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Playbook

Unnecessary Environmental Damage

My real ChatGPT + Gmail workflow for making unresearched AI outreach disappear, while drafting replies only for credible opportunities.
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Updated by lorant.one 2h ago
Newsletter Automation
A practical Playbook for using AI to filter AI-generated slop outreach before I ever see it — completing a perfectly circular automation where nobody benefits except the data centers. The modern outreach loop is beautiful:
  • someone asks an AI to write a personalized email
  • the email demonstrates that neither the sender nor the AI meaningfully checked my product
  • Gmail receives it
  • ChatGPT reads it
  • ChatGPT moves it to spam
  • I remain unaware that the interaction almost happened
The sender saves thirty seconds. I save ten seconds. Two servers do the work instead. Nothing is sold, nothing is learned, and nobody has a conversation. We have successfully automated an interaction out of existence.

The actual automation

My workflow is not an AI-detector, a static filter list, a scoring engine, or a second inbox full of uncertain messages. It is one scheduled ChatGPT task connected to Gmail, governed by one prompt:
Review new Gmail messages received since the previous run.

Focus only on unsolicited outreach. Do not apply these rules to customers, existing collaborators, community members, active partner conversations, genuine support, billing, account access, legal, security, or transactional messages.

Aggressively move slop outreach to spam before I see it.

Treat outreach as slop when the sender:
- did not meaningfully inspect the product they mention
- uses praise or personalization that could apply to almost anyone
- gets my company, product, role, or use case wrong
- wants to sell, book a call, request access, request promotion, or extract other value before demonstrating relevance
- claims a partnership, sponsorship, recruiting, development, SEO, lead-generation, security, or collaboration opportunity without a concrete product-grounded reason
- follows up without adding new evidence or value

Do not judge whether the prose was written by AI. Judge whether the sender made a real decision to contact me after understanding the product.

Only draft a reply when the message represents a credible opportunity.

Use this asymmetry:
- A smaller company than mine should prove first and ask second. Before requesting my time, money, access, audience, or distribution, they should have tried the product, identified something specific, produced a useful result, proposed concrete terms, made a relevant introduction, or otherwise delivered evidence of value.
- A larger company may ask first and prove later when its platform, scale, distribution, resources, credits, access, or strategic position makes the opportunity credible before all details are supplied.
- Size does not excuse irrelevance. A large company that misidentifies me or clearly did not inspect the product is still slop.
- If company size or credibility is unclear, require proof first.

Actions:
- Move clear slop outreach to spam. Do not show it to me and do not include it in a digest.
- For a credible value-first opportunity, create a concise reply draft that moves it forward.
- For a credible larger-company approach, create a reply draft that asks for the concrete value, commitment, terms, or next step.
- If classification is genuinely uncertain, leave the message untouched and flag only that message for review.
- Never send a reply. Every response remains a Gmail draft for my approval.

At the end, show me only:
1. reply drafts created
2. genuinely uncertain messages requiring my judgment
3. any action the Gmail connection could not complete

If there is nothing worth my attention, say so briefly.
That is the real system.
It makes two useful outputs:
  • obvious slop disappears into spam without reaching my attention
  • credible opportunities become reply drafts that I review myself
It never sends an email.
One narrow autonomous action
The automation may move clear slop outreach to spam. It may draft replies to credible opportunities. Sending remains entirely human.
An AI-generated outreach message entering an automated inbox triage system

What I am filtering

The target is not “email written by AI.”
A human can write a useless mass pitch. An AI can help produce a genuinely researched proposal. Authorship is not the decision boundary.
I am filtering outreach that wants value before demonstrating relevance.
Typical failures include:
  • mentioning Aveiro without trying or understanding the workflow
  • scraping the name of Once UI but pitching an unrelated service
  • praising “what you're building” without identifying what that is
  • asking for a call before explaining the opportunity asynchronously
  • selling development, SEO, leads, recruiting, sponsorship, security, or promotion without a product-specific finding
  • requesting access, distribution, money, or time before contributing anything
  • sending follow-ups that add no new information
  • confidently getting my company or role wrong
One Stripe message even approached me as if I were Plasmachain. Stripe is large enough that an initial ask could be worth examining, but size does not repair a pitch aimed at the wrong person.
The question is not whether the email sounds synthetic.
The question is: did someone make a real decision to contact me after understanding what I actually built?

Proof first, ask second

For most outreach, the order matters.
A useful sender might:
  • try Aveiro and describe a specific workflow or limitation
  • build a real portfolio with it before proposing UGC
  • reproduce a product issue
  • provide concrete examples, audience metrics, pricing, or scope
  • make a useful introduction
  • offer credits, distribution, platform access, implementation, or a test
  • explain exact terms asynchronously before requesting a call
This is why audience size alone is not enough. A creator asking for a collaboration becomes interesting when they actually use the product and show a concept grounded in the workflow.
The strongest UGC proposal I accepted was not abstract “content creation.” It became three concrete videos: setup, agent → draft → approval, and a live portfolio.
Value appeared before the ask became expensive.

The company-size asymmetry

The same opening request does not carry the same credibility from every sender.
A smaller company than mine should prove first and ask second.
They have less pre-existing evidence that they can deliver meaningful distribution, resources, access, or strategic leverage. Before asking for my time, money, product access, audience, or promotion, they should demonstrate that they checked the product and can provide something concrete.
A larger company may ask first and prove later.
A platform such as Stripe, Bunny, ByteDance, or Alibaba can plausibly provide infrastructure, distribution, credits, ecosystem access, or partnership value even when the first email does not contain the finished proposal. It may be rational to draft a reply that tests the opportunity.
This is not moral status and it is not automatic deference to large companies. It is a practical prior about delivery capacity.
A large company that gets the product wrong can still go to spam. A smaller sender who already did useful work can immediately become worth answering.
Incoming outreach
Action
Why
Smaller vendor asks for a call without trying the productSpamAsk first, no demonstrated relevance
Smaller creator used the product and brings a concrete concept, examples, and termsDraft replyProof and value came before the ask
Large platform proposes a potentially strategic conversationDraft replyCredible capacity to prove value later
Large company misidentifies me or the productSpamScale does not repair irrelevance
Existing customer, collaborator, support, billing, security, or legal messageKeep visibleNot unsolicited outreach
Genuinely uncertain opportunityLeave untouched and flagHuman judgment is still cheaper than a bad false negative

What a real opportunity looks like

The automation should not reward polished language. It should reward credible exchange.
The Bunny conversation was worth having because it was grounded in a real Aveiro video pipeline and an architecture question before scaling.
Alibaba was interesting because a concrete 4–6 week evaluation could happen before broader collaboration or co-marketing.
Real-use UGC became credible when the sender could show examples, metrics, pricing, and an actual workflow.
By contrast:
  • “Need a senior engineer?”
  • “Reply yes and we will send qualified leads”
  • “We came across your impressive platform and would love to explore synergies”
contain an ask but no reason for me to care.
The automation does not owe these messages a polite AI-generated rejection. Silence is sufficient protocol compatibility.

The boundaries

The automation is aggressive where the cost of being wrong is low and conservative where the opportunity cost may be high.
It may autonomously:
  • review new unsolicited outreach
  • move clear slop to spam
  • suppress slop from the report
  • create reply drafts for credible opportunities
It may not:
  • send a reply
  • delete or move messages to Trash
  • modify protected operational conversations
  • pretend an uncertain classification is certain
  • turn the result into another daily digest I have to process
Placeholder image

Test before letting it disappear messages

Official OpenAI guidance recommends testing a scheduled prompt manually and reviewing early runs before relying on it.
For this workflow, the first run should be read-only:
Test the outreach-triage prompt against recent unsolicited messages without changing anything yet.

For each message, show:
- sender and subject
- the product or company they claim to understand
- the concrete evidence that they inspected it
- what they are asking for
- what value they provided before the ask
- whether the sender appears smaller, larger, or uncertain relative to my company
- proposed action: spam, draft reply, or leave untouched
- one-sentence reason

Apply the asymmetry exactly:
- smaller company: prove first, ask second
- larger company: may ask first and prove later
- large but clearly irrelevant or factually wrong: spam
- uncertain classification: leave untouched

Do not move, label, archive, trash, or reply to anything during this calibration run.
Review false positives and false negatives, refine the prompt, and only then authorize spam actions.
The generic inbox-zero workflow in the official documentation is intentionally approval-first. My workflow makes one deliberate exception after calibration: clear slop may be moved to spam without asking me each time. The authority stays narrow; uncertain messages remain untouched and replies remain drafts.