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  • Call out now part of pi’s documentation
    Call out now part of pi’s documentation

    By

    @clementdelangue

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    Call out now also part of pi's docs. Just do it. Mario Zechner (@badlogicgames) People who like sharing agent traces. I've just published all my pi-mono coding agent sessions on @huggingface so you get to laugh at or pwn me! huggingface.co/datasets/badl… I suggest you do the same, see thread below. Let's make this a community effort. Here's pi-share-hf: github.com/badlogic/pi-share… If you are working on tools that help identify PII/sensitive data, get in touch. The better the classification is, the more willing people will be to share their traces. — https://nitter.net/badlogicgames/status/2041151967695634619#m

    → View original post on X — @clementdelangue, 2026-04-06 14:56 UTC

  • Morphic Workflows: Automated Video Creation from Single Image

    By

    @datachaz

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    Holy sh*t. Dropping one image in and watching it automatically predict 9 scenes, upscale every frame, animate them, and stitch together a compiled video  🤯 @Morphic just dropped ‘Workflows’. You bring the image. It handles everything after.
 Following closely 👀 Jaynti Kanani (JD) (@jdkanani) Introducing Workflows on @morphic. You know what you want, you just don’t know how to prompt for it. That’s what Workflows solve. Storyboarding? Three clicks. UGC ads? No prompting. Color grade? In seconds. Try now: morphic.com/workflows Live with 72 workflows today. More coming soon. With Workflows, you can capture repeatable creative tasks and reuse them without starting from scratch. Just select your assets and options while running a workflow. Minimal prompts required. And no nodes, of course. There’s a workflow for everything: filmmaking, social media, animation, fashion, marketing, and some just to have fun. Tag someone who'd make something wild with this. Here are my 5 favorite workflows: — https://nitter.net/jdkanani/status/2041154028034490867#m

    → View original post on X — @datachaz, 2026-04-06 14:54 UTC

  • AI as AI Ops: Automate Your AI Workflows for Higher ROI
    AI as AI Ops: Automate Your AI Workflows for Higher ROI

    By

    @alliekmiller

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    AI is better than you at working with AI. It's better at generating prompts for AI, teaching skills to other AI agents, coordinating messaging between AIs. AI is an AI ops whisperer. Think like a PM: go through the journey you're taking right now with your AI workflows and find high ROI ways to use AI to help your AI efforts. Yes, you can prompt AI to create an app. But you can also… …and this is overkill and would waste a lot of tokens but I want to dramatize it because when costs plummet, we will see strange usage patterns… Prompt an AI with the idea, and then it creates a much better prompt (see images), and then it creates 4 different versions of that prompt, and then it spawns parallel agents to research the product space from 4 different points of view, and then they all meet in an agent team war room and battle it out, and then spec a product together while 5 other agents with 5 different goals in parallel spec it out themselves, and then 3 more agents review and critique the specs, then another reviews all previous work and summarizes, and another one tees up open questions, then 10 more with radically different personas meet to evolve the best idea and spawn 50 more versions, then you run a simulation by 10000 personas to vote for the product with the fastest time to market, highest delight, and strongest ROI potential. And then you create the app. What I'm saying is: find where you are the intermediary and shouldn't be, and find higher order ways to plug yourself in. Take yourself out of the loop before the loop takes you out.

    → View original post on X — @alliekmiller, 2026-04-06 14:38 UTC

  • OpenSeeker: AI-Native Search Beyond Keyword Matching

    By

    @learnopencv

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    OpenSeeker: Rethinking Search With AI-Native Reasoning In this episode of Artificial Intelligence: Papers and Concepts, we explore OpenSeeker, an emerging approach to building AI-native search systems that go beyond traditional keyword matching. Instead of retrieving links based purely on queries, OpenSeeker focuses on reasoning over information helping users get structured, context-aware answers rather than a list of results. We break down how modern search is evolving with large language models, why retrieval alone is no longer enough, and how systems like OpenSeeker combine retrieval with reasoning to deliver more accurate and useful outputs. If you’re interested in AI-powered search, retrieval-augmented generation, or the future of information discovery, this episode explains why OpenSeeker represents a shift toward more intelligent and answer-driven search experiences. Resources: Paper Link: arxiv.org/abs/2603.15594v1 Interested in Computer Vision and AI consulting and product development services? Email us at contact@bigvision.ai or visit us at bigvision.ai

    → View original post on X — @learnopencv, 2026-04-06 14:30 UTC

  • RAG, AI Agent, Fine-Tuning, and LLM Customization Strategy Explained
    RAG, AI Agent, Fine-Tuning, and LLM Customization Strategy Explained

    By

    @gp_pulipaka

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    RAG, AI Agent, Fine-Tuning, LLM Customization Strategy Briefly Explained! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/RAG-AI-Agent

    → View original post on X — @gp_pulipaka, 2026-04-06 14:26 UTC

  • Google Cloud Cookbook: Big Data Analytics and Machine Learning Guide
    Google Cloud Cookbook: Big Data Analytics and Machine Learning Guide

    By

    @gp_pulipaka

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    Google Cloud Cookbook! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode geni.us/Google-Cloud-Cookboo…

    → View original post on X — @gp_pulipaka, 2026-04-06 14:26 UTC

  • Microsoft Open Sources VibeVoice-ASR for 60-Minute Speech Processing
    Microsoft Open Sources VibeVoice-ASR for 60-Minute Speech Processing

    By

    @sumanth_077

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    If you found it useful, reshare it with your network Follow me → @Sumanth_077 for more insights and tutorials on AI Engineering! nitter.net/Sumanth_077/status/204… Sumanth (@Sumanth_077) Microsoft just fixed a major speech recognition problem! They open sourced VibeVoice-ASR, a speech-to-text model that processes 60 minutes of audio in a single pass. Here's the problem with most ASR models. They slice audio into short chunks, usually 30 seconds or less. Process each chunk separately. Lose speaker context between segments. You get disconnected transcripts that can't track who said what across a full meeting. VibeVoice-ASR handles 60 minutes of continuous audio without chunking. The model maintains global context across the entire hour. The output is structured. Who spoke, when they spoke, what they said. Speaker diarization, timestamps, and transcription all in one pass. Key features: • 60-minute single-pass processing without chunking audio • Structured output: speaker labels, timestamps, and content combined • Customized hotwords: provide specific names or technical terms to improve accuracy • Multilingual support: 50+ languages • Joint ASR, diarization, and timestamping in one model The model is 7B parameters. Available on Hugging Face with finetuning code included. I've shared the repo link in the comments! — https://nitter.net/Sumanth_077/status/2041157100840051111#m

    → View original post on X — @sumanth_077, 2026-04-06 14:14 UTC

  • Microsoft Shares VibeVoice GitHub Repository

    By

    @sumanth_077

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    Github Repo: github.com/microsoft/VibeVoi… [Translated from EN to English]

    → View original post on X — @sumanth_077, 2026-04-06 14:12 UTC

  • Microsoft Open Sources VibeVoice-ASR for 60-Minute Speech Recognition
    Microsoft Open Sources VibeVoice-ASR for 60-Minute Speech Recognition

    By

    @sumanth_077

    –

    Microsoft just fixed a major speech recognition problem! They open sourced VibeVoice-ASR, a speech-to-text model that processes 60 minutes of audio in a single pass. Here's the problem with most ASR models. They slice audio into short chunks, usually 30 seconds or less. Process each chunk separately. Lose speaker context between segments. You get disconnected transcripts that can't track who said what across a full meeting. VibeVoice-ASR handles 60 minutes of continuous audio without chunking. The model maintains global context across the entire hour. The output is structured. Who spoke, when they spoke, what they said. Speaker diarization, timestamps, and transcription all in one pass. Key features: • 60-minute single-pass processing without chunking audio • Structured output: speaker labels, timestamps, and content combined • Customized hotwords: provide specific names or technical terms to improve accuracy • Multilingual support: 50+ languages • Joint ASR, diarization, and timestamping in one model The model is 7B parameters. Available on Hugging Face with finetuning code included. I've shared the repo link in the comments!

    → View original post on X — @sumanth_077, 2026-04-06 14:12 UTC

  • Try the Ad Concepter App Today New Tool

    By

    @runwayml

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    Try the Ad Concepter App today: bit.ly/4v9omu9

    → View original post on X — @runwayml, 2026-04-06 14:02 UTC