MAROKO133 Update ai: Nous Research's NousCoder-14B is an open-source coding model lan

📌 MAROKO133 Update ai: Nous Research's NousCoder-14B is an open-source coding

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

📌 MAROKO133 Hot ai: Plastic Surgeons Are Using Material From Dead People on New Pa

Cosmetic surgeons have turned to using a “fat filler” harvested from dead people in order to give patients new breasts, sexier buttocks and other body contouring tweaks, according to reporting by Business Insider — in a practice that not even “Frankenstein” author Mary Shelley could have dreamed up in her day.

The hot new product in question is called alloClae, manufactured by biomedical science business Tiger Aesthetics, and it’s become increasingly popular in plastic surgery circles despite its gruesome origins and aggressive price tag — ranging from $10,000 to $100,000 per procedure, according to BI — because it allows dramatically less healing time and doesn’t require patients being put under general anesthesia.

“People are paying for the convenience,” New York City plastic surgeon Sachin Shridharani told the site.

Historically, plastic surgeons have relied on implants or siphoning fat from another section of your body and injecting it into another part in order to perform body contouring. Using alloClae gives them a flexible new option.

Another reason why it’s become popular is that weight loss GLP-1 agonists drugs like Ozempic have made faces and bodies saggy — and that has them clamoring for specialists like Shridharani to fix their body issues.

“In their own words, ‘I’ve got no ass,’” Shridharani said. “‘My trousers look like they’re falling off.’”

So how does somebody’s fat end up in a plastic surgeon’s syringe?

What typically happens is that people donate their bodies to science, with organs going to donation or scientific research. Tissue banks also harvest fat calls from dead people’s abdomen’s, though, and Tiger Aesthetics buys it and processes the fat cells into alloClae.

“As of the beginning of 2026, we’ll be producing a ton more alloClae so that we can service the real demand that’s out there,” Tiger Aesthetics preident Caroline Van Hove told BI.

The practice isn’t entirely unprecedented. There’s already a product called Renuva, for instance, with the same origin but used for facial injections.

Is it weird to have your body sculpted with the fat from a dead person? Maybe, but you clearly can’t argue with results — or the horror of body issues.

More on plastic surgery: Insecure Dudes Are Getting Beard Transplants

The post Plastic Surgeons Are Using Material From Dead People on New Patients appeared first on Futurism.

🔗 Sumber: futurism.com