# CLI Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/cli
> Markdown URL: https://aitinkerers.org/technologies/cli.md
> Technology record last updated: 2026-02-23T23:07:06Z
> Generated: 2026-09-22T02:43:21Z

The Command Line Interface (CLI): Your direct, text-based terminal for executing commands and automating system operations with maximum efficiency.

CLI is the essential interface for system administration and development: It bypasses the overhead of a Graphical User Interface (GUI) for faster, scriptable workflows. Shells like Bash, Zsh, and PowerShell interpret typed commands (e.g., `ls -l`, `git commit -m`) to manage files, execute programs, and control hardware. The core advantage is automation: Complex, multi-step tasks can be chained and executed instantly via scripts, delivering significant time savings for repetitive operations (often 10x faster than manual GUI clicks).

- Official technology site: https://clig.dev
- Public AI Tinkerers demos and talks: 9
- Result page: 1 of 1

## Recent Public Talks and Demos

### [From AI Prospect Memos to a Scored Warm-Intro Queue](https://poland.aitinkerers.org/talks/rsvp_k0DCDscxWlA)

I built a dogfood workflow on top of graph.one that connects AI-assisted company qualification to a relationship-graph work queue. I'm the founder of graph.one, but this demo is about the internals of the build, not a product pitch. It starts in Prospector: a Node CLI pipeline that creates frozen public-source packets, runs AI-generated prospect memos, validates structured outputs, and stores decisions in append-only JSONL ledgers. The handoff is a qualified-company record. Intro Queue then takes a relationship-graph org-path export with organization identity, relationship owner, optional connector, target person/role, and strength fields. It caches the export in SQLite, collapses duplicate and noisy paths into scored route options, groups selected routes by connector, tracks per-company outcomes, and surfaces fallback routes as a human action queue. In the demo I'll follow one fully synthetic target company from source packet to memo to graph path row to scored route option to failed route/fallback. The queue output is a person-centric ask docket for a human operator; there is no send pipeline. I'll use fully synthetic graph data and redacted schema/log excerpts only.

- Event context: AI Tinkerers Warsaw: GTM Engineering Track — 2026-06-24 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_k0DCDscxWlA

### [Jules: Uma Raposa Que Toca o Meu Negócio Inteiro no Claude Code](https://saopaulo.aitinkerers.org/talks/rsvp_dm9xlYot2e4)

AI collaborator system on top of Claude Code that helps run my whole business and a live recording studio, Jules Live Studio, that turns it into a real two-way conversation with my AI cohost Jules (a fox). The system has two layers worth showing builders: The skills layer. - Custom workflow skills — /think, /dialogue, /write, /research, and a routing rule that picks between them — force the model through structural gates instead of letting it default to the two failure modes every LLM has: enthusiastically agreeing with whatever I just said, and anchoring on its first answer instead of asking what I actually think. Each skill is a small workflow with explicit phases — elicit, challenge, adversarial review, only-then-recommend — that the model cannot skip. The studio layer. - Jules Live Studio is a local recording app that wires the skills layer into a live voice and video loop: speech-to-text in with Whisper, Claude reasoning, ElevenLabs voice out, fox avatar on screen, and OBS recording. The audience watches a real conversation, not a terminal scrollback. For an AI Tinkerers audience, the demo focuses on the parts that matter to builders: - how the routing layer sends different kinds of requests to different workflows - how a skill structurally forces the model to push back instead of agree - how memory and feedback loops keep it useful across sessions - how the live voice/video loop is wired together with off-the-shelf parts - how far you can get without piling on heavyweight agent frameworks

- Event context: AI Tinkerers SP - Meetup de Maio - Kiro & AWS — 2026-05-28 — São Paulo
- Public talk page: https://saopaulo.aitinkerers.org/talks/rsvp_dm9xlYot2e4

### [GTM in the Terminal — Agent-driven personalized outbound](https://sf.aitinkerers.org/talks/rsvp_O2H7CQUJ6IU)

Cyrus is the cofounder of The Synthesis Company (YC S24). Previously, he was a core contributor to Stanford NLP's DSPy framework and cofounder of WOMBO.ai (100M+ downloads). He'll be demoing GTM-OS, a CLI and TUI he used to programmatically drive hyper-personalized LinkedIn outbound. For AI tinkerers he built usesocial.dev, a CLI to connect your agents to LinkedIn / X and build your own workflows and systems.

- Event context: AI Tinkerers San Francisco: GTM Engineering Track — 2026-05-19 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_O2H7CQUJ6IU

### [Building a Real-Time Terminal Messenger on LinkedIn's Private APIs with Bun, Ink, and a Git-Backed Message Store](https://seattle.aitinkerers.org/talks/rsvp_0AqXN-gxTTM)

Allman is a two-layer system for LinkedIn messaging from the terminal: a CLI that syncs, sends, and streams LinkedIn messages into a git-versioned file store, and a TUI that renders it as a full two-pane Ink/React terminal messenger with live updates, auto-backfill, and real-time presence. The demo shows the TUI in action — navigating conversations, composing replies, watching the status bar update as messages stream in over LinkedIn's SSE channel, and kicking off a sync that backfills an entire conversation history while showing live progress counts. Under the hood, the TUI never touches the network directly. Every write — sends, syncs, searches — shells out to the lilac binary, which is embedded inside the compiled TUI executable as a bundled asset. Every read — conversation list, message thread, slug lookup — goes straight to JSONL files on disk, so navigation is instant with zero subprocess overhead per keystroke. The key learning or takeaway is that private APIs, even when obfuscated, are easily reverse-engineered by coding harnesses. Claude Opus 4.6 processed the compiled LinkedIn binary overnight, completely autonomously.

- Event context: AI Tinkerers Seattle: GTM Engineering — April Meetup — 2026-04-23 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_0AqXN-gxTTM

### [ThreadHop: local-first context sharing across coding agent sessions](https://montreal.aitinkerers.org/talks/rsvp_72wf-elK3uc)

ThreadHop is a local TUI and CLI app that turns isolated coding-agent sessions into a connected workspace with a chat history view, cross-session Full Text Search (FTS5), a kanban board for session tagging, automatically extracted decisions and TODOs, bookmarks, and compressed handoffs. In this demo, I want to show how conversation history can be reused instead of ignored. ThreadHop indexes local transcripts, lets you organize chats on a kanban board with simple states like backlog, in progress, in review, and done, and makes it easy to bring the right context into a new session — whether that is a bookmarked turn, the open TODOs and decisions a background observer has already pulled out, or a full handoff through the /threadhop:handoff skill that packages decisions, TODOs, ADRs, and cross-session conflicts so work can carry forward. The goal is to make multi-agent coding feel like one continuous workspace rather than a pile of disconnected chats.

- Event context: AI Tinkerers Montreal - April Demo Night — 2026-04-22 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_72wf-elK3uc

### [Build MCP apps the easy way](https://zurich.aitinkerers.org/talks/rsvp_yY7TrFFu3wo)

mcp-use is one of the most popular frameworks for building MCP servers and MCP apps. At Manufact we love open source and we are creating the open source developer platform for MCP. in this demo, I will show how you can use the CLI or Manufact Vibe to create an MCP app in a couple of minutes, either through code or with Vibe coding, and then show how you can submit your app to the OpenAI marketplace

- Event context: AI Tinkerers Zurich April 9th — 2026-04-09 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_yY7TrFFu3wo

### [cli_engineer](https://manizales.aitinkerers.org/talks/rsvp_ON22Vh5vLuE)

El desarrollo y funcionamiento de un sistema agencial de desarrollo de software 100% automatizado.

- Event context: 🚀 ¡Sexto Encuentro de AI Tinkerers Manizales! 🤖 — 2025-08-27 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_ON22Vh5vLuE

### [Bodhi App - Run LLMs locally](https://bengaluru.aitinkerers.org/talks/rsvp_AylPIDyUhYk)

Bodhi App allows you to run LLMs locally, saving you cost and giving you complete privacy for your data. Whereas ollama gives you ability to do the same, Bodhi is targeted to wider audience and does not assume that you understand API, CLI, frontend/backend, and unlocks the power of open source LLMs for everyone with a device.

- Event context: AI Tinkerers - Bengaluru - March Meetup — 2025-03-04 — Bengaluru
- Public talk page: https://bengaluru.aitinkerers.org/talks/rsvp_AylPIDyUhYk

### [A new CLI/Library for evals](https://austin.aitinkerers.org/talks/rsvp_tmBFag9qz7M)

I'd like to show off a new configuration-based evaluation system for LLMs that I've been working on. The project is a an evaluation CLI for running evaluations on prompts with datasets or for existing model output, either from a production system or from some other run mechanism.

- Event context: July Meetup: Community AI Demos — 2024-07-11 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_tmBFag9qz7M

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