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

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

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: US firm’s new facility to build 1,500 tons of Iron Nitride per

World’s first full-scale Iron Nitride permanent magnet facility is set to be built in Sartell, Minnesota. Wood has secured the engineering, procurement and construction management (EPCM) contract for Niron Magnetics’ Plant 1.

The company revealed that this groundbreaking plant will manufacture 1,500 tons of Iron Nitride permanent magnets annually once fully operational in 2027.

Resilient US supply chain for magnets used in critical industries

This is expected to create a resilient US supply chain for the magnets used in critical industries including automotive, defense systems, industrial motors, and consumer electronics.

Unlike traditional magnets that rely on rare earth materials and intensive mining, Niron Magnetics’ technology starts with iron and nitrogen- two abundant, easily sourced materials – to deliver magnets made with a fully-domestic supply chain and sustainable manufacturing processes, according to a press release.

Game-changer for sustainable magnet production

“The Sartell Plant 1 project is a game-changer for sustainable magnet production at scale,” said John Day, president of Projects Western Hemisphere at Wood.

“Having delivered the initial design, we’re now taking this project from concept to execution. Leveraging our EPCM expertise and first-of-a-kind scale-up experience, we’re enabling Niron Magnetics to deliver a high-production facility that reduces reliance on rare earths, helps power the future of sustainable energy and mobility, and creates a resilient supply chain for the U.S. economy.”

Wood’s scope will be delivered by a team of over 80 engineering and project delivery specialists based in the U.S.

Advanced manufacturing process

Niron Magnetics is scaling the world’s first advanced manufacturing process for the mass production of permanent magnets powered by its breakthrough material formulation. The company’s proprietary magnet technology, based on Iron Nitride, enables magnets that are inherently high magnetisation, free of rare earths and other critical materials, and will drive innovation in various industries.

Niron underlined that magnets power the devices that shape our daily lives — from motor vehicles to turbines to cellphones. But the global magnet supply chain faces vulnerabilities that threaten entire industries. Niron Magnetics was founded to solve this challenge with its Iron-Nitride technology made entirely from abundant, 100% domestically-sourced iron and nitride.

The Sartell plant builds on the success of Niron Magnetics’ commercial pilot plant that opened in Minneapolis in 2024. With investors and commercial partners including Stellantis, Samsung, Allison Transmission, Magna, and many others already sampling products from the pilot facility, the Sartell plant is positioned to deliver immediate economic impact for the City of Sartell, Minnesota, and the dozens of U.S. industries that depend on permanent magnets, according to Niron.

The new 190,000-square-foot facility will expand Niron Magnetics’ capacity to supply rare-earth-free permanent magnets for data center cooling pumps, automobile motors, robotics, consumer electronics, defense and drone equipment, and other applications critical to the U.S. economy. The plant will be operational in early 2027 and will create over 175 full-time jobs in manufacturing, engineering, and operations.

🔗 Sumber: interestingengineering.com