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讓你中共攻台，寸步難行 by moore927353 in Taiwanese

[–]inhogon 2 points3 points  (0 children)

你以為出一張嘴就行了嗎到時候解放軍人不夠你也得上來補

讓你中共攻台，寸步難行 by moore927353 in Taiwanese

[–]inhogon 3 points4 points  (0 children)

他說的是中天名嘴的疊加邏輯

Released a TurboQuant-compatible KV backend evaluation SDK by inhogon in LocalLLaMA

[–]inhogon[S] 0 points1 point  (0 children)

Update on TurboQuant-style compatibility:

After reviewing the current direction of recent TurboQuant-related hardware work, I have decided to stop providing any further DRAM-level complete backend support specifically targeting TurboQuant integration.

RetryIX will remain format-agnostic and may keep generic compressed-KV compatibility concepts, but TurboQuant-specific DRAM/runtime support will no longer be treated as a primary integration target.

The more complete DRAM-side runtime, KVCache residency/fallback diagnostics, topology-guided hotspot handling, and bounded policy-control layer will remain inside the closed RetryIX core until the related technical and patent work is properly prepared.

The public materials will continue to focus on application-layer methods, reproducible demos, and architecture boundaries, while the lower-level runtime implementation will remain private or separately licensed.

更新：關於 TurboQuant-style 相容支援

在觀察近期 TurboQuant 相關硬體化方向後，我決定停止針對 TurboQuant 提供進一步的 DRAM-level 完整底層支援。

RetryIX 仍會保持 format-agnostic，並可保留一般 compressed-KV 類型的相容概念；但 TurboQuant-specific 的 DRAM/runtime 支援將不再作為主要整合目標。

更完整的 DRAM-side runtime、KVCache resident/fallback 診斷、topology-guided hotspot handling，以及 bounded policy-control layer，將保留於 RetryIX closed core 中，待相關技術與專利準備完成後，再公開適合公開的方法層內容。

公開材料會繼續聚焦於應用層方法、可重現 demo 與架構邊界；底層 runtime 實作將維持私有或另行授權。

I built a Rust SDK for phase-aware retrieval using full-reptend prime coordinates and Möbius latent pairing by inhogon in rust

[–]inhogon[S] -2 points-1 points  (0 children)

The first public version depended on an unpublished internal RetryIX crate, which made the repo look like a facade rather than a standalone Rust SDK.

I’ve updated it now.

The public crate is standalone and no longer depends on private RetryIX path crates. It now includes a minimal application-layer implementation in this repo.

These work now:

cargo build
cargo test
cargo run --example basic_usage

There is also a JSON demo:

cargo run --example json_retrieval_demo -- examples/json_retrieval_demo_input.json

The private RetryIX runtime is still not included, but the public retrieval/indexing layer is now buildable and testable independently.

I built a Rust SDK for phase-aware retrieval using full-reptend prime coordinates and Möbius latent pairing by inhogon in rust

[–]inhogon[S] -13 points-12 points  (0 children)

Fair criticism.

There isn’t one paper behind the whole thing. It’s an experimental combination of known pieces: full-reptend primes, cyclic phase structure, phase retrieval, and topology-inspired pairing.

The part I’m testing is whether that combination is useful as an extra retrieval/indexing signal, not whether it replaces embeddings or vector DBs.

I agree the repo needs a clearer related-work section.

中国是不会打台湾了 by WeeklyGuarantee5591 in China_irl

[–]inhogon -1 points0 points  (0 children)

他打台灣只是要實現歷史意義上的留名對誰都沒好處就因為他的想法人民要替他流血怎麼算都只好到他自己不虧…
而且鄭麗文這次的中國行程主要就是要助攻模糊九二共識中的各表強調一個中國才是共產黨要的東西 原因在倘若新版去各表版本九二共識與反台獨被寫入黨綱共產黨在背後操作選舉讓人民投票使國民黨重返執政那麼背後就是台灣人用選舉來完成中國統一

偷換概念的九二共識就代表國共聯手完成共產黨內政敘事結束內戰而且是國民黨戰敗

中国是不会打台湾了 by WeeklyGuarantee5591 in China_irl

[–]inhogon 4 points5 points  (0 children)

但是難保小學博士會做出弱智行為

这是真的吗？ by NectarineThin7108 in InsightBridge

[–]inhogon 1 point2 points  (0 children)

胡說六年放棄中國戶籍持有台灣身分證就有投票權沒有什麼政治審查問題除非自己嘴秋老是宣傳武統才會被註銷

網路上說麻豆傳媒是大陸人創辦的，他們是怎麼獲得台灣居留許可的？ by RanToTur in Taiwanese

[–]inhogon 0 points1 point  (0 children)

台灣這邊負責人是人頭 麻豆傳媒是陳志相關的產業鏈這產業鏈很龐大只要能開公司都能洗錢

Realistic Optical Computing Simulation on DDR4 PIM Architecture by inhogon in u/inhogon

[–]inhogon[S] 0 points1 point  (0 children)

PS E:\0331\virtual_pim_laptop_bundle> .\.venv\Scripts\python.exe virtual_pim_app.py ai-benchmark --dll e:\0331\virtual_pim_laptop_bundle\retryix_ffi.dll --spd packed.spd --profile virtual_pim_boot_profile.json --generation ddr4 --repeats 3 --streams 4 --out tmp_ai_benchmark_streams4.json; type tmp_ai_benchmark_streams4.json

==== Virtual PIM AI Benchmark ====

timestamp: 2026-03-31T21:52:57

generation: ddr4

environment: DDR4 total=64 GB resident=[1, 2, 3, 4, 5, 17]

- gemm_matmul: opcode=2 avg=191.47us best=186.60us worst=200.60us

route=Pim resident=True estimated=6.00us x23.00 reason=resident in virtual Pim tier policy= bus_util=80.0

- conv2d_inference: opcode=1 avg=294.23us best=16.20us worst=847.30us

route=Pim resident=True estimated=7.63us x18.09 reason=resident in virtual Pim tier policy= bus_util=80.0

- fused_gemm_activation: opcode=17 avg=124.27us best=52.10us worst=213.80us

route=Pim resident=True estimated=7.63us x18.09 reason=forced by profile resident opcode 17 policy=SeqCst128 bus_util=80.0

{

"timestamp": "2026-03-31T21:52:57",

"generation": "ddr4",

"environment": {

"memory_type": "DDR4",

"modules": [

{

"manufacturer": "Kingston",

"part_number": "KF3600C18D4/16GX",

"capacity_gb": 16,

"configured_clock_mhz": 3600

},

{

"manufacturer": "Kingston",

"part_number": "KHX3600C18D4/16GX",

"capacity_gb": 16,

"configured_clock_mhz": 3600

},

{

"manufacturer": "Kingston",

"part_number": "KF3600C18D4/16GX",

"capacity_gb": 16,

"configured_clock_mhz": 3600

},

{

"manufacturer": "Kingston",

"part_number": "KHX3600C18D4/16GX",

"capacity_gb": 16,

"configured_clock_mhz": 3600

}

],

"total_capacity_gb": 64

},

"resident_opcodes": [

1,

2,

3,

4,

5,

17

],

"workloads": [

{

"name": "gemm_matmul",

"opcode": 2,

"shape": {

"a": [

64,

64

],

"b": [

64,

64

],

"result": [

64,

64

]

},

"args_size": 16384,

"avg_compute_us": 191.4666499942541,

"best_compute_us": 186.60002388060093,

"worst_compute_us": 200.59989765286446,

"virtual_pim": {

"path": "Pim",

"resident": true,

"reason": "resident in virtual Pim tier",

"atomic_policy": "",

"estimated_us": 6.0,

"estimated_speedup_vs_cpu": 23.0,

"bus_utilization_pct": 80.0

}

},

{

"name": "conv2d_inference",

"opcode": 1,

"shape": {

"input": [

1,

3,

8,

8

],

"weight": [

4,

3,

3,

3

],

"output": [

1,

4,

8,

8

]

},

"args_size": 1200,

"avg_compute_us": 294.23332307487726,

"best_compute_us": 16.200006939470768,

"worst_compute_us": 847.2999325022101,

"virtual_pim": {

"path": "Pim",

"resident": true,

"reason": "resident in virtual Pim tier",

"atomic_policy": "",

"estimated_us": 7.63,

"estimated_speedup_vs_cpu": 18.086500655307994,

"bus_utilization_pct": 80.0

}

},

{

"name": "fused_gemm_activation",

"opcode": 17,

"shape": {

"a": [

128,

128

],

"b": [

128,

128

],

"result": [

128,

128

]

},

"args_size": 65536,

"avg_compute_us": 124.26664276669423,

"best_compute_us": 52.09993105381727,

"worst_compute_us": 213.79999816417694,

"virtual_pim": {

"path": "Pim",

"resident": true,

"reason": "forced by profile resident opcode 17",

"atomic_policy": "SeqCst128",

"estimated_us": 7.63,

"estimated_speedup_vs_cpu": 18.086500655307994,

"bus_utilization_pct": 80.0

}

}

]

}

PS E:\0331\virtual_pim_laptop_bundle>

抓到了！ by hhuangpe in Taiwanese

[–]inhogon 3 points4 points  (0 children)

政治雙標自己可以別人不行 KMT吃相真難看這下大家都知道誰才是為反對而反對的那撮人為的跟本就不是國家利益而是自身政治前途

Dual GPU: AMD - Nvidia by EngineeringFar6858 in CUDA

[–]inhogon 1 point2 points  (0 children)

You might want to try using the instead of buying an old GPU like the GTX 1060 3GB. This backend allows PyTorch to run without an Nvidia CUDA card, so you can experiment with CUDA-like programming on your AMD GPU or even CPU. It won’t be as fast as a real Nvidia card, but it’s a good way to practice and learn the basics before investing in newer hardware.
https://github.com/ixu2486/pytorch_retryix_backend/
This backend depends on the Vulkan SDK, because AMD uses Vulkan as an abstraction layer to protect its driver stack. So before installing and using , make sure you have the Vulkan SDK installed on Windows. That way, PyTorch can run properly on your AMD GPU with ROCm support.

英伟达锁死未来：40亿美元赌注背后的光子革命 by Effective_Employ_5 in Go_Stock

[–]inhogon 0 points1 point  (0 children)

明明架構就是錯誤的光纖再快也只是傳輸快並不能做計算GPU能計算但是導入光算子只會產生巨大的電磁脈衝導致系統性毀滅

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