🧠⚡ Claude Code Local

Run Claude Code 100% on-device with local AI on Apple Silicon.
No cloud, no API key, no proxy — an MLX-native server that speaks the Anthropic API.
🥊 Pick your fighter: Gemma 4 31B · Llama 3.3 70B · Qwen 3.5 122B · DeepSeek V4 Flash (1M context via ds4).

🛑 Usage Limit · 🤔 What Is This · 🚀 Quick Start · 🥊 Lineup · 🎮 Modes · 🔒 Privacy · 📊 Benchmarks · 🎤 Voice · 🌐 Browser · 📱 Phone · 🔌 MCP · 🛣️ Roadmap

🛑 Hit your Claude usage limit?

If Claude Code just told you "you've reached your usage limit" and gave you a reset time hours away, that's what this is for. You keep working — same Claude Code, same terminal, same project — except the model answering is running on your own Mac.

curl -fsSL https://raw.githubusercontent.com/nicedreamzapp/claude-code-local/main/install.sh | bash

No API key. No second subscription. No waiting until 3pm. It works on a 16 GB MacBook and gets better the more RAM you have — see what runs on your Mac.

Real session, unedited. Claude Code reads and edits the file — the model answering is Gemma 4 31B on the laptop.

🤔 What Is This?

Your Mac has a powerful GPU built right into the chip. This project uses that GPU to run massive AI models — the same kind that power ChatGPT and Claude — entirely on your computer, and plugs them into Claude Code so the whole coding experience works offline.

🚫 No internet needed 💰 No monthly subscription 🔒 No one sees your code or data ✅ Full Claude Code experience — write code, edit files, manage projects, control your browser, or run a full hands-free voice session

         📱 You (Mac or Phone)
          │
     🤖 Claude Code           ← the AI coding tool you know
          │  HTTP localhost:4000
     ⚡ MLX Native Server      ← this repo (~1000 lines of Python)
          │
     🥊 Pick your fighter     ← Gemma 4 31B · Llama 3.3 70B · Qwen 3.5 122B
          │
     🖥️  Apple Silicon GPU    ← your M-series chip does all the work

The trick: Claude Code speaks the Anthropic API. Local model servers speak the OpenAI API. So everyone bolts a translation proxy in between — and the proxy is slow and fragile. This server speaks Anthropic natively. One process, zero translations:

🐌 What everyone else does	🚀 What we did
Claude Code → Proxy → Ollama → Model	Claude Code → Our Server → Model
3 processes, 2 API translations	1 process, 0 translations
133 seconds per task	17.6 seconds per task

🎯 That one change — eliminating the proxy — made it 7.5× faster.

🎬 Watch It Run — AirGap AI

A real NDA. Llama 3.3 70B. Wi-Fi physically OFF. lsof running live. Watch a 70-billion-parameter model audit a confidential legal document, on-device, with the receipts on screen.

AirGap is this whole build running as one private workstation — a capability, not a product. Everything you need is in this repo. If your firm needs one built, here's what it looks like.

More local-AI demos on the channel:

Video	What happens
🌌 The Rematch	4 AI engines build northern lights, 3 fully local — the local challenger painted the best aurora
🏁 Hexagon Shootout	Gemma 31B vs Llama 70B vs cloud Claude, same physics prompt, live counters — 2 of 3 with zero cloud calls
🐳 DeepSeek Three-Way	DeepSeek V4 Flash local beats cloud Claude on wall-clock, same MacBook
🎤 NarrateClaude	Speak to Claude Code, hear replies in a cloned voice — 100% on-device
🏠 Mac mini as home AI	Chat with the Mac mini at home from any browser on any phone

🥊 The Lineup — Pick Your Fighter

We started with one model. Now we ship a roster. Same MLX server, same Anthropic API — swap one env var and you swap the brain. Plus the ds4 engine for DeepSeek V4 Flash via its own native Metal runtime.

	🟡 Hermes 4 14B	🟢 Gemma 4 31B	🟠 Llama 3.3 70B	🔵 Qwen 3.5 122B	🐳 DeepSeek V4 Flash ⭐
Nickname	The One That Runs On Your Laptop	The Quick One	The Wise One	The Beast	The 1M-Context Whale
Build	4-bit abliterated	4-bit IT abliterated	8-bit abliterated	4-bit MoE (A10B)	2-bit asymmetric (ds4 GGUF)
Speed	not benchmarked yet	~15 tok/s	~7 tok/s	65 tok/s 🚀	~32 tok/s
Params	14 B dense (Qwen3 base)	31 B dense	71 B dense	122 B / 10 B active	284 B / 37 B active
Context	40 K	128 K	128 K	256 K	1 M tokens
RAM	~8 GB	~18 GB	~70 GB	~75 GB	~81 GB
Min RAM to run	16 GB	32 GB	96 GB	96 GB	128 GB
Best at	Everyday edits on a stock MacBook	Daily coding	Hardest reasoning, full precision	Max throughput, active sparsity	Long context, agentic loops
Engine	MLX Native	MLX Native	MLX Native	MLX Native	`antirez/ds4`
Launcher	`Claude Local.command`	`Gemma 4 Code.command`	`Llama 70B.command`	`Claude Local.command`	`DeepSeek V4 Flash.app`

💻 Got a 16 GB MacBook Air? Start with Hermes. setup.sh picks it for you automatically — you don't need 96 GB of RAM to use this.

💡 Fun fact: Qwen wins raw speed because it's an MoE — only 10B of 122B params activate per token. DeepSeek V4 Flash is even bigger (284B) but only ~37B active per token, and it ships with on-disk KV cache so a 25k-token Claude Code system prompt prefills exactly once, ever.

🐳 DeepSeek V4 Flash via `ds4`

We tested it the day Antirez (the Redis guy) shipped ds4. Local DeepSeek beat cloud Claude on wall-clock time on the same MacBook, same prompt — watch the three-way.


🧠 Engine	`antirez/ds4` — pure C + Metal kernels, ~few thousand lines
🤗 Weights	`antirez/deepseek-v4-gguf` (q2: 81 GB, q4: 153 GB)
📦 Server wrapper	`~/.local/bin/ds4-server-up` (boots on demand)
🚀 Claude Code wrapper	`~/.local/bin/claude-ds4` (drop-in replacement for `claude`)
📏 Context	1 M tokens; 200 K is sane for most agent runs
💾 Disk KV cache	Persists across restarts — first prefill is the only one that ever happens

⭐ Our Own MLX Abliterated Uploads

The models in this lineup aren't from generic mirrors — we package and upload our own abliterated MLX builds to HuggingFace so anyone running this repo can pull them with one command. Browse the full set at huggingface.co/divinetribe.

# Llama 3.3 70B — full-precision feel
MLX_MODEL=divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx \
  bash scripts/start-mlx-server.sh

# Gemma 4 31B — fast daily driver
MLX_MODEL=divinetribe/gemma-4-31b-it-abliterated-4bit-mlx \
  bash scripts/start-mlx-server.sh

# Hermes 4 14B — sweet spot for 16/32 GB Macs
MLX_MODEL=divinetribe/Hermes-4-14B-abliterated-4bit-mlx \
  bash scripts/start-mlx-server.sh

Model	Quant	Disk	Params	Context	Best for
`Llama-3.3-70B-Instruct-abliterated-8bit-mlx`	8-bit, g64	~75 GB	71 B dense	128 K	Hardest reasoning on 96 GB+ Macs
`gemma-4-31b-it-abliterated-4bit-mlx`	4-bit, g64	~17 GB	31 B dense	128 K	Daily coding on a 32 GB+ Mac
`Hermes-4-14B-abliterated-4bit-mlx`	4-bit, g64	~8 GB	14 B dense (Qwen3 base)	40 K	16 GB Macs, instruction-following, tool use

Abliteration sources: huihui-ai (Llama, Gemma) and Babsie (Hermes). MLX conversion + quantization by us. See what abliteration means.

⚠️ Use it responsibly. "Abliterated" suppresses the model's built-in refusal direction so it doesn't refuse benign-but-edgy requests. It is not a general capability upgrade, and you remain bound by each upstream license (Llama 3.3, Gemma, Hermes/Qwen3).

🎮 The Modes

Four ways to run the lineup. Each one is a double-clickable launcher in launchers/.

Mode	What it does	Launcher
🤖 Code	Run Claude Code with a local model — same UX, no API key	`Claude Local.command`, `Gemma 4 Code.command`, `Llama 70B.command`
🌐 Browser	Local AI controls real Brave browser via Chrome DevTools	`Browser Agent.command`
🎤 Hands-Free Voice	Speak in, hear replies in your cloned voice — full loop, 100% on-device	`Narrative Gemma.command` + NarrateClaude
📱 Phone	iMessage in → text/image/video out, via claude-screen-to-phone	`~/.claude/imessage-*.sh`

💻 What You Need

Your Mac	RAM	What `setup.sh` installs for you
MacBook Air / base M1-M4	16 GB	🟡 Hermes 4 14B — yes, this works
M1/M2/M3/M4 Pro	32-48 GB	🟢 Gemma 4 12B
M2/M3/M4/M5 Max	64-95 GB	🟢 Gemma 4 31B
M3/M4/M5 Max · Ultra	96 GB+	🔵 Qwen 3.5 122B, 🟠 Llama 70B, 🐳 DeepSeek

Also need:

🐍 Python 3.12+ (for MLX)
🤖 Claude Code (npm install -g @anthropic-ai/claude-code)

🚀 Quick Start (One Command)

curl -fsSL https://raw.githubusercontent.com/nicedreamzapp/claude-code-local/main/install.sh | bash

Or clone it yourself if you'd rather read the script first:

git clone https://github.com/nicedreamzapp/claude-code-local
cd claude-code-local
bash setup.sh

setup.sh auto-detects your RAM, picks a model from the lineup, downloads it, installs the MLX server, and creates a Claude Local.command launcher on your Desktop.

Then double-click Claude Local.command. You're coding locally.

🐛 If the launcher asks you to sign in to a Claude account: your claude CLI is too old. The launchers pass --bare to force local-only API-key auth; older CLIs don't support it. Fix: npm install -g @anthropic-ai/claude-code

🛠️ Note for contributors: setup.sh installs the server as a symlink at ~/.local/mlx-native-server/server.py pointing back at this repo's proxy/server.py. Edit the file in the repo, restart the server, done — one source of truth, no silent drift.

Or do it manually

# 1. Set up the MLX virtualenv
python3.12 -m venv ~/.local/mlx-server
~/.local/mlx-server/bin/pip install mlx-lm

# 2. Pick a fighter and download (one time, ~18-75 GB)
bash scripts/download-and-import.sh gemma   # or 'llama' or 'qwen'

# 3. Start the server
MLX_MODEL=divinetribe/gemma-4-31b-it-abliterated-4bit-mlx \
  bash scripts/start-mlx-server.sh

# 4. Launch Claude Code
ANTHROPIC_BASE_URL=http://localhost:4000 \
ANTHROPIC_API_KEY=sk-local \
claude --model claude-sonnet-4-6

🔧 How It Works

┌──────────────────────────────────────────────────┐
│              Your MacBook (M-series)             │
│                                                  │
│  📝 You type ──> 🤖 Claude Code                  │
│                      │                           │
│                      ▼                           │
│                 ⚡ MLX Server (port 4000)        │
│                      │                           │
│                      ▼                           │
│                 🥊 Local model ──> 🖥️  GPU        │
│                 (Gemma·Llama·Qwen)               │
│                      │                           │
│                      ▼                           │
│  📝 Answer <─── ✨ Clean response                │
│                                                  │
│         🔒 Nothing leaves this box. Ever.        │
└──────────────────────────────────────────────────┘

The server (proxy/server.py) is one file, ~1000 lines. It does six things:

📦 Loads the model — Apple's MLX framework, native Metal GPU, unified memory. Handles Gemma's RotatingKVCache quirk automatically.
🔌 Speaks Anthropic API — Claude Code thinks it's talking to Anthropic's cloud. It's not.
🔧 Translates tool use — Three tool-call formats in and out: Gemma 4 native, Llama 3.3 raw JSON, and HuggingFace <tool_call> JSON (Qwen and others). All converted ↔ Anthropic tool_use blocks, with garbled-output recovery for small models.
🧹 Cleans the output — A real-time ThinkingFilter strips <think> blocks token-by-token during generation, then clean_response handles stop markers and reasoning preamble.
⚡ Reuses prompt caches across requests — Claude Code's system prompt doesn't get re-prefilled every turn. Huge speedup for short questions.
🎯 Code mode — auto-detects Claude Code coding sessions, swaps the ~10K-token harness prompt for a slim ~150-token one, and strips verbose tool descriptions to name + parameter types. A 28× prompt reduction that cuts prefill from ~60 s to ~2 s on Gemma 4 31B.

🛤️ The Journey

We didn't start here. Three generations in one night:

Gen	What We Tried	Speed	💡 What We Learned
1️⃣	Ollama + custom proxy	30 tok/s	Ollama works but Claude Code can't talk to it directly
2️⃣	llama.cpp TurboQuant + proxy	41 tok/s	TurboQuant compresses KV cache 4.9x, but the proxy is the bottleneck
3️⃣	MLX native server	65 tok/s	Kill the proxy. Speak Anthropic API directly. 7.5x faster.
4️⃣	The lineup	65 / 15 / 7 tok/s	Three brains, one server — swap one env var to change the fighter

🔒 Privacy + How the Data Flows

This is the part we're proudest of. Your code never leaves your Mac. Not for a model call. Not for telemetry. Not for "anonymous analytics". Not ever.

   ┌─────────────────────────────────────────────────────────────┐
   │                    🖥️  YOUR MACBOOK                          │
   │                                                             │
   │   📝 Your code ──> 🤖 Claude Code ──> ⚡ MLX Server          │
   │                     (localhost:4000)      │                 │
   │                                           ▼                 │
   │                    🧠 Local model ──> 🖥️  Apple GPU          │
   │                                                             │
   │             🚫 ZERO outbound network calls                  │
   │             🚫 ZERO telemetry                               │
   │             🚫 ZERO phone-home                              │
   └─────────────────────────────────────────────────────────────┘
                   │
                   ✗  ←  Nothing from our code crosses this line.
                   │
   ┌─────────────────────────────────────────────────────────────┐
   │                    ☁️  THE INTERNET                          │
   │                  (your code never goes here)                 │
   └─────────────────────────────────────────────────────────────┘

🔍 What We Audited (Every Component)

Component	Source	Outbound calls	Verdict
server.py (ours)	We wrote it line by line	0	✅ Safe
browser agent	nicedreamzapp/browser-agent — we wrote it	0 (localhost CDP only)	✅ Safe
mlx-lm	Apple ML team	0	✅ Safe
MLX framework	Apple	0	✅ Safe
Model weights	HuggingFace verified repos	0 at runtime	✅ Safe
Claude Code CLI	Anthropic (closed-source binary)	0 with our launchers — `lsof`-verified	✅ Safe

✅ Verified offline. Claude Code's own binary previously reached out to api.anthropic.com on startup for telemetry, statsig feature flags, marketplace auto-install, and the autoupdater. The launchers plug all four channels via documented Anthropic env vars (thanks @tadrianonet, PR #32):
CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1
DISABLE_AUTOUPDATER=1
CLAUDE_CODE_DISABLE_OFFICIAL_MARKETPLACE_AUTOINSTALL=1
CLAUDE_CODE_DISABLE_BACKGROUND_TASKS=1
Verify it yourself: run lsof -p $(pgrep -f claude) during a session — you'll see only localhost:4000. Run lsof -i -P while the server is up — nothing leaves your Mac.

⚠️ We removed LiteLLM after supply-chain attack concerns. Every dependency was re-audited from scratch. If a package had unexplained network calls, it didn't ship.

✈️ When To Use This

Situation	Use This?	Why
On a plane (no wifi)	✅	Full AI coding, no internet needed
NDA / sensitive client code	✅	Nothing leaves your machine — air-gapped, `lsof`-verified
Healthcare / legal / finance review	✅	100% on-device, audit-friendly
Don't want API fees	✅	$0/month forever
Want fastest possible	☁️	Cloud Sonnet is still slightly faster
Need Claude-level reasoning	☁️	Local models are good, not Claude-level

📊 Benchmarks

Three generations of optimization. Each one got faster.

⚡ Speed Comparison

Generation	Approach	Speed	Real Claude Code task
🐌 Gen 1	Ollama + Proxy	30 tok/s	133 s
🏃 Gen 2	llama.cpp + Proxy	41 tok/s	133 s
🚀 Gen 3	MLX Native (ours)	65 tok/s	17.6 s

🥊 Lineup Comparison

Model	tok/s	RAM	Best For
🟢 Gemma 4 31B Abliterated	~15	~18 GB	Daily coding on a 64 GB Mac
🟠 Llama 3.3 70B Abliterated	~7	~70 GB	Hardest reasoning, full precision
🔵 Qwen 3.5 122B-A10B	65	~75 GB	Maximum throughput, MoE sparsity

☁️ vs Cloud APIs

	🖥️ Our Local Setup	☁️ Claude Sonnet	☁️ Claude Opus
Speed	65 tok/s	~80 tok/s	~40 tok/s
Monthly cost	$0 🎉	$20-100+	$20-100+
Privacy	100% local 🔒	Cloud	Cloud
Works offline	Yes ✈️	No	No

💡 Our local setup beats cloud Opus on raw speed (65 vs 40 tok/s) at $0/month. Qwen numbers measured on M5 Max 128 GB — full details in BENCHMARKS.md.

🔧 Tool-Call Reliability

Local models don't format tool calls perfectly. They want to call a tool but mix XML and JSON syntax — Claude Code sees no valid tool call, re-prompts, and the model garbles it the same way again. The result: infinite loops where the AI says "let me do that" but never does anything.

We fixed this with 4 changes to server.py:

Change	What	Why
KV Cache	4-bit → 8-bit, quantization starts at token 1024	Model retains conversation context
Temperature	0.7 → 0.2	Less randomness = more consistent tool formatting
Garbled Recovery	`recover_garbled_tool_json()`	Catches XML-in-JSON hybrids, infers tool names from parameter keys
Retry Logic	Up to 2 retries when tool intent is detected but parsing fails	Re-prompts with explicit formatting instructions

🧪 Results: 98/98 tests passed across 7 consecutive runs. Zero failures. The multi-step scenario that used to trigger infinite loops — create 12 month folders, delete all but September, verify — now passes every time. Run it yourself:

python3 scripts/test_mlx_server.py

⚙️ Tuning

Variable	Default	What It Does
`MLX_MODEL`	`divinetribe/gemma-4-31b-it-abliterated-4bit-mlx`	Pick which fighter to load
`MLX_KV_BITS`	`8`	KV cache quantization bits (4 saves memory, 8 improves coherence)
`MLX_KV_QUANT_START`	`1024`	Token position where KV quantization begins
`MLX_TOOL_RETRIES`	`2`	Max retries when a garbled tool call is detected
`MLX_MAX_TOKENS`	`8192`	Max output tokens per response
`MLX_SUPPRESS_THINKING`	`1`	Skip the model's reasoning chain (~1 min/request saved). Set `0` to let it think.
`MLX_BROWSER_MODE`	`0`	Optimize for chrome-devtools MCP sessions — keeps only the 9 essential browser tools (~99% fewer tokens)

📚 More

Everything above gets you running. These live in docs/ so this page stays short:


🎤 Hands-Free Voice Mode	Talk to Claude Code, hear it answer in a cloned voice
🌐 Browser Agent	Let the local model drive your real browser
📱 Control From Your Phone	Run a session on your Mac from anywhere
🔌 MCP Servers	Claude Code's whole plugin ecosystem, 100% local
📁 What's In This Repo	File-by-file tour
📊 Benchmarks · 🔧 Tool-Call Reliability	The numbers and how they were measured
📱 Apps From The Same Workshop · 🙏 Credits	Everything else

🧩 The Local-First Stack

claude-code-local is the brain. It pairs with sibling repos — each stands alone, together they take Claude Code off the keyboard and off the screen:

Repo	Role	What it does
🤖 claude-code-local	Brain (you are here)	MLX Anthropic server · launcher lineup · tool-call translation
🎤 NarrateClaude	Ears + Mouth	Talk to Claude, hear replies in your cloned voice — both directions on-device
🌐 browser-agent	Hands	Drives real Brave via CDP — iframes, Shadow DOM, ProseMirror
📱 claude-screen-to-phone	Remote	iPhone → Claude Code over iMessage; text/screenshots/videos back
🛟 claude-failover	Backstop	Keep cloud Claude primary, flip one command to local when limits pinch or Anthropic is down

🔬 More Local-AI Work

Not part of this stack — separate projects, same rule: the model runs on your machine, not on somebody's server.

Repo	What it is
🧠 nemotron-omni-mlx	NVIDIA's tri-modal Nemotron Omni — text, vision and audio — in pure MLX. The vision and audio towers had no Apple Silicon runtime, so I wrote one. 23/23 parity tests against NVIDIA's PyTorch reference. 67 tok/s with an image, wifi off.
🎬 story-forge	A script goes in, a finished film comes out — stills, motion, voices, music, grade and sound, start to finish on one laptop. Flux · LTX-2 · Piper · ACE-Step · ffmpeg.
🎙️ song-forge	Describe a song, get a finished original in any of 19 languages. ACE-Step + Gemma + seed-vc voice swap, rendered locally, never stored.
👁️ VisionBuilder	Label photos and train your own on-device vision model straight from the camera roll. The dataset never leaves the phone.
🗓️ Family-Planner	Self-hosted family command center for a spare iPad — voice control, OCR document scanning, meal planning. No cloud, no subscription.

📱 There are four apps on the App Store and Google Play too — see them here.

🛣️ What's Next

We ship fast and in public. If any of these excite you, hit Watch to get the release ping.

🟡 Full Qwen 3.5 122B benchmark suite — reliability, tool-call pass rate, long-context behavior vs Gemma
🟡 Fully-local Whisper fallback — alternative to the Apple SFSpeechRecognizer path for older Macs and non-English voices
🟡 One-click DMG installer — no terminal needed
🟡 MLX_MODEL=<hf-url> — point at any HuggingFace repo and auto-register a new fighter
🟡 More fighters — open to PRs adding launchers for DeepSeek, Mistral, Phi, anything MLX-compatible

💡 Want something that's not on this list? Open an issue → Every serious request gets read and usually replied to within 24h.

🤝 Contributing

Ideas, bug reports, a new launcher for a model I don't run, a better code-mode prompt — open an issue or a PR, I read them all. Especially interested in: folks on older Apple Silicon (M1/M2, 16–36 GB) who know which models actually fit; anyone stress-testing the voice loop on different hardware or accents; TTS recipes beyond Pocket TTS (Piper, MLX-TTS, Kyutai Moshi); and edge cases I'll never hit on an M5 Max with 128 GB.

🙏 Credits

🧑‍🔧 Contributors

Every one of these landed on hardware I don't own, on a bug I hadn't hit. Thank you.

Who	What they fixed
@0xshugo	Client disconnects handled, retries skipped when there are no tools (#4)
@asdmoment	Gemma inference crash — auto-disable KV quantization (#7)
@kulveersingh	`ArraysCache` has no attribute `offset` (#10)
@tripathiprateek	`uninstall.sh` — reverses `setup.sh` cleanly (#23)
@tadrianonet	Mac base/Pro 16 GB support: Qwen 2.5 14B, ChatML stop markers, `<tools>` parser, offline leak fix (#32)
@kevbarns	Gemma 4 thinking suppression + slimmer tool descriptions — ~4× latency cut (#33)
@KaoCSC	Stop on the tokenizer's real EOS, and tolerate empty env ints (#41) · bare JSON tool calls, which took Qwen 2.5 Coder from 0/12 to 14/14 (#43)

Tested on Apple M5 Max with 128 GB unified memory.

Built by Matt Macosko in Arcata, CA — part of Nice Dreamz LLC. More open-source at nicedreamzwholesale.com/software · demos at youtube.com/@nicedreamzapps.

📜 MIT License — Use it however you want.

💬 Builders hang out on Discord — share what you're building, swap MLX tips.

⭐ Star this repo if it helped you! ⭐

Upstream projects this is built on are listed in docs/CREDITS.md.

Name		Name	Last commit message	Last commit date
Latest commit History 109 Commits
.github		.github
NarrativeGemma		NarrativeGemma
assets		assets
docs		docs
launchers		launchers
proxy		proxy
scripts		scripts
smart-router		smart-router
.gitignore		.gitignore
IMESSAGE_MEDIA_PIPELINE.md		IMESSAGE_MEDIA_PIPELINE.md
LICENSE		LICENSE
README.md		README.md
install.sh		install.sh
setup.sh		setup.sh
uninstall.sh		uninstall.sh

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🧠⚡ Claude Code Local

🛑 Hit your Claude usage limit?

🤔 What Is This?

🎬 Watch It Run — AirGap AI

🥊 The Lineup — Pick Your Fighter

🐳 DeepSeek V4 Flash via `ds4`

⭐ Our Own MLX Abliterated Uploads

🎮 The Modes

💻 What You Need

🚀 Quick Start (One Command)

Or do it manually

🔧 How It Works

🛤️ The Journey

🔒 Privacy + How the Data Flows

🔍 What We Audited (Every Component)

✈️ When To Use This

📊 Benchmarks

⚡ Speed Comparison

🥊 Lineup Comparison

☁️ vs Cloud APIs

🔧 Tool-Call Reliability

⚙️ Tuning

📚 More

🧩 The Local-First Stack

🔬 More Local-AI Work

🛣️ What's Next

🤝 Contributing

🙏 Credits

🧑‍🔧 Contributors

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🧠⚡ Claude Code Local

🛑 Hit your Claude usage limit?

🤔 What Is This?

🎬 Watch It Run — AirGap AI

🥊 The Lineup — Pick Your Fighter

🐳 DeepSeek V4 Flash via ds4

⭐ Our Own MLX Abliterated Uploads

🎮 The Modes

💻 What You Need

🚀 Quick Start (One Command)

Or do it manually

🔧 How It Works

🛤️ The Journey

🔒 Privacy + How the Data Flows

🔍 What We Audited (Every Component)

✈️ When To Use This

📊 Benchmarks

⚡ Speed Comparison

🥊 Lineup Comparison

☁️ vs Cloud APIs

🔧 Tool-Call Reliability

⚙️ Tuning

📚 More

🧩 The Local-First Stack

🔬 More Local-AI Work

🛣️ What's Next

🤝 Contributing

🙏 Credits

🧑‍🔧 Contributors

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