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
660 Comments
Tomi Engdahl says:
https://github.blog/ai-and-ml/github-copilot/how-github-copilot-enables-zero-dns-configuration-for-github-pages/
Tomi Engdahl says:
Ex-Microsoft engineer rebuilds Notepad in 2.5KB using nothing but stuff Windows already had
https://www.windowslatest.com/2026/07/04/ex-microsoft-engineer-rebuilds-notepad-in-2-5kb-using-nothing-but-stuff-windows-already-had/
Tomi Engdahl says:
https://www.infoworld.com/article/4194096/visual-studio-update-rejiggers-github-copilot-usage-tracking.html
Tomi Engdahl says:
https://towardsdatascience.com/llm-wikis-are-over-engineered-i-replaced-mine-with-a-pure-python-compiler/
Tomi Engdahl says:
https://thenewstack.io/killing-the-code-review/
Tomi Engdahl says:
https://www.langchain.com/blog/introducing-openwiki-an-open-source-agent-for-repo-documentation
Tomi Engdahl says:
Nika: Open-source code analysis tool
Many serious security bugs in web applications sit across several files at once. Request data enters through a controller, moves through data objects and service layers, and turns dangerous only when it reaches a sensitive operation such as a database query or a file action. A scanner that reads one file at a time can miss that path entirely.
https://www.helpnetsecurity.com/2026/07/01/nika-open-source-code-analysis-tool/
Tomi Engdahl says:
https://linuxiac.com/qsoe-0-1-debuts-as-a-qnx-inspired-open-source-os-for-risc-v/
Tomi Engdahl says:
https://towardsdatascience.com/time-series-llms-explained-with-t0-alpha/
Tomi Engdahl says:
https://kotimikro.fi/oheislaitteet/kayttojarjestelma/android/androidin-tyopoytatila-nyt-puhelinta-voi-kayttaa-tietokoneena?fbclid=Iwb21leASyRG9jbGNrBLJEZ2V4dG4DYWVtAjExAHNydGMGYXBwX2lkDDM1MDY4NTUzMTcyOAABHpgtc8GD6-gdPmDdFjvJdhtxroe_3s6mAXk1_dISqpTCnVAaWIRzQE8GTzmE_aem_XXsBx5rU_u-c1nvEBNyDQw