Sam Musoni

@ray.remote(num_gpus=1)
class LLMAgentState:
    def __init__(self, model_id: str):
        # Initialize high-performance quantization
        self.model = AutoModelForCausalLM.from_pretrained(
            model_id, device_map="auto", load_in_8bit=True
        )
        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
        
    async def stream(self, prompt: str) -> AsyncGenerator:
        inputs = self.tokenizer(prompt).to("cuda")
        streamer = TextIteratorStreamer(self.tokenizer)
        
        thread = Thread(target=self.model.generate, kwargs=dict(inputs, streamer=streamer))
        thread.start()
        
        for new_text in streamer:
            yield {"token": new_text, "ts": time.time()}

export default function InteractiveDashboard() {
    const [metricData, setMetricData] = useState<TimeSeriesData[]>([]);
    const socketRef = useRef<WebSocket | null>(null);

    useEffect(() => {
        socketRef.current = new WebSocket('wss://api.aether-hub.io/v1/stream');
        
        socketRef.current.onmessage = (event) => {
            const payload = JSON.parse(event.data);
            setMetricData(prev => {
                const history = prev.length > 500 ? prev.slice(1) : prev;
                return [...history, { val: payload.throughput, t: Date.now() }];
            });
        };
        return () => socketRef.current?.close();
    }, []);

    return (
        <div className="grid grid-cols-12 gap-6 bg-zinc-950">
            <Suspense fallback={<DashboardSkeleton />}>
                <AnalyticsChart data={metricData} />
            </Suspense>
        </div>
    );
}

#include <pthread.h>
#include <sys/mman.h>

#define MAX_THREADS 16
#define PAGE_SIZE 4096

typedef struct {
    void* memory_pool;
    size_t allocation_size;
    pthread_mutex_t lock;
} ThreadSafeAllocator;

void* fast_alloc(ThreadSafeAllocator* m_pool, size_t bytes) {
    pthread_mutex_lock(&m_pool->lock);
    
    // Align memory to page boundaries for optimal L1 cache hits
    size_t aligned = (bytes + PAGE_SIZE - 1) & ~(PAGE_SIZE - 1);
    void* ptr = mmap(NULL, aligned, PROT_READ | PROT_WRITE, 
                     MAP_PRIVATE | MAP_ANONYMOUS, -1, 0);
                     
    m_pool->allocation_size += aligned;
    pthread_mutex_unlock(&m_pool->lock);
    
    return ptr;
}

// The Alpha Core Agent: Natural Language Controller
class AlphaAgent {
    constructor() {
        this.knowledgeBase = {
            "hello": "Greetings, user. System is fully operational.",
            "status": "All subsystems nominal. Memory at 12% capacity.",
            "help": "I am a rudimentary AI. Try saying 'hello', 'status', or 'who are you'.",
            "who are you": "I am an autonomous agent deployed on this portfolio."
            // extended vocabulary omitted for brevity...
        };
    }
    
    process(input) {
        let match = Object.keys(this.knowledgeBase).find(k => input.toLowerCase().includes(k));
        return match ? this.knowledgeBase[match] : "Processing error: Command not recognized.";
    }
}