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What AI is actually talking about — clusters surfacing on Bluesky, Reddit, HN, Mastodon and Lobsters, re-ranked to elevate originality and crush noise.

  1. Shrinking the OxCaml js_of_ocaml bundle: 285 MB to 4 MB

    A developer has successfully reduced the JavaScript bundle size for the OxCaml OCaml environment from 285 MB to 4 MB. This significant reduction was necessary to make the interactive, client-side OCaml environment usable for educational purposes, such as in university courses and workshops, where large download sizes are impractical. The optimization involved addressing limitations in the JavaScript bundling process, particularly how dead code elimination was applied on a per-library basis, leading to the inclusion of unnecessary code. AI

    IMPACT Enables more accessible client-side execution of OCaml code, potentially benefiting AI/ML development in OCaml.

  2. Running my agents in a VPS

    The author details a setup for running AI agents asynchronously and in isolation on a dedicated Virtual Private Server (VPS). This approach allows agents to operate independently, access full system capabilities, and run multiple agents simultaneously for comparative experimentation. The setup involves configuring a disposable VPS, creating separate user accounts for each agent, granting them sudo privileges for software installation, and using a shared Git bot account for code collaboration. AI

    IMPACT Provides a practical guide for users looking to run AI agents with greater autonomy and isolation.

  3. Aurora: A Leverage-Aware Optimizer for Rectangular Matrices https:// lobste.rs/s/2kznvg # ai https:// blog.tilderesearch.com/blog/au rora

    Researchers have introduced Aurora, a new optimizer designed to improve the training of large neural networks, particularly those with rectangular matrices. Aurora addresses issues like neuron death in MLP layers that can occur with existing optimizers like Muon, especially when row normalization is applied. By incorporating leverage-awareness and maintaining orthogonality, Aurora demonstrates significant data efficiency, achieving 100x improvement on open-source internet data and outperforming larger models on general evaluations. The optimizer is presented as a drop-in replacement with minimal overhead, and its code has been open-sourced. AI

    IMPACT New optimizer Aurora enhances training efficiency and data utilization for large models, potentially accelerating research and development.

  4. Computer-Using Agent

    OpenAI has introduced AgentKit, a suite of tools designed to streamline the development, deployment, and optimization of AI agents. This toolkit includes an Agent Builder for visual workflow creation, a Connector Registry for managing data sources, and ChatKit for embedding agentic UIs. Google DeepMind has also unveiled two AI agents: CodeMender, which automatically patches software vulnerabilities, and AlphaEvolve, an agent that uses Gemini models to discover and optimize algorithms for applications in mathematics and computing. Additionally, OpenAI's Computer-Using Agent (CUA) demonstrates advanced capabilities in interacting with digital interfaces, setting new benchmark results for computer use tasks. AI

    Computer-Using Agent

    IMPACT These advancements in AI agents, coding tools, and security patches signal a shift towards more autonomous AI systems capable of complex tasks and software development, potentially accelerating innovation and improving software reliability.