Legacy Database Migrations to PostgreSQL. Evolution Not Revolution.

Database Solutions That Work

We specialize in PostgreSQL internals, custom extensions, and migrating legacy systems to modern architectures. Whether you need performance optimization, C/C++ extension development, or a path off your aging database platform, we can help.

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About dataStone

dataStone is a PostgreSQL and C/C++ consulting firm founded by Dave Sharpe. We help organizations solve complex database challenges, especially migrating legacy systems to modern PostgreSQL architectures.

Founder: Dave Sharpe

Dave brings 20+ years of C/C++ development experience with deep specialization in PostgreSQL backend development. His expertise includes:

  • PostgreSQL internals, extension development, and FDW (Foreign Data Wrapper) implementation
  • PostgreSQL hooks, callbacks, and PL/pgSQL development
  • Relational database theory and SQL semantics
  • AI theory and algorithms, with a focus on applying AI to development workflows

Topics We Cover

AI News PostgreSQL Productivity SQL

Recent Posts

  • Automating the PARA Weekly Review with Claude Projects and Notion MCP AIProductivity

    The PARA weekly review is supposed to be a habit. In practice, it competes with everything else on a Monday morning and (for me) loses. Tiago Forte describes the process clearly and if you do it you feel better, but manually working through six databases and writing a summary takes sustained focus that is easy to defer. You could open a Claude conversation instead, but you’d spend the first ten minutes copy-pasting database schemas, re-explaining your workflow, and reconstructing context that evaporates the moment you close the tab. By the time Claude is useful, you’ve burned more energy than the review itself would have taken.

  • Your Best Ideas Come at the Worst Time: Building a Low-Friction Capture System for Developers ProductivityAI

    I was mid-feature in a Claude Code session when a /simplify pass triggered a bigger idea: retrofit simplification systematically across the whole codebase. Claude gave me a 300-word response sketching how that might work. Useful idea, concrete starting point.

    But the idea had nothing to do with what I needed to finish NOW. I told myself I would come back to it … I did not come back to it.

  • Catching Spec-Kit Task Phantom Completions with /speckit.verify-tasks AI

    AI agents sometimes mark tasks [X] without doing the work. These phantom completions are rare (~0.36% in my data), but each one is a false claim you’ll either accept or spend precious mental energy figuring out the truth. verify-tasks is a spec-kit community extension that runs a multilayer verification cascade against every [X] completion in your tasks.md and passes a verdict on whether the work was actually done.

    In The [X] Problem, I documented phantom completions: tasks that AI agents mark [X] complete without doing the work. Across ~830 structured tasks spanning Claude Code /plan and spec-kit workflows, I found three phantom completions, about 0.36%. The preceding post introduced /verify-plan to catch this in Claude Code’s /plan workflow, but nothing equivalent existed for spec-kit’s task-based workflows, where hundreds of [X] marks in tasks.md go unchecked after /speckit.implement finishes.

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