SZL SENTIENT ZERO LABS
Newsletter for builders

Practical AI insights
for founders & engineers

Two focused series. Fourteen issues. Zero hype. Each ends with a checklist you can use immediately.

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14 Issues published
2 Focused series
14 Actionable checklists

Two series. Two audiences.

Whether you're deciding what to build or building it yourself, there's a series for you.

Founders 6 issues

Pragmatic AI for Founders

Evaluate AI opportunities, avoid the wrapper trap, measure real ROI, and build defensible AI products. Written for non-technical founders and product leaders.

  • Mental models for AI
  • Prompt engineering (without code)
  • Agent design for business
  • Build vs Buy strategy
Read the series →
Technical 8 issues

Agent Design Fieldbook

Design, build, and operate AI agents in production. An 8-layer architecture from data layer to RAG, with real code and battle-tested patterns.

  • 8-layer agent architecture
  • Schema design & field registries
  • Intent classification & filter extraction
  • Memory, ranking & RAG patterns
Read the series →

What makes this different

01

Starts with a real story

Every issue opens with a concrete failure or success. No abstract theory — real systems, real outcomes.

02

One idea at a time

Each issue covers one concept deeply. You'll finish knowing it well enough to use it, not just recognize it.

03

Ends with a checklist

Every issue closes with a scorecard or checklist you can apply to your own product immediately.

Latest issues

Founders

#6
Build vs Buy vs Embed: AI Strategy That Actually Works Most AI strategy failures come from building when you should buy, buying vendors that can't handle your data layer, or measuring ROI without counting the disasters you prevented.
→ #5
Silent Failures and Monitoring AI in Production The scariest AI failures are silent -- no errors thrown, just slow degradation -- and monitoring drift is the only way to catch them before users leave.
→ #4
Where AI Breaks and The Data Layer Solution AI fails predictably in two ways -- hallucination and bad retrieval -- and designing your data layer first prevents 80% of production disasters.
→

Technical

#8
The Product Deep-Dive Layer: When Users Want More Than Specs When users want to go deep on a product -- reading reviews, understanding thermal performance, checking compatibility -- RAG lets you search unstructured knowledge that doesn't fit in structured columns.
→ #7
The Sort & Rank Layer: Ordering Results Intelligently Sorting isn't just ORDER BY -- define what 'best' means for each field, handle JSONB with SQL expressions, and let the LLM infer user intent from keywords like 'cheapest' or 'best'.
→ #6
The Memory Layer: Conversations That Persist Agents need memory -- not just chat history, but structured context (what was fetched, what was selected, what tokens were used) persisted to database for multi-turn conversations.
→

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