Many articles on dev.to are about coding, but we are rapidly entering a world where this job is done largely by AI and where humans review the code produced. This has consequences for which skills matter, so I want to focus here on the relatively neglected but increasingly vital skill of code review, and later describe a working example — Doclets — a small tool you can run, read and adapt.
This is a personal view, and it is meant to be argued with.
Recognition is easier than writing
As time goes by, you are ever more unlikely to be asked to write code — that job is fast becoming one performed almost exclusively by AI. Your job will be first to prompt the AI, and then to review the code presented in the language chosen for the project. Review relies not on writing but on reading and recognition, and it becomes increasingly effective the closer the language comes to a human language. Most coding tools still focus on writing code, assuming human programmers will continue to do it. Tools that put readability first are the exception — and AllSpeak is a deliberate one.
AllSpeak is a coding tool closely resembling natural human languages. It can do most of the things normally done by Python or JavaScript, but its focus is on keeping code readable by humans. Most things can be described clearly using natural language, so it makes sense for AI to be asked to code using constrained, unambiguous dialects of the languages used by the humans in charge.
Assertion
If a thing can be described in English, it should, if at all possible, be coded in something resembling English. If it's too complex to describe in English, the likelihood is it's simply too complex. Period.
AI has no trouble writing AllSpeak code; in our experience it even makes fewer mistakes than when using mainstream languages. This is partly owing to the constrained syntax of AllSpeak and partly to the comprehensive guidance provided to AI when using it.
Like natural language, AllSpeak relies on an extensive vocabulary instead of complex syntax. When you read a book on an unfamiliar subject or even in a foreign language, the structure of the text seldom causes you any difficulty; it's usually the words themselves. You consult a dictionary for the terms you don't know. When reviewing AllSpeak code, the dictionary is an AI agent: an unfamiliar word or construct in the code is just a question away. "What does this word mean?" or "What is this block doing?" — and the agent explains it in plain language. Recognition, assisted by the dictionary, is enough to review what was written. When complexity is handled by words in the language rather than by syntax, this process becomes far easier. Most of us find complex syntax hard to follow, whereas we can absorb a huge range of vocabulary with little effort.
Why programmers resist
Many programmers find the foregoing argument hard to accept — so used are they to regarding code as something they own. "Reading the code" has always been unpopular, and mostly for good reason. Every cycle, someone promises readable code and fails to deliver — so the scepticism is earned. The difference this time is that the code is no longer written by hand at all. When using AllSpeak, AI is under strict instruction to provide clear and comprehensive documentation for each block of code, for the benefit both of itself and of the humans who will review the code. If this stipulation is enforced, then regardless of what coding language is used, at last we can rely on documentation that's both accurate and complete. But even the best documentation will not save the reader from having to know the language itself — and the expertise that mainstream languages demand is precisely what will be increasingly hard to gain. A language that reads like one you already speak removes that barrier.
I have recently begun to avoid the word "programmer" when referring to those creating software, because in this scenario the term stops being useful. The people who remain are engineers — and the skills they need are very different from traditional ones. When we delegate to AI the job of creating and managing complex structures, we are free to step back and view the big picture or to zoom in to examine specific details. This is the essence of review, and natural language adds to this by opening the door of opportunity to those previously denied access.
Review is the coming skill
It is widely accepted that there are fewer and fewer opportunities to gain coding expertise. Review is the coming skill — and if toolchains remain as they are now, few will possess it effectively unless the language of code moves closer to human language.
The only alternative to accepting — and embracing — more accessible forms of language is to abdicate software generation to machines entirely. That is not a future most of us would choose willingly — and it is the default if we do nothing.
AllSpeak is a first step, not a destination
AllSpeak is not a final destination; it is a first step towards a new paradigm, one that must gradually become familiar to engineers. The closer the language comes to a human language, the more effective recognition becomes, and the less foreign the code feels.
A working example: Doclets
The Doclets project is a working example of the pattern. Neither hand-coded nor vibe-coded, it was produced through exactly the process described above — a succession of deliberately small steps in which AI writes and humans review, block by block. It is an example of a pattern — one of the few patterns that explicitly acknowledges the gap between coding and review and points the way to bridging it.
Doclets started as a solution to a real-world problem, that of how to organise information, either for an individual or for a small team. Information is typically strewn over a wide range of documentation tools, social media, email, web pages and more.
The chosen solution was to create a central, searchable repository of Markdown documents ("doclets"), organised into topics, to be read and edited in a browser on any device. A small server process owns the documents and talks to the browser over MQTT.
Plain search is a substring match over every doclet's title and content:
Clicking a result line opens the doclet in prettified (HTML) format, with an option to switch to Markdown for editing.
A second way to search uses a local language model: tick "LLM query" and ask questions like "list the main topics covered by the doclets in the AllSpeak topic" — a short prose answer comes back, derived from the collection itself, running entirely on your own machine.
For small teams, each topic has an owner and a visibility setting: public topics are readable by anyone, private topics only by the owner and named readers, with separate grants for creating, modifying and deleting, and a simple activity log of who did what and when.
Why it is easy to customise
The construction is deliberately simple — and that is the point. One language for the whole stack (the browser client and the server are both AllSpeak), screens defined declaratively, MQTT for request/reply, and the one heavy component (searching, securing, and the LLM integration) consigned to a Python plugin that adds a small vocabulary of its own to the language.
Customising Doclets — a new field, a different screen, a renamed command — is often a matter of recognising and adjusting existing code, not writing new code from scratch. That is the review skill this article argues for, applied to a tool you can run today.
This is a general picture, not a specification — the details live in the repository.
Ask your agent
Doclets is deliberately and unashamedly an AI project. If you want the detail, point your AI agent at the Doclets repository and ask for a synopsis, a full technical breakdown, or anything in between. Every section of the code carries a doc block explaining why it exists, so the agent — and you — can read it block by block. Recognition, with the dictionary always at hand.
The code is not the point; what the code does is the point. If AI writes the code and we review it well, software production becomes a skill of judgment rather than of syntax. That is a change worth embracing.
Photo by Cookie the Pom on Unsplash
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