📌 TOPINDIATOURS Breaking ai: Furious AI Users Say Their Prompts Are Being Plagiari

Move over, Ship of Theseus — there’s a new paradoxical thought experiment in town.

Some power users of generative AI have grown so comfortable with their new tools — especially image-generating ones — that they now feel entitled to the specific prompts they use to churn out slop, as if the entire technology wasn’t based on the work of human artists that had been ingested without consent.

Consider Amira Zairi, a self-professed “AI educator” and “ambassador” for Adobe, LeonardoAI, and TripoAI, who posted a scathing rant this week on X-formerly-Twitter to her 49,000 followers. Her complaint? Other people were “plagiarizing” her unique AI prompts.

“‘Make your own prompts’ isn’t advice. It’s basic integrity,” Zairi wrote, using syntax that reads suspiciously like text generated by ChatGPT. “I’m honestly fed up. Changing a few words, renaming the prompt, or slightly rephrasing it doesn’t make it yours, the idea is still the same, the vibe is the same, and the results are obviously similar.”

“And no, this isn’t about one or two people, and it didn’t happen once!!!!” Zairi continued. “Creating your own prompts is actually easier than copying someone else’s work! Try it.”

While Zairi is only the latest AI hound to bark about stolen prompts, she’s certainly not the first. Examples abound, as the Daily Dot pointed out back in December: consider a poster who railed about “prompt thieves in the AI art community,” or the “AI artist” who went on a tangent after someone aped his prompt “without knowing it’s mine.”

There’s even a niche market for preventative tools among cybersecurity developers. Late in 2024, an AI researcher named Xinyue Shen developed a tool called PromptShield to guard against so-called “prompt stealing.”

It’s all pretty rich, given that these AI tools were all trained on troves of human-made art and media without permission. In order to create generative AI models, tech companies systematically scrape vast amounts of copyrighted art from the web without consent, licensure, or compensation for the artists. This data is then used to train generative AI models that synthesize and churn out derivative images, an outcome some ethicists argue amounts to labor exploitation.

Put simply, AI bros are mad that people are stealing their recipe for the plagiarism machine — an irony which is pretty hard to ignore.

“What you are describing and complaining about is the fundamental function of the tech you’re advocating for, inextricable from it,” digital artist Rory Blank replied under Amira Zairi’s post. “Hope that helps.”

More on AI: AI Researchers Say They’ve Invented Incantations Too Dangerous to Release to the Public

The post Furious AI Users Say Their Prompts Are Being Plagiarized appeared first on Futurism.

🔗 Sumber: futurism.com

📌 TOPINDIATOURS Breaking ai: Nous Research's NousCoder-14B is an open-source

Nous Research, the open-source artificial intelligence startup backed by crypto venture firm Paradigm, released a new competitive programming model on Monday that it says matches or exceeds several larger proprietary systems — trained in just four days using 48 of Nvidia's latest B200 graphics processors.

The model, called NousCoder-14B, is another entry in a crowded field of AI coding assistants, but arrives at a particularly charged moment: Claude Code, the agentic programming tool from rival Anthropic, has dominated social media discussion since New Year's Day, with developers posting breathless testimonials about its capabilities. The simultaneous developments underscore how quickly AI-assisted software development is evolving — and how fiercely companies large and small are competing to capture what many believe will become a foundational technology for how software gets written.

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NousCoder-14B achieves a 67.87 percent accuracy rate on LiveCodeBench v6, a standardized evaluation that tests models on competitive programming problems published between August 2024 and May 2025. That figure represents a 7.08 percentage point improvement over the base model it was trained from, Alibaba's Qwen3-14B, according to Nous Research's technical report published alongside the release.

"I gave Claude Code a description of the problem, it generated what we built last year in an hour," wrote Jaana Dogan, a principal engineer at Google responsible for the Gemini API, in a viral post on X last week that captured the prevailing mood around AI coding tools. Dogan was describing a distributed agent orchestration system her team had spent a year developing — a system Claude Code approximated from a three-paragraph prompt.

The juxtaposition is instructive: while Anthropic's Claude Code has captured imaginations with demonstrations of end-to-end software development, Nous Research is betting that open-source alternatives trained on verifiable problems can close the gap — and that transparency in how these models are built matters as much as raw capability.

How Nous Research built an AI coding model that anyone can replicate

What distinguishes the NousCoder-14B release from many competitor announcements is its radical openness. Nous Research published not just the model weights but the complete reinforcement learning environment, benchmark suite, and training harness — built on the company's Atropos framework — enabling any researcher with sufficient compute to reproduce or extend the work.

"Open-sourcing the Atropos stack provides the necessary infrastructure for reproducible olympiad-level reasoning research," noted one observer on X, summarizing the significance for the academic and open-source communities.

The model was trained by Joe Li, a researcher in residence at Nous Research and a former competitive programmer himself. Li's technical report reveals an unexpectedly personal dimension: he compared the model's improvement trajectory to his own journey on Codeforces, the competitive programming platform where participants earn ratings based on contest performance.

Based on rough estimates mapping LiveCodeBench scores to Codeforces ratings, Li calculated that NousCoder-14B's improvemen t— from approximately the 1600-1750 rating range to 2100-2200 — mirrors a leap that took him nearly two years of sustained practice between ages 14 and 16. The model accomplished the equivalent in four days.

"Watching that final training run unfold was quite a surreal experience," Li wrote in the technical report.

But Li was quick to note an important caveat that speaks to broader questions about AI efficiency: he solved roughly 1,000 problems during those two years, while the model required 24,000. Humans, at least for now, remain dramatically more sample-efficient learners.

Inside the reinforcement learning system that trains on 24,000 competitive programming problems

NousCoder-14B's training process offers a window into the increasingly sophisticated techniques researchers use to improve AI reasoning capabilities through reinforcement learning.

The approach relies on what researchers call "verifiable rewards" — a system where the model generates code solutions, those solutions are executed against test cases, and the model receives a simple binary signal: correct or incorrect. This feedback loop, while conceptually straightforward, requires significant infrastructure to execute at scale.

Nous Research used Modal, a cloud computing platform, to run sandboxed code execution in parallel. Each of the 24,000 training problems contains hundreds of test cases on average, and the system must verify that generated code produces correct outputs within time and memory constraints — 15 seconds and 4 gigabytes, respectively.

The training employed a technique called DAPO (Dynamic Sampling Policy Optimization), which the researchers found performed slightly better than alternatives in their experiments. A key innovation involves "dynamic sampling" — discarding training examples where the model either solves all attempts or fails all attempts, since these provide no useful gradient signal for learning.

The researchers also adopted "iterative context extension," first training the model with a 32,000-token context window before expanding to 40,000 tokens. During evaluation, extending the context further to approximately 80,000 tokens produced the best results, with accuracy reaching 67.87 percent.

Perhaps most significantly, the training pipeline overlaps inference and verification — as soon as the model generates a solution, it begins work on the next problem while the previous solution is being checked. This pipelining, combined with asynchronous training where multiple model instances work in parallel, maximizes hardware utilization on expensive GPU clusters.

The looming data shortage that could slow AI coding model progress

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🔗 Sumber: venturebeat.com