Turn This Idea Into A Clear Plan
Start with the big idea, then make it easier to act on. This view helps you name the direction, keep the scope small, break the work into smaller parts, and say how you will know it worked.
This creates plan cards only. You can review them first, then start work from the plan you want to use.
Direction
Clarify the first real-world outcome PTX carriers up the ML stack on RTX (FFN/attention/block/llama) + kernel-graph runtime + Android SPIR-V should create and the person it helps first.
Scope
Keep the first version tight: one walkable flow, one visible result, and only the minimum support needed. Extend the proven form-ptx lane (matvec f32/f16/bf16 + affine-train, four-way verdict 63, bit-exact on RTX via driver JIT -O0) up the layer stack to reach Metal parity and enable full models on all t…
Smaller parts
Split the idea into promise, flow, and proof so each plan stays small enough to build and check.
Proof of success
A human can understand the value, walk the flow, and answer this open question next: How can we improve this idea, show whether it is working yet, and make that proof clearer over time?
Plans you place here carry the name you choose.
You can shape the preview below now. To place a plan, first be welcomed: choose a name. You will return here, and the plans will carry your name.
PTX carriers up the ML stack on RTX (FFN/attention/block/llama) + kernel-graph runtime + Android SPIR-V: promise and audience
Lock the first audience and promise for PTX carriers up the ML stack on RTX (FFN/attention/block/llama) + kernel-graph runtime + Android SPIR-V so the first version says something concrete.
Define who this helps first, what tension it resolves, and what the first visible result should be. Extend the proven form-ptx lane (matvec f32/f16/bf16 + affine-train, four-way verdict 63, bit-exact on RTX via driver JIT -O0) up the layer stack to reach Metal parity and enable full models on all three backends. Three…
How you will know it worked: A new user can tell who this is for, why it matters, and where to start without extra explanation.
Be welcomed to place this planExpected impact 28.0 | Work size 10.0 PTX carriers up the ML stack on RTX (FFN/attention/block/llama) + kernel-graph runtime + Android SPIR-V: end-to-end human flow
Turn PTX carriers up the ML stack on RTX (FFN/attention/block/llama) + kernel-graph runtime + Android SPIR-V into one walkable flow instead of a loose collection of internal actions.
Map the creation, update, review, and follow-up loop in plain language. Use this open question as a design constraint: How can we improve this idea, show whether it is working yet, and make that proof clearer over time?
How you will know it worked: Someone can complete the core journey locally without needing hidden IDs, inside language, or hard-to-find navigation.
Be welcomed to place this planExpected impact 32.0 | Work size 18.0 PTX carriers up the ML stack on RTX (FFN/attention/block/llama) + kernel-graph runtime + Android SPIR-V: measurement, trust, and follow-up
Make progress for PTX carriers up the ML stack on RTX (FFN/attention/block/llama) + kernel-graph runtime + Android SPIR-V credible by showing what changed, what is blocked, and what should happen next.
Define the status signals, proof markers, human review points, and follow-up rules that keep the first version trustworthy.
How you will know it worked: The experience makes value movement, blockers, and next actions visible without extra technical interpretation.
Be welcomed to place this planExpected impact 20.0 | Work size 12.0