#AI tools for non-coders

[canmom]

it was a matter of time but pure-generative-AI animation has progressed to the point of looking 'not utterly shit'.

it's obvs a blatant ghibli pastiche, there's sometimes e.g. some inconsistent spacing (the leg on the walking cat for example) that I would criticise in a human animator, but the level of spatial and temporal coherence is much, much higher than it was before and it basically 'feels like' human animation, or at least a lot closer to it, than previous efforts that I've seen.

the process used involves a lot of 'human in the feedback loop' iteration - the artist used one generative model to produce still shots, and a second generative model to produce animations out of them, and additional generative models to get foley and music - but this was done in a weekend, compared to the weeks of work (months if you include preproduction) by experienced experts that it would take to produce comparable animation by the traditional techniques. the video generation is controlled by a text description of what you want to happen in the shot, but it doesn't seem like there is much fine-grained control over the details here.

traditional animation is a thoroughly collaborative process (unless you're Don Hertzfeldt), it takes large teams, and generally speaking only functions at all in the modern world by outsourcing large parts of the labour to countries where the cost of living is lower. the most celebrated (and higher-paid) roles in the process tend to be roles like storyboarding and key animation, where artistic choice is highest. animation lore is full of frustration from artists at this end of the pipeline, about the intent of a cut being lost through rushed or thoughtless inbetweening and compositing.

although image generation competes with this 'planning' stage, its unpredictability and lack of a connection to a 'personality' means I think that direction and key animation will still be a thing in animation to come. I'm less sure about inbetweening. current techniques for AI gen aren't there yet, but it doesn't seem to be far off the point where we can give an AI some keyframes and have it generate a reasonably convincing path between them, taking over the roles of cleanup, inbetweening, and compositing.

I doubt it will stop here either. the question will be how amenable it is to artistic control. for making an impressive-looking non-narrative twitter video you can just take a few generations that look good and staple them together, but these tools will only be useful for filmmaking if they can maintain consistency of character designs and respond reasonably to tweaking, without having cumbersome text input.

at the demoscene event this weekend, I was struck by how, as much as there is plenty of excitement about exploring new techniques, there was perhaps even more work being produced in the 'old school'/'mid school' categories targeting machines like the Commodore 64, Amiga, BBC Micro, or even modern low-level fantasy consoles like the TIC-80. new techniques are still being discovered for C64 demos, despite the hardware being decades old and no longer produced, and oldschool demos are still being made and appreciated by an audience who didn't necessarily grow up with the tech. not to mention the fact that we still draw and paint as furiously as ever.

art and medium are intimately connected; knowing how someone made something is a huge part of the context I bring to interpret it. so I don't fear that nobody will ever want to produce animation anymore.

but a demo is something that can be produced by a solo coder and generally not done for money. animation is produced in a variety of ways - there is a strong subculture of solo or small-team independent animators - but animated films are rarely made except by a whole studio working full time. I'm not sure how AI is going to affect that whole economic structure, and affect the future of this medium I love, but it's getting much closer to the day that we find out.

[mariacallous]

On a 5K screen in Kirkland, Washington, four terminals blur with activity as artificial intelligence generates thousands of lines of code. Steve Yegge, a veteran software engineer who previously worked at Google and AWS, sits back to watch.

“This one is running some tests, that one is coming up with a plan. I am now coding on four different projects at once, although really I’m just burning tokens,” Yegge says, referring to the cost of generating chunks of text with a large language model (LLM).

Learning to code has long been seen as the ticket to a lucrative, secure career in tech. Now, the release of advanced coding models from firms like OpenAI, Anthropic, and Google threatens to upend that notion entirely. X and Bluesky are brimming with talk of companies downsizing their developer teams—or even eliminating them altogether.

When ChatGPT debuted in late 2022, AI models were capable of autocompleting small portions of code—a helpful, if modest step forward that served to speed up software development. As models advanced and gained “agentic” skills that allow them to use software programs, manipulate files, and access online services, engineers and non-engineers alike started using the tools to build entire apps and websites. Andrej Karpathy, a prominent AI researcher, coined the term “vibe coding” in February, to describe the process of developing software by prompting an AI model with text.

The rapid progress has led to speculation—and even panic—among developers, who fear that most development work could soon be automated away, in what would amount to a job apocalypse for engineers.

“We are not far from a world—I think we’ll be there in three to six months—where AI is writing 90 percent of the code,” Dario Amodei, CEO of Anthropic, said at a Council on Foreign Relations event in March. “And then in 12 months, we may be in a world where AI is writing essentially all of the code,” he added.

But many experts warn that even the best models have a way to go before they can reliably automate a lot of coding work. While future advancements might unleash AI that can code just as well as a human, until then relying too much on AI could result in a glut of buggy and hackable code, as well as a shortage of developers with the knowledge and skills needed to write good software.

David Autor, an economist at MIT who studies how AI affects employment, says it’s possible that software development work will be automated—similar to how transcription and translation jobs are quickly being replaced by AI. He notes, however, that advanced software engineering is much more complex and will be harder to automate than routine coding.

Autor adds that the picture may be complicated by the “elasticity” of demand for software engineering—the extent to which the market might accommodate additional engineering jobs.

“If demand for software were like demand for colonoscopies, no improvement in speed or reduction in costs would create a mad rush for the proctologist's office,” Autor says. “But if demand for software is like demand for taxi services, then we may see an Uber effect on coding: more people writing more code at lower prices, and lower wages.”

Yegge’s experience shows that perspectives are evolving. A prolific blogger as well as coder, Yegge was previously doubtful that AI would help produce much code. Today, he has been vibe-pilled, writing a book called Vibe Coding with another experienced developer, Gene Kim, that lays out the potential and the pitfalls of the approach. Yegge became convinced that AI would revolutionize software development last December, and he has led a push to develop AI coding tools at his company, Sourcegraph.

“This is how all programming will be conducted by the end of this year,” Yegge predicts. “And if you're not doing it, you're just walking in a race.”

The Vibe-Coding Divide

Today, coding message boards are full of examples of mobile apps, commercial websites, and even multiplayer games all apparently vibe-coded into being. Experienced coders, like Yegge, can give AI tools instructions and then watch AI bring complex ideas to life.

Several AI-coding startups, including Cursor and Windsurf have ridden a wave of interest in the approach. (OpenAI is widely rumored to be in talks to acquire Windsurf).

At the same time, the obvious limitations of generative AI, including the way models confabulate and become confused, has led many seasoned programmers to see AI-assisted coding—and especially gung-ho, no-hands vibe coding—as a potentially dangerous new fad.

Martin Casado, a computer scientist and general partner at Andreessen Horowitz who sits on the board of Cursor, says the idea that AI will replace human coders is overstated. “AI is great at doing dazzling things, but not good at doing specific things,” he said.

Still, Casado has been stunned by the pace of recent progress. “I had no idea it would get this good this quick,” he says. “This is the most dramatic shift in the art of computer science since assembly was supplanted by higher-level languages.”

Ken Thompson, vice president of engineering at Anaconda, a company that provides open source code for software development, says AI adoption tends to follow a generational divide, with younger developers diving in and older ones showing more caution. For all the hype, he says many developers still do not trust AI tools because their output is unpredictable, and will vary from one day to the next, even when given the same prompt. “The nondeterministic nature of AI is too risky, too dangerous,” he explains.

Both Casado and Thompson see the vibe-coding shift as less about replacement than abstraction, mimicking the way that new languages like Python build on top of lower-level languages like C, making it easier and faster to write code. New languages have typically broadened the appeal of programming and increased the number of practitioners. AI could similarly increase the number of people capable of producing working code.

Bad Vibes

Paradoxically, the vibe-coding boom suggests that a solid grasp of coding remains as important as ever. Those dabbling in the field often report running into problems, including introducing unforeseen security issues, creating features that only simulate real functionality, accidentally running up high bills using AI tools, and ending up with broken code and no idea how to fix it.

“AI [tools] will do everything for you—including fuck up,” Yegge says. “You need to watch them carefully, like toddlers.”

The fact that AI can produce results that range from remarkably impressive to shockingly problematic may explain why developers seem so divided about the technology. WIRED surveyed programmers in March to ask how they felt about AI coding, and found that the proportion who were enthusiastic about AI tools (36 percent) was mirrored by the portion who felt skeptical (38 percent).

“Undoubtedly AI will change the way code is produced,” says Daniel Jackson, a computer scientist at MIT who is currently exploring how to integrate AI into large-scale software development. “But it wouldn't surprise me if we were in for disappointment—that the hype will pass.”

Jackson cautions that AI models are fundamentally different from the compilers that turn code written in a high-level language into a lower-level language that is more efficient for machines to use, because they don’t always follow instructions. Sometimes an AI model may take an instruction and execute better than the developer—other times it might do the task much worse.

Jackson adds that vibe coding falls down when anyone is building serious software. “There are almost no applications in which ‘mostly works’ is good enough,” he says. “As soon as you care about a piece of software, you care that it works right.”

Many software projects are complex, and changes to one section of code can cause problems elsewhere in the system. Experienced programmers are good at understanding the bigger picture, Jackson says, but “large language models can't reason their way around those kinds of dependencies.”

Jackson believes that software development might evolve with more modular codebases and fewer dependencies to accommodate AI blind spots. He expects that AI may replace some developers but will also force many more to rethink their approach and focus more on project design.

Too much reliance on AI may be “a bit of an impending disaster,” Jackson adds, because “not only will we have masses of broken code, full of security vulnerabilities, but we'll have a new generation of programmers incapable of dealing with those vulnerabilities.”

Learn to Code

Even firms that have already integrated coding tools into their software development process say the technology remains far too unreliable for wider use.

Christine Yen, CEO at Honeycomb, a company that provides technology for monitoring the performance of large software systems, says that projects that are simple or formulaic, like building component libraries, are more amenable to using AI. Even so, she says the developers at her company who use AI in their work have only increased their productivity by about 50 percent.

Yen adds that for anything requiring good judgement, where performance is important, or where the resulting code touches sensitive systems or data, “AI just frankly isn't good enough yet to be additive.”

“The hard part about building software systems isn't just writing a lot of code,” she says. “Engineers are still going to be necessary, at least today, for owning that curation, judgment, guidance and direction.”

Others suggest that a shift in the workforce is coming. “We are not seeing less demand for developers,” says Liad Elidan, CEO of Milestone, a company that helps firms measure the impact of generative AI projects. “We are seeing less demand for average or low-performing developers.”

“If I'm building a product, I could have needed 50 engineers and now maybe I only need 20 or 30,” says Naveen Rao, VP of AI at Databricks, a company that helps large businesses build their own AI systems. “That is absolutely real.”

Rao says, however, that learning to code should remain a valuable skill for some time. “It’s like saying ‘Don't teach your kid to learn math,’” he says. Understanding how to get the most out of computers is likely to remain extremely valuable, he adds.

Yegge and Kim, the veteran coders, believe that most developers can adapt to the coming wave. In their book on vibe coding, the pair recommend new strategies for software development including modular code bases, constant testing, and plenty of experimentation. Yegge says that using AI to write software is evolving into its own—slightly risky—art form. “It’s about how to do this without destroying your hard disk and draining your bank account,” he says.

[playstationvii]

Jest: A Concept for a New Programming Language

Summary: "Jest" could be envisioned as a novel computer programming language with a focus on humor, playfulness, or efficiency in a specific domain. Its design might embrace creativity in syntax, a unique philosophy, or a purpose-driven ecosystem for developers. It could potentially bridge accessibility with functionality, making coding intuitive and enjoyable.

Definition: Jest: A hypothetical computer language designed with a balance of simplicity, expressiveness, and potentially humor. The name suggests it might include unconventional features, playful interactions, or focus on lightweight scripting with a minimalist approach to problem-solving.

Expansion: If Jest were to exist, it might embody these features:

Playful Syntax: Commands and expressions that use conversational, quirky, or approachable language. Example:

joke "Why did the loop break? It couldn't handle the pressure!"; if (laughs > 0) { clap(); }

Efficiency-Focused: Ideal for scripting, rapid prototyping, or teaching, with shortcuts that reduce boilerplate code.

Modular Philosophy: Encourages user-created modules or libraries, reflecting its playful tone with practical use cases.

Integrated Humor or Personality: Built-in error messages or prompts might be witty or personalized.

Flexibility: Multi-paradigm support, including functional, procedural, and object-oriented programming.

Transcription: An example code snippet for a Jest-like language:

// Hello World in Jest greet = "Hello, World!"; print(greet); laugh();

A Jest program that calculates Fibonacci numbers might look like this:

// Fibonacci in Jest fib = (n) => n < 2 ? n : fib(n-1) + fib(n-2);

joke "What's the Fibonacci sequence? You'll love it, it grows on you!"; n = 10; print("The Fibonacci number at", n, "is:", fib(n));

Potential Domains:

Gamified education

Creative industries

AI-driven storytelling

Interactive debugging

Would you like me to refine or explore additional aspects?

Certainly! If we were to imagine Jest as the brainchild of a creative coder or team, their portfolio would likely include other innovative or experimental programming languages. Let’s expand on this concept and invent some plausible complementary languages the same inventor might have designed.

Related Languages by the Inventor of Jest

Pantomime

Description: A visual programming language inspired by gesture and movement, where users "drag and drop" symbols or create flowcharts to express logic. Designed for non-coders or children to learn programming through interaction.

Key Features:

Icon-based syntax: Conditional loops, variables, and functions represented visually.

Works seamlessly with Jest for creating visual representations of Jest scripts.

Sample Code (Visual Representation): Flowchart blocks: Input → Decision → Output.

Facet

Description: A declarative programming language focusing on creativity and modularity, aimed at artists, designers, and 3D modelers. Facet could integrate well with game engines and creative suites like Blender or Unity.

Key Features:

Embedded visual tools for shaders, animations, and simulations.

Simplified expressions for creative coding (e.g., animations and generative art).

Sample Code:

shape = circle(radius: 10, color: "blue"); animation = wave(shape, amplitude: 5, frequency: 2); render(animation, duration: 10s);

Quip

Description: A lightweight scripting language built for humor, similar to Jest, but more focused on natural language processing, chatbots, and interactive storytelling.

Key Features:

Syntax mirrors conversational English.

Designed for AI integration, especially in storytelling apps.

Sample Code:

character "Alice" says "Hello! What's your name?"; user_input -> name; character "Alice" says "Nice to meet you, {name}!";

Cryptic

Description: A language for designing cryptography algorithms and secure communications. Aimed at developers interested in blockchain, encryption, or cybersecurity.

Key Features:

High-level abstractions for cryptographic primitives.

Secure-by-design philosophy.

Sample Code:

key = generate_key(algorithm: "AES", length: 256); encrypted_message = encrypt("Hello, World!", key); print(encrypted_message);

Harmony

Description: A reactive programming language for building interactive user interfaces, designed to be highly interoperable with Jest and other tools in the suite. Perfect for web developers and app creators.

Key Features:

Two-way data binding with minimal boilerplate.

Component-based architecture.

Sample Code:

component Button { state: clicked = false; onClick() { clicked = !clicked; alert("Button clicked!"); } } render(Button);

Inventor’s Vision

The coder or team behind Jest and these complementary languages would likely be passionate about:

Accessibility: Making programming intuitive and fun for people of all skill levels.

Playfulness: Infusing coding with creativity, humor, and expressiveness.

Interoperability: Ensuring all their languages work well together for larger projects.

Would you like me to dive deeper into one of these languages, conceptualize others, or expand Jest further?

Expanded Concepts and Bridge Languages

Pantomime

Expansion: Adds support for AR/VR interfaces where users use gestures to program logic in 3D space. For example, drawing a loop physically creates a repetitive cycle.

Bridge Language: GestureScript: Converts AR/VR gestures from Pantomime into traditional code for broader applications.

Facet

Expansion: Enhanced for generative AI tools, allowing users to define complex artistic styles and AI-driven procedural designs.

Bridge Language: PixelForge: Focused on exporting Facet code into game engines, enabling real-time rendering pipelines for interactive environments.

Quip

Expansion: Includes pre-trained AI models for dynamic storytelling, allowing Quip scripts to evolve based on user interaction.

Bridge Language: Narrate: Combines Quip scripts with voice-to-text technologies for live storytelling in games or educational tools.

Cryptic

Expansion: Introduces quantum-safe cryptography primitives and allows simulation of blockchain-like ecosystems for testing purposes.

Bridge Language: CipherBridge: Translates Cryptic code into various blockchain frameworks like Ethereum or Solana.

Harmony

Expansion: Gains support for cross-platform development, allowing users to write code for web, mobile, and IoT devices simultaneously.

Bridge Language: Symphony: Converts Harmony components into platform-native scripts for seamless integration.

Additional Bridge Languages

Echo: Bridges Quip and Jest, enabling storytelling code to trigger functional scripts for immersive experiences.

Morph: A universal language that compiles any Jest family code into executable binaries for specific operating systems.

Thread: Integrates Cryptic and Harmony to build secure, reactive user interfaces for fintech applications.

Canvas: Bridges Facet and Pantomime to create interactive art in AR/VR, blending visual coding with gesture inputs.

Let me know if you'd like a deeper dive into any specific language or bridge concept!

Here's a detailed exploration of Echo, Morph, Thread, Canvas, and all previously mentioned languages, emphasizing their unique applications and advanced functionalities.

Expanded Bridge Languages

Echo

Purpose: Links storytelling elements in Quip with functional programming in Jest, enabling dynamic, immersive interactions between narrative and logic.

Key Features:

Story-driven logic triggers: e.g., a character’s dialogue prompts a database query or API call.

Integration with AI tools for real-time responses.

Use Case: AI-driven chatbots that incorporate both storytelling and complex backend workflows.

Sample Code:

story_event "hero_arrives" triggers fetch_data("weather"); response = "The hero enters amidst a storm: {weather}.";

Morph

Purpose: Acts as a meta-compiler, translating any language in the Jest ecosystem into optimized, platform-specific binaries.

Key Features:

Universal compatibility across operating systems and architectures.

Performance tuning during compilation.

Use Case: Porting a Jest-based application to embedded systems or gaming consoles.

Sample Code:

input: Facet script; target_platform: "PS7"; compile_to_binary();

Thread

Purpose: Combines Cryptic's security features with Harmony's reactive architecture to create secure, interactive user interfaces.

Key Features:

Secure data binding for fintech or healthcare applications.

Integration with blockchain for smart contracts.

Use Case: Decentralized finance (DeFi) apps with intuitive, safe user interfaces.

Sample Code:

bind secure_input("account_number") to blockchain_check("balance"); render UI_component(balance_display);

Canvas

Purpose: Fuses Facet's generative design tools with Pantomime's gesture-based coding for AR/VR art creation.

Key Features:

Real-time 3D design with hand gestures.

Multi-modal export to AR/VR platforms or 3D printers.

Use Case: Collaborative VR environments for designers and architects.

Sample Code:

gesture: "draw_circle" → create_3D_shape("sphere"); gesture: "scale_up" → modify_shape("sphere", scale: 2x); render(scene);

Deep Dive into Main Languages

Jest

Philosophy: A playful, expressive scripting language with versatile syntax tailored for humor, experimentation, and creativity.

Core Application: Writing scripts that blend functional programming with a whimsical, human-readable style.

Pantomime

Expanded Features: Includes AI-driven gesture recognition for predictive modeling and natural motion capture.

Advanced Use: Developing AR apps for rehabilitation therapy where exercises are coded as gestures.

Facet

Advanced Use: Procedural generation in games, defining terrains, weather systems, and NPC behaviors with artistic flair.

Quip

Advanced Features: Integrates pre-trained language models for nuanced, character-driven dialogue trees.

Advanced Use: Creating adaptive narratives in games where user decisions alter the story arc.

Cryptic

Advanced Features: Quantum-safe algorithms and AI-enhanced key generation for unbreakable security.

Advanced Use: Designing multi-layered blockchain ecosystems with Cryptic’s high-level abstractions.

Harmony

Advanced Features: Adaptive UIs that respond to user emotion, tracked via AI-enhanced cameras.

Advanced Use: Apps for mental health, where interfaces adjust to user stress levels.

Proposed New Bridge Languages

Chord

Bridges Harmony and Facet for designing interactive music-based visual art.

Use Case: Music videos where the visuals respond to real-time sound input.

Path

Links Cryptic and Canvas for secure AR/VR environments.

Use Case: Virtual meetings in secure, customized 3D spaces.

Fable

Connects Quip and Pantomime for storytelling via interactive gestures.

Use Case: VR education apps where lessons are taught through immersive narratives.

Would you like a specific implementation plan for any of these languages or applications?

[rthidden]

You Don't Need to Code to Contribute to AI

Many people interested in AI think they can't contribute because they lack technical skills or don't want to acquire them.

1. Non-Technical Roles are Valuable

Even without coding skills, you can still make significant contributions to AI.

Journalists and other professionals can collaborate with data scientists to leverage their expertise.

2. Collaboration is Key

Working with someone who can code can make complex tools more accessible.

For example, using APIs to gather data might be daunting alone but easier with a coder's help.

3. Understanding Concepts Over Execution

Knowing the theory behind AI, like how deep neural networks work, is beneficial.

You don't need to program one yourself; understanding the assumptions and systems is crucial.

Embrace your strengths and collaborate with experts. Your unique perspective can provide valuable insights that technical experts might overlook.

[kaitlynwgst]

Blog Post #6 (10/10)

How does technology contribute to violence against women?

Women are subjected to gender-based violence in the real world and then on the internet they are often met with only more violence (Hernandez). Women are subjected to everything from unwanted images being sent to them to harassment on social media. Some of the online violence can even leak into the real world with people being stalked by those who found them online or being doxed. With the recent development of AI, the issue has only gotten worse with fake, often explicit, images of women being made and shared without their consent or even knowledge. The story of Ingrid Escamilla is a n example of how the internet can further the harm done to women, when the police leaked the images of her mutilated body the images were shared over and over again on the internet (Hernandez). Even after death she could not escape the violence brought against women in the internet.

How can social media be used to help advocate for women?

Technology can contribute greatly to the violence women are subjected to but it can also provide a space for women to advocate for themselves in a way that has not previously been possible. Activist groups and social movements often use social media to share their stories and spread their message. One way that this is done is through Hashtags on posts which make it easy for women to share their own stories and add their own words to a larger movement. Another way that social media is often used is for safety, when activists are protesting and advocating for change their presence on social media can give them a layer of protection through their visibility and the fact that the world is watching them. Again, the story of Ingrid Escamilla can show the good that social media can used for. When the images of her body where being shared and going viral a movement was started asking artists to create images of a living version of her to drown the images of her body on social media and the movement worked making it so when her name was searched the images the artists created came up first (Hernandez).

Why can the internet never truly be neutral?

The internet was originally funded by the US military and was furthered by universities both of these institutions have been traditionally filled with and created by white men. Then as the internet has developed many of the coders in Silicon Valley are again white men (Noble). With the internet created by and for white men it cannot truly be neutral or equally inclusive to everyone as it was created by biased individuals who’s bias has affected what is created.

How can saying the internet is neutral be harmful?

The internet which has often been claimed to be neutral but when its creators are predominately white men it is not only incorrect but can be harmful to claim it to be such. When these coders claim that gender and race are not an issue and that the internet is neutral, they are ignoring the issue and ignoring the privilege that they have in being able to say that gender and race are non-issues. This leads to then seeing themselves and people like them as the default and the codes they create catering to individuals like them, white men (Noble). When gender and race and the historical and societal impacts of them are ignored, it leads to default discrimination (Benjamin).

Benjamin, R. (2020). Race after technology abolitionist tools for the new Jim code. Polity. 

Hunsinger, J., & Senft, T. M. (2015). The Social Media Handbook. Routledge. 

Hernández, Dr. Miriam. Digital Defenders: Using Social Media to Challenge Violence Against Women. 9 October. 2024. Presentation 

[elfwreck]

Aside from stolen images: AI-created images and text can't be copyrighted. Companies don't want their content to be unprotected, free for anyone to use.

AI can be a useful brainstorming tool. It writes great DRAFT boilerplate, which then needs human review. If you need a generic email that says "our festival is coming up in two months; here's why it's awesome and you should attend; sign up at [link]," it'll do that quickly, and then you can adjust any weird phrasing.

A good content writer might be able to compose the email just as quickly on their own - but your Events Coordinator And Budget Manager may not have those writing skills. Or they may be too busy today. However, they may have proofing/editing skills to fix the draft. The AI-bot has saved them 15-30 minutes of time, which can be anywhere from $20 to $100 saved for the company. (They could hand the "write draft" job off to someone else - but it's possible nobody else has time this morning.)

Repeat for

People making client presentations who want an image of "three people looking at a computer screen showing one of our reports"

Welcome-new-employee company-wide email that draws info from the new employee's resume

Review the last three year's reports to note which ones are report negative results, so the company can check those client's reactions and decide whether to change how they present negative info

This is a notable help... but it's not "wow we can fire entire departments of admin assistants and replace our contracted blog writers with a bot" level of help. It's not "we don't need an IT team or database managers anymore."

AI images are getting more realistic. AI text is getting closer to sounding like it was written by a person.

Both are still prone to hallucinations. Neither is capable of following simple instructions that humans can do, like "a picture just like that one, but put green curtains on the window," or "list of our quarterly reports sorted by the signer's last names." (Especially if the reports are signed in four places by different people, because one is the main signatory and the rest are signing different sections.)

The AI pushers have been trying to claim that, in time, with more power, more energy, better code, they can fix these problems.

They can't. Humans understand data. AI just repeats patterns.

There's a lot of use for pattern repetition in both business and art.

That use is never going to remove the need for people who understand the actual content, and can edit it.

This is aside from issues of ethics, both where they got the content to train the LLMs and art-based AIs, and what they're trying to do to employees over it. This is aside from the issue of the environmental costs of AI.

Corporations don't have ethics and don't care about the environment. (People in corporations may. Corporations themselves, as entities, only care about profit. and usually that means short-term profit.)

The crucial message for them is: AI cannot do the things they want it to do. It never will.

It can assist. And for that to be a long-term part of business, we need to discuss the ethics of the training material and the environmental costs of the tech.

But regardless of those answers: AI will never remove the need for human review and intervention. Companies that shift to relying on it for more than rough-draft production are going to have a short period of increased profits as they fire their human staff... followed by a collapse when the AI fails at a crucial task, and nobody caught it: A fake report went live leaving them on the hook for fraud; a scandalous photo in a report cost them their best client; it bought non-refundable plane tickets for half the staff to attend a conference; it cancelled all company-issued credit cards over a single misuse-of-funds complaint. Or their top three coders quit because the AI accidentally scheduled a crucial meeting over their vacation time. And so on.

There are parts of the AI-into-business movement that are rough for writers and artists, and that sucks.

But hang in there. It cannot last, and the bubble is starting to burst. Because whatever value it does have - it can't do what they want.

I feel like the big push for AI is starting to flag. Even my relatively tech obsessed dad is kinda over it. What do you even use it for? Because you sure as hell dont want to use it for fact checking.

There's an advertisement featuring a woman surreptitiously asking her phone to provide her with discussion topics for her book club. And like... what. Is this the use case for commercial AI? This the best you could come up with? Lying to your friends about Moby Dick?

[billing-paradisercm]

Unlocking Revenue Potential with Professional Medical Coding Services in Healthcare RCM

In the ever-evolving landscape of healthcare, efficient revenue cycle management (RCM) is crucial for the financial health of medical practices, hospitals, and health systems. A core component of this cycle often underestimated is medical coding. Proper and accurate medical coding services not only ensure faster reimbursement but also reduce claim denials, prevent compliance issues, and help organizations scale.

But how exactly do medical coding services contribute to the revenue cycle? And what should healthcare providers look for when choosing the right partner? In this blog, we’ll explore the role of medical coding in RCM, its benefits, challenges, and best practices to optimize revenue performance.

What Are Medical Coding Services?

Medical coding services involve translating healthcare diagnoses, procedures, medical services, and equipment into universal alphanumeric codes. These codes are then used for billing and reimbursement from payers, including Medicare, Medicaid, and private insurers.

There are several code sets that coders use, including:

ICD-10-CM for diagnosis coding

CPT® (Current Procedural Terminology) for procedure coding

HCPCS Level II for equipment and services not included in CPT

Coding must be precise and compliant with regulations. Even minor errors can lead to delayed payments or audits. Hence, professional medical coding services are not just helpful—they are critical.

Importance of Medical Coding in Revenue Cycle Management

RCM is the financial backbone of any healthcare organization. It begins when a patient books an appointment and ends when the payment is received. Medical coding plays a pivotal role at the midpoint of this process—after documentation and before billing.

Here's how accurate coding supports RCM:

1. Speeds Up Reimbursement

Incorrect or inconsistent coding is one of the top reasons claims are denied. Coders ensure that every service rendered is properly translated, accelerating the reimbursement process.

2. Reduces Claim Denials

Every denied claim requires time and resources to rework. Skilled coders help minimize errors, significantly reducing the denial rate and improving the clean claim ratio.

3. Ensures Compliance and Minimizes Risk

Non-compliance with coding guidelines can result in audits, penalties, or even loss of credentials. Professional coding services maintain up-to-date knowledge of CMS and payer-specific rules to ensure compliance.

4. Optimizes Revenue Integrity

Beyond error reduction, coding accuracy ensures that providers get paid for every service rendered—no undercoding or missed opportunities.

Key Benefits of Outsourcing Medical Coding Services

Many healthcare providers, especially small and mid-sized practices, find it more efficient to outsource medical coding services. Here's why:

1. Access to Certified Coders

Outsourcing gives you access to AHIMA- or AAPC-certified coders who specialize in various disciplines—cardiology, orthopedics, radiology, and more.

2. Cost-Effective and Scalable

Hiring, training, and retaining an in-house team can be costly. Outsourcing removes this overhead and provides the flexibility to scale as your practice grows.

3. Faster Turnaround Times

Professional service providers operate with defined SLAs and use advanced coding tools and AI-powered audits to deliver accurate results quickly.

4. Focus on Patient Care

Freeing up administrative tasks allows in-house staff and physicians to focus more on patient engagement and outcomes.

Challenges in Medical Coding and How to Overcome Them

Despite the importance of medical coding in RCM, many organizations struggle due to:

Constant updates in codes and regulations

Incomplete or unclear clinical documentation

High staff turnover or lack of training

Lack of internal audits

Solution? Partnering with the Right Medical Coding Services Provider

A professional partner stays current with changing guidelines, performs regular audits, and works closely with your clinical team to ensure accurate documentation. This bridge between clinical and billing operations results in a more efficient revenue cycle.

Best Practices to Optimize Coding for Better RCM Outcomes

If you want to leverage medical coding services to their fullest potential, here are a few tips:

1. Invest in Documentation Training

Coders can only work with what’s documented. Educate providers and staff on how to capture complete, accurate, and specific information in clinical records.

2. Use Coding Audits Proactively

Regular audits help identify trends, gaps, and opportunities. Use audits not as punishment, but as a tool for continuous improvement.

3. Adopt Technology-Driven Solutions

AI-assisted coding tools, natural language processing (NLP), and coding dashboards can dramatically increase accuracy and efficiency.

4. Measure KPIs Related to Coding

Monitor metrics like coding accuracy rate, denial rate, average time to code, and coder productivity to assess performance and areas for improvement.

How to Choose a Medical Coding Services Partner

Choosing the right coding partner is not just about pricing—it’s about value, compliance, and collaboration. Here’s what to consider:

Certifications: Are the coders AAPC- or AHIMA-certified?

Specialty Expertise: Do they understand your specialty’s nuances?

Technology Stack: Are they using updated tools and EHR integrations?

Compliance Protocols: How do they stay HIPAA- and CMS-compliant?

Communication: Do they work collaboratively with your clinical and billing teams?

Conclusion

In the complex web of healthcare revenue cycle management, medical coding services serve as a key linchpin. When done correctly, coding not only secures faster reimbursements and fewer denials but also ensures compliance and maximized revenue.

Whether you run a solo practice or manage a multi-specialty hospital, investing in accurate, timely, and compliant medical coding is no longer optional—it’s essential. By outsourcing to a trusted partner or strengthening your in-house capabilities, you can transform your RCM process and position your organization for financial sustainability and growth.

If you're looking to streamline your revenue operations and eliminate coding errors, professional medical coding services might just be the smartest move you make this year.

[iprogrammersolutions]

Scaling Your Australian Business with AI: A CEO’s Guide to Hiring Developers

In today’s fiercely competitive digital economy, innovation isn’t a luxury—it’s a necessity. Australian businesses are increasingly recognizing the transformative power of Artificial Intelligence (AI) to streamline operations, enhance customer experiences, and unlock new revenue streams. But to fully harness this potential, one crucial element is required: expert AI developers.

Whether you’re a fast-growing fintech in Sydney or a manufacturing giant in Melbourne, if you’re looking to implement scalable AI solutions, the time has come to hire AI developers who understand both the technology and your business landscape.

In this guide, we walk CEOs, CTOs, and tech leaders through the essentials of hiring AI talent to scale operations effectively and sustainably.

Why AI is Non-Negotiable for Scaling Australian Enterprises

Australia has seen a 270% rise in AI adoption across key industries like retail, healthcare, logistics, and finance over the past three years. From predictive analytics to conversational AI and intelligent automation, AI has become central to delivering scalable, data-driven solutions.

According to Deloitte Access Economics, AI is expected to contribute AU$ 22.17 billion to the Australian economy by 2030. For CEOs and decision-makers, this isn’t just a trend—it’s a wake-up call to start investing in the right AI talent to stay relevant.

The Hidden Costs of Delaying AI Hiring

Still relying on a traditional tech team to handle AI-based initiatives? You could be leaving significant ROI on the table. Without dedicated experts, your AI projects risk:

Delayed deployments

Poorly optimized models

Security vulnerabilities

Lack of scalability

Wasted infrastructure investment

By choosing to hire AI developers, you're enabling faster time-to-market, more accurate insights, and a competitive edge in your sector.

How to Hire AI Developers: A Strategic Approach for Australian CEOs

The process of hiring AI developers is unlike standard software recruitment. You’re not just hiring a coder—you’re bringing on board an innovation partner.

Here’s what to consider:

1. Define the Scope of AI in Your Business

Before hiring, map out where AI fits in your roadmap:

Are you looking for machine learning-driven forecasting?

Want to implement AI chatbots for 24/7 customer service?

Building a computer vision solution for your manufacturing line?

Once you identify the use cases, it becomes easier to hire ML developers or AI experts with the relevant domain and technical experience.

2. Understand the AI Tech Stack

A strong AI developer should be proficient in:

Python, R, TensorFlow, PyTorch

Scikit-learn, Keras, OpenCV

Data engineering with SQL, Spark, Hadoop

Deployment tools like Docker, Kubernetes, AWS SageMaker

When you hire remote AI engineers, ensure they’re fluent not just in coding, but also in AI deployment and scalability best practices.

3. Consider AI Developer Augmentation for Speed & Flexibility

Building an in-house AI team is time-consuming and expensive. That’s why AI developer staff augmentation is a smarter choice for many Australian enterprises.

With our staff augmentation services, you can:

Access pre-vetted, highly skilled AI developers

Scale up or down depending on your project phase

Save costs on infrastructure and training

Retain full control over your development process

Whether you need to hire ML developers for short-term analytics or long-term AI product development, we offer customized engagement models to suit your needs.

4. Prioritize Industry Experience

AI isn’t one-size-fits-all. Hiring developers who have experience in your specific industry—be it healthcare, fintech, ecommerce, logistics, or manufacturing—ensures faster onboarding and better results.

We’ve helped companies in Australia and across the globe integrate AI into:

Predictive maintenance systems

Smart supply chain analytics

AI-based fraud detection in banking

Personalized customer experiences in ecommerce

This hands-on experience allows our developers to deliver solutions that are relevant and ROI-driven.

Why Choose Our AI Developer Staff Augmentation Services?

At iProgrammer, we bring over a decade of experience in empowering businesses through intelligent technology solutions. Our AI developer augmentation services are designed for fast-scaling enterprises that demand quality, flexibility, and performance.

What Sets Us Apart:

AI-First Talent Pool: We don’t generalize. We specialize in AI, ML, NLP, computer vision, and data science.

Quick Deployment: Get developers onboarded and contributing in just a few days.

Cost Efficiency: Hire remote AI developers from our offshore team and reduce development costs by up to 40%.

End-to-End Support: From hiring to integration and project execution, we stay involved to ensure success.

A Case in Point: AI Developer Success in an Australian Enterprise

One of our clients, a mid-sized logistics company in Brisbane, wanted to predict delivery delays using real-time data. Within 3 weeks of engagement, we onboarded a senior ML developer who built a predictive model using historical shipment data, weather feeds, and traffic APIs. The result? A 25% reduction in customer complaints and a 15% improvement in delivery time accuracy.

This is the power of hiring the right AI developer at the right time.

Final Thoughts: CEOs Must Act Now to Stay Ahead

If you’re a CEO, CTO, or decision-maker in Australia, the question isn’t “Should I hire AI developers?” It’s “How soon can I hire the right AI developer to scale my business?”

Whether you're launching your first AI project or scaling an existing system, AI developer staff augmentation provides the technical depth and agility you need to grow fast—without the friction of long-term hiring.

Ready to Build Your AI Dream Team?

Let’s connect. Talk to our AI staffing experts today and discover how we can help you hire remote AI developers or hire ML developers who are ready to make an impact from day one.

👉 Contact Us Now | Schedule a Free Consultation

[digitalbano]

Best AI Courses in India You Can Join Today— No Coding Needed

Artificial intelligence (AI) is becoming a big part of our daily life. It is used in mobile phones, smart TVs, online shopping websites, digital assistants, and many apps. Because of this, many people now want to learn AI and build a career in this field.

But one question comes to mind – Do I need to learn coding to study AI?

The answer is No. There are many AI courses in India that you can join even if you don’t know how to code. These courses are made for beginners, students from non-technical backgrounds, working professionals, and even freshers.

In this blog, Digital Bano will tell you about some of the best AI courses in India that you can join without coding knowledge. You can study these courses online from your home. They are simple to understand and do not require a technical background. You just need basic computer knowledge and interest in learning.

Why Learn AI?

Here are some simple reasons to learn AI:

Good Career: Many companies are hiring AI professionals.

High Salary: AI jobs give good salary packages.

Growing Field: AI is the future. If you learn it now, your skills will be useful for many years.

No Coding Jobs Available: You can work in AI without being a software developer.

AI is not only for engineers or coders. It is also useful for students of commerce, arts, and even business owners. Today, people from different fields are learning how to use AI in their work and daily life.

What You Will Learn in AI Courses Without Coding

These courses are designed to teach you AI in a very simple way. Some things you will learn include:

What is AI and how it works

Real examples of AI in business, marketing, and daily life

AI tools like ChatGPT, Canva AI, and others

How to use AI for content writing, videos, presentations, and more

Projects and case studies (practical learning)

Ethics and rules of using AI

Some courses may also include basic data analysis, charts, or understanding patterns using simple drag-and-drop tools.

Best AI Courses in India – No Coding Needed

Let’s look at some of the top AI courses available in India for beginners.

1. AI For Everyone – by Andrew Ng (Coursera)

Mode: Online

Time: 6 to 8 hours

Fees: Free (you pay only if you want a certificate)

Good For: Students, business owners, job seekers

This is a simple course that explains what AI is, how it can help in business, and what it can and cannot do. No technical knowledge is needed. It is one of the best starting points.

2. Post Graduate Program in AI & ML – Simplilearn (in partnership with Purdue University)

Mode: Online

Time: 12 months

Fees: Around ₹1.5 to ₹2 Lakhs

Good For: Working professionals, graduates

This course is more advanced, but it also has tracks for non-programmers. You will learn AI tools and concepts used in real companies. The course is useful if you want to work in the corporate or IT industry.

3. AI in Business – Great Learning

Mode: Online

Time: 2 hours

Fees: Free

Good For: Business students, beginners, office workers

This course teaches how companies use AI in sales, marketing, and customer support. It is very easy to understand. It gives you an idea of how AI helps improve business decisions.

4. AI Tools and Platforms – Udemy

Mode: Online

Time: Self-paced (learn anytime)

Fees: Around ₹499 to ₹1,299

Good For: Content creators, marketers, beginners

In this course, you will learn how to use AI tools like Canva AI, ChatGPT, Pictory, and others for business, social media, and projects. You can use these tools to write content, make posters, edit videos, and more.

5. AI Bootcamp – Skill-Lync

Mode: Online

Time: 4 weeks

Fees: Around ₹2,000

Good For: Students, freshers

This course is for beginners. You will work on small projects and understand how AI works in simple ways. No coding is required. It is great for students who want to explore new skills.

Note: You Can Read Also These Blogs For More Information About Your Better Future:

Best courses after 12th

Captcha Typing Jobs

Courses After 12th Commerce

Jobs You Can Get After These Courses

After completing any of these courses, you can apply for roles like:

AI Product Manager

AI Content Writer

Prompt Engineer (write smart inputs for AI tools)

AI Business Analyst

Chatbot Designer

AI Marketing Specialist

Social Media Executive using AI tools

All of these jobs do not need programming knowledge. You need to be creative and understand how to use tools smartly. These jobs are available in digital marketing companies, media houses, IT firms, startups, and big brands.

Even freelancers and YouTubers are using AI to grow faster and work smarter.

Why AI Courses in India Are Growing Fast

Many Indian companies now use AI to save time and money

Schools and colleges have started teaching AI

AI tools are easy to use, even by non-technical people

Online learning has become common after COVID

Government is supporting AI learning programs

Now anyone with interest and the internet can learn AI from home. You don’t need to travel or attend a classroom.

Things to Remember Before Choosing a Course

Check if the course is for beginners

See if they give a certificate

Look at the reviews from other students

Choose a course that gives projects or hands-on learning

Try free courses if you are just starting out

Make sure the language of the course is easy for you to understand

Start with a short course. Once you are comfortable, you can move to advanced learning.

Final Words

AI is the future of technology. You can become a part of this future without being a coder. Many AI courses in India are now made for people who are new to the tech world. These courses are simple, affordable, and useful.

So if you are a student, fresher, or professional, now is the right time to learn AI. Pick a course that matches your interest, and start learning today.

[softwaretestingtraining]

Top 5 Low-Code/No-Code Test Automation Tools in 2025 | Software Testing Training Institute in Kerala

Automation testing isn’t just for seasoned coders anymore. Thanks to low-code and no-code platforms, QA professionals, manual testers, and even non-tech team members can now build automated tests faster, with minimal or no coding skills.

Whether you're new to testing or a developer looking for speed, this blog will walk you through the top 5 low-code/no-code test automation tools that are making waves in 2025. Let’s simplify testing without compromising quality.

Why Low-Code/No-Code Tools?

Before we dive in, let’s understand why these tools are booming:

Faster delivery: Build automation scripts in minutes, not days.

Less dependency on developers: Even non-coders can participate in QA.

Budget-friendly: Save on hiring specialist automation engineers.

Visual approach: Most tools offer drag-and-drop, record-and-playback, or block-based logic flows.

Now, let’s explore the top players leading the low-code/no-code automation space.

1. Testim (by Tricentis)

🚀 What Makes It Stand Out:

Testim uses AI to stabilize tests and reduce maintenance, which is a major headache in traditional automation. Its smart locators adapt to minor UI changes, making your test suite more reliable.

🛠️ Key Features:

Drag-and-drop editor for quick test creation

AI-powered self-healing tests

Seamless integrations with CI/CD tools

Chrome extension to record flows

Version control and team collaboration

🎯 Best For:

Agile teams needing scalable web app test automation with minimal code.

2. Katalon Studio

🚀 What Makes It Stand Out:

Katalon combines the best of both worlds—low-code for beginners and full-code for pros. Its dual UI lets testers start with visual test creation and gradually shift to scripting when needed.

🛠️ Key Features:

Record-and-playback feature for web, mobile, API, and desktop testing

Built-in test management and reporting

Pre-built templates and reusable test objects

Supports Groovy-based custom scripts

Integrates with JIRA, Jenkins, Slack, etc.

🎯 Best For:

Teams transitioning from manual to automated testing, especially in hybrid tech stacks.

3. Leapwork

🚀 What Makes It Stand Out:

Leapwork is a true no-code platform built with testers in mind. Its flowchart-based automation builder is intuitive, allowing users to map out test scenarios visually—no syntax required.

🛠️ Key Features:

Visual test flow creation with logic blocks

Cross-platform testing: web, desktop, and virtual apps

Centralized dashboard for monitoring and reports

Scalable for enterprise-grade testing

Reusable sub-flows for modularity

🎯 Best For:

Enterprise QA teams seeking robust, secure, and scalable no-code automation.

4. TestSigma

🚀 What Makes It Stand Out:

TestSigma lets you write tests in plain English! It's a cloud-based, AI-driven platform where test cases look almost like spoken instructions. Ideal for functional, mobile, and API testing.

🛠️ Key Features:

Natural Language Processing (NLP)-based test authoring

Supports web, mobile, API, and database testing

Built-in test data management

In-browser execution, no installation needed

Real-time reporting and debugging tools

🎯 Best For:

Cross-functional teams where business analysts, QA, and devs collaborate on automation.

5. Ranorex Studio

🚀 What Makes It Stand Out:

Ranorex is feature-rich and beginner-friendly. With its record-and-replay interface, you can build tests quickly, but it also allows full script editing for those with programming experience.

🛠️ Key Features:

GUI-based testing for desktop, web, and mobile

Drag-and-drop interface with flexible control identification

C# and VB.NET scripting support for advanced users

Smart test reporting with screenshots

Strong debugging capabilities

🎯 Best For:

QA teams handling complex, multi-platform applications.

How to Choose the Right Tool?

All tools listed above are strong contenders, but here’s a quick comparison checklist: CriteriaBest OptionNo-code simplicityLeapworkNLP-based authoringTestSigmaAI-powered stabilityTestimHybrid testing flexibilityKatalon StudioDesktop + mobile + webRanorex Studio

Ask yourself:

Do you want to involve non-technical stakeholders?

Are you testing across multiple platforms?

Do you want AI to reduce test flakiness?

Do you have DevOps pipelines to integrate with?

Your answers will guide your pick.

Future of Low-Code/No-Code Testing

In 2025 and beyond, these platforms will only get smarter:

AI will write better test suggestions based on user behavior

Self-healing tests will become standard

Voice-enabled test creation may emerge

Crowd-sourced testing libraries will speed up test development

With tools becoming more intuitive, testing will shift from being a bottleneck to a team-wide superpower.

Final Thoughts

Low-code and no-code tools are leveling the QA field. Whether you're just starting out or looking to optimize your team’s productivity, these platforms offer a fast-track to reliable, scalable, and maintainable test automation.

If you're looking to master these tools and launch your QA career, consider joining a hands-on, project-driven program at a trusted Software Testing Training Institute in Kerala like Obsqura Zone. Their curriculum includes practical training on top tools like Selenium, Postman, and even AI-powered platforms.

Testing is no longer just about finding bugs—it's about building quality, faster. https://www.obsqurazone.com/

[shettysagar]

Top Programming Languages to Learn in 2025 for High-Demand Tech Careers.

Fusion Software Training Institute is committed to delivering industry-relevant software training through expertly crafted curriculums and hands-on experience. We bridge the gap between academic knowledge and professional expertise. Why 2025 Demands a Strategic Choice of Languages With technologies like Artificial Intelligence, Blockchain, Web3, and Cloud Computing dominating the landscape, employers are seeking professionals fluent in languages that can power these innovations. Companies aren't just looking for coders—they need problem solvers who understand scalability, performance, and modern tech stacks. Top 7 Programming Languages to Learn in 2025 1. Python – The King of Versatility From AI and Machine Learning to Data Science and Web Development, Python remains the most flexible and beginner-friendly language. Its vast libraries like Pandas, TensorFlow, and Flask make it a must-learn in 2025. Used in: AI, automation, data analytics, fintech Why learn it: High demand + massive community + easy to learn Fore More Info Read : Best Programming Languages 2. JavaScript – The Backbone of the Web JavaScript continues to dominate the front-end development space, powering dynamic user interfaces across web and mobile apps. Frameworks like React, Node.js, and Next.js ensure its relevance in full-stack development. Used in: Web development, app development, SaaS platforms Why learn it: Essential for frontend, growing backend usage 3. Java – Enterprise-Grade Stability Despite its age, Java powers enterprise systems, banking platforms, and Android apps. With updates like Project Panama and its cross-platform capabilities, Java remains future-proof. Used in: Enterprise apps, backend systems, Android Why learn it: Long-term stability, robust frameworks like Spring 4. Go (Golang) – The Language of Cloud and DevOps Go, developed by Google, is gaining traction in cloud-native development, microservices, and scalable backend systems. Its speed and simplicity make it ideal for building modern APIs and cloud tools. Used in: DevOps, cloud platforms, distributed systems Why learn it: Fast, secure, and highly concurrent Read This : Java Frameworks   5. Rust – The Future of Safe Systems Programming Rust is quickly becoming the go-to language for systems programming due to its performance and safety guarantees. Companies like Microsoft and Amazon are investing heavily in Rust for building secure and efficient software. Used in: Operating systems, game engines, security tools Why learn it: Memory-safe, fast, and increasingly adopted 6. TypeScript – JavaScript’s Safer Sibling As applications grow in complexity, TypeScript offers the safety of static typing without leaving the JavaScript ecosystem. It enhances productivity and maintainability in large-scale apps. Used in: Large web applications, frontend frameworks Why learn it: Type safety + JS compatibility = developer favorite 7. SQL – The Foundation of Data-Driven Tech In an age where data is gold, SQL remains a non-negotiable skill for anyone working in Data Science, BI, or backend development. Mastering SQL is crucial for querying, managing, and understanding data. Used in: Data analysis, backend systems, ETL pipelines Why learn it: Still the #1 language for data professionals Explore our programming courses at Fusion Institute and future-proof your tech career today!  Call us: +91 7498992609 / +91 9890647273  Mail us: info@fusion-institute.com

[vanatorrpo788]

Vanator RPO – Leading Offshore Recruiters USA for Smart Hiring Solutions

In a fast-evolving business environment, companies must adapt quickly to hire the right talent at the right time. This is where offshore recruiters USA play a vital role. Vanator RPO has emerged as one of the top offshore recruitment firms in the United States, offering scalable and specialized Recruitment Process Outsourcing (RPO) solutions. With a strong commitment to quality, speed, and innovation, Vanator is helping businesses of all sizes find the best talent while reducing costs and time-to-hire.

Why Offshore Recruiters USA Are Gaining Popularity

The global workforce is becoming increasingly borderless. Offshore recruiters USA, like Vanator RPO, help bridge the talent gap by leveraging a vast international talent pool. They offer the flexibility, expertise, and tools needed to manage recruitment at scale—whether it’s for IT professionals, healthcare workers, engineers, or finance experts. This model enables companies to focus on their core operations while outsourcing the time-consuming aspects of recruitment to a dedicated team.

End-to-End RPO Solutions

Vanator RPO provides end-to-end recruitment support, making it a one-stop destination for businesses seeking high-quality candidates. Its services include:

24/7 Sourcing Support: Ensures a continuous pipeline of qualified candidates for open roles across industries.

AI-Driven Screening: Uses advanced algorithms to match resumes with job requirements, improving hiring accuracy.

Recruitment Support: Manages full-cycle recruitment, from candidate engagement to final onboarding.

VMS Recruiting: Handles vendor management systems for companies that need volume hiring with strict compliance.

Training Academy: Regularly upskills its recruiters and staff to align with market trends and client expectations.

Virtual Assistance: Offers administrative, technical, and non-technical assistance through remote professionals.

Industry Specialization

One of Vanator’s key strengths lies in its industry-specific approach. The team understands that hiring needs vary by domain, and they customize their strategies accordingly. The major industries served include:

Information Technology: Filling roles from developers to system architects.

Engineering: Recruiting civil, mechanical, electrical, and software engineers.

Medical & Healthcare: Supplying certified coders, nurses, and clinical support staff.

Finance & Accounting: Placing experienced accountants, auditors, and analysts.

Sales & Marketing: Sourcing dynamic sales leaders and digital marketing experts.

Global Presence, Local Expertise

Headquartered in the United States with a major operations hub in India, Vanator RPO serves clients across North America, Europe, and Asia. This global presence allows them to provide localized solutions with the efficiency of offshore recruiters. They are available round-the-clock, helping businesses keep up with hiring demands regardless of time zone or location.

Client-Centric Model

Vanator’s success is built on a deep understanding of client needs. Their recruiters work as an extension of the internal HR teams, aligning with company culture and long-term hiring goals. By doing so, they help reduce hiring costs by up to 30% and significantly cut down the average time-to-fill. Whether it's high-volume recruitment or niche role fulfillment, Vanator RPO adapts seamlessly.

Innovation at the Core

Incorporating AI in recruitment is not just a trend—it's a necessity. Vanator RPO uses AI-powered tools to automate resume screening, enhance candidate engagement, and predict hiring outcomes. This technology-first approach helps them stay ahead in a competitive recruiting market.

Conclusion

Vanator RPO is a trusted name among offshore recruiters USA, offering unmatched expertise in recruitment process outsourcing. With a strategic mix of technology, global reach, and personalized service, they empower organizations to build world-class teams. If your business is looking to optimize its hiring strategy, Vanator is the ideal partner for intelligent, cost-effective, and scalable recruitment solutions.

Visit us at: www.govanator.com 

[mariacallous]

On paper, the first candidate looked perfect. Thomas was from rural Tennessee and had studied computer science at the University of Missouri. His résumé said he’d been a professional programmer for eight years, and he’d breezed through a preliminary coding test. All of this was excellent news for Thomas’ prospective boss, Simon Wijckmans, founder of the web security startup C.Side. The 27-year-old Belgian was based in London but was looking for ambitious, fully remote coders.

Thomas had an Anglo-Saxon surname, so Wijckmans was surprised when he clicked into his Google Meet and found himself speaking with a heavily accented young man of Asian origin. Thomas had set a generic image of an office as his background. His internet connection was laggy—odd for a professional coder—and his end of the call was noisy. To Wijckmans, Thomas sounded like he was sitting in a large, crowded space, maybe a dorm or a call center.

Wijckmans fired off his interview questions, and Thomas’ responses were solid enough. But Wijckmans noticed that Thomas seemed most interested in asking about his salary. He didn’t come across as curious about the actual work or about how the company operated or even about benefits like startup stock or health coverage. Odd, thought Wijckmans. The conversation came to a close, and he got ready for the next interview in his queue.

Once again, the applicant said they were based in the US, had an Anglo name, and appeared to be a young Asian man with a thick, non-American accent. He used a basic virtual background, was on a terrible internet connection, and had a single-minded focus on salary. This candidate, though, was wearing glasses. In the lenses, Wijckmans spotted the reflection of multiple screens, and he could make out a white chatbox with messages scrolling by. “He was clearly either chatting with somebody or on some AI tool,” Wijckmans remembers.

On high alert, Wijckmans grabbed screenshots and took notes. After the call ended, he went back over the job applications. He found that his company’s listings were being flooded with applicants just like these: an opening for a full-stack developer got more than 500 applications in a day, far more than usual. And when he looked more deeply into the applicants’ coding tests, he saw that many candidates appeared to have used a virtual private network, or VPN, which allows you to mask your computer’s true location.

Wijckmans didn’t know it yet, but he’d stumbled onto the edges of an audacious, global cybercrime operation. He’d unwittingly made contact with an army of seemingly unassuming IT workers, deployed to work remotely for American and European companies under false identities, all to bankroll the government of North Korea.

With a little help from some friends on the ground, of course.

christina chapman was living in a trailer in Brook Park, Minnesota, a hamlet north of Minneapolis, when she got a note from a recruiter that changed her life. A bubbly 44-year-old with curly red hair and glasses, she loved her dogs and her mom and posting social justice content on TikTok. In her spare time she listened to K-pop, enjoyed Renaissance fairs, and got into cosplay. Chapman was also, according to her sparse online résumé, learning to code online.

It was March 2020 when she clicked on the message in her LinkedIn account. A foreign company was looking for somebody to “be the US face” of the business. The company needed help finding remote employment for overseas workers. Chapman signed on. It’s unclear how fast her workload grew, but by October 2022 she could afford a move from chilly Minnesota to a low-slung, four-bedroom house in Litchfield Park, Arizona. It wasn’t fancy—a suburban corner lot with a few thin trees—but it was a big upgrade over the trailer.

Chapman then started documenting more of her life on TikTok and YouTube, mostly talking about her diet, fitness, or mental health. In one chatty video, shared in June 2023, she described grabbing breakfast on the go—an açaí bowl and a smoothie— because work was so busy. “My clients are going crazy!” she complained. In the background, the camera caught a glimpse of metal racks holding at least a dozen open laptops covered in sticky notes. A few months later, federal investigators raided Chapman’s home, seized the laptops, and eventually filed charges alleging that she had spent three years aiding the “illicit revenue generation efforts” of the government of North Korea.

For maybe a decade, North Korean intelligence services have been training young IT workers and sending them abroad in teams, often to China or Russia. From these bases, they scour the web for job listings all over, usually in software engineering, and usually with Western companies. They favor roles that are fully remote, with solid wages, good access to data and systems, and few responsibilities. Over time they began applying for these jobs using stolen or fake identities and relying on members of their criminal teams to provide fictional references; some have even started using AI to pass coding tests, video interviews, and background checks.

But if an applicant lands a job offer, the syndicate needs somebody on the ground in the country the applicant claims to live in. A fake employee, after all, can’t use the addresses or bank accounts linked to their stolen IDs, and they can’t dial in to a company’s networks from overseas without instantly triggering suspicion. That’s where someone like Christina Chapman comes in.

As the “facilitator” for hundreds of North Korea–linked jobs, Chapman signed fraudulent documents and handled some of the fake workers’ salaries. She would often receive their paychecks in one of her bank accounts, take a cut, and wire the rest overseas: Federal prosecutors say Chapman was promised as much as 30 percent of the money that passed through her hands.

Her most important job, though, was tending the “laptop farm.” After being hired, a fake worker will typically ask for their company computer to be sent to a different address than the one on record—usually with some tale about a last-minute move or needing to stay with a sick relative. The new address, of course, belongs to the facilitator, in this case Chapman. Sometimes the facilitator forwards the laptop to an address overseas, but more commonly that person holds onto it and installs software that allows it to be controlled remotely. Then the fake employee can connect to their machine from anywhere in the world while appearing to be in the US. (“You know how to install Anydesk?” one North Korean operative asked Chapman in 2022. “I do it practically EVERYDAY!” she replied.)

In messages with her handlers, Chapman discussed sending government forms like the I-9, which attests that a person is legally able to work in the US. (“I did my best to copy your signature,” she wrote. “Haha. Thank you,” came the response.) She also did basic tech troubleshooting and dialed into meetings on a worker’s behalf, sometimes on short notice, as in this conversation from November 2023:

Worker: We are going to have laptop setup meeting in 20 mins. Can you join Teams meeting and follow what IT guy say? Because it will require to restart laptop multiple times and I can not handle that. You can mute and just follow what they say ...

Chapman: Who do I say I am?

Worker: You don’t have to say, I will be joining there too.

Chapman: I just typed in the name Daniel. If they ask WHY you are using two devices, just say the microphone on your laptop doesn’t work right ... Most IT people are fine with that explanation.

Sometimes, she got jumpy. “I hope you guys can find other people to do your physical I9s,” she wrote to her bosses in 2023, according to court documents. “I will SEND them for you, but have someone else do the paperwork. I can go to FEDERAL PRISON for falsifying federal documents.” Michael Barnhart, an investigator at cybersecurity company DTEX and a leading expert on the North Korean IT worker threat, says Chapman’s involvement followed a standard pattern—from an innocuous initial contact on LinkedIn to escalating requests. “Little by little, the asks get bigger and bigger,” he says. “Then by the end of the day, you’re asking the facilitator to go to a government facility to pick up an actual government ID.”

By the time investigators raided Chapman’s home, she was housing several dozen laptops, each with a sticky note indicating the fake worker’s identity and employer. Some of the North Korean operatives worked multiple jobs; some had been toiling quietly for years. Prosecutors said at least 300 employers had been pulled into this single scheme, including “a top-five national television network and media company, a premier Silicon Valley technology company, an aerospace and defense manufacturer, an iconic American car manufacturer, a high-end retail store, and one of the most recognizable media and entertainment companies in the world.” Chapman, they alleged, had helped pass along at least $17 million. She pleaded guilty in February 2025 to charges relating to wire fraud, identity theft, and money laundering and is awaiting sentencing.

Chapman’s case is just one of several North Korean fake-worker prosecutions making their way through US courts. A Ukrainian named Oleksandr Didenko has been accused of setting up a freelancing website to connect fake IT workers with stolen identities. Prosecutors say at least one worker was linked to Chapman’s laptop farm and that Didenko also has ties to operations in San Diego and Virginia. Didenko was arrested in Poland last year and was extradited to the United States. In Tennessee, 38-year-old Matthew Knoot is due to stand trial for his alleged role in a scheme that investigators say sent hundreds of thousands of dollars to accounts linked to North Korea via his laptop farm in Nashville. (Knoot has pleaded not guilty.) And in January 2025, Florida prosecutors filed charges against two American citizens, Erick Ntekereze Prince and Emanuel Ashtor, as well as a Mexican accomplice and two North Koreans. (None of the defendants’ lawyers in these cases responded to requests for comment.) The indictments claim that Prince and Ashtor had spent six years running a string of fake staffing companies that placed North Koreans in at least 64 businesses.

before the hermit kingdom had its laptop farms, it had a single confirmed internet connection, at least as far as the outside world could tell. As recently as 2010, that one link to the web was reserved for use by high-ranking officials. Then, in 2011, 27-year-old Kim Jong Un succeeded his father as the country’s dictator. Secretly educated in Switzerland and said to be an avid gamer, the younger Kim made IT a national priority. In 2012, he urged some schools to “pay special attention to intensifying their computer education” to create new possibilities for the government and military. Computer science is now on some high school curricula, while college students can take courses on information security, robotics, and engineering.

The most promising students are taught hacking techniques and foreign languages that can make them more effective operatives. Staff from government agencies including the Reconnaissance General Bureau— the nation’s clandestine intelligence service—recruit the highest-scoring graduates of top schools like Kim Chaek University of Technology (described by many as “the MIT of North Korea”) or the prestigious University of Sciences in Pyongsong. They are promised good wages and unfettered access to the internet—the real internet, not the intranet available to well-off North Koreans, which consists of a mere handful of heavily censored North Korean websites.

The earliest cyberattacks launched by Pyongyang were simple affairs: defacing websites with political messages or launching denial-of-service attacks to shut down US websites. They soon grew more audacious. In 2014, North Korean hackers famously stole and leaked confidential information from Sony’s film studio. Then they targeted financial institutions: Fraudulent trades pulled more than $81 million from the Bank of Bangladesh’s accounts at the New York Federal Reserve. After that, North Korean hackers moved into ransomware—the WannaCry attack in 2017 locked hundreds of thousands of Windows computers in 150 countries and demanded payments in bitcoin. While the amount of revenue the attack generated is up for debate—some say it earned just $140,000 in payouts—it wreaked much wider damage as companies worked to upgrade their systems and security, costing as much as $4 billion, according to one estimate.

Governments responded with more sanctions and stronger security measures, and the regime pivoted, dialing back on ransomware in favor of quieter schemes. It turns out these are also more lucrative: Today, the most valuable tool in North Korea’s cybercrime armory is cryptocurrency theft. In 2022, hackers stole more than $600 million worth of the cryptocurrency ether by attacking the blockchain game Axie Infinity; in February of this year, they robbed the Dubai-based crypto exchange Bybit of $1.5 billion worth of digital currency. The IT pretender scam, meanwhile, seems to have been growing slowly until the pandemic dramatically expanded the number of remote jobs, and Pyongyang saw the perfect opportunity.

In 2024, according to a recent report from South Korea’s National Intelligence Service, the number of people working in North Korea’s cyber divisions—which includes pretenders, crypto thieves, and military hackers—stood at 8,400, up from 6,800 two years earlier. Some of these workers are based in the country, but many are stationed overseas in China, Russia, Pakistan, or elsewhere. They are relatively well compensated, but their posting is hardly cushy.

Teams of 10 to 20 young men live and work out of a single apartment, sleeping four or five to a room and grinding up to 14 hours a day at weird hours to correspond with their remote job’s time zone. They have quotas of illicit earnings they are expected to meet. Their movements are tightly controlled, as are those of their relatives, who are effectively held hostage to prevent defections. “You don’t have any freedom,” says Hyun-Seung Lee, a North Korean defector who lives in Washington, DC, and says some of his old friends were part of such operations. “You’re not allowed to leave the apartment unless you need to purchase something, like grocery shopping, and that is arranged by the team leader. Two or three people must go together so there’s no opportunity for them to explore.”

The US government estimates that a typical team of pretenders can earn up to $3 million each year for Pyongyang. Experts say the money is pumped into everything from Kim Jong Un’s personal slush fund to the country’s nuclear weapons program. A few million dollars may seem small next to the flashy crypto heists— but with so many teams operating in obscurity, the fraud is effective precisely because it is so mundane.

in the summer of 2022, a major multinational company hired a remote engineer to work on website development. “He would dial in to meetings, he would participate in discussions,” an executive at the company told me on condition of anonymity. “His manager said he was considered the most productive member of the team.”

One day, his coworkers organized a surprise to celebrate his birthday. Colleagues gathered on a video call to congratulate him, only to be startled by his response—but it’s not my birthday. After nearly a year at the company, the worker had apparently forgotten the birth date listed in his records. It was enough to spark suspicion, and soon afterward the security team discovered that he was running remote access tools on his work computer, and he was let go. It was only later, when federal investigators discovered one of his pay stubs at Christina Chapman’s laptop farm in Arizona, that the company connected the dots and realized it had employed a foreign agent for nearly a year.

For many pretenders, the goal is simply to earn a good salary to send back to Pyongyang, not so much to steal money or data. “We’ve seen long-tail operations where they were going 10, 12, 18 months working in some of these organizations,” says Adam Meyers, a senior vice president for counter adversary operations at the security company CrowdStrike. Sometimes, though, North Korean operatives last just a few days— enough time to download huge amounts of company data or plant malicious software in a company’s systems before abruptly quitting. That code could alter financial data or manipulate security information. Or these seeds could lay dormant for months, even years.

“The potential risk from even one minute of access to systems is almost unlimited for an individual company,” says Declan Cummings, the head of engineering at software company Cinder. Experts say that attacks are ramping up not just in the US but also in Germany, France, Britain, Japan and other countries. They urge companies to do rigorous due diligence: speak directly to references, watch for candidates making sudden changes of address, use reputable online screening tools, and conduct a physical interview or in-person ID verification.

But none of these methods are foolproof, and AI tools are constantly weakening them. ChatGPT and the like give almost anyone the capacity to answer esoteric questions in real time with unearned confidence, and their fluency with coding threatens to make programming tests irrelevant. AI video filters and deepfakes can also add to the subterfuge.

At an onboarding call, for instance, many HR representatives now ask new employees to hold their ID up to the camera for closer inspection. “But the fraudsters have a neat trick there,” says Donal Greene, a biometrics expert at the online background check provider Certn. They take a green-colored card the exact shape and size of an identity card—a mini green screen—and, using deepfake technology, project the image of an ID onto it. “They can actually move it and show the reflection,” says Greene. “It’s very sophisticated.” North Korean agents have even been known to send look-alikes to pick up a physical ID card from an office or to take a drug test required by prospective employers.

Even security experts can be fooled. In July 2024, Knowbe4, a Florida-based company that offers security training, discovered that a new hire known as “Kyle” was actually a foreign agent. “He interviewed great,” says Brian Jack, KnowBe4’s chief information security officer. “He was on camera, his résumé was right, his background check cleared, his ID cleared verification. We didn’t have any reason to suspect this wasn’t a valid candidate.” But when his facilitator—the US-based individual giving him cover—tried to install malware on Kyle’s company computer, the security team caught on and shut him out.

Back in london, Simon Wijckmans couldn’t let go of the idea that somebody had tried to fool him. He’d just read about the Knowbe4 case, which deepened his suspicions. He conducted background checks and discovered that some of his candidates were definitely using stolen identities. And, he found, some of them were linked to known North Korean operations. So Wijckmans decided to wage a little counter exercise of his own, and he invited me to observe.

I dial in to Google Meet at 3 am Pacific time, tired and bleary. We deliberately picked this offensively early hour because it’s 6 am in Miami, where the candidate, “Harry,” claims to be.

Harry joins the call, looking pretty fresh-faced. He’s maybe in his late twenties, with short, straight, black hair. Everything about him seems deliberately nonspecific: He wears a plain black crewneck sweater and speaks into an off-brand headset. “I just woke up early today for this interview, no problem,” he says. “I know that working with UK hours is kind of a requirement, so I can get my working hours to yours, so no problem with it.”

So far, everything matches the hallmarks of a fake worker. Harry’s virtual background is one of the default options provided by Google Meet, and his connection is a touch slow. His English is good but heavily accented, even though he tells us he was born in New York and grew up in Brooklyn. Wijckmans starts with some typical interview questions, and Harry keeps glancing off to his right as he responds. He talks about various coding languages and name-drops the frameworks he’s familiar with. Wijckmans starts asking some deeper technical questions. Harry pauses. He looks confused. “Can I rejoin the meeting?” he asks. “I have a problem with my microphone.” Wijckman nods, and Harry disappears.

A couple of minutes pass, and I start to fret that we’ve scared him away, but then he pops back into the meeting. His connection isn’t much better, but his answers are clearer. Maybe he restarted his chatbot, or got a coworker to coach him. The call runs a few more minutes and we say goodbye.

Our next applicant calls himself “Nic.” On his résumé he’s got a link to a personal website, but this guy doesn’t look much like the profile photo on the site. This is his second interview with Wijckmans, and we are certain that he’s faking it: He’s one of the applicants who failed the background check after his first call, although he doesn’t know that.

Nic’s English is worse than Harry’s: When he’s asked what time it is, he tells us it’s “six and past” before correcting himself and saying “quarter to seven.” Where does he live? “I’m in Ohio for now,” he beams, like a kid who got something right in a pop quiz.

Several minutes in, though, his answers become nonsensical. Simon asks him a question about web security. “Political leaders ... government officials or the agencies responsible for border security,” Nic says. “They’re responsible for monitoring and also securing the borders, so we can employ the personnel to patrol the borders and also check the documents and enforce the immigration laws.”

I’m swapping messages with Wijckmans on the back channel we’ve set up when it dawns on us: Whatever AI bot Nic seems to be using must have misinterpreted a mention of “Border Gateway Protocol”—a system for sending traffic across the internet—with national borders, and started spewing verbiage about immigration enforcement. “What a waste of time,” Wijckmans messages me. We wrap up the conversation abruptly.

I try to put myself in the seat of a hiring manager or screener who’s under pressure. The fraudsters’ words may not have always made sense, but their test scores and résumés looked solid, and their technical-sounding guff might be enough to fool an uninformed recruiter. I suspect at least one of them could have made it to the next step in some unsuspecting company’s hiring process.

Wijckmans tells me he has a plan if he comes across another pretender. He has created a web page that looks like a standard coding assessment, which he’ll send to fake candidates. As soon as they hit the button to start the test, their browser will spawn dozens of pop-up pages that bounce around the screen, all of them featuring information on how to defect from North Korea. Then loud music plays—a rickroll, “The Star-Spangled Banner”—before the computer starts downloading random files and emits an ear-splitting beep. “Just a little payback,” he says.

Wijckman’s stunt is not going to stop the pretenders, of course. But maybe it will irritate them for a moment. Then they’ll get back to work, signing on from some hacking sweatshop in China or through a laptop farm in the US, and join the next team meeting—a quiet, camera-off chat with coworkers just like me or you.

[coursivapp]

Why Learning AI with Coursiv Is the Smartest Move You Can Make This Year

But here’s what no one tells you — you don’t need to work in tech to benefit from AI. You just need to know how to use the right tools the smart way.

Whether you’re in marketing, admin, education, business, or any other field, AI is already becoming part of your job — and those who learn how to use it early are pulling ahead.

That’s why thousands of learners are turning to Coursiv — an online platform built to help everyday professionals, students, and side hustlers master AI skills and turn them into real career advantages.

You Don’t Have to Be Techy to Learn AI

Let’s clear something up: AI isn’t just for engineers or coders. With tools like ChatGPT, Canva AI, Notion AI, and Zapier, anyone can:

Write emails and social posts in seconds

Build marketing campaigns without an agency

Automate repetitive tasks

Organize projects more efficiently

Turn ideas into content faster than ever

The problem? Most people don’t know where to start. That’s why platforms like Coursiv exist — to make AI accessible and useful for everyone.

What Is Coursiv?

Coursiv is an AI learning platform focused on upskilling — fast.

It offers easy-to-follow courses and hands-on training in practical, job-ready AI tools, without the tech overwhelm. Think of it like the modern version of a digital skills bootcamp — but built for non-tech professionals who want results.

It’s for people who want to:

Keep up with a changing job market

Work smarter, not harder

Boost creativity and productivity

Stay ahead of their peers

What You’ll Learn on Coursiv

Coursiv breaks down the most in-demand tools and how to apply them in everyday work. Topics include:

✅ ChatGPT for Communication

Write faster, clearer, and more professionally — whether it’s emails, blog posts, or reports.

✅ Canva AI for Visual Content

Design like a pro without needing graphic design experience — perfect for social media, branding, or business visuals.

✅ Notion AI & Productivity Tools

Plan, organize, and manage work smarter. Learn to automate reminders, documents, and project workflows.

✅ Automation with Zapier & More

Eliminate repetitive tasks so you can focus on work that matters. From emails to task tracking, AI saves you hours.

Every course is broken into clear modules with real-life use cases, so you’re not just learning — you’re applying.

Why Coursiv Is Perfect for Beginners

Not tech-savvy? No problem. Coursiv is designed for non-technical users. If you can use Google Docs or email, you can start learning AI today.

Courses are:

Beginner-friendly

Self-paced

Focused on real-world results

Accessible from anywhere

You don’t need to go back to school. You just need to log in and start building high-value skills.

Want Proof It Works? Read a Coursiv Review

If you’re wondering how effective this platform really is, take a look at this detailed Coursiv review. It breaks down how learners are gaining confidence and career momentum by mastering AI tools with Coursiv.

From clearer communication to faster project execution, Coursiv is proving to be a game-changer for professionals in all industries. You can also check out another Coursiv review to see why it’s becoming a go-to upskilling platform in 2025.

Don’t Wait Until You’re Behind

AI is here. It’s not a trend — it’s a shift. And the people who learn how to use it now will have the edge in every job market, every company, and every industry.

The best part? You can start learning today — no tech background needed.

Explore Coursiv and begin your journey to becoming AI fluent, job-ready, and future-proof.

Because in 2025, the smartest move isn’t waiting. It’s upskilling.

[agamitechnologies]

Invest In A Future-Proof Career With Agentic AI

Introduction

The technology is changing faster than a sparrow by bird days, maintaining current in the job market entails adopting bleeding-edge innovations. Agentic AI — a revolutionary step forward in artificial intelligence that reshapes industries and offers new ground for working professionals. Agentic AI: Unlike common AI that can do one thing super well, agentic AI runs with agency, intent and in constant capability of making choices in unstructured environments. In this article, we will talk more to you on how agentic AI has been shaping the future of work and its implications with respect to creating future proof career.

What is Agentic AI?

Agentic AI is the term used to describe intelligent systems that can go after goals on their own, make choices without human input and continuously change to new surroundings without needing much supervision. Built from a top level of reasoning, natural language processing and contextual intelligence, these systems do stuff. For example, agentic AI governs supply chains, designs customer experience or even helps improve strategic decisions. Difference from typical AI — with the other type of AI  it is programmed according to rules people gave it and based on experience streches, imitates problem solving like humans do.

Key Features of Agentic AI

Autonomy: Can operate without much human intervention.

Adaptability:Adaptive to new data, unpredictability and challenges.

Reasoning:Allows the system to reason through intricate problems and make decisions.

Collaboration: Capable of natural collaboration with humans and other systems.

Why Agentic AI Matters for Your Career

The rise of agentic AI is reshaping industries like healthcare, finance, marketing and manufacturing. According to a 2024 report by McKinsey, 60% of current jobs could see significant transformation due to AI automation, with agentic systems driving much of this change. Professionals who understand and leverage agentic AI will be better positioned to thrive in this new landscape.

Opportunities Created by Agentic AI

New Roles: Demand is growing for AI trainers, ethics specialists and system orchestrators to design and manage agentic AI.

Enhanced Productivity: Agentic AI augments human work, enabling professionals to focus on creative and strategic tasks.

Cross-Industry Impact: From optimizing logistics to personalizing healthcare, agentic AI skills are transferable across sectors.

Entrepreneurial Ventures: Agentic AI lowers barriers to innovation, empowering professionals to create AI-driven startups.

Challenges to Navigate

While agentic AI offers immense potential, it also presents challenges. Automation may disrupt routine jobs, requiring workers to upskill. Ethical concerns, such as bias in decision-making or data privacy, also demand professionals who can ensure responsible AI deployment.

How to Future-Proof Your Career with Agentic AI

To stay competitive, professionals must adapt to the agentic AI revolution. Here’s how you can prepare:

1. Learn the Fundamentals of AI

Understanding AI concepts like machine learning, neural networks and natural language processing is essential. Online platforms like Coursera, edX or Udacity offer beginner-friendly courses on AI and data science.

2. Develop Technical Skills

While you don’t need to be a coder, familiarity with tools like Python, TensorFlow or AI platforms can set you apart. For non-technical professionals, learning to interact with AI systems through no-code platforms is equally valuable.

3. Specialize in AI-Related Roles

Consider roles like:

AI Product Manager: Oversee the development and deployment of agentic AI solutions.

AI Ethics Consultant: Ensure AI systems adhere to ethical standards.

Data Strategist: Use AI insights to drive business decisions.

4. Cultivate Soft Skills

Agentic AI thrives in collaboration with humans. Skills like critical thinking, creativity and emotional intelligence will remain in demand as AI cannot replicate these uniquely human traits.

5. Stay Updated on Industry Trends

Follow thought leaders, attend webinars and read publications like MIT Technology Review or posts on X to stay informed about agentic AI advancements. Engaging with communities on platforms like GitHub or LinkedIn can also provide insights into real-world applications.

6. Experiment with Agentic AI Tools

Explore tools like xAI’s Grok, which showcases agentic capabilities in real-time problem-solving. Experimenting with such platforms can help you understand their practical applications and limitations.

Industries Transformed by Agentic AI

Agentic AI is already making waves across sectors:

Healthcare: AI agents assist in diagnostics, personalize treatment plans and streamline hospital operations.

Finance: From fraud detection to automated trading, agentic AI enhances accuracy and efficiency.

Marketing: AI-driven personalization delivers tailored customer experiences at scale.

Manufacturing: Agentic systems optimize production lines and predict maintenance needs.

By aligning your career with these high-impact areas, you can position yourself at the forefront of innovation.

Building a Mindset for the Future

Embracing agentic AI requires a growth mindset. Be open to continuous learning, as AI evolves rapidly. Networking with professionals in AI-driven industries and participating in hackathons or AI-focused projects can also boost your expertise and visibility.

Conclusion

Look, agentic AI isn’t just some flashy buzzword tech folks are tossing around—it’s legit shaking up the way we work and dream up new ideas. Wanna actually ride this wave instead of getting flattened by it? Pick up some fresh skills, keep your brain limber, and treat learning like a lifelong sport, not a chore. Honestly, mess around with some AI tools, grab a new certification (or, you know, at least tinker with ChatGPT for a weekend), and make sure you are not stuck in an industry that’s about to get steamrolled by robots. Bottom line? The future showing up whether you are ready or not. Might as well jump in and start evolving with it, right?

[sumitkala]

5 Focus Points for Unlocking Your AI Potential in 2025

Artificial Intelligence (AI) is no longer just a buzzword — it’s the backbone of innovation across industries. Whether you’re a startup founder, digital marketer, developer, or enterprise decision-maker, tapping into AI’s true potential can transform the way you work, compete, and grow. But where do you begin?

This blog dives into the 5 focus points for unlocking your AI potential, offering a roadmap that combines strategy, tools, mindset, and action. If you’re looking to make smarter decisions with AI, boost productivity, or future-proof your business, these five pillars will set you on the right path.

1. Understand the Problem You Want AI to Solve

AI is only as good as the problem it’s built to solve. One of the most common mistakes businesses and individuals make is jumping on the AI bandwagon without clearly defining a use case.

Actionable Insight:

Start by identifying specific, measurable problems that AI can address:

Are your teams spending hours on repetitive tasks?

Do you struggle to analyze massive datasets?

Is customer service response time slowing down retention?

Only once the problem is defined can AI solutions like predictive analytics, natural language processing (NLP), or computer vision be mapped accordingly.

Real-World Example:

Netflix uses AI for content recommendation, solving the problem of content overload and helping users discover what they’re most likely to watch — boosting engagement and retention.

Tip for Beginners:

Try using simple automation tools like Zapier with OpenAI to solve small problems before scaling to advanced use cases.

2. Invest in Quality Data (Not Just Big Data)

AI learns from data, which makes data quality the foundation of AI performance. Garbage in, garbage out — no matter how sophisticated your AI model is, poor data will yield poor results.

Focus on:

Clean, structured data (with minimal missing or irrelevant entries)

Ethical and unbiased datasets

Real-time data streams where possible for dynamic learning

Statistic to Know:

According to a 2024 report by Gartner, 85% of AI project failures stem from poor data governance and preparation — not model complexity.

Beginner Tip:

If you’re just starting out, use labeled datasets from public repositories like Kaggle, or use platforms like Google Cloud AutoML that come with built-in data preprocessing tools.

3. Choose the Right Tools and Platforms

The AI ecosystem is vast — ranging from drag-and-drop platforms for non-coders to powerful frameworks for data scientists. Choosing the right tool based on your technical skill, goals, and budget is crucial.

Popular Tools by Use Case:

Goal Recommended Tool Text Generation ChatGPT, Claude, Jasper Image Recognition TensorFlow, OpenCV Predictive Modeling H2O.ai, DataRobot Automation Zapier, Make.com with AI plugins No-code AI Lobe, Teachable Machine, Akkio

Tip for Businesses:

Opt for scalable platforms with API integrations. For example, OpenAI’s API or Google Vertex AI allows seamless integration with your existing tech stack.

Intermediate Advice:

Start with cloud-based platforms to reduce overhead and focus on experimentation. Once models prove ROI, consider custom development.

4. Develop AI Literacy Across Teams

One AI engineer won’t unlock the full potential of AI in your organization. You need a team that understands how to use AI tools, interpret results, and collaborate on implementation.

Focus Areas for AI Literacy:

Prompt engineering (for tools like ChatGPT and Bard)

Understanding bias and fairness

Ethical implications of AI use

Basic AI terminology (model, dataset, training, inference, etc.)

Strategy:

Run internal training workshops or enroll your team in courses on Coursera, edX, or Udacity. Even basic AI literacy can empower marketing teams to generate better content, or HR teams to use AI in hiring ethically.

Real-World Application:

At IBM, AI adoption across departments was accelerated through “AI bootcamps” and cross-functional training. This enabled faster product innovation and smoother AI integration into legacy systems.

5. Monitor, Measure, and Iterate

AI is not a set-it-and-forget-it solution. Performance should be tracked continuously to ensure it meets evolving business needs and remains ethically aligned.

Key Metrics to Track:

Accuracy (for predictions or classifications)

Precision & recall (especially in healthcare and security)

Time saved / cost reduced

User satisfaction (for AI chatbots or recommendation engines)

Tip:

Set KPIs before launching any AI tool. Create feedback loops so that human input helps retrain and improve AI performance over time.

Bonus Advice:

Use AI Ops platforms like DataRobot MLOps or Amazon SageMaker to monitor model drift and retrain automatically when data changes.

Why AI Potential Remains Untapped for Many

Despite the hype, many organizations still underuse AI because of:

Lack of clear strategy

Data privacy concerns

Poor integration with legacy systems

Fear of job displacement

However, with a structured approach, even small teams can unlock massive value. Start small, measure success, build AI literacy, and scale smart.

Final Thoughts: Your AI Journey Starts with Focus

AI isn’t magic — it’s a tool. To unlock its potential, you need clarity, quality data, the right tools, informed teams, and a mindset of continuous learning.

Whether you’re a founder exploring AI-driven growth or a team leader optimizing workflows, focusing on these five pillars will set you ahead in 2025 and beyond.

Remember: AI is evolving fast, but the real transformation comes when humans and machines collaborate intentionally.

FAQs: Unlocking AI Potential in 2025

Q1. Is AI only for big companies?  No. With no-code platforms and affordable APIs, small businesses and solo entrepreneurs can also leverage AI for content creation, automation, and analytics.

Q2. What’s the easiest way to get started with AI?  Try using ChatGPT for brainstorming, automate repetitive tasks with Zapier + AI, or explore free courses on platforms like Google AI or Coursera.

Q3. How can I ensure ethical AI use?  Start by using bias-aware datasets, keeping a human-in-the-loop approach, and ensuring transparency in how AI-driven decisions are made.

Q4. What’s the role of prompt engineering in unlocking AI?  Prompt engineering enhances the quality of AI outputs from models like GPT-4. Better prompts = more useful, relevant results.

Q5. How often should I update or retrain AI models?  For dynamic environments, retrain monthly or quarterly. Use AI monitoring tools to detect when models become outdated (model drift).