$30/mo runs my entire cold outbound scrape -> research -> enrich -> write -> judge -> send -> classify replies -> book calls. 50 emails/day, fully personalized, and every draft is scored against a voice spec before it lands in my inbox for review. here's the actual stack: 17 consecutive cron jobs. 1 deterministic yaml for icp settings. 1 voice file the copywriter is forced to match. 1. scout -> firecrawl scrapes public sources, or private ones if i provide the api key 2. researcher -> scrapes the web for company info, decision-makers, contacts. classifies icp match 3. enricher -> waterfall email enrichment via smtp (same method apollo uses), falls back to hunter/apollo 4. copywriter -> drafts in my tone, defined in a separate .md 5. judge -> reviews the draft against the same voice file. fails it -> back to the copywriter 6. notion sync -> pushes leads all attached intel into my db 7. notifier -> telegram ping when drafts are ready for review 8. health digest -> watches errors, bounces, ai budget. daily status to telegram 9. orchestrator -> watches metrics, writes new policies, adjusts every previous agent. the brain. improves on data. 10. inbox monitor -> polls every 15 min 11. email classifier -> tags intent, routes the reply to the right notion db 12. booker -> if intent confirmed, pulls free slots from my calendar, proposes them, sets up the google meet on confirm 13. sender -> dispatches approved emails every 34 min. handles new-account warmup on a 5 -> 10 -> 15 -> 25 daily ramp. 14. (the yaml voice files are the spine. change them and the whole system shifts.) infra: - $12/mo vps - $20/mo ai budget on deepseek-v4-pro (i don't even spend half of it) - ~$30/mo all in. the lesson from building this: building ai agents that do their job well is shockingly, EXTREMELY difficult. ai output quality is inconsistent. SO, i ripped ai out of every step that didn't strictly need it. scout, enricher, sender, inbox watcher, sync, notifier are all deterministic code. no ai here ai stays only where judgement is unavoidable: writing, judging, classifying replies, negotiating calendar slots. adding ai everywhere makes it mess. just add only there, where it really matters. and in 90% of the cases, you won't need it.

hell yeah, a new era begins

this post already brought me 2 leads, which i converted to clients, with a total value of $5,000 it's genuinely crazy that this is possible. p.s. these people have never engaged with any of my posts

signed my first client for this agent from this exact post and it only got 124 views.

tell me you’re desperately broke without telling me you’re desperately broke

so, opus 4.8 changes exactly nothing for 90% of the tasks missing 4.5 release. what a revolution it was

i genuinely don't understand people who push for one ai model over the other gpt didnt kill claude. and claude didnt kill gpt. and that's a dumb argument to even start in the first place. both are good

$30/mo runs my entire cold outbound scrape -> research -> enrich -> write -> judge -> send -> classify replies -> book calls. 50 emails/day, fully personalized, and every draft is scored against a voice spec before it lands in my inbox for review. here's the actual stack: 17 consecutive cron jobs. 1 deterministic yaml for icp settings. 1 voice file the copywriter is forced to match. 1. scout -> firecrawl scrapes public sources, or private ones if i provide the api key 2. researcher -> scrapes the web for company info, decision-makers, contacts. classifies icp match 3. enricher -> waterfall email enrichment via smtp (same method apollo uses), falls back to hunter/apollo 4. copywriter -> drafts in my tone, defined in a separate .md 5. judge -> reviews the draft against the same voice file. fails it -> back to the copywriter 6. notion sync -> pushes leads all attached intel into my db 7. notifier -> telegram ping when drafts are ready for review 8. health digest -> watches errors, bounces, ai budget. daily status to telegram 9. orchestrator -> watches metrics, writes new policies, adjusts every previous agent. the brain. improves on data. 10. inbox monitor -> polls every 15 min 11. email classifier -> tags intent, routes the reply to the right notion db 12. booker -> if intent confirmed, pulls free slots from my calendar, proposes them, sets up the google meet on confirm 13. sender -> dispatches approved emails every 34 min. handles new-account warmup on a 5 -> 10 -> 15 -> 25 daily ramp. 14. (the yaml voice files are the spine. change them and the whole system shifts.) infra: - $12/mo vps - $20/mo ai budget on deepseek-v4-pro (i don't even spend half of it) - ~$30/mo all in. the lesson from building this: building ai agents that do their job well is shockingly, EXTREMELY difficult. ai output quality is inconsistent. SO, i ripped ai out of every step that didn't strictly need it. scout, enricher, sender, inbox watcher, sync, notifier are all deterministic code. no ai here ai stays only where judgement is unavoidable: writing, judging, classifying replies, negotiating calendar slots. adding ai everywhere makes it mess. just add only there, where it really matters. and in 90% of the cases, you won't need it.

tokenmaxxing is the most retarded shit i’ve ever seen

is it just me or building agent with openclaw/hermes is very overexciting

AGI will never be able to have human level intelligence, NEVER, and it’s not a resource problem, it’s a fundamental design FLAW, if you don’t believe me, i’ll prove it to you now. AI is not magic, we manage to brute force patterns in our language(s) that we are unable to formalize into a bunch weights using transformers. think about it like that: formalize the logic of languages is too hard for us, so we took as much examples as we could and we forced out the right “equation” from it. llms are nothing more than that, hidden language logic encoded in a long list of numbers. period. there is something llms will never be able to do tho, not if they’re built like this, and this is the fundamental difference between human intelligence and llms intelligence. models have nothing else to interpret context other then the text itself, humans on the other hand have something fundamentally different, our context interpretation, even if you don’t realize it, is deeply grounded in MUCH MORE different information. every word is attached to a HUGE hidden substrate of emotions, touch, vision, memories, pain, pleasure, fear, body state, goals and every other thing that is deeply connected with our biological body and NOT ENCODED into text that models just doesn’t have. take this sentence for example: “the cup fell off the table” when you read it you do not just process the words “the”, “cup”, “fell” and so on, but in your head you implicitly simulate gravity, objects, surfaces, breakability, sound, surprise, maybe a hand reaching out, maybe annoyance. All data that is not in the sentence. it is supplied by your embodied world model that’s why language is compressed. humans can say very little because the listener fills in a lot from shared reality. this is one reason why llms writing can feel subtly off, it knows the phrase and the context where the phrase appears, but it may not have the same grounded constraint behind it. it can say “this design feels heavy” but it does not feel weight, effort, fatigue, or manipulation resistance. it can model the linguistic pattern of that judgment, but the judgment is not anchored in the same sensorimotor reality this is why we all fell that way ”AI has no taste”, because “taste” as we intended is EXACTLY THIS! it information encoded in embodied experiences that is not present into text.

i've built 30 marketing ai agents for my clients i've rejected every single client who didn't have an EXCEPTIONALLY well-defined, proven process. here's a fascinating reason: you can't automate something you don't understand. truth is that, i am not a marketer, and never will be so, when i come into a company, i expect them to have a proven and tested SOP of the job they're trying to automate/delegate to ai if they don't - i reject. it's a waste of time and money. lesson is this: use ai for what you already doing very well, not for things you're bad at. few.

everyone's shitting on vibecoding NOTHING is inherently wrong with it ai can write code faster than you can type but it cant outtaste you if you have no standards, your ai won't either i vibecode 90% of mine. 0 problems vibe coding was never the problem. the operator was

people flexing their 100% usage limit in claude are retards that aint a flex that's a sign you don't know shit about how to use ai properly

alright it's time to touch some grass

pro tip: download all of your claude data filter it out then ask your claude code to ingest all of the conversations massive.

i sometimes genuinely forget to post here because i am too busy talking to claude. what a time to be alive.