Thai Nguyen Trong @gin97_

I built an open-source Hinglish TTS that beats every model on the market. I had zero research background. last week I was using Ira AI companion. Got curious about the company. Started digging into their stack. I set a stupid goal to build the world's best Hinglish TTS from scratch. Documented every dead end, every architectural decision, every mistake and how i approached this. Full build log (open source): harrrshall.github.io/hinglish-tts/ @aiweekendsxyz @blrwritersroom

《Introduction to Modern Statistics》Second Edition Free Math Book, 510 pages, CC License. This book is available as a PDF and in paperback. also read the book online openintrostat.github.io/ims/ A college-level open-source textbook with a modern approach highlighting multivariable relationships and simulation-based inference Hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods. 1、Statistics is an applied field with a wide range of practical applications. 2、You don’t have to be a math guru to learn from interesting, real data. 3、Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world. Part 1: Introduction to data. Part 2: Exploratory data analysis. Part 3: Regression modeling. Part 4: Foundations for inference. Part 5: Statistical inference. Part 6: Inferential modeling. This is a book from the Open Textbook Initiative Approved Textbooks. x.com/huihoo/status/…

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灰狐 @huihoo

2 months ago

Open Textbook Initiative The American Institute of Mathematics (AIM) seeks to encourage the adoption of open source and open access mathematics textbooks. The AIM Editorial Board has developed evaluation criteria to identify the books that are suitable for use in traditional

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现在大家都在做 Agent，但一上复杂业务：架构怎么搭、记忆怎么管、多智能体怎么配合，往往越做越迷糊。 我最近看到一本开源书 Agentic Design Patterns，把智能体设计模式从入门讲到企业级，脉络清晰、拆解到位。 全书 21 章 + 7 个附录，按难度分四大部分；每章配套 Jupyter Notebook，边读边跑，理论和代码紧贴在一起。 GitHub：github.com/evoiz/Agentic-… 前半段打底：提示链、路由、并行、反思、工具调用、多智能体协作等核心模式，一次讲透“怎么设计”。 后半段落地：记忆管理、异常恢复、人机协作、安全护栏、性能评估等生产必修课，直接对准“怎么上线”。 附录还补齐框架对比和高级提示技巧，适合查漏补缺、随用随翻。 想把 Agent 从概念学到可落地的系统方法，这本开源书值得收藏，慢慢啃。

Are we done with object detection? What about tiny objects beyond 200 meters? 🔎 Telescope 🔭 addresses long-range perception by explicitly tackling extreme scale imbalance ⚖️ in images. It hinges on a learnable hyperbolic foveation transform from a low-resolution image, magnifying distant regions 🔍 while compressing nearby ones - effectively normalizing object scales with minimal computational overhead. Objects are detected in the transformed (Riemannian) space using a novel bounding box parameterization and are then mapped back to the original image. Project: light.princeton.edu/telescope/

Private, multi-model, local alternative to Notebook LM github.com/lfnovo/open-no…

This might be the wildest AI engineering breakdown on the internet right now 🤯 After the Anthropic leak… Someone turned the ENTIRE Claude Code system into a readable playbook. 👉 claude-code-from-source.com⁠ We’re talking: * 500K+ lines of real production AI agent logic * Broken down into 18 chapters you can actually learn from * Multi-agent systems, tool pipelines, memory, orchestration… all exposed This isn’t theory This is how a top-tier AI coding agent actually works under the hood Key ideas you’ll steal instantly: → Agent loops with async execution → Multi-agent “teams” coordinating tasks → File-based memory (no DB 🤯) → Context compression tricks → Tool execution pipelines at scale Basically… Instead of guessing how to build AI agents you now have a blueprint from a real system used by thousands of devs claude-code-from-source.com Crazy part? The whole thing was analyzed + rewritten in HOURS using AI agents claude-code-from-source.com If you're building: • AI agents * Dev tools * LLM products * or learning MLOps This is not optional This is a cheat code

@JuveGiovanili Solid on the ball but a bit slow in transition. Still he’s got that confidence to make something happen

@DaltonCassar @NicoSchira Nobody does it better than him. Who else should we consider?

@thomas_novello9 Pellegrini

@Jfamily3502 He cant if the corrupt system still exists

@gualanodavide Great stuff! Juve faced Stuttgart recently so Stiller is definitely on their radar. Heard rumors they’re scouting English-based midfielders like Tyler Morton, Hayden Hackney... What’s your take on them?

Finally a good VAD github.com/FireRedTeam/Fi… so many developers use bad but popular VAD these days

Someone literally built a free AI university - all in one repo, covering real-world AI systems step by step Link - github.com/jamwithai/arxi… Here’s what you’ll learn: Week 1 - Setup everything Docker, FastAPI, databases Beginner-friendly foundation Week 2 - Feed it real data Automatically fetch research papers Fully automated data pipeline Week 3 - Teach it to search BM25 keyword search implementation Your own Google-like search system Week 4 - Make it smarter Hybrid search enabled Understands meaning, not just keywords Week 5 - It talks back Complete RAG system Ask questions, get accurate answers Week 6 - Production ready Caching and monitoring added Runs like a real product Week 7 - Give it a brain Agentic AI with LangGraph Even works with Telegram

Just open sourced LavaSR v2: an incredibly fast model that can enhance 5000 seconds of audio in 1 second. V2 is much higher quality than a 6gb model now and v1. It's great to - Enhance TTS models - Fix Audio Datasets - Restore low quality audio. Repo: github.com/ysharma3501/La…

Wanna learn how to fine tune a small model to excel at function calling? Here's an example with full source code ↓ github.com/Mandark-droid/…

There's a big gap between building an AI agent in a notebook and running one in production. The article below goes over the full path, from #LangGraph graph design and ReAct patterns to deploying on AWS #AgentCore with memory, guardrails, and observability baked in. The decisions that matter in production like when to route to a lightweight model vs. a capable one, how to manage short and long term memory with AgentCore, and how to set up evaluation loops that keep working after you ship are discussed. Joud W. Awad covers architecture, tooling, deployment, and monitoring in this. Check it out! via @nexus_share lckhd.eu/cYuuVc

This `Music Source Splitter` is a killing @streamlit app from @grsFabio! 🤯 🔗 huggingface.co/spaces/fabiogr…

@Forza1_Juventus @JuveeTifoso @itshaydo1900 @juvenewslive We have 2 tough matches coming up against Parma and Atalanta, so this is not bad

@ConnorHolden00 Conte's 3-4-3 is rigid and lacks flexibility