One person, no hires, no hand-written code, no spend: keep 100 real opportunities live at all times, find the person who can actually say yes, research them properly, and hand each one a whole pitch — a site, an ad set and an analytics deck — sitting at companyname.danilovicioso.com. The machine finds the people and their accounts itself. The whole funnel is edited by chatting, and it deploys itself.
Mass automated messaging through Apollo and Clay converted badly. Fewer, manually written, better-targeted messages lifted reply rates a lot. So volume is not the lever — relevance is, and the machine should protect that.
The bottleneck moved to research: reading dozens of posts to work out what one person actually cares about. That is the part worth automating. Automate the research and the artefacts; keep judgement on the sending.
End to end, hands off. Finding the people, resolving their accounts and contact details, producing every artefact and publishing it — none of it is a manual step I do between runs. My only jobs are supplying the templates and a single approve on the things that go public.
No recurring tool spend. Anything with a per-credit or per-seat bill — enrichment vendors, scraping platforms, hosting tiers — needs a free equivalent or it doesn't go in.
Every template is mine — the landing page, the ad base, the deck. The machine fills them, I can edit anything, and a single approve gates the things that matter: the site before it goes live, and the outreach before it sends. Everything else runs without me.
Everything is set up and changed by asking for it in chat. I never open an editor.
"Free" and "fully automated" collide at the scraping and enrichment layer — Apify, email lookup and LinkedIn automation are the paid parts today. Resolving this is the first real design decision.
A durable profile of what I've actually done — COO at Tabs, the operating problems I've solved, the numbers I moved, what I'm good at and what I want next. Every downstream stage reads from it: matching, hooks, the pitch, the deck. It gets richer over time rather than being re-explained each round.
A standing pool of 100 opportunities I could genuinely do, kept topped up as roles fill and new ones appear. Two kinds, held in the same pool: open roles matched against my profile, and companies with a problem I could fix whether or not they've posted anything.
For each opportunity, identify the person who can actually say yes — the founder, the exec who owns the P&L, the one whose problem this is — and route around recruiters and HR queues. That person, not the job post, is who enters the list of 150.
Scrape LinkedIn posts, profiles and comment threads into local SQLite. Classify what each person and company actually posts about. Mine comment threads for people already describing the problem. Surface uncommon commonalities — shared employers, schools, volunteer work.
Then look at what the company is already doing in market: pull their live Facebook and Instagram ads from the Meta Ad Library, their current landing pages and positioning. That evidence is what makes the ad batch and the deck an argument rather than a guess.
Connection requests and message sequences drafted per person off their research file, held for my single approve, then sent and logged: what went out, what came back, which hooks earn replies.
Generated from an existing prompt: a primalqueen.com-style landing page rebuilt around their product, published at companyname.danilovicioso.com.
Generated from an ad-base prompt against the same brief as the site, so page and creative say the same thing.
A short deck arguing the opportunity for that specific company, built from the research file into my deck template — a real argument, not a logo swap.
The subdomain is the deliverable: site, ad set and deck reachable from one address, so the outreach message only ever has to carry one link.
Everything above is edited by chatting — change the targeting, rewrite a message template, restyle a prospect's page, regenerate the ad batch — and the change deploys without a build step or an engineer. This is the requirement that makes the rest survivable for one person.
Open: the publishing route. A method for pushing generated pages live straight from the design tool is being evaluated (r/ClaudeDesign thread) — needs checking against wildcard subdomains on danilovicioso.com, per-prospect isolation, and how fast a page can be regenerated after an edit.
| # | Requirement | Status |
|---|---|---|
| R1 | Hold a deep, durable profile of my background (COO at Tabs, operating wins, strengths, what I want next) that every stage reads from | To build |
| R1a | Keep 100 live opportunities in the pool at all times — open roles I could do, plus companies I could add value to | To build |
| R1b | Score each opportunity against my profile, and refresh the pool as roles fill | To build |
| R1c | For each opportunity, resolve the real decision maker — not the hiring manager or recruiter | To build |
| R1d | Build and maintain the working list of ~150 from those decision makers, with email + LinkedIn identity resolved | To build |
| R2 | Scrape posts, profiles and comment threads into local SQLite; nothing leaves the machine | Have (insaight) · Apify is paid |
| R3 | Classify each person and company by what they actually post about | Have |
| R4 | Mine comment threads for people already describing the problem | Have |
| R5 | Extract uncommon commonalities from full profiles as outreach hooks | Have |
| R6 | Draft per-person email + LinkedIn messages from the research file; edit before send | To build |
| R7 | Log sends and replies; report which hooks earn replies | Have (partial) |
| R8 | Generate a per-prospect landing page from the primalqueen-style prompt | Prompt exists |
| R9 | Generate ~50 ad variants per prospect from the ad-base prompt | Prompt exists |
| R10 | Generate a short analytical deck per prospect from the research file | To build |
| R11 | Publish all three to companyname.danilovicioso.com behind one index | Open |
| R12 | Edit any stage by chat; changes redeploy with no manual build | Open |
| R13 | Run end-to-end with no hires and no hand-written code | Constraint |
| R14 | Discover people and resolve their accounts and contact details automatically — no manual list building | To build |
| R15 | Run the full pipeline hands-off on a schedule, not as a sequence of prompts I babysit | To build |
| R15a | Present each prospect as one approval screen — message, site, ads and deck side by side | To build |
| R15b | Single approve gates the public artefacts: the site before it goes live, the outreach before it sends | To build |
| R15c | I can edit anything on that screen — copy, sections, targeting — or send it back to regenerate | To build |
| R15d | All templates are supplied by me; the machine fills them, it does not invent formats | Constraint |
| R16 | Zero recurring cost: every component free-tier or self-hosted | Constraint · at risk |
| R17 | Pull a company's existing advertising — Meta/Facebook Ad Library, live landing pages, current positioning — into the research file | To build |
| R17a | Use that evidence in the pitch: what they run now, what's missing, what my 50 would do differently | To build |
Multi-user access, a CRM, paid ad spend and campaign management, any hosted service holding the scraped data, and volume beyond 150 in a round.