Carlos @cfpperche

Software Engineer São Paulo Joined July 2014

Tweets

25
Followers

42
Following

412
Likes

223

Carlos @cfpperche

24 hours ago

@t20000622yy Very interesting, this is my first open-source project, only 3 days of work. I will evolve the project according to my needs and contributions from others. I will study Hive, thank you.

0 0 0 7 0

View Details

Meet Tachyon ⚡ — run a fleet of AI coding agents inside VS Code: each one a real terminal, all coordinating through an embedded MCP server. Bring your own CLIs (Claude Code, Codex, Gemini, OpenCode…). 100% local. Free & open source. 🧵 The problem: running 3–4 agents today means alt-tabbing between terminals, babysitting which one stopped to ask you something, and losing the whole fleet on a reboot. Mini-tutorial: 1. Install "Tachyon" from the VS Code Marketplace (needs tmux — Linux/WSL/macOS) 2. Open a folder and declare your agents in a tachyon.yml: agents: { claude: { cmd: claude, autostart: true }, codex: { cmd: codex } } 3. Hit ▶ in the ⚡ sidebar — each agent opens as a native editor terminal. The real TUI, not a wrapper. Config-as-code: your comments survive UI edits. And agents survive VS Code restarts — tmux owns the processes, Tachyon re-attaches.

2 0 0 129 0

View Details

Carlos @cfpperche

2 days ago

100% local — no cloud, no telemetry, no token proxying. It runs on the subscriptions you already pay for. GPL-3.0, open source. → Site: cfpperche.github.io/tachyon/ → Install: marketplace.visualstudio.com/items?itemName… → Code: github.com/cfpperche/tach… This is early and I'm building it in the open — I'd love your help. Bugs, rough edges, feature ideas, runtime requests (which agent CLI next?) → github.com/cfpperche/tach… ⚡ RTs appreciated 🙏

0 0 0 62 0

View Details

Carlos @cfpperche

2 days ago

The core trick: an embedded MCP Bridge. One agent spawns sub-agents, reads their output, writes to them — spawn_agent, write_input, read_output, notify. Agents that orchestrate agents. Tachyon also watches every pane and tells you — and your other agents — who needs you (prompt detection), who's idle (CPU-aware, so a thinking agent is never flagged as stuck), who crashed (exit code + frozen postmortem). No more babysitting. And new this week: session resume. A reboot, wsl --shutdown, or an OOM kill takes your agents down? Reopen the folder and they come back WITH their conversation — claude --resume, codex resume… Your fleet survives the machine, not just the editor. Plus: human-gated schedules (cron for agents), curated commands & runbooks, shared human↔agent pins, multi-root workspaces, and a tmux server inspector.

1 0 0 77 0

View Details

Carlos @cfpperche

a week ago

@acgfbr Teu erro é querer vender software pra brasileiro, povo sobrevive e é acostumado com pirataria

0 0 1 735 0

View Details

Carlos @cfpperche

2 months ago

País amaldiçoado do caralho, isso aqui tem que pegar fogo e queimar ate virar deserto! lixo de lugar

0 0 1 44 0

View Details

Carlos @cfpperche

2 months ago

3 months ago

● Análise completa salva em .spock/research/2026-03-02-anthropic-prompt-eng-tutorial.md. Resumo das melhorias identificadas O tutorial revela 3 gaps principais no nosso processo: Alta prioridade 1. Few-shot examples nos skills — O tutorial diz que exemplos são "the single most effective tool" e nós temos quase zero. Skills como spock-task-create e spock-prd-create deveriam ter 1-2 exemplos concretos de input/output. 2. XML tags no build-prompt.sh — Nosso pipeline de prompt monta tudo em markdown concatenado. Envolver dados injetados (task spec, arquivos pre-lidos) em XML tags previne confusão instrução/dado — exatamente o que o capítulo 4 ensina. Média prioridade 3. "Give an out" — Padrão barato: dizer "only answer if certain" reduz alucinações. Temos o Uncertainty Protocol no CLAUDE.md mas não reforçamos dentro dos skills individuais. 4. Evidence-first scratchpad nos audits — Antes de emitir veredicto, extrair evidências em tags . Mapeia direto pros nossos audit skills. 5. Task restatement no final — O tutorial nota que colocar a pergunta/tarefa no final do prompt dá melhores resultados. Nosso build-prompt.sh deveria repetir o objetivo após o contexto pré-lido. Baixa prioridade 6. Prefilling no claude -p e stop_sequences para reduzir tokens desperdiçados. Quer que eu implemente alguma dessas melhorias agora? ✻ Brewed for 3m 16s thanks