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

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

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 Eksklusif ai: China turns buried $50m tunnel machine failure into land

Chinese engineers have managed to turn disaster into a masterful engineering feat by rescuing a stranded tunnel boring machine (TBM) using its twin. Conducted under the Yangtze River, this impressive engineering success is something the team, understandably, is very proud of.

The problem initially arose during the construction of the Jiangyin–Jingjiang Yangtze River Tunnel, a 4-mile (6.4 km) road tunnel under the Yangtze River. This was being completed using a 52.5-foot (16-meter) wide, multi-million-dollar TBM.

These machines can excavate the ground while supporting the ground above as the tunnel progresses. Such machines are also able to line and support the tunnel in their wake.

Work was progressing as expected until the TBM catastrophically failed. At this point, the TBM was around 177 feet (54 meters) underground and subject to enormous water pressure from above.

Write-off or recover?

Under these conditions, the TBM was unable to reverse and couldn’t be safely dismantled for recovery. It also couldn’t be repaired in situ. Things looked very bleak indeed for the stranded machine and the project as a whole.

The engineering team faced one of several equally disastrous choices. The first was to completely abandon the machine and absorb the loss. The second choice was to completely redesign or cancel the entire tunnel project.

In all cases, this would lead to years of delay and much embarrassment for all involved. However, the team decided to try a third option: rescue the TBM using its twin.

The idea was to launch the second TBM from the opposite bank of the river and drive it straight at the stranded one. While this sounds simple on the surface, the undertaking was not going to be a walk in the park.

To succeed, they had to predict ground movement under a massive river, while also controlling direction over kilometres with millimetre precision. The team also had to avoid even tiny vertical or horizontal errors that could cause collapse or flooding.

Above all, the target error margin was smaller than the thickness of a coin! Incredibly, the team managed to pull it off, and all with a vertical error of just 2mm.

Truly impressive engineering feat

The final horizontal misalignment was effectively zero and was completed under high pressure in soft sediments and water-saturated ground. The two machines met cleanly underground (a procedure called a mid-tunnel docking), which is one of the hardest operations in underground civil engineering.

This allowed engineers to not only access the failed TBM but also recover the tunnel project. It also enabled them to continue the project instead of scrapping it completely.

This nws in not just about saving the tunnel project, but is also an interesting showcase of what can be achieved with a little bit of lateral thinking and planning. Notably, it demonstrates that deep underground rescue is possible, even under rivers, and that large TBM failures don’t have to be terminal anymore.

It also highlighted how precision guidance systems for tunnelling have reached a new level. Looking ahead, the lessons learned from the near-disaster could be used for future projects like sub-sea tunnels, metro systems, and work in high-risk geological environments.

🔗 Sumber: interestingengineering.com