2026-01-13 17:39:33
\ Scalability and resilience problems no longer exist in the technology conferences as the buzz term. They offer a lifeline to an online business wherein a second of unavailability or low access rate can put the users on the back button. The most striking detail of this growth is the strategic three, Java, Kafka, and AWS that amalgamate and create the systems that do not only have the power to continue loading, but also to welcome it. Since the nature of how the developers build and implement applications has become transformed in the environment of the microservices architecture, the requirement of the presence of a consistent and viable communication of the autonomous services has become a technical necessity, and has become the basis of competitive creation.
This begins with the information on what the business requires. One of the outlets is an online store, and it is not a conventional shop. It is living and alive with moving inventory reports, payment gateways, customer data, recommendation engines, among others, that are running behind your neck. It will be founded on Java magic and developed on the foundation of object-oriented and mature base because the independent component of the larger system, user authentication, product catalog, checkout process, and order monitoring. But Java alone isn’t enough. Information flow does not only exist between these services, but flow of information must be efficient and error-free.
This is where Kafka comes in with the role of an effective conductor who is leading an orchestra. The normal API-based communicated service may easily result in tight coupling. One of the services falls into pieces, and the remaining of them are brought in a kind of domino. This is what Kafka re-implanted where he dis-integrated services by event communication. The user does not have to wait when the user makes an order because the order service is waiting to be informed about the inventory system, payment system, or shipping system respectively. Rather, it is employed to merely inform on an occurrence in a Kafka topic. When the consumers are prepared, the message is passed to them in other forms of services. This renders the whole system more responsive, more responsive, and most significantly more enduring.
In reality, there are material communicational barriers of microservices. The larger they are, the more difficult it becomes to sustain and with minimal dependencies, nightmares in maintenance. Kafka broke this chain in the process of making communication asynchronous. Waiting times – synchronous waiting times – will not always be an issue in services. And since Kafka is equipped with the capacity of sending messages even when one of the consumer services is not online, in an instance when the service is capable of storing the messages it lost, whereby it can re-enter the network again, can celebrate the messages it lost again. It is not design but rather more it is survival architecture of the world of survival of the fittest.
Neither is Kafka and Java in bad marriage. Java Kafka API and Spring integration allow application developers to develop powerful and lightweight consumers and producers. The heavy lifting that otherwise must be done to facilitate inter-service coordination can be done by publishing and subscribing to service-to-service messaging. This accelerates the development and fewer bugs and services can be developed. This is like the scenario whereby all the musicians of an orchestra will not have the nerve to play their parts the way they will be done best even when the conductor is on leave.
On the one hand, a scaled system is easily developed. It is a second-size game to play. AWS offers the infrastructure that was tested on the battlefield in order to deploy, run, and administer microservices in Java and expressed in the form of Kafka. Cloud enables scaling to unexpected load without reducing the system irrespective of how it is staged by the usage of Kubernetes clusters or pre-staged Kafka controlled services. AWS is also useful in the integration and delivery pipelines which must be regularly integrated, and when one presses a button, they can refresh them.
The other interesting fact is that the scaling of services can be done separately by the topic of Kafka and volume of messages. When it comes to flash sale of order processing, an addition of order topic consumers suffices. The other services are untouchable. The game of the DevOps teams which must inevitably be engaged in in high-traffic applications changes in such a granular control.
Take an example of an online Black Friday store. Thousands of people are registering, browsing, throwing their products in the cart, and checking out at the same speed. The Java-based microservices are independent of all the capabilities of the operations which comprise the checkout to the login. Kafka has made sure that these functions are interacting between themselves and they are not overloading the system of one of them. The other later indicator of the payment gateway is not the freezing of the entire operation. In the meantime, AWS applies the on-need scaling of on-need services that respond directly to the spikes of traffic. No crashes. No bottlenecks. It is impossible to compare the process of shopping with simplicity and purity.
Gone are the days of monolithic applications that are glued and handwritten API calls. The contemporary software platform is a breathing, living network of services that can be capable of communicating at an extremely high rate, scale, and recover gracefully with relatively little trouble. The infinite of Java legs and Kafka words and playground exists with AWS. They all lead to a three that is not only an inevitable powerhouse, but a necessity in the contemporary digital ecosystem that is much needed.
\
:::tip This story was distributed as a release by Sanya Kapoor under HackerNoon’s Business Blogging Program.
:::
\
2026-01-13 17:24:49
One of the factors of financial management that is important is the external audit since it gives an independent assurance that the financial statement of an organization is accurate and reliable. The process of finding the audit planning was traditionally carried out by using past workpapers, assumptions and sampling. The general approach adopted by these teams was the experience and gut feeling, which was inefficient and often overlooked risks. But this is changing. Data analytics is also making the auditors reconsider planning and carrying out the auditing process. They are no longer applying guesswork, but instead use data to tailor audit plans which are more accurate, less time-consuming, and smarter to guide their audits.
There is no need now to make narrow samples or use outdated records of the past years in order to carry out external audit. The analytics will allow the auditors to initiate full data digging before the fieldwork is conducted. The tendencies previously hidden are brought out. The variation in the expense accounts, the period of the recognition of the revenue, the number of transactions were not the ones that can be ascertained manually anymore. They are done at the acute youth level on an automatic basis. Such a plan will result in improved discussion with clients, more targeted audit plans, and risk evaluation based on current realities rather than assumptions.
The data analytics allow the auditors concentrate on the high risk transactions or accounts with irregularities compared to the rest rather than random sampling on transactions in the regions. The data analytics will allow the auditors to concentrate on the problems that matter. It anticipates and funds are saved in case of deviation of a given account. What has been actualized is rather a history planning process with the current risk profile and in comparison to attachment to the past.
The most significant thing about the transition is the data transformation tool which is effective and known as Power Query from Microsoft. It does not specifically emphasize taking it in the audit procedure, but it is one of the key tools the contemporary audit teams should keep in mind, as it is flexible and possibilities to work with it. It enables the auditors to create information with a high number or significant number of sources, summarize and convert them to a format that is easy to analyze without a lot of strain. There are five systems of clients, and each of them exports their financial data differently. Power Query will help an auditor to unify and access data from different systems in a single form and identify trends and anomalies that can serve as indicators of areas of concern. This freedom of degree will be invaluable in the interaction where the complex business or global organization is involved.
The capability to process full datasets is among the finest qualities of Power Query as it is free from the flaws of small-sample analysis. In the traditional sampling, in most cases, the red flags can be walked off without being realized because the selected sample is not focusing on the red flags. Through the assistance of Power Query, it will demonstrate the option to explore the entire data set and not to remain focused on the serious threats. It also removes subjectivity that will always be encountered through the manual selection and this makes the auditors more confident with their findings.
So long as the interest of auditors in acquiring a specific solution tailored to external audits is high, the CCH suite will provide an integrated system that simplifies risk assessment, documentation, and resource management. It does not just mean that the checklists will be automatized. It also co-locates all the requirements of risk assessment, resource management, and documentation control to be under one environment. This system also facilitates the concerned risk analysis that allocates the risk scores to the audit areas and that the abnormalities are determined with the help of the supplied intelligence that allows the teams to prioritize their work and do it efficiently.
An opportunity to assemble the list of document requests and it is anchored on the identified risks is one of its strongest points. The system also aids the auditors in establishing what they need to answer certain areas of risks compared to general or blanket requests by auditors to the clients. This saves time and documents that may have been lost are evaded and contribute to the maintenance of the audit schedule.
CCH also allows auditors to reuse the audit plans from the previous year and update them dynamically with new client information. This helps in saving time, new standards are adhered to and also audit methodology is adapted to the changing profile of client risks. The historical information will be an asset and not a support on the basis of dynamism. The system continues to experience increased changes throughout the engagement. Along with the new introduction of data, changes of risk profiles are occurring. This has been credited to the ongoing feedback that makes sure that the audit plan is not fixed on what is actually occurring and not what was originally forecasted at the beginning of the planning session.
All these innovations create a more refined, progressive approach to external audits. The auditors can now put their energies in a different area, which is in demand as opposed to squandering it. It can be a case of finding a vendor, or a customer, whose buy record can be matched with the signature of an established fraud, or change in the cash flow, which would be better tracked down, but saved by the tools. They do not replace the professional judgment on which the process relies, but rather enhance it.
So is the manner in which the outside audits are evolving. They are no longer characterized by predetermined plans, quite evident examples, and backward analysis. They are turning into ubiquitous, insightful, and objective. Such benefits are self-explanatory to the firms that are ready to embrace the change. The end result of it will be more efficient and faster audits that will be more value-added and insightful to the clients. The external audit teams are now in a better vantage position where they can identify, quantify, and respond to actual risk with the help of the relevant tools and a data-driven mindset.
\
:::tip This story was distributed as a release by Sanya Kapoor under HackerNoon’s Business Blogging Program.
:::
\
2026-01-13 15:10:57
How are you, hacker?
🪐Want to know what's trending right now?:
The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here.
## Should We Be Worried About Losing Jobs? Or Just Adapt Our Civilization to New Reality? 
By @chris127 [ 10 Min read ]
The question isn't whether jobs will disappear—it's whether our traditional work model is still valid. Read More.
](https://hackernoon.com/iso-27001-compliance-tools-in-2026-a-comparative-overview-of-7-leading-platforms)** 
By @stevebeyatte [ 7 Min read ]
Breaking down the best ISO 27001 Compliance tools in the market for 2026. Read More.
By @ipinfo [ 7 Min read ] IPv6 breaks digital ad measurement. Learn how IPinfo’s research-driven, active-measurement model restores accuracy across CTV and all channels. Read More.
By @denisp [ 4 Min read ] The 3 AM Reality Check: When your autonomous AI agent stalls, who fixes it? A candid look at the risks and rewards of deploying Agentic AI. Read More.
By @paoloap [ 12 Min read ] Master AI agents in 6-9 months with this complete learning roadmap. From math foundations to deploying production systems, get every resource you need. Read More.
By @denisp [ 5 Min read ] Don't let your AI fly before it can walk. Why jumping straight to fully autonomous systems is a recipe for disaster (and what to do instead). Read More.
By @denisp [ 12 Min read ] An AI agent without memory is just a script. An agent without guardrails is a liability. The 7 critical pillars of building production-grade Agentic AI. Read More.
By @denisp [ 23 Min read ] Success isn't building the agent; it's managing it. From "AgentOps" to ROI dashboards, here is the operational playbook for scaling Enterprise AI. Read More.
By @yuliiaharkusha [ 8 Min read ] AI boosts individual performance but weakens organisational thinking. Why smarter workers and faster tools can leave companies less intelligent than before. Read More.
By @denisp [ 17 Min read ] Avoid the "AI Slop" trap. From runaway costs to memory poisoning, here are the 7 most common failure modes of Agentic AI (and how to fix them). Read More.
By @hacker39947670 [ 15 Min read ] Bundlers are the bridge between account abstraction and the execution layer. Read More.
By @ainativedev [ 3 Min read ] Cursor’s acquisition of Graphite aims to unify code creation and review, and in the process brings the company closer to territory long dominated by GitHub. Read More.
By @chris127 [ 8 Min read ] When we think of war, we imagine soldiers, weapons, and physical destruction. But there's another type of war that affects millions of people worldwide… Read More.
By @stevebeyatte [ 12 Min read ] Modern midsize companies need platforms that balance sophistication with agility, offering powerful features without overwhelming complexity. Read More.
By @stevebeyatte [ 4 Min read ] Even the most automated systems still need an underlying philosophy. Read More.
By @ipinfo [ 9 Min read ] IPinfo reveals how most VPNs misrepresent locations and why real IP geolocation requires active measurement, not claims. Read More.
By @ishanpandey [ 3 Min read ] Ledger data breach via Global-e exposes customer info. No crypto stolen, but phishing attempts surge. Third-party risks examined. Read More.
By @erelcohen [ 2 Min read ] In a polarized 2025 market, enterprise software companies can no longer win through broad consensus—only through brand clarity. Read More.
By @leonrevill [ 8 Min read ] Is AI making developers faster or just worse? A CTO builds a complex platform from scratch to test the "Stability Tax, and why "Vibe Coding" is dead. Read More.
By @romanaxelrod [ 7 Min read ]
AI-powered XR won’t be won by smart glasses alone. Why Big Tech is stuck optimizing and how deep tech, AI-driven R&D, and new materials are reshaping computing Read More.
🧑💻 What happened in your world this week? It's been said that writing can help consolidate technical knowledge, establish credibility, and contribute to emerging community standards. Feeling stuck? We got you covered ⬇️⬇️⬇️
ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME
We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.
See you on Planet Internet! With love,
The HackerNoon Team ✌️
.gif)
2026-01-13 11:56:29
\ Security is treated as a critical priority in modern software organizations. It appears in roadmaps, compliance documents, architectural reviews, and post-incident reports. Yet there is one place where security is still largely invisible: the hiring pipeline.
Most engineering teams invest heavily in security tools, audits, and policies, yet devote little effort to evaluating whether new hires can write secure code. The assumption is simple and widespread. Secure coding can be taught later. What matters during hiring is speed, problem-solving ability, and technical breadth. Security, somehow, will follow. That assumption is wrong and increasingly dangerous.
In practice, hiring pipelines prioritize what is easiest to test and compare. Candidates are evaluated on syntax familiarity, algorithmic reasoning, framework usage, and high-level system design. These signals are convenient and scalable, but they reveal little about how developers reason about trust, failure, and misuse. Security understanding is treated as implicit knowledge, something candidates are expected to absorb over time. This gap is widening as AI-assisted development becomes the norm, shifting developers from writing code line by line to reviewing, adapting, and approving logic generated by AI tools.
Hiring is the first architectural decision a company makes. When secure coding ability is excluded from that decision, insecurity is embedded into the system before the first line of production code is written. The result is a growing disconnect between what organizations claim to value and what they actually select for during recruitment.
Modern hiring pipelines are optimized for efficiency rather than signal quality. This is not the result of negligence or bad intent. It is a structural outcome of how hiring processes are designed to scale.
Secure coding ability does not fit neatly into standardized interviews. It is contextual and situational, and it is resistant to simple scoring. Evaluating it requires discussion, judgment, and a willingness to explore ambiguity. That makes it expensive in both time and attention, especially under pressure to hire quickly.
Secure coding becomes a secondary concern, as interviews prioritize what is easy to measure over what truly matters in production. Yet secure coding is not a checklist skill. It is a way of thinking.
Strong secure coding requires anticipating how code could be misused, understanding how data flows across trust boundaries, recognizing how errors propagate or fail silently, and reasoning carefully about defaults, assumptions, and edge cases.
These qualities do not surface in trivia questions, typical JavaScript interview questions, or time-boxed coding challenges. They appear when developers are asked to explain why something is safe rather than just how it works.
Because secure coding ability does not produce a single correct answer, it is often excluded from interviews entirely. Hiring teams prefer deterministic evaluation, even if it selects for the wrong attributes.
A common justification for ignoring secure coding during hiring is the belief that security can be taught after onboarding. This view underestimates how strongly early development decisions shape a system.
Developers write foundational code at the start of a project, including authentication logic, authorization boundaries, or error-handling patterns. These decisions become implicit assumptions that future code builds on.
When security reasoning is missing at this stage, the problem is not a single vulnerability but a structural weakness. Retrofitting security later requires reworking core logic, not just fixing isolated bugs. That effort is costly, slow, and often resisted because it challenges existing design choices.
So, security debt begins as a mindset issue rather than a technical one. If developers are hired without the ability to reason about risk, those gaps propagate through the codebase. By the time security teams engage, insecurity is already embedded into the system.
Another reason secure coding is ignored in hiring is the belief that it is the domain of security specialists rather than developers. Security teams can guide and review, but they cannot write or maintain every critical code path.
Secure coding is not a separate role. It is a baseline engineering competency. When hiring pipelines fail to evaluate it, risk is pushed downstream, and security teams are left compensating for gaps that could have been avoided at the point of entry.
Finally, security tooling is essential, but it is not sufficient. SAST and DAST are effective at detecting known patterns, yet they cannot understand intent, context, or business logic. They cannot determine whether a trust boundary was correctly identified or whether a fallback behavior is actually safe.
That reasoning belongs to developers, even when the code itself is produced by AI systems. Secure coding depends on recognizing assumptions, understanding who controls inputs, and judging what happens when expected conditions break. No tool can perform this reasoning on a developer’s behalf. When organizations rely on security tools to compensate for missing reasoning skills, they create a false sense of safety.
Secure coding ability is often mistaken for familiarity with vulnerability lists, standards, or security tooling. In practice, as I mentioned above, it is a reasoning skill. It reflects how a developer thinks about uncertainty, not how many security terms they recognize.
Developers with strong secure coding skills can articulate why a piece of code is safe. They can identify where trust changes within a system and explain the implications of those transitions. When reviewing logic, they naturally consider how the code might behave outside its intended use.
Just as important, they are comfortable making tradeoffs explicit. Rather than hiding uncertainty behind confidence or tooling, they surface assumptions and explain the risks those assumptions introduce. When something is unclear, they explore the consequences rather than guess. In AI-assisted workflows, this ability becomes even more critical because developers are often asked to approve, modify, or deploy logic they did not originally design.
Evaluating secure coding ability does not require turning interviews into security exams or asking candidates to enumerate vulnerabilities. The goal is not to test security knowledge in isolation, but to observe how candidates reason when correctness, risk, and uncertainty intersect.
One effective shift is moving interviews away from producing the right solution and toward examining imperfect ones. Presenting a small, flawed code sample and asking how a candidate would review it reveals far more than asking them to build something from scratch. What matters is not whether they immediately identify a specific issue, but how they reason about assumptions, trust boundaries, and failure paths. This mirrors the modern AI-assisted development, where the primary skill is not generation but judgment.
In practice, engineers who can be trusted with secure production code tend to demonstrate these behaviors:
From a business perspective, this approach scales better than security-heavy interviews. It does not require specialist interviewers or long assessments. It requires interviewers to listen for reasoning rather than speed or confidence. Over time, this aligns hiring with the realities of operating and protecting software and systems, rather than with abstract notions of technical brilliance.
Modern software systems fail less often due to missing tools than to misplaced confidence. When understanding is assumed instead of examined, risk becomes invisible. Hiring decisions quietly decide where that invisibility will surface.
Secure coding ability is ultimately about judgment: knowing when something is safe enough, when it is not, and when the right answer is to pause rather than proceed. That judgment cannot be automated, delegated to AI, or retrofitted. It only exists if it is present from the beginning.
Organizations that treat hiring as a throughput problem will continue to accumulate fragile systems. Those that treat it as a trust decision will build software that can withstand change, pressure, and uncertainty. Security does not begin with defenses. It begins with discernment.
2026-01-13 11:55:10
\
Engineers, architects, and developers share a common flaw: we hate ambiguity. We build systems based on logic, predictable inputs, and measurable outputs. Yet, the most important metric of our existence—Happiness—is usually treated as a vague, ethereal concept that "just happens."
I don't like things that "just happen." I like things I can track, optimize, and debug.
If life is a system, then Happiness is the output. If the output is inconsistent, the code is buggy. To fix it, I realized I needed to stop treating happiness like magic and start treating it like math.
I developed a simple mental model called the Happiness Formula, and then I wrote a script to run it.
The core philosophy is binary. There are things that charge your battery, and things that drain it.
The formula is simple:
\ $$H = (V1 + V2 + V3…) - (F1 + F2 + F3…)$$
You rate every item on a scale of 0 to 100 based on intensity.
If you have a Variable like "Deep Work" that gives you immense satisfaction, it might be a 90. If you have a Friction like "Chronic Back Pain," that might be a -80.
The goal isn't just to "be happy." The goal is to maximize H.
\
\ When you visualize it this way, "getting happier" stops being an abstract wish and becomes an engineering ticket. You either need to push a feature update (add a new Variable) or patch a bug (remove a Friction).
I didn't just want a theory; I wanted a tool. I whipped up a high-contrast, dark-mode calculator that allows me to input these values dynamically.
You can host this on GitHub Pages for free. The logic is lightweight. Here is the core function that drives the score:
function calculateHappiness() {
// 1. Sum up the Variables (The Good)
let vSum = 0;
document.querySelectorAll('.v-score').forEach(input => {
let val = parseFloat(input.value);
if (!isNaN(val)) vSum += val;
});
// 2. Sum up the Frictions (The Bad)
let fSum = 0;
document.querySelectorAll('.f-score').forEach(input => {
let val = parseFloat(input.value);
if (!isNaN(val)) fSum += val;
});
// 3. The Formula
let h = vSum - fSum;
// 4. Render the Reality Check
const resultArea = document.getElementById('result-area');
if (h > 0) {
// Green: System is stable
resultArea.style.borderColor = '#00ff00';
msg = "POSITIVE H. Your drivers outweigh your friction.";
} else {
// Red: System critical
resultArea.style.borderColor = '#ff0000';
msg = "NEGATIVE H. Focus on minimizing your top frictions.";
}
}
I ran my own life through the calculator. I listed my drivers (creative work, family) and subtracted my frictions.
My H-Score came out to 35.
This is a positive integer, which means my system is stable. However, it’s not 100. This tells me that while my variables are strong, my frictions are likely creating too much drag.
If your score is Negative: You are in technical debt. No amount of "positive thinking" (adding small Variables) will fix a massive Friction score. You need to refactor. If your job causes you 90 points of friction, and your weekend hobby only brings 20 points of joy, the math will never work in your favor. You have to remove the friction.
If your score is Positive: You have a surplus. You can now afford to take risks, invest in new skills, or optimize your Variables to push that number higher.
We spend all day optimizing code, refactoring architectures, and cleaning up databases. Why do we accept spaghetti code in our personal lives?
Fork the repo. Run the numbers. Debug your life.
[https://github.com/damianwgriggs/The-Happiness-Formula]()
\ My Favorite Part: The Art
I made the header image today whilst thinking about this article and my formula. I wanted to have the canvas be yellow to represent happiness. The other colors, black, blue, (and some others I am unsure about) were selected to form a piece that conveys the messiness of happiness. Sometimes there are black spots, sometimes we are blue, but what matters most is that we are yellow (not cowardly lol) more than the splotches that can appear in our life. Below is the original image without the crop:
\ \ I would also encourage you to upload to socials and share your results. You can tag me @damianwgriggs!
2026-01-13 11:53:32
A Comprehensive Engineering and Operational Analysis of the Internet Archive
If you stand quietly in the nave of the former Christian Science church on Funston Avenue in San Francisco’s Richmond District, you can hear the sound of the internet breathing. It is not the chaotic screech of a dial-up modem or the ping of a notification, but a steady, industrial hum—a low-frequency thrum generated by hundreds of spinning hard drives and the high-velocity fans that cool them. This is the headquarters of the Internet Archive, a non-profit library that has taken on the Sisyphean task of recording the entire digital history of human civilization.
Here, amidst the repurposed neoclassical columns and wooden pews of a building constructed to worship a different kind of permanence, lies the physical manifestation of the "virtual" world. We tend to think of the internet as an ethereal cloud, a place without geography or mass. But in this building, the internet has weight. It has heat. It requires electricity, maintenance, and a constant battle against the second law of thermodynamics. As of late 2025, this machine—collectively known as the Wayback Machine—has archived over one trillion web pages.1 It holds 99 petabytes of unique data, a number that expands to over 212 petabytes when accounting for backups and redundancy.3
The scale of the operation is staggering, but the engineering challenge is even deeper. How do you build a machine that can ingest the sprawling, dynamic, and ever-changing World Wide Web in real-time? How do you store that data for centuries when the average hard drive lasts only a few years? And perhaps most critically, how do you pay for the electricity, the bandwidth, and the legal defense funds required to keep the lights on in an era where copyright law and digital preservation are locked in a high-stakes collision?
This report delves into the mechanics of the Internet Archive with the precision of a teardown. We will strip back the chassis to examine the custom-built PetaBox servers that heat the building without air conditioning. We will trace the evolution of the web crawlers—from the early tape-based dumps of Alexa Internet to the sophisticated browser-based bots of 2025. We will analyze the financial ledger of this non-profit giant, exploring how it survives on a budget that is a rounding error for its Silicon Valley neighbors. And finally, we will look to the future, where the "Decentralized Web" (DWeb) promises to fragment the Archive into a million pieces to ensure it can never be destroyed.5
To understand the Archive is to understand the physical reality of digital memory. It is a story of 20,000 hard drives, 45 miles of cabling, and a vision that began in 1996 with a simple, audacious goal: "Universal Access to All Knowledge".7
The heart of the Internet Archive is the PetaBox, a storage server custom-designed by the Archive’s staff to solve a specific problem: storing massive amounts of data with minimal power consumption and heat generation. In the early 2000s, off-the-shelf enterprise storage solutions from giants like EMC or NetApp were prohibitively expensive and power-hungry. They were designed for high-speed transactional data—like banking systems or stock exchanges—where milliseconds of latency matter. Archival storage, however, has different requirements. It needs to be dense, cheap, and low-power.8
Brewster Kahle, the Archive's founder and a computer engineer who had previously founded the supercomputer company Thinking Machines, approached the problem with a different philosophy. Instead of high-performance RAID arrays, the Archive built the PetaBox using consumer-grade parts. The design philosophy was radical for its time: use "Just a Bunch of Disks" (JBOD) rather than expensive RAID controllers, and handle data redundancy via software rather than hardware.4
The trajectory of the PetaBox is a case study in Moore's Law applied to magnetic storage. The first PetaBox rack, operational in June 2004, was a revelation in storage density. It held 100 terabytes (TB) of data—a massive sum at the time—while consuming only about 6 kilowatts of power.1 To put that in perspective, in 2003, the entire Wayback Machine was growing at a rate of just 12 terabytes per month. By 2009, that rate had jumped to 100 terabytes a month, and the PetaBox had to evolve.1
The engineering specifications of the PetaBox reveal a relentless pursuit of density:
| Specification | Generation 1 (2004) | Generation 4 (2010) | Current Generation (2024-2025) | |----|----|----|----| | Capacity per Rack | 100 TB | 480 TB | ~1.4 PB (1,400 TB) | | Drive Count | ~40-80 drives | 240 drives (2TB each) | ~360+ drives (8TB+ each) | | Power per Rack | 6 kW | ~6-8 kW | ~6-8 kW | | Heat Dissipation | Utilized for building heat | Utilized for building heat | Utilized for building heat | | Processor Arch | Low-voltage VIA C3 | Intel Xeon E7-8870 (10-core) | Modern High-Efficiency x86 | | Cooling | Passive / Fan-assisted | Passive / Fan-assisted | Passive / Fan-assisted |
1
The fourth-generation PetaBox, introduced around 2010, exemplified this density. Each rack contained 240 disks of 2 terabytes each, organized into 4U high rack mounts. These units were powered by Intel Xeon processors (specifically the E7-8870 series in later upgrades) with 12 gigabytes of RAM. The architecture relied on bonding pair of 1-gigabit interfaces to create a 2-gigabit pipe, feeding into a rack switch with a 10-gigabit uplink.10
By 2025, the storage landscape had shifted again. The current PetaBox racks provide 1.4 petabytes of storage per rack. This leap is achieved not by adding more slots, but by utilizing significantly larger drives—8TB, 16TB, and even 22TB drives are now standard. In 2016, the Archive managed around 20,000 individual disk drives. Remarkably, even as storage capacity tripled between 2012 and 2016, the total count of drives remained relatively constant due to these density improvements.11
In its quest for efficient storage, the Archive also experimented with modular data centers. In 2007, the Archive became an early adopter of the Sun Microsystems "Blackbox" (later the Sun Modular Datacenter). This was a shipping container packed with Sun Fire X4500 "Thumper" storage servers, capable of holding huge amounts of data in a portable, self-contained unit.
The Blackbox at the Archive was filled with eight racks of servers running the Solaris 10 operating system and the ZFS file system. This experiment validated the concept of containerized data centers - a model later adopted by Microsoft and Google—but the Archive eventually returned to its custom PetaBox designs for their primary internal infrastructure, favoring the flexibility and lower cost of their own open-source hardware designs over proprietary commercial solutions.12
One of the most ingenious features of the Archive’s infrastructure is its thermal management system. Data centers are notoriously energy-intensive, often spending as much electricity on cooling (HVAC) as they do on computing. The Internet Archive, operating on a non-profit budget, could not afford such waste.
The solution was geography and physics. The Archive's primary data center is located in the Richmond District of San Francisco, a neighborhood known for its perpetual fog and cool maritime climate. The building utilizes this ambient air for cooling. There is no traditional air conditioning in the PetaBox machine rooms. Instead, the servers are designed to run at slightly higher operational temperatures, and the excess heat generated by the spinning disks is captured and recirculated to heat the building during the damp San Francisco winters.9
This "waste heat" system is a closed loop of efficiency. The 60+ kilowatts of heat energy produced by a storage cluster is not a byproduct to be eliminated but a resource to be harvested. This design choice dramatically lowers the Power Usage Effectiveness (PUE) ratio of the facility, allowing the Archive to spend its limited funds on hard drives rather than electricity bills. It is a literal application of the "reduce, reuse, recycle" mantra to the thermodynamics of data storage.3
With over 28,000 spinning disks in operation, drive failure is a statistical certainty.3 In a traditional corporate data center, a failed drive triggers an immediate, frantic replacement protocol to maintain "five nines" (99.999%) of reliability. At the Internet Archive, the approach is more pragmatic.
The PetaBox software is designed to be fault-tolerant. Data is mirrored across multiple machines, often in different physical locations (including data centers in Redwood City and Richmond, California, and copies in Europe and Canada).12 Because the data is not "mission-critical" in the sense of a live banking transaction, the Archive can tolerate a certain number of dead drives in a node before physical maintenance is required.
This "low-maintenance" design allows a very small team—historically just one system administrator per petabyte of data—to manage a storage empire that rivals those of major tech corporations. The system uses the Nagios monitoring tool to track the health of over 16,000 distinct check-points across the cluster, alerting the small staff only when a critical threshold of failure is reached.8
If the PetaBox is the brain of the Archive, the web crawlers are its eyes. Archiving the web is not a passive process; it requires active, aggressive software that relentlessly traverses the links of the World Wide Web, copying everything it finds. This process, known as crawling, has evolved from simple script-based retrieval to complex browser automation.
For much of its history, the Archive relied on a crawler called Heritrix. Developed jointly in 2003 by the Internet Archive and Nordic national libraries (Norway and Iceland), Heritrix is a Java-based, open-source crawler designed specifically for archival fidelity.16
Unlike a search engine crawler (like Googlebot), which cares primarily about extracting text for search relevance, Heritrix cares about the artifact. It attempts to capture the exact state of a webpage, including its images, stylesheets, and embedded objects. It packages these assets into a standardized container format known as WARC (Web ARChive).18
The WARC file is the atomic unit of the Internet Archive. It preserves not just the content of the page, but the "HTTP headers"—the digital handshake between the server and the browser that occurred at the moment of capture. This metadata is crucial for historians, as it proves when a page was captured, what server delivered it, and how the connection was negotiated.19
Heritrix operates using a "Frontier"—a sophisticated queue management system that decides which URL to visit next. It adheres to strict "politeness" policies, respecting robots.txt exclusion protocols and limiting the frequency of requests to avoid crashing the target servers.16
However, Heritrix was built for a simpler web—a web of static HTML files and hyperlinks. As the web evolved into a platform of dynamic applications (Web 2.0), social media feeds, and JavaScript-heavy interfaces, Heritrix began to stumble.
Heritrix captures the initial HTML delivered by the server. But on a modern site like Twitter (now X) or Facebook, that initial HTML is often just a blank scaffolding. The actual content is loaded dynamically by JavaScript code running in the user's browser after the page loads. Heritrix, being a dumb downloader, couldn't execute this code. The result was often a broken, empty shell of a page—a digital ghost town.17
To combat the "dynamic web," the Archive had to evolve its tooling. The modern archiving stack includes Brozzler and Umbra, tools that blur the line between a crawler and a web browser.
Brozzler (a portmanteau of "browser" and "crawler") uses a "headless" version of the Google Chrome browser to render pages exactly as a user sees them. It executes the JavaScript, expands the menus, and plays the animations before capturing the content. This allows the Archive to preserve complex sites like Instagram and interactive news articles that would be invisible to a traditional crawler.17
Umbra acts as a helper tool, using browser automation to mimic human behaviors. It "scrolls" down a page to trigger infinite loading feeds, hovers over dropdown menus to reveal hidden links, and clicks buttons. These actions expose new URLs that are then fed back to the crawler for capture.17
This shift requires significantly more computing power. Rendering a page in Chrome takes orders of magnitude more CPU cycles than simply downloading a text file. This has forced the Archive to be more selective and targeted in its high-fidelity crawls, reserving the resource-intensive browser crawling for high-value dynamic sites while using lighter tools for the static web.17
Perhaps the most significant technological shift in recent years has been the democratization of the crawl. The Save Page Now feature allows any user to instantly trigger a crawl of a specific URL. This bypasses the scheduled, algorithmic crawls and inserts a high-priority job directly into the ingestion queue.
Powered by these browser-based technologies, Save Page Now has become a critical tool for journalists, researchers, and fact-checkers. In 2025, it is often the first line of defense against link rot, allowing users to create an immutable record of a tweet or news article seconds before it is deleted or altered.1
It is impossible to discuss the Archive's crawling history without mentioning Alexa Internet. Founded by Brewster Kahle in 1996 alongside the Archive, Alexa was a for-profit company that crawled the web to provide traffic analytics (the famous "Alexa Rank").
For nearly two decades, Alexa was the primary source of the Archive's data. Alexa would crawl the web for its own commercial purposes and then donate the crawl data to the Internet Archive after an embargo period. This symbiotic relationship provided the Archive with a massive, continuous stream of data without the need to run its own massive crawling infrastructure. However, with Amazon (which acquired Alexa in 1999) discontinuing the Alexa service in May 2022, the Archive has had to rely more heavily on its own crawling infrastructure and partners like Common Crawl.7
Running a top-tier global website usually requires the budget of a Google or a Meta. The Internet Archive manages to operate as one of the world's most visited websites on a budget that is shockingly modest. How does an organization with no ads, no subscription fees for readers, and no data mining revenue keep 200 petabytes of data online?
According to financial filings (Form 990) and annual reports, the Internet Archive’s annual revenue hovers between $25 million and $30 million.7 In 2024, for example, the organization reported approximately $26.8 million in revenue against $23.5 million in expenses.25
The primary revenue driver is Contributions and Grants, which typically account for 60-70% of total income. This includes:
The second major revenue stream is Program Services, specifically digitization and archiving services. The Archive is not just a library; it is a service provider.
The expense side of the ledger is dominated by Salaries and Wages (roughly half the budget) and IT Infrastructure. However, the Archive’s "PetaBox economics" allow it to store data at a fraction of the cost of commercial cloud providers.
Consider the cost of storing 100 petabytes on Amazon S3. At standard rates (~$0.021 per GB per month), the storage alone would cost over $2.1 million per month. The Internet Archive’s entire annual operating budget—for staff, buildings, legal defense, and hardware—is less than what it would cost to store their data on AWS for a year.
By owning its hardware, using the PetaBox high-density architecture, avoiding air conditioning costs, and using open-source software, the Archive achieves a storage cost efficiency that is orders of magnitude better than commercial cloud rates.25
The Internet Archive’s mission is "Universal Access to All Knowledge." This mission is morally compelling but legally perilous. As the Archive expanded beyond simple web pages into books, music, and software, it moved from the relatively safe harbor of the "implied license" of the web into the heavily fortified territory of copyright law.
The tension exploded in 2020 during the COVID-19 pandemic. With physical libraries closed, the Archive launched the "National Emergency Library," removing the waitlists on its digitized book collection. This move prompted four major publishers—Hachette, HarperCollins, Wiley, and Penguin Random House—to sue, alleging massive copyright infringement.31
The legal core of the Archive’s book program was Controlled Digital Lending (CDL). The theory argued that if a library owns a physical book, it should be allowed to scan that book and lend the digital copy to one person at a time, provided the physical book is taken out of circulation while the digital one is on loan. This "own-to-loan" ratio mimics the constraints of physical lending.33
However, in a crushing decision in March 2023, a federal judge rejected this defense, ruling that the Archive’s scanning and lending was not "fair use." The court found that the digital copies competed with the publishers' own commercial ebook markets. The Archive’s argument that its use was "transformative" (making lending more efficient) was rejected. In September 2024, the Second Circuit Court of Appeals upheld this decision, and by late 2024, the Archive announced it would not appeal to the Supreme Court.31
The settlement in the Hachette case was a significant blow. The Archive was forced to remove roughly 500,000 books from its lending program—specifically those for which a commercial ebook version exists. This "negotiated judgment" fundamentally altered the Archive's book strategy, forcing it to pivot back to older, out-of-print, and public domain works where commercial conflicts are less likely.31
While the book battle raged, a second front opened on the audio side. The Great 78 Project aimed to digitize 78rpm records from the early 20th century. These shellac discs are brittle, obsolete, and often deteriorating. The Archive argued that digitizing them was a preservation imperative.37
Major record labels, including Sony Music and Universal Music Group, disagreed. They sued in 2023, claiming the project functioned as an "illegal record store" that infringed on the copyrights of thousands of songs by artists like Frank Sinatra and Billie Holiday. They sought damages that could have reached over $600 million—an existential threat to the Archive.38
In September 2025, this lawsuit also reached a settlement. While the terms remain confidential, the resolution allowed the Archive to avoid a potentially bankruptcy-inducing trial. However, the immediate aftermath saw the removal of access to many copyrighted audio recordings, restricting them to researchers rather than the general public. This pattern—settlement followed by restriction—marks the new reality for the Internet Archive in 2025: a retreat from the "move fast and break things" approach to a more cautious, legally circumscribed preservation model.39
In a major strategic win amidst these losses, the Internet Archive was designated as a Federal Depository Library (FDL) by the U.S. Senate in July 2025.7 This status is more than just a title; it legally empowers the Archive to collect, preserve, and provide access to U.S. government publications.
This designation provides a crucial layer of legal protection for at least a portion of the Archive’s collection. While it doesn't protect copyrighted music or commercial novels, it solidifies the Archive's role as an essential component of the nation's information infrastructure, making it politically and legally more difficult to shut down entirely.7
The legal threats of 2020-2025 exposed a critical vulnerability: centralization. If a court order or a catastrophic fire were to hit the Funston Avenue headquarters, the primary copy of the web’s history could be lost. The Archive’s strategy for the next decade is to decentralize survival.
The Archive is a primary driver behind the DWeb movement, which seeks to build a web that is distributed rather than centralized. The goal is to store the Archive’s data across a global network of peers, making it impossible for any single entity—be it a government, a corporation, or a natural disaster—to take it offline.5
Technologically, this involves integrating with protocols like IPFS (InterPlanetary File System) and Filecoin.
Every four years, the Archive leads a massive effort to crawl (dot)gov and (dot)mil websites before a presidential transition. The 2024/2025 crawl was the largest in history, capturing over 500 terabytes of government data.45 This project highlights the Archive's role as a watchdog of history, ensuring that climate data, census reports, and policy documents don't vanish when a new administration takes office.
I emailed Brewser Kahle regarding 2025 and generative AI, and here is his quote:
\
“Generative AI has caused some web sites to pursue dollar signs by block their sites or launch lawsuits. This does not help the cultural heritage institutions, such as the Internet Archive and often hurts users in general. The web seems to have turned much more adversarial as many pursue monetization.
The Internet Archive will stay free and open to try to help people get a handle on our changing world. The Archive offers open datasets for AI researchers and companies to leverage for their services. As an organization the Internet Archive has been using generative AI tools to help speed metadata assignment and scanning activities.”
As we move deeper into the 21st century, the Internet Archive stands as a paradox. It is a technological behemoth, operating at a scale that rivals Silicon Valley giants, yet it is housed in a church and run by librarians. It is a fragile institution, battered by lawsuits and budget constraints, yet it is also the most robust memory bank humanity has ever built.
The events of 2025—the "trillionth page" milestone, the painful legal settlements, and the pivot toward decentralized storage—mark a maturing of the organization. It is no longer the "wild west" of the early web. It is a battered but resilient institution, adapting its machinery and its mission to survive in a world that is increasingly hostile to the concept of free, universal access. And the rising popularity of generative AI adds yet another unpredictable dimension to the future survival of the public domain archive.
Inside the PetaBox, the drives continue to spin. The heat they generate warms the building, keeping the fog of the Richmond District at bay. And somewhere on those platters, amidst the trillions of zeros and ones, lies the only proof that the digital world of yesterday ever existed at all. The machine remembers, so that we don't have to.
Wayback Machine - Wikipedia, accessed January 8, 2026, https://en.wikipedia.org/wiki/Wayback_Machine
Looking back on “Preserving the Internet” from 1996 | Internet Archive Blogs, accessed January 8, 2026, https://blog.archive.org/2025/09/02/looking-back-on-preserving-the-internet-from-1996/
Petabox - Internet Archive, accessed January 8, 2026, https://archive.org/web/petabox.php
PetaBox - Wikipedia, accessed January 8, 2026, https://en.wikipedia.org/wiki/PetaBox
IPFS: Building blocks for a better web | IPFS, accessed January 8, 2026, https://ipfs.tech/
internetarchive/dweb-archive - GitHub, accessed January 8, 2026, https://github.com/internetarchive/dweb-archive
Internet Archive - Wikipedia, accessed January 8, 2026, https://en.wikipedia.org/wiki/Internet_Archive
Making Web Memories with the PetaBox - eWeek, accessed January 8, 2026, https://www.eweek.com/storage/making-web-memories-with-the-petabox/
PetaBox - Internet Archive Unoffical Wiki, accessed January 8, 2026, https://internetarchive.archiveteam.org/index.php/PetaBox
The Fourth Generation Petabox | Internet Archive Blogs, accessed January 8, 2026, https://blog.archive.org/2010/07/27/the-fourth-generation-petabox/
Internet Archive Hits One Trillion Web Pages - Hackaday, accessed January 8, 2026, https://hackaday.com/2025/11/18/internet-archive-hits-one-trillion-web-pages/
The Internet Archive's Wayback Machine gets a new data center - Computerworld, accessed January 8, 2026, https://www.computerworld.com/article/1562759/the-internet-archive-s-wayback-machine-gets-a-new-data-center.html
Internet Archive to Live in Sun Blackbox - Data Center Knowledge, accessed January 8, 2026, https://www.datacenterknowledge.com/business/internet-archive-to-live-in-sun-blackbox
Inside the Internet Archive: A Meat World Tour | Root Simple, accessed January 8, 2026, https://www.rootsimple.com/2023/08/inside-the-internet-archive-a-meat-world-tour/
Internet Archive Preserves Data from World Wide Web - Richmond Review/Sunset Beacon, accessed January 8, 2026, https://richmondsunsetnews.com/2017/03/11/internet-archive-preserves-data-from-world-wide-web/
Heritrix - Wikipedia, accessed January 8, 2026, https://en.wikipedia.org/wiki/Heritrix
Archive-It Crawling Technology, accessed January 8, 2026, https://support.archive-it.org/hc/en-us/articles/115001081186-Archive-It-Crawling-Technology
WARCreate: Create Wayback-Consumable WARC Files From Any Webpage - ODU Digital Commons, accessed January 8, 2026, https://digitalcommons.odu.edu/cgi/viewcontent.cgi?article=1154&context=computerscience_fac_pubs
The WARC Format - IIPC Community Resources, accessed January 8, 2026, https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/
What is heritrix? - Hall: AI, accessed January 8, 2026, https://usehall.com/agents/heritrix-bot
Archiving Websites Containing Streaming Media, accessed January 8, 2026, https://library.imaging.org/admin/apis/public/api/ist/website/downloadArticle/archiving/14/1/art00004
March | 2025 | Internet Archive Blogs, accessed January 8, 2026, https://blog.archive.org/2025/03/
Alexa Crawls - Internet Archive, accessed January 8, 2026, https://archive.org/details/alexacrawls
Alexa Internet - Wikipedia, accessed January 8, 2026, https://en.wikipedia.org/wiki/Alexa_Internet
Internet Archive - Nonprofit Explorer - ProPublica, accessed January 8, 2026, https://projects.propublica.org/nonprofits/organizations/943242767
Update on the 2024/2025 End of Term Web Archive - Ben Werdmuller, accessed January 8, 2026, https://werd.io/update-on-the-20242025-end-of-term-web-archive/
Archive-It | History as Code, accessed January 8, 2026, https://www.historyascode.com/tools-data/archive-it/
Pricing - Internet Archive Digitization Services, accessed January 8, 2026, https://digitization.archive.org/pricing/
The random Bay Area warehouse that houses one of humanity's greatest archives - SFGATE, accessed January 8, 2026, https://www.sfgate.com/tech/article/bay-area-warehouse-internet-archive-19858332.php
Vault Pricing Model - Vault Support, accessed January 8, 2026, https://vault-webservices.zendesk.com/hc/en-us/articles/22896482572180-Vault-Pricing-Model
Hachette v. Internet Archive - Wikipedia, accessed January 8, 2026, https://en.wikipedia.org/wiki/Hachette_v._Internet_Archive
Hachette Book Group, Inc. v. Internet Archive | Copyright Cases, accessed January 8, 2026, https://copyrightalliance.org/copyright-cases/hachette-book-group-internet-archive/
Hachette Book Group, Inc. v. Internet Archive, No. 23-1260 (2d Cir. 2024) - Justia Law, accessed January 8, 2026, https://law.justia.com/cases/federal/appellate-courts/ca2/23-1260/23-1260-2024-09-04.html
Hachette Book Group v. Internet Archive and the Future of Controlled Digital Lending, accessed January 8, 2026, https://www.library.upenn.edu/news/hachette-v-internet-archive
Internet Archive's Open Library and Copyright Law: The Final Chapter, accessed January 8, 2026, https://www.lutzker.com/ip_bit_pieces/internet-archives-open-library-and-copyright-law-the-final-chapter/
What the Hachette v. Internet Archive Decision Means for Our Library, accessed January 8, 2026, https://blog.archive.org/2023/08/17/what-the-hachette-v-internet-archive-decision-means-for-our-library/
Labels settle copyright lawsuit against Internet Archive over streaming of vintage vinyl records - Music Business Worldwide, accessed January 8, 2026, https://www.musicbusinessworldwide.com/labels-settle-copyright-lawsuit-against-internet-archive-over-streaming-of-vintage-vinyl-records/
Internet Archive Settles $621 Million Lawsuit with Major Labels Over Vinyl Preservation Project - Consequence.net, accessed January 8, 2026, https://consequence.net/2025/09/internet-archive-labels-settle-copyright-lawsuit/
An Update on the Great 78s Lawsuit | Internet Archive Blogs, accessed January 8, 2026, https://blog.archive.org/2025/09/15/an-update-on-the-great-78s-lawsuit/
Music Publishers, Internet Archive Settle Lawsuit Over Old Recordings - GigaLaw, accessed January 8, 2026, https://giga.law/daily-news/2025/9/15/music-publishers-internet-archive-settle-lawsuit-over-old-recordings
Internet Archive Settles Copyright Suit with Sony, Universal Over Vintage Records, accessed January 8, 2026, https://www.webpronews.com/internet-archive-settles-copyright-suit-with-sony-universal-over-vintage-records/
July | 2025 - Internet Archive Blogs, accessed January 8, 2026, https://blog.archive.org/2025/07/
Decentralized Web FAQ - Internet Archive Blogs, accessed January 8, 2026, https://blog.archive.org/2018/07/21/decentralized-web-faq/
Decentralized Web Server: Possible Approach with Cost and Performance Estimates, accessed January 8, 2026, https://blog.archive.org/2016/06/23/decentalized-web-server-possible-approach-with-cost-and-performance-estimates/
Update on the 2024/2025 End of Term Web Archive | Internet …, accessed January 8, 2026, https://blog.archive.org/2025/02/06/update-on-the-2024-2025-end-of-term-web-archive/
Progress update from The End of Term Web Archive: 100 million webpages collected, over 500 TB of data : r/DataHoarder - Reddit, accessed January 8, 2026, https://www.reddit.com/r/DataHoarder/comments/1ijkdjl/progress_update_from_the_end_of_term_web_archive/
\n
\
