chadbrewbaker

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

// Raw dog Interaction Calculus demo in C
// Demonstrates superpositions (SUP), lambdas, apps, global scoping via env
// Uses function pointers for reduction rules and term kinds

typedef enum {
    TERM_VAR,

chadbrewbaker

./build/bin/llama-server \
  -m ~/models/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf \
  -ngl 99 \
-c 262144 \
  -np 1 \
  -fa on \
--jinja --reasoning-format deepseek \
  --cache-type-k q4_0 \
  --cache-type-v q4_0 \
  --host 0.0.0.0

chadbrewbaker

76     420680387                                             THE TRUSTEES OF GRINNELL COLLEGE      $1,086,636,902.00
227    421143702                                             IOWA STATE UNIVERSITY FOUNDATION        $292,636,627.00
491    420698295                                                         MERCY MEDICAL CENTER         $97,889,322.00
555    844628214                                University of Iowa Strategic Initiatives Fund         $82,026,731.00
637    426139033                                          COMMUNITY FDN OF GREATER DES MOINES         $66,046,696.00
711    420680460                                                             DRAKE UNIVERSITY         $53,492,350.00
760    420796760                                          STATE UNIVERSITY OF IOWA FOUNDATION         $48,287,270.00
771    510233180                                       MERCY HOSPITALCEDAR RAPIDSIA ENDOWMENT         $47,162,540.00
815    420703280                                                        ST AMBROSE UNIVE

chadbrewbaker

         ORG_EIN                                                                  ORG_NAME_L1 SF_01_FRGN_REG_TOT_EXP
0      980571483                                                             NOVO HOLDINGS AS     $23,648,391,120.00
1      941156365                                          THE BOARD OF TRUSTEES OF THE LELAND     $18,850,601,940.00
2      941105628                                                  KAISER FOUNDATION HOSPITALS     $12,144,993,009.00
3      210634501                                         The Trustees of Princeton University     $11,728,687,974.00
4      350868188                                              University of Notre Dame du Lac     $11,239,145,760.00
5      980593375                                                                GAVI ALLIANCE     $10,845,619,280.00
6      590735717                                              Howard Hughes Medical Institute     $10,526,015,441.00
7       60646973                                                              Yale Unive

chadbrewbaker

Yes — this is exactly what llama-server's slot system (+ your queue dispatcher) was built for. You can run one llama-server instance on a single GGUF, expose several independent message queues, and give each queue explicit time slices that are long enough to amortize the cost of flushing the KV cache for a fresh context window. Model weights stay hot in VRAM/CPU the whole time; only per-slot KV caches are flushed/recomputed when you want a truly fresh start.Why this works (no cache thrashing)Slots = isolated KV caches: Start the server with -np N (N = number of queues + 1–2 buffer). Each slot gets its own KV cache. The server automatically assigns requests to slots (or you pin with "slot_id": X in the JSON payload). Model weights (the GGUF) are loaded once and stay cached.  
Fresh context = intentional flush: In every request from a queue, include "cache_prompt": false. This forces the server to discard/recompute the KV cache for that request (fresh prefill, no reuse from prior work in the slot).  
No thrashi

chadbrewbaker

from __future__ import annotations
import sys, argparse, typing, re, unicodedata, json, uuid, time, functools, itertools
from dataclasses import dataclass
from tinygrad import Tensor, nn, UOp, TinyJit, getenv, function
from tinygrad.uop.ops import resolve
from tinygrad.helpers import partition, DEBUG, Timing, GlobalCounters, stderr_log, colored, Context
from tinygrad.viz.serve import TCPServerWithReuse, HTTPRequestHandler

class SimpleTokenizer:
  def __init__(self, normal_tokens:dict[str, int], special_tokens:dict[str, int], preset:str="llama3"):

chadbrewbaker

llama-server \
  --model "/Volumes/Lexar/ollama_models/Qwen3.5-0.8B-Claude-4.6-Opus-Reasoning-Distilled.gguf" \
  --n-gpu-layers 99 \
  --ctx-size 24576 \
  --flash-attn on \
  --mlock \
  --port 8080

chadbrewbaker

Sub FixTabularData()
    Dim rng As Range
    Dim cell As Range
    
    ' Only act on the selected cells
    Set rng = Selection

    On Error Resume Next
    ' Optimization: Only process cells that contain numbers
    ' This prevents the macro from ruining your text headers

chadbrewbaker

# vibe code of https://arxiv.org/pdf/2511.02960 via Google Gemeni 3 flash preview
import numpy as np

class EpsilonRobustGallerySolver:
    def __init__(self, vertices, epsilon=0.1):
        self.vertices = [np.array(v, dtype=float) for v in vertices]
        self.n = len(vertices)
        self.epsilon = epsilon
        self.edges = [(self.vertices[i], self.vertices[(i+1)%self.n]) for i in range(self.n)]
        self.lengths = [np.linalg.norm(e[1]-e[0]) for e in self.edges]

chadbrewbaker

#include <IOKit/IOKitLib.h>
#include <CoreFoundation/CoreFoundation.h>
#include <stdio.h>
#include <stdint.h>
#include <mach/mach_port.h>

typedef struct {
    uint32_t spaceType;
    uint32_t bitWidth;
    uint64_t options;