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Nov 7

Models BuildersMulti-source03:59

Chinese contentRipple-65B Preview 发布：Apache 2.0 开源，R&D 成本 3200 万美元

美国团队开源了个 65B 的编程模型，TerminalBench 2.1 上开源美国模型里分最高，还是 Apache 2.0 随便商用，不过其它基准输给了 Inkling 2 Small。

#Ripple-65B #TerminalBench #开源模型 #编程助手

Event

Official accountDecoder@Matthias BastianSource

Nov 6

Models US vs ChinaMulti-source10:54

Chinese contentRicursive Intelligence 发布 Ripple-65B Preview 开放权重编程模型

一个 65B 的小模型在 TerminalBench 上打赢 816B 的大块头，还支持 1M 上下文，写代码的可以试试权重。

#Ripple-65B #Ricursive Intelligence #开源模型 #编程助手

Event

IT之家Source

Models BuildersSelected01:56

ripple-65b preview from Ricursive launches on Together AI

A fully open-weight Apache 2.0 model scoring 80.5 on terminal-bench 2.1 gives teams a self-hostable alternative to closed coding agents, with 1M context for long-repo workflows. Builders evaluating coding assistants can run the preview on Together AI and benchmark it against proprietary options before licensing decisions. Availability through Together AI makes it accessible across Southeast Asia without local deployment.

#ripple-65b #Ricursive #Together AI #open-source models

Official accountTogether AI@togethercomputeSource

Models US vs China01:08

Chinese contentRipple-65B Preview 开源：65B 稠密模型，100 万上下文

有人用 Olmo 3 和 StepFun 的方案从头训了个 65B 开源模型，成本 3200 万美元，上下文 100 万，还说是 Qwen 70B 的平替。

#Ripple-65B #Olmo 3 #StepFun #开源模型

Gorden Sun@Gorden_SunSource

Models BuildersMulti-source00:48

Chinese contentRipple-65B 首个 GGUF 量化版本发布，可本地运行

Ripple-65B 终于能在自己电脑上跑了，已经移植到 llama.cpp，还有小显存版本在路上。

#Ripple-65B #llama.cpp #GGUF #量化

Event

@atomic_chat_hq@atomic_chat_hqSource

Nov 5

Models BuildersMulti-source14:35

Chinese contentOpenAI 参与 Linux 内核安全，Kimi K3.1 Pro 开放权重

这期日报一次讲三件事：OpenAI 做了 Linux 内核安全，Kimi K3.1 Pro 放出了开放权重，还有 Ricursive 的 Ripple-65B，想追开源模型的可以看看。

#Kimi K3.1 Pro #Ripple-65B #OpenAI #开源模型

Event

Official accountLatent Space (swyx)@swyx .aiSource

Models BuildersMulti-source07:02

Chinese contentRicursive Intelligence 发布 Ripple-65B Preview 开源模型

65B 的小模型居然在编程智能体任务上打过了 800B 级别的大家伙，美国开源阵营又有新选择。

#Ripple-65B #Ricursive Intelligence #开源模型 #编程助手

Event

elvis@omarsar0Source

Models BuildersOfficial06:57

Chinese contentRicursive AI 发布 Ripple-65B Preview：开源权重模型 Terminal-Bench 2.1 得分 80.5

Ricursive AI 放出了 65B 的开源模型 Ripple-65B Preview，Apache 2.0 许可，Terminal-Bench 2.1 拿了 80.5 分，比它大十几倍的开源模型都没它高。

#Ripple-65B #Terminal-Bench #开源模型 #Ricursive AI

Primary sourcemarktechpost@Asif RazzaqSource

Models Builders05:25

Chinese contentRipple-65B Preview 刷新美国开源模型 Terminal-Bench 2.1 成绩

五人小团队从零训了个 65B 稠密模型，拿了美国开源在 Terminal-Bench 2.1 的最好成绩，训练细节写得挺实在。

#Ripple-65B #Terminal-Bench #开源模型 #RicursiveAI

Stanford AI Lab@StanfordAILabSource

Models BuildersMulti-sourceSelected04:19

Ripple-65B Preview: Stanford-affiliated Ricursive Intelligence releases 65B open-weight model

A 65B dense model beating 500B-plus open-weight rivals on agentic coding would reshape assumptions about how much capacity agents actually need. Teams choosing self-hosted coding models should benchmark Ripple against Nemotron 3 Ultra-550B and the other named releases before committing to larger deployments. No benchmark names or independent evaluations are given yet, so treat the state-of-the-art claim as the lab's own.

#Ripple-65B #Ricursive Intelligence #open-source models #agents

Event

Stanford AI Lab@StanfordAILabSource

Models Builders04:13

Chinese contentRipple-65B Preview 训练成本仅 3200 万美元

65B 开源编程模型把成本压到上代 SOTA 的十分之一，训练细节都在这条推文里，做模型的人可以看看。

#Ripple-65B #开源模型 #智能体编程 #训练成本

swyx (AI Engineer)@swyxSource

Models BuildersSelected03:49

Ricursive Intelligence Releases Ripple-65B Preview Under Apache 2.0

A 65B dense model matching or beating a 501B sparse model on Terminal-Bench 2.1 would shift assumptions about how much parameter count matters for agentic coding. The Apache 2.0 licence and local runnability mean teams can self-host a competitive coding agent without API dependence. Benchmark figures here are company-reported, so independent verification should be the first stop before adopting it.

#Ripple-65B #Ricursive Intelligence #Terminal-Bench #open-source models