Best #Books to Read! for 2023 – by @bookauthority
! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
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Best Books to Read 2023: Data Science and AI
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Adaptive Block-Scaled Data Types for Efficient 4-bit LLM Quantization
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“Adaptive Block-Scaled Data Types” A lot of 4-bit LLM quantization assumes every block should use the same number format, this paper argues that’s the wrong abstraction. So they let each 16-value block choose between FP4 and scaled INT4, depending on which gives lower error. This format, IF4 (Int/Float 4), reuses the unused sign bit of the shared FP8 scale factor to store the choice, so it gets adaptivity with no extra storage overhead. This is because at 4 bits, precision is so scarce that matching the format to the local value distribution really matters. The result is lower quantization error and better performance than existing 4-bit block-scaled formats in both training and PTQ.
→ View original post on X — @askalphaxiv, 2026-04-02 06:54 UTC
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Early Recognition of Siri’s AI Potential and Impact
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Siri was launched in my house, and the three guys who started it say that only two people really "got it" back then: me and Steve Jobs. I saw it a few months before Steve did, and I instantly understood its importance because it made using a phone so much easier and more
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Agent Improvement Loop: Tracing Foundation for Better AI Agents
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Great stat in here: Claude Code went from 17% to 92% on our eval set once it had access to LangSmith traces and Skills. A coding agent without trace data is just guessing at fixes LangChain (@LangChain) New conceptual guide: 🔄 The agent improvement loop starts with a trace Tracing is the foundational primitive for improving agents. A trace gives you the full behavioral record of what an agent actually did. From there, teams can enrich traces with evals and human feedback, turn recurring failures into test cases, validate fixes before shipping, and repeat. This guide breaks down the full improvement loop and why reliable agents are built through trace-centered iteration, not one-off debugging. Read more → langchain.com/conceptual-gui… — https://nitter.net/LangChain/status/2039028327030079565#m
→ View original post on X — @langchain, 2026-04-02 05:33 UTC
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New Bootcamp Upgrades: Recordings Access and AI Pro Subscriptions
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🔥 Big Upgrade for Our Bootcamp Family! You asked. We listened. And now we’re delivering. 😄 Going forward Anyone who will be learning with us through any of our bootcamps will now get access to previous bootcamp recordings as well! And that’s not all 👇 We’re soon launching Quarterly and Half-Yearly Subscription Plans for our AI Pro program along with Industry-Ready Projects designed to make you job-ready faster. More learning. More projects. More growth. 🚀 Stay tuned — exciting things are coming! #AI #Bootcamp #LearningNeverStops #DataScience #GenerativeAI
→ View original post on X — @krishnaik06, 2026-04-02 05:07 UTC
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CLI for Code Deep Work, Apps for General Knowledge Tasks
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cli for deep code, app for anything else, knowledge work
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Seedance 2.0 launches with advanced video creation for Teams
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Seedance 2.0 is live on OpenArt – for Teams & Enterprise users first.
— OpenArt (@openart_ai) 2 avril 2026
-Up to 9 images, 3 videos, and 3 audio files as references
-Director-level camera control
-90%+ usable output on the first attempt
-Cinematic multi-shot storytelling.
-Physics that actually holds.
60% off… pic.twitter.com/C5jgqs9jZISeedance 2.0 is live on OpenArt – for Teams & Enterprise users first. -Up to 9 images, 3 videos, and 3 audio files as references -Director-level camera control -90%+ usable output on the first attempt -Cinematic multi-shot storytelling. -Physics that actually holds. 60% off the annual Teams plan. US & individual users: join the waitlist for early access 🔗 lnkd.in/g3HYxWkd
→ View original post on X — @aihighlight, 2026-04-02 03:58 UTC
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Anthropic’s hidden Tamagotchi system inside Claude Code
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4. Anthropic's "Claude Buddy" Anthropic hid a full Tamagotchi system inside Claude Code. 18 species, five stat categories (Debugging, Patience, Chaos, Wisdom, Snark), and a unique pet per developer. It was supposed to be an April Fools' surprise, but an npm leak spoiled it a
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Atlas 1: Willow’s Speech-to-Text Model Tops Transcription Leaderboard
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🆕 Meet Atlas 1 — Willow's new frontier speech-to-text model just dropped, and it's already rewriting the leaderboard.
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 2 avril 2026
It outperforms ElevenLabs, Deepgram, and OpenAI on transcription accuracy. Not by a little. By a wide margin.
Built on the first scalable, human-powered… pic.twitter.com/mdnb0MmggVMeet Atlas 1 — Willow's new frontier speech-to-text model just dropped, and it's already rewriting the leaderboard. It outperforms ElevenLabs, Deepgram, and OpenAI on transcription accuracy. Not by a little. By a wide margin. Built on the first scalable, human-powered