A trader reportedly used Claude 5.5 Opus to build a Quant Bot — and generated +$115,615 on Polymarket. Here’s what caught my attention:👇 • ~41,972 predictions • ~39% win rate • 22+ trades/hour • ~$1,501 average daily profit • ~$219.7K in deposits • ~52.6% reported ROI • 80-day performance window The strategy wasn’t about finding one massive winner. It was about repeating a small edge at scale. The bot reportedly: → Scans short-duration crypto markets → Detects pricing inefficiencies → Enters before odds fully adjust → Repeats the setup across thousands of trades Some reported winners:👇 $62 → $1,745 $28 → $1,365 $80 → $1,350 The interesting part isn’t the biggest trade. It’s the system behind the repetition. AI agents can potentially handle the workflow: Scan → Analyze → Price → Execute → Monitor → Repeat Tools like Jev Bot or Grok can be used to explore similar agent-based workflows. 🎁 GIVEAWAY I’m sharing my complete breakdown of this Quant Bot system. To enter: → Comment “QUANT” → Like + Repost → Follow @mostofakamal00 → Stay active — I’ll DM selected winners No follow = no DM. Winners will be contacted within 48 hours. 🚀

Today, we're launching Typeless for Linux. Typeless is now available on Mac, Windows, iOS, Android — and Linux. The Linux app brings the full Typeless desktop experience, with all the core features available on Mac. But this launch is about more than adding another platform. For us, every new platform is another step toward a bigger goal: making voice the default way for people to interact with computers — wherever they think, create, and work. For decades, the keyboard has been the default interface between our thoughts and computers. And we've gotten so used to it that we rarely question the friction. Thoughts come fast, messy, and nonlinear. Before you communicate them, you normally have to turn them into sentences in your head first. Then you slow everything down again to type. We built Typeless to remove both layers of friction. With Typeless, you can just think out loud. Follow your thoughts wherever they go. Change your mind halfway through. Add context later. Repeat yourself. Speak imperfectly. Typeless turns that messy stream of thought into clear, polished writing in real time. It removes filler words and repetition, structures your ideas, adapts to your writing style, understands your personal vocabulary, and gives you writing that fits the app you're working in. You can: • Dictate — turn messy thoughts into clear writing, 4x faster than typing • Translate — speak in your language and write naturally in another • Ask anything — use your voice to write from scratch, or select text to edit it, ask questions, and take actions • Build your dictionary — names, acronyms, and specialized terms get recognized correctly • Personalize your style — Typeless learns how you write and adapts to where you're writing • Speak 100+ languages — including naturally mixing languages while you talk And it's private by design. Linux users tend to be some of the people who spend the most time interacting deeply with computers. Developers. Engineers. Researchers. Builders. They should be able to use voice as naturally on Linux as anywhere else. Our long-term belief is simple: Voice will become the default way people interact with computers. Not because speaking is merely faster than typing. But because voice gives us a way to communicate with computers much closer to the speed and form in which thoughts actually happen. To us, that's what real freedom in computing looks like: expressing what you mean without adapting how you think. And for that future to happen, it can't belong to one device, one operating system, or one workflow. It has to be available wherever you work. Linux. Mac. Windows. iOS. Android. One interface, wherever you work. Typeless for Linux is here.

用 Ling-3.0-flash-VL 做了个图文识别小工具。 功能很简单：图片丢进去，直接识别里面的文字、数字、公式，同时尽可能保留原来的排版和内容关系。 为了看看它处理复杂图片到底怎么样，测试素材故意选得杂了一点： • 数学论文：正文、公式、上下标、希腊字母混排
• IDE：小字号代码、Terminal 输出、各种符号
• 图表：表格、数字、文字挤在一起
• 暗光菜单：低对比度、中英文混排
• 手写笔记：字形不标准、排版随意
• 发票：字段密集，而且金额和数字不能认错 全部跑完以后，Ling-3.0-flash-VL 这部分的表现比预想中完整。 数学公式里的上下标和符号关系基本都能保住，小字号代码没有因为界面信息太密而丢掉大量字符；表格、发票这种更考验位置关系的图片，字段和数字之间的对应也没有被打乱。 暗光和手写反而挺有意思。 这两类图片肉眼看都没有标准文档那么舒服，但模型依然能从背景里把主要内容抽出来。不是只能认几个醒目的大字，边角的小字和局部信息也能顾到。 不过这次测下来，比较值得说的其实不是“OCR 准不准”。 传统 OCR 很容易把一张复杂页面最后变成一长串文本。字可能都认识，但标题属于哪里、公式跟着哪一段、数字对应表格哪一列，这些关系一丢，后面还得重新整理。 Ling-3.0-flash-VL 对这部分处理得更像是在“读页面”。 标题还是标题，正文跟着正文，公式和表格也能尽量保持原来的关系。原图里的视觉结构，没有在转成文字之后直接消失。 这会直接影响后面的用法。 论文截图可以继续转 Markdown / LaTeX，票据可以继续抽字段，软件截图可以拿去做知识库，成批的图片也能进一步整理成结构化数据。 图文识别本身并不是什么新鲜功能。 但如果模型能把一张人能读懂的复杂图片，尽量完整地变成机器也能继续处理的内容，那它能做的事情就比“识字”多得多了。 这也是这次用 Ling-3.0-flash-VL 做完这个小工具后，感觉最有实际价值的地方。 🔗GitHub：
github.com/sggg221/ling-desk… 🔗 Ling-3.0-flash-VL 在线体验
目前在 OpenRouter 上可免费体验：
openrouter.ai/inclusionai/li… 模型：Ling-3.0-flash-VL
官方：@AntLingAGI

Screenshot → functional webpage. 🔥 Ling-3.0-flash-VL makes AI-powered web creation feel incredibly practical. 🚀