@Component({
selector: 'trw-target-editor-virtual-row',
host: {
'[attr.contenteditable]': 'true',
onbeforeinput: 'return false',
},
})
export class TargetVirtualEditorRowComponent {
keys$ = fromEvent<KeyboardEvent>(this.host, 'keydown').pipe(
concatMap((event) =>
isTextKey(event)
? defer(() => this.insertText(event))
: defer(() => this.handleSpecial(event)),
),
);
insertText(event: KeyboardEvent) {
event.preventDefault();
const char = typographicVersion(event.key, this.language, this.textBeforeCaret());
TextInputHandler.insertChar(this, char);
applyFrenchDiphthongRule(this);
this.undoRedo.record(this);
}
}for name, fn in parallel_nodes:
builder.add_node(name, fn)
builder.add_edge(START, name)
builder.add_edge(name, "assemble_context")
builder.add_edge("assemble_context", "llm_call")
builder.add_conditional_edges("llm_call", should_use_tools)
builder.add_edge("execute_tools", "mark_docs_processed")
builder.add_edge("mark_docs_processed", "llm_call")
def chat_json[T: BaseModel](system: str, user: str, schema: type[T]) -> T:
for _attempt in range(2):
try:
content = call_model(messages, schema)
content = _strip_think(content)
return schema.model_validate_json(content)
except ValidationError as exc:
messages.append({"role": "assistant", "content": content})
raise LLMError(...)select * into state_row from public.auction_states
where league_id = p_league_id for update;
if p_amount <= state_row.current_bid then
raise exception 'Bid must exceed current bid';
end if;
if p_amount > team_row.credits then
raise exception 'Insufficient credits';
end if;
update public.auction_states set
current_bid = p_amount, current_bidder_team_id = p_team_id,
status = 'bidding',
timer_expires_at = now() + make_interval(secs => league_row.timer_seconds),
version = version + 1
where league_id = p_league_id
returning * into state_row;
async function getUserCalendar({ from, to }, config) {
const days = (Date.parse(to) - Date.parse(from)) / 86_400_000 + 1;
if (days > MAX_DAYS) {
throw new Error(
`Range of ${days} days, maximum ${MAX_DAYS}. Split it into several calls.`,
);
}
const { calendar } = readEmployee(config.context.userId);
return calendar.filter((e) => e.from <= to && e.to >= from);
}
const words = new RBush<WordBox>();
words.load(overlays.map(({ element, word, suggestions }) => {
const r = element.getBoundingClientRect();
return { minX: r.left, minY: r.top, maxX: r.right, maxY: r.bottom, word, suggestions };
})); Now AI Engineering Lab — module 07, agents & orchestration › Luca Sargenti
Senior Product Engineer — Fullstack (Angular, Node.js, AWS)LLM, RAG & tool calling
I build products across the full stack: Angular frontends, React Native apps, Node.js microservices and serverless AWS backends. Lately I've been focused on the LLM layer of products: retrieval, agentic RAG, tool calling and MCP, and measuring what actually works.