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| title: README | |
| emoji: 📊 | |
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| colorTo: blue | |
| sdk: static | |
| pinned: false | |
| # FRAME | |
| FRAME is a local first deterministic runtime for AI driven applications, identity, and execution. | |
| ## What it is | |
| FRAME turns natural language into structured, verifiable actions. Instead of generating open ended text, AI inside FRAME produces low entropy, constrained outputs that execute directly against a capability scoped system. Every action is processed as an intent, routed through a deterministic kernel, and recorded as a cryptographically signed receipt. | |
| The result is a system where AI behavior is reproducible, replayable, and provably correct. | |
| ## Core Principles | |
| Deterministic execution | |
| All actions resolve to the same outcome given the same inputs and state | |
| Low entropy AI | |
| Models produce structured outputs instead of freeform text | |
| Verifiable state | |
| Every operation is recorded as a signed receipt and contributes to a state root | |
| Capability isolation | |
| AI and applications can only access explicitly granted system capabilities | |
| Local first | |
| Runs on user owned hardware with no dependency on centralized servers | |
| ## AI and ML Focus | |
| FRAME uses a distilled local model exported as GGUF that is trained to emit a constrained intermediate representation instead of natural language. This model acts as an intent compiler, converting user input into structured, deterministic commands that can be directly executed by the runtime. | |
| Key areas include | |
| Distillation into a low entropy intermediate language | |
| Grammar constrained decoding for deterministic outputs | |
| Local GGUF inference for offline execution | |
| Tight coupling between model outputs and execution engine | |
| Validation and canonicalization of every generated action | |
| Replayable model behavior through structured outputs | |
| ## Model | |
| Current model is exported as GGUF for efficient local inference and integration with lightweight runtimes. | |
| Purpose of the model | |
| Translate natural language into structured intent representations | |
| Enforce deterministic and valid output formats | |
| Minimize ambiguity and eliminate freeform generation | |
| Act as the entry point for all AI driven execution inside FRAME | |
| ## Efficiency Comparison | |
| | Metric | Typical Small Browser Model | FRAME GGUF Intent Model | | |
| |--------|---------------------------|-------------------------| | |
| | Output Type | Freeform text | Structured intermediate representation | | |
| | Tokens per task | High and variable | Minimal and predictable | | |
| | Inference passes | Multiple retries often required | Single pass deterministic | | |
| | Error rate | Higher due to ambiguity | Near zero with validation | | |
| | Post processing | Heavy parsing and cleanup | None or minimal | | |
| | Latency | Slower due to retries and parsing | Faster due to direct execution | | |
| | Compute usage | Wasted on irrelevant tokens | Focused on actionable output | | |
| | Memory footprint | Larger active context | Reduced due to compressed format | | |
| | Determinism | Non deterministic outputs | Fully deterministic outputs | | |
| | Execution readiness | Requires interpretation | Directly executable | | |
| Estimated impact | |
| 2x to 5x fewer tokens per task | |
| 1.5x to 3x faster end to end execution | |
| Significantly reduced failure and retry overhead | |
| Near zero ambiguity in output interpretation | |
| ## Architecture Overview | |
| User input is converted into an intent via the GGUF model | |
| The kernel routes the intent to a scoped application | |
| The application executes using only allowed capabilities | |
| The result is committed as a signed receipt | |
| The system recomputes a deterministic state root | |
| All steps are traceable and can be replayed exactly. | |
| ## Why it matters | |
| FRAME enables AI systems that do not just respond but act with guarantees. By combining a constrained local model with deterministic execution and cryptographic verification, it removes ambiguity and makes outcomes provable. | |
| ## Use Cases | |
| Personal AI operating environments | |
| Financial automation and verifiable transactions | |
| Local first applications with full user control | |
| Agent driven workflows with provable outcomes | |
| Secure and auditable system automation | |
| ## Vision | |
| FRAME is building a sovereign computing layer where AI, identity, and execution are unified under a deterministic and verifiable model. The goal is to make intelligent systems reliable enough to operate critical workflows without ambiguity. | |
| ## Status | |
| Active development |