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Every published item, in order · 1031 items
Sep 24
Sep 23
ModelsUS vs ChinaMulti-sourceSelected19:27
Step 5 Preview tested on two real coding tasks

Benchmark scores can hide whether a model survives long, multi-step builds, so the value here is watching Step 5 Preview handle mechanical synchronisation and real tooling rather than canned puzzles. Developers choosing between Step 5, GLM-5.3, Kimi K3 and Claude Opus 5 get a practical cost-versus-reliability comparison for agentic coding workflows. The stated price gap makes it a concrete candidate for teams in Southeast Asia and beyond cutting per-task inference spend.

Event
掘金本周最热@猫猫头啊6 sourcesSource
IndustryUS vs ChinaMulti-sourceSelected73°15:02
Goldman Sachs: US Outspends China on AI Compute $806bn vs $110bn

The compute gap is smaller than it looks, because efficiency gains let Chinese labs deliver competitive inference at a fraction of the infrastructure cost. Developers and procurement teams choosing between US and Chinese models should track OpenRouter usage rankings and token pricing rather than headline capex figures. Goldman expects the next competitive front to be virtual-worker use cases in finance, legal and accounting workloads.

Event
AI Will@FinanceYF54 sourcesSource
PapersBuildersSelected09:09
Post-Training Delivery Benchmark Tests LLM Agents as Forward-Deployed Engineers

The benchmark reframes agent evaluation around deliverable outcomes and payment-gated acceptance, a shift from metric-chasing benchmarks that miss corrupted training runs. Teams building or buying post-training-as-a-service can use the acceptance gate and corruption detector to screen automated fine-tuning pipelines. The human FDE baseline gives a concrete performance reference against all four frontier agents.

arXiv: DeepSeek@Weihang Ding, Junfei ZhanSource
PapersBuildersSelected73°04:04
Jev tested across three Zilliz open-source RAG projects

The benchmarks show where cheap small models can replace larger ones in RAG pipelines: stopping decisions and relationship filtering see large latency and cost savings with matched recall. Engineers building retrieval systems can use the published code to pick where to route between Jev, GPT-5-mini and Voyage rerank-3. The evaluation includes Chinese-language queries, useful for teams serving bilingual users.

Milvus@milvusioSource
Sep 22
Sep 21
ModelsBuildersSelected09:16
GameASG-Bench: Benchmarking Autonomous Software Generation for Game Development

This paper provides a detailed benchmark for evaluating the performance of AI models in generating autonomous software for game development, which is relevant for anyone interested in the capabilities and limitations of these models.

arXiv: DeepSeek@Xiuhui Zhang et al. (7)Source
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