Was looking to speed up coding and reduce token usage. Theory is an LLM can provide a plan to a deterministic local coder faster and with less token use than writing the code itself, and so far that s panned out. Revisions and teaching the coder new local skills can take time and tokens in the short run, but Sif remembers the new skills and doesn t have to be taught those skills again. Name: Sif 1.0 License: Apache 2.0 Developed and tested on Windows. Most of my testing involved converting Python to C++, though I have experimented with general coding tasks. Python to C++ conversion has worked well so far. Takes about 250-300 tokens for a frontier model, 400 to 500 tokens for a flash open source model to produce the plan. If Sif has experienced all requirements before, can produce one shot conversion with no repairs needed. Several test/benchmark reports can be found in the repository highlighting successes and failures along the way if interested. For those who are interested, I would greatly appreciate any feedback offered.