fix: Update repository references

.github/CODEOWNERS

@@ -3,17 +3,17 @@
 # Order matters: the last matching pattern wins.
 
 # Default owner for everything in the repo.
-*                       @shifulegend
+*                       @sherwin-casem
 
 # Build + CI configuration.
-/Makefile               @shifulegend
-/CMakeLists.txt         @shifulegend
-/.github/               @shifulegend
+/Makefile               @sherwin-casem
+/CMakeLists.txt         @sherwin-casem
+/.github/               @sherwin-casem
 
 # Core engine hot paths (kernels, weight loading, transformer).
-/src/math/              @shifulegend
-/src/core/              @shifulegend
-/src/transformer/       @shifulegend
+/src/math/              @sherwin-casem
+/src/core/              @sherwin-casem
+/src/transformer/       @sherwin-casem
 
 # Project memory / AI-dev system (canonical docs).
-/docs/ai/               @shifulegend
+/docs/ai/               @sherwin-casem

.github/FUNDING.yml

@@ -1 +1 @@
-github: shifulegend
+github: sherwin-casem

.github/ISSUE_TEMPLATE/config.yml

@@ -4,11 +4,11 @@
 blank_issues_enabled: true
 contact_links:
   - name: 💬 Questions & Discussion
-    url: https://github.com/shifulegend/project-zero/discussions
+    url: https://github.com/sherwin-casem/project-zero/discussions
     about: Ask usage questions, propose ideas, or discuss architecture here.
   - name: 🏁 Community Benchmark Challenge
-    url: https://github.com/shifulegend/project-zero/discussions/3
+    url: https://github.com/sherwin-casem/project-zero/discussions/3
     about: Run the engine on your hardware and share your tok/s result.
   - name: 🔐 Report a Security Vulnerability
-    url: https://github.com/shifulegend/project-zero/security/advisories/new
+    url: https://github.com/sherwin-casem/project-zero/security/advisories/new
     about: Please report vulnerabilities privately — do not open a public issue.

.github/ROADMAP.md

@@ -116,7 +116,7 @@ Verified on [OpenBenchmarking.org](https://openbenchmarking.org/result/2606063-S
 | Phase | Feature | Status | Notes |
 |-------|---------|--------|-------|
 | **34** | GGUF reader — metadata, tokenizer, tensor loading | ✅ | DeepSeek-V2-Lite-Chat Q4_K_S runs correctly |
-| **34-MoE** | MoE expert weight scatter — contiguous access at inference time | 🆘 **BLOCKING** | 13× behind llama.cpp. Root cause: non-contiguous DRAM offsets. [Discussion #1](https://github.com/shifulegend/project-zero/discussions/1) |
+| **34-MoE** | MoE expert weight scatter — contiguous access at inference time | 🆘 **BLOCKING** | 13× behind llama.cpp. Root cause: non-contiguous DRAM offsets. [Discussion #1](https://github.com/sherwin-casem/project-zero/discussions/1) |
 | **35** | GGUF MLA+MoE architecture router — deepseek2 tensor mapping, stacked expert slicing | ❌ | Fully specified in `IMPLEMENTATION_PLAN.md §35` |
 | **37.1** | Q2_K dequant | ✅ | Needed for DeepSeek-V2-Lite-Chat-Q2_K |
 | **37.2** | Q3_K dequant | ❌ | 256 elems, 110 bytes/block |
@@ -172,7 +172,7 @@ The two highest-impact contributions that unblock all MoE-related phases:
 
 **Fix:** Repack expert weights at model load time so the top-K experts for common activation patterns are physically contiguous. llama.cpp does this in `llama_model_load_internal()`. The challenge: preserving Q4_K superblock boundaries during the repack.
 
-**Discussion + full profiling data:** [Discussion #1](https://github.com/shifulegend/project-zero/discussions/1)
+**Discussion + full profiling data:** [Discussion #1](https://github.com/sherwin-casem/project-zero/discussions/1)
 
 ### 2. Native Q4_K Matmul Kernel
 
@@ -184,7 +184,7 @@ The two highest-impact contributions that unblock all MoE-related phases:
 
 ## Community Benchmarks
 
-Run the engine on your hardware and add your result: [Discussion #3](https://github.com/shifulegend/project-zero/discussions/3)
+Run the engine on your hardware and add your result: [Discussion #3](https://github.com/sherwin-casem/project-zero/discussions/3)
 
 ---
 

CITATION.cff

@@ -9,10 +9,10 @@ abstract: >-
   tools, and RAG persistent memory. No GPU and no ML framework.
 type: software
 authors:
-  - alias: shifulegend
+  - alias: sherwin-casem
     name: "Project Zero Contributors"
 license: MIT
-repository-code: "https://github.com/shifulegend/project-zero"
+repository-code: "https://github.com/sherwin-casem/project-zero"
 keywords:
   - llm
   - inference

CONTRIBUTING.md

@@ -8,7 +8,7 @@ Thank you for your interest in contributing! Project Zero is a from-scratch CPU-
 
 1. **Read [`GOLDEN_RULES.md`](GOLDEN_RULES.md)** — mandatory for all contributors and AI agents. No hardcoding, test after every change, document every step.
 2. **Read [`DEVELOPER_ONBOARDING.md`](DEVELOPER_ONBOARDING.md)** — architecture overview, testing protocol, branching strategy.
-3. **Check open Discussions** — especially [Discussion #1](https://github.com/shifulegend/project-zero/discussions/1) (MoE help wanted) and [Discussion #2](https://github.com/shifulegend/project-zero/discussions/2) (phase roadmap).
+3. **Check open Discussions** — especially [Discussion #1](https://github.com/sherwin-casem/project-zero/discussions/1) (MoE help wanted) and [Discussion #2](https://github.com/sherwin-casem/project-zero/discussions/2) (phase roadmap).
 
 ---
 
@@ -60,8 +60,8 @@ The full phase roadmap is at [`.github/ROADMAP.md`](.github/ROADMAP.md). The two
 
 | Area | Current state | Target | Discussion | Skill needed |
 |---|---|---|---|---|
-| **MoE expert weight repacking** | DeepSeek MoE at ~1 tok/s — 86% L3 miss rate due to non-contiguous expert offsets in GGUF | ≥ 9 tok/s | [Discussion #1](https://github.com/shifulegend/project-zero/discussions/1) | C, GGUF Q4_K layout, memory layout |
-| **Native Q4_K matmul kernel** | Dense layers dequant Q4_K → FP32 before matmul | 4× speedup on DeepSeek dense layers | [Discussion #1](https://github.com/shifulegend/project-zero/discussions/1) | C, AVX-512 intrinsics |
+| **MoE expert weight repacking** | DeepSeek MoE at ~1 tok/s — 86% L3 miss rate due to non-contiguous expert offsets in GGUF | ≥ 9 tok/s | [Discussion #1](https://github.com/sherwin-casem/project-zero/discussions/1) | C, GGUF Q4_K layout, memory layout |
+| **Native Q4_K matmul kernel** | Dense layers dequant Q4_K → FP32 before matmul | 4× speedup on DeepSeek dense layers | [Discussion #1](https://github.com/sherwin-casem/project-zero/discussions/1) | C, AVX-512 intrinsics |
 
 See [`MOE_RESEARCH_AND_FIX_PLAN.md`](docs/architecture/MOE_RESEARCH_AND_FIX_PLAN.md) for 8 previous fix attempts (P1–P8) with profiling data — read this before starting on expert repacking.
 
@@ -112,7 +112,7 @@ These phases are completely spec'd in [`IMPLEMENTATION_PLAN.md`](docs/architectu
 
 ## 💬 Questions?
 
-Open a [Discussion](https://github.com/shifulegend/project-zero/discussions) — we prefer discussions over issues for questions.
+Open a [Discussion](https://github.com/sherwin-casem/project-zero/discussions) — we prefer discussions over issues for questions.
 
 ---
 

README.md

@@ -36,7 +36,7 @@ Project Zero runs [PrismML's Ternary-Bonsai-27B](https://huggingface.co/prism-ml
 Run it yourself — one binary, no Python (model download ~7.2 GB):
 
 ```bash
-git clone https://github.com/shifulegend/project-zero && cd project-zero && make release
+git clone https://github.com/sherwin-casem/project-zero && cd project-zero && make release
 curl -fL -o models/Ternary-Bonsai-27B-Q2_0.gguf \
   https://huggingface.co/prism-ml/Ternary-Bonsai-27B-gguf/resolve/main/Ternary-Bonsai-27B-Q2_0.gguf
 ./adaptive_ai_engine --model models/Ternary-Bonsai-27B-Q2_0.gguf \
@@ -160,7 +160,7 @@ All 16 screenshots (t=1..8 × 2 engines): [`benchmark_results/sweep_2026-06-21/s
 |---|---|
 | [![Xeon result](docs/openbenchmarking_xeon_vs_bitnetcpp.png)](https://openbenchmarking.org/result/2606207-SHIF-PROJECT42) | [![i5 result](docs/openbenchmarking_i5_vs_llamacpp.png)](https://openbenchmarking.org/result/2606208-SHIF-PROJECT03) |
 
-**Run it yourself and post your result:** [Discussion #3 — community benchmarks](https://github.com/shifulegend/project-zero/discussions/3)
+**Run it yourself and post your result:** [Discussion #3 — community benchmarks](https://github.com/sherwin-casem/project-zero/discussions/3)
 
 ### Community Baselines & Known Bottlenecks
 
@@ -356,7 +356,7 @@ Full RCA, methodology, and status: [`docs/ai/mistakes.md`](docs/ai/mistakes.md)
 **Option A — pre-built binary (Linux x86-64, no compiler needed):**
 
 ```bash
-wget https://github.com/shifulegend/project-zero/releases/download/v0.1.0/adaptive_ai_engine-0.1.0-x86_64-linux.tar.gz
+wget https://github.com/sherwin-casem/project-zero/releases/download/v0.1.0/adaptive_ai_engine-0.1.0-x86_64-linux.tar.gz
 tar xf adaptive_ai_engine-0.1.0-x86_64-linux.tar.gz
 ./adaptive_ai_engine --model models/bitnet-b1.58-2B-4T.bin \
   --tokenizer models/bitnet-b1.58-2B-4T_tokenizer_proper.bin \
@@ -366,7 +366,7 @@ tar xf adaptive_ai_engine-0.1.0-x86_64-linux.tar.gz
 **Option B — build from source (60 seconds):**
 
 ```bash
-git clone https://github.com/shifulegend/project-zero.git
+git clone https://github.com/sherwin-casem/project-zero.git
 cd project-zero
 make demo   # builds engine + downloads SmolLM2-135M + runs a test prompt
 ```
@@ -383,7 +383,7 @@ No GPU. No Python at runtime. No API key. GCC or Clang + `make` + `curl` — not
 
 > If you like my work or if it helped you, buy me a coffee or A100😉
 > 
-> [![Sponsor Nitika](https://img.shields.io/badge/Sponsor-%E2%9D%A4-red)](https://github.com/sponsors/shifulegend)
+> [![Sponsor Nitika](https://img.shields.io/badge/Sponsor-%E2%9D%A4-red)](https://github.com/sponsors/sherwin-casem)
 
 ## Help Wanted
 
@@ -393,10 +393,10 @@ Two open problems where outside expertise would make a real difference:
 |---|---|---|
 | **MoE expert weight repacking** | DeepSeek-V2-Lite runs at 1.90 tok/s — 7× behind `llama.cpp`. Top-K expert weights sit at non-contiguous GGUF offsets: **~86% L3 cache miss rate per token**. Fix: repack selected expert weights into contiguous memory at load time, matching llama.cpp's interleaved layout. | ≥ 9 tok/s |
 | **Native Q4_K matmul kernel** | Current dense-model path dequants Q4_K → F32 before multiply. A fused mixed-precision kernel would close the remaining gap to `llama.cpp` on dense 4-bit GGUF models. | — |
-| **Re-benchmark classifier INT8/INT4 throughput post-quality-fix** | `matmul_i4_task`/`matmul_i8_task` (`src/math/parallel_matmul.c`) were rewritten 2026-07-31 to fix a real quantization-quality gap (one scale per row → one scale per 32-element block with error-minimizing search — see `docs/ai/decision-log.md`, [GitHub issue #27](https://github.com/shifulegend/project-zero/issues/27)). That rewrite dropped the VNNI `dpbusds`/VBMI fast paths (their row-wide bias-correction trick doesn't hold once every block has its own scale) in favor of a portable per-block FMA path — the specific SSE-interleave-unpack throughput issue this row used to describe no longer applies to the current code, but the *net* throughput effect of the whole rewrite hasn't been re-measured against the historical sweep above. | Confirm INT8/INT4 throughput post-fix; a genuine multi-row/multi-block SIMD kernel (not full VNNI dpbusds, which the new per-block scaling structurally can't reuse) could recover some of the dropped throughput without regressing quality |
+| **Re-benchmark classifier INT8/INT4 throughput post-quality-fix** | `matmul_i4_task`/`matmul_i8_task` (`src/math/parallel_matmul.c`) were rewritten 2026-07-31 to fix a real quantization-quality gap (one scale per row → one scale per 32-element block with error-minimizing search — see `docs/ai/decision-log.md`, [GitHub issue #27](https://github.com/sherwin-casem/project-zero/issues/27)). That rewrite dropped the VNNI `dpbusds`/VBMI fast paths (their row-wide bias-correction trick doesn't hold once every block has its own scale) in favor of a portable per-block FMA path — the specific SSE-interleave-unpack throughput issue this row used to describe no longer applies to the current code, but the *net* throughput effect of the whole rewrite hasn't been re-measured against the historical sweep above. | Confirm INT8/INT4 throughput post-fix; a genuine multi-row/multi-block SIMD kernel (not full VNNI dpbusds, which the new per-block scaling structurally can't reuse) could recover some of the dropped throughput without regressing quality |
 
 Existing SIMD work documented in [`docs/KERNEL_INTERNALS.md`](docs/KERNEL_INTERNALS.md).
-MoE repacking thread: [Discussion #1](https://github.com/shifulegend/project-zero/discussions/1)
+MoE repacking thread: [Discussion #1](https://github.com/sherwin-casem/project-zero/discussions/1)
 
 ---
 

SECURITY.md

@@ -11,7 +11,7 @@
 
 **Please do NOT open a public GitHub issue for security vulnerabilities.**
 
-To report a security vulnerability, please open a **[GitHub Security Advisory](https://github.com/shifulegend/project-zero/security/advisories/new)** (private disclosure).
+To report a security vulnerability, please open a **[GitHub Security Advisory](https://github.com/sherwin-casem/project-zero/security/advisories/new)** (private disclosure).
 
 Include:
 - Description of the vulnerability

benchmark_results/pr31_moe_threading_test_2026-07-22/README.md

@@ -1,6 +1,6 @@
 # PR #31 verification — `--moe-threading=rowsplit` vs `=legacy`
 
-**Date:** 2026-07-22 · **PR under test:** [#31](https://github.com/shifulegend/project-zero/pull/31) (`Amlaach/project-zero@fba3225`)
+**Date:** 2026-07-22 · **PR under test:** [#31](https://github.com/sherwin-casem/project-zero/pull/31) (`Amlaach/project-zero@fba3225`)
 
 ## Setup
 

docs/GROWTH_STRATEGY.md

@@ -29,7 +29,7 @@ Project Zero is a pure-C CPU LLM engine. On an Intel Xeon (2.10 GHz, AVX-512 VNN
 the same binary hits 40.4 tok/s on BitNet b1.58-2B-4T and 142 tok/s on SmolLM2-135M.
 
 Run it on YOUR CPU and drop your numbers here:
-    git clone https://github.com/shifulegend/project-zero && cd project-zero && make demo
+    git clone https://github.com/sherwin-casem/project-zero && cd project-zero && make demo
 
 Please include: CPU model, core/thread count, RAM type, the `[gen] … tok/s` line,
 and the auto-detected SIMD backend. I'll keep a leaderboard table in this thread.

docs/KERNEL_INTERNALS.md

@@ -155,7 +155,7 @@ At model load, sort and interleave expert weights so that any set of top-K activ
 
 The challenge with Q4_K: each "superblock" in Q4_K quantization contains 256 weights with a shared scale+offset. Repacking must preserve superblock boundaries or recompute scales. See `ggml_type_size(GGML_TYPE_Q4_K)` and the `block_q4_K` struct in ggml-quants.h for the layout.
 
-**Discussion with more context:** https://github.com/shifulegend/project-zero/discussions/1
+**Discussion with more context:** https://github.com/sherwin-casem/project-zero/discussions/1
 
 If you've worked on ggml Q4_K layout or llama.cpp's expert repacking, your input there would be extremely valuable.
 

docs/ai/change-trace.md

@@ -4,7 +4,7 @@
 > Update after each meaningful sub-step. Last updated: 2026-08-04.
 
 ### 2026-08-04 — DeepSeek MoE benchmark run: project-zero vs colibri vs llama.cpp
-- What: user asked to run DeepSeek MoE on project-zero, colibri (`shifulegend/colibri`), and
+- What: user asked to run DeepSeek MoE on project-zero, colibri (`sherwin-casem/colibri`), and
   llama.cpp and report results. Found colibri has no DeepSeek-arch loader (only GLM-5.2/Inkling/
   Kimi K3/OLMoE, one engine per family — DeepSeek referenced only as prior-art in `colibri.c`)
   and project-zero has no OLMoE-arch loader (`gguf_loader.c`'s MoE dispatch only special-cases

docs/reports/PR21_K6_LUT_KERNEL_REVIEW.md

@@ -1,6 +1,6 @@
 # PR #21 Review: K-6 LUT Ternary Kernel — Bug Fix & Benchmark Report
 
-**PR:** [shifulegend/project-zero#21](https://github.com/shifulegend/project-zero/pull/21) — adds a new
+**PR:** [sherwin-casem/project-zero#21](https://github.com/sherwin-casem/project-zero/pull/21) — adds a new
 5-trit-per-byte ("K-6 LUT") AVX-512BW ternary matmul kernel for BitNet-style models.
 **Reviewed:** 2026-07-04 · **Fix commit:** `8a4905f` on `bench/lut-k6-20260627`
 **Test rig:** this session's sandbox — Intel Xeon Scalable (cloud vCPU), 4 cores, AVX-512 VNNI + AVX-512BW (see hardware table below).

docs/reports/SECURITY_FINDINGS.md

@@ -1,6 +1,6 @@
 # Security and Functional Test Report: project-zero
 
-**Repository:** https://github.com/shifulegend/project-zero.git
+**Repository:** https://github.com/sherwin-casem/project-zero.git
 **Assessment Date:** 2026-03-12 (updated)
 **Branch assessed:** `master`
 **HEAD commit:** See latest commit on branch

docs/reports/qa_report.md

@@ -5,7 +5,7 @@
 ---
 
 **Prepared by:** Independent Principal QA Architect / Senior Software Security Auditor  
-**Repository:** https://github.com/shifulegend/project-zero  
+**Repository:** https://github.com/sherwin-casem/project-zero  
 **Language / Runtime:** Pure C99, POSIX + Win32 abstraction, zero external runtime dependencies  
 **Assessed Phases:** 0–9 (Phases 10+ planned but not implemented)  
 **Policy:** Zero-Trust — all tests designed from first principles; no existing test artefacts consulted

docs/reports/test_execution_report.md

@@ -1,6 +1,6 @@
 # Test Execution Report — project-zero
 **Status:** [TO FIX] — Unmitigated risks identified  
-**Repository:** https://github.com/shifulegend/project-zero  
+**Repository:** https://github.com/sherwin-casem/project-zero  
 **Engine:** CPU LLM Ternary Inference Engine (C99)  
 **Execution Date:** March 13, 2026  
 **Policy:** Zero-Trust — all tests written from scratch, existing tests treated as untrusted  
@@ -293,4 +293,4 @@ It is registered in CMake and runs as test #19 under `ctest`.
 ---
 
 *End of Report — Prepared under Zero-Trust policy. Total: 3,367 tests executed, 3,367 passed, 0 failed, 0 ASAN/UBSAN violations.*  
-*Source: https://github.com/shifulegend/project-zero*
+*Source: https://github.com/sherwin-casem/project-zero*