#ai-coding

[infosprint]

Stop Writing Code! AI Code Assistants & No-Code Are The Future

Is AI taking the future of software development with its code and no-code? More than 50% non-experts are already using AI-driven coding and no-code platforms - often without being familiar with it. Platforms like WordPress, Wix, and Mailchimp utilize no-code or low-code development to create templates, emails, websites, and more. This technology is popular for its ease of use, which doesn’t require coding expertise, and its fast deployment, among other benefits.

According to a Gartner survey, Over 70% of new applications were built using this technology. This shift is not just for beginners—even experienced developers are embracing AI-powered code assistants to enhance productivity and efficiency.

The software industry is going through a significant shift, one that could redefine how applications are built. Infosprint Technologies is a software company leading the digital transformation with AI technology to enhance productivity and efficiency. Explore the software transformation and what the future holds for this technological evolution.

Demystifying AI Code Assistants: What You Need to Know

AI code assistants are tools designed to assist software developers in writing, debugging, and optimizing their code. These platforms utilize artificial intelligence (AI) and machine learning (ML) to analyze vast open-source code and programming documentation sets. This enables them to provide intelligent suggestions, ultimately enhancing the efficiency of the software development process.

AI-code Assistants: The Key Features

Code autocompletion: AI codes help developers by suggesting complete or missing lines in a code, reducing manual efforts.

Bug detection and fixes: This helps fix any errors and bugs and suggests an alternate code that will enhance and improve the deployment time of the application.

Code refactoring: Helps improve code structure and readability while maintaining functionality.

Code translation: involves converting code from one programming language to another.

Security Analysis: Detects vulnerabilities and suggests security best practices.

Integration with IDEs: Works seamlessly within development environments like VS Code, JetBrains, and GitHub.

A recent GitHub report found that developers using AI assistants like Copilot completed coding tasks 55% faster than those who didn't.

No-Code is Here: Why You Should Pay Attention

No-code development is a method for creating applications without writing any code. Unlike traditional coding, no-code development uses programs that let users drag and drop objects. This approach includes pre-built integrations and automation tools, making app development accessible to non-technical users. The popularity of this technology is growing because:

1. Demarcation of development

This technology removes technical barriers between software developers, non-developers, marketers, and business owners.

Reduces the dependency on developers, making business agile.

2. Cost-Effective:

Lower development costs by eliminating the need to hire expensive developers.

It doesn't require technical skills to maintain infrastructure and updates.

This technology is optimal for startups that need to launch quickly with limited budgets.

3. Rapid prototyping:

Reduces the time to market for new products.

Developers can test MVP(minimal viable products) without worrying about budgetary constraints.

4. Enhanced collaboration:

Business and IT teams can work together without a technical bottleneck.

Marketing, operations, and sales teams can create internal tools without waiting for developer support.

Encourages a more agile development process.

Popular No-code platforms

Bubble: An illustration program that allows users to build dynamic web apps.

Webflow: A no-code website builder with advanced design customization to design responsive websites.

Zaiper: Connect different apps to create automated workflows without coding.

What's Next in Software Development?

AI assistance has evolved rapidly from merely suggesting incomplete code to analyzing, modifying, and debugging current code. These advancements are changing how we develop software, what we create, and the very nature of the developer's role.Adopting these adjustments will be critical for success in the years ahead.

1. AI-First Development

Artificial Intelligence can analyze business needs and suggest optimal architecture, frameworks, and programming languages based on the project scope. This helps developers save time for manual research and planning.

AI-powered tools can analyze user behavior trends and suggest design improvements for better engagement, thus leading to higher-quality products with improved user experience.

Organizations have improved software quality with fewer defects as AI can enforce best coding practices, reducing errors and improving maintainability.

Before applications research the market, AI uses simulations with real-world scenarios to predict potential system failures before deployment.

2. Expansion of no-code in enterprise

No-code technology reduces development, infrastructure, and maintenance expenses through pre-built integrations. This technology is being adopted faster by startups and small business owners to minimize development costs and accelerate time-to-market deployment. According to a recent survey, around 70% of new applications are built by no-code technology.

No-code platforms use drag-and-drop interfaces and pre-built components, significantly reducing development time compared to custom coding.

Organizations can rapidly prototype, test, and deploy applications without long deployment cycles. They can automate workflows and streamline operations without waiting for IT backlogs.

With the increasing need for software developers, organizations are struggling to find the right developer. Still, with no-code technology, one can use this technology to build software without requiring high technical skills.

3. Hybrid Workflows: AI + No-Code – The Future of Scalable and Flexible Innovation

As businesses adopt no-code development, they leverage AI-powered code assistants to create hybrid workflows that enhance efficiency, scalability, and innovation. This approach enables enterprises to enjoy the ease of no-code creation while also benefiting from AI-driven automation, informed decision-making, and advanced features.

AI-powered automation can efficiently manage data processing, predictive analytics, and intelligent decision-making in no-code applications.

Platforms using No-code technology offer defined functionalities, while AI-driven code assistants like GitHub Copilot and OpenAI Codex can generate custom scripts when needed.

Artificial intelligence can enhance business workflows by analyzing patterns and recommending improvements for automation.

No-code tools enable quick development, while AI offers scalability by managing big data, making predictions, and facilitating dynamic decision-making.

4. Beyond Coders: The Expanding World of Software Development

No-code development has given rise to a new profession: citizen developers. These individuals are business professionals who can build applications using intuitive business platforms despite lacking traditional coding knowledge. This shift democratizes software development, making innovation across various industries more accessible.

Faster innovation and agility through no-code development. Build faster prototypes, reduce IT bottlenecks, and accelerate digital transformation.

Save money by utilizing pre-built integrations of the no-code platform, reducing custom development efforts.

Bridge the IT gap by empowering non-technical teams to build applications so software teams can focus on more technical challenges rather than routine business.

5. Ethical & Security Considerations in AI & No-Code Development

No-code technology is increasingly adopted across organizations, and as they embrace AI and ML, organizations must address the critical ethical and security challenges to ensure responsible adoption.

Modern no-code platforms include built-in security and compliance features, ensuring safe development without requiring technical expertise.

Organizations can establish centralized governance policies to promote the responsible use of technology.

AI-driven no-code platforms now include bias detection algorithms, which help ensure fairness in automation.

No-code platforms have built-in enterprise-grade security, enabling organizations to innovate without facing security-related obstacles.

Clear and transparent AI decision-making, along with responsible automation frameworks, helps build trust and ensures long-term success.

The Impact on Professional Developers: How AI & No-Code Enhance Development

One might wonder : Will no-code technology replace professional developers? No, rather than replacing them, these technologies serve as powerful augmenters. By using this technology, developers are experiencing better efficiency, code quality, and reduced development bottlenecks.

AI-driven code assistance suggests codes based on best practices reducing time spent on writing repetitive logic.

Its detection capabilities identify common programming errors and enforce industry best practices, making the codebase more scalable and maintainable.

AI enhances developers' creativity, problem-solving, and decision-making skills allowing them to automate repetitive tasks, focus on high-level architecture and system design, and continuously learn and improve coding techniques.

AI-powered tools are especially useful for handling intricate software challenges such as refactoring large codebases and generating boilerplate code. This will help developers spend less time understanding the whole codebase.

More Accessible, Faster, and Cost-Effective: The Future of Software is Here

The future of software development is changing quickly. AI code assistants improve traditional coding efficiency, while no-code platforms democratize development by allowing anybody to create applications. The confluence of these technologies is ushering in a new era in which software creation is more accessible, faster, and cost-effective.

While traditional coding will never become outdated, the way we create code is fundamentally changing. Whether you are a developer or a business professional, adopting AI-powered development and no-code solutions can help you stay ahead of the digital transition.

[aiweirdness]

“Slopsquatting” in a nutshell:

1. LLM-generated code tries to run code from online software packages. Which is normal, that’s how you get math packages and stuff but

2. The packages don’t exist. Which would normally cause an error but

3. Nefarious people have made malware under the package names that LLMs make up most often. So

4. Now the LLM code points to malware.

https://www.theregister.com/2025/04/12/ai_code_suggestions_sabotage_supply_chain/

[shepscapades]

Surely this will have no negative consequences whatsoever!

[ask-the-rag-dolly]

anyways good episode

[jaynemcinnes1975blog]

[sreegs]

the past few years, every software developer that has extensive experience, and knows what they're talking about, has had pretty much the same opinion on LLM code assistants: they're OK for some tasks but generally shit. Having something that automates code writing is not new. Codegen before AI were scripts that generated code that you have to write for a task, but is so repetitive it's a genuine time saver to have a script do it.

this is largely the best that LLMs can do with code, but they're still not as good as a simple script because of the inherently unreliable nature of LLMs being a big honkin statistical model and not a purpose-built machine.

none of the senior devs that say this are out there shouting on the rooftops that LLMs are evil and they're going to replace us. because we've been through this concept so many times over many years. Automation does not eliminate coding jobs, it saves time to focus on other work.

the one thing I wish senior devs would warn newbies is that you should not rely on LLMs for anything substantial. you should definitely not use it as a learning tool. it will hinder you in the long run because you don't practice the eternally useful skill of "reading things and experimenting until you figure it out". You will never stop reading things and experimenting until you figure it out. Senior devs may have more institutional knowledge and better instincts but they still encounter things that are new to them and they trip through it like a newbie would. this is called "practice" and you need it to learn things

[neat-deadandlive-things]

Extremely grateful for every single person who still posts coding tutorials on YouTube and Stacks etc. so I can avoid using AI.

[inkzectz]

Bésame con tus ojos de miel, que me hablan de un mundo que ya no conozco

[42wv]

I started watching Code Geass and Suzaku immediately jumped up into my top list of favorite fictional characters

[aeolianblues]

I don't like that the dev community picks on people who are most fluent in Python, when the ChatGPT-using "vibe coders" are right there. At least Python babies are coding. Bully the non-coders instead.

[nixcraft]

The Comedy of Errors : Developers edition

[yourfaveisintersex]

Miss Francis Clara Censordoll from Moral Orel is canonically intersex-coded! It's implied she may have a variation such as Complete Androgen Insensitivity Syndrome (CAIS), due to her fixation on eggs—especially fertilized eggs—because of the fact her gonads were nonconsensually removed during infancy, which is all-too common in individuals with CAIS due to the (arguably false) belief the testes will have a high chance of cancer.

Requested by @trans-dwightschrute + more under the cut!

The requester elaborates: "During a phone call with her Mom she says this:

"My lack of eggs is not a hinderance, it is an asset. Don't push me down like that when you did this to me."

"Well what good was it relieving me of my reproductive parts if I could not be an expert on the matter? I was an infant when I had them, I was an infant when you had them removed."

Some fans theorize that the unconscentual [sic] surgery implies that she's intersex."

[mostlysignssomeportents]

“Humans in the loop” must detect the hardest-to-spot errors, at superhuman speed

I'm touring my new, nationally bestselling novel The Bezzle! Catch me SATURDAY (Apr 27) in MARIN COUNTY, then Winnipeg (May 2), Calgary (May 3), Vancouver (May 4), and beyond!

If AI has a future (a big if), it will have to be economically viable. An industry can't spend 1,700% more on Nvidia chips than it earns indefinitely – not even with Nvidia being a principle investor in its largest customers:

https://news.ycombinator.com/item?id=39883571

A company that pays 0.36-1 cents/query for electricity and (scarce, fresh) water can't indefinitely give those queries away by the millions to people who are expected to revise those queries dozens of times before eliciting the perfect botshit rendition of "instructions for removing a grilled cheese sandwich from a VCR in the style of the King James Bible":

https://www.semianalysis.com/p/the-inference-cost-of-search-disruption

Eventually, the industry will have to uncover some mix of applications that will cover its operating costs, if only to keep the lights on in the face of investor disillusionment (this isn't optional – investor disillusionment is an inevitable part of every bubble).

Now, there are lots of low-stakes applications for AI that can run just fine on the current AI technology, despite its many – and seemingly inescapable - errors ("hallucinations"). People who use AI to generate illustrations of their D&D characters engaged in epic adventures from their previous gaming session don't care about the odd extra finger. If the chatbot powering a tourist's automatic text-to-translation-to-speech phone tool gets a few words wrong, it's still much better than the alternative of speaking slowly and loudly in your own language while making emphatic hand-gestures.

There are lots of these applications, and many of the people who benefit from them would doubtless pay something for them. The problem – from an AI company's perspective – is that these aren't just low-stakes, they're also low-value. Their users would pay something for them, but not very much.

For AI to keep its servers on through the coming trough of disillusionment, it will have to locate high-value applications, too. Economically speaking, the function of low-value applications is to soak up excess capacity and produce value at the margins after the high-value applications pay the bills. Low-value applications are a side-dish, like the coach seats on an airplane whose total operating expenses are paid by the business class passengers up front. Without the principle income from high-value applications, the servers shut down, and the low-value applications disappear:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Now, there are lots of high-value applications the AI industry has identified for its products. Broadly speaking, these high-value applications share the same problem: they are all high-stakes, which means they are very sensitive to errors. Mistakes made by apps that produce code, drive cars, or identify cancerous masses on chest X-rays are extremely consequential.

Some businesses may be insensitive to those consequences. Air Canada replaced its human customer service staff with chatbots that just lied to passengers, stealing hundreds of dollars from them in the process. But the process for getting your money back after you are defrauded by Air Canada's chatbot is so onerous that only one passenger has bothered to go through it, spending ten weeks exhausting all of Air Canada's internal review mechanisms before fighting his case for weeks more at the regulator:

https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-the-wrong-information-now-the-airline-has-to-pay-for-the-mistake-1.6769454

There's never just one ant. If this guy was defrauded by an AC chatbot, so were hundreds or thousands of other fliers. Air Canada doesn't have to pay them back. Air Canada is tacitly asserting that, as the country's flagship carrier and near-monopolist, it is too big to fail and too big to jail, which means it's too big to care.

Air Canada shows that for some business customers, AI doesn't need to be able to do a worker's job in order to be a smart purchase: a chatbot can replace a worker, fail to their worker's job, and still save the company money on balance.

I can't predict whether the world's sociopathic monopolists are numerous and powerful enough to keep the lights on for AI companies through leases for automation systems that let them commit consequence-free free fraud by replacing workers with chatbots that serve as moral crumple-zones for furious customers:

https://www.sciencedirect.com/science/article/abs/pii/S0747563219304029

But even stipulating that this is sufficient, it's intrinsically unstable. Anything that can't go on forever eventually stops, and the mass replacement of humans with high-speed fraud software seems likely to stoke the already blazing furnace of modern antitrust:

https://www.eff.org/de/deeplinks/2021/08/party-its-1979-og-antitrust-back-baby

Of course, the AI companies have their own answer to this conundrum. A high-stakes/high-value customer can still fire workers and replace them with AI – they just need to hire fewer, cheaper workers to supervise the AI and monitor it for "hallucinations." This is called the "human in the loop" solution.

The human in the loop story has some glaring holes. From a worker's perspective, serving as the human in the loop in a scheme that cuts wage bills through AI is a nightmare – the worst possible kind of automation.

Let's pause for a little detour through automation theory here. Automation can augment a worker. We can call this a "centaur" – the worker offloads a repetitive task, or one that requires a high degree of vigilance, or (worst of all) both. They're a human head on a robot body (hence "centaur"). Think of the sensor/vision system in your car that beeps if you activate your turn-signal while a car is in your blind spot. You're in charge, but you're getting a second opinion from the robot.

Likewise, consider an AI tool that double-checks a radiologist's diagnosis of your chest X-ray and suggests a second look when its assessment doesn't match the radiologist's. Again, the human is in charge, but the robot is serving as a backstop and helpmeet, using its inexhaustible robotic vigilance to augment human skill.

That's centaurs. They're the good automation. Then there's the bad automation: the reverse-centaur, when the human is used to augment the robot.

Amazon warehouse pickers stand in one place while robotic shelving units trundle up to them at speed; then, the haptic bracelets shackled around their wrists buzz at them, directing them pick up specific items and move them to a basket, while a third automation system penalizes them for taking toilet breaks or even just walking around and shaking out their limbs to avoid a repetitive strain injury. This is a robotic head using a human body – and destroying it in the process.

An AI-assisted radiologist processes fewer chest X-rays every day, costing their employer more, on top of the cost of the AI. That's not what AI companies are selling. They're offering hospitals the power to create reverse centaurs: radiologist-assisted AIs. That's what "human in the loop" means.

This is a problem for workers, but it's also a problem for their bosses (assuming those bosses actually care about correcting AI hallucinations, rather than providing a figleaf that lets them commit fraud or kill people and shift the blame to an unpunishable AI).

Humans are good at a lot of things, but they're not good at eternal, perfect vigilance. Writing code is hard, but performing code-review (where you check someone else's code for errors) is much harder – and it gets even harder if the code you're reviewing is usually fine, because this requires that you maintain your vigilance for something that only occurs at rare and unpredictable intervals:

https://twitter.com/qntm/status/1773779967521780169

But for a coding shop to make the cost of an AI pencil out, the human in the loop needs to be able to process a lot of AI-generated code. Replacing a human with an AI doesn't produce any savings if you need to hire two more humans to take turns doing close reads of the AI's code.

This is the fatal flaw in robo-taxi schemes. The "human in the loop" who is supposed to keep the murderbot from smashing into other cars, steering into oncoming traffic, or running down pedestrians isn't a driver, they're a driving instructor. This is a much harder job than being a driver, even when the student driver you're monitoring is a human, making human mistakes at human speed. It's even harder when the student driver is a robot, making errors at computer speed:

https://pluralistic.net/2024/04/01/human-in-the-loop/#monkey-in-the-middle

This is why the doomed robo-taxi company Cruise had to deploy 1.5 skilled, high-paid human monitors to oversee each of its murderbots, while traditional taxis operate at a fraction of the cost with a single, precaratized, low-paid human driver:

https://pluralistic.net/2024/01/11/robots-stole-my-jerb/#computer-says-no

The vigilance problem is pretty fatal for the human-in-the-loop gambit, but there's another problem that is, if anything, even more fatal: the kinds of errors that AIs make.

Foundationally, AI is applied statistics. An AI company trains its AI by feeding it a lot of data about the real world. The program processes this data, looking for statistical correlations in that data, and makes a model of the world based on those correlations. A chatbot is a next-word-guessing program, and an AI "art" generator is a next-pixel-guessing program. They're drawing on billions of documents to find the most statistically likely way of finishing a sentence or a line of pixels in a bitmap:

https://dl.acm.org/doi/10.1145/3442188.3445922

This means that AI doesn't just make errors – it makes subtle errors, the kinds of errors that are the hardest for a human in the loop to spot, because they are the most statistically probable ways of being wrong. Sure, we notice the gross errors in AI output, like confidently claiming that a living human is dead:

https://www.tomsguide.com/opinion/according-to-chatgpt-im-dead

But the most common errors that AIs make are the ones we don't notice, because they're perfectly camouflaged as the truth. Think of the recurring AI programming error that inserts a call to a nonexistent library called "huggingface-cli," which is what the library would be called if developers reliably followed naming conventions. But due to a human inconsistency, the real library has a slightly different name. The fact that AIs repeatedly inserted references to the nonexistent library opened up a vulnerability – a security researcher created a (inert) malicious library with that name and tricked numerous companies into compiling it into their code because their human reviewers missed the chatbot's (statistically indistinguishable from the the truth) lie:

https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/

For a driving instructor or a code reviewer overseeing a human subject, the majority of errors are comparatively easy to spot, because they're the kinds of errors that lead to inconsistent library naming – places where a human behaved erratically or irregularly. But when reality is irregular or erratic, the AI will make errors by presuming that things are statistically normal.

These are the hardest kinds of errors to spot. They couldn't be harder for a human to detect if they were specifically designed to go undetected. The human in the loop isn't just being asked to spot mistakes – they're being actively deceived. The AI isn't merely wrong, it's constructing a subtle "what's wrong with this picture"-style puzzle. Not just one such puzzle, either: millions of them, at speed, which must be solved by the human in the loop, who must remain perfectly vigilant for things that are, by definition, almost totally unnoticeable.

This is a special new torment for reverse centaurs – and a significant problem for AI companies hoping to accumulate and keep enough high-value, high-stakes customers on their books to weather the coming trough of disillusionment.

This is pretty grim, but it gets grimmer. AI companies have argued that they have a third line of business, a way to make money for their customers beyond automation's gifts to their payrolls: they claim that they can perform difficult scientific tasks at superhuman speed, producing billion-dollar insights (new materials, new drugs, new proteins) at unimaginable speed.

However, these claims – credulously amplified by the non-technical press – keep on shattering when they are tested by experts who understand the esoteric domains in which AI is said to have an unbeatable advantage. For example, Google claimed that its Deepmind AI had discovered "millions of new materials," "equivalent to nearly 800 years’ worth of knowledge," constituting "an order-of-magnitude expansion in stable materials known to humanity":

https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/

It was a hoax. When independent material scientists reviewed representative samples of these "new materials," they concluded that "no new materials have been discovered" and that not one of these materials was "credible, useful and novel":

https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/

As Brian Merchant writes, AI claims are eerily similar to "smoke and mirrors" – the dazzling reality-distortion field thrown up by 17th century magic lantern technology, which millions of people ascribed wild capabilities to, thanks to the outlandish claims of the technology's promoters:

https://www.bloodinthemachine.com/p/ai-really-is-smoke-and-mirrors

The fact that we have a four-hundred-year-old name for this phenomenon, and yet we're still falling prey to it is frankly a little depressing. And, unlucky for us, it turns out that AI therapybots can't help us with this – rather, they're apt to literally convince us to kill ourselves:

https://www.vice.com/en/article/pkadgm/man-dies-by-suicide-after-talking-with-ai-chatbot-widow-says

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/04/23/maximal-plausibility/#reverse-centaurs

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

[euarchontoglires]

kodaka games: big tiddy girl: do you wanna touch my boobs 😏 protagonist: uhhh…no…? *blushing*

uchikoshi games: big tiddy girl: do you wanna touch my boobs 😏 protagonist: hell yeah! protagonist's female partner: i will have your head on a stick if you even take so much as one more step towards that woman big tiddy girl: here's my cat his name is boobs

[starfall-isle]

U think your life is hard? Try being a tween A.I girl!

4/24/23