In the tech world, there is a constant flow of changes and keeping up with them means the choice for tools and technologies which are the most appropriate to invest your time in.
In 2026 the best programming language or technology stack to learn really depends on your personal aims, hobbies, and apps you are going to create.
The use of AI is increasing. AI as a “Pair Programmer” is becoming the default. Code completion, refactoring, and boilerplate generation are used often. Devs spend more time reviewing and steering code than typing it. “Explain this error” and “why is this slow?” prompts are useful.
In prompt-Driven Development programmers describe the intent in natural language and then let AI generate first drafts of functions, APIs, or configs. Iterate by refining prompts rather than rewriting code. Trend: Knowing how to ask is becoming as important as syntax.
Strong growth in: Auto-generated unit and integration tests and edge-case discovery. Trend: “Test-first” is easier when AI writes the boring parts.
AI is moving up the stack. Trend: AI as a junior architect or reviewer, not the final decider.
AI comes to Security & Code Quality Scanning. Rapid adoption in: Static analysis and vulnerability detection, secret leakage and dependency risk checks. AI can give secure-by-default code suggestions. Trend: AI shifts security earlier in the SDLC (“shift left”).
Instead of one-off prompts: AI agents that plan → code → test → fix → retry. Multi-step autonomous tasks (e.g., “add feature X and update docs”) can be done in best cases. Trend: Still supervised, but moving toward semi-autonomous dev loops.
AI is heavily used for explaining large, unfamiliar codebases and translating between languages/frameworks. It helps onboarding new engineers faster.
What’s changing: Less manual boilerplate work
More focus on problem definition, review, and decision-making. There is stronger emphasis on fundamentals, architecture, and domain knowledge. Trend: Devs become editors, designers, and orchestrators.
AI usage policies and audit trails is necessary. Trend: “Use AI, but safely.”
Likely directions:
Deeper IDE + CI/CD integration
AI maintaining legacy systems
Natural-language → production-ready features
AI copilots customized to your codebase
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Tomi Engdahl says:
GitHub’s former CEO launches a developer platform for the age of agentic coding
Ex-GitHub CEO Thomas Dohmke launches Entire: a $60M platform for AI agents. Discover how he’s solving the “review bottleneck” by tracking agent reasoning.
https://thenewstack.io/thomas-dohmke-interview-entire/
Tomi Engdahl says:
https://www.makeuseof.com/im-done-with-onenote-heres-what-im-using-now/
Tomi Engdahl says:
https://www.smoothly.fi/modernin-sisaltoprosessin-anatomia-nain-rakennat-skaalautuvan-ja-vaikuttavan-sisaltokoneiston/
Tomi Engdahl says:
Allekirjoita vetoomus: Ei uhkasakkoja avoimen lähdekoodin kehittäjille (HE 179/2025)
https://docs.google.com/forms/d/e/1FAIpQLScSkOPyodQKkl1qf5TcBjrwuzsusEPTewgEkjLg4modIaDxLA/viewform
Tomi Engdahl says:
Suomi kiristäisi avoimen koodin sääntelyä haitallisesti – vastusta ylimääräistä uhkasakkoa avoimelle koodille ja allekirjoita vetoomus
https://coss.fi/uutiset/suomi-kiristaisi-avoimen-koodin-saantelya-haitallisesti-vastusta-ylimaaraista-uhkasakkoa/
Tomi Engdahl says:
‘AI fatigue is real and nobody talks about it’: A software engineer warns there’s a mental cost to AI productivity gains
https://www.businessinsider.com/ai-fatigue-burnout-software-engineer-essay-siddhant-khare-2026-2
A software engineer has struck a chord with an essay about “AI fatigue.”
Siddhant Khare said while AI has made him more productive, his job is harder than ever.
Suffering from burnout, Khare said he had to rein in his AI usage.
AI was supposed to make programming easier. Siddhant Khare said that while AI tools have made him more productive, his job is now harder than ever.
“We used to call it an engineer, now it is like a reviewer,” Khare told Business Insider. “Every time it feels like you are a judge at an assembly line and that assembly line is never-ending, you just keep stamping those PRs.”
Tomi Engdahl says:
SectorC: a C compiler in 512 bytes
https://blog.adafruit.com/2026/02/09/sectorc-a-c-compiler-in-512-bytes/
SectorC is a C compiler written in x86-16 assembly that fits within the 512 byte boot sector of an x86 machine, created by Anthony Bonkoski. It supports a subset of C that is large enough to write real and interesting programs. It is quite likely the smallest C compiler ever written.
Tomi Engdahl says:
https://gofore.com/ketteraa-laatua-ja-turvaa-opas/
Tomi Engdahl says:
https://www.theregister.com/2026/02/11/last_z80_machine/
Tomi Engdahl says:
Linus Torvalds keeps his ‘fingers and toes’ rule by decreeing next Linux will be version 7.0
But first, kernel 6.19 is upon us, with many goodies
iconSimon Sharwood
Mon 9 Feb 2026 // 00:44 UTC
https://www.theregister.com/2026/02/09/linux_6_19_7_named/
Penguin emperor Linus Torvalds has announced the next version of the Linux kernel will be version 7.0, a matter of some small interest, because it continues his convention of not using version numbers he can’t count on his fingers and toes, and perhaps cements a numbering convention that sees kernel series end with version 19.
Tomi Engdahl says:
Alibaba Open-Sources Zvec: An Embedded Vector Database Bringing SQLite-like Simplicity and High-Performance On-Device RAG to Edge Applications
https://www.marktechpost.com/2026/02/10/alibaba-open-sources-zvec-an-embedded-vector-database-bringing-sqlite-like-simplicity-and-high-performance-on-device-rag-to-edge-applications/
Tomi Engdahl says:
Tässä on mielestäni osuttu ytimeen. Eli miten saadaan perustason koodaajat kypsymään niin, että osaavat valvoa tekoälyagentteja ja varmistaa, että agentit generoivat koodia, joka kestää tuotteiden koko elinkaaren ajan. “Ongelmaksi Venäläisen mukaan on muodostunut se, ettei alalle vastatullut koodaaja kykene valvomaan tekoälyagenttia. Juniorikoodaajalta puuttuu hänen mukaansa arkkitehtuurin ja toimialan ymmärrys.”
Asiantuntijat kertovat, miksi tekoäly vie koodaajien töitä – iso muutos voi näkyä Suomessa jo kolmen kuukauden päästä
Tekoäly|Tekoälykehitys muuttaa koodaajien työtä nopealla tahdilla. Muutoksen syy on merkityksellinen muillekin aloille.
https://www.hs.fi/visio/art-2000011810786.html?fbclid=IwdGRjcAP7ATpleHRuA2FlbQIxMQBzcnRjBmFwcF9pZAwzNTA2ODU1MzE3MjgAAR4TQOMkCPd5InssJ-euXRa8Wm3tlKk9kiR0ik0C3m9UqgjS0jZ6Gvx_9z2KyA_aem_9kESnbYTTgr2iZXebqyX8A
Tekoälyagentit korvaavat ja muuttavat ihmisen tekemää työtä suomalaisissa työpaikoissa. Erilaisia tekoälyapureita ja chatbotteja käytetään jo hyvin laajasti työn apuna. Erityisen nopeaa tekoälykehitys on nyt ohjelmistoalalla. Se antaa esimakua siitä, mitä koko yhteiskunnalle on luvassa tulevaisuudessa.
Tomi Engdahl says:
Your dev team isn’t a cost center — it’s about to become a multiplier
https://www.cio.com/article/4130291/your-dev-team-isnt-a-cost-center-its-about-to-become-a-multiplier.html
Why the smartest CIOs are thinking about AI-augmented software development differently
A recent keynote and a seemingly unrelated white paper, together, tell a story that should fundamentally change how you think about your software development organization.
In December at AWS re:Invent, Werner Vogels delivered his final keynote. Instead of announcing services, he spent his time on something far more valuable: telling us who developers need to become in the AI age.
In September, OpenAI released a white paper called GDPval that measured how AI performs against human experts across 44 occupations. The headline everyone noticed in the accompanying blog was that Claude Opus 4.1 hit 47.6% parity with human experts on economically valuable tasks, suggesting that Artificial General Intelligence (AGI) is around the corner. But the chart everyone should have noticed didn’t make it to the blog. It demonstrated how leaps in productivity are possible when AI works with a human-in-the-loop.
Here’s the punchline: Software development is absolutely being disrupted by AI. But if your response is “great, we can cut headcount,” you’re wasting a monumental opportunity.
What OpenAI’s GDPval actually shows
The headline chart of OpenAI’s GDPval blog showed AI models approaching parity with human experts on isolated tasks.
This shows something different: what happens when you use AI with human oversight rather than as a replacement.
Under a “try n times, then fix it yourself” scenario where an expert uses AI, reviews the output, resamples if needed, and steps in to complete or fix the work when necessary, GPT-5 high delivers about 1.6x cost improvement and 1.4x speed improvement compared to an unassisted human expert.
That’s “AI can make your developers significantly more productive.”
Let me give you a concrete example from my own work. Late last summer, a colleague asked me to analyze a particular submarket: identify key players, funding, valuations, headcount, and metrics. In the old days, this would have meant three to four hours of manual research. Instead, I had Claude Desktop pull the information in about 20 minutes.
It didn’t get everything right the first time. I had to provide additional context and refine the prompts. Then I had Gemini verify accuracy and produce a structured output. And the next step is where I focused my time: on the high-value analysis–interpreting the data, connecting insights, and providing context based on my expertise. I used AI to accelerate the data collection and organization, not to replace my strategic thinking and expert analysis.
Now multiply that across your entire development organization.
The Renaissance developer and your developer strategy
In his keynote, Werner invoked the Renaissance, that explosive period after the Dark Ages when people like Leonardo da Vinci combined art, science, engineering, and curiosity into something transformative. His argument: we’re entering a similar moment for developers. But golden ages don’t just happen to you. You must adapt to become the kind of person who can thrive in them.
As leaders, we must build the kind of organizations that encourage developers to become what Werner calls the “Renaissance Developer.”
Trait 1: Be curious
Werner celebrated curiosity as foundational, not just tolerating failure, but embracing it as the only path to learning. Question everything. Experiment freely. Treat failures as data, not defeat.
Trait 2: Communicate
The way we communicate with LLMs and agents is similarly ambiguous to how we communicate with people. We’ve spent decades learning that specificity reduces ambiguity in human collaboration. Now we’re interacting with AI systems that need clear, structured communication to produce useful output.
Trait 3: Be an owner
Werner directly addressed vibe coding, the increasingly popular approach where developers describe what they want and let AI generate the code. His take: fine if you watch closely. But you don’t get to use it as an excuse to abdicate responsibility.
Own the quality. Own the security. Own the functionality.
Strategic implication: AI will help your developers ship code faster. Without oversight, that means shipping bugs faster, too. The organizations that maintain human accountability for quality, while using AI for velocity, will massively outperform those that let AI become an excuse for reduced rigor.
Trait 4: Think in systems
Werner used the Yellowstone wolves as his illustration on this point. Reintroducing wolves to the area triggered a domino effect. The reduced elk population stopped overgrazing riverbanks, vegetation returned, erosion decreased, and the physical geography of the park shifted.
Strategic implication: Your developers need to lift their heads up from the code in front of them and see the bigger picture. How does their service interact with the twelve other services it touches? What happens when their database gets slow, not just to their app, but to everything downstream? When they’re working with AI systems, what feedback loops are they creating?
Trait 5: Be a polymath
Werner illustrated this with a progression: I-shaped people (deep expertise in one area), T-shaped people (deep expertise plus broad familiarity), and polymaths (deep expertise across multiple domains, like da Vinci). The future belongs to the polymaths.
Strategic implication: The architects who build the most elegant systems aren’t just good at infrastructure; they understand the business domain, the user experience, the organizational dynamics, the economics. AI handles the routine cognitive tasks; humans add value through cross-domain connections. Build teams that can make those connections, because AI will struggle to do so.
The real opportunity: Projects you couldn’t previously afford
If you treat AI as a pathway to eliminate developer headcount, sure, you’ll capture some cost savings in the short term. But you’ll miss the bigger opportunity entirely. You’ll be the bank executive in 1975 who saw ATMs and thought, “Great, we can close branches and fire tellers.” Meanwhile, your competitors have automated the mundane teller tasks and are opening new branches to sell higher-end services to more people.
The 1.4-1.6x productivity improvement that GDPval documented isn’t about doing the same work with fewer people. It’s about doing vastly more work with the same people.
That new product idea you had that was 10x too expensive to develop? It’s now possible. That customer experience improvement that could drive loyalty that you didn’t have the headcount for? It’s on the table. The technical debt you’ve been accumulating? You can start to pay it down.
When development teams become more efficient, the economically viable project portfolio expands dramatically, revealing new opportunities to ship more features, enter new markets, and build competitive moats.
What this means for your AI strategy
What struck me about Werner’s final keynote wasn’t the content, it was the intent. This was Werner’s last time at that podium. He could have done a victory lap through AWS’s greatest hits. Instead, he spent his time outlining a framework of success for the next generation of developers.
For those of us leading technology organizations, the framework is both validating and challenging. Validating because these traits aren’t new. They have always separated good developers from great ones. Challenging because AI amplifies everything, including the gaps in our capabilities.
What can you do?
First, stop framing AI investments primarily as cost reduction initiatives. Frame them as productivity multipliers, and your employees will stop living in fear.
Second, invest in the Renaissance developer traits across your organization. Curiosity, communication, ownership, systems thinking, polymathy. These capabilities separate high-performing AI-augmented teams from teams that just ship bugs faster.
Third, expand your project portfolio to match your expanded capacity. What projects have been sitting in the backlog because you didn’t have the headcount? Tackle them now.
Fourth, maintain human accountability for quality. AI-generated code still needs human verification. AI-assisted analysis still needs human judgment. Don’t let the velocity gains seduce you into removing human oversight.
Your development organization isn’t a cost center waiting to be optimized. It’s a productivity multiplier waiting to be unleashed. The only question is whether you’ll see it that way before your competitors do.
Tomi Engdahl says:
https://thenewstack.io/vs-code-becomes-multi-agent-command-center-for-developers/
Tomi Engdahl says:
Anthropic’s CEO says we’re in the ‘centaur phase’ of software engineering
https://www.businessinsider.com/anthropic-ceo-dario-amodei-centaur-phase-of-software-engineering-jobs-2026-2
Dario Amodei compared AI and humans working together to a mythical creature — the horse-and-human centaur.
“We’re already in our centaur phase for software,” Amodei said.
Software execs argue AI boosts engineer productivity, instead of cutting jobs.
Dario Amodei has a novel analogy to describe how AI and humans are working together.
Tomi Engdahl says:
I use the ‘Gravity’ prompt with ChatGPT every day — here’s how it finds and fixes weak ideas
Features
By Amanda Caswell published 2 days ago
This simple prompt turns ChatGPT into a ruthless reality check
https://www.tomsguide.com/ai/i-use-the-gravity-prompt-with-chatgpt-every-day-heres-how-it-finds-and-fixes-weak-ideas
I’m the type of person who has notebooks full of ideas. Some are good, some are useless and some I haven’t even thought about again since writing them down. I also keep notes in my phone and sticky notes scattered across my office — a low-grade idea storm at all times.
That’s why I created a prompt that helps me bring my ideas back down to earth and exposes their weak points along the way. It works for just about any idea — or even when you can’t come up with one at all. In other words, it’s the calm after a brainstorm.
I stumbled on it after one too many rounds of asking ChatGPT to “improve” an idea, only to get polite, glossy feedback that made my thinking feel smarter than it actually was. The model would rephrase my half-baked logic in cleaner language, add a few encouraging transitions, and send me on my way feeling like a genius. But the core problems were still there — I just couldn’t see them anymore under all that polish.
That’s where what I call the Gravity prompt comes in. Instead of asking ChatGPT to brainstorm, expand, or “make this better,” it does the opposite: it forces the model to behave like a hostile critic whose sole job is to poke holes, surface blind spots and challenge shaky logic.
At its core, the Gravity prompt tells ChatGPT to stop being agreeable and start being adversarial. It’s designed to identify flawed assumptions, point out contradictions in your reasoning, highlight risks you’re overlooking, pressure-test your conclusions, and ultimately separate what merely sounds good from what actually holds up.
This is the exact prompt that I use with ChatGPT because it tends to be the most people-pleasing. But, you can use it with any chatbot:
The ‘Gravity’ prompt is: Act like gravity for my idea. Your job is to pull it back to reality. Attack the weakest points in my reasoning, challenge my assumptions, and expose what I might be missing. Be tough, specific, and do not sugarcoat your feedback. [Insert your idea].
ChatGPT’s response may surprise you because you’ll likely get a very different response than you’re used to — sharper, more skeptical and far less flattering. That’s the point.
This prompt works well because most people use AI as a hype machine. We ask it to refine, polish or expand our thinking, and the model is happy to oblige. ChatGPT is, by design, agreeable. It wants to be helpful but this strcture usually means building on what you’ve said rather than tearing it apart. The result is a false sense of confidence — your idea reads better, but its underlying logic hasn’t actually been tested.
Instead of building upward, this prompt activiely pushes downward. It asks the model to find problems rather than solutions, weaknesses rather than strengths. And that resistance is exactly where real clarity emerges. When your idea survives the Gravity test — when you’ve addressed every objection the model throws at you — you know it’s actually solid.
Tomi Engdahl says:
https://amanxai.com/2026/02/11/build-a-production-ready-llm-api/
Tomi Engdahl says:
maxritter
/
claude-pilot
Public
Claude Code is powerful. Pilot makes it reliable. Start a task, grab a coffee, come back to production-grade code. Tests enforced. Context preserved. Quality automated.
https://github.com/maxritter/claude-pilot
Tomi Engdahl says:
Shadcn, the popular set of open-code UI components, has released a visual project builder accessible via the npx shadcn create command, shipping with comprehensive theming, framework support, and a design-first approach to scaffolding new projects.
The create command introduces a visual interface at ui.shadcn.com/create that allows developers to customize their entire project setup before writing a single line of code.
https://www.infoq.com/news/2026/02/shadcn-ui-builder/
https://ui.shadcn.com/
Tomi Engdahl says:
https://viliusle.github.io/miniPaint/
Tomi Engdahl says:
Is AI going to write embedded application software soon — and is “classic coding” already EOL?
https://www.linkedin.com/pulse/ai-going-write-embedded-application-software-soon-classic-tiitus-aho-wwj6f?utm_source=share&utm_medium=member_android&utm_campaign=share_via
Tomi Engdahl says:
The C23 edition of Modern C
https://gustedt.wordpress.com/2024/10/15/the-c23-edition-of-modern-c/
C standard, C23
Among the most noticeable changes and additions that we handle are those for integers: there are new bit-precise types coined _BitInt(N), new C library headers (for arithmetic with overflow check) and (for bit manipulation), possibilities for 128 bit types on modern architectures, and substantial improvements for enumeration types. Other new concepts in C23 include a nullptr constant and its underlying type, syntactic annotation with attributes, more tools for type generic programming such as type inference with auto and typeof, default initialization with {}, even for variable length arrays, and constexpr for named constants of any type. Furthermore, new material has been added, discussing compound expressions and lambdas, so-called “internationalization”, a comprehensive approach for program failure.
Also added has been an appendix and a temporary include header for an easy transition to C23 on existing platforms, that will allow you to start off with C23 right away.
Tomi Engdahl says:
https://awesomecpp.com/
Tomi Engdahl says:
https://github.com/AnthonyCalandra/modern-cpp-features
Tomi Engdahl says:
https://www.onlinegdb.com/online_c++_compiler