Kimingi_27

Automation logic🪄
Machine learning,
Geometry beats calculus ( manifold projection)
AI enthusiast (vibe coder)🧙🏽‍♂️
civil engineering technician 🚧

[Kimingi_27 @Kimingi_]

🔗 https://t.co/z4fxdyxLxY
📄 DOI: 10.5281/zenodo.20036883 @karpathy @ylecun @huggingface @Modular @GergelyOrosz @bcherny @ZENODO_ORG

[Kimingi_27 @Kimingi_]

@_lennoxomondi Hiyo soda ujue ni 350😂😂maziwa ni 200 na indomie 150. Kula at your own risk. Iko mahali nilikula snacks time ya kucheck out naona ati iko kwa bill😭

[Kimingi_27 @Kimingi_]

@wayne_gakuo How much does it cost?

[Kimingi_27 @Kimingi_]

@Goddie_Ke Mi nangoja email tuanze kazi july

[Kimingi_27 @Kimingi_]

@Kelvinmwendwa__ And apparently, trailers are free to exceed. They said they don't handle lorries. That's a different department

[Kimingi_27 @Kimingi_]

@Kelvinmwendwa__ They got me a while ago for going 62 on a 50

[Kimingi_27 @Kimingi_]

@Coopbankenya Tuko mwezi wa tatu😂kamechapa aki..si mniwekee za sabuni🙏🏽

[Kimingi_27 @Kimingi_]

. https://t.co/WF9JmWHkeF https://t.co/EnmeuwNS8y

[Kimingi_27 @Kimingi_]

@claudeai @OpenAI @karpathy @Modular @huggingface @Microsoft @ylecun @ycombinator @GergelyOrosz @elonmusk @nvidia @NVIDIAGeForce @IBMResearch

[Kimingi_27 @Kimingi_]

RAG at 1M entries on 4GB: killed by the OS.
GDE at 1M entries on 4GB: 0.073ms.
Clone it. Run it. Break it.
All benchmarks reproducible. MIT licensed. DOI archived.
https://t.co/z4fxdyxLxY

[Kimingi_27 @Kimingi_]

The same word produces the same byte address on every machine.
neural0 → 94,111,959,646
Verified on 4GB and 16GB independently. Identical.
signal vs sensor: 10.32 geometric distance
orbit vs planet: 56.11
The coordinates know what words mean. Hash functions don't.

[Kimingi_27 @Kimingi_]

I BEAT FAISS 10X WITH NO GPU ON 4GB RAM! 🧵..
FAISS HNSW is fast. 0.188ms per query at 1M entries.
The GDE Mojo pipeline on a 4GB laptop in Nairobi: 0.073ms.
No GPU. No approximation. No scanning. 1,000,000 queries. O(1). Every time. FAISS is approximate. GDE is exact.

[Kimingi_27 @Kimingi_]

@claudeai @OpenAI @karpathy @Modular @huggingface @Microsoft @ylecun @ycombinator @GergelyOrosz @elonmusk @nvidia @NVIDIAGeForce @IBMResearch

[Kimingi_27 @Kimingi_]

I BEAT FAISS 10X WITH NO GPU ON 4GB RAM! 🧵..
FAISS HNSW is fast. 0.188ms per query at 1M entries.
The GDE Mojo pipeline on a 4GB laptop in Nairobi: 0.073ms.
No GPU. No approximation. No scanning. 1,000,000 queries. O(1). Every time. FAISS is approximate. GDE is exact.

[Kimingi_27 @Kimingi_]

RAG at 1M entries on 4GB: killed by the OS.
GDE at 1M entries on 4GB: 0.073ms.
Clone it. Run it. Break it.
All benchmarks reproducible. MIT licensed. DOI archived.
https://t.co/z4fxdyxLxY

[Kimingi_27 @Kimingi_]

The same word produces the same byte address on every machine.
neural0 → 94,111,959,646
Verified on 4GB and 16GB independently. Identical.
signal vs sensor: 10.32 geometric distance
orbit vs planet: 56.11
The coordinates know what words mean. Hash functions don't.

[Kimingi_27 @Kimingi_]

@Krepublics2_ You should see trump in South park

[Kimingi_27 @Kimingi_]

RAG fails on constrained hardware. GDE thrives on it.
The benchmark code is open. Run it yourself.
🔗 https://t.co/z4fxdyxLxY
📄 DOI: 10.5281/zenodo.20036883
📊 README has full results tables

[Kimingi_27 @Kimingi_]

4GB and 16GB — same code, same 100GB address space. Both returned bit-identical results. A at byte 0. Z at byte 107,374,182,399. The geometry holds across different hardware architectures.
This is not a faster search engine. It is a different class of system entirely.

[Kimingi_27 @Kimingi_]

RAG didn't slow down. The OS killed it. Out of memory. Hardware termination. It physically cannot complete the task.
GDE on the same 4GB machine: 0.019 seconds. 800,000 entries. Zero collisions.
We then ran both machines simultaneously.

[Kimingi_27 @Kimingi_]

GDE at 1M entries: 0.355ms per query. Unchanged from the 10K result.
That is 19,491x faster. Not 345x anymore. The ratio grows because RAG is O(n) and GDE is O(1). The bigger the knowledge base, the worse RAG looks.
Then we ran it on the 4GB laptop.