📌 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

📌 TOPINDIATOURS Eksklusif ai: Chinese scientists build mini womb on a chip to stud

Every human life starts with a risky and delicate event—a microscopic embryo finding a place to settle inside the uterus. If this process fails, pregnancy never begins, no matter how healthy the embryo looks. 

For decades, scientists have struggled to study this moment in humans because of ethical restrictions and limited access to early pregnancy tissue. 

Now, a team of researchers in China has crossed this barrier by producing a miniature womb on a chip that faithfully recreates how human embryos attach to and burrow into the uterine lining.

“This system successfully recapitulates key events of human implantation and early post-implantation development,” the researchers note. 

This advancement opens a new way to understanding infertility, improving IVF success, and testing treatments in ways that weren’t possible before.

Implantation has been almost impossible to study

Embryo implantation happens just days after fertilisation, when a five- to six-day-old embryo reaches the uterus. At this stage, the embryo must complete three tightly controlled steps. It first brushes against the uterine lining, then locks onto it, and finally pushes its way inside to establish a connection that will support pregnancy.

Studying these steps directly in humans is extremely difficult. Ethical rules limit experiments on natural embryos, and early pregnancy tissue is usually available only from rare medical procedures such as hysterectomies. 

Existing lab models, including flat cell cultures and endometrial organoids, capture only fragments of the process and miss the complex three-dimensional interaction between embryo and uterus. 

As a result, many cases of implantation failure, especially in IVF, remain poorly understood. To address this gap, researchers from the Chinese Academy of Sciences designed a three-dimensional model of the human endometrium, the tissue that lines the uterus. 

Creating the uterus lining on a chip

They began by embedding human endometrial cells into gel-like layers, allowing the cells to grow and organise themselves into a structure that closely resembles real uterine tissue. This engineered tissue, known as an endometrioid, was placed inside a microfluidic chip—a small device that can control the movement of fluids and nutrients. 

The chip environment allowed the tissue to behave more as it does inside the body, rather than in a traditional petri dish. More importantly, the endometrial cells used to build the model could be obtained from a single biopsy, and the system was also compatible with cells collected non-invasively from menstrual blood.

Once the artificial uterine lining was ready, the team introduced two types of embryos into the chip. One was a real human blastocysts, which contain about 100 to 200 rapidly dividing cells. 

The other was blastoids, lab-made structures created from stem cells that closely mimic natural blastocysts and can be produced in large numbers with consistent genetic properties. Inside the chip, both blastocysts and blastoids went through the full implantation sequence. 

They made initial contact with the uterine surface, formed stable attachments using molecular signals, and then actively invaded the tissue, embedding themselves just as they would in early pregnancy. This level of detail had not been achieved with earlier two-dimensional models. 

“Our in-chip 3D endometrioid-based implantation model offers a streamlined platform that captures all major stages of implantation – apposition, attachment, and invasion – as well as early post-implantation development,” the study authors said.

When the researchers built womb chips using cells from women diagnosed with recurrent implantation failure—defined as repeated IVF failures—the embryos showed a much lower ability to implant. This mirrored real-world clinical outcomes, confirming that the model can capture patient-specific differences. 

The team then used the platform to screen over 1,000 FDA-approved drugs and identified compounds that improved implantation performance, demonstrating the chip’s value as a drug-testing tool.

A way of improving infertility treatment

With infertility affecting roughly one in six adults worldwide, understanding why embryos fail to implant is a major medical priority. This womb-on-a-chip approach offers a way to study implantation safely, ethically, and in a highly controlled setting. 

It could help doctors identify why IVF fails in certain patients and tailor treatments based on individual uterine responses rather than trial and error. 

However, this approach still has limitations. It does not yet include blood vessels or immune cells, both of which play crucial roles in reshaping the uterus and supporting a growing placenta. 

Hopefully, the researchers will incorporate these missing elements in future studies to make the model even closer to real human biology.

The study is published in the journal Cell.

🔗 Sumber: interestingengineering.com