❯ cd /data/projects/coding_agent_session_search ❯ cc
▐▛███▜▌ Claude Code v2.0.54 ▝▜█████▛▘ Opus 4.5 · Claude Max ▘▘ ▝▝ /data/projects/coding_agent_session_search
read AGENTS.md and the README and explore the project deeply. Use ultrathink
● I'll start by reading the AGENTS.md and README files, then explore the project structure in depth.
It turns out that MacOS Tahoe can generate and use secure-enclave backed SSH keys! This replaces projects like https://github.com/maxgoedjen/secretive
There is a shared library /usr/lib/ssh-keychain.dylib that traditionally has been used to add smartcard support
to ssh by implementing PKCS11Provider interface. However since recently it also implements SecurityKeyProivder
which supports loading keys directly from the secure enclave! SecurityKeyProvider is what is normally used to talk to FIDO2 devices (e.g. libfido2 can be used to talk to your Yubikey). However you can now use it to talk to your Secure Enclave instead!
| import os | |
| os.environ['PYTORCH_CUDA_ALLOC_CONF']='expandable_segments:True' | |
| import soundfile as sf | |
| from transformers import Qwen3OmniMoeForConditionalGeneration, Qwen3OmniMoeProcessor, BitsAndBytesConfig | |
| from qwen_omni_utils import process_mm_info | |
| import torch | |
| MODEL_PATH = "Qwen/Qwen3-Omni-30B-A3B-Instruct" |
Here are the exact dimensions placed horizontally measured in mm:
TL;DR: We're witnessing the end of graphical user interfaces. AI agents like Claude Code are eliminating the need for windows, menus, and clicks, replacing them with natural language. The computer is finally learning to speak human, not the other way around.
Last week, I realized something profound: I haven't opened Finder in months. Not once.
| #!/usr/bin/env bun | |
| "use strict"; | |
| const fs = require("fs"); | |
| const { execSync } = require("child_process"); | |
| const path = require("path"); | |
| // ANSI color constants | |
| const c = { | |
| cy: '\033[36m', // cyan |
We propose an AI agent architecture in PyTorch that integrates state-of-the-art components to meet the following goals: (1) advanced reasoning with transformer models, (2) ingestion of large documents or histories via long context windows, (3) persistent memory without traditional vector-database RAG, (4) tool use for actions (API calls, code execution, etc.) similar to Anthropic’s MCP standard, and (5) declarative, goal-driven behavior with autonomous planning. The system will be compatible with both CPU and GPU environments. Below, we detail recommended models, libraries, and design choices for each aspect, followed by an overall architecture and example implementation steps.
Model Selection: Use modern transformer-based LLMs known for strong reasoning and multitasking. For example, Meta’s LLaMA 2 (open-source, 7B–70B parameters) or **Mist
Imagine a chip that learns like a brain — not by uploading data to train on later, but by adjusting itself in real time, using almost no power. That’s what the new “Super-Turing” AI chip does. Instead of separating learning and inference like traditional neural networks (train first, deploy later), this chip learns and makes decisions at the same time, directly in hardware.
At the heart of this system is a device called a synstor — a synaptic transistor that acts both as memory and as a learning engine. It doesn’t just store weights like a normal neural network. It changes them dynamically based on electrical pulses, mimicking how biological synapses adjust when neurons fire. This change happens through a mechanism called Spike-Timing Dependent Plasticity (STDP) — if a signal comes in just before the output neuron fires, the connection strengthens; if it comes after, it weakens. All of this happens instantly and locally
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
The SPARC Automated Development System (claude-sparc.sh) is a comprehensive, agentic workflow for automated software development using the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion). This system leverages Claude Code's built-in tools for parallel task orchestration, comprehensive research, and Test-Driven Development.