this is my AI marketing engine
say I have an idea I want to turn into a campaign. a guide, a cohort, a webinar, something we have been planning for a while, or just something cool I came across and want to build around.
it usually starts as one thing, an idea or an evergreen piece, and this engine is how that one thing becomes a full campaign and fans out across every vertical
the engine is a graph, a general step-by-step the idea moves through. at each step I can swap the harness, the loop, the tool, or the model to fit the campaign
right now I'm testing gstack, superpowers, and matt's skills, engineer tools I'm bending toward marketing
the models can write and design well now. what takes work is the context you feed them, the routing per job, and the evals that catch what is weak
there are eval stops all through the graph. both human and other agents evaluating and reviewing output, and then looping it back if it doesnt
here is the path it travels, from a raw idea down to a shipped campaign
PLANNING
1. the idea in
I dictate the whole thing out loud, every half-formed thought, and let the skill bundle catch the mess and hand me back a starting brief. this is the karpathy point, get it out of your head first and clean it up after
2. ideation
off that brief it opens the idea into angles and directions to choose from. I throw most of them out
3. research and context
this is where I pull context, and how much I need depends on the campaign, sometimes a ton of internal history, sometimes barely any. the internal side is our company brain (gBrain), the voice, the past campaigns, what converted, the offers, the ICP. externally I pull the market, the competitors, the hooks working this week
4. synthesis
different models merge all of that into a draft plan. the plan itself, the architecture and the trade-offs, runs on opus 5, and the narrow work underneath gets cheap fast models. that split is model routing
5. the sign-off
nothing crosses into execution until I sign it off. I read the draft against our marketing protocols, the voice rules, the brand, the SOPs, and I cut, sharpen, or send it back
EXECUTION
6. handing it to the build
once the plan clears the sign-off it goes into the build, and the idea splits into all the parts a campaign needs. here I run two shapes depending on the job. when a piece is one task that has to clear a bar I run a loop, the agent drafts, checks itself, fixes, and keeps circling until it is good.
the bigger many-part pieces I build as a graph, drawing the steps and routes ahead of time so the agents travel the map I laid down. it is usually a bit of both
7. the models doing it
routing runs in execution too, you do not pay opus prices to resize a thumbnail for example
8. what stays with people
some of it I coordinate, some assets I make myself, and the work that needs taste, a relationship, or a client in the room I hand to the agency team
that one idea comes out the other end as a full campaign across every vertical, landing pages, blogs and guides, video scripts, email, PR, paid, and the social cuts
then the results come back in, what got bookmarked, what converted, what died, and that updates the brain for the next campaign
everyone has the same models, so the edge is the graph, the brain, and the protocols, and those you have to build yourself
Anthropic engineer just released a free 2-hour workshop.
How to build agentic graphs from scratch:
04:43 - RAG and graphs from zero
26:30 - Nodes, edges, and graph engineering
1:09:11 - Index agent data in graphs
1:30:50 - The three graph layers behind agents
1:48:15 - Build adaptive RAG that verifies itself
2:16:37 - The future of graph engineering
Most people build agents as isolated loops.
Graphs give them structure, memory, and control.
This workshop is worth more than most $500 agent engineering courses.
Bookmark and watch it today
Then read the full graph engineering guide below
As an AI Engineer. Please learn
>Harness engineering, not just prompt engineering
>Context engineering, not just long prompts
>Prompt caching vs. semantic caching tradeoffs
>KV cache management, eviction, reuse, and memory pressure at scale
>Prefill vs. decode latency and why they optimize differently
>Continuous batching, paged attention, and throughput optimization
>Speculative decoding vs. quantization vs. distillation tradeoffs
>INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality
>Structured output failures, schema validation, repair loops, and fallback chains
>Function calling reliability, tool contracts, argument validation, and idempotency
>Agent guardrails, loop budgets, tool budgets, and termination conditions
>Model routing, graceful fallback logic, and degraded-mode UX
>RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness
>Retrieval evals: recall, precision, grounding, attribution, and citation quality
>Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals
>LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift
>Cost attribution per feature, workflow, tenant, and user journey not just per model
>Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries
>Multi-tenant isolation, cache safety, and cross-user context contamination prevention
>Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool
>Latency, quality, cost, and reliability tradeoffs across the full inference stack
>Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions
This man named “Satyam Pandit” assaulted innocent GenZ kids who came to protest at Jantar Mantar, which is clearly jurisdiction of @DelhiPolice
Will his visible closeness to CM @gupta_rekha desist @CPDelhi from lodging immediate FIR ?
Let’s see.
We are relieved and grateful that Sonam sir has ended his hunger strike after 26 days.
Thank you, sir, for your extraordinary courage and sacrifice. By putting your own life on the line, you awakened the conscience of an entire nation. Your life is far too precious to this country.
The Cockroach Janta Party’s peaceful protest at Jantar Mantar will continue until Dharmendra Pradhan resigns.
Lol.. The official govt-funded public broadcaster of India @DDNewslive which is primarily funded by Indian taxpayers.
Is now reaching out to school kids asking them to give positive statements and to praise PM Modi after his latest tweet on Paper Leak, Students protest in Jantar Mantar and various other states.
Wah @narendramodi Wah! 🤣🤣
We are relieved and grateful that Sonam sir has ended his hunger strike after 26 days.
Thank you, sir, for your extraordinary courage and sacrifice. By putting your own life on the line, you awakened the conscience of an entire nation. Your life is far too precious to this country.
The Cockroach Janta Party’s peaceful protest at Jantar Mantar will continue until Dharmendra Pradhan resigns.