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Nous Research Releases ‘Hermes Agent’ to Fix AI Forgetfulness with Multi-Level Memory and Dedicated Remote Terminal Access Support

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In the current AI landscape, we’ve become accustomed to the ‘ephemeral agent’—a brilliant but forgetful assistant that restarts its cognitive clock with every new chat session. While LLMs have become master coders, they lack the persistent state required to function as true teammates. Nous Research team released Hermes Agent , an open-source autonomous system designed to solve the two biggest bottlenecks in agentic workflows: memory decay and environmental isolation. Built on the high-steerability Hermes-3 model family, Hermes Agent is billed as the assistant that ‘grows with you.’ The Memory Hierarchy: Learning via Skill Documents For an agent to ‘grow,’ it needs more than just a large context window. Hermes Agent utilizes a multi-level memory system that mimics procedural learning. While it handles short-term tasks through standard inference, its long-term utility is driven by Skill Documents . When Hermes Agent completes a complex task—such as debugging a specific microserv...
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Tailscale and LM Studio Introduce ‘LM Link’ to Provide Encrypted Point-to-Point Access to Your Private GPU Hardware Assets

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For the modern AI developer productivity is often tied to a physical location. You likely have a ‘Big Rig’ at home or the office—a workstation humming with NVIDIA RTX cards—and a ‘Travel Rig,’ a sleek laptop that’s perfect for coffee shops but struggles to run even a quantized Llama-3 variant. Until now, bridging that gap meant venturing into the ‘networking dark arts.’ You either wrestled with brittle SSH tunnels, exposed private APIs to the public internet, or paid for cloud GPUs while your own hardware sat idle. This week, LM Studio and Tailscale launched LM Link , a feature that treats your remote hardware as if it were plugged directly into your laptop. The Problem: API Key Sprawl and Public Exposure Running LLMs locally offers privacy and zero per-token costs, but mobility remains the bottleneck. Traditional remote access requires a public endpoint, which creates two massive headaches: Security Risk: Opening ports to the internet invites constant scanning and potential e...
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How to Build an Elastic Vector Database with Consistent Hashing, Sharding, and Live Ring Visualization for RAG Systems

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In this tutorial, we build an elastic vector database simulator that mirrors how modern RAG systems shard embeddings across distributed storage nodes. We implement consistent hashing with virtual nodes to ensure balanced placement and minimal reshuffling as the system scales. We visualize the hashing ring in real time and interactively add or remove nodes to observe how only a small fraction of embeddings move. We use this setup to connect infrastructure theory directly to practical behavior in distributed AI systems. Copy Code Copied Use a different Browser !pip -q install networkx ipywidgets import hashlib import bisect import random from dataclasses import dataclass from typing import Dict, List, Optional import numpy as np import networkx as nx import matplotlib.pyplot as plt from IPython.display import display, clear_output import ipywidgets as widgets We set up the execution environment and install the required libraries needed for visualization and interactivi...
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New ETH Zurich Study Proves Your AI Coding Agents are Failing Because Your AGENTS.md Files are too Detailed

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In the high-stakes world of AI, ‘Context Engineering’ has emerged as the latest frontier for squeezing performance out of LLMs. Industry leaders have touted AGENTS.md (and its cousins like CLAUDE.md ) as the ultimate configuration point for coding agents—a repository-level ‘North Star’ injected into every conversation to guide the AI through complex codebases. But a recent study from researchers at ETH Zurich just dropped a massive reality check. The findings are quite clear: if you aren’t deliberate with your context files, you are likely sabotaging your agent’s performance while paying a 20% premium for the privilege. https://ift.tt/OHdZqua The Data: More Tokens, Less Success The ETH Zurich research team analyzed coding agents like Sonnet-4.5 , GPT-5.2 , and Qwen3-30B across established benchmarks and a novel set of real-world tasks called AGENTBENCH . The results were surprisingly lopsided: The Auto-Generated Tax : Automatically generated context files actually reduced ...
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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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