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  • Google Launches Gemma 4: Advanced Open Models for Developers

    By

    @googleai

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    Today, we’re launching Gemma 4, our most intelligent open models to date. Built with the same breakthrough technology as Gemini 3, Gemma 4 brings advanced reasoning to your personal hardware and devices. Here’s what Gemma 4 unlocks for developers: — Intelligence-per-parameter: Our 31B (Dense) and 26B (MoE) models deliver state-of-the-art performance for their size, outcompeting models 20x their size on @arena — Commercial flexibility: Released under a permissive Apache 2.0 license for complete developer flexibility and digital sovereignty — Agentic workflows: Native support for function-calling and structured JSON output allows you to build reliable, autonomous agents — Multimodal edge AI: The E2B and E4B models bring native vision, audio, and low latency to mobile and IoT devices — Long-context reasoning: Up to 256K context windows allow you to process entire repositories or large documents in a single prompt Whether you're building global applications in 140+ languages or local-first AI code assistants, Gemma 4 is built to be your foundation. Explore in @GoogleAIStudio or download the weights on @HuggingFace, @Kaggle, and @Ollama.

    → View original post on X — @googleai, 2026-04-02 16:03 UTC

  • Gemma 4: New Open Models for Advanced Reasoning and Agents

    By

    @nandodf

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    Meet Gemma 4: our new family of open models you can run on your own hardware. Built for advanced reasoning and agentic workflows, we’re releasing them under an Apache 2.0 license. Here’s what’s new 🧵

    → View original post on X — @nandodf, 2026-04-02 16:03 UTC

  • Abacus AI Simplifies OpenClaw Agent Deployment Without Setup

    By

    @abacusai

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    OpenClaw is overhyped. Most people can’t even run it properly – setup is messy, security is risky, and it breaks easily Abacus AI fixed that. Run OpenClaw-style agents with Abacus AI Agent. no setup, no configs, just real workflows running end-to-end.

    → View original post on X — @abacusai

  • mngr: Open Source Tool for Managing Parallel Claude Code Sessions

    By

    @plinz

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    mngr: programmatically manage 100s of claude code sessions in parallel 🤖 open source today. lets you do things like: — for each open GitHub issue, create a PR — for each flaky test in the past week, fix it — for each rule in style guide, scan codebase & fix all instances runs any agent: @claudeai, codex, @opencode, etc. runs on any compute: locally, @modal, @Docker, or anything you can ssh into.

    → View original post on X — @plinz, 2026-04-02 15:31 UTC

  • Mistral TTS Local Deployment Challenges Explained

    By

    @tunguz

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    Yeah dude, that latest Mistral TTS model is a major pain in the a** to figure out how to run locally.

    → View original post on X — @tunguz

  • Memory Sparse Attention Framework Enables 100M Token Processing
    Memory Sparse Attention Framework Enables 100M Token Processing

    By

    @jiqizhixin

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    Can AI models finally process context the size of a lifetime? Evermind, Shanda Group, and Peking University present Memory Sparse Attention (MSA)! This new framework gives AI a massively scalable, end-to-end trainable long-term memory. It uses an innovative sparse attention architecture and other techniques to handle hundreds of millions of tokens with linear efficiency, maintaining exceptional precision. MSA processes 100M tokens on 2xA800 GPUs with less than 9% precision degradation from 16K. It significantly outperforms frontier LLMs, SOTA RAG systems, and leading memory agents in long-context benchmarks, paving the way for lifetime-scale AI memory. MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens Code: github.com/EverMind-AI/MSA Paper: zenodo.org/records/19103670 Our report: mp.weixin.qq.com/s/FHJA4kALc… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-02 14:25 UTC

  • Kilocode VS Code Extension Updates with Open-Source Core
    Kilocode VS Code Extension Updates with Open-Source Core

    By

    @datachaz

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    Holy Moleyyy @kilocode just dropped a massive update to their VS Code extension Going with an open-source core (OpenCode) is a bold move. ..but spinning up multiple agents on isolated git worktrees so they don't step on each other's code? Too cool!

    → View original post on X — @datachaz

  • PSF Security Reports LiteLLM and Telnyx Supply Chain Attacks

    By

    @simonw

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    PSF Security developers have published incident reports on the LiteLLM & Telnyx #supplychain attacks. Read what happened, who's affected, and what developers & maintainers can do to prepare and protect themselves from future incidents. #security #python blog.pypi.org/posts/2026-04-…

    → View original post on X — @simonw, 2026-04-02 13:55 UTC

  • LLaMA-Factory: Fine-Tune 100+ LLMs Without Coding
    LLaMA-Factory: Fine-Tune 100+ LLMs Without Coding

    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/203… Sumanth (@Sumanth_077) Fine-Tune 100+ LLMs without writing a single line of code! LLaMA-Factory lets you train and fine-tune open-source LLMs and VLMs without writing any code. Here's why it's a game changer for fine-tuning: • Fine-tune 100+ LLMs/VLMs with built-in templates (LLaMA, Gemma, Qwen, Mistral, DeepSeek, and more). • Zero-code CLI & Web UI for training, inference, merging, and evaluation. • Supports full-tuning, LoRA, QLoRA, freeze-tuning, PPO/DPO, OFT, reward modeling, and multi-modal fine-tuning. • Speeds up training/inference with FlashAttention-2, RoPE scaling, Liger Kernel, and vLLM backend. • Integrates experiment tracking via LlamaBoard, TensorBoard, Weights & Biases, MLflow, and SwanLab. It's 100% Open Source Link to the Github Repo in the comments! — https://nitter.net/Sumanth_077/status/2039701710659272775#m

    → View original post on X — @sumanth_077, 2026-04-02 13:50 UTC

  • LLaMA-Factory: Fine-Tune 100+ LLMs Without Code
    LLaMA-Factory: Fine-Tune 100+ LLMs Without Code

    By

    @sumanth_077

    –

    Fine-Tune 100+ LLMs without writing a single line of code! LLaMA-Factory lets you train and fine-tune open-source LLMs and VLMs without writing any code. Here's why it's a game changer for fine-tuning: • Fine-tune 100+ LLMs/VLMs with built-in templates (LLaMA, Gemma, Qwen, Mistral, DeepSeek, and more). • Zero-code CLI & Web UI for training, inference, merging, and evaluation. • Supports full-tuning, LoRA, QLoRA, freeze-tuning, PPO/DPO, OFT, reward modeling, and multi-modal fine-tuning. • Speeds up training/inference with FlashAttention-2, RoPE scaling, Liger Kernel, and vLLM backend. • Integrates experiment tracking via LlamaBoard, TensorBoard, Weights & Biases, MLflow, and SwanLab. It's 100% Open Source Link to the Github Repo in the comments!

    → View original post on X — @sumanth_077, 2026-04-02 13:49 UTC