Audit
Technical audit
We inspect the crisis first — then write findings and a fix path. Boards below map the assets we actually work: terminal coder agents, LLM / llama.cpp latent structure, transformers, DiT and diffusion generation, tensors, ASIC spectrogram, encode/decode, and hardware superstructure. Bond Network mesh lives on the home Systems section.
Terminal coder agents
We optimize the agent shell itself — memory management, rule management, skill management, and the terminal coder path — so coding agents run tight instead of thrashing context.
Latent structure
For LLMs, code models, and local stacks such as llama.cpp / Llama class models: keep latent and data layouts addressable so the model is not fed a raw dump.
Transformer optimization
Transformer stacks: attention path, layer budget, and mid-span signal — tune how the model spends compute without publishing the recipe.
DiT and diffusion
Diffusion Transformer and diffusion paths for text, image, video, music, and voice generation — step locks, settle quality, modality routing.
Tensor optimization
Tensor layouts and packs shaped for the silicon you actually run — less waste between model math and device memory.
Spectrogram on ASIC
Spectral processing for voice and signal work on ASIC paths — band lanes read before transform, not one blind pipe.
Encode / decode
Custom encode and decode software for ASIC and render devices — move work onto the silicon path that owns it.
Hardware and superstructure
Hardware optimization for CPU, GPU, NPU, ASIC, and extreme render stacks — affinity, host links, and the superstructure that holds the run together.
Deep attention
Stronger selection than a wide pass so mid-prompt instructions survive — used across LLM and agent workloads.
Prompt optimization
Extra prompt layers on the model path — select, order, bound, lock — so local and remote models take the same disciplined steer.
Speculative decode across genetics
Speculative decode style — with a twist. Draft models do not have to share a family. A SAP path lets unrelated small models propose, then the large path verifies. Same idea as speculative decoding; different genetics on the draft side.
Shard dissolve decode
Shard the large model and open multi-channel lanes. Small-model words stream in as fragments and dissolve into the large body — continuous fuse, not a single draft pass.
Fee and timing
$100 USD (ETH accepted via support).
Turnaround typically 48 hours to one week, sooner when schedule allows.
What you receive
- Written findings in plain language
- A proposed board at engagement level
- A separate quote for build work