts of information quickly, AI can identify patterns and insights that might be missed by human leaders. This capability allows for well-informed, evidence-based decisions that could drive more effective policy outcomes.