100 palabras de inglés tech: glosario para devs, founders y product

deploy

publicar código a producción

“We deploy every Friday at 4pm”

ship

lanzar / entregar feature

“Let's ship it and iterate later”

refactor

reescribir código sin cambiar funcionalidad

“I need to refactor the auth module”

tech debt

deuda técnica

“We're drowning in tech debt”

scope creep

expansión no planificada del scope

“This sprint had massive scope creep”

code review (CR)

revisión de código por par

“Can you review my PR by EOD?”

PR (pull request)

petición de merge

“I opened a PR for the bug fix”

merge / rebase

fusionar / re-aplicar commits

“Rebase your branch on main”

hotfix

fix urgente en prod

“We need a hotfix for the checkout bug”

rollback

revertir deploy

“The deploy broke prod — rollback now”

monitoring / observability

vigilancia de sistemas

“Set up Datadog for observability”

SLA / SLO / SLI

service level agreement/objective/indicator

“Our SLA is 99.9% uptime”

uptime / downtime

tiempo activo / inactivo

“We had 12 min downtime last night”

endpoint

URL de API

“The /users endpoint returns 500”

payload

datos enviados en request

“The payload is too large”

rate limit

límite de peticiones

“We're hitting Stripe's rate limit”

PRD (product requirements doc)

documento de requisitos

“Did you read the PRD?”

MVP (minimum viable product)

producto mínimo viable

“Ship the MVP next month”

user story

historia de usuario (formato As a… I want… so that…)

“Write 5 user stories for onboarding”

north star metric

métrica principal

“Our north star is weekly active users”

GTM (go-to-market)

estrategia de lanzamiento al mercado

“What's your GTM for Q3?”

PMF (product-market fit)

encaje producto-mercado

“We're not at PMF yet”

stakeholder

persona con interés en el proyecto

“Align with stakeholders first”

deliverable

entregable

“What are the deliverables for this sprint?”

scope / out of scope

alcance / fuera del alcance

“Mobile is out of scope for v1”

roadmap

ruta de productos a futuro

“Update the roadmap with the new features”

discovery

investigación previa a build

“We're still in discovery phase”

user research

investigación de usuarios

“Run user research before designing”

pain point

punto de dolor del usuario

“Slow checkout is their biggest pain point”

feature flag

interruptor para activar/desactivar feature

“Roll out behind a feature flag”

runway

meses de cash que quedan

“We have 14 months of runway”

burn rate

cash que gastás por mes

“Our burn is $80k/month”

MRR / ARR

monthly/annual recurring revenue

“We just hit $1M ARR”

churn

% de clientes que se van

“Churn jumped to 8% last month”

CAC / LTV

customer acquisition cost / lifetime value

“Our LTV:CAC ratio is 4:1”

seed / Series A/B/C

rondas de inversión

“We're raising a Series A”

term sheet

documento preliminar de inversión

“Sequoia sent us a term sheet”

cap table

tabla de propiedad accionaria

“Show me the cap table”

dilution

dilución de propiedad por nueva ronda

“Founders will dilute 15% this round”

exit

venta o IPO

“What's your exit strategy?”

founder-led sales

ventas hechas por founders

“Founder-led sales is critical pre-Series A”

moat

ventaja competitiva sostenida

“Our data is our moat”

standup / daily

reunión diaria corta

“Standup is at 9:30”

sprint

ciclo de 1-2 semanas de trabajo

“This sprint we ship onboarding”

retro (retrospective)

revisión al fin del sprint

“What went well in retro?”

backlog

lista de tareas pendientes

“Groom the backlog Friday”

story points

estimación de esfuerzo

“This is 5 story points”

blocker

obstáculo que para tu trabajo

“My blocker is the API spec”

ticket / issue

tarea en Jira/Linear

“Open a ticket for that bug”

LLM (large language model)

modelo de lenguaje grande

“Which LLM should we use?”

prompt / prompting

instrucción al modelo

“Iterate on the prompt”

fine-tuning

ajuste de modelo con datos propios

“We fine-tuned GPT on our data”

RAG (retrieval-augmented generation)

LLM + búsqueda de contexto

“Build a RAG pipeline with Pinecone”

embedding

representación vectorial de texto

“Generate embeddings with OpenAI”

hallucination

modelo inventa información

“The model hallucinated the citations”

context window

tokens que el modelo puede procesar

“Claude has a 200k context window”

inference

ejecución del modelo en producción

“Inference costs are killing us”