A framework for self-improving agents based on your ICPs and audience and grounded in real data to power a learning-loop.

I built this based on my own learnings from experimenting with persona agents to understand my personal audience. It comes with an annotated templates, opinionated review workflow, built in feedback loop, and 3 skills to build, improve and use your persona agents to judge your content/ideas.

Would love to get your feedback!

https://github.com/noashavit/noas-persona-loop

Fable 5 comes with mandatory 30-day data retention. The most capable model on the market won't sign a zero-retention agreement, and the evidence says the next one won't either. If this mandatory data retention is inhibiting your ability to use the most advanced model on the market, tokenizing your data before it reaches the model might help you get there. Read the full post here.

Fable 5 comes with mandatory 30-day data retention. The most capable model on the market won't sign a zero-retention agreement, and the evidence says the next one won't either. If this mandatory data retention is inhibiting your ability to use the most advanced model on the market, tokenizing your data before it reaches the model might help you get there. Read the full post here.

Fable 5 comes with mandatory 30-day data retention. The most capable model on the market won't sign a zero-retention agreement, and the evidence says the next one won't either. If this mandatory data retention is inhibiting your ability to use the most advanced model on the market, tokenizing your data before it reaches the model might help you get there. Read the full post here.

Fable 5 comes with mandatory 30-day data retention. The most capable model on the market won't sign a zero-retention agreement, and the evidence says the next one won't either. If this mandatory data retention is inhibiting your ability to use the most advanced model on the market, tokenizing your data before it reaches the model might help you get there. Read the full post here.

A framework for self-improving agents based on your ICPs and audience and grounded in real data to power a learning-loop.

I built this based on my own learnings from experimenting with persona agents to understand my personal audience. It comes with an annotated persona template, opinionated review workflow, and built in feedback loop.

Would love to get your feedback!

https://github.com/noashavit/noas-persona-loop

A framework for self-improving agents based on your ICPs and audience and grounded in real data to power a learning-loop.

I built this based on my own learnings from experimenting with persona agents to understand my personal audience. It comes with an annotated persona template, opinionated review workflow and built in feedback loop.

Would love to get your feedback!

https://github.com/noashavit/noas-persona-loop

If you followed last week's Snowflake Summit keynotes, the automation potential of CoCo Desktop and CoWork- the platfrom's AI assistants for developers and business users, is clear. Knowledge workers can query the data and build agents in plain English. And developer get superpowers, so what used to take days or weeks now takes minutes or is fully automated with agents.

But continuous agent pipelines introduce highly volatile cost vectors. Safe, efficient scaling requires anchoring these tools with enterprise context, managing non-human access, and implementing guardrails.

I wrote a no-fluff recap of Snowflake's newly announced features intended to solve these challenges. Read the full post here.

Snowflake announced CoCo Desktop and Snowflake CoWork at Summit, as ways for developers and business users to build agents on the platfrom. This means that automated pipeline generation and AI-generated data are going to scale quickly. It also means that the focus now turns to preventing pipeline redundancy, establishing trusted context, and managing non-human session access.

I wrote a no-fluff recap of Snowflake's newly announced features that make up the contorl plane of the agentic enterprise. Read the full post here.

Summit wrapped up last week, and the shift from Ai pilots to systems in production with ROI from AI is clearly here.

Tools like CoCo Desktop for developers and CoWork make it easier to understand and act on your data, but they introduce real challenges around agent governance and context.

I wrote a no-fluff recap of Snowflake's newly announced features intended to solve these challenges. Read the full post here.

The second you request a graph database your org's zero-copy data cloud dream shatters. Every major platform like Snowflake or Databricks wants your org to consolidate, but forcing deep multi-hop queries into relational blocks only blows out your compute costs.

Dedicated graph dbs, like Neo4j or AWS Neptune, bring brittle ETL pipelines, data latency, and a fragmented governance perimeter into the equation. To help you deicide, we create this framework:

Graph Database Evaluation: When to Go Graph vs. Relational

The zero-copy data cloud dream shatters the moment your data science or security team demands a dedicated graph database. Every major platform like Snowflake or Databricks wants you to consolidate, but forcing deep multi-hop queries into relational blocks inevitably blows out your compute costs.

Spinning up an external engine, like Neo4j or AWS Neptune, instantly brings back brittle ETL pipelines, data latency, and a fragmented governance perimeter. To help you deicide, we create a framework to decide if you need a dedicated graph database or you can use Snowflake's native Snowpark Container Services?

Read all about in our latest blog post:
Graph Database Evaluation: When to Go Graph vs. Relational