qwen3-coder-next
PulseAugur coverage of qwen3-coder-next — every cluster mentioning qwen3-coder-next across labs, papers, and developer communities, ranked by signal.
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Alibaba's Qwen3-Coder-Next achieves 70.6% on SWE-bench with efficient MoE architecture
The Qwen3-Coder-Next model, an 80 billion parameter Mixture-of-Experts model from Alibaba's Qwen team, has demonstrated impressive efficiency by achieving 70.6% on the SWE-bench Verified benchmark with only approximatel…
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Local LLM inference with 96GB VRAM fails to beat paid APIs on cost
A user detailed their two-week effort to optimize a local LLM setup with 96GB of VRAM across four RTX 3090 GPUs, aiming to replace paid cloud APIs. Despite achieving approximately 105 tokens/second and implementing opti…
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Krasis LLM runtime rewritten in Rust, boosts speed
The Krasis LLM runtime has been updated to version 1.0, featuring a complete rewrite in Rust for improved performance and efficiency. This update removes Python from the critical execution path, leading to faster prefil…
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Alibaba's Qwen3.7-Max debuts with 1M context, autonomous coding
Alibaba has released Qwen3.7-Max, an agent-first LLM with a 1 million token context window, capable of autonomous coding tasks. The model demonstrated a 35-hour coding session without human intervention, optimizing code…
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Qwen3-Coder-Next uses 3B params for 80B model, slashes coding costs
A new coding-focused AI model, Qwen3-Coder-Next, has been released, boasting an 80 billion parameter size while only activating 3 billion parameters during operation. This innovative approach significantly reduces compu…
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ForgeFlow system hits file modification deadlock with LLMs
After completing 12 projects using the ForgeFlow system, the developers identified a critical file modification boundary. Tasks involving the creation of new files were consistently successful, but attempts to modify ex…
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Alibaba's Qwen3-Coder-Next achieves 70.6 on SWE-Bench with sparse MoE
Alibaba's Qwen3-Coder-Next, an 80 billion parameter model with 3 billion active parameters, has achieved a 70.6 score on the SWE-Bench Verified benchmark. This performance is notable as it rivals top closed-source model…
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Local AI coding agent ForgeFlow passes 35 tests autonomously
A developer built a fully local AI coding agent named ForgeFlow on a MacBook Pro with 128GB of unified memory. This agent autonomously writes code and runs tests within a Docker sandbox, committing changes only when all…