You're already managing context for AI assistants - through CLAUDE.md files, project docs, manual reminders. DDAID isn't a revolution; it's an evolution of what you're doing now. The difference? Your context stays alive, synced, and actually works at scale.
Working with AI assistants today is like collaborating with a brilliant junior developer who forgets everything overnight. Every morning, you re-explain your architecture, your decisions, your patterns. The context you carefully documented last week? Already stale.
You know the cycle: spend an hour setting up context, get amazing results for a week, then watch it slowly decay until you're back to square one, explaining basic project structure to an AI that should already know.
DDAID doesn't replace your documentation - it defers to it. Your architectural decisions, your coding standards, your patterns remain the source of truth. DDAID just keeps them alive and in sync.
Without DDAID: AI is a junior dev with daily amnesia
With DDAID: They still forget, but show up having read yesterday's notes
The key insight: documentation becomes the shared language between human and AI. You write it once, maintain it naturally through git, and every AI assistant speaks it fluently.
DDAID treats AI as a collaborator, not a magical replacement for developers. The human remains in charge of architecture, decisions, and judgment. The AI handles the repetitive work of keeping context synchronized.
It's tunable and correctable - you can edit any documentation directly, and DDAID maintains it going forward. No black box, no mystery. Just reliable collaboration based on shared understanding.
Git-driven updates: When code changes, documentation updates automatically. No more stale ARCHITECTURE.md files.
Specialized agents: Different contexts for different concerns - architecture, API, security, performance. Each maintained by focused agents.
Universal format: One context standard that works across all AI tools. Your docs work the same in Claude, ChatGPT, or local models.
Human override: Edit docs anytime. DDAID defers to your changes and maintains them going forward.
Less repetition: Stop explaining the same architectural decisions every session.
Less drift: Documentation stays synced with code automatically.
More reliable collaboration: AI suggestions align with your established patterns.
Scales beyond toys: Works on real codebases, not just weekend projects.
You stay in control: Architecture and decisions remain human-driven. AI handles the maintenance.
Documentation becomes the shared language between human and AI. Write it once, maintain it naturally, speak it everywhere.
Loco is our reference implementation - proving DDAID works entirely offline with local models:
.loco/ and travels with your repoNot vaporware. Not hype. Working code you can run today.
git clone https://github.com/billie-coop/loco go build && ./loco
See the GitHub repository for more details.
While DDAID is a philosophy that any AI tool could adopt, Loco is a specific exploration: What would a local AI coding assistant look like if we tried to replicate the sophisticated patterns of cloud AI providers, but entirely offline?
It's both a reference implementation of DDAID and a testbed for ideas about local AI orchestration. These are patterns that emerged from asking: "Without Anthropic's infrastructure, how can we still achieve powerful AI assistance?"
Instead of throwing one large model at everything, Loco uses a progressive approach:
Tier 1: Quick Overview (3B model, 2-3 seconds)
Rapid project structure understanding, language detection, framework identification.
Tier 2: Detailed Analysis (7B model, 30s-2min)
Parallel file-by-file analysis, importance scoring, dependency mapping.
Tier 3: Knowledge Synthesis (14B+ model, 2-5min)
Deep architectural understanding, pattern recognition, comprehensive documentation.
This mirrors how cloud providers likely handle different tasks with different models, but proves it's possible locally.
Rather than one model trying to understand everything, Loco explores using specialized models:
Each model does what it's best at. Together, they achieve more than any single model could.
Loco pioneered several efficiency techniques that make local AI practical:
These aren't just optimizations - they're explorations of how to make AI assistance sustainable without cloud resources.
"What if you could have Claude-quality assistance without sending your code anywhere?"
Loco experiments with:
We're actively exploring how to make local AI even more powerful:
We're implementing semantic search capabilities to give models better context awareness:
This lets smaller models access exactly the context they need, when they need it - making 7B models perform like much larger ones.
The next frontier we're exploring:
Imagine a model that not only understands your project's context but has actually learned from it - adapting to your team's conventions, architectural decisions, and coding patterns.
Cloud AI providers have massive infrastructure advantages. But Loco demonstrates that with clever orchestration, local models can achieve surprisingly sophisticated results. RAG makes small models smarter. LoRA will make them personal. Together, they challenge the assumption that powerful AI requires cloud infrastructure.
Every technique in Loco is an experiment: Can we replicate cloud-scale AI capabilities using only what runs on a developer's machine? With RAG operational and LoRA on the horizon, the answer is becoming a resounding yes.
Loco is open source and actively exploring new ideas:
These are just ideas being explored - not prescriptions. Fork it, modify it, take it in new directions. The goal is to push forward what's possible with local AI.
If you're interested in local AI, model orchestration, or just want AI assistance that respects your privacy and works offline, join us.
None of this would be possible without the incredible work of others:
DDAID and Loco exist as part of this larger movement toward better AI-assisted development. We're not trying to replace these tools - we're exploring one specific gap: how to make context management automatic and universal.
Thank you to everyone pushing this field forward. 🙏
AI is transforming the world at breathtaking speed. What was science fiction months ago is now daily reality. The ripple effects are already visible - in creative industries, in coding, in education. This isn't coming; it's here.
This technology carries both profound risks and incredible possibilities:
The risks are real:
But so are the possibilities:
When transformative technologies emerge, who controls them determines who benefits. The internet could have been purely corporate-controlled, but open protocols and free software created space for everyone. The same choice faces us with AI.
Building local, open source AI tools isn't just about software - it's about ensuring there are alternatives to a future where AI is something done to people rather than with them. Every open model, every local tool, every piece of community-built infrastructure is a small act of ensuring AI remains pluralistic.
There are many valid responses to AI's emergence - regulation, education, organizing, building. For those choosing to build, there's a particular opportunity: creating tools that put control back in users' hands. Not because it's the only way, but because it's one necessary piece of a larger ecosystem.
If you're concerned about AI's impact on your community, if you want tools you can trust and control, if you believe technology should amplify human agency rather than replace it - then projects like this exist for you. Not as the answer, but as one attempt among many to shape a better outcome.
The tools are here. The models are open. The future is being written now.
How we choose to build determines what that future looks like.