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

📌 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

Buried in Li's <a href="https://nousresearch.com/nouscoder-14b-a-co…

Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com

📌 MAROKO133 Eksklusif ai: China ready to move its J-35 stealth fighter jet into ma

New footage suggests that China might be close to mass-producing advanced stealth fighter jets, not just prototyping them as previously believed. What’s more, this appears to mean China’s latest fifth-generation stealth fighter, the J-35, rather than its older, more well-known J-20.

This has been prompted by the release of images and videos of the J-35 sporting a green coating. According to analysts, the color means that the jets are newly built and are still being tested.

These aircraft haven’t received their final paint because the design may still change, and it’s cheaper and easier to inspect in primer. Therefore, seeing multiple green aircraft together strongly suggests serial (repeat) production, not one-off prototypes.

To this end, it would appear that China’s J-35 could well be ready for mass production and is no longer to be considered experimental.

Mass-producing stealth

Developed by China’s Shenyang Aircraft Corporation (SAC), a subsidiary of Aviation Industry Corporation of China (AVIC), the company has also pledged to double its overall warplane production over the next three to five years.

The SAC is the product of over 8.6 billion yuan ($1.2bn) by Beijing, and the factory alone covers a reported area of 1.62 square miles (4.2 square-kilometers). According to reports, the SAC will be part of a much larger “Shenyang Aerospace City” spanning ~79 km² (roughly Hong Kong Island).

The fact that China’s desire to rapidly increase capacity mirrors that of the US with its F-35 production has also not gone unnoticed.

Another interesting point is that the J-35 isn’t just an air force jet. It has a naval version designed for carriers and so is likely destined to operate from China’s most advanced aircraft carrier, the Fujian.

This ship is China’s first entirely domestically-produced carrier and comes, it is claimed, with working electromagnetic catapults, like US supercarriers. Interestingly, prototypes of the J-35 have already taken off via catapult and performed arrested landings on the carrier.

That means it’s operationally credible, not theoretical, and gives China a stealth carrier air wing, something only the US Navy currently fields at scale.

A warning to the West

It is also important to note that China typically keeps advanced war machine production under wraps. Showing off jets on runways and multiple units together in one place appears to be intentional messaging from Beijing.

Looking at the bigger picture, China appears to want the world to know that it can now (it claims) match the US in terms of aircraft development and production.

This, it would appear, is intentionally to show the US that a future war between them would be determined not just by who has the best kit. But also, which side can absorb and replace losses the fastest.

For Taiwan, this is also potentially bad news as the jhe J-35 is optimized for regional power projection. Carrier-based stealth jets, like the J-35, could complicate Taiwan’s air-defence planning

This pairs with increasing PLA drills and pressure operations

In short, China isn’t just building a stealth fighter anymore. It’s demonstrating that it can mass-produce fifth-generation jets and sustain a modern air war, especially at sea.

🔗 Sumber: interestingengineering.com