What made it especially interesting to me was that it moved beyond the familiar question of how much AI can speed up an individual developer.

Instead, it raised a more compelling one: how do you organize software development when one engineer is no longer working with a single AI tool, but coordinating five, six, or seven agents at once?

AI adoption in software teams has evolved through three stages

The conversation featured Jordan Topoleski, COO at Cursor, and Pallavi Mahajan, CTO at Nokia, and was moderated by Bloomberg’s Peter Elstrom. Topoleski’s view of how AI adoption in software teams has evolved through three stages is particularly revealing, as it clearly illustrates just how quickly the nature of developers’ work has changed.

The first stage was defined by tools such as autocomplete, designed to speed up existing tasks.

The second began with the emergence of AI agents, which developers can already delegate entire tasks to.

The third, which is only just beginning, brings a much deeper shift: companies no longer need to introduce more capable tools, but to rethink how development, teams, and responsibilities are organized when a single engineer is managing multiple agents working in parallel.

We need to redesign the system for a world where one person can work with five, six, or seven different agents at the same time.

Topoleski believes this shift also requires a different way of thinking about productivity. Figures such as AI spend per developer or token usage say very little on their own about the value a company actually receives.

That is why companies are increasingly shifting their attention from inputs to concrete outcomes, asking what the team actually managed to deliver because of that investment.

Cursor already sees the limits of simply adding AI tools to the existing development process at large companies without making deeper changes to how that process works. 

Topoleski says many organizations plateau at around 35 to 45 percent of AI-generated code eventually reaching production, which suggests that giving developers more capable tools alone will not take them to the next stage.

To fully use their potential, companies also need to adapt both their organization and their development process to an AI-native way of working.

AI is also changing how teams share responsibility

One consequence of this shift is a different distribution of time across the software development lifecycle. Technical teams have traditionally spent a large part of their time writing and reviewing code, and AI can now speed up both activities particularly well.

However, as teams speed up these tasks, the bottleneck simply moves to another stage of the process.

As a result, planning and design, as well as review, testing, and deployment, become increasingly important parts of the development cycle, because faster code generation means little if the rest of the system cannot keep up.

The shift also affects team size. Topoleski pointed to a large insurance company that split its teams of eight or nine engineers into smaller teams of about four, and the smaller teams moved through the organization much faster thanks to the lower coordination burden. The company saw roughly a threefold increase in output, suggesting that AI can change the economics of teamwork itself.

Nokia already sees a similar transformation inside its own organization, while Mahajan believes technology itself rarely creates the biggest obstacle to this kind of change:

Technology is never the bottleneck. People, culture, and the operating model are the bottleneck.

That is why the changes do not stop with the tools developers use. They also affect the traditionally clear boundaries between roles in the development process.

Nokia previously had separate positions such as software engineer, test engineer, release qualification engineer, and product manager, but Mahajan says those boundaries are now gradually fading and merging into a broader role she calls the “product builder.”

In other words, AI is changing code writing as well as how teams distribute responsibility.

How much AI should developers be allowed to use?

As developers use more advanced models, companies need to find a balance between controlling budgets and giving developers the freedom to use AI when it creates value.

At one point, Nokia experimented with quotas for engineers, but Mahajan said the approach caused negative reactions. The company then moved to a more flexible model:

Instead of strict restrictions, Nokia now uses “soft limits.” When developers exceed a certain quota, they receive a notification, but they can continue using the resources if they decide they need them.

Cursor also supports a more dynamic approach. The most advanced models do not need to be the default choice for every task. Some jobs benefit from frontier models, while teams can handle others with much cheaper models without significantly reducing quality.

In one project with a large hardware company, Topoleski says Cursor helped reduce costs by 26 percent while the organization continued to increase the share of code that made it into production.

The real ROI may not come from developers doing less

However, one of the most interesting points from the panel was that AI’s biggest value may not come from cutting costs.

Topoleski argues that many companies have never lacked ideas. The challenge has been turning those ideas into reality. When AI takes over some of the execution, developers can spend more time thinking about what they actually want to build:

This also changes how companies look at their roadmaps, as projects that were previously pushed aside because of limited time and resources can now become possible.

Instead of simply helping companies complete their existing roadmap faster, AI can expand what they are able to build in the first place.

With AI agents and systems that run multiple agents in parallel, the impact goes far beyond writing code faster. Developers are changing the size of development teams, the boundaries between roles, and the entire process of turning an idea into production.

The post Cursor and Nokia: AI Matters Less for Speed Than Expanding Developers’ Capabilities appeared first on ShiftMag.

A few years ago, building an AI app was hard. Today, anyone can put together an LLM, connect a few APIs, and build a strong demo in a day. But turning that demo into a production app is still much harder.

At the AI Engineer World Fair, I talked with Mike Chambers, a Developer Advocate at AWS who focuses on generative AI, about what makes production AI systems different from conference demos. His answer kept coming back to one thing developers often overlook: everything around the model.

It’s not AI, it’s harness

Developers spend a lot of time comparing models, tweaking prompts, and testing frameworks, but far less time on the infrastructure that keeps an AI app running. In his talk, Mike said many production failures don’t come from the model itself, they come from what he calls the harness.

There’s two different types of agents: an agent that I’m using and an agent that I’m building, and we need to think about those very differently.

For developers using coding assistants, the harness is mostly about standards, workflows, and how AI fits into the team’s development process. But for engineers building AI products, it means something completely different, as Mike put it:

You have to think about how you’re actually going to deploy that out to infrastructure and deploy it at scale. And so doing that right and not just putting everything inside of one container, that’s the top tip here.

That distinction matters because a lot of AI talk treats every agent as the same, but Mike says the engineering challenges are very different depending on whether you’re using an AI tool or building one for other people.

Observability needs to be built in early

Today’s AI world is full of impressive demos. They can write code, answer questions, and automate tasks well enough to impress a crowd. But production systems need much more, so I asked Mike what teams usually get wrong when they move from demo to production:

The number one thing is observability, followed closely by evaluations.

Mike said that used to be less important. Teams would build something cool, show it off, and stop there. Now, production AI needs much more discipline from the start.

Modern AI systems, Mike says, work differently from traditional software. Their outputs can vary, their reasoning isn’t always easy to trace, and agents may take several steps to reach a result. That’s why observability needs to be built in early – so teams can understand what the system is doing before they try to ship it to production.

Too often, AI projects are still judged mainly by model quality, when what really matters is whether the system is visible and controllable in practice.

Memory doesn’t have to live inside the agent

Memory is one of the biggest topics in AI engineering, and it’s often treated as essential for useful agents. So I asked Mike what engineers misunderstand most about it.

He said the biggest misconception is that an agent can’t work without memory. Short-term memory is just the current conversation, while long-term memory carries over into future interactions. The key question is where that processing happens:

All of that memory extraction can happen outside of your agent somewhere else because you don’t need it today. You need it tomorrow when the user comes back to the agent again.

Rather than making every agent responsible for managing its own knowledge, developers can separate those concerns into different systems. That keeps agents simpler while still allowing applications to remember previous interactions.

Are multi-agent systems the future?

Another topic attracting plenty of attention are multi-agent architecture. Conference demos often showcase teams of specialized agents working together on different tasks. I was curious whether Mike sees that as the future of AI systems, so I asked him directly.

He said that he doesn’t dismiss that approach, but he sees a different reason for using multiple agents in production.

Multi-agent is super useful because you’re parallelizing the work. When developers build customer-facing AI systems, however, the motivation shifts. Often multi-agent is about separating out context. So it’s actually now becoming more of a context engineering conversation.

Instead of assigning agents to different jobs simply because it’s fashionable, teams should use them to isolate information and avoid overwhelming a single model with every piece of available context. The architecture becomes less about adding more intelligence and more about deciding which agent should see which information.

Engineers need to build a strong intuition for generative AI itself

Mike’s most practical advice had little to do with frameworks or infrastructure.

When I asked him what developers should focus on over the next few years, he didn’t point to another SDK or the latest model release. Instead, he argued that engineers need to build a strong intuition for generative AI itself – what large language models can do, and just as importantly, what they can’t.

New models will keep arriving, but right now, he said, the biggest improvements are happening around the model, in the harness.

His final observation neatly summarizes the engineering mindset he hopes developers adopt:

You want to be using this technology. You don’t want this technology to be using you.

The post We Usually Blame the AI Model, But the Harness Is What Breaks Production appeared first on ShiftMag.

Mike Krieger, Instagram co-founder and Member of Technical Staff at Anthropic, said hand-reviewing every line of code is no longer the best use of a developer’s time.

The job, he argued, is moving toward understanding what AI is trying to do, judging whether its decisions make sense, and checking whether the end result actually solves the user’s problem.

For startups and small teams, that shift could be a major advantage: AI makes it cheaper to experiment, pivot, and learn faster from real feedback.

‘AI will not replace developers’

If a founder (or a developer) can test three or four product versions or approaches in the time it used to take to build one, the biggest gain is speed of learning.

In Mike’s view, that doesn’t mean AI replaces creativity, ideation, or product instinct, it just helps strong ideas get validated, or rejected, much faster.

The real challenges developers and founders face were never technical to begin with, he argues. Spotting the actual problem, understanding what users need, and finding a product the market will genuinely adopt is far harder than writing the code itself.

AI will not replace developers or threaten the startup ecosystem. It will let small teams build, test, and iterate faster with fewer resources.

That shift will not be easy for everyone, and plenty of teams will struggle, quit, or pivot along the way. But blaming AI for a rough patch in the industry is not fair. Because as Mike says, what was hard before AI is still hard now.

Code review matters less now

One of the biggest changes Mike sees is not how much code AI can write, but how people assign work to it.

Developers are moving away from planning every step in detail and doing it all by hand. Now they more often set the goal, give the AI context, and then review the results, decisions, and trade-offs.

At Anthropic AI is increasingly used less as a simple tool for individual tasks and more like a team member that holds context, tracks specific problems, and proactively suggests solutions.

That is also changing how code gets reviewed. Traditional code review, where someone goes line by line and checks the implementation, matters less than understanding the intent behind a change.

Instead of asking “is every line of this code correct,” the more useful question becomes “is this the right way to solve the problem, and what are the consequences of this decision.”

I don’t review every line of code in a pull request. Instead, I talk to Claude about the code and ask the questions I would normally ask the person who wrote it.

That does not mean people disappear from the process. Human judgment matters more, especially for architectural decisions and complex changes. AI can speed up implementation, but someone still needs to understand the product, the users, and the long-term consequences of a decision.

It is easy to lose perspective in the AI race

Beyond the changing workflow, Mike also spoke about the pressure in the AI industry. With new models, products, and regulatory shifts coming almost daily, it is easy to feel like you always have to stay one step behind.

He said that by midweek, Anthropic is already reviewing the ”state of AI news,” and the list of updates is often long. His point was that you have to keep perspective, you can’t track or react to every single change.

That pressure, he added, is also what makes burnout such a risk in an industry moving this fast. People often tie their sense of success to daily results, competitors’ launches, or product releases, even though real wins and failures are usually measured over much longer stretches of time.

No job is important enough that you can’t be offline for a few days.

He closed by saying teams need to get used to talking about frustrations and failures, not just celebrating wins. Being open about problems, he argued, helps teams solve them faster and keep a healthy working culture, even as the industry moves at an extraordinary pace.

The post Instagram’s Co-Founder Says He Doesn’t Review Every Line of Code Anymore appeared first on ShiftMag.

Put together, they form a consistent picture – not an official ladder, but a very real one that shows how engineers grow, gain influence, and move from writing code to shaping entire systems.

Compensation reveals the real hierarchy

At its core, Adobe uses a P-level system (Professional levels) that maps engineering growth from entry-level roles to company-wide technical leadership.

Adobe doesn’t hand out impressive titles quickly. But behind the modest titles, what’s actually expected of you keeps growing at every level. The ladder looks flat from the outside. From the inside, the gap between levels is real.

If titles are understated, compensation isn’t: across multiple sources, total compensation for software engineers at Adobe ranges from roughly $150K at the entry level to well above $500K at the top of the individual contributor track.

The numbers matter, but so does the curve. Pay grows steadily through early and mid-level roles and then jumps sharply after senior. That’s where Adobe starts paying for influence, not just output.

Senior level: where leverage begins

At P10 and P20, the job is straightforward: ship code, learn the systems, and figure out how Adobe builds and scales things. The goal is to become someone the team can rely on.

By P30, something shifts. Engineers stop executing tasks and start owning problems (taking a feature end-to-end), making real technical calls, and thinking about why something should be built, not just how.

At P40, the job changes for real. Senior engineers design systems, not just features. They cross team boundaries, shape architectural decisions, and lead bigger initiatives. For many, this is a long-term home – the next step demands a fundamentally different kind of growth.

Staff: the real career breakpoint

The jump from Senior (P40) to Staff (P50) is the most important one on the ladder. Same title family, completely different job.

Staff engineers operate as technical leaders without formal authority. They define architecture, guide technical direction, and shape roadmaps across teams. At Staff, you’re measured by what others can build because of you and compensation starts to reflect that.

Beyond Staff, engineering becomes increasingly strategic. Senior Staff engineers (P55) operate across domains, aligning engineering efforts with business goals and driving initiatives that span multiple teams.

Principal engineers (P60) move to a company-wide level of influence. They define technical vision, tackle ambiguous problems, and shape decisions that impact entire product lines. At this level, engineering is less about building and more about direction-setting.

Cross-company level mapping

One useful way to understand Adobe’s ladder is to map it against more transparent systems at companies like Microsoft. While titles and expectations vary slightly, the underlying progression is broadly aligned across Big Tech. Adobe’s levels tend to appear slightly compressed in naming, but comparable in scope, especially from Staff level onward.

The important nuance is that while the mapping is directionally accurate, scope matters more than exact title equivalence. A P50 at Adobe may operate closer to a strong L6 at Google or even edge into L7 territory, depending on the organization, reinforcing the idea that Adobe’s ladder is less about labels and more about impact.

What Adobe actually values?

One pattern runs through the whole ladder: scope drives everything.

• Early levels – Can you execute?
• Mid levels – Can you own?
• Senior – Can you design systems?
• Staff+ – Can you influence outcomes across teams?

That’s the real progression. The ladder feels invisible from the outside because titles aren’t the point — expanding impact is.

Adobe’s ladder stands out for how quietly it operates. No playbook, no loud framing, just one consistent logic: as you grow, you move from writing code to shaping systems to shaping decisions. At the top, one question defines everything: how much of the company changes because of your work?

The post Let’s Take A Look Inside Adobe’s Complete Career Ladder appeared first on ShiftMag.

Top experts from global tech leaders will share how they build and scale some of the world’s most complex systems, with real-world lessons from production environments.

Across multiple stages, the program features panels, workshops, and talks focused on deep technical insight and practical experience. Speakers include Katie Gamanji (Apple), Igor Dmochowski (NVIDIA), Sven Peters, Bette Lyon Delsordo, and many others.

You don’t need silicon valley for world-class tech

Igor Dmochowski, Head of Developer Relations for Central and Eastern Europe at NVIDIA, said that Infobip Shift is already internationally recognized, and added that he is especially excited to meet local developers whose potential is increasingly visible on the global stage:

Croatia has exceptional technological talent and a strong AI community, and I’m glad to be part of an event that puts this into a global context. True tech innovation comes from connecting people and ideas, and Shift has that energy.

On the other hand, Ivan Brezak Brkan, the Director of Developer Experience at Infobip, said that Infobip Shift is rightfully the largest tech conference in this part of Europe and one of the largest in the world.

You don’t have to, especially in today’s conditions, travel to the other side of the world to exchange experiences with people whose technological solutions power the world. If you want to advance your career not only through knowledge but also through connections, you need to be at Shift.

No professional speakers and marketing presentations

“Infobip Shift is built on two foundations: high-quality content and a relaxed atmosphere that encourages networking. We ensure strong content by carefully selecting speakers who share real-world experience from people actively working in the industry,” said Stipe Cigic, Director of the Infobip Shift, adding:

We avoid professional speakers, marketing-driven presentations, and topics overloaded with buzzwords and hype. That has become especially challenging today, when roughly every second submitted topic is somehow related to AI. Our speakers are real developers – people whose knowledge our audience truly values.

He also said the second foundation of Shift is its relaxed, laid-back atmosphere – a vibe the team aims to create each year. It can’t be forced, but they work to make the event feel light, summery, and welcoming. That kind of atmosphere makes networking more natural and fun.

Meeting new people is the biggest opportunity for attendees. I know at least 2 projects that started from casual chats at last year’s Shift. High-quality content plus a summer vibe, that’s what makes it memorable.

Knowledge gets you started, networking takes you further

With increasing reports of layoffs and disruption in the global IT industry, networking is becoming even more important. Stipe says the goal is for developers to leave Shift not only with new knowledge, but also with greater confidence and reassurance in their skills, value, and future in the industry.

I believe that, when it comes to networking and professional connections, Shift has no real competition in this part of Europe within the developer industry. A junior developer can casually grab a coffee with a senior engineer from companies like Netflix. The exchange of knowledge and experience in conversations like that is something our community rarely finds at nearby conferences.

Stipe concluded that Shift is a long-term platform developers can rely on, not just to meet peers, but also potential employers, clients, and partners. It offers many opportunities, whether for networking or simply enjoying great talks, food, and drinks under the Mediterranean sun.

Interested in Infobip Shift? Explore what we have planned!

The post Infobip Shift 2026 Returns to Zadar, Bringing Apple and Nvidia to the Stage appeared first on ShiftMag.

If you’ve ever published on Dev.to, built an audience there, or just scrolled through its feed looking for a useful tutorial, you probably had a few question marks over your head when news of the acquisition dropped.

Major League Hacking (MLH) has officially acquired Dev.to.

It sounds strategic. MLH runs hackathons, fellowships, and developer programs worldwide, and Dev.to is one of the most recognizable developer publishing platforms on the internet. Community meets talent pipeline. Content meets events. Growth meets distribution.

But if you’re a developer (especially one who’s spent years building a presence on Dev.to) the real challenge is figuring out how this shift will affect your work and your audience.

A new chapter for Dev.to?

MLH announced the acquisition as a step toward “building the largest community for software professionals.” They’ve also emphasized that Dev.to will remain community-first.

The open-source project that powers Dev.to will continue to exist.

Nothing radical, nothing alarming – at least based on what’s been shared so far. Still, developers naturally have questions. When platforms change ownership, people who’ve invested time and trust in them want to understand what the long-term impact might be.

The uncomfortable questions devs are asking

Five years ago, Lane Wagner argued in his post The Collapsing Quality of Dev.to that the platform’s overall content quality was declining, with repetitive, low-effort, and beginner-focused posts crowding out deeper, more insightful content – and that was long before the rise of the current AI-driven flood.

The author highlighted a lack of effective moderation tools and incentives that favor quantity over quality, making it difficult for higher-value content to stand out. As a result, Dev.to risks becoming less useful and less credible for experienced developers.

On the other hand, Samuel Zacharie raised the question on Dev.to itself: Has the platform become a victim of its own success? He argues that as Dev.to expanded, its content increasingly leaned toward beginner-focused, repetitive, and bootcamp-style posts, the kind that are easy to produce and tend to perform well in search rankings.

According to him, that shift makes it harder for more advanced perspectives to surface, ultimately diluting the diverse, high-quality developer voices that once defined the platform.

These aren’t just random opinions; they reflect a broader frustration with how developer publishing platforms scale. And that’s where the acquisition comes in.

Could quality drop further? Here’s what authors think

This is where the reactions start to split. 

To better understand how authors perceive the situation, we reached out to several developers who actively publish on Dev.to and asked them how they feel about the acquisition, and whether they are optimistic or concerned about what comes next. Jacob (northerndev) said:

My initial reaction to the MLH acquisition is a mix of optimism and caution. Dev.to is valuable because it is raw and community-driven. MLH has a great track record with hackathons and getting people to build things, which is fantastic for energy and momentum.

However, his main concern as an author is the signal-to-noise ratio. Hackathons naturally produce a lot of fast, chaotic output. Combined with the current wave of AI-generated content, there is a real risk that the platform could become flooded with low-effort posts.

If MLH uses their resources to filter out the noise and highlight genuine, hard-learned engineering experiences, this acquisition is a huge win. If it just becomes a marketing channel for hackathon projects, we lose the core value of the site. Signal needs to win over noise.

Hackathons spark momentum, experimentation, and projects, but they also produce rapid output. With today’s AI-assisted writing tools, the barrier to publishing is even lower.

That’s the context for this acquisition: not 2019, not pre-ChatGPT, but in the heart of what many developers call the AI slop era.

On the other hand, not everyone sees this as a threat.

Maame, other author at the platform, is optimistic:

As someone who writes about the student developer journey, Python, and Git on my Dev.to profile, I find this news very exciting. In my view, bringing together the community-driven storytelling of Dev.to with the hands-on energy of Major League Hacking (MLH) is the perfect combination. 

Hackathons are where much innovation begins, and Dev.to is where it’s documented and shared. Maame believes that closer ties between these collaboration-heavy events and the platform naturally benefit both authors and the wider developer ecosystem:

I’m optimistic that this will lead to more integrated ways for developers to showcase what they build.

Two very different outlooks. 

So, what does this mean for you?

Ultimately, what this acquisition means depends on which side of the platform you’re on and how it unfolds.

If you’re an author, now is the time to watch closely. Notice what gets promoted: thoughtful engineering deep-dives, or fast hackathon-style recaps and AI-assisted tutorials. Your reach, your positioning, and even your choice to keep publishing here could hinge on how incentives shift.

If you’re a reader, the question is simpler: does the content get better or noisier? Will you find real-world lessons from builders, or just surface-level posts optimized for speed and visibility? MLH could bring structure, resources, and stronger curation to Dev.to, but growth pressures might also amplify existing quality concerns.

For now, it’s too early to call it a win or a loss. The coming months will show whether this turns into a story of revitalization or dilution.

The post Dev.to Acquired. Is This the End of the Beloved Developer Blog Network? appeared first on ShiftMag.

As of the beginning of 2025, the estimated global developer population is over 47 million, according to a market research study by SlashData.

The number of professional developers increased from 21.8 million to 36.5 million, whereas the amateur developer population declined by over 1 million in the last year.

That’s 50% more software developers than in Q1 2022. The growth rate has varied, with a 15% increase from 2022 to 2023 and a 21% spike from 2023 to 2024, attributed to post-pandemic investments and demand for digital services.

However, growth has decelerated to 10% in the last 12 months, indicating a shift towards a plateau in the developer ecosystem.

The population estimates are derived using SlashData’s independent bottom-up methodology, which is based on reliable measurements from various sources. Data sources include the number of GitHub and Stack Overflow accounts, along with employment statistics from the USA and the European Union.

The methodology also incorporates data from their global developer survey, which has run 29 waves and reached over 10,000 developers globally in each wave, said Costas Korakitis, the director of research at SlashData.

Professional and Amateur Developers

The professional developer population has grown significantly by 70% from 2022 to 2025, while the amateur developer segment has begun to contract. The number of professional developers has increased from 21.8 million to 36.5 million, while the amateur developer population experienced a decline of over 1 million in the last year.

This trend indicates that professionals remain in the field longer, while the traditional entry-level population is shrinking.

Developer Population is Ageing

The demographic profile of developers is gradually aging, with a decline in the percentage of developers aged 18 to 24 from 33% in early 2022 to 23% in early 2025.

At the same time, the share of developers aged 35 to 44 has increased from 22% to 26% during the same period.

Western Europe and North America continue to dominate the global developer population, each with approximately 9.5 million developers. However, significant growth is happening in other regions, particularly in South Asia, which has nearly doubled its developer population from 4 million in 2022 to 7.5 million in 2025.

Greater China has experienced explosive growth, tripling its developer population from 2.4 million to 5.8 million, driven by investments in education and local platforms. South America has also seen growth, increasing from 1.7 million to 3.4 million developers, with countries like Brazil and Argentina emerging as tech outsourcing hubs.

More Backend and AI, Less Mobile Developers

Web development remains the most popular application area, with over 23 million developers engaged in front-end and back-end applications.

Backend services and data science/ML/AI applications are also significant focus areas for developers. At the same time, emerging sectors include the development of applications for third-party ecosystems and embedded software, which have experienced substantial growth since 2022.

Mobile app development and desktop applications have declined, possibly due to market saturation and rising acquisition costs.

28 million JavaScript Developers

JavaScript remains the most widely used programming language, with around 28 million users, followed closely by Java and Python, each with approximately 23 million users. Rust has emerged as the fastest-growing language, more than doubling its user base since 2022, driven by its focus on safety and performance.

Developer preferences are diversifying, with many using multiple languages depending on project requirements, indicating a shift towards selecting the right tools for specific tasks.

The post There are 47 Million Developers in the World appeared first on ShiftMag.

Developers learn through various sources, from traditional books to informal online communities like subreddits. But with so much online content, how can you know what to focus on? To help you navigate this, we’ve curated a selection of evergreen books, blogs, and newsletters to help you enhance your skills and become an expert in your field.

Books as Essential Resources

Aware of the progress of technology, the expansion of AI tools, and other traps of “misinformation,” renowned experts have taken on the role of evangelists, generously sharing their knowledge with other members of the developer community.

Professional literature has become more accessible. We’ve selected ten evergreen books:

“Clean Code: A Handbook of Agile Software Craftsmanship” by Robert C. Martin

As the title says – this book prepares you to write clean, readable, and maintainable code, which is crucial for long-term success in programming and a guarantee that no machine will replace you!

“The Pragmatic Programmer: Your Journey to Mastery” by Andrew Hunt and David Thomas

This book is rich with practical programming advice, career tips, and how to improve your problem-solving skills. It’s a must-have in every programmer’s library at the beginning of their career!

“Design Patterns: Elements of Reusable Object-Oriented Software” by Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides

It provides the foundation for understanding design patterns in object-oriented programming, helping developers create scalable and reusable solutions.

“Refactoring: Improving the Design of Existing Code” by Martin Fowler

It is a book that explains how to refactor code to improve its structure and design without changing its functionality, making it indispensable for maintaining and improving existing codebases.

“The Mythical Man-Month: Essays on Software Engineering” by Frederick P. Brooks

A true classic of programming literature! It explores the human side of software development, focusing on the challenges of managing software projects and teams.

“Code Complete: A Practical Handbook of Software Construction” by Steve McConnell

As the ultimate guide to software construction, this book dives deep into best practices for writing high-quality code and provides comprehensive techniques.

“The Clean Coder: A Code of Conduct for Professional Programmers” by Robert C. Martin

If you’re wondering how to become a professional programmer – a true expert in your field – this book focuses on discipline, ethics, and attitude in software development.

“Working Effectively with Legacy Code” by Michael Feathers

The development of technologies constantly requires improvements to existing code. Therefore, this book is crucial. It focuses on working with legacy code and teaches strategies for managing and improving older code while ensuring it remains functional and maintainable.

“Introduction to Algorithms” by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein

Known as CLRS, this book is a comprehensive guide to algorithms and data structures, indispensable for developers who want to deepen their understanding of the core principles of computer science.

“Structure and Interpretation of Computer Programs” by Harold Abelson, Gerald Jay Sussman

How can you think like a computer and use programming to solve complex problems? This approach will turn you into a human-machine. With this book, not only will you manage computers, but you’ll become one!

Blogs and Online Communities

Although books represent the best way to acquire knowledge, we cannot overlook that we live in an instant-online world, where people prefer to instantly get the information they need. They enjoy concise and summarized data, and when backed by personal experience, it creates a win-win situation!

While you probably already have accounts on many of these platforms, here’s a list of the most relevant online platforms, ranked by traffic and number of members.

Stack Overflow

Until recently the most popular platform for asking and answering technical questions, bringing together developers worldwide. This platform allows developers to ask questions, share their knowledge, and learn from other community members, making it an invaluable resource. Stack Overflow significance has been replaced by gen AI assistants for the most part, but sometimes there’s nothing like copying and pasting code form StackOverflow.!

GitHub

A community-driven platform where developers collaborate on open-source projects, share code, and contribute to various repositories. GitHub has become an indispensable place for collaborative work, innovation, and progress in software engineering.

dev.to

This community-driven platform allows developers to post articles and tutorials and discuss various programming-related topics. Dev.to is a rich source of knowledge and a space for creative idea exchange among developers.

Reddit

Subreddits like r/programming and r/learnprogramming provide a platform for discussing programming challenges, sharing advice and news, and exchanging experiences among developers. These communities allow for quick connection and real-time learning.

daily.dev

A mix of Reddit-like forums, a news feed of the best engineering content, and a developer community daily.dev was founded with the idea to bring scattered online source of knowledge for engineers to one place. Don’t forget to join ShiftMag Squad there to stay up to date!

Hacker News

This social news site is dedicated to technology and entrepreneurship, offering a space for sharing articles, asking questions, and discussing the latest developments in the tech world. Hacker News gathers enthusiasts who want to stay updated with all the important industry topics.

Medium (Programming section)

Medium is a platform that offers a dedicated section for programming. Developers can find articles, tutorials, personal experiences, and advice there. It is a space for a deeper understanding of new technologies and professional skill development.

CSS-Tricks

Specializing in CSS and web design, this blog covers front-end development and design topics, offering many resources, tips, and tutorials. CSS-Tricks has become a key tool for anyone involved in web design and looking to enhance their skills.

Code Project

Code Project is a community that provides free access to articles, tutorials, and code samples covering a wide range of topics related to software development and programming. It is a true haven for developers of all levels, offering resources for all technologies and platforms.

SitePoint

SitePoint is a comprehensive online resource offering tutorials, articles, and courses on web development, design, and programming technologies. It is an extremely valuable resource for any developer who wants to expand their knowledge and improve their skills in the industry.

Newsletters for Real Geeks

There are indispensable newsletters for those who don’t want to miss any important news and prefer to have the latest updates from the programming world delivered straight to their inbox.

We bring you a list of the most popular newsletters, perfect for all developers and those who want to stay up-to-date with the latest trends and knowledge in the industry.

JavaScript Weekly

JSW is a weekly roundup of news and articles about JavaScript, one of the most popular programming languages.

Smashing Newsletter

This newsletter offers bi-monthly web design and development updates featuring articles, resources, and tutorials.

Frontend Focus

Focused on HTML, CSS, and news related to browsers, providing essential information for frontend developers.

CSS-Tricks

It offers tips, tricks, and techniques for using CSS, making it a valuable resource for developers working with styles and layouts.

Python Weekly

Python Weekly is a newsletter that delivers news, articles, and tutorials specific to Python, one of the most widely used programming languages.

React Status

This newsletter provides updates and articles on React and related technologies, keeping developers up-to-date with the latest in the React ecosystem.

Golang Weekly

Golang Weekly brings the latest news from the Go programming language community. Go is popular among developers who focus on simplicity and performance.

Ruby Weekly

Ruby Weekly is a weekly digest of news and articles about Ruby, a dynamic, open-source programming language focused on simplicity.

Hacker Newsletter

It is a weekly newsletter curating the best articles about startups, technology, programming, and more for tech enthusiasts.

DevOps Weekly

This newsletter focuses on DevOps and infrastructure, offering insights into best practices, tools, and trends in modern software development.

New Ways of Learning: Gamification and AI-Powered Tools

In complement to conventional learning methods, developers today benefit from clever ways to enhance their skills. Platforms like CodinGame, Swift Playgrounds, and Codewars offer gamified learning, making problem-solving more interactive and fun.

These platforms simulate real-world scenarios, promoting critical thinking and strategy development. Moreover, AI-driven tools such as GitHub Copilot, ChatGPT, and Tabnine transform how developers write code. By providing real-time suggestions and automating repetitive tasks, these tools help developers focus on more complex challenges, streamlining the learning process and making it more efficient.

The post Where and how developers learn in 2025? appeared first on ShiftMag.

While some designers despair that this tool has turned its focus to developers with the new update, others still believe the new interface was designed to improve collaboration between designers and developers.

Furthermore, Figma CTO Kris Rasmussen stated at this year’s We Are Developers conference that Figma is a tool that addresses the gap between design and development by creating tools that cater to both.

In this text, we have analyzed whether the CTO’s claims are true, whether the dissatisfaction of certain designers is justified, and whether it is just their twisting things from reality and inventing non-existent concepts to gain more attention from the global audience.

What does Figma offer to developers, and what does it provide to designers?

Figma is the current market leader in UI design and is a natural evolution of solutions in this space. Sketch paved the way for Figma by moving us from graphic design paradigms in Photoshop to more web-based mental models.

This tool took the next step in that evolution by adding native multiplayer (in a cloud-based, platform-agnostic tool), prototyping, and developer handoff capabilities.

Either way, it still serves the same goal of visually exploring and documenting product and design decisions. Still, it does it better and way more collaboratively than most tools before.

Collaborative Design Systems: Figma has evolved into a platform for designers to create user interfaces and facilitate the development process. Features like shared libraries and design systems enable developers to access design assets easily, making it simpler to find and export images, icons, and other necessary resources without needing to request them separately.

Integration with Development Tools: Figma integrates various development tools, such as code generation features that allow designers to export components directly usable by developers. This minimizes friction in the handoff process and promotes faster implementation of designs into production.

Real-time Collaboration: These features allow multiple users to work on a project simultaneously and help bridge the gap between teams. This capability ensures that developers can provide immediate feedback on design decisions, fostering a more iterative approach to product development.

Prototyping and Developer Handoff: With enhanced prototyping capabilities, designers can create interactive mockups that developers can reference directly. This minimizes misunderstandings during the development phase and ensures that the end product aligns closely with the original design intentions.

Developer-Focused Updates: Figma has rolled out updates focusing on developer needs, such as support for various viewports, code export options, and improved accessibility features. These enhancements make it easier for developers to understand and implement designs.

They do improve the design and developer ergonomics

Figma simplifies the job for developers in several ways:

• Code Generation: Figma provides the option to generate CSS code snippets for design elements. This can accelerate the development process since developers can directly use these snippets, reducing the time spent writing styles from scratch.
• Integration with Other Tools: Figma’s compatibility with various development tools (such as version control systems and testing environments) allows developers to integrate designs more easily into their existing workflows.

This ultimately allows developers to focus more on coding and implementation than deciphering designs or clarifying specifications.

In the current product and web development paradigm, UI design output (especially the kind Figma enables) is mostly input for developers. So, it is an absolute no-brainer that developers coming into the tool to explore the visual and technical specs should have a pleasant experience. Otherwise, you’re regressing and creating more work for designers to export and update those files continually.

The post Developers will love Figma now, but what about designers? appeared first on ShiftMag.

This opens up exciting possibilities, like remotely controlling vehicles (imagine an interactive self-driving car two thousand kilometers away!) and enhancing user experiences.

So, how can developers tap into this potential?

Network APIs can boost app performance using telecom power

At the Infobip Shift conference, Shkumbin Hamiti from Nokia showed how network APIs make the most of 5G and easily fit into apps, with demonstrations from experts Matija Razem and Boris Drilo highlighting their real-world uses.

Shkumbin defines its API business system as a way to deliver network capabilities that seamlessly integrate into applications, enhancing the digital experience for end-users. Network APIs are how developers enter the world of telecommunications.

They provide access to valuable user data collected by operators and manage the way your applications communicate with the network. From protecting bank customers to enabling remote control of ships or cars, network APIs are already having a significant impact on various human activities.

With Networks as a code API, you don’t need deep knowledge to unlock the power of mobile networks.

Network-Based Platform: Shkumbin says that Nokia has developed a platform called “network-based”, aimed at enabling developers to access and utilize complex network capabilities effectively.

API Development: The company is working on APIs that provide advanced functionalities, such as ensuring the quality of service for sessions and improving security through trusted device locations, instead of relying solely on-device data.

Developers can easily integrate 5G features into apps

Network Science: A significant focus is on “network science” within the company, providing specialized network creation for specific applications (e.g., 5G video devices) while minimizing legal complexities for users. Network API-s enable developers to integrate advanced 5G network capabilities into their applications seamlessly.

These APIs are giving you access to the power of the 5G network and unleashing a new era of innovation and revenue opportunities.

Main benefits to app developers: Hamiti mentioned the simplicity of using their APIs – requiring only a few lines of code to achieve advanced network capabilities, despite the complex technology running in the background. Simple APIs and ergonomics SDKs fit your existing workflows and make integration a breeze.

With Networks as a code API, you don’t need deep knowledge to unlock the power of mobile networks.

Network APIs in action!

This can bring users and developers to new use cases, better and more secure applications, and network monetization. If we speak about real use cases, for example, that would be:

1. Remote logistic operations;
2. Drone operations;
3. Shared, remote, and automated mobility;
4. Finance, Health and safety Asset management;
5. Stadium, event, and gaming experiences;
6. VR technology.

Partnerships and Collaboration: Successful implementation of these technologies often involves partnerships with other companies. They are emphasizing that collaboration is key to innovating and delivering such services.

These details emphasize the significance of network APIs in enhancing application capabilities. The company’s commitment to making these technologies accessible, and the various innovative use cases being explored.

The post Unlock advanced network capabilities with just a few lines of code appeared first on ShiftMag.