const agent = await spawn('comway-ai');
// connecting to neural network
if (model.ready) {
deploy('production');
return ok();
}
agent.listen('ws://comway.ai');
await train(dataset);
// 99.7% accuracy
checkpoint.save();
gradient.step();
loss = 0.0341;
export default agent;
// neural inference
weights.load();
for (i of tokens) {
embed(i);
attention.apply();
}
softmax(logits);
return response;
async function classify() {
const y = model(x);
return y.argmax();
}
// loss converging
while (epoch < 100) {
optimize();
}
router.post('/api/agent', async (req) => {
const reply = await agent.chat(req.body);
return reply;
});
const tools = ['search', 'code', 'plan'];
agent.register(tools);
db.migrate();
// build pipeline ok
deploy.go('comway.ai');
import { LLM } from 'comway/ai';
const llm = new LLM({
model: 'opus-4.7',
temp: 0.2,
});
await llm.init();
// agent online
session.start();
respond(input);
const out = trace.end();
metrics.push(out);
function build() {
bundle();
optimize();
deploy();
}
build();
// 100% test pass
return { ok: true };
agent.monitor();
// uptime 99.97%
model.load('./weights.bin');
inference.run(input);
// confidence 0.992
detection.classes();
async bbox(img) {
return yolo(img);
}
trace.flush();
vector = embed(text);
similarity = cosine(a, b);
if (sim > 0.85) match;
// rag pipeline
chunks.filter(top_k);
prompt = build(chunks);
response = llm(prompt);
