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AlphaSignal
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AlphaSignal
@AlphaSignalAI
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  • user avatar
    AlphaSignal
    @AlphaSignalAI
    1h
    ByteDance ran AI agents for 38,000 hours across 134 real-world tasks, from gravitational-wave detection to CPU design. One clean mathematical curve predicted their progress almost perfectly across five frontier models. Agents with memory beat fresh restarts by a wide margin,
    00:00
    user avatar
    AlphaSignal
    @AlphaSignalAI
    16h
    Article cover image
    Article
    ByteDance Finds a Scaling Law for Long-Horizon Agents
    A 38,000-hour benchmark shows agents learn better when they retain experience instead of restarting. In 4 minutes, you’ll learn why long-horizon agents need feedback, memory, and time to improve....
    354
  • user avatar
    AlphaSignal
    @AlphaSignalAI
    15h
    Bigger context windows won't fix your agent's memory problem. Memory isn't a longer prompt. It's a read-write discipline: what to save, recall, and forget. Most agents have four kinds, and skip most of them: > working: what's on the desk > episodic: what it already tried >
    user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 2
    Article cover image
    Article
    Agentic AI 101 Survey Research Paper: The Stack Between a Model and a System
    What separates an agent from a workflow, one layer at a time In ~10 mins: the agent loop, the model-to-agentic-system ladder, the full stack from model to UI, a workflow-vs-agent-vs-multi-agent...
    1.2K
    user avatar
    AlphaSignal
    @AlphaSignalAI
    15h
    Paper: arxiv.org/abs/2606.24937 Subscribe at alphasignal.ai/newsletter for 5-min daily AI signals. Read by 300,000+ subscribers.
    arXiv logo
    arxiv.org
    The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
    The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment,...
    157
  • user avatar
    AlphaSignal
    @AlphaSignalAI
    16h
    Article cover image
    Article
    ByteDance Finds a Scaling Law for Long-Horizon Agents
    A 38,000-hour benchmark shows agents learn better when they retain experience instead of restarting. In 4 minutes, you’ll learn why long-horizon agents need feedback, memory, and time to improve....
    953
    user avatar
    AlphaSignal
    @AlphaSignalAI
    16h
    Blog edge-bench.org Paper edge-bench.org/paper.pdf GitHub github.com/ByteDance-Seed… Dataset huggingface.co/datasets/ByteD… Check out alphasignal.ai/newsletter to get a daily summary of the latest breakthrough news, models, papers and repos. Read by 300,000+ devs.
    edge-bench.org
    EdgeBench | Scaling Laws of Environment Learning
    EdgeBench studies how agents learn from real-world environments across 134 day-long executable tasks.
    207
  • user avatar
    AlphaSignal
    @AlphaSignalAI
    20h
    Top AI Repos of the Week (June 26 - July 3) 1. SimpleMem: Efficient lifelong memory for LLM agents 2. Loopy: A library of practical AI-agent loops 3. Agent Reach: Give your AI agent eyes to see the entire internet 4. Supervision: Open source Python library for CV
    718
    user avatar
    AlphaSignal
    @AlphaSignalAI
    20h
    Replying to @AlphaSignalAI
    /6 Harvard just open-sourced a full curriculum for Machine Learning Systems. It teaches AI engineering beyond model training, covering distributed training, production inference, edge deployment, hardware limits, fault tolerance, and governance. > Two-volume textbook >
    176
    user avatar
    AlphaSignal
    @AlphaSignalAI
    20h
    Thanks for reading! Follow @AlphaSignalAI for more content like this. Check out alphasignal.ai/newsletter to get a daily summary of the latest breakthrough news, models, papers, and repos. Read by 300,000+
    alphasignal.ai
    Daily AI Newsletter for Engineers
    Get a daily 5-minute summary of the latest AI breakthroughs, research papers, and new models. Join 300,000+ engineers and subscribers. Free.
    143
  • user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 3
    Anthropic made Claude Sonnet 5 more agentic, but also more controversial. The new model is built to plan longer, use tools more often, check its own work, and stay inside coding loops with less hand-holding. Sounds like an upgrade, but the reaction has been mixed. Some users
    user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 2
    Article cover image
    Article
    Claude Sonnet 5 Is Here, But Should You Switch?
    I gathered the technical details and changes that everyone should know about Sonnet 5. Anthropic has released Claude Sonnet 5, but we do not want this article to be another line-by-line recap of the...
    908
  • user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 2
    Your agent is probably just a workflow, and one 603-page paper proves it. A workflow runs the steps you wrote but an agent decides its own next step. The Hitchhiker's Guide to Agentic AI, a paper by Haggai Roitman, maps that whole gap. It treats an agent as a model inside a
    user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 2
    Article cover image
    Article
    Agentic AI 101 Survey Research Paper: The Stack Between a Model and a System
    What separates an agent from a workflow, one layer at a time In ~10 mins: the agent loop, the model-to-agentic-system ladder, the full stack from model to UI, a workflow-vs-agent-vs-multi-agent...
    3.8K
    user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 2
    Paper: arxiv.org/abs/2606.24937 Subscribe at alphasignal.ai/newsletter for 5-min daily AI signals. Read by 300,000+ subscribers.
    arXiv logo
    arxiv.org
    The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
    The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment,...
    291
  • user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 2
    Article cover image
    Article
    Agentic AI 101 Survey Research Paper: The Stack Between a Model and a System
    What separates an agent from a workflow, one layer at a time In ~10 mins: the agent loop, the model-to-agentic-system ladder, the full stack from model to UI, a workflow-vs-agent-vs-multi-agent...
    6.7K
  • user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 2
    Top AI Educational Posts of the Week (June 25 - July 2) 1. You can now turn any source into a 60-second video in NotebookLM 2. Learn how loop engineering helps AI agents build software with human feedback. 3. CS 153 at Stanford: Learn AI systems from the people building the
    930
    user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 2
    Replying to @AlphaSignalAI
    /6 Decoding AI’s open-source course teaches you how to build a personal AI assistant with LLMs, agents, RAG, fine-tuning, and LLMOps. It covers: > Data pipelines > Dataset generation > Fine-tuning > Advanced RAG The stack includes ZenML, Opik, Comet, Unsloth, MongoDB, Hugging
    user avatar
    Dan Kornas
    @DanKornas
    Jul 1
    Your second brain needs more than a chatbot demo second-brain-ai-assistant-course is an open-source course repo from Decoding AI for builders who want to create a personal knowledge-base assistant with LLMs, agents, RAG, and LLMOps. It helps you move from scattered notes to an
    230
    user avatar
    AlphaSignal
    @AlphaSignalAI
    Jul 2
    Thanks for reading! Follow @AlphaSignalAI for more content like this. Check out alphasignal.ai/newsletter to get a daily summary of the latest breakthrough news, models, papers, and repos. Read by 300,000+
    alphasignal.ai
    Daily AI Newsletter for Engineers
    Get a daily 5-minute summary of the latest AI breakthroughs, research papers, and new models. Join 300,000+ engineers and subscribers. Free.
    115