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Allstate Is Demanding That We Delete These Quotes by Its Exec About Using How It's Using AI to Write Insurance Emails

NEXT Insurance, a digital insurtech specialising in small businesses, has announced it has entered into strategic partnerships with US primary insurer

NEXT Insurance enters partnerships with Allstate and Allianz X

Last valued at $4 billion, Next Insurance raised $265 million in funding from Allstate and Allianz X, it said Wednesday. Why it matters: Insurtech was the most-funded sector within fintech in the third quarter, per FT Partners data. Strategic investors are helping play a role in that.

SMB insurance company Next raises $265 million

Allstate and Allianz have made a $265 million strategic equity investment in small business-focused insuretech startup Next Insurance.

Allstate, Allianz invest $265 million in Next Insurance in a big bet on insuretech

NEXT Insurance, a leading technology-first small business insurer, today announced its strategic partnership with Allstate that will combine the companies' expertise in small business insurance to develop and offer innovative and unique new products to a large underserved market,

NEXT Insurance Enters Partnerships with Allstate and Allianz X, Supported by $265 Million Strategic Investment

The small businesses digital insurance company has entered a strategic partnership with Allstate and Allianz X. Israeli insurtech company Next Insurance today announced its strategic partnership with Allstate and Allianz X,

Israeli co Next Insurance raises $265m

An in-depth review of American Family car insurance, including coverage options, pricing, customer satisfaction and how AmFam compares to other insurance companies.

American Family car insurance review 2023

Allstate Corporation, a leading insurance company based in the United States, has made a significant impact on the global insurance market through its growth and expansion strategies. With a strong focus on customer satisfaction and innovative products ...

The Growth and Expansion of Allstate Corporation in the Global Insurance Market

Allstate Corporation is a well-known insurance company that has been providing coverage to individuals and businesses for over 90 years. Understanding the history and evolution of this company is crucial to understanding its unique approach to insurance.

Understanding Allstate Corporation’s Unique Approach to Insurance

That follows similar decisions by Allstate and State Farm, earlier this year. The most recent move is by smaller insurers. Merastar Insurance Company; Unitrin Auto and Home Insurance Company; Unitrin Direct Property and Casualty Company and Kemper ...

4 more insurers are leaving California after Allstate and State Farm flee wildfire risk

Allstate

allstate

A pair of newly public documents filed with a California administrative law judge show experts accusing the company of systematically overcharging customers it believed to be the most loyal around $1 billion over the past decade. This practice of charging higher premiums to customers an insurance company suspects are unlikely to defect to a competitor is termed “price optimization” and was the subject of a 2020 Markup that found Allstate was attempting to use a new pricing algorithm for auto insurance in Maryland that would have unfairly targeted its highest-paying customers—and that the algorithm had been approved in several other states. investigation Nearly every state, including California, bars insurers from setting car insurance rates on factors apart from the actual risk the drivers pose. Insurance regulators in 18 states and Washington, D.C., have explicitly declared price optimization illegal. The new documents, which were initially filed in late October by the California Department of Insurance and Consumer Watchdog, a consumer advocacy group allowed by the state to intervene in the case and provide expertise, consist of written testimony from insurance industry experts who examined how Allstate set its prices. They allege that Allstate was engaging in price optimization by giving smaller than appropriate discounts to the least-price-sensitive among its customers with clean driving records who held multiple policies with the company or who had several decades of driving experience. Edward Cimini Jr., a senior casualty actuary with the California Department of Insurance, he reviewed internal Allstate documents, documents the company submitted to the state describing its auto insurance pricing plan, and depositions of company employees and found that Allstate gave smaller discounts to drivers with more than 39 years of experience, a group he said is unlikely to shop around. said “Since Allstate’s selections were not based on underlying costs, the final rates that Allstate charged these policyholders were actuarily unsound and unfairly discriminatory,” he said. Allan Schwartz, an actuarial consultant hired by Consumer Watchdog to review Allstate’s pricing practices, that Allstate overcharged California drivers who were owed discounts “about $1 billion.” estimated Since Allstate’s selections were not based on underlying costs, the final rates that Allstate charged … were actuarily unsound and unfairly discriminatory. Edward Cimini Jr., California Department of Insurance “Those policyholders were known by Allstate to have a lower elasticity of demand and were more likely to renew with Allstate even though they were charged premiums in excess of those based upon an actuarially sound estimate of the cost of risk transfer,” he said. The company denies the allegations. “Allstate does not employ, and has never employed, price optimization in determining premiums in California because Allstate does not take into account an individual’s or class’s willingness to pay a higher premium relative to other individuals or classes,” Allstate spokesperson Ben Corey wrote in an email to The Markup. In its court , Allstate points out that in 2011 the California Department of Insurance reviewed and approved the 2011 plan without highlighting the issues now raised in the long-running class action that triggered this hearing. filings The company was sued in 2015 over alleged price optimization practices in California. Allstate moved to have the case thrown out, arguing in part that it wasn’t a matter for civil courts but rather for the state department of insurance. In 2016, the United States District Court for the Northern District of California put the case on hold and referred it to the authority of the Commissioner of the California Department of Insurance for its opinion, which triggered the administrative law proceeding. Last week, the parties agreed to voluntary mediation. enter Two years ago, The Markup published an revealing that a new method of calculating rates for car insurance customers that Allstate was trying to implement across the country attempted to charge higher rates to customers who were already paying a lot for their car insurance—essentially creating a “suckers list” of big spenders and squeezing even more money out of them. investigation The investigation was based on details the insurer provided to regulators in Maryland as part of its 2014 filing there that revealed the current and proposed rate for policyholders in the state. Using statistical regressions, we found that the rating factor, called CGR, would charge more—and severely limit discounts—to big spenders. Maryland regulators rejected Allstate’s plan over price optimization concerns. We found Allstate was using similar algorithms in 10 other states, but we were not able to determine if they worked exactly the same way in those states because we lacked the data we had in Maryland. Allstate did not answer our questions about the algorithms. Consumer Watchdog founder Harvey Rosenfield, who has long been critical of Allstate’s pricing practices, said that the company found a different method of achieving the same goals in California, which only allows insurers to use a limited number of approved factors—like driving record, type of car, or the number of years behind the wheel—to determine how much customers have to pay in premiums. “What we’re contending is they took their knowledge of price elasticity and figured out a pretty straightforward way of doing it without saying so,” Rosenfield said. “Their actuaries selected relativities to set people’s premiums that punished people who Allstate knew were inelastic, who fit the profile of being less sensitive to price changes. They charged those people more.” California isn’t the only place where Allstate has faced litigation over its use of price optimization. Shortly after the publication of The Markup’s Maryland investigation, the company was hit with a that directly referenced The Markup’s reporting. class action lawsuit in Texas That suit that the company was “charging higher premiums to its more tenured policyholders than it charges otherwise identically-situated newer policyholders for the same or materially the same coverages.” alleges That case is still ongoing. Correction The story originally stated that the Texas class action lawsuit against Allstate had settled last year. It is still ongoing. The story has been updated to correct the error. Written by: Aaron Sankin Photo by on Vlad Deep Unsplash Also published here

Allstate Accused of Overcharging Loyal California Customers $1 Billion

A joint investigation by The Markup and Consumer Reports was cited in a lawsuit over discriminatory car insurance rates On April 28, a group of current and former Allstate Corp. insurance policyholders sued the company, alleging that it had engaged in discriminatory pricing practices, including using a that The Markup and Consumer Reports uncovered and reported about in February. The was filed in the Austin division of the United States District Court for the Western District of Texas. secret algorithm complaint The suit alleges that “Allstate essentially has to pick the rate that it will charge each policyholder” using the secret algorithm. It claims that Allstate viewed longtime customers as less sensitive to premium price changes and alleges that those customers were “generally charged higher premiums for the same policies and coverages than otherwise identically situated customers with less tenure.” carte blanche Allstate essentially has carte blanche to pick the rate that it will charge each policyholder. The suit against Allstate The suit cites the joint investigation by The Markup and Consumer Reports, whose of Allstate customer data from a Maryland filing (which was withdrawn before it was put into effect) found that customers who were already paying some of the highest rates would have also been charged rate hikes of up to 20 percent, compared to more limited increases of up to 5 percent for similarly situated customers who had been paying cheaper rates. The analysis also found that no customer would have received a substantial decrease, with rate decreases capped at half of one percent. analysis Allstate spokesperson Shaundra Turner Jones responded to the report by The Markup and Consumer Reports saying that Allstate’s rating plans comply with state laws and regulations and that the reporting on the Maryland filing was “inaccurate and misleading” because it was “based on a rating plan that was never used.” The Texas lawsuit also claims that Allstate charged customers differently depending on what “book” they were in. Allstate maintains several auto insurance companies (referred to as “books of business” in the insurance industry) that issue insurance policies to customers. While some of those books are “open,” meaning that they take new customers, several of the Allstate books operating in Texas are “closed,” meaning that they only work with pre-existing customers. The suit alleges that “the premiums that Allstate charges new policyholders in the Open Books are generally significantly lower (often, very significantly so) than the premiums Allstate charges existing policyholders in the Closed Books for the same or materially the same coverages.” In an email to The Markup, Allstate spokesperson Roberto DeLeon said, “There is no merit to this complaint. We pair quality, customized coverage with great savings and reward long-term customers for their loyalty.” Texas law prohibits insurers from treating policyholders differently “without a sound actuarial basis.” The suit alleges that Allstate knowingly violated this prohibition as early as 2015, when the company was sent by the Texas Office of Public Insurance Counsel (OPIC), an agency independent from the state’s insurance department, stating that the retention model it was proposing violated multiple Texas laws. a letter Deeia Beck, public counsel for OPIC, wrote that “allowing clearly non-risk-and-cost-based rating factors opens the door to further conditions where certain classes of consumers are unfairly priced out of the market or shunted into low quality products.” In explaining the differences between accepted insurance practices and what Allstate was doing, Beck noted that “discriminatory intent is introduced” when insurers introduce “for example, a 25% rate cap for older drivers and a 40% cap for youthful drivers.” The Markup’s and Consumer Reports’ of Allstate customer data in Maryland found that customers hit with a 20 percent increase were more likely to be middle-aged. analysis It seems Allstate continued its use of the retention model even after receiving the OPIC letter: The Markup and Consumer Reports found a from Allstate representatives to the Ohio Department of Insurance that claimed that the company was still using the retention model in Texas at that time but that it was under “active review.” July 2016 letter Credits Originally published as " Texas Drivers Sue Allstate over Secret “Suckers List” " with the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

What Is “Suckers List” And Why Texas Drivers Sued Its Creator

Allstate's Car Insurance Algorithm: How Insurance Algorithm Was Analyzed

Allstate's Car Insurance Algorithm: How Insurance Algorithm Squeezes Big Spenders

Want to Get to the Important Truth Inside Any Privacy Policy? Here's How

As someone who's worked in the trenches of financial markets, I've seen firsthand the importance of real-time data processing. During my time at Two Sigma and Bloomberg, I witnessed how even minor delays can have significant consequences. In this article, I'll share my insights on the challenges of real-time data processing in distributed systems, using examples from the financial industry. Data Consistency: The Achilles' Heel of Distributed Systems Imagine you're a trader, relying on real-time market data to make split-second decisions. But what if the data you're receiving is inconsistent? Perhaps one server thinks the price of Apple is $240, while another sees it at $241. This discrepancy might seem minor, but in the world of high-frequency trading, it can be catastrophic. To ensure data consistency, financial institutions employ various techniques, such as: Event sourcing: By storing all state changes as a sequence of events, systems can reconstruct the latest state, even in the event of a failure.
Distributed consensus algorithms: Protocols like Paxos or Raft enable nodes to agree on a single source of truth, even in cases of network partitioning. Event sourcing: By storing all state changes as a sequence of events, systems can reconstruct the latest state, even in the event of a failure. Event sourcing Distributed consensus algorithms: Protocols like Paxos or Raft enable nodes to agree on a single source of truth, even in cases of network partitioning. Distributed consensus algorithms However, these solutions can introduce additional complexity, particularly in high-throughput environments. Latency: The Need for Speed In financial markets, latency can make or break a trade. High-frequency trading firms invest heavily in infrastructure to minimize latency, and even the smallest inefficiency can have significant consequences. Real-time market data must be processed and delivered to consumers with extremely low latency. To address latency, financial institutions employ strategies such as: Edge processing: By processing data closer to its source, systems can reduce latency and improve performance.
Hardware acceleration: Utilizing specialized hardware, such as FPGA, can significantly reduce processing times for critical tasks.
Optimized message brokers: Choosing the right message broker, such as Kafka or Pulsar, is crucial in ensuring that data is pushed to consumers as quickly as possible. Edge processing: By processing data closer to its source, systems can reduce latency and improve performance. Edge processing Hardware acceleration: Utilizing specialized hardware, such as FPGA, can significantly reduce processing times for critical tasks. Hardware acceleration Optimized message brokers: Choosing the right message broker, such as Kafka or Pulsar, is crucial in ensuring that data is pushed to consumers as quickly as possible. Optimized message brokers Fault Tolerance: What Happens When Things Go Wrong? No system is immune to failure, but in financial markets, fault tolerance is paramount. If a single node or service goes down, consumers cannot afford to lose critical market data. To ensure fault tolerance, financial institutions employ strategies such as: Replication: Market data is replicated across multiple servers or regions to ensure continuity in the event of a failure.
Automatic failover: Systems are designed to detect failure and route traffic to healthy servers without human intervention.
Distributed logging: Distributed logs, such as Kafka's log-based architecture, ensure that all actions are recorded and can be replayed in the event of a crash. Replication: Market data is replicated across multiple servers or regions to ensure continuity in the event of a failure. Replication Automatic failover: Systems are designed to detect failure and route traffic to healthy servers without human intervention. Automatic failover Distributed logging: Distributed logs, such as Kafka's log-based architecture, ensure that all actions are recorded and can be replayed in the event of a crash. Distributed logging Scalability: Handling Explosive Growth Financial markets are inherently unpredictable, and systems must be designed to handle sudden surges in traffic. Scalability is critical to ensure that systems can handle explosive growth without degrading performance. To achieve scalability, financial institutions employ strategies such as: Sharding: Market data is divided into smaller chunks and distributed across different servers to improve performance.
Load balancing: Incoming data is efficiently distributed across multiple nodes or servers to avoid bottlenecks.
Elasticity: Systems are designed to scale up or down based on demand, ensuring that resources are allocated efficiently. Sharding: Market data is divided into smaller chunks and distributed across different servers to improve performance. Sharding Load balancing: Incoming data is efficiently distributed across multiple nodes or servers to avoid bottlenecks. Load balancing Elasticity: Systems are designed to scale up or down based on demand, ensuring that resources are allocated efficiently. Elasticity Security: Protecting Critical Market Data Finally, security is paramount in financial markets. Distributed systems, by their nature, involve multiple servers, databases, and services spread across various regions, making them vulnerable to attacks. To ensure security, financial institutions employ strategies such as: Encryption: Data is encrypted both in transit and at rest to prevent eavesdropping or unauthorized access.
Authentication and authorization: Only authorized parties can access sensitive market data feeds, using techniques like OAuth and API keys.
DDoS mitigation: Systems are designed to detect and prevent Distributed Denial of Service (DDoS) attacks, ensuring availability and performance. Encryption: Data is encrypted both in transit and at rest to prevent eavesdropping or unauthorized access. Encryption Authentication and authorization: Only authorized parties can access sensitive market data feeds, using techniques like OAuth and API keys. Authentication and authorization DDoS mitigation: Systems are designed to detect and prevent Distributed Denial of Service (DDoS) attacks, ensuring availability and performance. DDoS mitigation Conclusion Real-time data processing in distributed systems is a complex challenge, particularly in high-stakes environments like financial markets. By understanding the challenges of data consistency, latency, fault tolerance, scalability, and security, financial institutions can design and implement more efficient, resilient, and scalable systems. As the financial industry continues to evolve, the quest for near-zero latency, high availability, and real-time data processing will only become more critical. By sharing my insights and experiences, I hope to contribute to the ongoing conversation about the challenges and opportunities in real-time data processing. Image Courtesy: https://www.nber.org/digest/202110/race-exploit-stock-price-differences-between-exchanges
https://www.linkedin.com/pulse/distributed-systems-design-pattern-lease-based-stock-shanoj-kumar-v-0bawc/
https://blog.e-boks.com/understanding-data-encryption-a-comprehensive-guide-for-regulated-industries https://www.nber.org/digest/202110/race-exploit-stock-price-differences-between-exchanges https://www.nber.org/digest/202110/race-exploit-stock-price-differences-between-exchanges https://www.linkedin.com/pulse/distributed-systems-design-pattern-lease-based-stock-shanoj-kumar-v-0bawc/ https://www.linkedin.com/pulse/distributed-systems-design-pattern-lease-based-stock-shanoj-kumar-v-0bawc/ https://blog.e-boks.com/understanding-data-encryption-a-comprehensive-guide-for-regulated-industries https://blog.e-boks.com/understanding-data-encryption-a-comprehensive-guide-for-regulated-industries

Challenges of Real-Time Data Processing in Financial Markets

**BOSTON, USA, March 11th, 2025/CyberNewsWire/--**GitGuardian, the security leader behind GitHub's most installed application, today released its comprehensive "2025 State of Secrets Sprawl Report," revealing a widespread and persistent security crisis that threatens organizations of all sizes. The report exposes a 25% increase in leaked secrets year-over-year, with 23.8 million new credentials detected on public GitHub in 2024 alone. Most concerning for enterprise security leaders: 70% of secrets leaked in 2022 remain active today, creating an expanding attack surface that grows more dangerous with each passing day. "The explosion of leaked secrets represents one of the most significant yet underestimated threats in cybersecurity," said Eric Fourrier, CEO of GitGuardian. "Unlike sophisticated zero-day exploits, attackers don't need advanced skills to exploit these vulnerabilities—just one exposed credential can provide unrestricted access to critical systems and sensitive data."Eric Fourrier points to the 2024 U.S. Treasury Department breach as a warning: "A single leaked API key from BeyondTrust allowed attackers to infiltrate government systems. This wasn't a sophisticated attack—it was a simple case of an exposed credential that bypassed millions in security investments." "The explosion of leaked secrets represents one of the most significant yet underestimated threats in cybersecurity," said Eric Fourrier, CEO of GitGuardian. "Unlike sophisticated zero-day exploits, attackers don't need advanced skills to exploit these vulnerabilities—just one exposed credential can provide unrestricted access to critical systems and sensitive data."Eric Fourrier points to the 2024 U.S. Treasury Department breach as a warning: "A single leaked API key from BeyondTrust allowed attackers to infiltrate government systems. This wasn't a sophisticated attack—it was a simple case of an exposed credential that bypassed millions in security investments." Key Findings for Security Leaders The report identifies several critical trends that demand immediate attention: The Blind Spot: Generic Secrets Despite GitHub's Push Protection helping developers detect known secret patterns, generic secrets—including hardcoded passwords, database credentials, and custom authentication tokens—now represent more than half of all detected leaks. These credentials lack standardized patterns, making them nearly impossible to detect with conventional tools. Private Repositories: A False Sense of Security The analysis reveals a startling truth: a full 35% of all private repositories scanned contained at least one plaintext secret, shattering the common assumption that private repositories are secure: AWS IAM keys appeared in plaintext in 8.17% of private repositories—over 5× more frequently than in public ones (1.45%)
Generic passwords appeared nearly 3× more often in private repositories (24.1%) compared to public ones (8.94%)
MongoDB credentials were the most frequently leaked secret type in public repositories (18.84%) AWS IAM keys appeared in plaintext in 8.17% of private repositories—over 5× more frequently than in public ones (1.45%) Generic passwords appeared nearly 3× more often in private repositories (24.1%) compared to public ones (8.94%) MongoDB credentials were the most frequently leaked secret type in public repositories (18.84%) "Leaked secrets in private code repositories must be treated as compromised," emphasized Eric Fourrier. "Security teams must recognize that secrets should be treated as sensitive data regardless of where they reside." "Leaked secrets in private code repositories must be treated as compromised," emphasized Eric Fourrier. "Security teams must recognize that secrets should be treated as sensitive data regardless of where they reside." Beyond Code: Secrets Sprawl Across the SDLC Hardcoded secrets are everywhere, but especially in security blind spots like collaboration platforms and containers environments where security controls are typically weaker: Slack: 2.4% of channels within analyzed workspaces contained leaked secrets
Jira: 6.1% of tickets exposed credentials, making it the most vulnerable collaboration tool
DockerHub: 98% of detected secrets were embedded exclusively in image layers, with over 7,000 valid AWS keys currently exposed Slack: 2.4% of channels within analyzed workspaces contained leaked secrets Jira: 6.1% of tickets exposed credentials, making it the most vulnerable collaboration tool DockerHub: 98% of detected secrets were embedded exclusively in image layers, with over 7,000 valid AWS keys currently exposed The Non-Human Identity Crisis Non-human identities (NHIs)—including API keys, service accounts, and automation tokens—now vastly outnumber human identities in most organizations. However, these credentials often lack proper lifecycle management and rotation, creating persistent vulnerabilities. A security leader at a Fortune 500 company acknowledged this challenge: "We aim to rotate secrets annually, but enforcement is difficult across our environment. Some credentials have remained unchanged for years." A security leader at a Fortune 500 company acknowledged this challenge: "We aim to rotate secrets annually, but enforcement is difficult across our environment. Some credentials have remained unchanged for years." Secrets Managers: Not a Complete Answer Even organizations using secrets management solutions remain vulnerable. A study of 2,584 repositories leveraging secrets managers revealed a 5.1% secret leakage rate —far from the near-zero we anticipate. This surpasses the overall GitHub average of 4.6%. Common issues include: Secrets extracted from secrets managers and hardcoded elsewhere
Insecure authentication to secrets managers exposing access credentials
Fragmented governance due to secrets sprawl across multiple secrets managers Secrets extracted from secrets managers and hardcoded elsewhere Insecure authentication to secrets managers exposing access credentials Fragmented governance due to secrets sprawl across multiple secrets managers The Path Forward: Comprehensive Secrets Security As AI-generated code, automation, and cloud-native development accelerate, the report forecasts that secrets sprawl will only intensify. While GitHub's Push Protection has reduced some leaks, it leaves significant gaps—particularly with generic secrets, private repositories, and collaboration tools. "For CISOs and security leaders, the goal isn't just detection—it's the remediation of these vulnerabilities before they're exploited," said Eric Fourrier. "This requires a comprehensive approach that includes automated discovery, detection, remediation, and stronger secrets governance across all enterprise platforms." "For CISOs and security leaders, the goal isn't just detection—it's the remediation of these vulnerabilities before they're exploited," said Eric Fourrier. "This requires a comprehensive approach that includes automated discovery, detection, remediation, and stronger secrets governance across all enterprise platforms." The report concludes with a strategic framework for organizations to address secrets sprawl through: Deploying monitoring for exposed credentials across all environments
Implementing centralized secrets detection and remediation
Establishing semi-automated rotation policies for all credentials
Creating clear developer guidelines for secure vault usage Deploying monitoring for exposed credentials across all environments Implementing centralized secrets detection and remediation Establishing semi-automated rotation policies for all credentials Creating clear developer guidelines for secure vault usage To read the full 2025 State of Secrets Sprawl Report, users can visit GitGuardian.com. GitGuardian.com GitGuardian.com Additional resources GitGuardian - Website GitGuardian - Website GitGuardian - Website The State of Secrets Sprawl 2025 The State of Secrets Sprawl 2025 The State of Secrets Sprawl 2025 About GitGuardian GitGuardian is an end-to-end NHI security platform that empowers software-driven organizations to enhance their Non-Human Identity (NHI) security and comply with industry standards. With attackers increasingly targeting NHIs, such as service accounts and applications, GitGuardian integrates Secrets Security and NHI Governance. GitGuardian GitGuardian This dual approach enables the detection of compromised secrets across your dev environments while also managing non-human identities and their secrets' lifecycles. The platform is the world's most installed GitHub application and supports over 450+ types of secrets, offers public monitoring for leaked data, and deploys honeytokens for added defense. Trusted by over 600,000 developers, GitGuardian is the choice of leading organizations like Snowflake, ING, BASF, and Bouygues Telecom for robust secrets protection. Contact Media Contact Holly Hagerman Connect Marketing hollyh@connectmarketing.com +1(801) 373-7888 This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

GitGuardian Report: 70% Of Leaked Secrets Remain Active For Two Years, Urging Immediate Remediation

Доклад на GitGuardian: 70% от изтеклите тайни остават активни в продължение на две години, настоявайки за незабавно отстраняване

Informe de GitGuardian: El 70 % de los secretos filtrados permanecen activos durante dos años, lo que insta a una solución inmediata.

דוח GitGuardian: 70% מהסודות שדלפו נשארים פעילים במשך שנתיים, ודוחקים לתיקון מיידי

GitGuardian jelentés: A kiszivárgott titkok 70%-a két évig aktív marad, azonnali orvoslást sürget

GitGuardian レポート: 漏洩した機密情報の 70% が 2 年間有効のまま、即時の修復が必要

GitGuardian Willakuy: 70% Pakakuna filtrado kaqmanta Iskay Watakama llamk'achkan, chaylla allichayta kallpachaspa

Raport GitGuardian: 70% dintre secretele scurse rămân active timp de doi ani, solicitând remedierea imediată

GitGuardian Chirevo: 70% Yezvakavanzika Zvakaburitswa Zvirambe Zvichishanda KweMakore Maviri, Kukurudzira Kugadziriswa Kwekare

Извештај ГитГуардиана: 70% процурелих тајни остаје активно две године, позивајући се на хитну санацију

Ripoti ya GitGuardian: Asilimia 70 ya Siri Zilizovuja Husalia Hai kwa Miaka Miwili, Ikihimiza Marekebisho ya Haraka

GitGuardian Raporu: Sızdırılan Sırların %70'i İki Yıl Aktif Kalıyor, Acil Düzeltme Çağrısı Yapılıyor

Звіт GitGuardian: 70% витоку секретів залишаються активними протягом двох років, закликаючи до негайного виправлення

Intelligence is a broad, relative, highly subjective variable that humans have yet to adequately quantify. Generally, if an organism is “alive” and “autonomous,” we classify it as intelligent life.
Nowhere else does this abstract opaqueness materialize so vividly as in Crypto. From degens crafting dissertations on the cult value of Fartcoin to governments exploring Strategic Bitcoin Reserves to scholars developing overengineered vaporware, the Crypto industry is one gigantic magical pandora’s box of paradoxes. In this hypomanic world, winners are few, and losers are plenty. What sets these two classes apart is the single, simple metric of intelligence as gauged by portfolio performance. If you want to win at the game of crypto you must be anything but average. Either a Sith lord with 200 IQ or an absolute degenerate ape with 20; anything in the middle, and you are toast. The greatest insult one can achieve in crypto is being called average intelligence. (Not to mention the emotional and financial pain associated with it). What is the Crypto Curve? Based on the principles of the “Bell Curve,” the Crypto Curve is a meme used to depict the distribution of intelligence among market participants. The Crypto IQ Curve Minorities on the outskirts (winners). Majority in the middle (losers). Thus, is the vicious, competitive, zero-sum nature of financial games. Coming in on the left side with a single brain cell and little-to-no self-awareness, we have the 20 IQ apes. On the right side, with an almost supernatural level of mathematic and socio-psychological understanding, we have the 200 IQ gigabrains. And standing smack in the middle, we have the hardworking and emotionally dependent average Joe. Let's break these down a little further: Left Curve Thinking like a single-celled organism. This audacious bunch is not afraid of taking risks; they are out there on the front lines of the trenches, experimenting, failing, and learning without the loss of motivation. Highly eccentric and, at times, autistic, these gorgeous creatures are supportive of one another and skilled at the art of meme-craft. While their ferocious commitment makes it difficult to draw the line between degeneracy and gambling, that same energy carries them over into the elite group of Diamond hands and first adopters. Generally non-emotional, Left Curvers have a natural inclination to understand human psychology by virtue of divine intervention. Asset types: predominantly Memecoins, but open to all categoriesExamples: GOAT, SHIB, SOL, PEPEPhilosophy: Less is More, Catcoin Gud coin, HODL, WAGMIReturn delta: -99% ← → + 10,000%Takes Profit?: Sometimes Mid Curve The herd of exit liquidity. Heavily reliant on technical analysis/charting tools, mid-curvers are meticulous thinkers who think they are diligent by obsessing over false narratives and abstaining from experimentation. Scared to be the first, but always think they won't be last. This emotionally sensitive group of top buyers & bottom sellers go by many names: Late rotators, retail, Paper hands, Bag Holders, and everything in between. There is not much else to say about these guys other than how important they are to the industry. Without them, there would be no profits. Asset types: predominantly scams and slow growersExamples: EOS, BTC, HEXPhilosophy: Buy High-Sell Low, Panic, Blame Manipulative MarketsReturn delta: -99% ← → + 100%Takes Profit?: Lol Right Curve The Alpha and the Omega. Frontrunners and even creators of narratives, these people predict and control trends. Admittance into this group requires specialized knowledge of cryptography, economics, finance, sociology, psychology, computer science, statistics, and other intellectually dense subject matters. Hypercognizant of their emotional state, right-curvers are obsessive about human psychology and extraordinarily patient. These people do not bend the knee when it comes to social pressures. Not afraid of taking a loss or admitting their mistakes, this open-minded sector of people are flexible. Asset types: predominantly Memecoins, but open to all categoriesExamples: SOL, GOAT, PEPE, OMPhilosophy: HODL, BUIDL, DCA, No LeverageReturn delta: -99% ← → + 10,000%Takes Profit?: Yes, Just the right amount To understand what IQ generally looks like in the real world, let's rile up nationalism and stir some emotions by taking a take a peek at some data; *Data sets vary depending on their sources; thus below, I present a compressed micro-list with the most consistent/persistent information across multiple sources that search engines can find: The Average IQ worldwide is ~94 (range from 70–110)Average IQ inAmerica is ~97 (varying from state to state 95–103)Average IQ inChina is ~ 104 (106 in Hong Kong)*Average IQ in Russia is ~96 (Top 4 of countries with sub $10k earnings)Average IQ inIndia is ~77 (could be cultural & due to huge population*) Please take this data with a grain of salt; it is almost guaranteed that the census method used to collect and aggregate this information is somehow skewed/biased. If you are triggered by this data, congratulations, you are likely somewhere in the middle. Don't be offended, be aware. Where Do You Want to Be? Far left or far right? (Political Pun not intended) Pick your preference. Human nature, in the form of pride, will have many assume that they belong on the right side. Some will humbly not care (left curvers), but the simple fact is that the vast majority of people will end up in the middle. Regardless of which side you pick remember that being average is the only wrong answer and that: “He who chases two rabbits,Catches Neither” How to Understand Where You Truly Are? While the type of asset is certainly a strong indicator, understanding how to quantify where on the spectrum somebody lands is a mixture of factors ranging from their emotional and psychological well-being during the decision-making process, the logical reasoning (or absence of it), aptitude to handle social feedback, and of course the final outcome of the decision. Ultimately, just get objective. Be real with yourself. Understand your risk preference and take action. Go out on a Limb,That is where the Fruit grows. Peace, Love, & Absolute Abundance to all (regardless of where on the Curve you land) See you on chain 🥂 Intelligence is a broad, relative, highly subjective variable that humans have yet to adequately quantify. Generally, if an organism is “alive” and “autonomous,” we classify it as intelligent life. Nowhere else does this abstract opaqueness materialize so vividly as in Crypto. Intelligence is a broad, relative, highly subjective variable that humans have yet to adequately quantify. Generally, if an organism is “alive” and “autonomous,” we classify it as intelligent life. Intelligence is a broad, relative, highly subjective variable that humans have yet to adequately quantify. Generally, if an organism is “alive” and “autonomous,” we classify it as intelligent life. Nowhere else does this abstract opaqueness materialize so vividly as in Crypto. Nowhere else does this abstract opaqueness materialize so vividly as in Crypto. From degens crafting dissertations on the cult value of Fartcoin to governments exploring Strategic Bitcoin Reserves to scholars developing overengineered vaporware, the Crypto industry is one gigantic magical pandora’s box of paradoxes. In this hypomanic world, winners are few, and losers are plenty. What sets these two classes apart is the single, simple metric of intelligence as gauged by portfolio performance. If you want to win at the game of crypto you must be anything but average. Either a Sith lord with 200 IQ or an absolute degenerate ape with 20; anything in the middle, and you are toast. The greatest insult one can achieve in crypto is being called average intelligence. (Not to mention the emotional and financial pain associated with it). (Not to mention the emotional and financial pain associated with it). What is the Crypto Curve? Based on the principles of the “ Bell Curve ,” the Crypto Curve is a meme used to depict the distribution of intelligence among market participants. Bell Curve The Crypto IQ Curve Minorities on the outskirts ( winners ). winners Majority in the middle ( losers ). losers Thus, is the vicious, competitive, zero-sum nature of financial games. Coming in on the left side with a single brain cell and little-to-no self-awareness, we have the 20 IQ apes. On the right side, with an almost supernatural level of mathematic and socio-psychological understanding, we have the 200 IQ gigabrains. And standing smack in the middle, we have the hardworking and emotionally dependent average Joe. Let's break these down a little further: Left Curve Thinking like a single-celled organism. This audacious bunch is not afraid of taking risks; they are out there on the front lines of the trenches, experimenting, failing, and learning without the loss of motivation. Highly eccentric and, at times, autistic, these gorgeous creatures are supportive of one another and skilled at the art of meme-craft. While their ferocious commitment makes it difficult to draw the line between degeneracy and gambling, that same energy carries them over into the elite group of Diamond hands and first adopters. Generally non-emotional, Left Curvers have a natural inclination to understand human psychology by virtue of divine intervention. Asset types: predominantly Memecoins, but open to all categories Examples: GOAT, SHIB, SOL, PEPE Philosophy: Less is More, Catcoin Gud coin, HODL, WAGMI Return delta: -99% ← → + 10,000% Takes Profit?: Sometimes Asset types: predominantly Memecoins, but open to all categories Examples: GOAT, SHIB, SOL, PEPE Philosophy: Less is More, Catcoin Gud coin, HODL, WAGMI Return delta: -99% ← → + 10,000% Takes Profit?: Sometimes Mid Curve The herd of exit liquidity. Heavily reliant on technical analysis/charting tools, mid-curvers are meticulous thinkers who think they are diligent by obsessing over false narratives and abstaining from experimentation. think Scared to be the first, but always think they won't be last. This emotionally sensitive group of top buyers & bottom sellers go by many names: Late rotators, retail, Paper hands, Bag Holders, and everything in between. There is not much else to say about these guys other than how important they are to the industry. Without them, there would be no profits. Asset types: predominantly scams and slow growers Examples: EOS, BTC, HEX Philosophy: Buy High-Sell Low, Panic, Blame Manipulative Markets Return delta: -99% ← → + 100% Takes Profit?: Lol Asset types: predominantly scams and slow growers Examples: EOS, BTC, HEX Philosophy: Buy High-Sell Low, Panic, Blame Manipulative Markets Return delta: -99% ← → + 100% Takes Profit?: Lol Right Curve The Alpha and the Omega. Frontrunners and even creators of narratives, these people predict and control trends. Admittance into this group requires specialized knowledge of cryptography, economics, finance, sociology, psychology, computer science, statistics, and other intellectually dense subject matters. Hypercognizant of their emotional state, right-curvers are obsessive about human psychology and extraordinarily patient. These people do not bend the knee when it comes to social pressures. Not afraid of taking a loss or admitting their mistakes, this open-minded sector of people are flexible. Asset types: predominantly Memecoins, but open to all categories Examples: SOL, GOAT, PEPE, OM Philosophy: HODL, BUIDL, DCA, No Leverage Return delta: -99% ← → + 10,000% Takes Profit?: Yes, Just the right amount Asset types: predominantly Memecoins, but open to all categories Examples: SOL, GOAT, PEPE, OM Philosophy: HODL, BUIDL, DCA, No Leverage Return delta: -99% ← → + 10,000% Takes Profit?: Yes, Just the right amount To understand what IQ generally looks like in the real world, let's rile up nationalism and stir some emotions by taking a take a peek at some data; *Data sets vary depending on their sources; thus below, I present a compressed micro-list with the most consistent/persistent information across multiple sources that search engines can find: *Data sets vary depending on their sources; thus below, I present a compressed micro-list with the most consistent/persistent information across multiple sources that search engines can find: The Average IQ worldwide is ~94 (range from 70–110) Average IQ in America is ~97 (varying from state to state 95–103) Average IQ in China is ~ 104 (106 in Hong Kong)*Average IQ in Russia is ~96 (Top 4 of countries with sub $10k earnings) Average IQ in India is ~77 ( could be cultural & due to huge population *) worldwide ~94 (range from 70–110) America ~97 (varying from state to state 95–103) (varying from state to state 95–103) China ~ 104 ( 106 in Hong Kong )*Average IQ in Russia is ~96 ( Top 4 of countries with sub $10k earnings ) 106 in Hong Kong Russia ~96 Top 4 of countries with sub $10k earnings India ~77 could be cultural & due to huge population Please take this data with a grain of salt; it is almost guaranteed that the census method used to collect and aggregate this information is somehow skewed/biased. If you are triggered by this data, congratulations, you are likely somewhere in the middle. Don't be offended, be aware. Where Do You Want to Be? Far left or far right? (Political Pun not intended) (Political Pun not intended) Pick your preference. Human nature, in the form of pride, will have many assume that they belong on the right side. Some will humbly not care (left curvers), but the simple fact is that the vast majority of people will end up in the middle. Regardless of which side you pick remember that being average is the only wrong answer and that: “He who chases two rabbits,Catches Neither” “He who chases two rabbits, Catches Neither” How to Understand Where You Truly Are? While the type of asset is certainly a strong indicator, understanding how to quantify where on the spectrum somebody lands is a mixture of factors ranging from their emotional and psychological well-being during the decision-making process, the logical reasoning (or absence of it), aptitude to handle social feedback, and of course the final outcome of the decision. Ultimately, just get objective. Be real with yourself. Understand your risk preference and take action. Go out on a Limb,That is where the Fruit grows. Go out on a Limb, That is where the Fruit grows. Peace, Love, & Absolute Abundance to all (regardless of where on the Curve you land) (regardless of where on the Curve you land) See you on chain 🥂

The Crypto Curve: Left, Right, and Center

Крипто кривата: отляво, отдясно и в центъра

La curva criptográfica: izquierda, derecha y centro

暗号通貨の曲線：左、右、中央

Crypto Curve: 왼쪽, 오른쪽, 중앙

Courbe ya Crypto: Gauche, Droite, mpe Centre

The Crypto Curve: Ka go le letshadi, ka go le letona le Bogare

Inzira ya Crypto: Ibumoso, Iburyo, na Hagati

Kryptokurvan: vänster, höger och mitten

கிரிப்டோ வளைவு: இடது, வலது மற்றும் நடுவு

Crypto Curve: ซ้าย ขวา และตรงกลาง

Đường cong mật mã: Trái, Phải và Trung tâm

ICrypto Curve: Ekhohlo, Ekunene, kunye neZiko

A proposed class-action lawsuit has been filed against the maker of family-tracking app Life360, alleging it sold users’ location data without permission. The federal suit was brought on behalf of a Florida minor and his family, who say they never would have used Life360 had they known about the data sales. They allege “unjust enrichment,” citing a December 2021 Markup investigation that revealed Life360 was selling the precise locations of millions of users—largely kids and families—to about a dozen different location data brokers. Life360 disclosed the lawsuit in its May 15 quarterly earnings filing with the Securities and Exchange Commission (SEC). It was filed in the Northern District of California Jan. 12. The complaint alleges that the data sold by Life360 can be used to identify users and their visits to sensitive locations, “including places of religious worship, places that may be used to infer an LGBTQ+ identification, domestic abuse shelters, medical facilities, and welfare and homeless shelters.” The complaint claims this “poses an unwarranted intrusion into the most private areas of consumers’ lives,” it continues, and could expose them to “stigma, discrimination, physical violence, emotional distress, and other harms.” The suit is demanding a jury trial, and seeks “compensatory, statutory, and punitive damages” to be determined by the court. Ed Tagliaferri, a Life360 spokesperson, told The Markup in an emailed statement, “While we don’t comment on pending litigation, Life360 remains committed to transparency and choice. We disclose our data practices and give members meaningful choices about how their data is used, processed, and shared, including the ability to opt out of even aggregated data sales.” In its SEC filing, the company said it “intends to defend against the claim.” Life360 announced it would stop selling precise location data to the dozen or so data brokers it had been working with a month after The Markup published its investigation, cutting off one of the largest sources of data to the multibillion-dollar location data industry. Instead, the company said, it would sell aggregated data to Placer.AI, a major figure in the location data industry. It would also continue to sell precise location data only to Arity, an Allstate company. On May 26, 2023, the judge in the case approved an order to delay proceedings until after a mutually agreed upon private mediation session on Aug. 21, 2023. The parties have until Aug. 25 to submit a status report to the court. The Markup will continue to follow this case. By Jon Keegan Also published here Photo by Dennis Kummer on Unsplash A proposed class-action lawsuit has been filed against the maker of family-tracking app Life360, alleging it sold users’ location data without permission. The federal suit was brought on behalf of a Florida minor and his family, who say they never would have used Life360 had they known about the data sales. They allege “unjust enrichment,” citing a December 2021 Markup investigation that revealed Life360 was selling the precise locations of millions of users—largely kids and families—to about a dozen different location data brokers. revealed Life360 disclosed the lawsuit in its May 15 quarterly earnings filing with the Securities and Exchange Commission (SEC). It was filed in the Northern District of California Jan. 12. filing The complaint alleges that the data sold by Life360 can be used to identify users and their visits to sensitive locations, “including places of religious worship, places that may be used to infer an LGBTQ+ identification, domestic abuse shelters, medical facilities, and welfare and homeless shelters.” The complaint claims this “poses an unwarranted intrusion into the most private areas of consumers’ lives,” it continues, and could expose them to “stigma, discrimination, physical violence, emotional distress, and other harms.” The suit is demanding a jury trial, and seeks “compensatory, statutory, and punitive damages” to be determined by the court. complaint Ed Tagliaferri, a Life360 spokesperson, told The Markup in an emailed statement, “While we don’t comment on pending litigation, Life360 remains committed to transparency and choice. We disclose our data practices and give members meaningful choices about how their data is used, processed, and shared, including the ability to opt out of even aggregated data sales.” In its SEC filing, the company said it “intends to defend against the claim.” Life360 announced it would stop selling precise location data to the dozen or so data brokers it had been working with a month after The Markup published its investigation, cutting off one of the largest sources of data to the multibillion-dollar location data industry . Instead, the company said, it would sell aggregated data to Placer.AI, a major figure in the location data industry. It would also continue to sell precise location data only to Arity, an Allstate company. announced multibillion-dollar location data industry On May 26, 2023, the judge in the case approved an order to delay proceedings until after a mutually agreed upon private mediation session on Aug. 21, 2023. The parties have until Aug. 25 to submit a status report to the court. The Markup will continue to follow this case. By Jon Keegan By Jon Keegan Jon Keegan Also published here Also published here here Photo by Dennis Kummer on Unsplash Photo by Dennis Kummer on Unsplash Dennis Kummer Unsplash

Life360 Is Being Sued for Selling Location Data: What You Need to Know

Building Immutable foundations from the ground TL;DR: Pass essential attributes during object creation to reduce mutability and eliminate getters and setters. Problems Addressed Mutability
Getters
Setters
Unclear object state
Violating the least surprise principle Steps Identify essential attributes required for object creation
Create a constructor that accepts all essential attributes
Remove setter and getter methods
Update object creation calls to pass all required attributes upfront Sample Code Before public class CreditCard {
    private String cardNumber;
    private String cardHolderName;
    private String expirationMonthYear;
    private int cvv;

    public CreditCard() {} // Empty Constructor

    public void setCardNumber(String cardNumber) { 
        this.cardNumber = cardNumber; 
    }
    public void setCardHolderName(String cardHolderName) { 
        this.cardHolderName = cardHolderName; 
    }
    public void setExpirationMonthYear(String expirationMonthYear) { 
        this.expirationMonthYear = expirationMonthYear;
    }
    public void setCvv(int cvv) { 
        this.cvv = cvv; 
    }

    public String getCardNumber() { 
        return cardNumber; 
    }
    public String getCardHolderName() {
        return cardHolderName;
    }
    public String getExpirationMonthYear() {
        return expirationMonthYear; 
    }
    public int getCvv() {
        return cvv; 
    }
}

CreditCard card = new CreditCard();
card.setCardNumber("1234-5678-9012-3456");
card.setCardHolderName("Lilywhite Lilith");
card.setExpirationMonthYear("12/25");
card.setCvv(123); After public class CreditCard {
    private final String cardNumber;
    private final String cardHolderName;
    private final String expirationMonthYear;
    private final int cvv;

    public CreditCard(String cardNumber,
                      String cardHolderName,
                      String expirationMonthYear,
                      int cvv) {
        // 1. Identify essential attributes for object creation
        // 2. Create a constructor that accepts all essential attributes
        this.cardNumber = cardNumber;
        this.cardHolderName = cardHolderName;
        this.expirationMonthYear = expirationMonthYear;
        this.cvv = cvv;
    }

    // 3. Remove setter and getter methods 
    
    // Find real behavior related to credit card usage
  
}

// 4. Update object creation calls to pass all required attributes upfront
CreditCard card = new CreditCard("1234-5678-9012-3456",
                                 "Lilywhite Lilith", 
                                 "12/25", 
                                 123); Type Semi-Automatic This is a step-by-step refactor. Safety This refactoring is generally safe if you ensure you pass all essential attributes during object creation. You must update all object creation sites, which may require refactoring tools and careful review in larger codebases. Why is the code better? The refactored code enforces object integrity by requiring all essential attributes at creation time. The objects become thread-safe reducing the risk of being in an inconsistent state. AI Correction Most AI tools can correct this code with explicit instructions. You can have clear pre-prompts requesting all your code samples to favor immutability. Try Them! Remember AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Gemini

Gemini Tags Mutability Related Refactorings Refactoring 001 - Remove Setters See also https://hackernoon.com/nude-models-part-i-setters-5tm3u3y?embedable=true https://hackernoon.com/nude-models-part-ii-getters-sjo3ua2?embedable=true https://hackernoon.com/is-it-crystal-clear-for-everybody-that-a-date-should-not-mutate-wuoy3z03?embedable=true Credits Image by Denis on Pixabay This article is part of the Refactoring Series. How to Improve your Code With Easy Refactorings Building Immutable foundations from the ground Building Immutable foundations from the ground TL;DR: Pass essential attributes during object creation to reduce mutability and eliminate getters and setters. TL;DR: Pass essential attributes during object creation to reduce mutability and eliminate getters and setters. Problems Addressed Mutability Getters Setters Unclear object state Violating the least surprise principle Mutability Mutability Getters Getters Setters Setters Unclear object state Violating the least surprise principle Steps Identify essential attributes required for object creation Create a constructor that accepts all essential attributes Remove setter and getter methods Update object creation calls to pass all required attributes upfront Identify essential attributes required for object creation Create a constructor that accepts all essential attributes Remove setter and getter methods Update object creation calls to pass all required attributes upfront Sample Code Before public class CreditCard {
    private String cardNumber;
    private String cardHolderName;
    private String expirationMonthYear;
    private int cvv;

    public CreditCard() {} // Empty Constructor

    public void setCardNumber(String cardNumber) { 
        this.cardNumber = cardNumber; 
    }
    public void setCardHolderName(String cardHolderName) { 
        this.cardHolderName = cardHolderName; 
    }
    public void setExpirationMonthYear(String expirationMonthYear) { 
        this.expirationMonthYear = expirationMonthYear;
    }
    public void setCvv(int cvv) { 
        this.cvv = cvv; 
    }

    public String getCardNumber() { 
        return cardNumber; 
    }
    public String getCardHolderName() {
        return cardHolderName;
    }
    public String getExpirationMonthYear() {
        return expirationMonthYear; 
    }
    public int getCvv() {
        return cvv; 
    }
}

CreditCard card = new CreditCard();
card.setCardNumber("1234-5678-9012-3456");
card.setCardHolderName("Lilywhite Lilith");
card.setExpirationMonthYear("12/25");
card.setCvv(123); public class CreditCard {
    private String cardNumber;
    private String cardHolderName;
    private String expirationMonthYear;
    private int cvv;

    public CreditCard() {} // Empty Constructor

    public void setCardNumber(String cardNumber) { 
        this.cardNumber = cardNumber; 
    }
    public void setCardHolderName(String cardHolderName) { 
        this.cardHolderName = cardHolderName; 
    }
    public void setExpirationMonthYear(String expirationMonthYear) { 
        this.expirationMonthYear = expirationMonthYear;
    }
    public void setCvv(int cvv) { 
        this.cvv = cvv; 
    }

    public String getCardNumber() { 
        return cardNumber; 
    }
    public String getCardHolderName() {
        return cardHolderName;
    }
    public String getExpirationMonthYear() {
        return expirationMonthYear; 
    }
    public int getCvv() {
        return cvv; 
    }
}

CreditCard card = new CreditCard();
card.setCardNumber("1234-5678-9012-3456");
card.setCardHolderName("Lilywhite Lilith");
card.setExpirationMonthYear("12/25");
card.setCvv(123); After public class CreditCard {
    private final String cardNumber;
    private final String cardHolderName;
    private final String expirationMonthYear;
    private final int cvv;

    public CreditCard(String cardNumber,
                      String cardHolderName,
                      String expirationMonthYear,
                      int cvv) {
        // 1. Identify essential attributes for object creation
        // 2. Create a constructor that accepts all essential attributes
        this.cardNumber = cardNumber;
        this.cardHolderName = cardHolderName;
        this.expirationMonthYear = expirationMonthYear;
        this.cvv = cvv;
    }

    // 3. Remove setter and getter methods 
    
    // Find real behavior related to credit card usage
  
}

// 4. Update object creation calls to pass all required attributes upfront
CreditCard card = new CreditCard("1234-5678-9012-3456",
                                 "Lilywhite Lilith", 
                                 "12/25", 
                                 123); public class CreditCard {
    private final String cardNumber;
    private final String cardHolderName;
    private final String expirationMonthYear;
    private final int cvv;

    public CreditCard(String cardNumber,
                      String cardHolderName,
                      String expirationMonthYear,
                      int cvv) {
        // 1. Identify essential attributes for object creation
        // 2. Create a constructor that accepts all essential attributes
        this.cardNumber = cardNumber;
        this.cardHolderName = cardHolderName;
        this.expirationMonthYear = expirationMonthYear;
        this.cvv = cvv;
    }

    // 3. Remove setter and getter methods 
    
    // Find real behavior related to credit card usage
  
}

// 4. Update object creation calls to pass all required attributes upfront
CreditCard card = new CreditCard("1234-5678-9012-3456",
                                 "Lilywhite Lilith", 
                                 "12/25", 
                                 123); Type Semi-Automatic Semi-Automatic This is a step-by-step refactor. Safety This refactoring is generally safe if you ensure you pass all essential attributes during object creation. You must update all object creation sites, which may require refactoring tools and careful review in larger codebases. Why is the code better? The refactored code enforces object integrity by requiring all essential attributes at creation time. The objects become thread-safe reducing the risk of being in an inconsistent state. AI Correction Most AI tools can correct this code with explicit instructions. You can have clear pre-prompts requesting all your code samples to favor immutability. Try Them! Remember AI Assistants make lots of mistakes Remember AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Gemini

Gemini Without Proper Instructions

With Specific Instructions ChatGPT

ChatGPT Claude

Claude Perplexity

Perplexity Gemini

Gemini Without Proper Instructions With Specific Instructions Without Proper Instructions Without Proper Instructions With Specific Instructions With Specific Instructions ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT Claude Claude Claude Claude Claude Claude Claude Claude Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Gemini Gemini Gemini Gemini Gemini Gemini Gemini Gemini Tags Mutability Mutability Related Refactorings Refactoring 001 - Remove Setters Refactoring 001 - Remove Setters See also https://hackernoon.com/nude-models-part-i-setters-5tm3u3y?embedable=true https://hackernoon.com/nude-models-part-i-setters-5tm3u3y?embedable=true https://hackernoon.com/nude-models-part-ii-getters-sjo3ua2?embedable=true https://hackernoon.com/nude-models-part-ii-getters-sjo3ua2?embedable=true https://hackernoon.com/is-it-crystal-clear-for-everybody-that-a-date-should-not-mutate-wuoy3z03?embedable=true https://hackernoon.com/is-it-crystal-clear-for-everybody-that-a-date-should-not-mutate-wuoy3z03?embedable=true Credits Image by Denis on Pixabay Denis Pixabay This article is part of the Refactoring Series. How to Improve your Code With Easy Refactorings How to Improve your Code With Easy Refactorings

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