AI isn't the solution to all your problems. AI Alternatives are plenty and specific to your use case. Understanding the nature of your query is the first step for finding an alternative or using AI smart and responsibly.
Summary: Don't use AI unless you need to! Prefer local, lightweight and privacy-preserving services like Ecosia AI or Thaura for simple requests and Claude, Sonnet, Copilot for coding assistance. Rediscover seemingly obsolete AI alternatives!
What are Alternatives to AI Assistants?
- human experts (developers, translators, artists, ...)
- specialized algorithmic tools like linters and IDEs
- search engines like Google, Ecosia, DuckDuckGo
- documentation
- forums
- StackOverflow
- research, learning by doing
- creative and analytic thinking
Techniques like rubber duck dialog development, feature branches, merge/pull requests, code reviews, explicit acceptance criteria, and test-driven development can also be used when working alone.
Why does it matter?
AI is prone to problems affecting its output: hallucinations, incompleteness, inconsistency, and bias. AI usage is costly, and the popular free services might require expensive paid plans or downgrade to sponsored light versions at any time. AI is costly for humanity, even if it's cheap or free for you: computation, databases and training consume energy, hardware, and human assistance wasting precious resources and threatens to accelerate climate crisis and threaten established business models that provide jobs for experienced experts.
Abstract and Article Contents
- What are alternatives to AI assistants?
- Why does it matter?
- Abstract and Article Contents
- Are Algorithms better or worse than AI?
- Reasons for not using AI tools
- Does AI Harm or Benefit Learning?
- What do people use AI for?
- Users Pay for Incorrect AI Answers
- Effort and Laziness as Human Virtues
There are numerous possible reasons for not using AI tools, so it's good to have an alternative. Alternatives to AI assistants differ based on what AI assistants are specifically used for, like text processing, debugging, inspiration, research, learning, code creation, code explanation, refactoring, and generating images. Algorithms, linters and classic coding tools can be helpful alternatives. AI alternatives like algorithms can be hard to tell apart from LLM-based AI from a users's perspective, and even a historic chatbot like Eliza can act as a "rubber duck" dialog partner to help people focus and find answers.
AI assistants based on large-language models excel at processing text, but fail to understand existing code and generate production-level code without introducing bugs. Prompt-based image generation is likely to produce low-quality output. Instead of learning how to prompt better, people should focus on their strengths and use both AI and human experts when quality matters.
Are Algorithms better or worse than AI?
It depends. Algorithms aren't better or worse than AI in general, they're different strategies and their typical use cases overlap. From the end-users' perspective, both lack transparency and their recommendations are prone to bias and manipulation. Algorithms are more predictable and much more efficient. AI is worth a try if nothing else helps. However, using a big data model to produce unreliable output violates several best practices of software development, including the rule of least power.
Overusing AI weakens your intellect and creativity like couch potatoeing weakens your muscles and counteracts years spent in the gym or on the courts quite quickly.
Reasons for not using AI tools
There are many valid reasons against using a speicifc AI model or banning AI usage completely. You have a point against using AI when...
- there is a more suitable tool
- the AI's answers or code doesn't help you
- you have been blocked
- you have used up your tokens
- the AI service is down
- policies (you are not allowed to use it)
- ethical and ecological concerns
- you want to practice and learn.
Lies and Hallucinations
Marketing and enthusiastic blog posts fuel unrealistic expectations that no AI can fulfil. Worse, reliability of AI results seem to deteriorate with increasing complexity of its models and more restrictive usage limits despite alleged competition and a race for user retention.
According to my own experience as a professional web developer, AI is as unreliable as ever, except for a few straighforward use cases like boilerplate code adaption and summarizing and checking large amounts of written text. Anything else is still a gamble that's not getting sustantially better. In 2026, Several American AI startups have gone into the outfield already:
- Perplexity keeps insisting on misread and made-up quotations and hallucinations, gambling its lead over other AIs in research and reflection skills. Perplexity's lies have gone so far overboard that The Wall Street Journal and New York Post filed a lawsuit against Perplexity, explicitly alleging that the engine generates "fake news hallucinations" by attributing fabricated quotes and events to their journalists, damaging the publications' reputations for accuracy.
The Times also claimed that the startup's generative AI products created fabricated content, or "hallucinations," and falsely attributed them to the newspaper by displaying them alongside its registered trademarks.
Source: Reuters, 6 December 2025
According to my personal observation, Perplexity hallucinates more than Gemini or Claude and sticks more stubbornly to false claims within a conversation.
Ethical Concerns
However, Google's Gemini and its Google Search AI mode, formerly known as Bard, and their tendencies to hallucinate and prioritize low quality sources, is not the only reason users seek AI alternatives. A growing ethical movement in Europe focuses on reducing dependence on U.S. American technology giants. Surveys in early 2026 report that 73% of Europeans believe their societies are far too dependent on US tech companies. And among these, 83% said they’re concerned about this dependence, with 54% actively seeking European alternatives to regain control over their digital lives.
- ChatGPT faced a "QuitGPT" shitstorm after OpenAI President Greg Brockman and his wife donated millions to the MAGA Inc. organziation supporting Donald Trump in September 2025.
Google has long dropped its former "don't be evil" motto. In 2025, Google has adopted a more collaborative approach toward the Trump administration "to align with federal priorities and mitigate regulatory pressure" (quoting Google's AI mode). Google's holding company, Alphabet, removed mentions of its DEI commitment from its annual investor report, changed the title of its "Chief Diversity Officer" and executed a major reversal of its DEI policies, citing the need to comply with new executive orders and a changing legal environment. (Source: CBS News, February 2025)
The Chinese DeepSeek and Musk's Grok are no real alternatives for similar reasons. Powerful technology control does not belong in the hands of individuals or undemocratic regimes.
Fear of a Disruptive Catastrophe
Oxford professor Michael Wooldridge compared "AI" to 1930s Zeppelin airlines, i.e. an immature technology pushed to market by greedy investors resulting in a disruptive crash eventually. After the "Hinderburg" caught fire before killing many people preparing to land in New Jersey in 1937, the Zeppelin era was over.
However, let's assume that no such disruption has happended yet and that you, as a user, have a working AI service handy as an assistant answering your questions. Can such an AI help you to acquire and improve professional skills or should you avoid using AI as an aspiring developer?
Does AI Harm or Benefit Learning?
Using AI for learning has been discussed controversially. AI can help the clueless, but so can tutorials and documentation. In my experience, tutorials and AI can both make learners focus on irrelevant aspects and lead to an illusion of competence.
Getting good at Shoveling Dirt
Apart from its real limitations, AI assistants often fail to understand assumptions or don't understand your requirements. As an example, it took me three attempts to make the JetBrains AI assistant, which is integrated in my IDE, to consider the code snippet already highlighted in the open editor, to answer a specific question. Finding the answer without AI might have been much quicker, and practicing to code still provides more long-term value than practicing to prompt AI questions. Quoting Erik Dietrich's Surviving the Great Commoditizer, we shouldn't get "good at shovelling dirt."
What do people use AI for?
AI isn't always useless. Otherwise it wouldn't have become so popular. AI isn't only used by lazy or penniless people either. Let’s explore when it makes sense to use AI and which alternatives might be more suitable in which situation.
Text processing
Large language operations are what LLMs were made for: digesting, transforming and creating text, especially long text about topics that have already been written about extensively. You can ask the AI to analyze a text and suggest improvements based on criteria like readability, interestingness, or consistency. Asking an AI to summarize an essay's topics and central claims can be quick and easy. But even if you have no time or talent to read long text with your own eyes, there is an alternative: human experts. Pro: higher quality and accuracy, if you chose the right one. Con: experts cost time and money. But human experts often provide better value for money once you have to pay for AI and consider long-term total costs, customer conversion, or learning and practicing your professional skills.
Combining the advantages of all possible solutions, you could start with your own thoughts and take notes, then use AI or a search engine for more inspiration and aspect that you might have overlooked.
Write and let your thoughts flow. Read, edit, repeat.
Later, you can ask an AI and a human expert to review and suggest improvements. Don't rely on AI but choose an experienced human proof reader if quality is important!
Debugging and Development Support
A perceived strength of AI assistants that reminds me of ELIZA, an early chatbot that used simple text patterns to answer people who were made believe they were talking to a psychologist. That's also known as the teddy bear technique or rubber duck debugging, a method developers use to debug code or clarify their own understanding by explaining the problem out loud, often to an inanimate object or an imaginary interlocutor.
AI as an Advanced ELIZA
You can try out ELIZA at the New Jersey Institute of Technology's website.
Talking about your problems and answering simple further inquiries helps thinking and finding solutions. I used to draft several bug reports and StackOverflow questions that I discarded unsent because providing the necessary details and context in a readable way, preferably with a minimal reproducible code example, sometimes revealed a solution that might seem obvious in hindsight. A recent example quoted in Meme Monday:
(Source: Steph Smith, quoted by Matt Novak, quoted on Meme Monday) stephsmithio paleofuture.bsky.social https://dev.to/best_codes/comment/2pe6p
AI folks have now discovered "thinking":
Sometimes in the process of writing a good enough prompt for ChatGPT, I end up solving my own problem, without even needing to submit it.
Alternatives:
- thinking
- analytic thinking
- write down your problem
- try to explain your problem to somebody
Why it works: thinking is a uniquely human skill that improves the more we do it. No algorithm or deep learning computer system has yet been able to mimic the magic that happens when humans think and become creative.
Inspiration, Ideation
Another perceived strength of AI that might turn into a trap: asking for help too early before trying yourself can bias your thoughts and ideas around those answers and prevent better or more creative alternatives that were already in your head - or somewhere else. The process of inspiration often seems random. Many creatives like to go for a walk in a park or a forest, or change location, sit in a café or a library or a shed in the countryside.
If you are looking for real random inspiration, you can pick a book and open random pages and underline words before opening your eyes or use a deck of Tarot cards. If that seems too random, may you don't need inspiration but you're already researching.
Less randomly, blogging platforms like medium, substack, hashnode, or hackernoon are full of posts that you can navigate by topics or see who's following who to explore an inspiring author's inspirations. I'm a little-known substack author right now, and I still post on my existing medium account from time to time (mostly in German now).
FAQ, Q&A, Research
Use a search engine or see Google alternatives like discussion boards and official documentation. The latter is probably the most underrated source of information at your fingertips without investing much time, money, or energy.
Commonplace advice about topics that you're not familiar with are traditionally found in books in a bookstore, online, or in a library. You might also try and find a knowledgeable person to talk to.
AI for decision making is doubtful. Where do they get their info from? Is it outdated or biased? When you research, you can at least decide from case to case if you trust the source or if it sounds shady. Fake Reviews, marketing content might make a trend sound too good to be true. Edge case problems might be irrelevant in your situation. Alternatives? Research and if possible ask people you know about their experience , inside a large company or a community.
Popular alternatives included StackOverflow but SO's guidelines used to forbid questions that tend to attract opinionated answers, explicitly including questions about best practice. In 2026, this restriction is no longer enforced, as StackOverflow struggles to retain its active community and desperately seeks to attract new users.
Documentation, again, can be a valuable authoritative source of truth, and - much like StackOverflow - ideally be the result of other experts' diligent research and discussion, even more so if that documentation is an official or a de-facto standard or most popular recommendation.
If you want to use AI for decision making, make sure to be specific. Ask critical further questions and insist that it respects both common knowledge and your specific requirements and insist that it does not neglect important aspects. Ask where it got its facts and make it search the web (might require premium paid plan) for up-to-date information.
Code Creation, Explanation and Refactoring
Coding assistance tasks range from single-line auto completion suggestions and simple contextual questions to context actions like refactoring and static code analysis to complex code generation.
The context action for "finding problems" in code has already spared me and my code reviewers unnecessary refinement rounds. Linters and static code analysis tools can be used alternatively or together with AI to improve code quality.
Maybe AI assistants will finally popularize test-driven development. Most developers don't like writing tests or documentation. AI-generated tests might be better than no tests at all, but on the other hand, they might give a false feeling of safety while testing the obvious in a naive or wrong way, and they might become a liability when maintaining a code base over time.
One Step Forward, Two Steps Back
Developers think AI makes them 24% faster. The data says 19% slower, according to a METR study published in January 2026. The meter is always running.
Developers spend more time reading code than writing it, but it's hard to anticipate edge cases and possible problems and misunderstandings in advance. Even if we try to write test-driven, type-safe, clean code with descriptive naming and concise documentation, the result often becomes legacy code sooner or later, that future developers have a hard time to understand.
Can AI help to Understand Legacy Code?
Explaining existing code needs context, analysis of existing code and recognition of common design patterns. That can't be done googling or browsing a text book. In my personal experience as a web developer, all AI tools that I have evaluated recently (in 2025 and 2026) failed to explain legacy code and provide correct and complete answers suitable to fix or extend an existing project.
When working on legacy projects, especially those written by other developers that aren't available to answer questions anymore, my experience and intuition usually helps me more than AI does.
In 2027, this might have changed at least in theory, at least for open-source code openly discussed and inspected without fearing to give sensitive customer data into the wrong hands. In theory, there should be enough context and historical examples to understand a legacy Symfony, Typo3 or WordPress project. However, based on the AI's past development, I haven't seen any real progress from 2025 to 2026 concerning understanding existing code that's not TypeScript, Python, Rust or React. AI still can't understand legacy code.
Maybe AI providers already focus on what they're good at. That would be a legitimate choice. However, it would also be fair to admit this openly to their customers.
Complex Code Generation vs. Reading the Manual
While AI can speed up creating a proof of concept quickly to evaluate a new framework or for for throw-away for a client demo or to illustrate your requirements, don't use its code as a basis for production-level software. AI tends to introduce unnecessary technical debt and waste resources for fixing obscure bugs in the same time could be doing proper development.
AI works best in well-documented, simple scenarios, like creating a CRUD React app using Tailwind to style a boring frontend. But do we reall need costly language models to do that?
Alternatives to AI-Assisted Code Generation
There are libraries of complex code often intended as boilerplate code with helpful comments, and there are wizards like the (discontinued) create-react-app. Some tech stack have a client showcase or ready-made live demos that give everyone a better impression than some AI-generated guess that seldom work properly.
In general, developers should prefer tools designed for specific tasks, following the UNIX philosophy and the principle of single responsibility.
- domain-specific code creation tools
- example code found in official documentation
- example code found on GitHub
- learning by doing
- ask or hire a senior expert
Pro: human experts can solve problems where AI assistants fail. Seniors can use their experience and intuition. Humans know about the real world and might be better at thinking beyond explicit requirements (although most can't or don't seem to care). Cons: humans make mistakes, too. Humans get tired, have an ego and you need to give them coffee or money to make them work.
Alternatives to AI-assisted Image Generation
Use charting tools to create technical diagrams. Use a graphics tablet, vector drawing software, or your own hands, to draw creative cartoons. Seriously!
In 2025, I asked AI to draw a cartoon to illustrate the claim that "human programmers get tired, make mistakes, have an ego and you need to give them coffee or money to make them work." AI doesn't complain about my lazy prompt with words partially overheard in some other developers' discussion, not a real joke, and no idea about the desired outcome. If it's not too busy, it proceeds to create an image that you might mistake for a funny cartoon if you don't read the text on it.
I suspect that this OpenAI-generated cartoon was probably copying someone's style without warning me, and the crash-test-dummy-lookalike using screen and keyboard is so stupid that it doesn't work without additional text.
OpenAI vs. Google Gemini
Google's Gemini is not much better, taking more liberties trying to be "creative" and possibly mimicking another uncredited artist's style. Fast-forward one year, Google's image generator is called Nano Banana and I made it remake its original cartoon into a softer pseudo-vintage style retaining the same characters and postures.
AI assistants behave like the metallic robot in the second picture: "prompt received, code generated." Who's Grang, by the way? And Laz?
Only that the code often doesn't work, or when it does, it contains subtle bugs and technical debt that you'll find out about later.
Users Pay for Incorrect AI Answers
This is an actual discussion with an AI chatbot (Claude Sonnet) in 2026 where it literally admits that AI providers make their users pay for erroneous answers:
Please clarify: do your "thinking" answers with those errors still count against my AI token budget or are they for free?
Great question - yes, my responses absolutely count against your token budget, errors and all.
Every token I generate - whether it's correct, incorrect, repetitive, or just me rambling through mistakes - uses your budget. The "thinking" you're referring to might be extended reasoning that some AI models do, but regardless, all output tokens cost you.
So when I:
- First gave you incomplete advice (suggesting GUI tools)
- Then gave you wrong information (text domain in filenames)
- Then had to correct myself multiple times
...you paid for all of that. Which is frustrating, and I understand the 😵💫 reaction even more now.
There's no "free error correction" tier. Every token in my responses - good or bad - counts the same against your budget.
There is no free "artificial intelligence".
Effort and Laziness as Human Virtues
Make an effort! Don't neglect learning, practicing and real human interaction, and learn to be lazy! Laziness is praised as a virtue in hacker culture. Being positively lazy increases productivity, leading to better solutions, automating repetitive tasks, and refusing to do what's unnecessary (YAGNI principle: "you ain't gonna need it!")
Recovery for AI-Dependent Coders
Take a break away from the computer and get some inspiration and interaction in the real world! Use your hands and dare to be inefficient! Don't strive for efficiency, strive to be effective and individual!
"I might be lazy and starving for c0ffee, but at least, I'm original!"
Further reading: a practical guide by Cesar Aguirre ...
... and the other parts of this article series:
Top comments (16)
Yeah the way I see it is: the alternative to AI is to keep doing what we were already doing before AI !
And that includes Google ("classical" search), Stack Overflow, etc - yes, that "reviled" Stack Overflow, which you argued we should find 'alternatives' for, might actually be our best friend and one of our "secret weapons" to fight the onslaught of "AI domination" ... ? Think about that for a moment :)
Good point @leob , that's why StackOverflow came only as top 6, and they're all into the AI hype now, anyway. I wish that search engines got any better, instead whenever asking AI, I have to insist it shows me sources to prove its results.
Likewise, if Google search was smarter and wouldn't prioritize their advertising partners' spam over my actual search, I wouldn't need Gemini at all in most cases. Google, just fix your search instead of wasting energy with that guessing bot incorrectly refered to as "artificial intelligence"!
I don't need an AI to help me brute-force trial and error coding.
If there was "just"
why would we need "AI assistants" to get our work done?
I'm glad you wrote this, and I really like & appreciate what you're saying, but it's sad that we have to tell people that they don't have to and shouldn't use AI for everything. I'm hoping that the twitter screenshot you included is either a needle in the haystack or satire, but it's still terrifying to think that there are people out there who simply don't think anymore. Really great article though. I always enjoy reading your posts.
Weird place for the article content, that can only be done by a human.
haha, yes, a human who hates when articles start with a page full of TOC links instead of the first paragraphs. I think that blog posts should jump right into the content, more like a short story, not a large book you might find in a university library.
I was curious and I found that you can add the details and summary tags.
The fix for your TOC hate. Which I totally get.
Thanks, that will save me typing next time!
I updated this article again in 2026 to reflect current and upcoming legal, ethical, and technological aspects to help you decide if and how you'll use AI in 2027. A focus on content creation and image generation can be found in my follow-up publication, If Writing still Matters, How to Do it Right and Avoid AI Suspicion? on DEV.to.
Here is a recent DEV Meme Monday cartoon crafted using prompt engineering and human photography to create a brown colored cartoon style avoiding obvious clichés of generic AI image generation defaults:
Cartoon: "Prompt engineering be like": Coffee ordering engineer: "I'm a coffee ordering engineer! I speak the secret language to the baristas for you. I provide a valuable service bridging the gap!" Customer: "I just want a black coffee."
(Source: Meme Monday)
StackOverflow 2025 Developer Survey results highlight AI sentiment and usage: "84% of respondents are using AI tools this year. 66% of developers are frustrated with AI solutions that are almost right. More developers actively distrust the accuracy of AI tools than trust it. Positive sentiment to AI tools has decreased in 2025."
Another observation, as common wisdom claims that AI coding assistance works better with new, more explicit and type-safe front-end code like Astro, React, Vite, and TypeScript, the npm-based web development ecosystem still suffers the issue of recurring breaking changes and incompatible peer dependencies, and AI does not handle that much better than human trial-and-error plus watching unresolved GitHub issues did
I'd prefer stable software, good documentation, and relevant search results to AI-assisted guesswork.
I have updated the posts and added a paragraph about using dialog techniques and code reviews even when working alone.
Great article, Ingo. It’s refreshing to see someone highlight the value of human tools and discipline in a time when AI is everywhere.
That said, for devs who do want to use AI but need tighter control over data privacy and hallucination prevention, I’d suggest checking out TeraVera. I work there, and we built a secure AI platform with strict tenant-level data isolation. It’s designed for people who want the benefits of AI without the risks of data leakage or model overreach.
right... This isn't really an article
I was expecting some out of the world alternatives. But it seems you just listed the ways we used to do it before AI.
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