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Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - AronDaron/dataset-generator: No-code desktop app for generating high-quality synthetic datasets to fine-tune LLMs — plan-then-execute pipeline, LLM-as-judge, HuggingFace upload. GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL).
GitHub - genedeng-ca/ai-mac-migration: AI-powered Mac-to-...
2026-04-10 · via Hacker News - Newest: "LLM"

Install as Claude Code Skill

The fastest way to use this is as a Claude Code skill. One command, then just talk to Claude.

# Clone into your Claude Code skills directory
git clone https://github.com/genedeng-ca/ai-mac-migration.git ~/.claude/skills/mac-migration

Or if you just want the skill file without the full repo:

mkdir -p ~/.claude/skills/mac-migration/skill/scripts
curl -o ~/.claude/skills/mac-migration/skill/SKILL.md \
  https://raw.githubusercontent.com/genedeng-ca/ai-mac-migration/master/skill/SKILL.md
curl -o ~/.claude/skills/mac-migration/skill/scripts/scan.sh \
  https://raw.githubusercontent.com/genedeng-ca/ai-mac-migration/master/skill/scripts/scan.sh
curl -o ~/.claude/skills/mac-migration/skill/scripts/verify.sh \
  https://raw.githubusercontent.com/genedeng-ca/ai-mac-migration/master/skill/scripts/verify.sh
chmod +x ~/.claude/skills/mac-migration/skill/scripts/*.sh

Then tell Claude: "Help me migrate my Mac"

Claude will walk you through scanning your source Mac, building an intelligent migration plan, executing the transfer, and verifying everything landed correctly.

Standalone scripts

You can also use the helper scripts directly without Claude Code:

# Run on the source Mac to get a full system scan
./skill/scripts/scan.sh           # Human-readable report
./skill/scripts/scan.sh --json    # JSON output for automation

# Run on the target Mac after migration to verify
./skill/scripts/verify.sh user@source-mac.local

I haven't reinstalled macOS in 20 years. Every Mac I've owned has been migrated forward -- from PowerBook G4 to M5 MacBook Pro, across 8 machines, carrying two decades of muscle memory, scripts, configs, and digital life. Apple's Migration Assistant got me 80% there each time. AI got me the rest.


The Problem

Apple Migration Assistant is a black box. It copies everything -- the 50GB of Intel-only apps that will never run on Apple Silicon, the three conflicting Python installations, the orphaned preference files from software you uninstalled in 2019. You watch a progress bar for 6 hours and pray.

When it's done, you spend the next two weeks discovering what broke:

  • That app needs Rosetta and runs at half speed
  • Your SSH keys migrated but the permissions are wrong
  • 30GB of duplicate photos because iCloud and local copies both came over
  • VMware Fusion config migrated, but the actual VMs didn't
  • Software licenses tied to hardware IDs are now invalid
  • Homebrew packages compiled for Intel are silently failing

Migration Assistant treats your Mac like a disk image. Your Mac is not a disk image. It's a living system.

The Solution

AI Mac Migration replaces blind copying with intelligent transfer. It scans, understands, decides, and verifies -- using a 122-billion-parameter LLM running locally on your Mac. No cloud. No API calls. No telemetry. Just a smarter migration.

Source Mac ──SSH──> AI Analysis ──rsync──> Target Mac
                      │
               Local LLM on M5 Max
               Qwen3.5 122B via OpenClaw
               "Should this transfer?
                Is there a better version?
                Will this even work on ARM?"

How it works

  1. Scan -- Inventory both machines: apps, configs, packages, hardware capabilities
  2. Analyze -- LLM evaluates every item: architecture compatibility, version currency, size vs. value
  3. Plan -- Generate a ranked transfer plan with explanations for every decision
  4. Execute -- Adaptive rsync with real-time health monitoring
  5. Verify -- Post-transfer checksums and functional testing

Features

  • Intelligent app scanning -- Detects Intel vs ARM binaries, flags Rosetta-dependent apps, suggests native alternatives
  • Cross-machine package reuse -- If another machine on your network already has the ARM version of a package, grab it from there instead of downloading
  • Real-time hardware health detection -- Monitors disk I/O, CPU thermal, network throughput during transfer; backs off before your fans hit jet-engine mode
  • Adaptive transfer speed optimization -- Dynamically adjusts rsync parallelism and block size based on network conditions and disk speed
  • Smart exclusion lists -- AI-generated .migrationignore that understands what cache files, build artifacts, and temp data can be safely skipped
  • Selective config migration -- Migrates shell configs, SSH keys, git configs with permission fixup and path rewriting for the new machine
  • Automated photo dedup and organization -- Detect and eliminate duplicate photos across iCloud, local library, and manual backups before transfer
  • Software license migration -- Detect apps with hardware-locked licenses, generate a deactivation checklist for the old machine before wiping
  • End-to-end verification with checksums -- Post-migration integrity check comparing source and destination with per-file SHA-256 verification
  • Rollback snapshots -- APFS snapshot on target before migration starts; one-command rollback if anything goes wrong
  • Profile-based migration -- Pre-built profiles for "Developer", "Creative Pro", "Business" that prioritize what matters for each workflow

The Story

This tool was born from migrating an M4 Max MacBook Pro to an M5 Max MacBook Pro -- a machine carrying 20 years of accumulated digital life across 160TB of family data, dozens of development environments, and a fleet of AI services.

The migration was powered by Qwen3.5 122B running locally on the M5 Max itself via OpenClaw -- no cloud APIs, no subscriptions. The AI scanned both machines over SSH, made real-time decisions about what to transfer, and caught problems that Migration Assistant would have silently propagated.

What should have been a weekend of pain became an afternoon of supervised automation. The AI caught things a human would miss:

  • 12 Intel-only apps that had native ARM replacements available
  • 8GB of orphaned container images from a Docker version two releases old
  • SSH config referencing hosts that no longer exist
  • Homebrew packages that could be pulled from a local fleet machine instead of downloading 4GB over the internet

Tech Stack

Component Role
Python 3.12+ Core orchestration
Qwen3.5 122B via OpenClaw Decision engine running locally on M5 Max (no cloud dependency)
SSH Secure cross-machine communication
rsync Adaptive file transfer with resume support
APFS snapshots Pre-migration safety net
Homebrew Package reconciliation and cross-architecture detection

Quickstart

# On your NEW Mac:
git clone https://github.com/genedeng-ca/ai-mac-migration.git
cd ai-mac-migration
pip install -r requirements.txt

# Point it at your old Mac:
python migrate.py --source user@old-mac.local --target .

# Review the AI-generated migration plan before anything transfers:
# The tool will show you exactly what it wants to do and why.

Lessons Learned (The Hard Way)

Real migrations taught us what AI gets wrong. These are now guardrails in the tool:

Mistake What Happened The Fix
Photo loss AI classified "IMG_" prefixed files as iOS duplicates. Some were unique screenshots. Never delete -- only flag. Human reviews all dedup suggestions.
App folder miss Some apps store data in ~/Library/Application Support/ with a different name than the .app bundle. Map app bundles to their data directories using lsregister dump.
VMware forget Migration Assistant copied VMware Fusion but not the VMs in ~/Virtual Machines/. Explicit VM detection: scan for .vmx, .vmdk, .qcow2, Parallels bundles.
License gap Migrated apps that needed per-machine activation. Old machine was wiped. Licenses lost. Pre-migration license audit with deactivation reminders.
Permission drift SSH keys and GPG keyrings migrated with wrong permissions. Silent auth failures. Post-transfer permission validator: 700 for .ssh/, 600 for private keys.
Homebrew architecture mismatch Intel Homebrew (/usr/local) packages copied to ARM Mac. Binaries crash silently. Detect architecture, reinstall from source or pull from fleet ARM cache.

Architecture

ai-mac-migration/
├── migrate.py              # Entry point
├── scanner/
│   ├── apps.py             # Application inventory & architecture detection
│   ├── packages.py         # Homebrew/pip/npm package analysis
│   ├── configs.py          # Shell, SSH, git config discovery
│   └── hardware.py         # Hardware capability comparison
├── analyzer/
│   ├── llm_engine.py       # Local LLM interface (OpenClaw compatible)
│   ├── compatibility.py    # ARM/Intel compatibility checker
│   └── dedup.py            # File & photo deduplication engine
├── executor/
│   ├── transfer.py         # Adaptive rsync orchestration
│   ├── health_monitor.py   # Real-time system health during transfer
│   └── verify.py           # Post-transfer integrity checks
└── plans/
    └── migration_plan.json  # AI-generated, human-reviewed transfer plan

Contributing

This started as a personal tool for migrating my own Mac fleet. If you've felt the pain of Migration Assistant and want something better, contributions are welcome.

# Run tests
python -m pytest tests/

# Run a dry-run migration (no files transferred)
python migrate.py --source user@old-mac.local --target . --dry-run

License

MIT License. See LICENSE for details.


Built by someone who has mass-migrated too many Macs and finally got tired of doing it manually. Powered by Qwen3.5 122B running locally on Apple Silicon -- no cloud, no API keys, just your Mac understanding itself.