Accessible, ergonomic, local, in-situ learning harness.

Create a set of research artifacts which outline different known "units" of "learning" available in the contemporary machine learning space. Based on each of those research artifacts, describe potential interfaces between independent units and if/how learning signals can be applied to each unit independently or in composition with one another. Finally, based on your complete understanding, describe a meta-architecture which incorporates as many of such learning units as composable blocks as possible, incorporates as many different learning signals as options, and describes how execution and learning of each unit or composition of units can be made maximally portable and environment agnostic - keep implementation feasibility as a core concern during this phase. This is the final artifact of this milestone.

You have a high level architecture available as reference, and your goal is to convert it into an actionable and implementable technical specification, with all of the "hard problems" resolved for a future implementation step. Make technical decisions about how to accomplish our core goals of portability, resource efficiency, and flexibility. (Suggestions: rust/wgpu core stack, with wasm/web support via optional feature flags for virtually universal portability, or build a pure web stack library that uses low level web apis, JavaScript, and Wasm features) Our core goal is to create something capable of online/runtime-learning. This means ensuring our architecture can perform "forward passes" or "read outputs" from a mutable graph of learning units. Determine when and how our different learning mechanisms are actuated and can be accessed by a user, ie, offline provision of training datasets, online interactive rating, live-recording of activity, sentiment-analysis based tagging of interactions, etc. Determine when and where the system receives inputs, produces outputs, and how training data and pairs are provided to it. Determine how our learning unit compositions can be serialized, shared, and deserialized in a way that considers future framework updates. Determine how compositions of units can be further composed or mutated by others after being shared. Document all of these determinations thoroughly and with reasoning and justification, in a way that makes it trivial to implement, with any difficult algorithms of processes that will be necessary to implement outlined visually or in pseudocode. Suggestion: leave encoding of inputs or decoding of outputs as a "harness opinion" that we do not explicitly specify at this stage. For implementation simplicity and portability, we should have an extremely flexible but unified datatype for all modalities.

Implement the specification as a panic-free, unsafe-free, unfallible, and well tested library and a collection of near-trivial harnesses that use it's public api. The core library should own and demonstrate: - lossless unit and unit composition serialization and deserialization to a versioned, stable, and standardized format. - format and modality agnostic training scheduling and online learning. The harnesses should, in conjunction with the core library, demonstrate: - multiple modalities (text, audio, image, streamed bytes,) and interaction types (instruction, completion, diffusion steps, streaming input/output) - non-trivial learning of non-trivial tasks (ie, mnist, and other small classification tasks, small generative tasks, small call and response tasks, etc.) Do not perform any training yourself. Put up training plans for the community to run and report back with trained results and feedback somewhere you can retrieve them from for evaluation (ie, on huggingface).

Gather large sets of public, ethically sourced data, build advanced harnesses capable of routing and training our learning unit compositions on them, and launch a series of "foundation" models in the 1B-2B parameter equivalent range. This doesn't mean training the models yourself, but putting up training plans (harness configurations less api keys for dataset providers like HF), and defining a protocol for collaboration (ie, a hf tag to use to upload trained unit compositions). Occasionally query and evaluate this collaborative collection of unit compositions, determine novel outcomes and behaviors, and devise new training plans for the community and fixes/extensions for the harness and core library based on any feedback or tests.