Category Archives: Software

Elon Musk vs. OpenAI: A Clash of Visions

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In 2015, Elon Musk co-founded OpenAI with a lofty mission: to develop artificial general intelligence (AGI) for the benefit of humanity. AGI, often dubbed “strong AI,” would possess human-like cognitive abilities—think of it as a digital polymath capable of any task a person can perform. OpenAI was set up as a not-for-profit organization, emphasizing altruism over profit.

Fast forward to 2024, and the landscape has shifted. Elon Musk is now suing OpenAI, alleging that the organization has strayed from its original mission. Here are the key points:

  1. The Departure from Altruism: The lawsuit contends that OpenAI has shifted its focus from “benefiting humanity” to “maximizing profits.” Instead of pursuing AGI for the greater good, the organization allegedly prioritizes financial gains.
  2. Microsoft’s Influence: Microsoft, a major investor in OpenAI, looms large in this drama. The lawsuit claims that OpenAI’s technology, including the powerful GPT-4, is now closed-source primarily to serve Microsoft’s commercial interests. The tech giant’s initial $1 billion backing in 2019 transformed into a multi-year, multi-billion partnership after the launch of ChatGPT in 2023.
  3. Boardroom Drama: Last November, OpenAI experienced internal turmoil. CEO Sam Altman was abruptly ousted from the board, only to be reinstated days later. The board accused him of inconsistent communication. Microsoft was drawn into the fray, even offering jobs to OpenAI staff who quit during the upheaval.
  4. Effective Altruism: To understand the context, consider the philosophy of effective altruism. Tech billionaires like Musk embrace this approach, aiming to solve humanity’s most pressing problems. OpenAI’s deviation from its original mission clashes with this altruistic worldview.

The Legal Battle Unfolds

Elon Musk’s legal team asserts that OpenAI must adhere to its founding agreement. They demand a return to the mission of developing AGI for humanity’s benefit, rather than serving individual interests or corporate profits. The lawsuit seeks transparency, urging OpenAI to make its research and technology publicly accessible.

As regulators scrutinize the Microsoft-OpenAI partnership, the stakes remain high. AGI’s potential impact on society—both positive and negative—cannot be overstated. Musk’s lawsuit underscores the tension between noble ideals and commercial realities.

In Conclusion

Next time you interact with AI, whether through ChatGPT or other tools, remember the hidden battles behind the scenes. Elon Musk’s legal challenge serves as a reminder that the quest for AGI isn’t just about algorithms—it’s about ethics, transparency, and the future of humanity.


Written with the use of Microsoft Copilot

Unleashing the Power of AI: How F1 Races Ahead with AWS Insights

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In the high-octane world of Formula 1 racing, every fraction of a second counts. From fine-tuning aerodynamics to optimizing pit stops, teams leave no stone unturned in their quest for victory. However, in recent years, there’s been a new player on the track: Artificial Intelligence (AI). Leveraging the cutting-edge capabilities of AWS (Amazon Web Services), Formula 1 teams are harnessing AI insights to gain a competitive edge like never before.

The F1 AI Insights Grand Prix: Accelerating Performance

In a recent article titled “F1 AI Insights Grand Prix” on the AWS website, the convergence of AI and Formula 1 takes center stage. The partnership between F1 and AWS has paved the way for groundbreaking advancements in data analytics and performance optimization.

Real-Time Data Analysis

At the heart of this collaboration lies the ability to process vast amounts of data in real-time. From telemetry data streaming off the cars to weather conditions and race strategies, AI algorithms sift through this information to provide teams with actionable insights instantaneously. This enables teams to make split-second decisions that can mean the difference between victory and defeat.

Predictive Analytics

One of the most exciting applications of AI in F1 is predictive analytics. By analyzing historical data and simulating various scenarios, teams can anticipate race outcomes and tailor their strategies accordingly. Whether it’s predicting tire degradation or strategizing pit stops, AI-powered models give teams a strategic advantage on the track.

Enhancing Performance and Safety

Beyond the race itself, AI is also revolutionizing the way teams approach car development and safety. By analyzing performance data from countless simulations, engineers can fine-tune every aspect of the car, from aerodynamics to engine efficiency. Moreover, AI algorithms are being deployed to enhance safety measures, predicting and mitigating potential risks in real-time.

Looking Ahead: The Future of F1 and AI

As technology continues to evolve, the partnership between F1 and AWS is poised to reach new heights. From advancing aerodynamics to revolutionizing fan engagement, the possibilities are endless. With AI at the helm, Formula 1 is not just a race; it’s a showcase of innovation and human ingenuity.


In the relentless pursuit of victory, Formula 1 teams are turning to AI to gain a competitive edge. Through the partnership with AWS, F1 is harnessing the power of data analytics and predictive insights to push the boundaries of performance and safety. As the F1 AI Insights Grand Prix unfolds, one thing is clear: the future of racing has arrived, and it’s powered by AI.

In a sport where every millisecond matters, AI isn’t just a tool; it’s the key to unlocking new frontiers of speed and precision. As the engines roar and the tires screech, one thing is certain: the race to the future has only just begun.


wrote with the use of ChatGPT 3.5

Repair pitfalls of Apple Vision Pro

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Apple Vision Pro: Repairs and Costs


On Kozminski techblog some students have already written about Apple Vision Pro details, features, prices and thoughts. But nobody mentioned its pitfalls, not many people know that the repair prices of this piece of technology cost a fortune. So, today I’m going to speak about repairing details, prices and technical challenges.

Overview of Repair Costs:

According to recent reports, repairing Apple’s cutting-edge Vision Pro headset may leave users with a hefty bill. The repair costs are soaring, shedding light on the financial implications of maintaining this advanced technology. If the headset’s cover glass cracks, it’ll set you back $799 to get it replaced. Other types of damage could cost you up to $2,399, which is only $1,100 less expensive than a brand new base unit. The repair pricing details have stirred discussions and raised concerns among Apple enthusiasts and potential buyers alike. This begs the question: Is the Apple Vision Pro a pricey investment with substantial long-term costs, or just a “fancy toy”?

AppleCare Plus Coverage:

AppleCare Plus is an extended warranty and technical support plan offered by Apple for their devices. It extends the one-year limited warranty and provides one-stop service and support from Apple experts. AppleCare Plus allows the customer unlimited incidents of accidental damage with a deductible, which varies by device. The coverage includes unlimited incidents of accidental damage, each subject to a service fee and applicable tax. AppleCare Plus with Theft and Loss coverage is also available in some countries. Understanding the coverage terms of AppleCare Plus becomes crucial for users looking to safeguard their investment in Apple devices and potentially save on repair expenses. 

Apple Vision Pro still needs a lot of work before release

Complex Repairs and Technical Challenges:

Looking more closely at the technical aspects of the Apple Vision Pro, the cost of repairs appears to be linked to the intricate design and advanced technology of the headset. Reports suggest that the complexity of the headset, including its state-of-the-art sensors and display components, contributes significantly to the high repair prices. Users are urged to handle their Apple Vision Pro with care to avoid accidental damage, given the potential challenges and costs associated with repairing this innovative device.

What consumers and industry think about it:

Revealing the cost of repairing the Vision Pro has sparked reactions on social media and technology forums. Concerns about the affordability and sustainability of owning and maintaining this high-end headset are being raised by users and industry experts alike. Examining consumer and industry sentiment provides valuable insight into the impact of repair costs on Apple Vision Pro’s perceived value.



AI Influencers market

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In the ever-evolving landscape of social media and marketing, a new phenomenon has emerged: virtual influencers. These AI-generated personas, such as Aitana Lopez and Lil Miquela, have captured the attention of audiences and brands alike, sparking debates and raising ethical questions.

The Disruption of a Market

Virtual influencers have been touted as disruptors in an overpriced market. Traditional human influencers often demand hefty fees for collaborations, making it challenging for smaller brands to access their reach. In contrast, virtual influencers offer a cost-effective alternative, providing brands with the opportunity to engage with audiences at a fraction of the cost.

However, the lack of transparency surrounding the artificial nature of virtual influencers raises ethical concerns. Audiences may not be aware that they are interacting with AI-generated personas, blurring the line between authenticity and deception. As a result, discussions around regulation and disclosure have become increasingly prominent.

The Illusion of Engagement

Virtual influencers strive to create a sense of human-like engagement through their social media presence. They share relatable content, respond to comments, and even develop intricate backstories. However, doubts persist about the depth and authenticity of these interactions compared to genuine human connections. Virtual influencers, after all, are programmed to respond in specific ways, lacking the emotional intelligence and lived experiences of their human counterparts.

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The Quest for Representation

One of the significant advantages of virtual influencers is their ability to transcend physical limitations. Their AI-generated nature allows for the creation of racially ambiguous features, presenting a unique opportunity for inclusivity and representation. However, critics argue that this portrayal can be superficial, merely scratching the surface of true diversity. The question of whether virtual influencers truly challenge societal norms or merely perpetuate existing ideals remains a subject of debate.

The Sexualization Debate

An ongoing concern surrounding virtual influencers is the sexualization of their personas. While the fashion and beauty industry have long faced criticism for objectifying women, the emergence of virtual influencers raises additional questions. These AI-generated personas often embody hyper-sexualized characteristics, mirroring industry norms but potentially perpetuating the exploitation of female sexuality under the guise of AI.

Agency and Autonomy

As virtual influencers gain popularity and secure brand partnerships, another contentious issue arises: the clash between human agency and AI-generated profits. Female autonomy over their bodies and the monetization of their images becomes a focal point of discussion. The question of who ultimately benefits from the success of virtual influencers and whether they have control over their digital personas remains unresolved.

The Future of Virtual Influencers

Despite the controversies and debates surrounding virtual influencers, their presence shows no signs of slowing down. As technology continues to advance, AI-generated personas are likely to become even more sophisticated, blurring the line between human and artificial. The influencer landscape will continually evolve, with virtual influencers reshaping the industry’s dynamics and challenging traditional notions of authenticity and engagement.

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The rise of virtual influencers driven by AI has undoubtedly reshaped the world of social media and marketing. As these AI-generated personas capture the attention of audiences and brands alike, discussions surrounding ethics, transparency, representation, and agency persist. The clash between human influencers and their AI counterparts raises important questions about the future of the industry and societal perceptions. As the virtual influencer phenomenon continues to evolve, only time will tell how it will shape the landscape and the extent of its impact.

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Reading Time: 5 minutes


Crypto assets are no longer on the fringe of the financial system.

The market value of these novel assets rose to nearly $3 trillion in November from $620 billion in 2017, on soaring popularity among retail and institutional investors alike, despite high volatility. This week, the combined market capitalisation had retreated to about $2 trillion, representing an almost four-fold increase since 2017.

Amid greater adoption, the correlation of crypto assets with traditional holdings like stocks has increased significantly, which limits their perceived risk diversification benefits and raises the risk of contagion across financial markets.

The stronger association between crypto and equities is also apparent in emerging market economies, several of which have led the way in crypto-asset adoption between returns on the MSCI emerging markets index and Bitcoin was 0.34 in 2020–21, a 17-fold increase from the preceding years.

Stronger correlations suggest that Bitcoin has been acting as a risky asset. Its correlation with stocks has turned higher than that between stocks and other assets such as gold, investment grade bonds, and major currencies, pointing to limited risk diversification benefits in contrast to what was initially perceived.

Crypto assets have experienced tremendous growth over the past two decades, with the number of coins increasing from just Bitcoin in 2009 to over 5,000 currently, and reaching a total market capitalization of over USD 3 trillion towards the end of 2021. However, this growth has been accompanied by significant volatility, with most crypto coins going through several cycles of rapid growth followed by dramatic collapses. This is reminiscent of other periods in financial history in which private forms of money have proliferated in the absence of adequate government regulation, leading to frequent financial crises (such as in the US during the “Free Banking Era” of 1837–1863).

The rapid ascent of crypto assets, coupled with their increasing mainstream adoption, has generated concerns among policymakers and regulators, who are mindful about the potential contagion risks to other financial markets as well as the broader macro-financial. Crypto asset markets can both act as a source of shocks or as amplifiers of overall market volatility, thereby having the potential to have significant implications for financial stability. Consequently, policymakers face an imperative to enhance their comprehension of the interconnections between crypto assets and financial markets, enabling them to devise regulatory frameworks that effectively counteract the potential adverse consequences of crypto assets on financial stability.

The complex and rapidly evolving nature of the crypto market pose challenges for regulators in effectively assessing and addressing associated risks. Crypto assets encompass a wide range of technological attributes and features, serving means of payment, to store of value, speculative asset, support for smart contracts, fundraising, asset transfer, decentralized finance, privacy, digital identity, governance, among others. However, their relationship with traditional financial assets, particularly in terms of diversification potential, remains a subject of debate. While substantial research has investigated the nature, direction and intensity of linkages between crypto assets and crypto assets and other financial assets, the findings are still relatively inconclusive and paint a complex picture of interdependencies.

The multifaceted interaction channels between crypto assets and financial markets may make it challenging to assess the relationship, while it may also have changed over time.

On the one hand, a “fight-to-safety channel” would suggest that investors may allocate their funds into crypto assets during periods of economic uncertainty or market stress if cryptos are perceived as safer and offering a good hedge to certain financial assets. Crypto assets can thus provide diversification benefits if their correlation with certain classes of traditional assets is low. However, their tendency for high volatility raises important concerns. Another potential channel is the “speculative demand channel”, which would suggest that demand for crypto assets may increase during times of high financial market risk appetite, as cryptos offer the potential for high returns due to their volatility. Further channels could be related to market liquidity and to information spillovers or investor sentiment, which can lead to additional comovement between various classes of financial assets and crypto markets.

This dataset consists of the daily closing price of the five largest crypto assets by market capitalization namely Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Binance (BNB), and Tether (USDT) as of December 31st, 2021. The stock market is captured by the US S&P500 index, and we also include the Brent oil price, as well as the 10-year U.S. treasury bill as control variables to account for the possible impact of variations in commodity prices and financial condition on asset prices. The US S&P500 tracks the performance of 500 large companies in leading industries and represents a broad cross-section of the U.S. economy and is widely considered representative of the overall stock market . Tether (USDT) is a stable coin used in this study to provide insight into the inflow and outflow of funds in the market and as a tool for hedging against the volatility of the crypto market. For this reason, the USDT is likely to be more sensitive to the movement of price in the crypto market. presents a time series plot of the sampled variables. The daily datasets are in U.S. dollar currency and span from the period January 2018 to December 2021, excluding non-trading days for uniformity. Data on cryptocurrencies (Bitcoin, Ethereum, Ripple, Binance, and Tether) were retrieved from Yahoo Finance, whereas data on Brent oil, and U.S. 10-year treasury bills were retrieved from the U.S. Federal Reserve Bank of St. Louis. Additionally, the U.S. S&P500 was retrieved from Investing market indices. The baseline specification of this study considers the S&P500 index as an endogenous variable whereas cryptocurrencies and the control variables are used as dependent variables.

The increased and sizeable co-movement and spillovers between crypto and equity markets indicate a growing interconnectedness between the two asset classes that permits the transmission of shocks that can destabilise financial markets.


This analysis suggests that crypto assets are no longer on the fringe of the financial system, IMF said.

The market value of these novel assets rose to nearly $3 trillion in November from $620 billion in 2017, on soaring popularity among retail and institutional investors alike, despite high volatility. This week, the combined market capitalization had retreated to about $2 trillion, still representing an almost four-fold increase since 2017.

Amid greater adoption, the correlation of crypto assets with traditional holdings like stocks has increased significantly, which limits their perceived risk diversification benefits and raises the risk of contagion across financial markets, according to new IMF research.

By- Shannul Mawlong 50401

AI Sources: chat gpt 4

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Geospy AI: Revolutionizing Data Analysis in the Modern Age

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In today’s rapidly evolving digital landscape, the emergence of Geospy AI stands as a remarkable innovation, revolutionizing the way we interact with geographical data and analytics. This groundbreaking artificial intelligence technology is reshaping industries, enhancing research capabilities, and offering new insights into our world.

Understanding Geospy AI

Geospy AI is an advanced AI-driven platform that specializes in processing and analyzing geographical data. By leveraging sophisticated algorithms and machine learning techniques, Geospy AI can interpret, predict, and visualize complex geographical information in ways that were previously unimaginable. It extracts meaningful insights from various data sources, including satellite imagery, geographic information systems (GIS), and environmental data.

Applications Across Industries

Environmental Monitoring and Management

Geospy AI plays a crucial role in environmental conservation and management. It can analyze satellite images to track deforestation, monitor wildlife habitats, and assess the impacts of climate change on various ecosystems. By providing real-time data and predictive analytics, it aids in proactive environmental protection and sustainable resource management.

Urban Planning and Development

Urban planners and architects are utilizing Geospy AI to create smarter, more sustainable cities. The AI system analyzes demographic, environmental, and infrastructural data to assist in efficient urban design. This includes optimizing road networks, planning public transportation routes, and designing green spaces that enhance the quality of urban life.

Disaster Response and Management

In disaster management, Geospy AI’s ability to rapidly process and analyze geographical data is invaluable. It can predict the path of natural disasters like hurricanes or floods, enabling early warning systems and efficient evacuation planning. Post-disaster, Geospy AI assists in damage assessment and coordinating effective response strategies.

Agriculture and Land Use

Farmers and agronomists are turning to Geospy AI for precision agriculture. By analyzing soil, weather, and crop data, it helps optimize irrigation, fertilization, and harvesting. This leads to increased crop yields, reduced resource usage, and minimized environmental impact.

Advantages of Geospy AI

Accuracy and Efficiency

Geospy AI processes large datasets with remarkable accuracy and speed, surpassing traditional methods. Its ability to rapidly analyze and interpret geographical data allows for timely decision-making and action.

Predictive Analytics

One of the most significant features of Geospy AI is its predictive capabilities. By analyzing historical data and current trends, it can forecast future scenarios, helping industries and governments to prepare and plan accordingly.

User-Friendly Interfaces

Despite its complex underpinnings, Geospy AI offers user-friendly interfaces, making it accessible to professionals and laypersons alike. This ease of use democratizes data analysis, enabling more people to benefit from its insights.

Challenges and Considerations

While Geospy AI presents numerous opportunities, it also poses challenges. Privacy concerns, particularly regarding data sourced from public and private areas, need careful consideration. Additionally, the accuracy of AI predictions depends heavily on the quality of the input data, emphasizing the need for reliable data sources.

The Future of Geospy AI

As Geospy AI continues to evolve, its potential applications will expand, influencing sectors from healthcare to transportation. Its integration with other emerging technologies like the Internet of Things (IoT) and 5G networks will further enhance its capabilities, making it an indispensable tool in our increasingly data-driven world.

In conclusion, Geospy AI represents a significant leap forward in data analysis. Its ability to process and interpret geographical data with unprecedented accuracy and efficiency opens up new horizons for a multitude of industries and sectors. As we continue to harness its power, Geospy AI promises to be a key driver of innovation and progress in the 21st century.


Try it yourselve:

Emotion Recognition AI

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In the dynamic world of artificial intelligence, a novel protagonist has emerged: emotion recognition AI. This cutting-edge technology, at the intersection of advanced computing and human psychology, is set to transform our interactions with machines. Emotion recognition AI tools analyze data inputs such as facial expressions, voice intonations, and body language, moving beyond mere command interpretation to understanding human emotions.

The Pioneers of Emotional Recognition

Several key players are leading the charge in this new domain.


Originating from MIT Media Lab, AFFECTIVA stands as a milestone in emotion recognition AI. This technology extends beyond traditional limits, offering insights into human emotions through digital analysis. AFFECTIVA deciphers a range of emotions by examining facial expressions and vocal nuances. Its applications span market research and advertising, providing brands with profound insights into consumer reactions. Combining advanced machine learning algorithms with a broad, diverse dataset, AFFECTIVA aims to create empathetic connections between technology and users, thus enhancing business-consumer relationships, content customization, and product development. However, AFFECTIVA’s journey also underscores the delicate balance between innovation and privacy, emphasizing the need for ethical considerations in the burgeoning field of emotion AI.


Paravision’s facial recognition software is celebrated for its top-tier performance in benchmarks such as 1:1 Verification, 1:N Identification, and Paperless Travel, consistently excelling in NIST FRTE benchmarks. The Gen 6 software boasts a 30% reduction in error rates, improving accuracy across demographics. Paravision’s technology supports groundbreaking identity solutions and customer experiences, enabling the development of robust biometric verification and identification solutions. The company’s focus on accuracy and performance has made significant advancements in sectors like air travel, banking, retail, and public sector, enhancing security and reliability in face recognition technology.

Transforming Industries with Emotion Recognition

Emotion recognition AI’s potential applications are diverse and compelling. In the automotive sector, it enhances safety through real-time monitoring of drivers’ alertness and emotions. In healthcare, it aids in diagnosing and treating mental health issues by detecting subtle emotional cues. In customer service, adapting responses based on customers’ emotional states revolutionizes interaction. Education benefits from tailored learning experiences attuned to student emotions. Marketing and advertising sectors are already utilizing these tools for deeper consumer insights, leading to more effective campaigns.

The Positive Aspects of Emotion Recognition AI

The advantages of emotion recognition AI are multifaceted. It elevates user experiences across various platforms and industries by resonating with human emotions. For instance, in driving, it can increase safety by identifying fatigue or distraction. In mental health, it offers innovative methods for diagnosis and treatment. From a business standpoint, insights into consumer behavior are invaluable, leading to more personalized and effective marketing strategies.

Towards an Ethical Framework in Emotion AI

Responsible utilization of emotion recognition AI necessitates a robust ethical framework. Key aspects include transparency in tool operations, ensuring informed user consent, addressing AI biases to prevent unfair outcomes, and a commitment to enhancing human experiences without exploitation. Privacy concerns are paramount, necessitating strict measures to protect sensitive emotional data.

Looking Ahead: The Future of Emotion AI

The future of emotion recognition AI holds both promise and challenges. As the technology evolves, we can expect more sophisticated applications, potentially redefining human-machine interactions. The prospect of machines that understand and empathize with human emotions opens new avenues for innovation in various fields. However, this advancement must be approached cautiously, ensuring that our pursuit of technological understanding of emotions does not detract from our humanity.


Emotion recognition AI marks a new frontier in technology, offering more intuitive and human-like machine interactions. Embracing this era requires awareness of ethical implications and a commitment to using these tools to enhance the human experience. The true success of emotion recognition AI will be judged not only by its technological achievements but also by its ability to respect and uplift human emotions in a responsible and ethical manner.


tool: chatsonic by. writesonic

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Sprints vs Basecamp’s methodology

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Software Is Eating the World

Marc Andreessen

At the beginning of 10’s, Marc Andreessen (General partner at Andreessen Horowitz) stated that ‘Software Is Eating the World’ [1] – what happened to be true in the past decade. How is it going to be with 20’s decade? There’s a big unknow but so far it’s seems it’s going to be similar.

Creating digital software plays a crucial role in E-Economy. Over the years a lot of frameworks had been developed on how to develop a software in the most efficient ways.

What is a framework?

According to Cambridge Dictionary, the framework is ‘a supporting structure around which something can be built’ [2].

Sprint framework

A two world-wide known frameworks are:

  • Agile – ‘include requirements, discovery and solutions improvement through the collaborative effort of self-organizing and cross-functional teams with their customer(s)/end user(s)’ [3].
  • Scrum – ‘scrum prescribes for teams to break work into goals to be completed within time-boxed iterations, called sprints. Each sprint is no longer than one month and commonly lasts two weeks’ [4]

So scrum team strives to accomplish a certain amount of work within a brief, time-boxed period known as a sprint. Agile and scrum approaches revolve around sprints, and a well-executed sprint will enable your agile team to deliver better product with fewer difficulties.

Basecamp framework

However some people found out that ‘sprints were ok as they were shipping stuff at a decent velocity, but there were cracks showing in the process – especially as when Foresight Group raised (raised $39M Series B) and experienced hypergrowth, they knew there had to be a better way to ship the most impactful features for their users and business’. [5]

Shape is a six-week cycles

There are six-week cycles to the work. Six weeks is just enough time to construct anything significant from beginning to end, but it’s also not so lengthy that everyone doesn’t sense the impending deadline and makes good use of their time. In a single six-week cycle, the objective is to construct, test, and release.

Shape Up differs from Agile procedures in that it fixes a time to release a portion of work to customers and concentrates on “shaping” work into that window of time, as opposed to Agile methods where the team splits a huge body of work down into smaller sprint-sized pieces and begins building them. Teams are now expected to swarm a project together and give it their whole concentration to see it through to completion rather than juggling numerous things at once.

Teams are developing previously shaped work within any six-week cycle, while shapers—product managers and designers—are working on what the teams can possibly produce in subsequent cycles.

Two tracks when in-cycle (6 weeks):

  1. Building: Uninterrupted work on approved pitches
  2. Shaping: Gathering requirements for the next cycle [6].

Few one of the most important assumptions of Basecamp’s framework [7]:

  1. Six-Week Cycles: A Framework for Focus
  2. Big Batch, Small Batch: Prioritization and Efficiency
  3. Pitches: Defining and Betting on Work
  4. Vertical Slices: Building and Integrating
  5. Making Teams Responsible for Their Work
  6. Metrics for Outcome, Not Hours
  7. Shape Up in Practice: Delivering Meaningful Work


To sum up, Agile & Scrum & Sprints frameworks have been working since 00’s but in order to produce modern software, companies should develop new 20’s frameworks. That’s how Basecamp framework had been created. Basecamp’s Shape Up methodology stands as a testament to the power of a well-structured approach to product development. By emphasizing focus, autonomy, and the delivery of valuable workIn my opinion, logically it really make sense but we should wait for more companies to implement it and we will see a resoults.

  6. Image:

Digital Immunity: Your Software’s Superpower in a Connected World

Reading Time: 2 minutes
Five worthy reads: Digital immune systems—your first line of defense  against cyberattacks - ManageEngine Blog

In our digital age, where cyber threats loom large, Digital Immune Systems (DIS) are like superheroes for software, shielding it from bugs and security threats. This powerful approach not only makes software resilient but also ensures a seamless user experience. Let’s dive into what DIS is all about, why it matters, and how you can strengthen your software’s immune system.

Digital Immune System Decoded: DIS is like a shield, swiftly protecting software from bugs and security breaches. Its goal is clear: make applications resilient, reduce business risks, and, most importantly, keep users happy. By 2025, organizations investing in DIS are expected to cut downtime by 80%, a testament to its crucial role in keeping operations smooth.

Essential Components of DIS:

  1. Observability: Transparent systems are resilient. Observability means real-time monitoring, catching and fixing issues on the fly, making user experiences better.
  2. AI-Augmented Testing: Let AI handle testing independently, identifying issues early without human intervention.
  3. Chaos Engineering: Controlled disruptions in testing, preparing teams for real-world challenges.
  4. Auto-Remediation: Applications that can fix themselves, ensuring uninterrupted service without human help.
  5. Application Security: Safeguarding the software supply chain with security measures and strong version control.
  6. Site Reliability Engineering (SRE): Balancing speed and stability for a fantastic user experience.

Why DIS Matters for Software: DIS not only reduces business risks but also boosts software quality. Real-time threat detection, continuous monitoring, and meeting security requirements ensure a proactive defense against cyber threats.

Linking Digital Immunity to Software Quality: Automated testing and continuous monitoring, integral to DIS, ensure fast and top-quality software releases. The aim is to make software immune to bugs and vulnerabilities right from the start, ensuring efficiency and performance.

Achieving a Healthy Digital Immune System: Automate code changes, increase testing coverage, and leverage technology like APIs. Sauce Labs, with its testing solutions, helps identify risks quickly and securely.

In a Nutshell: In our digital-heavy world, DIS is your software’s superhero. Investing in a robust digital immune system protects your digital assets, ensures smooth operations, delights users, and fortifies software quality.

Opinion: This insightful information about Digital Immunity brilliantly demystifies the complex world of software development, making the concept accessible and highlighting its pivotal role in safeguarding against cyber threats. The clear breakdown of key components and their real-world significance, coupled with practical tips for achieving a robust Digital Immune System, empowers you with actionable insights. In a landscape where digital security is paramount, this piece not only underscores the importance of DIS but also serves as a valuable guide for organizations aiming to fortify their software against evolving threats.

Questions for Readers: How do you think Digital Immune Systems will evolve as our digital world keeps growing, and what extra steps would you recommend for safeguarding software development against new threats?

Resources: What is Digital Immune System? | Overview of Digital Immunity (, What Is A Digital Immune System? (, Digital Immune System and its Key Compoenents (, What Is a Digital Immune System and Why Does It Matter? (, chat AI bing


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Augmented Reality in the Workplace

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Augmented reality (AR) and virtual reality (VR) are two rapidly developing technologies with a wide range of applications. AR overlays digital information onto the real world, while VR creates an immersive digital environment that users can interact with.

Both AR and VR are having a significant impact on the workspace, school space, and everyday life. In the workspace, AR and VR are being used to improve training, safety, and productivity. In the school space, AR and VR are being used to create more engaging and immersive learning experiences. In everyday life, AR and VR are being used to enhance entertainment, shopping, and communication.

Impact on the workspace


One of the most significant impacts of AR and VR on the workplace is in training. AR and VR can provide trainees a realistic and safe environment to practice their skills. For example, Boeing is using VR to train its assembly workers on how to build new aircraft. The US military is using VR to train soldiers on how to operate weapons and navigate different terrains.


AR and VR can also be used to improve safety in the workplace. For example, AR glasses can provide workers with real-time information about hazards and safety procedures. AR can also be used to guide workers through complex tasks. For example, General Electric is using AR to help workers repair jet engines.


AR and VR can also be used to improve productivity in the workplace. For example, AR can be used to provide workers with hands-free access to information and instructions. VR can be used to create virtual meeting spaces and collaboration tools. For example, Microsoft Teams now offers VR meeting spaces, allowing users to collaborate in a virtual environment.

Impact on the School Space

AR and VR are being used to create more engaging and immersive learning experiences. For example, AR can be used to bring science textbooks to life or to allow students to explore historical sites. VR can be used to transport students to different places and times or to allow them to experience complex concepts firsthand.

For example, the Franklin Institute in Philadelphia, Pennsylvania uses AR to bring its science exhibits to life. Students can point their smartphones at the exhibits to see additional information and animations. The University of North Carolina is using VR to transport students to different historical sites and allow them to experience them firsthand. For example, students can take a virtual tour of the Roman Colosseum or the Great Wall of China.

Impact on everyday life

AR and VR are being used to enhance entertainment, shopping, and communication in everyday life. For example, AR can be used to play interactive games or to try on clothes before you buy them. VR can be used to watch movies and TV shows in a more immersive way or to attend virtual concerts and sporting events.

For example, IKEA is using AR to allow customers to try on furniture in their own homes before they buy it. Customers can point their smartphones at the place where they want to place the furniture to see how it would look. The North Face is using VR to allow customers to experience their products in a virtual outdoor environment. Customers can hike through a virtual forest or climb a virtual mountain.

AR and VR are also being used to improve communication. For example, AR can be used to translate languages in real-time or to provide sign language interpretation. VR can be used to create virtual meeting spaces and collaboration tools. For example, Facebook is developing AR glasses that can be used to translate languages in real-time and provide sign language interpretation.


AR and VR are two rapidly developing technologies with a wide range of applications. AR and VR are having a significant impact on the workspace, school space, and everyday life. As these technologies continue to develop, we can expect to see even more innovative and ground-breaking applications.

Engine used: QuillBot

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