Competitive intelligence
your agents can query.
On your own machine.

Signals stored

2,600+

in the deployment behind the live preview

Companies tracked

13

across two segments, and it is configuration

Runtime deps

3

no build step, offline on a fresh clone

Monthly spend

$15–25

at the shipped models. Database and search on free tiers

1. Step 01Get it running

   The first command downloads it. The second builds a small database on your machine and fills it with real collected data. The third opens the dashboard in your browser. You can stop there and look around: everything in the three screens above is already populated.

   git clone https://github.com/gledach/signals && cd signals
   npm install
   npm run db:migrate   # your own database, with example data in it
   npm run view         # the dashboard, at 127.0.0.1:5180

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   What you need: a Mac, a Windows PC or a Linux machine, and Node 22. That is the whole list. The install guide assumes you have never used a terminal before and takes about fifteen minutes. If you have, it is closer to two. Run npm run doctor at any point and it will tell you what is working and what each thing you have not set up yet would unlock. The package name is gledach-signal. There is no signal command. npm already publishes unrelated packages named signal and signals.

2. Step 02Tell it who to watch

   It arrives watching thirteen AI coding tools, because a tool that opens empty teaches you nothing. Pointing it at your own market is one file: copy the example, write in the companies you care about, save.

   # copy the example, then edit your copy
   cp config/companies.default.mjs config/companies.local.mjs

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   config/companies.local.mjs the only file most people edit

   export const companies = {
     acme: {
       id: 'acme', name: 'Acme', domain: 'acme.com',
       query: '"Acme Corp" OR "acme.com"',
       repos: ['acme/platform'],
       subreddits: ['acme'],
     },
     // one block like that per competitor
   };

   The one field worth a moment's thought is query, because a company name is often an ordinary word. Cursor is also the thing blinking in a text box, so the query is written to catch the company and not the word. The shipped file has thirteen worked examples to copy the pattern from, each with a note on what it collides with.

   Everything else follows on its own. The news feeds it reads, the searches it runs, the code repositories it checks and the labels down the side of the dashboard are all rebuilt from that one list. There is no second place to keep in sync.

3. Step 03Leave it collecting

   One command runs every watcher once and leaves the day in your database. Put it on a schedule and you have a standing picture of the market that updates while you are not looking at it.

   npm run start      # one pass over every source, on whatever schedule you like
   npm run brief      # a 200-word morning read of what changed

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   From there you either read the dashboard, or you point an AI assistant at it and ask. Connecting it to Claude Code or any other MCP client is a single line, and the assistant then answers from your collected data rather than from whatever it remembers about the market.

The queue is public, including what was turned down and why. The form asks the three questions that decide whether anything is watchable at all: what you check by hand, where it is published, and whether seeing it needs an account. Requests go there rather than to an inbox, so everyone can see what was asked and what happened to it.