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a small startup contributor. doing A.I. things and collaboration tools .
Shanghai
Joined October 2009
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Keep your data safe is our mission.
The team will continually expending our ZDR model list.
connect the dots
rutis is also upgrade to 0.5.0
github.com/arcships/rutis/re…
aimux更新了0.5.0版本
支持了toolcall-repair以及ws代理等能力
同时我们对稳定性/安全性做了更多的修复与约束
github.com/arcships/aimux/re…
aimux更新了0.5.0版本
支持了toolcall-repair以及ws代理等能力
同时我们对稳定性/安全性做了更多的修复与约束
github.com/arcships/aimux/re…
Han Cheng e/acc retweeted
让 Dim Agent 在后台跑几个比较重的goal,用 DeepSeek V4.1 Flash 持续反编译和分析一个程序。属于长时间不停读代码、执行命令、分析结果、继续修改的那种 Agent 工作流。
跑了两天,后台统计大概调用了 6000 多次,产生约 16 亿 Token,消耗了 8400 Credits。
整个套餐大概还剩6成左右。
考虑到官方还送了1个RESET点数,这70块钱花得值啊。
有这么个不成熟的想法。
以后其实没必要开源代码。
开源build时的session trajectory 可能更好。
更raw更底层。
把dlook这个tui文件阅读器扩展了视频,网页和图片的渲染能力。
整个binary只有8MB,非常适合在vps上用。
github.com/eric8810/dlook
把dlook这个tui文件阅读器扩展了视频,网页和图片的渲染能力。
整个binary只有8MB,非常适合在vps上用。
github.com/eric8810/dlook
下午和夕阳针、小灰灰聊了两个小时,重温 DeepChat 的成长,也聊了我们在 Agent 时代的摸爬滚打,以及项目未来的方向。
模型在升级,我们也在努力给自己打补丁😂
和愿意动手、愿意分享的人一起折腾,挺开心。这场聊天收进了《为 AI 发电》第二期,欢迎收听,一起继续发电⚡️
xiaoyuzhoufm.com/episode/6ab…
light-ocr已经不知不觉500+ star了。
整个迭代过程除了api层有些设计,基本靠的都是metrics + sandbox 实验。一直想试试适配各种NPU,但是弄不到合适的硬件就作罢了。
github.com/arcships/light-oc…
Han Cheng e/acc retweeted
忘记说了, 现在最新beta版本的 dimcode 你可以 dim web 启动 web版来玩了. @DimAgentai
npm i -g dimcode
So true
efficiency, productivity, speed
we keep worshipping these words like they are what we live for
ship faster
merge more
manage more agents
compress every loop
remove every pause
turn every hour into output
but what is the point of endless production if there is no time left to think deeply?
not react
not optimize
not summarize
not clear the queue
think
to sit with a question long enough that your real intention starts to appear
to find the purpose underneath the motion
to ask whether the thing being accelerated should exist at all
ai could be a new canvas
a way to make strange things
personal things
impossible things
tools, worlds, poems, games, interfaces, films, languages, rituals
things that let more people touch the shape of their own imagination
but instead, so much of it is becoming slop factories
more content
more funnels
more tickets
more fake work pretending to be value
you can merge 2000 prs
you can manage 500 agents
you can wake up to dashboards proving that the machine kept moving while you slept
but what is the point if you only sleep 5 hours a day?
what is the point if you stop talking to other humans?
stop learning about the past?
stop playing?
stop wandering?
stop being surprised?
what is the point if your whole life becomes optimizing the system that is consuming you?
speed is not meaning
scale is not wisdom
automation is not freedom if it only makes everyone run faster
and where are we running?
toward what?
a world where every person becomes a manager of machines, and every machine produces more things no one had the time to truly want?
the danger is not that ai makes us lazy
the danger is that ai makes us endlessly busy
that it gives us infinite production before we have found intention
infinite execution before we have developed taste
infinite motion before we have learned how to be still
maybe the real frontier is not more speed
maybe it is discernment
knowing what not to make
knowing when to stop
knowing when the most productive thing is to sleep, read history, call a friend, play with a cat, walk without tracking it, or stare at the ceiling until the false urgency dissolves
tools should give us more life
not turn life into a tool
if ai is going to matter, it should not just help us produce more
it should help us become more human
more imaginative
more free
otherwise, it’s the same old factory
just without humans
Han Cheng e/acc retweeted
Introducing Contrastive Language Model (CLM): an ultra-fast System One Model trained with a contrastive learning objective that connects states and actions.
CLM-8B is pre-trained on internet-scale data and delivers up to 9× faster inference than Jev ⚡ while achieving comparable performance across computer-use, gaming, and tool-calling tasks.
With lightweight fine-tuning, CLM-8B sets a new SOTA on challenging agentic coding benchmarks, such as DeepSWE (81.6%) and Terminal-Bench 2.1 (87.6%). In contrast, Jev fails to serve as an effective verifier for these long-horizon tasks.
We also build an efficient training and serving infra for CLMs by disaggregating states and actions, allowing their embeddings to be cached and reused independently. This substantially reduces inference latency in settings where the state evolves continuously while the action set remains fixed.
Finally, we establish scaling laws for CLMs and show that the test contrastive loss decreases predictably as a power law in training compute, model size, and dataset size.
📄 Blog: contrastive-lm.notion.site
💻 Code: github.com/Contrastive-LM/CL…
🗣️ Discord: discord.gg/5dAQEDJBs
🤗 Data & Models: huggingface.co/Contrastive-L…
More details on CLM’s architecture, data recipe, and scaling laws in the thread below 🧵