# Enough is a feature

Intelligence doesn't need to be excessive to be good.

Keep what is essential, own what you have.

Understand what you use, use only what you need.

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The case for LowAI

## Less AI, more for everyone.

Artificial intelligence is fast becoming the core infrastructure of how we work, learn and decide — and it is being built in a shape that concentrates power, cost and control in remarkably few hands. A handful of firms own the frontier models, the data centres that run them, and the terms on which everyone else may use them. Every question is metered, logged, and routed through machines you will never see or govern.

None of this is a law of nature. It is a design choice — and design choices can be remade. LowAI is the wager that intelligence can run on hardware people already own, on open models anyone can inspect, with data that never leaves the device. Not merely because that is cheaper — though it is — but because privacy, autonomy and sustainability should be the floor a technology stands on, not a premium tier sold back to us.

The argument is structural, economic and ecological at once. Below: the flags worth raising, the numbers worth seeing, and the openings worth taking.

🏡Tiny-house living
💻Runs on what you own
🌿Low consumption
🛠️Repairable & yours

### Structural flags

🚩 Concentration of power A few companies set the models, the compute and the price of access for everyone else.
🚩 Rented, never owned Terms, prices and availability can change overnight on tools you don't control.
🚩 Metered surveillance Pay-per-token means every prompt becomes a data point about you, stored elsewhere.
🚩 Ecological footprint The race for ever-larger models scales energy and water use to industrial levels.
🚩 Designed-in exclusion Budgets, connectivity and permissions gate who gets to use AI at all.

LowAI's reply is deliberately modest: keep the model small enough to understand, local enough to own, and open enough to audit. Frugal AI is not a downgrade — it is AI that answers to you.

### Cloud vs Local, by design

Data that leaves your device
Cloud
100%
Local
0%
Works fully offline
Cloud
0%
Local
100%
You can audit the model & code
Cloud
~10%
Local
100%
Recurring cost to keep using it
Cloud
High
Local
~0

### Indicative figures

≈1–2%
of the world's electricity used by data centres today — and climbing.
$10s M
to train a single frontier model.
0
tokens, dollars or data leaving your machine with LowAI.

Figures are indicative, drawn from widely reported ranges; LowAI's are by design.

### Try it yourself

If you ask an AI 30 questions a day…

…and your computer already has 8 GB of RAM

How AI development got here

For a decade, progress in AI has been read as a single dial: scale. More parameters, more data, more compute — and, reliably, more capability. That scaling story produced genuine breakthroughs, but it also concentrated the field around whoever could afford the largest clusters. The result is an ecosystem optimised for centralisation: capability lives in the cloud, value accrues to platform owners, and users become a source of data and revenue rather than people to be served. Smaller, efficient, local models were always possible — quantisation, distillation and better architectures keep widening what a laptop can do — but they were under-funded precisely because they don't fit the rental business model. LowAI treats that neglected branch as the main road.

The opportunity: a better world through LowAI

A frugal, local, open approach is not only a defence — it is an opening. Intelligence that runs offline reaches the places connectivity doesn't. AI you can audit is AI you can trust in medicine, law and education. Models you own can be tuned to a person, a village, a language or a craft without surrendering the data that makes them useful. Energy spent once, locally, beats energy spent endlessly in a distant data centre. And a community building in the open compounds: every efficient model, every shared recipe, every reproducible tool lowers the floor for the next person. The goal is not a smaller AI for its own sake, but a wider one — owned by many, understood by its users, and gentle on the world it runs in.

🦉

## Athenea

Our first prototype: an LLM agent that runs 100% on your machine — chat, knowledge and RAG, fully offline. No tokens, no external APIs, no cloud.

How Athenea works

#### One local engine

Open GGUF models run on your own hardware. Nothing leaves your device — no tokens, no cloud.

#### Your own knowledge

Keyword RAG over your PDFs and EPUBs feeds the engine, with atomic storage in the `.mv2` format.

#### Two tools, one core

From that core branch an Assistant you talk to, and Content you generate from everything it knows.

#### Frugal & free

It all runs inside one frugal envelope: no key, no subscription, no lock — ~8 GB RAM, open formats you can export anytime.

### Assistant

Local conversational agent

Talk to open models running on your own hardware. Persistent memory, tools and knowledge — all offline, without a single byte leaving your machine.

#### How it works with your knowledge

- RAG over your documents: extracts text from PDF and EPUB.
- Incremental ingestion with `/learn`, `/fetch` and `/ingest`.
- OpenAI-compatible API server to connect your own apps.
- Atomic persistence in the `.mv2` format (conversations + knowledge).

#### Frugal under the hood

- TurboQuant KV-cache compression (f16/bf16/q8_0/q4_0/turbo2-4).
- Catalog of 20 pre-quantized models (340 MB – 8.4 GB): Qwen, Gemma, Phi, Llama.
- Runs on ~8 GB of RAM — no bleeding-edge GPU required.
- Modest hardware, long life: the tiny-house logic applied to AI.

### Content

Content generation & transformation

Produce and transform content from your own local knowledge: drafts, summaries and rewrites. Nothing sent to the cloud, no subscriptions.

#### Workflow

- Batch processing of documents and notes.
- Reusable templates for recurring tasks.
- Generates from the same sources your Assistant already ingested.
- Exports to standard formats (Markdown, plain text).

#### Reproducible, no vendor lock

- Pipeline wired to the same `.mv2` knowledge base.
- Same input → same output: reproducible generation, locally.
- Open models and formats: migrate and export whenever you want.
- Zero dependence on OpenAI, Google or any third-party API.

Built on Athenea

## Implementations

Projects that extend the Athenea core toward new capabilities.

### Personalized fine-tuner

on the nety network

Prototype

A personalized assistant that fine-tunes your own Athenea model on your data, spreading the compute across the nodes of the nety P2P network. Your AI, trained by and for you.

#### How it fits

- Starts from a local base model in Athenea's catalog.
- Tuning is distributed across nety's compute nodes.
- The result comes back as a GGUF model that runs offline on your machine.
- Your raw data never goes to a central server.

#### Why it matters

- Real personalization without proprietary GPU farms.
- Uses the P2P resource market — low cost by design.
- Proves the Athenea ⇄ nety bridge: local AI + decentralized network.

## Related projects

### Distributed computing

nety · Rust + libp2p

Active

The distributed compute layer of nety: a P2P network where nodes offer and consume resources. The infrastructure that makes the fine-tuner possible.

#### Pieces of the network

- Compute and storage nodes across the P2P mesh.
- Resource Manager: tracks CPU, RAM, GPU and disk.
- VM Runtime with WASM execution (Wasmtime / WASI).
- Tiered storage: local, IPFS, S3 or hybrid.

#### Market & reputation

- Market Protocol: peer-to-peer supply/demand of resources.
- Scheduler: matching, placement and reputation.
- GPU distribution (phase F9, in progress).
- Ed25519 + ChaCha20-Poly1305 crypto; CRDT reputation.

### Knowledge hypergraph

research

Research

Representing Athenea's knowledge as a hypergraph: instead of edges joining two nodes, hyperedges that connect many at once.

#### The idea

- One hyperedge links question ↔ documents ↔ concepts together.
- Models many-to-many relations a flat graph can't capture.
- Richer than the linear `JSONL` index of today's RAG.

#### Where it goes

- Retrieval by hyperedge neighborhood, not just keyword.
- Connects sources, concepts and agents in one structure.
- A basis to reason about why a document answers a question.

### Ephemerall

ephemeral by design

Concept

Data and compute that exist only as long as they are needed, then vanish. The simple-living instinct applied to memory: don't hoard what you can let go.

#### The idea

- Sessions, context and artifacts with a built-in lifespan.
- Nothing persists by accident — keeping is an explicit choice.
- Less stored state means less to leak, secure and maintain.

#### Why it matters

- Privacy as a default: what doesn't exist can't be exposed.
- Low consumption: ephemeral state is cheap state.
- Pairs with Athenea's local engine and nety's transient nodes.

## Featured companions

Hugging Face Athenea GGUF models
opencode Open-source coding agent
Docker Ready-to-run images
GitHub LowAiorg · floveorg

Community

### Founders

Once upon a time… some people gave their care and passion to set up this community. Know us closer.

Free software made us friends, a care for the world we share.

Open new tools, write the first specs, and steward a project's direction and quality.

Ship a small, frugal tool under the LowAI philosophy and propose it to the community.

Click to expand ▾

### Support us

Funding, infrastructure, advocacy and mentoring.

Support is what keeps frugal AI sustainable without selling it back to anyone.

Sponsor a project, donate compute or hosting, mentor newcomers, or simply spread the word.

Frugal, open tools rarely fit a rental business model — community support is what keeps them alive.

Click to expand ▾

### Lows

Tiny houses, Simple livings, Low techs… Low all!

No gatekeeping: using, sharing and improving the tools already makes you part of it.

Run tools locally, file issues, share models and recipes, and help others get started.

Clone a project, run it on your own machine, and open your first pull request.

Click to expand ▾
Governance Decisions happen in the open — in public issues and discussions, not behind closed doors.
Standards Shared frontend and frugal-model guidelines so tools feel consistent and stay easy to maintain.
Onboarding Step-by-step guides so a newcomer can clone, run and contribute in a single afternoon.
Recognition Every contribution is credited, however small — code, docs, models, translations or support.
Open by default Frugal over flashy Local over cloud Repairable Documented Welcoming

### Ways to get involved

🐙 Pick an issue github.com/LowAiorg 💡 Propose a project Open a discussion 💬 Join the conversation Issues & discussions 🤗 Share a model Hugging Face

New here? Start with the contributing guide, browse good first issues, or say hello in discussions. Building something small and frugal? Propose it as a project and we'll help you shape it — or publish a model on Hugging Face. Everything is open source and built in the open.

Ready to build something that asks for little?

Start on GitHub