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- The Next Input — Issue #054
The Next Input — Issue #054
An AI That Remembers Everything

⚡ The Briefing — 60 sec
Vibe coding turns senior devs into AI babysitters. $300K to be a vibe coder remediator? Not a bad life, hey.
UK plans AI systems to monitor offenders and prevent crimes. 1984? Big Brother?
Anthropic adds memory to Claude for some users. Finally! You remember!
🛠️ The Playbook — AI Memory Layer for Business Ops
Mission Build a persistent memory layer that gives your AI assistants context across sessions: remembering decisions, tracking projects, and surfacing the right docs without you having to re-prompt every time.
Difficulty Advanced | Build time 2–3 hours (pilot)
ROI Saves ≈ 15–20 h/month in repeated explanations and context setup.
Why This Matters
Without memory, every interaction with an AI agent is groundhog day: “Here’s our company context… again.” Memory changes the game by:
Eliminating repetition – no more restating rules, naming conventions, or key decisions.
Improving consistency – responses align with your processes over time.
Boosting trust – humans feel like the agent “gets them,” reducing tool fatigue.
The Architecture
Layer | Tool | Purpose |
|---|---|---|
Memory DB | Supabase / Pinecone | Store embeddings of key notes & session history. |
Orchestrator | LangChain / LlamaIndex | Manage retrieval & injection into prompts. |
Agent | Claude 3.5 / GPT-4o | Generate outputs with retrieved memory context. |
Interface | Slack / Notion / Chrome Agent | Where users interact and add new “memories.” |
Workflow
Capture Events
Every Slack Q&A, decision doc, or meeting note is logged to Supabase with metadata (
topic, owner, date, tags).
Vectorize + Store
Use embeddings (OpenAI text-embedding-3-large) to encode snippets.
Store as
{id, embedding, source, summary}.
Retrieve on Query
When user asks, orchestrator runs vector search for top-5 related memories.
Injects into system prompt: “Relevant memories from past interactions…”
Update Memory
After generating output, save summary of interaction.
Tag it automatically (
project: GTM Engine, status: complete).
Human Checkpoint
Slack button: 👍 Keep / 👎 Forget.
Keeps memory clean and prevents hallucinated context creep.
Example Use-Cases
Project Management: Agent remembers “Sprint retro notes” and references blockers next stand-up.
Customer Success: AI recalls last quarter’s QBR promises for a client before drafting renewal deck.
Sales: Memory stores prospect objections → next outreach email addresses them proactively.
Ops/Finance: Agent remembers categorization rules for messy vendors → applies without re-training.
The “Memory-Aware” Prompt
SYSTEM: You are an AI assistant with memory.
Before answering, review the attached memories.
Always ground your response in these if relevant.
If conflicting, show both and ask for clarification.
INPUT: {User query}
MEMORIES:
- {memory_1.summary} (source: {memory_1.source}, date: {date})
- {memory_2.summary} (source: {memory_2.source}, date: {date})
...
Scaling Memory
Short-Term: Store 30 days of chat + notes (fast recall).
Long-Term: Periodically summarise & compress into quarterly “Memory Books.”
Multi-Agent: Route memory by domain (Finance, Sales, Ops) so each agent stays sharp.
Privacy: Obfuscate PII and allow “forget” requests.
Pilot Plan — 60 Minutes
Pick one domain (e.g., Customer Success).
Connect Slack channel → Supabase → LangChain retrieval.
Save top 20 Q&As and 5 key docs as first “memories.”
Interact with agent for 1 week; tag Keep/Forget.
Measure: drop in repeated questions, response alignment.
Pro tip: Pair this with Claude’s new native memory (where available) but always keep a company-owned layer (Supabase/Pinecone) for portability.
🎯 The Arsenal — Tools & Prompts
Asset | What it does | Link |
|---|---|---|
Supabase | Simple Postgres + vector DB for memory. | |
LangChain Memory | Framework for memory injection. | |
Slack SDK | Build “Keep/Forget” buttons. | |
Prompt · Memory Cleaner | Summarises memory logs. |
Summarise these logs into 3 bullet memories.
Drop irrelevant chit-chat. Keep decisions, rules, and key facts only.
💡 Free Office Hours
Want a memory pilot for your business?
Book a free 15-minute Office Hours slot—no sales pitch, just workflows solved.
How Canva, Perplexity and Notion turn feedback chaos into actionable customer intelligence
Support tickets, reviews, and survey responses pile up faster than you can read.
Enterpret unifies all feedback, auto-tags themes, and ties insights to revenue, CSAT, and NPS, helping product teams find high-impact opportunities.
→ Canva: created VoC dashboards that aligned all teams on top issues.
→ Perplexity: set up an AI agent that caught revenue‑impacting issues, cutting diagnosis time by hours.
→ Notion: generated monthly user insights reports 70% faster.
Stop manually tagging feedback in spreadsheets. Keep all customer interactions in one hub and turn them into clear priorities that drive roadmap, retention, and revenue.
🕹️ Game Over
Ship one memory layer tonight—tomorrow’s workflows won’t start from zero.
Share your win; you could headline Issue #055.
— Aaron
Automating the boring. Amplifying the brilliant.
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