今日已更新 158 条资讯 | 累计 37407 条内容
关于我们

标签：#Git

找到 1710 篇相关文章

AI 资讯

Hands-on: dedicated Lumpcode daemon

Lumpcode is a git-first loop manager : a small CLI that runs long agent campaigns over your own repo, in reviewable slices. Git is the gate (one PR at a time) and the source of truth (what is left is read from remote history, not a distant database). You describe the campaign once, merge what is good, and the next tick continues with the rest. A lump is one campaign under .lumpcode/lumps/<name>/ . Each context is one isolated unit of work: one branch, one PR. You can run a tick by hand, or leave a daemon on a machine that stays on. This article is that dedicated-daemon path. You author on your laptop. A second clone, that you do not develop in, runs the scheduler. When a lump lands on the primary branch, the worker picks it up. The argument for why loops should plug into git is Codemods grew a brain. Our tooling didn't. . 1. Requirements The dedicated clone is a checkout you do not develop in. Put it on a remote machine if you want it to run forever. Pre-flight hard-resets that tree. You need: Git origin with fetch and push A coding agent CLI on PATH ( cursor-agent , copilot , claude , …), already logged in Node 22+ Nothing else. No extra service to stand up. Install the /lumpcode skill so your agent has current docs while you set this up and write configs: npx skills add lumpcode/skills Use /lumpcode in the session when you hit a config or CLI question. 2. Install the CLI On both machines: npm install -g @lumpcode/cli lumpcode --version 3. Laptop: project setup, shared mode From your day-to-day repo: lumpcode project-setup --primaryBranch main Use your real integration branch instead of main if that is what you merge to. .lumpcode/local.json is gitignored and per machine. On the laptop it should be: { "mode" : "shared" } Shared mode never touches this checkout. Runs go to ~/.lumpcode/project-copies/<projectName>/ . Install @lumpcode/cli-utils and @lumpcode/recipes into this repo now , before the first push. Later TypeScript lumps import them from the project's node

2026-08-20 原文 →
AI 资讯

1a vez trabalhando com git com time: tudo que você precisa saber

Faz mais de 5 anos que eu não abria um PR ou issue técnica no Github, mas essa semana tenho aprendido algumas boas práticas e termos que reuni neste artigo. Introdução Essa semana eu fiz uma coisa simples: atualizei o README de um projeto open source, o 4noobs , da comunidade He4rt. Troquei um badge, ajustei o contraste de um logo, organizei umas pastas e adicionei um índice pra facilitar a navegação. Nada muito complexo no fim das contas. Só que antes de chegar no "nada muito complexo", eu passei um tempo enrolada com uma pergunta boba: "E se eu mandar isso direto pra branch principal e bagunçar tudo?" Se tu já sentiu esse friozinho na barriga antes de mexer num repositório que não é só teu, esse artigo é pra ti. Não importa se tu é dev há anos ou se nunca abriu um terminal na vida... A lógica por trás de "como contribuir sem quebrar nada" é a mesma e bem mais simples do que parece. Definição de Git Colaborativo Quando eu aprendi git há uns anos, aprendi somente o versionamento e a enviar os arquivos pra dentro do Github, mas ele é bem mais que isso, né? É através dele que times enormes interagem a respeito de um mesmo projeto de forma organizada, comentando, gerenciando tarefas, sugerindo melhorias e conhecendo o que os outros envolvidos estão fazendo. Isso é a parte do Git Colaborativo . O Git resolve isso com um conceito central: branches (ou "ramificações"). Cada branch é tipo uma cópia paralela do projeto, onde tu pode mexer à vontade sem afetar a versão "oficial" (geralmente chamada de main ou master ). Quando tu termina sua parte, tu propõe que essas mudanças sejam incorporadas de volta pelo Pull Request (PR) . Ou seja, o fluxo básico é: Tu cria uma branch nova a partir do projeto principal Faz as alterações lá, no seu espaço isolado Envia ( push ) essa branch pro repositório remoto Abre um Pull Request pedindo pra essas mudanças serem revisadas e, se aprovadas, unidas ( merge ) à branch principal Ninguém mexe direto na versão "de produção" do projeto. Isso

2026-08-20 原文 →
AI 资讯

You Benchmarked the Model. Now Benchmark the Server.

You picked a free model because the answers looked good. Good answers are not an endpoint. An endpoint is the model plus the server plus the network. Demos pass. Pipelines stall. The model was rarely the problem. So why do we keep benchmarking only the model? Because it is easy. You paste a prompt. You read the output. You declare a winner. The server never gets a vote. This post is a reproducible benchmark. It measures the pair, not the model. Run it before you wire any free endpoint into CI. The Pair, Not the Model Most evaluations compare answers. You paste a prompt. You judge the output. You pick a winner. That measures the model. It ignores the server. Free model access usually means a shared endpoint. A free server option means shared tenancy. Other users share the CPU, memory, and network. Your latency is their latency. Your timeout is their timeout. Here is the scenario I keep seeing. A team evaluates a free model on Friday. The answers look great. They wire it into CI on Monday. By Wednesday, the pipeline is red. The model did not change. The server did. A neighbor started a batch job. Now every request queues behind it. I applied the same harness to MonkeyCode's free model access and their free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I did not trust the demo. I built a harness instead. The Harness A benchmark needs three things. A fixed prompt set. A concurrency ladder. A pass/fail table. Here is the harness I use. #!/usr/bin/env python3 """ Benchmark a model endpoint as a pair: model + server. """ import argparse import asyncio import json import statistics import time import httpx PROMPTS = [ " Say OK. " , " Classify this log line: ERROR disk full " , " Return one word: is 429 a retryable status? " , ] async def fire ( client , url , payload , sem , timeout = 30 ): async with sem : start = time . perf_counter () try : r = await client . post ( url , json = payload , timeout = timeout ) return r . sta

2026-08-20 原文 →
← 上一页 下一页 →