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The AI Engineer

Paolo Perrone

Become dangerously good at AI Engineering.

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Substack
PricingOnly free issuesPublishesTwice weekly
Issues49Founded9 months agoLast Issue5 days ago
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Latest Issues

RAG vs Fine-Tuning

RAG and fine-tuning solve different problems. RAG pulls external text into the prompt at query time, so the model answers from documents you can edit or cite. Fine-tuning retrains the model’s weights until a skill, format, or tone is built...

5 days ago
15

Should You Self-Host Inference?

Self-hosting inference pays off in two cases: you push past roughly two million tokens a day, or your data legally cannot leave your network. Below that line a hosted API is cheaper, and it spares your team from running GPUs at all. For mos...

10 days ago
14
3

AI Coding Tools: What Changed in the Last 6 Months

AI coding tools stopped competing on autocomplete and started competing on autonomy. Agents moved into the terminal and the cloud. Every major vendor shipped its own model. SWE-bench cleared 80% and bunched up, flat-rate plans gave way to m...

13 days ago
17
1

Mem0 vs Zep vs Letta vs cognee

cognee, Mem0, Zep, and Letta all give an AI agent memory that survives past a single conversation, in four different ways. Mem0 extracts facts into a store. Zep builds a temporal knowledge graph. Letta lets the agent edit its own memory. co...

17 days ago
18
4

What is LLM Observability?

LLM observability means giving your app a black box, the same idea as an airplane’s flight recorder: something that captures every step of a request so you can replay exactly what happened when the output is wrong.

🧭 Part 9 of the RAG &...

20 days ago
26
5

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  • Paolo Perrone

    Shipping Production AI: Agents, Inference, GPU. Read by 1M+ AI engineers.

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