The First Data Platform: What a 30-Person Company Actually Needs
Aaron Wise
A first data platform for a 30-person startup: one warehouse, managed ingestion, SQL transforms and one BI tool, plus the signals that justify more.
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Leadership & Management
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Reporting Engineering to the Board Without Vanity Metrics
Aaron Wise
Engineering metrics for the board: report predictability, reliability, cost, risk and capacity, drop activity counts, and adapt the page as you grow.
The Goldilocks Platform: How to Engineer Just Enough (and Not Too Much)
Aaron Wise
Right-sized platform engineering balances load, risk, and growth stage to avoid over- and under-building.
The Wrong Kind of Early: When Startups Aren’t Ready for Senior Engineering Leadership
Aaron Wise
Senior engineering leaders need a clear problem, founder-market fit, and rough business plan before joining a startup.
Product Management
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Build, Buy or Wait Is a Different Call at Each Stage
Aaron Wise
Build vs buy decisions by company stage: buy by default, build where you differ, wait with a trigger, and revisit every vendor call at Series B.
MLOps vs. LLMOps: Choosing Between MLOps and LLMOps for Business Impact
Aaron Wise
MLOps industrializes predictive models, while LLMOps operationalizes large language models for generative tasks.
Engineering
More in EngineeringThe First Data Platform: What a 30-Person Company Actually Needs
Aaron Wise
A first data platform for a 30-person startup: one warehouse, managed ingestion, SQL transforms and one BI tool, plus the signals that justify more.
Reporting Engineering to the Board Without Vanity Metrics
Aaron Wise
Engineering metrics for the board: report predictability, reliability, cost, risk and capacity, drop activity counts, and adapt the page as you grow.
Must Read
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Engineering, Insights, Leadership & Management, Must Read
The Wrong Kind of Early: When Startups Aren’t Ready for Senior Engineering Leadership
Senior engineering leaders need a clear problem, founder-market fit, and rough business plan before joining a startup.
Roadmap Roulette: Choosing the Right Prioritization Model for Every Stage of Growth
The right roadmap prioritization model depends on your company’s growth stage and strategic goals.
Data Science
More in Data ScienceEvaluations Are the Spec for an LLM Feature
Aaron Wise
LLM evaluation sets are the spec: write twenty good-answer test cases before building, with product owning the bar and engineering keeping it green.
Beyond Words: How NLP Turns Flat Text into Context
Aaron Wise
NLP adds context to flat text through statistical and neural models, enabling machines to act on meaning rather than symbols.
Compressing the Clock: Scaling from Data Disarray to AI Impact in 18 Months
Aaron Wise
Azure builds a reliable AI platform by combining native services with third-party tools, structured environments, and a cross-functional team.
Recent Posts
Search all postsCompressing the Clock: Scaling from Data Disarray to AI Impact in 18 Months
Azure builds a reliable AI platform by combining native services with third-party tools, structured environments, and a cross-functional team.
The Goldilocks Platform: How to Engineer Just Enough (and Not Too Much)
Right-sized platform engineering balances load, risk, and growth stage to avoid over- and under-building.
The Wrong Kind of Early: When Startups Aren’t Ready for Senior Engineering Leadership
Senior engineering leaders need a clear problem, founder-market fit, and rough business plan before joining a startup.
Who’s on the Rocket Ship? How Teams, Pitches, and Capital Structures Evolve Together
Team composition must align with capital stage and execution goals to build durable, scalable organizations.
Crossing Organizational Boundaries
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Cross Organizational, Engineering, Insights
Who’s on the Rocket Ship? How Teams, Pitches, and Capital Structures Evolve Together
Team composition must align with capital stage and execution goals to build durable, scalable organizations.
Cross Organizational, DataOps, Engineering
From Zero to Data Hero: How to Go from Blank Slate to Business Value
Building a data program from scratch requires cultivating trust, defining clear value drivers, and aligning people before platforms.