Category: Artificial death

Expand the evaluation to new languages and contexts.

In recent years, large language models (LLMs) have made tremendous strides in generating source code from natural language requests. However, most current evaluators, such as OpenAI's HumanEval, focus only on converting from English to Python, with a limited number of test cases. This leads to a situation where the performance of these models may be overestimated, while their true capabilities in more diverse contexts remain unknown.

Measurement and evaluation challenges in AI programming.

As large language models (LLMs) increasingly demonstrate impressive code generation capabilities, a key question arises: how can we reliably assess whether they truly understand and solve problems, or merely reproduce memorized patterns from training data? The challenge of measurement and evaluation is not just a technical issue, but also fundamental to determining the true progress of the field.

The Human-Machine Communication Gap: A Central Challenge in AI Programming

In the era of AI-assisted programming, a paradox exists: large language models (LLMs) are increasingly adept at generating source code, but the gap between programmer intent and machine output remains the biggest obstacle. As researchers from MIT CSAIL have pointed out, "code completion is the easy part; the hard part is everything else."

Challenges and trends in software development automation.

Automation in software development has made tremendous strides, especially with the explosion of artificial intelligence (AI). Tools like GitHub Copilot and large language models (LLMs) have demonstrated impressive code generation capabilities. However, as researchers from MIT CSAIL have pointed out, "code completion is the easy part; the hard part is everything else."

Automation and process optimization in software development.

In the context of modern software development, automation is not just an option but a vital requirement for maintaining speed and quality. From simple tasks like running scripts to complex CI/CD systems and intelligent AI agents, automation is changing the way programmers work.

Time and task management skills for programmers

In the fast-paced world of software development, programmers often face a multitude of pressures: fixing bugs urgently, meeting deadlines, responding to client change requests, and simultaneously dedicating time to critical tasks such as writing new code, reviewing code, or learning new technologies. This constant multitasking and interruptions leave many programmers in a state of "busy but inefficient," always racing against time and feeling exhausted.

Essential skills for programmers: The tools for success in the digital age.

Candido Aufderhar

In the context of the ongoing Fourth Industrial Revolution, the profession of programmer is no longer simply about writing code, but has become one of the most influential and in-demand professions globally. However, to become a skilled and sustainable programmer, one needs not only extensive professional knowledge but also a multifaceted skill set, ranging from technical skills (hard skills) to soft skills and a mindset for self-development.

Information technology and programming: foundations, languages, and the future.

Candido Aufderhar

In today's digital age, Information Technology (IT) and Programming have become indispensable terms, playing a crucial role in all aspects of modern life. From personal devices like smartphones and tablets to complex business management systems, financial transactions, healthcare, education, and entertainment, everything operates on the foundation of information technology and is controlled by computer programs.

Which professions are safe from AI?

With artificial intelligence (AI) penetrating almost every field, no job is completely "immune" to AI. However, certain occupational groups are emerging as strongholds of human labor, where unique human qualities—empathy, moral judgment, adaptability to changing physical environments—remain irreplaceable. Anthropic research even indicates that approximately 30% of the workforce currently falls into the "AI-free zone"—jobs requiring humans to operate in a world of unexpected and chaotic physical conditions.

The game of tech giants and the survival dilemma of journalism.

In the digital transformation journey of Vietnamese journalism, perhaps no challenge is greater or more unfair than the competition with global technology giants. This is a game where the participants are not of equal weight, do not follow the same rules, and are not even on the same playing field. While news organizations invest tens of thousands of personnel, millions of labor hours, and enormous financial resources to produce around 40 million news articles annually, platforms like Google, Meta, and AI models are exploiting those values to the fullest extent without paying a commensurate cost.

Built-in security in the AI Agent Connection Protocol

In 2026, AI agents are no longer just individual tools but have become autonomous agents capable of communicating and collaborating across systems and organizations. However, this opens up a new attack surface. Unlike traditional threats, attackers can exploit the very "intelligence" and autonomy of AI agents to manipulate them. The security built into protocols like A2A is the first line of defense, but as will be seen, it is only the foundation for a deeper protection strategy.

The practical benefits and challenges of the AI agent connection protocol

In 2026, protocols for connecting AI agents like A2A (Agent2Agent) are transitioning from the laboratory to the production environment. The global report of 2025 shows that 29% of businesses have operated agentic AI in production, and another 44% plan to participate within a year. A2A, initiated by Google and now managed by the Linux Foundation, has attracted more than 150 supporting organizations, including Microsoft, AWS, Salesforce, and SAP. However, the path from experimentation to large-scale deployment is fraught with practical benefits and significant challenges.

Standardization of technology for AI and AI agents: A foundation for a trustworthy ecosystem

In 2026, technical standardization is becoming the backbone of the AI ecosystem and AI agents. As AI evolves from individual applications to a network of autonomous agents that interact, communicate, and make decisions, the demand for clear, auditable, and interoperable technical standards becomes vital.

Standardization at the industry and organizational level: The foundation for trustworthy AI in 2026

As AI becomes an indispensable part of most business activities, standardization is not only limited to the project level but has risen to a more macro level: the organizational and industry-wide level. The year 2026 witnesses the maturation of a multi-layered AI standardization ecosystem, from certifiable risk management frameworks to groundbreaking legal regulations, and technical protocols that are shaping how autonomous AI agents interact with the world.

Standardization - The only way to master AI in Software Engineering by 2026

In 2026, the Software Engineering industry is facing an undeniable reality: nearly 48% of the source code is generated by AI. AI agents have become official members of most development teams. However, this very popularity has created a silent crisis: 55% of technical leaders are concerned that their teams are losing a common understanding of how the codebase evolves, and 39% are worried about the ability to deploy with confidence as AI writes more and more source code.

The importance of standardization in Software Development with AI in 2026

The year 2026 marks a turning point in the Software Engineering industry: AI agents are becoming official members of development teams, with nearly 48% of the source code generated by AI. However, this sudden increase poses a greater challenge: how to maintain the consistency, quality, and maintainability of the codebase when hundreds of AI agents, each operating with different "understandings," are involved in the development process?

Building a workflow with AI in practice: A comprehensive guide for software engineers in 2026

In 2026, working with AI is no longer an experiment but has become a core part of modern software engineering. However, the gap between an impressive demo and a reliable production process is still very large. The era of "vibe coding" - where you make vague requests and hope to get usable results - has ended in the professional environment. Instead, engineers need to adopt a disciplined, structured approach, viewing AI as a complex "engineering stack."

Setting up an e-commerce in 2026 without burning money: decisions that really matter

If you want to open an online store in 2026, the risk is not "not knowing how to make a website": it is making mistakes in margin, logistics, and acquisition. This guide lands practical decisions, focusing on Spain/EU and how to validate without going bankrupt.

"Working" in Russian: how to structure your job so that it doesn't consume you

Алина Ермакова

Working for hire often breaks down not due to tasks, but due to chaos: vague expectations, urgency, boundaries, money. I have created a practical system that helps maintain control, grow income, and avoid burnout — without heroism and without illusions.

In 2026, when the reserved interest rate falls below 2.5%, are ordinary people really paying an intelligence tax for buying insurance, or can it truly save lives?

In the era of low interest rates in 2026, the form of insurance products has undergone dramatic changes. How can ordinary people allocate insurance to avoid paying an intelligence tax and truly protect their family's financial bottom line?

Bankruptcy without drama: how to truly decide if it is beneficial for you to declare bankruptcy

Perlita Vázquez

Bankruptcy is not a cinematic ending: it is a mechanism to organize an insolvency that already exists. If your debts have grown faster than your peace of mind, this framework helps you decide with criteria, scenarios, and specific questions.

Credit Isn’t a Score to Chase: It’s a System You Can Make Boring (and Win)

Alexander Hunter

Credit feels personal, but it’s mostly a prediction system. If you’ve ever been rejected for an apartment or quoted a painful interest rate, this post breaks down what actually moves your credit profile—and how to improve it without paying unnecessary interest.

Anti Panic Tax Management: 1 Hour Routine that Makes Filing Tax Returns Feel Easy

Karya Oktaviani

Taxes often cause stress not because they are difficult, but because we start when it's already close to the deadline. I summarized the most practical ways to prepare documents, choose a reporting path, and avoid mistakes that lead to underpayment—so you can finish without drama.

Interest Isn’t Boring: It’s the Speed Your Money Is Moving

Interest is the price of time—and it can either drain you quietly or build you quietly. Here’s a practical, no-jargon way to audit your rates, prioritize debt vs savings, and set up small moves that flip interest from enemy to ally.

Integrate and expand with the existing ecosystem.

In the modern business environment, Master Data Management (MDM) systems cannot exist as an isolated "island." The true value of MDM is only realized when it is seamlessly integrated with an organization's diverse and complex technology ecosystem – from ERP and CRM systems to data warehouses, analytics platforms, and AI applications. However, this also presents one of the biggest challenges, as legacy systems, fragmented architectures, and organizational inconsistencies often create significant barriers.

Automated processing of unstructured data with AI generation.

In the era of digital transformation, unstructured data – including emails, contract documents, financial reports, medical records, and multimedia content – accounts for over 80% of total enterprise data. However, it is also the most difficult resource to exploit due to its diversity, lack of standardization, and contextual complexity. Traditional methods such as regular expressions (regex) or rule-based natural language processing (NLP) tools often prove inadequate in the face of the ever-changing nature of unstructured data.

GenAI and master data management: a transformation in the era of intelligent data.

In the modern business environment, master data – including information about customers, products, suppliers, and partners – serves as the foundation for all operations and strategies. Master Data Management (MDM) is the discipline that ensures this data is accurate, consistent, and managed throughout the organization. However, traditional MDM methods, which rely heavily on manual rules and human intervention, are proving insufficient to meet the speed, volume, and complexity of today's data.

Data flow automation – The backbone of smart logistics

In a modern supply chain, data moves at the speed of goods – and when data slows, the consequences are severe. A delay detected in the morning may have already led to erroneous decisions being made the night before. Logistics leaders realize the issue is no longer about visibility – they can see disruptions in real time. The failure is occurring at the execution layer: data is isolated in separate warehouses, not flowing seamlessly from one system to another, and humans still play the role of the "integration layer" copy-pasting information between unconnected platforms.

Human intelligence – our most valuable asset.

In an era where artificial intelligence is considered the "brain" of modern logistics, a profound paradox emerges: the more automation becomes, the more valuable human intelligence becomes. AI systems can process massive amounts of data, optimize routes, and forecast demand with accuracy far exceeding human capabilities, but they cannot replace inherent human qualities: the ability to make decisions in uncertain situations, empathy in partnerships, and strategic thinking in the face of unprecedented circumstances.

Humans remain the "integrator" – The paradox of digital transformation in logistics.

After years of investing billions of dollars in digitalization and artificial intelligence, a paradox is emerging in the logistics industry: people – not systems – remain the "integration layer" connecting the fragmented pieces of the supply chain. On average, a typical logistics workflow requires employees to interact with more than five different systems, and they must manually interpret, validate, and move information between these isolated platforms.

How can we ensure our data is good enough for AI to work with?

This question is haunting logistics leaders worldwide. A harsh reality has been confirmed: AI cannot fix poor data. As Loftware experts have warned, “If you put AI on top of bad data, you only get bad AI results.” This concern is not unfounded: 83% of logistics leaders consider data quality to be the biggest barrier to AI adoption. Numerous AI initiatives have failed not because of poor algorithms, but because of fragmented and unreliable data infrastructure.

Semantic normalization and data exchange – The foundation for connected logistics.

The global logistics industry is facing a paradox: while digital technology is advancing rapidly, systems still cannot "speak the same language." Each logistics provider uses its own track and trace solutions – and each solution "speaks" a different language, making coordination between parties in the supply chain difficult.

Data quality and connectivity – The foundation of AI failure in logistics.

Investment in artificial intelligence in logistics is at an unprecedented high, fueled by expectations of an automation revolution. However, the opposite is true: AI initiatives consistently fail early on, not because of poor algorithms, but because of fragmented data infrastructure. Many digital transformation efforts are failing at the operational level, where teams still manually translate, validate, and move information across isolated systems.

Combining AI and classical optimization – A breakthrough strategy for smart logistics.

For decades, the Vehicle Routing Problem (VRP) and its variations have been one of the most complex challenges in logistics. As an NP-hard problem, the computational complexity of the VRP increases exponentially with the number of stops, vehicles, and constraints. A small problem with 20 stops and 5 vehicles can be handled relatively easily, but when scaled up to thousands of stops with hundreds of vehicles and dozens of constraint types, even purely classical optimization algorithms – however powerful – reach their time and computational limits.

Route optimization and dynamic routing: The operational brain of modern logistics.

In the modern logistics world, a truck leaving a warehouse on a fixed route in the morning often cannot predict what will happen along the way: new customers placing orders after the truck has already departed, unexpected traffic accidents causing congestion on the main route, or bad weather slowing down travel. This is the context in which the Dynamic Vehicle Routing Problem (DVRP) was born, going beyond the limitations of traditional static models.