Tag: Software Engineering

The Modern Computer Science Skill Set: What Students Need for a Tech Career
A computer science degree can open many technology career paths, but employers are looking for more than transcripts and basic coding exercises. Today’s tech roles demand strong fundamentals in programming and algorithms, plus practical experience with AI, cloud systems, databases, cybersecurity, and real-world software development. The World Economic Forum ranks AI and big data among the fastest-rising skill areas, followed closely by networks, cybersecurity, and technology literacy.

Build Strong Programming and Problem-Solving Skills

A successful developer needs more than syntax knowledge. Languages like Python, TypeScript, Java, C++, Go, and Rust serve different technology areas. Python plays a major role in AI, data science, automation, and back-end development. GitHub reports that TypeScript became its most-used language in August 2025, ahead of Python and JavaScript, while Python remains essential for AI and data science. Developing in-depth skill in one primary language and basic comfort with a second creates a solid foundation.

Data structures and algorithms still matter. Arrays, trees, graphs, stacks, queues, recursion, dynamic programming, and Big-O analysis are valuable for technical interviews and system design. These skills also support code review, bug fixes, and architecture decisions. Algorithm knowledge should connect to real software work rather than stop at interview puzzles.

Develop AI Literacy With Human Judgment

AI now forms a major part of software development. The 2025 Stack Overflow Developer Survey found that 84% of respondents use or plan to use AI tools in development. However, 46% distrust AI output, while only 33% trust it. These numbers show why AI skills must be paired with human judgment.

Useful AI knowledge includes large language models, AI APIs, embeddings, vector databases, retrieval-augmented generation, tool calling, AI agents, model evaluation, and foundational machine learning concepts. GitHub reports more than 1.1 million public repositories with an LLM software development kit, with more than 693,000 such projects created in the past 12 months. AI skills go beyond prompt writing. Code review, testing, security checks, and clear model limitations remain essential. AI can accelerate tasks, but technical knowledge determines whether the final result actually works.

Master Software Engineering, Data, Cloud, and Security

Real technology work requires professional software development skills. Git, GitHub, testing, debugging, APIs, code review, documentation, and continuous integration should all be part of a computer science education.

Database knowledge also matters. SQL, PostgreSQL or MySQL, indexes, joins, transactions, query design, and data modeling provide a strong base. MongoDB and Redis add useful NoSQL options for specific system requirements.

Cloud skills add another important layer. AWS, Microsoft Azure, and Google Cloud offer services for compute, storage, databases, networking, identity, serverless systems, and monitoring. Linux, Docker, CI/CD, basic Kubernetes, cloud deployment, and infrastructure tools help turn a local project into a reliable service.

Cybersecurity needs daily attention. Authentication, authorization, HTTPS, encryption, secure passwords, API security, secrets management, dependency checks, and awareness of common web risks should be part of normal software work. The World Economic Forum places networks and cybersecurity among the fastest-rising skill areas for good reason.

Understand Systems and Choose a Specialty

Computer science fundamentals still form the technical foundation. Operating system concepts, computer networks, databases, computer architecture, concurrency, memory management, distributed systems, and system design explain what happens inside a real application. System design can begin with clients, servers, caches, queues, replicas, and databases, then expand to scaling, fault tolerance, and service architecture.

A strong career path does not require mastery of every technology. Core computer science knowledge should be paired with one clear area of depth:
- AI and machine learning: Python, statistics, neural networks, LLM systems, and model operations.
- Software engineering: Data structures and algorithms, back-end or front-end development, databases, cloud, and system design.
- Cloud and DevOps: Linux, networking, Docker, Kubernetes, CI/CD, and cloud platforms.
- Cybersecurity: Network security, application security, cryptography, and incident response.
Current data points to one clear career lesson: technical depth matters, but flexibility matters too. Stack Overflow reports that 69% of developers spent time in the past year learning a new coding technique or software language, while more than 36% studied AI-enabled tools for work or career growth. The World Economic Forum also identifies analytical thinking, creative thinking, resilience, flexibility, curiosity, and continuous learning as high-value skills.

Why This Matters

A computer science degree alone may not fully prepare students for modern technology roles. Employers need professionals with strong programming, problem-solving, AI, cloud, data, security, and software engineering skills. Learning these areas early helps students build meaningful projects, understand workplace expectations, adapt to new tools, and create a stronger foundation for long-term career growth.

Frequently Asked Questions

1. Which programming languages should computer science students learn?

Python, TypeScript, Java, C++, Go, and Rust are all useful options across different technology fields.

2. Does DSA still matter for tech careers?

Yes. Data structures and algorithms support problem-solving, technical interviews, software design, code quality, and system architecture.

3. Should every computer science student learn AI?

Basic AI literacy has become valuable across many technology roles, even for students who do not plan to enter AI research.

4. Why should students learn cloud computing?

Cloud knowledge helps students understand how modern applications use computing, storage, databases, networking, and deployment platforms.

5. Which skill should a student specialize in?

Students should build core computer science knowledge first, then develop deeper expertise in an area such as AI, software engineering, cybersecurity, cloud, or data engineering.
October 21, 2026
Beyond Clean Code: What Today’s Mobile Engineers Need to Succeed, According to Tomasz Kubiak

As mobile engineering expands beyond writing and shipping code, esteemed software engineer Tomasz Kubiak draws on his experience building an app used by millions to explain why reliability, judgment, and product thinking are raising the bar for mobile engineers.

Writing good code in mobile software engineering is no longer enough. A recent Forbes Technology Council article reports that median crash-free sessions have reached 99.95%, while 82% of users consider stability extremely important and 63% will uninstall an app after three crashes or fewer. As automation takes over more routine work, engineers have to bring more to the table. In this interview, Tomasz Kubiak, with 10+ years of experience, discusses the skills that increasingly separate strong mobile engineers from the rest and shares approaches that helped him grow and succeed in the field.

Tomasz is currently a Senior iOS Software Developer at Welltech, where he works on WalkFit, a wellbeing app with more than 10 million downloads. His product work includes A/B-tested changes that delivered a double-digit lift in early goal activation and a measurable gain in first-week retention. A nutrition module he developed was also associated with higher customer lifetime value among purchasers. Before joining Welltech, Tomasz developed iOS apps at Talixo and introduced CI improvements that halved build times. His broader engineering career includes three patent records, with patents granted in the United States and Europe, peer-reviewed research, and IEEE Senior Member status.

Q: The job of a mobile engineer looks very different today than it did even a few years ago. With more than a decade in engineering, what is expected from a strong mobile engineer today that wasn’t expected before?

I think the biggest shift is toward broader ownership. Mobile engineering has become much more connected to how a product performs in the real world. Technical decisions affect stability, delivery, user experience, and how a product evolves over time. Strong engineers need to understand the context around the code and think several steps ahead. The ability to see those connections and make sound decisions as complexity grows has become a much bigger part of the job.

Q: Your own path into mobile development was unusual: you started in mechanical and product engineering before moving into software. What did that experience teach you that later proved useful in mobile engineering?

I studied Mechatronics at Lodz University of Technology in Poland, which gave me an early understanding of how different parts of a system affect one another. After university, I joined Corning and later moved from Poland to its engineering organization in the U.S. Working on product development there taught me to pay close attention to failure modes and to understand the cause of an issue before making changes. When I moved into software, I brought that mindset with me. It has been especially useful in mobile engineering, where an application has to perform reliably across releases and real-world conditions. That background also shaped how I approach testing and stability today: I tend to think about the whole application’s behavior and the consequences an engineering decision may have beyond the feature being developed.

Q: At Talixo, you developed driver and customer iOS apps and nearly halved CI times while also working on modularization and release automation. When a mobile team needs to ship faster, where should it look for workflow improvements before asking engineers to code faster?

I would start with the development workflow itself. A major improvement came from introducing a more advanced CI/CD caching strategy, which reduced CI times. That meant less time waiting for builds and faster feedback during development. We also worked on modularization and release automation, which made the applications easier to maintain and reduced the risk of manual releases. For me, the broader lesson was that delivery speed depends heavily on the systems around the code. Improving that infrastructure gives engineers more time to focus on the product and makes releases more predictable.

Q: Today, you work on WalkFit, an app with more than 10 million downloads. Does building for an audience of that size change the way you think about engineering decisions, risk, and what happens after a feature is released?

The product’s main goal is to help people make walking a regular part of their daily routine. It combines activity tracking with walking plans and other features that support long-term consistency. At this scale, even a small engineering decision can affect a large, diverse group of users. A feature may work perfectly in isolation and still affect the broader experience in unexpected ways. That has made me pay much more attention to what happens after release and to treating real-world behavior as part of the engineering process.

Q: After resolving more than 50 production issues across eight iOS releases, what do you think separates strong engineers when something goes wrong in production?

In my view, what separates strong engineers is the willingness to look beyond the immediate bug. A production issue can point to a deeper weakness in how a feature or part of the system was designed, so the goal is to understand what made that failure possible and whether the same conditions exist elsewhere. This way of thinking also shaped my research on mobile architecture. In a recent paper, “A Local-First Architecture for Privacy-Preserving Personalization in iOS Health and Fitness Applications,” published in American Impact Review, I proposed an approach to iOS personalization that keeps sensitive health data on the device whenever possible. The work identifies potential failure points before implementation and places safeguards as close to the source as possible. The context is privacy, but the engineering principle is broader. A strong solution should address the underlying weakness and make the system more resilient, instead of simply making the visible problem disappear.

Q: The nutrition module you developed achieved a high-single-digit conversion rate and was associated with customer lifetime value increasing by well over half among purchasers. How much should mobile engineers understand about product and business performance, and how should that understanding influence technical decisions?

Understanding product and business metrics helps an engineer see the impact of technical work beyond the release itself. With the nutrition module, the conversion rate and customer lifetime value gave us a much clearer picture of its value after launch. That perspective also changes engineering priorities. We can direct time and complexity toward features that genuinely improve the product, and technical decisions become easier to tie to measurable outcomes. For me, that connection between engineering and product impact is central to senior-level work.

Q: Technical correctness alone does not make a mobile product successful. One of your A/B-tested improvements produced a double-digit lift in early goal activation and a mid-single-digit gain in first-week retention. How do you use experimentation and user behavior to determine whether a feature succeeds?

Experimentation helps connect engineering decisions with what people actually do with a product. Before releasing a change, I want to be clear about what behavior we expect it to influence and how we will know whether it worked. A/B testing gives us evidence from real usage, but I also look beyond the primary metric because improving one part of the experience can affect another. I brought that habit of considering both the intended result and the wider impact to my role as an invited judge at the recent AITEX Summit Summer 2026. The same principle applies when evaluating technology more broadly. A technically impressive solution is only part of the picture; it also needs to address a real need and create value in practice.

Q: Given these changes in the profession and your experience, what should mobile engineers invest in beyond coding if they want to stay valuable and keep growing over the next several years?

I would invest in judgment. Coding tools will keep changing, but engineers still need to understand the consequences of their decisions. That comes from seeing how systems behave in production, how users respond to a product, and how technical choices affect the business over time. I would also stay curious beyond the immediate mobile stack. Some of the most useful ideas in my own career came from moving between different areas of engineering and bringing those perspectives into software. I believe the ability to connect those pieces and make good decisions when the answer isn’t obvious will keep engineers valuable as the profession continues to change.

October 5, 2026
Why AI Coding Assistants Aren’t Speeding Up Mobile Projects (and What Will)

Ask a mobile team how much of their code an assistant drafts today, and the honest answer is a large share of it. Ask whether the last project shipped in half the time, and the tone changes. Both things are true at once: code appears faster than ever, but delivery dates have barely moved. That gap is not a failure of the tools, nor a reason to ignore them. It is a signal that typing was never the constraint.

Writing Code Was Never the Bottleneck

Break a mobile project into where the calendar actually goes, and code generation turns out to be a modest slice. There is deciding what to build, design iterations, integration with external systems, testing across a device matrix, the store submission cycle, and review—which has quietly become the busiest station on the line. Speeding up one step only shortens the chain if that step was the bottleneck. Generation was not. The saving is real but small, and it pushes the pressure elsewhere.

The Bottleneck Moved to Review

Reading code you did not write is slower than reading code you did. An assistant can produce a plausible 300-line change in the time it takes to describe the requirement. Understanding that change well enough to approve it takes what it always has. Output rose; review capacity did not. Pull requests get bigger, review gets shallower, and defects surface later, when they are more expensive. Teams that get real value impose three rules: keep diffs small, require an author who can explain every line, and never merge on a green test run alone.

Platform Knowledge Gaps

Suggestions are shaped by the code a model has seen, and public mobile code skews old. The assistant reaches for a deprecated API, an outdated permission flow, a threading pattern from before structured concurrency, or a library whose last release predates the current toolchain. All of it compiles. Some will not pass store review. Dependencies add further risk: Apple now requires privacy manifests and signatures for common SDKs, and every dependency carries a license, data collection disclosure, and maintenance risk. A model that suggests a package is not weighing any of that. None of these problems announce themselves; a compiler will not catch them.

Where AI Genuinely Excels

Assistants are excellent at boilerplate: data classes, serialization, wiring between layers. They are good at tests, especially edge cases people skip when tired. They excel at mechanical migrations (Java to Kotlin, Objective-C to Swift) and shorten the time to get oriented in an unfamiliar API. Throwaway code becomes almost free, changing what is worth prototyping. The gain is largest where work is mechanical, verifiable, and cheap to be wrong about. It shrinks to nothing where work depends on something the model has no access to: your data model, users, regulatory constraints, or historical decisions.

What This Changes About Buying Mobile Development

If the constraint has moved from production to verification, then the thing worth buying has moved too. Useful questions for a prospective partner now sound different: What is the review policy? Who is accountable for a line nobody wrote by hand? How does a new dependency get approved? What runs automatically on every commit, and on how many real devices? Those questions say more about the outcome than headcount or day rate. The mobile development teams that succeed today look less like typing and more like reviewing: fewer hands, more judgment, and a process that assumes code needs to be defended rather than merely delivered.

The Real Lever: Feedback Loops

If generation is fast and verification is slow, invest in verification. Continuous integration on every commit, an automated device matrix, static analysis and dependency scanning in the pipeline, builds that reach testers in minutes. The teams that actually got faster over the last two years are the ones that shortened the distance between writing something and finding out whether it works. The assistant is one input into that loop. It was never going to be the loop itself.

October 4, 2026
Agentic AI in Action: Real-World Use Cases and Industry Lessons
Agentic AI goes far beyond simple chat responses: it plans, acts, and executes tasks across systems, marking a shift from generative content creation to operational authority. While adoption is accelerating, most enterprise projects still fail to scale—not because the technology is weak, but because success hinges on setting clear boundaries for autonomous action.

Gartner projects that 40% of enterprise applications will incorporate task-specific AI agents by the end of 2026, up from under 5% in 2025. Yet McKinsey reports fewer than 10% of companies have scaled agents to deliver real value. Gartner also warns that over 40% of agentic AI projects will be canceled by 2027. The critical question for businesses is: where should AI be allowed to act without human intervention?

Where Agents Move Fastest

Customer service illustrates both the promise and the pitfalls. Klarna’s OpenAI-powered assistant handled 2.3 million conversations in its first month, cutting average resolution time from 11 minutes to under two and matching the output of roughly 700 full-time agents. However, by 2025, Klarna’s leadership acknowledged that automation had gone too far on service quality and began rehiring staff for escalated cases. This reversal underscores a key lesson: the boundary of what an agent should handle alone shifts as organizations learn where trust breaks down.

Software engineering is another area where agents are thriving. Tools like Claude Code, GitHub Copilot’s agent mode, and Cursor now read codebases, write fixes across files, run tests, and open pull requests with minimal manual input. Developers increasingly review diffs rather than writing every line themselves. This sector leads in scaled agent use because the humans supervising the output can quickly catch mistakes.

Where Risk Slows the Rollout

Finance and healthcare move more deliberately. Banks use agents to trace fraud patterns and draft suspicious-activity reports, but any action touching customer funds requires direct human approval. Healthcare systems employ ambient tools like Abridge and Nuance’s DAX Copilot to draft clinical notes and flag coding gaps, while leaving diagnosis and treatment fully in human hands. Insurance follows the same pattern: claims intake, document checks, and fraud screening run through agents, but settlement decisions sit behind an approval gate.

Manufacturing and Retail Emerging Patterns

In manufacturing, a vision system spots a recurring defect, and an agent traces it back to a specific supplier’s batch, then opens a corrective-action ticket automatically—a chain that once took a quality engineer days to reconstruct. Retailers use agents to track demand, generate purchase orders, and set delivery windows, stepping in only when a variance crosses a set threshold. What makes these scenarios truly agentic is the orchestration across systems, not just a single automated task.

The Real Determining Factor

Across industries, one pattern holds: adoption speed moves inversely to the cost of a wrong autonomous action. Customer service and coding advance quickly because mistakes are cheap to catch. Finance, insurance, and healthcare are cautious because a wrong action carries financial, legal, or clinical weight. These sectors are not rejecting agentic AI—they are gating it. Successful deployments share three habits:
- Give the agent a scoped job, not an open mandate.
- Map its permissions to the actual risk of each action, not a blanket setting.
- Define in advance exactly when a human takes over.
Projects that skip these steps tend to be canceled quietly. The ones that succeed are not the boldest deployments—they are the best governed ones.

Final Thought

The next phase of enterprise AI will not be measured by how many agents a company switches on. It will be measured by how precisely each one knows the edge of its own authority—and how quickly a business notices when that edge has been crossed.
September 22, 2026
Tech Careers 2036: The Best-Paying Roles on the Rise

Technology Jobs Enter a New Growth Era

Between 2026 and 2036, the technology industry is expected to create millions of new opportunities as organizations accelerate digital transformation. Artificial intelligence, automation, cloud computing, and cybersecurity are becoming core pillars of modern business. Professionals with advanced technical skills are well-positioned for sustained career growth as companies continue investing in innovation, digital infrastructure, and intelligent solutions across sectors.

Artificial Intelligence Roles Lead Demand

AI specialists are projected to remain among the most sought-after technology professionals over the next decade. Machine learning engineers, AI researchers, and automation experts will help organizations build smarter systems. With generative AI and intelligent platforms gaining wider adoption, these roles are expected to offer strong demand and competitive salaries in healthcare, finance, manufacturing, and technology.

Cybersecurity Expertise Becomes Essential

As digital threats grow, cybersecurity professionals are increasingly critical to protecting businesses and customer data. Security analysts, ethical hackers, and cloud security specialists are expected to stay in high demand. Rising global investment in digital protection will create long-term career paths for professionals with strong security knowledge and hands-on technical skills.

Cloud and Data Careers Expand Rapidly

Cloud architects, data engineers, and data scientists are set to see significant growth as organizations rely more on digital platforms. These professionals help manage large volumes of information and build scalable systems. Combining technical expertise with problem-solving, these roles are among the most promising career options for the next decade.

Software Development Remains a Strong Career Path

Software engineering remains one of the most reliable technology careers despite rapid change. Developers skilled in modern programming languages, AI tools, and contemporary application development will continue to be valuable. Companies will keep hiring engineers to build solutions, improve user experiences, and support evolving digital ecosystems.

Competitive Salaries Attract Talent

Technology careers draw attention because specialized roles often offer attractive pay. Professionals in AI, cloud computing, cybersecurity, and advanced software development frequently earn higher compensation due to growing demand. Continuous learning, certifications, and practical experience will be key differentiators for anyone pursuing success in the competitive tech industry.

What to Expect Through 2036

Tech employment is set to evolve with new opportunities and changing skill requirements over the next decade. Professionals who embrace emerging technologies will improve their chances of advancement. As digital transformation continues across industries, technology roles will remain among the most influential and financially rewarding career paths globally.

September 10, 2026
Karpathy’s Lord of the Rings AI Test Highlights LLM Gaps in Visual Reasoning and Planning

Andrej Karpathy, a prominent artificial intelligence researcher, has demonstrated a paradigm shift in how large language models (LLMs) are evaluated—moving from abstract coding benchmarks to complex, real-world software engineering tasks. In a recent experiment, Karpathy challenged Anthropic’s Claude Opus 5 to transform the opening paragraph of J.R.R. Tolkien’s The Lord of the Rings into a fully interactive 3D website, using the Three.js JavaScript library.

Instead of requesting small code snippets or static images, Karpathy provided the model with a token limit of one million (approximately $10 in cost) and instructed it to generate a complete, browser-based 3D scene. This test goes far beyond conventional evaluation methods, which typically focus on isolated tasks like code generation or text completion. Here, the model was required to think, write thousands of lines of code, manage resources, and produce an interactive product—all in one go.

From Benchmarks to Real-World Engineering

Karpathy’s approach reflects an industry-wide trend: as AI models grow more capable, engineers want to assess their performance on end-to-end workflows rather than narrow tests. The experiment underscores that the frontier of AI evaluation is shifting toward holistic, production-like challenges that reveal both strengths and persistent weaknesses.

Visual Reasoning and Scene Composition Still a Challenge

While Claude Opus 5 succeeded in generating an interactive 3D scene, the experiment also exposed critical shortcomings. According to Karpathy, visual rendering and scene composition remain difficult for even the most advanced AI systems. Additionally, evaluating the quality of such outputs is problematic: while code can be automatically tested, visual data often requires human judgment to assess accurately.

Raising the Bar for AI Evaluation

Karpathy’s experiment illustrates that expectations for LLMs are expanding beyond answering questions or writing code. Developers now want AI to build complete applications, reason across multiple steps, and generate interactive digital experiences with minimal human intervention. As AI capabilities continue to improve, real-world engineering challenges like this one will play a bigger role in measuring model performance, highlighting not only what these systems can accomplish but also the technical gaps that remain before they can consistently deliver production-ready software.

August 5, 2026
Master Dynamic Programming in 2026: Top Patterns and Interview Questions

Dynamic programming remains one of the most important coding interview topics in 2026. Mastering common DP patterns, understanding company-specific expectations, and practicing representative problems can significantly improve problem-solving skills and increase success in technical interviews at top technology companies.

Why Pattern Recognition Beats Memorization

Interview data from recent hiring cycles suggests that around 87% of coding questions at Google, Meta, Apple, Netflix, and Amazon are built around just 10 to 12 core problem-solving patterns, with dynamic programming forming one of the most versatile among them. The core idea behind DP is simple even when the problems aren’t: break a larger problem into smaller, overlapping subproblems, solve each one exactly once, and store the results, typically through memoization or a tabulation array, so the same subproblem is never recomputed. Any time a question asks for a maximum, minimum, or count of ways to do something under constraints, there’s a strong chance dynamic programming applies.

The Core Patterns Worth Mastering

DP breaks down into a manageable number of recognizable categories rather than one giant, undifferentiated topic. Knapsack-style problems (0/1 and unbounded variants) cover resource allocation and optimization questions. Grid-based 2D DP tests a candidate’s ability to reason across two-dimensional state spaces with multiple transition directions. String DP handles text-processing challenges like edit distance and longest common subsequence, patterns with direct application in areas like version control and bioinformatics. Interval DP applies to scheduling and matrix chain multiplication-style problems, while state-machine DP models transitions governed by specific rules, useful in problems involving buy/sell timing or step-based decisions.

Frequently Asked DP Problems in 2026

Company-Specific Differences Worth Knowing: Every organization does not follow the same approach toward DP problems. The first difference between companies is that Google requires higher DP expertise compared to other organizations, not only in 1D problems such as staircase problems but 2D Grid, String DP and even DP on Trees/Graphs problems and an overall preference for harder problems in general. The next two organizations, Meta and Amazon, have more focus on array and string DP problems with medium-level difficulty. Microsoft avoids going into hard problems related to DP and prefers medium-level difficult problems along with good explanations. This shows that preparation according to the requirements of a particular company pays off in a bigger way.

How to Actually Prepare for DP Interviews

The most optimal form of preparation is no different from the tried and tested methods of pattern-based preparation. The method is to select a canonical example for each pattern, practice solving it until the logic of the solution takes under two minutes to describe and then use that logic for other variations. Interviewers are much more concerned about the ability of candidates to recognize the structure of subproblems, verbally derive a recurrence relation for the same and explain their trade-offs on time and space than knowing whether the candidate has seen the problem or not. Considering the frequent appearance of DP questions on arrays, strings, trees and grids, spending the effort to really comprehend a few basic patterns goes a long way.

Why This Matters

Dynamic programming is one of the most frequently tested problem-solving techniques in software engineering interviews. Understanding its recurring patterns helps candidates solve complex coding challenges efficiently, improve algorithmic thinking, and perform better in technical interviews across leading technology companies.

FAQs

What is dynamic programming in programming?

Dynamic programming (DP) is an algorithmic technique used to solve complex problems by breaking them into smaller overlapping subproblems. Instead of solving the same subproblem repeatedly, DP stores previously computed results using memoization or tabulation, significantly improving time efficiency.

Why is dynamic programming important for coding interviews?

Dynamic programming tests a candidate’s problem-solving ability, optimization skills, and understanding of algorithms. Many leading technology companies include DP questions because they evaluate logical thinking, recurrence formulation, state transitions, and the ability to optimize brute-force solutions into efficient algorithms.

Which companies frequently ask dynamic programming questions?

Dynamic programming questions commonly appear in interviews at Google, Meta, Amazon, Microsoft, Apple, Netflix, Adobe, Uber, and many other technology companies. Google generally asks more advanced DP problems, while Amazon and Meta often focus on medium-difficulty array and string-based DP questions.

What are the most common dynamic programming patterns?

The most frequently tested DP patterns include 0/1 Knapsack, Unbounded Knapsack, Grid DP, String DP, Interval DP, State Machine DP, Tree DP, and Bitmask DP. Learning these core patterns enables candidates to solve a wide variety of interview problems efficiently.

How should beginners start learning dynamic programming?

Beginners should first understand recursion before learning memoization and tabulation. They should then practice simple problems like Fibonacci, Climbing Stairs, and House Robber before progressing to Knapsack, Longest Common Subsequence, Coin Change, and more advanced DP patterns.

July 13, 2026
The Quiet Revolution: How AI Is Reshaping Mobile App Development in 2026
A year ago, adding AI to a mobile app meant bolting on a chat widget and calling it a feature. That era is over. In 2026, AI has moved from a visible add-on to an infrastructure decision, fundamentally changing what “building an app” means for development teams.

The shift is evident in usage data. According to Sensor Tower’s year-end report, the total U.S. audience for AI assistants surpassed 200 million by the end of 2025, with more than half accessing those assistants exclusively on mobile devices—up from roughly 13 million mobile-only users the year before. This is no longer a niche behavior; it’s the default way most people interact with AI. The app is no longer a wrapper around the model—the app is the product.

From Cloud Round-Trips to On-Device Inference

For years, AI features followed a predictable pattern: capture input, send it to a server, wait for a model response, and render the result. That pattern is breaking down—not because cloud models got worse, but because users now expect AI features to behave like native app features: instant, available offline, and private by default.

Apple’s Neural Engine, Google’s Tensor chips, and Qualcomm’s on-device AI silicon have matured to the point where running a capable model locally is a realistic option for consumer apps. This changes the engineering conversation from “which API do we call” to “which parts of this feature can run on the device, and which parts genuinely need the cloud.” Getting that split wrong is expensive in ways that don’t surface until the app is already in users’ hands.

The Unglamorous Engineering Problem Nobody Mentions

On-device AI is a battery, memory, and fallback-path problem before it’s a model problem. A locally running model that drains a phone’s battery in an afternoon or locks up the UI thread during inference will get uninstalled regardless of output quality. The model is the easy part. The packaging, memory management, graceful fallback for devices without the necessary hardware, and testing across dozens of device and OS combinations are where most AI feature timelines go sideways.

This pushes product teams toward dedicated mobile app development partners rather than retrofitting AI onto an existing roadmap. Teams that get this right treat AI features as an architecture decision made early, not a plugin added late. Retrofitting almost always costs more because the data flow, caching layer, and offline state all have to be rethought rather than extended.

Why iOS Has Become Its Own Specialized Lane

The platform split has also sharpened. Apple’s approach to on-device intelligence—from Apple Intelligence’s privacy-first design to the App Intents framework—is deliberately different from Android’s. A model and pipeline built for one platform’s AI stack rarely transfers cleanly without real rework.

This has pushed specialized iOS development expertise higher on the priority list for any team shipping AI features on Apple’s platform. Knowing how to call a model API is no longer enough. Teams need to understand Apple’s specific constraints around on-device processing, App Store disclosure requirements for AI-generated content, and the Neural Engine’s actual performance ceiling on the device generations their user base is running. Get that platform-specific layer wrong, and a feature that demoed perfectly in a controlled environment becomes sluggish or inconsistent in the real world.

What This Means for Decision-Makers

For product and engineering leaders deciding how to staff an AI-feature build, a few questions separate a smooth rollout from a six-month detour:
- Does the team have hands-on experience with on-device inference, not just API integration?
- Is there a clear, tested fallback path for devices that can’t run the model locally?
- Has the architecture been designed for AI from the outset, rather than adapted after the fact?
- Does the team understand the platform-specific rules, especially on iOS, where Apple’s review process and hardware constraints are unusually strict?
A development partner without direct experience answering these questions will end up learning the hard parts on the client’s budget and timeline—an expensive lesson that is avoidable with the right questions asked before a single line of code is written.

The Bigger Shift

What’s happening in mobile development right now isn’t really about AI getting smarter. It’s about AI becoming infrastructure—the same way push notifications, offline sync, and biometric auth became infrastructure a decade ago. Once something becomes infrastructure, the teams that win aren’t the ones with the flashiest demo. They’re the ones who treated it as a core architectural decision from day one.

That’s the quieter story behind every AI feature that feels instant, private, and reliable on a phone in 2026. The visible part is the interface. The real work happened underneath it.
July 2, 2026
Top Online Learning Platforms Every Software Engineer Should Explore

Software engineering is a field that demands continuous learning. Whether you are a beginner or an experienced developer, choosing the right online learning platform can accelerate your career growth. Below are some of the best platforms known for their quality content, hands-on projects, and industry-recognized certifications.

Coursera

Offers university-backed software engineering courses, professional certificates, cloud computing, AI, cybersecurity, and hands-on projects with recognized industry credentials worldwide.

Udemy

Features affordable programming courses covering Python, Java, web development, DevOps, mobile apps, and practical projects for developers of every experience level.

Pluralsight

Delivers structured technology learning paths, skill assessments, cloud certifications, cybersecurity training, software architecture, and advanced developer courses for professional growth.

Educative

Provides interactive text-based programming courses with built-in coding environments, specializing in system design, interview preparation, algorithms, and backend engineering concepts.

Codecademy

Teaches programming through interactive browser-based exercises covering Python, JavaScript, SQL, data science, web development, and computer science fundamentals effectively.

freeCodeCamp

Offers completely free coding certifications, responsive web development, JavaScript, Python, machine learning, and real-world projects supporting practical software engineering skills.

Frontend Masters

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July 1, 2026
Cisco Slashes 471 California Jobs in Major Software Engineering Restructuring

Cisco is cutting 471 positions across three California offices as part of a broader restructuring initiative announced in May. The layoffs affect employees in San José, Milpitas and San Francisco, according to notices filed with the California Employment Development Department.

The networking and cybersecurity giant previously stated it would eliminate fewer than 4,000 jobs worldwide, representing less than 5% of its workforce. The latest filings offer granular detail on the California roles included in that plan.

Layoffs Spread Across Bay Area Offices

Cisco will eliminate 236 positions in San José, 154 in Milpitas and 81 in San Francisco. Employees received notice on May 14, with terminations scheduled to begin July 13. Some affected workers may leave within 13 days of that date.

The reductions are permanent, as stated in the company’s Worker Adjustment and Retraining Notification letters. California law mandates at least 60 days’ notice before a large layoff or facility closure.

Cisco declined to provide additional information beyond the May restructuring announcement, which the company described as an effort to lower costs in certain areas while reallocating resources toward targeted investments.

CEO Chuck Robbins told employees that Cisco expects to benefit from AI growth but reaching that goal requires “making hard decisions — about where we invest, how we’re organized, and how our cost structure reflects the opportunity in front of us.”

Software Engineering Roles Hit Hardest

The layoffs span departments including software development, product management, design and business operations. Software engineers represent the largest single group, with 56 positions eliminated: 34 in Milpitas, 19 in San José and three in San Francisco. Additionally, 39 software engineering technical leader positions will be cut, with 27 in Milpitas.

Other affected roles include 17 engineering product managers, 15 software engineering leaders, 12 directors of software engineering, 12 engineering program managers, nine directors of product management and eight business operations managers. Seven site reliability engineers, seven program managers and seven software quality assurance engineers are also included.

A Cisco business partner executive described the cuts as a familiar pattern, calling it “the regular Cisco cadence” before quarterly earnings announcements. Cisco did not confirm that assessment.

AI Investment Rises Amid Broader Tech Layoffs

The layoffs come as Cisco posts stronger financial results: quarterly revenue of $15.8 billion, its highest for a fiscal third quarter, and net income up 35% to $3.4 billion. The company markets its networking products as critical AI infrastructure, with demand for data centers and AI computing systems boosting spending on networking and cybersecurity.

Nevertheless, many tech firms are cutting staff while increasing AI investment. Meta, Snap, Block, Oracle and Amazon have all announced job cuts this year, with some executives linking workforce changes to automation and AI tools.

US technology companies announced 123,653 job cuts from January to May, a 66% increase from the same period in 2025, according to Challenger, Gray & Christmas. The firm noted that AI was cited more often than any other reason for layoffs, though it questioned whether AI alone explains the broader trend.

Cisco reported approximately 86,200 employees as of July 2025. Its planned global reduction of fewer than 4,000 positions would affect less than 5% of that workforce.

June 29, 2026