mammadmaheri7/TeleSparseRepo

TeleSparseRepo is a research-oriented prototype addressing the primary bottleneck of Zero-Knowledge Machine Learning (ZKML): the massive computational cost of generating proofs for dense neural network inferences. By leveraging sparsity, the project attempts to minimize the number of constraints in the Halo2 circuit. While the conceptual combination is sound, the project has zero velocity, very few stars (4), and minimal forks, suggesting it is a stagnant academic or personal experiment rather than a living tool. It faces extreme competition from well-funded and highly active ZKML frameworks like EZKL, Giza, and Modulus Labs, which are developing more generalized and optimized toolchains for model conversion and proving. The defensibility is low because the core innovation (sparsity for ZKML) is a known research direction that is being integrated into more robust, production-grade stacks. A technical investor would view this as a legacy reference implementation rather than a viable project.