Business ideas for 2026, backed by real demand.

Develop embedded telemetry firmware to collect high-frequency system metrics and stream data to host memory with minimal impact on performance, delivered through standard software upgrade mechanisms Build automated firmware build, test, and release infrastructure, and support manufacturing test integration with production-ready images

GitHub Stacked PRs Your approach is the most correct use of Git. Split work into pretty units of backwards-compatible work, PR one part, get it merged and then present the next PR. Unfortunately, this discipline is rare and the age of AI pull requests accelerates the decay. I am reviewing giant PRs where it is difficult (due to scope or personal skills) to split the work into meaningful separate PRs. This is where GitHub PR stacking will improve the life of the reviewer; The committer can split the workload into multiple logic pieces of smaller PRs but require that the PRs are merged all at once.

The Senior Manager, Information Technology Solution Architect is responsible for defining, governing, and evolving the organization’s IT architecture, technology strategy, cybersecurity capability, security risk and compliance alignment, design governance, project delivery assurance, operational resilience, and lifecycle management. The role provides senior technology leadership across enterprise platforms, infrastructure, cloud services, cybersecurity, integration, data, applications, operations, and technology operating models, ensuring that technology investments are secure, scalable, resilient, supportable, cost-effective, and aligned with business priorities, enterprise architecture principles, and long-term organizational strategy.

Lecture: Building a GPU Compiler From Scratch. Lowering PyTorch Graphs to CUDA via Multi-Level IRs (GPU_MODE video + article) [](https://preview.redd.it/lecture-building-a-gpu-compiler-from-scratch-lowering-v0-f7fplshp53vh1.png?width=1400&format=png&auto=webp&s=c5a63bb1ebbdfd868168e95163d1b4fe3af7463d) https://preview.redd.it/3ekngxk224vh1.png?width=1080&format=png&auto=webp&s=9bfe5c92033e17d5b2fb4529f1320c99455a138b I built a GPU compiler and wrote an article about it, focusing on concepts shared by modern compilers like T[](https://github.com/cloudrift-ai/emmy)VM, Modular, and PyTorch Inductor. The compiler works with vLLM for full end-to-end model serving, so it may be useful if you're interested in building LLM serving engines. The article breaks CUDA kernel generation into a sequence of passes and shows how a common operation (RMSNorm, Matmul, Attention) is progressively lowered to generate a CUDA kernel. Each pass introduces its own IR that is optimized for specific tasks like kernel fusion or schedule enumeration. https://preview.redd.it/tm16quo324vh1.png?width=1080&format=png&auto=webp&s=4435d5b7f7aacd90de2841731597ae77f2ecbca7 [](https://preview.redd.it/lecture-building-a-gpu-compiler-from-scratch-lowering-v0-b9wty7nb43vh1.png?width=1498&format=png&auto=webp&s=1bfe01922c8e88b32fd9cd1106c527b56ef2289c) Each example is reproducible, so you can write PyTorch code and generate code at a specific IR level, which makes the flow easier to understand. % emmy compile -c 'nn.Linear(3, 2, bias=False)(torch.randn(4, 3))' --ir loop for a0 in 0..4         for a1 in 0..2             for a2 in 0..3                 in0 = load input[a0, a2] in1 = load linear_wt[a2, a1] v0 = multiply(in0, in1) acc0 <- add(acc0, v0) linear[a0, a1] = acc0 On the benchmarked workloads, many outperform Torch Inductor and cuBLAS. Generated kernels use modern features like tensor cores and TMA. https://preview.redd.i...