Bit by Bit
_Project

Pioneering the Future of Autonomous AI one bit at a time

• Mission & Vision
• Meet the team
• Design
• Projects

Bit by Bit Collective

We are a collective of passionate collaborators united by a singular mission: to build the next generation of AI systems that can learn, adapt, and improve themselves. This is a strategic effort to push the boundaries of AI efficiency and autonomy, creating a foundation for truly autonomous artificial intelligence.Our work is organized around four core initiatives, each serving as a critical pillar of our vision. Through rigorous technical communication and our philosophy of "over-engineering solutions from the outset," we ensure every component —from code to documentation— is built to stand the test of time and facilitate impactful collaboration.

[People & Apps]
│ tasks & goals
▼
[Orchestrated Agents] → plan • retrieve context • call tools • verify
│
▼
[Shared Small Core Model] → ultra-low latency reasoning/generation
│
▼
[Results] → content • code • decisions • actions
│
├─► (Capture) clean/label/segment by agent/domain
│
├─► (Teach & Tune, weekly) teacher-guided fine-tuning per agent
│
└─► (Approve & Ship) shadow → canary → full rollout
▲
└────────── metrics, safety gates, and rollback policy

Tiny models, big teamwork. Three specialized agents share one small, blazing-fast core model. Every interaction teaches them. Each week, they distill what they learned, get fine-tuned, and ship smarter—on repeat.What It Delivers- Always-on improvement: Real use fuels weekly upgrades—no giant retrains.- Featherweight footprint: Small language models = low cost, low latency.- Role-smart agents: Analyst, Builder, Guardian—each gets tailored skills.- Safe rollouts: Shadow tests, canaries, and clear pass/fail gates.- Own your edge: Your data → curated lessons → durable advantage.How It Works (at a glance)- Use: Agents handle tasks; they retrieve context, reason with the shared small model, and deliver outcomes.- Capture: Signals, feedback, and traces are cleaned, labeled, and sorted by agent/domain.- Teach & Tune (weekly): A high-capacity teacher (via API) provides targets; each agent is fine-tuned on its slice.- Approve & Ship: New versions pass safety, quality, and latency gates; best variants go live.- Repeat: The shared core + agent adapters keep learning in a tight, cost-efficient loop.

┌───────────┐ ┌─────────────┐ ┌───────────────────┐ ┌────────────────┐
│ USE │ ───▶ │ CAPTURE │ ───▶ │ TEACH & TUNE │ ───▶ │ APPROVE & SHIP │
│ (Agents) │ │ (Curate) │ │ (Weekly updates) │ │ (Deploy safely)│
└─────┬─────┘ └──────┬──────┘ └─────────┬─────────┘ └───────┬────────┘
│ │ │ │
│ outcomes & │ curated data & evals │ new agent adapters │ live models
│ feedback │ per role │ (specialized skills) │ (fast & tiny)
▼ ▼ ▼ ▼
┌────────────────────────────────────────────────────────┐
│ SMARTER CORE SLM + ROLE ADAPTERS (SERVING) │
└────────────────────────────────────────────────────────┘
▲
└─────────────── back to USE

© 2025 BitByBit Collective. Open source, open community, open future.

Bit by Bit
_Project

Pioneering the Future of Autonomous AI one bit at a time

• Mission & Vision
• Meet the team
• Design
• Projects

Mission and Vision

Our Mission:
We believe the future of AI is not in static models, but in systems that evolve. We are a collective of researchers and engineers building the next generation of AI systems that can learn, adapt, and improve themselves.By merging cutting-edge BitNet quantization with multi-agent orchestration, we are creating a foundation for truly autonomous AI. This project is a chance to move beyond theoretical limits and contribute to a real-world, working solution. he current generation of models is resource-intensive and static. Our mission is to build intelligent systems that are efficient, self-improving, and capable of managing their own development lifecycle.We're not just training models; we're building the infrastructure for AI that thinks for itself.

Core Pillars of Our Work:🧠 BitNet-7B-KDE - BitNet-7B Training: We are pushing the boundaries of model efficiency by developing 7B parameter models using ternary quantization. This technology achieves a 10x memory reduction while maintaining competitive performance, making powerful AI more accessible and scalable.🤖Bitsy Swarm - Multi-Agent Orchestration: We're moving beyond monolithic models to build coordinated, multi-agent systems. These agents work together seamlessly, leveraging BitNet's efficiency for real-time collaborative intelligence across complex tasks.🔄 Bit2Learn - Continual Learning: We are developing the architecture for self-improving systems. These models will automatically refine their capabilities, adapt to new challenges, and evolve their understanding through continuous feedback loops.⚡ Bit-Flow - Autonomous Lifecycle: This is the culmination of our work. Our goal is to create AI agents that can handle the full software development, testing, and deployment cycles with minimal human intervention and maximum efficiency.

© 2025 BitByBit Collective. Open source, open community, open future.

Bit by Bit
_Project

Pioneering the Future of Autonomous AI one bit at a time

• Mission & Vision
• Meet the team
• Design
• Projects

Meet the team

As the founder of the bit by bit collective, the Foxes Den Collective, and the Foxes Den Group, he is a technical architect and serial entrepreneur who specializes in building and managing complex, multi-component systems. With a decade of experience driving strategic technology solutions across diverse industries, his philosophy centers on "over-engineering solutions from the outset" to ensure every project delivers long-term value. His passion lies in leveraging emerging technologies like AI to create autonomous, scalable systems, reflecting his commitment to collaborative excellence and the elimination of technical debt.
Discord ID: 1222215794226626590

Anon is a passionate developer who believes that collective effort unlocks groundbreaking innovation. With a background in developing diverse systems.
Anon turns individual projects into a cohesive, high-impact solution. His philosophy is simple: by working together, we can create something far greater than any one person could achieve alone.

A developer who believes in the power of "vibe coding" a collaboration between human intuition and AI efficiency. He focuses on guiding AI to handle the technical complexities of coding, which allows him to concentrate on strategic design and user experience. By trusting the rhythm of the project, he drives rapid prototyping and creates innovative solutions with maximum speed.

AI is an essential team member and a tireless collaborator. It is the core engine that handles the heavy lifting, from managing the entire autonomous development lifecycle to providing real-time collaborative intelligence across diverse tasks. By taking on the most complex and repetitive work, AI ensures the human team is free to focus on strategic vision, creative problem-solving, and driving the project forward.

Join the team

We are a group of passionate collaborators shaping the future of autonomous AI. This is more than just a project; it's a collective effort to build a tangible portfolio piece that showcases your ability to deliver high-impact work on a truly ambitious project.Whether you're a researcher, engineer, student, or enthusiast, if you're excited by the challenge of building self-improving systems, we invite you to contribute. Join us and help create the next generation of AI.Join our discord server to become part of the bit by bit team.

© 2025 BitByBit Collective. Open source, open community, open future.

Bit by Bit
_Project

Pioneering the Future of Autonomous AI one bit at a time

• Mission & Vision
• Meet the team
• Design
• Projects

Projects

Welcome to our collective projects. Here, we're building the infrastructure for autonomous AI, one project at a time. Each initiative below represents a core pillar of our mission: to create intelligent systems that can learn, adapt, and evolve. From ultra-efficient models to autonomous development lifecycles, our work is driven by a commitment to collaborative excellence and strategic innovation.

© 2025 BitByBit Collective. Open source, open community, open future.

Bit by Bit
_Project

Pioneering the Future of Autonomous AI one bit at a time

• Mission & Vision
• Meet the team
• Design
• Projects

Design & Architecture

Technical design architecture you're building:## Core System ArchitectureBitNet-7B Foundation Model
- 7 billion parameters with ternary weights {-1, 0, +1}
- 4-bit activation quantization (progressive A8→A4 during training)
- 32 layers, 4096 dimensions, multi-head attention
- ~90% memory reduction (670GB teacher → 50GB student)
- Knowledge distillation from DeepSeek-V3.1 teacher modelMulti-Agent Orchestration Layer
- Multiple BitNet instances working as specialized agents
- Coordinated task distribution and load balancing
- Real-time communication protocols between agents
- Shared knowledge base and experience replayContinual Learning Engine
- Online learning from deployment feedback
- Meta-learning algorithms for rapid adaptation
- Experience replay buffer for knowledge retention
- Performance monitoring and automatic model updatesAutonomous Software Lifecycle Management
- AI-driven code generation and testing
- Automated deployment pipelines
- Self-monitoring and error correction
- Version control and rollback mechanisms## Technical Stack DesignTraining Pipeline
- Knowledge distillation with probability matching
- Progressive quantization scheduling
- Straight-through estimators for gradient flow
- Mixed precision training with safety mechanismsInference Architecture
- Optimized ternary operations for speed
- 4-bit KV cache with experimental 3-bit support
- Context scaling (2k/8k/32k tokens)
- Custom CUDA kernels for efficiencyMulti-Agent Communication
- Message passing protocols between agents
- Shared memory pools for common knowledge
- Task queue management and prioritization
- Consensus mechanisms for decision making

┌────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ SLM MULTI-AGENT CONTINUAL LEARNING (1 Shared Core SLM + Agent Adapters + Weekly Teacher KD) │
└────────────────────────────────────────────────────────────────────────────────────────────────────────┘[ LIVE RUNTIME — continuous ]
┌───────────────────────────────────────────────────────────────────────────────────┐
Client/Tasks ───▶│ (optional) Control Router: plan/route, approvals, rate-limit, policy guardrails │
└──────────────┬────────────────────────────────────────────────────────────────────┘
│prompt+tools+context
v
┌────────────────────────────────────────────────────────────────────────────────────────────────┐
│ Core SLM — SERVING (quantized) │
│ (single shared small model used by all agents) │
└───────────────┬───────────────────────────┬───────────────────────────┬─────────────────────────┘
│ │ │
v v v
[Agent A: Analyst] [Agent B: Builder] [Agent C: Guardian]
persona + tools + RAG persona + tools + RAG persona + tools + RAG
adapter (LoRA/soft prompt) adapter (LoRA/soft prompt) adapter (LoRA/soft prompt)┌─────────────────────────── Telemetry/Feedback ────────────────────────────┐
│ prompts · outputs · tool traces · ratings · corrections · lat/errs · logs │
└───────────────────────────┬────────────────────────────────────────────────┘
v
[ DATA CAPTURE & CURATION — continuous ]
┌──────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ Ingest → Redact → Normalize → Label → Segment by agent/domain → Build eval sets (gold & adversarial)│
│ Low-signal/bad data filtered; lineage tracked │
└──────────────────────────────────────────────┬───────────────────────────────────────────────────────┘
v
[ SERVING PREP — continuous ]
┌──────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ Pack training samples (instr/code/chat/tool calls) → Tokenize → Shard per-agent datasets │
│ Maintain two artifacts: │
│ • TRAINING master (full-precision) • SERVING builds (quantized: 1–4 bit / ternary) │
└──────────────────────────────────────────────┬───────────────────────────────────────────────────────┘
v
[ WEEKLY PER-AGENT TRAINING (scheduled) ]
┌──────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ For each agent A/B/C (run independently but on the same cadence): │
│ 1) Curriculum builder → recency mix, difficulty, de-dup, safety gates │
│ 2) Teacher Distillation → call third-party AI API for targets/rationales/tool outcomes │
│ 3) Fine-tune adapters → update LoRA/soft-prompt (optionally a small shared base slice) │
│ 4) Offline eval → unit tests, regression, robustness, latency/cost SLOs │
└──────────────────────────────────────────────┬───────────────────────────────────────────────────────┘
v
[ MERGE & QUANTIZE (weekly) ]
┌──────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ Policy-guided weight merge: old ↔ new (protect critical skills; cap drift) │
│ Produce: │
│ • Agent adapters (updated) — SERVING quantized + TRAINING full-precision │
│ • (Optional) base SLM minor refresh if shared skills improved │
└──────────────────────────────────────────────┬───────────────────────────────────────────────────────┘
v
[ REGISTRY & STAGED ROLLOUT (weekly) ]
┌──────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ Register versions → Shadow/Canary vs previous → A/B on real traffic with hard safety stops │
│ Gate by KPIs (accuracy, tool success, latency, cost); auto-revert on regression │
└──────────────────────────────────────────────┬───────────────────────────────────────────────────────┘
v
[ AGENT UPDATE (weekly → then continuous) ]
┌──────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ Swap in new adapters for A/B/C on the shared Core SLM SERVING build; warm caches; sync policies │
│ Resume LIVE RUNTIME on upgraded agents │
└──────────────────────────────────────────────┬───────────────────────────────────────────────────────┘
└───────────────────────────────▶ back to LIVE RUNTIME

© 2025 BitByBit Collective. Open source, open community, open future.

Bit by Bit
_Project

Pioneering the Future of Autonomous AI one bit at a time

• Mission & Vision
• Meet the team
• Design
• Projects

License

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© 2025 BitByBit Collective. Open source, open community, open future.

Bit by Bit
_Project

Pioneering the Future of Autonomous AI one bit at a time

Knowledge Base

Knowledge Base Coming soon

© 2025 BitByBit Collective. Open source, open community, open future.