World Wire

Liquid AI has released LFM2.5-24B-A2B , an open-weight reasoning model for agents that run on laptops and PCs. LFM2.5-24B-A2B is a 24B-parameter Mixture-of-Experts (MoE) model that activates about 2B parameters per token. It is the largest and final member of the LFM2.5 family. Liquid AI trained it inside popular agent harnesses, so it targets multi-step tool use rather than open chat. Base and post-trained weights are available on Hugging Face . TL;DR Size: 24B total, 2B active (2.45B exact per the model card), 131,072-token context. Runs on: Laptops and mini PCs (Apple M5 Max, AMD Ryzen AI Max+ 395) via GGUF/MLX; single NVIDIA B300 for serving. FP8, GGUF, ONNX and MLX 4/5/6/8-bit builds. Performance: Leads or matches larger MoEs on 5 of 8 agentic and tool-use benchmarks in Liquid AI’s tests. Best: AutomationBench 27.69, more than 3x Qwen3.6-35B-A3B (8.72). Worst: SWE-Bench Verified (OpenCode) 51.00, versus 66.20 for Qwen3.6-35B-A3B. Bottom line...

Google DeepMind has made Gemini 4 Argon generally available to developers, enterprises and paid Gemini subscribers. Gemini 4 Argon is a sparse mixture-of-experts frontier model from Google built for long, multi-step work. Its target domains are coding, finance, legal, science and cybersecurity. Google first announced Argon on September 30 for a small group of cyber defenders. It now reaches the paid Gemini API at $2 input and $10 output per million tokens. The key change for developers is output length: Argon can write up to 1M tokens in one response. TL;DR Size: Parameter count not disclosed (sparse MoE). 1M token window shared by input and output. Runs on: Google’s cloud only, via Gemini API, AI Studio and Gemini platform. Trained on TPUs. No open weights. Performance: Leads Google’s table on knowledge work, long context and video. Mixed on agentic coding. Best: 91.7% on LVBench (long video understanding). Worst: 55.0% on FrontierSWE v2, la...

In this tutorial, we implement the Rebalancer , Meta’s assignment solver, with a C++ core and a Python binding. We describe objects, containers, dimensions, scopes, and partitions, add constraints and goals, then choose local search or the exact mixed-integer solver HiGHS. We build a small datacenter, drain a busy host, and compare the two solvers on toy models, mutually exclusive hosts, synthetic clusters, and 60 published OR-Library instances. We judge every plan by recounting the returned assignment rather than by the solver’s flags. In this build, across 24 solves, the solved flag read True in 14 solves that still left memory over capacity. HARD under local search returned a violating start without raising; misspelled spec keys were ignored silently, and enum names are rejected. The timings come from an Apple M4 with the macOS wheel, and Colab’s Linux wheel may time differently. Copy Code Copied Use a different Browser import os import re import sys import jso...