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Liquid AI’s New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs

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The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks, the conversation is shifting from raw parameter counts to architectural efficiency. Liquid AI team is leading this charge with the release of LFM2-24B-A2B , a 24-billion parameter model that redefines what we should expect from edge-capable AI. https://ift.tt/C3VNmOc The ‘A2B’ Architecture: A 1:3 Ratio for Efficiency The ‘A2B’ in the model’s name stands for Attention-to-Base . In a traditional Transformer, every layer uses Softmax Attention, which scales quadratically (O(N 2 )) with sequence length. This leads to massive KV (Key-Value) caches that devour VRAM. Liquid AI team bypasses this by using a hybrid structure. The ‘Base ‘ layers are efficient gated short convolution blocks , while the ‘Attention ‘ layers utilize Grouped Query Attention (GQA) . In the LFM2-24B-A2B configuration, the model uses a 1:3 ratio: Total Layers: 40...
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Meta AI Open Sources GCM for Better GPU Cluster Monitoring to Ensure High Performance AI Training and Hardware Reliability

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While the tech folks obsesses over the latest Llama checkpoints, a much grittier battle is being fought in the basements of data centers. As AI models scale to trillions of parameters, the clusters required to train them have become some of the most complex—and fragile—machines on the planet. Meta AI Research team just released GCM (GPU Cluster Monitoring) , a specialized toolkit designed to solve the ‘silent killer’ of AI progress: hardware instability at scale. GCM is a blueprint for how to manage the hardware-to-software handshake in High-Performance Computing (HPC). https://facebookresearch.github.io/gcm/docs/getting_started/ The Problem: When ‘Standard’ Observability Isn’t Enough In traditional web development, if a microservice lags, you check your dashboard and scale horizontally. In AI training, the rules are different. A single GPU in a 4,096-card cluster can experience a ‘silent failure’—where it technically stays ‘up’ but its performance degrades—effectively poisonin...
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A Coding Implementation to Simulate Practical Byzantine Fault Tolerance with Asyncio, Malicious Nodes, and Latency Analysis

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In this tutorial, we implement an end-to-end Practical Byzantine Fault Tolerance (PBFT) simulator using asyncio. We model a realistic distributed network with asynchronous message passing, configurable delays, and Byzantine nodes that intentionally deviate from the protocol. By explicitly implementing the pre-prepare, prepare, and commit phases, we explore how PBFT achieves consensus under adversarial conditions while respecting the theoretical 3f+1 bound. We also instrument the system to measure consensus latency and success rates as the number of malicious nodes increases, allowing us to empirically observe the limits of Byzantine fault tolerance. Copy Code Copied Use a different Browser import asyncio import random import time import hashlib from dataclasses import dataclass, field from typing import Dict, Set, Tuple, Optional, List import matplotlib.pyplot as plt PREPREPARE = "PREPREPARE" PREPARE = "PREPARE" COMMIT = "COMMIT" @dat...
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RAG vs. Context Stuffing: Why selective retrieval is more efficient and reliable than dumping all data into the prompt

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Large context windows have dramatically increased how much information modern language models can process in a single prompt. With models capable of handling hundreds of thousands—or even millions—of tokens, it’s easy to assume that Retrieval-Augmented Generation (RAG) is no longer necessary. If you can fit an entire codebase or documentation library into the context window, why build a retrieval pipeline at all? The key distinction is that a context window defines how much the model can see, while RAG determines what the model should see. A large window increases capacity, but it does not improve relevance. RAG filters and selects the most important information before it reaches the model, improving signal-to-noise ratio, efficiency, and reliability. The two approaches solve different problems and are not substitutes for one another. In this article, we compare both strategies directly. Using the OpenAI API, we evaluate Retrieval-Augmented Generation against brute-force context stuf...
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Composio Open Sources Agent Orchestrator to Help AI Developers Build Scalable Multi-Agent Workflows Beyond the Traditional ReAct Loops

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For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes. But as any software engineer who has tried to move these agents into production knows, simple loops are brittle. They hallucinate, they lose track of complex goals, and they struggle with ‘tool noise’ when faced with too many APIs. Composio team is moving the goalposts by open-sourcing Agent Orchestrator . This framework is designed to transition the industry from ‘Agentic Loops’ to ‘Agentic Workflows’—structured, stateful, and verifiable systems that treat AI agents more like reliable software modules and less like unpredictable chatbots. https://ift.tt/nkeQlKC The Architecture: Planner vs. Executor The core philosophy behind Agent Orchestrator is the strict separation of concerns. In traditional setups, the LLM is expected to both plan the strategy and execute the technical details simultaneously. This often leads to ‘greedy’ decisio...
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Beyond Simple API Requests: How OpenAI’s WebSocket Mode Changes the Game for Low Latency Voice Powered AI Experiences

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In the world of Generative AI, latency is the ultimate killer of immersion. Until recently, building a voice-enabled AI agent felt like assembling a Rube Goldberg machine: you’d pipe audio to a Speech-to-Text (STT) model, send the transcript to a Large Language Model (LLM), and finally shuttle text to a Text-to-Speech (TTS) engine. Each hop added hundreds of milliseconds of lag. OpenAI has collapsed this stack with the Realtime API . By offering a dedicated WebSocket mode , the platform provides a direct, persistent pipe into GPT-4o’s native multimodal capabilities. This represents a fundamental shift from stateless request-response cycles to stateful, event-driven streaming. The Protocol Shift: Why WebSockets? The industry has long relied on standard HTTP POST requests. While streaming text via Server-Sent Events (SSE) made LLMs feel faster, it remained a one-way street once initiated. The Realtime API utilizes the WebSocket protocol ( wss:// ) , providing a full-duplex communicati...
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