#ai software developer

[johnnysantiagovaldezcalderon]

Johnny Santiago Valdez Calderon on Building the Future with AI Software

What does it truly mean to build the future with AI? For Johnny Santiago Valdez Calderon, an AI software developer based in Sanford, North Carolina, the answer lies in asking the right questions, not just writing code. At a time when artificial intelligence is reshaping industries from healthcare to finance, Calderon isn’t focused on hype—he’s focused on solving problems with clarity and purpose.

How does AI move from concept to real-world utility?

Johnny Santiago Valdez Calderon believes the transformation begins with identifying tangible pain points. Whether it’s inefficient logistics in e-commerce or slow diagnostic tools in medical imaging, his approach begins with asking: What are we really trying to fix? Calderon’s projects are driven by a desire to create systems that don’t just function but adapt, learn, and evolve with real-world data. To him, AI isn’t magic. It’s architecture. And like any structure, it needs a foundation built on insight.

What makes a strong AI system reliable and ethical?

Ethics in AI isn’t just a trending topic—it’s an operational requirement, according to Calderon. He is a vocal advocate for designing transparent and explainable models. He questions: Can we explain why a model made a decision? And if not, should we trust it? These queries push his development process beyond the usual scope of accuracy metrics. Fairness, accountability, and reproducibility are principles that guide his architecture choices. For Calderon, if AI is going to shape the future, it has to do so responsibly.

How do you balance innovation with usability?

One of the biggest challenges Calderon highlights is the disconnect between developers and end users. Are we designing for engineers, or for the people who actually use the tools? It’s a question he frequently raises in collaborative sessions. Whether working on a predictive maintenance system for industrial machinery or developing a chatbot for healthcare scheduling, Calderon’s methodology includes deep user testing and feedback loops. He sees the user interface not as an afterthought but as the real test of innovation.

What’s the role of continuous learning in AI development?

In Calderon’s view, a model that doesn’t adapt is a model that fades. AI systems must evolve—not just technically, but contextually. Is your model still valid six months after deployment? he asks. This focus on lifecycle management has led him to integrate active learning pipelines and real-time feedback systems into many of his solutions. AI, to him, should never be static—it should be as dynamic as the world it aims to interpret.

Why should we care about who builds the AI we use?

It’s a question Johnny Santiago Valdez Calderon doesn’t shy away from. Behind every intelligent algorithm is a set of assumptions, values, and decisions. Who’s asking the questions that shape those systems? His commitment to mentoring junior developers and fostering diversity in AI development isn’t about optics—it’s about outcomes. A diverse team builds more inclusive, and ultimately more effective, systems.

In a field dominated by buzzwords and billion-dollar valuations, Johnny Santiago Valdez Calderon brings something increasingly rare—clarity. He isn’t chasing the next trend in generative models or jumping on every open-source release. Instead, he’s quietly asking tough questions, refining his code, listening to users, and building systems that don’t just perform, but improve lives.

[ethanparker9692]

"How Generative AI is Revolutionizing Software Development in 2025"

In 2025, the world of software development is undergoing a seismic shift, thanks to the power of generative AI. No longer just a futuristic concept, generative AI in software development is rapidly transforming how applications are built, tested, and deployed. Developers are now leveraging AI to automate tasks, generate code, and solve complex problems at unprecedented speeds. If you’re an AI software developer, staying ahead means mastering these tools and embracing AI-powered innovation.

The Generative AI Advantage in Software Development

Imagine cutting development time in half while boosting the quality of your code. AI developers are now using generative AI to streamline workflows, reducing bugs and accelerating time-to-market. Whether you're building a startup app or scaling enterprise software, AI-generated code and automated testing are becoming essential components of the development lifecycle.

By earning an AI professional certificate or enrolling in AI certification programs, software engineers gain hands-on experience with tools that are reshaping the industry. These certifications help developers harness the full potential of AI, ensuring they stay competitive in a rapidly evolving job market.

Key Benefits of Generative AI for Developers

Automated Code Generation - Generative AI can write functional code snippets, significantly speeding up the development process.

Efficient Bug Detection - AI models analyze and identify potential bugs faster than manual reviews, leading to cleaner code and more robust applications.

Enhanced Productivity - Routine tasks are automated, allowing developers to focus on high-level design and strategy.

Scalable Solutions - AI tools adapt to project demands, making it easier to build and scale applications efficiently.

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Why Pursue AI Certification?

For those looking to capitalize on this tech revolution, AI certification programs provide the knowledge and credentials to lead the charge. A certification validates your expertise, making you a sought-after asset in the industry. Whether you're an entry-level coder or a senior architect, an AI professional certificate ensures you’re equipped to thrive in the age of AI-driven development.

Generative AI isn't just the future—it's the present. The developers who adapt to this new landscape will shape the next generation of technology. Invest in your growth by exploring AI certification programs and lead the way as an innovative AI software developer.

The revolution has begun. Are you ready to code the future?

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Contact : +41444851189

[techenthuinsights]

Know how a Medical Imaging AI Software Developer can accelerate your product's go-to-market strategy (GTM) with specialized expertise.

[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

[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]

Imagine being this stupid to drink Kool-Aid and giving a remote LLM tool full access to your codebase, and, in many cases, not maintaining backups or using proper Git with permissions. How these guys are getting hired to write code is beyond me.

[cyle]

I know you're on paternity leave so feel free to ignore this if you don't want to think about it, but has there been any progress on open-sourcing Tumblr's front-end? Inquiring minds would like to know

i hadn’t seen any progress on it before i left. there’s a strong willingness to do it, it’s just a big task to get it open-source-able in a sustainable way. a lot of our CI/CD processes rely on stuff that would need to be rebuilt from scratch, i think. totally doable, just not a priority.

but maybe there’s been progress since i left, i dunno! 🤞

[moose-mousse]

Remember

When you write utility tools that are not specific to your workplace enviroment and you might want to use them at the next place you work.

Then make sure there is no way to prove that your code was made at your work.

Don't check it into a remote git ( just write git init and do it locally). Don't let Onedrive see it.

And then just move it back and fourth to update versions with a flash drive.

Remember.

It is not just ok to steal from your workplace, it is the ethical choice.

Think of it as "wealth distribution towards equality"

Or "Eat the rich"

Or "copyright is only a thing for your corperate masters. Your copyright is called "you are training data""

[frank-olivier]

The story of BASIC’s development began in 1963, when Kemeny and Kurtz, both mathematics professors at Dartmouth, recognized the need for a programming language that could be used by non-technical students. At the time, most programming languages were complex and required a strong background in mathematics and computer science. Kemeny and Kurtz wanted to create a language that would allow students from all disciplines to use computers, regardless of their technical expertise.

The development of BASIC was a collaborative effort between Kemeny, Kurtz, and a team of students, including Mary Kenneth Keller, John McGeachie, and others. The team worked tirelessly to design a language that was easy to learn and use, with a syntax that was simple and intuitive. They drew inspiration from existing programming languages, such as ALGOL and FORTRAN, but also introduced many innovative features that would become hallmarks of the BASIC language.

One of the key innovations of BASIC was its use of simple, English-like commands. Unlike other programming languages, which required users to learn complex syntax and notation, BASIC used commands such as “PRINT” and “INPUT” that were easy to understand and remember. This made it possible for non-technical users to write programs and interact with the computer, without needing to have a deep understanding of computer science.

BASIC was first implemented on the Dartmouth Time-Sharing System, a pioneering computer system that allowed multiple users to interact with the computer simultaneously. The Time-Sharing System was a major innovation in itself, as it allowed users to share the computer’s resources and work on their own projects independently. With BASIC, users could write programs, run simulations, and analyze data, all from the comfort of their own terminals.

The impact of BASIC was immediate and profound. The language quickly gained popularity, not just at Dartmouth, but also at other universities and institutions around the world. It became the language of choice for many introductory programming courses, and its simplicity and ease of use made it an ideal language for beginners. As the personal computer revolution took hold in the 1970s and 1980s, BASIC became the language of choice for many hobbyists and enthusiasts, who used it to write games, utilities, and other applications.

Today, BASIC remains a popular language, with many variants and implementations available. While it may not be as widely used as it once was, its influence can still be seen in many modern programming languages, including Visual Basic, Python, and JavaScript. The development of BASIC was a major milestone in the history of computer science, as it democratized computing and made it accessible to a wider range of people.

The Birth of BASIC (Dartmouth College, August 2014)

Friday, April 25, 2025

[mentalisttraceur-software]

DeepSeek R1 First Impressions

DeepSeek R1 is almost as good as me at belabored exhaustive analysis and application of C89 rules. For practical purposes, it's equally good.

I asked: "How would you implement zig-zag encoding in strictly portable C89?" It was spitting out thinking output for at least a minute, but it got a basically-perfect solution on first try:

unsigned int zigzag_encode(int n) { return (((unsigned int)n << 1) ^ ((n < 0) ? -1 : 0); }

It also provided a `zigzag_encode_long`.

Note that this code will optimize on modern C compilers to the best assembly you could write. There is no branch in the produced code with even just `-O1` (`clang`, `gcc`), the branch is how we portably tell the compiler the right idea.

The only thing DeepSeek did "wrong" vs the above, was redundantly add an `(unsigned int)` cast to the `-1`. I mentioned this as I would to a person: that the usual arithmetic conversions would take care of it at the `^`. It reasoned the rest on its own: yes, because the left operand is already at least an unsigned int, so integer promotion will make the left side an unsigned int as well.

We talked at length about how we can prove that the above is portable to the most pathological C89-conformant implementations. It kept taking longer to "think", but it didn't show any weakness until the very last question.

I asked it to help me rigorously prove if the maximum value of unsigned integers is required by the C standard to be a Mersenne number (2^n-1). To have all bits one, that is.

What if an implementation just decided to arbitrarily not use one or more of the top values? I.e., why not `#define UINT_MAX 0xFFFFFFFE`?

DeepSeek R1 didn't seem to conceive of this possibility until I made it explicit. (But it did a great job of ruling out all others.)

Finally, it gave a longer, non-trivial argument, which I don't find convincing. Basically, it seemed to be saying that since integers used "pure binary representation", and every value bit could be either one or zero, well then the maximum value always has all value bits one - in other words, it seemingly assumed that just because each value bit individually was allowed to be one or zero, the possibility of them all being one at once must be both legal and used to represent a distinct value.

I see a shorter argument, which follows directly from what the standard does say: C89 has two definitions of `~`:

flip all the bits;

subtract from maximum value of that unsigned integer type.

The only way both can be true at once is if the maximum value is all value bits one. DeepSeek R1 agreed.

So what does this all mean?

This is an insane level of competence in an extremely niche field. Less than a year ago I tested LLAMA on this, and LLAMA and I didn't even get past me hand-holding it through several portability caveats. DeepSeek R1 and I just had a full-blown conversation that most devs I've talked to couldn't have with me. DeepSeek R1 managed to help me think in an extremely niche area where I'm basically a world-class expert (since the area in question is C89 portability, "world-class expert" is derogatory, but still).

If it's this good in one domain, it's this good in most domains. I bet it can do comparably well in Python, Go, JavaScript, C++, and so on.

In other words, it's already better than many devs in areas like this. I've seen plenty of devs making 6-figure USD salaries who didn't bother to know any of their day job tech stack this deeply. There's a market adjustment coming. Knowledge and expertise are about to become dirt-cheap commodities.

AI will eat current software dev jobs even faster than even I thought - and I already thought it would be sooner than most expect. Meanwhile, much of the industry is busy rationalizing from human intuition and ignorance that it just can't happen.

For years I've thought that the future is human devs delegating to teams of AI. That future is almost upon us, and this AI is good enough that I will be seriously experimenting with making that future a reality. I think if you hack together the right script to hook it up to a sandbox with dev tools, and prompt it just right... you might already be able to get this thing to actually do useful dev work.

[atcuality3]

Simplify Decentralized Payments with a Unified Cash Collection Application

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[magebird]

Sometimes I feel like the discourse about AI art misses the actual point of why it’s not a good tool to use.

“AI art isn’t ‘real’ art.” —> opinion-based, echoes the same false commentary about digital art in general, just ends up in a ‘if you can’t make your own store-bought is fine’ conversation, implies that if art isn’t done a certain way it lacks some moral/ethical value, relies on the emotional component of what art is considered “real” or not which is wildly subjective

“AI art steals from existing artists without credit.” —> fact-based, highlights the actual damage of the tool, isn’t relying on an emotional plea, can actually lead to legally stopping overuse of AI tools and/or the development of AI tools that don’t have this problem, doesn’t get bogged down in the ‘but what if they caaaaan’t make art some other way’ argument

Like I get that people who don’t give a shit about plagiarism aren’t going to be swayed, but they weren’t going to be swayed by the first argument either. And the argument of “oh well AI art can’t do hands/isn’t as good/can’t do this thing I have decided indicates True Human Creativity” will eventually erode since… the AI tools are getting better and will be able to emulate that in time. It just gets me annoyed when the argument is trying to base itself on “oh this isn’t GOOD art” when AI does produce interesting and appealing images and the argument worth having is much more about the intrinsic value of artists than the perceived value of the works that are produced.

[learn-ai-free]

How to Build Custom AI Agents in Minutes Using Chai (Vibe Code)

Most business teams are still struggling to push the idea of an AI agent from the whiteboard to production—Why? The majority of professionals are non-technical and do not have a deep understanding of what goes on behind the scenes.

What is Chai by Langbase? 📌

Chai by Langbase is a prompt‑first service that builds, deploys, and scales AI agents straight from plain English. In much simpler terms, Chai can build AI agents for you. Users can vibe code production-ready AI agents within minutes after entering the prompt/ agent idea.

What sets Chai apart? 📌

Langbase describes Chai with three simple verbs—"Prompt. Sip. Ship," which literally means enter a prompt for your agent, sip chai tea while it vibe codes the agent for you, and ship it to your clients.

How to Build Custom AI Agents in Minutes Using Chai (Vibe Code) 📌

Step 1️⃣: Visit Chai.new.

Step 2️⃣: Enter a prompt for the AI agent.

Step 3️⃣: Chai will start by thinking and creating an overview of the AI agent.

Step 4️⃣: Deploy the AI agent.

↗️ Full Read: https://aiagent.marktechpost.com/post/how-to-build-custom-ai-agents-in-minutes-using-chai-vibe-code

[smak-annihilation]

yeah sorry guys but the machine escaped containment and is no longer in my control or control of any human. yeah if it does anything mortifying it's on me guys, sorry

[abathurofficial]

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