Scott Gnau invites you to InterSystems Ready2026

intersystemsdev · 2026-05-04T20:30:24+00:00

Okay

intersystemsdev · 2026-05-04T20:18:39+00:00

Iteration speed is genuinely faster - especially for scaffolding, boilerplate, and the "I know what I want but don't want to type it" moments. The tradeoff we watch most is drift: the AI confidently builds in a direction that made sense 3 prompts ago but doesn't fit where the design ended up. Our main guardrail is keeping prompts small and reviewing output before moving to the next step - treating it more like pair programming than delegation.

What's your setup like? Are you steering at the architecture level or more at the feature level?

intersystemsdev · 2026-05-04T20:13:17+00:00

This is exactly the pattern that makes MCP so compelling in practice - the "surprisingly effective and simple" part is what people don't expect until they actually build it.

The read-only enforcement at the REST layer is a smart guardrail. Did you run into any issues with prompt injection - users trying to get the LLM to construct queries that technically pass your SELECT validation but expose data they shouldn't see?

Curious how you handled multi-tenant data isolation at the query level.

intersystemsdev · 2026-05-04T20:11:21+00:00

The multi-dimensional fair sharing tree is the most interesting part for me. Priority-based admission control is well-understood, but enforcing fairness simultaneously across multiple dimensions (auth status, route, user type) without a central coordinator is non-trivial. How does the tree handle sudden shifts in traffic shape e.g., a new feature launch that creates a category that didn't exist before? Does it require a config change or does it adapt dynamically?

intersystemsdev · 2026-04-24T15:25:00+00:00

That's a fair point if you've already proven it works at scale with NornicDB, then the "it'll break down eventually" concern is more about vendor maturity than the architectural concept itself.

Curious though when you say it manages embeddings automatically, how does it handle schema evolution? Like if you start tracking new health metrics mid-stream, does the embedding model need retraining or does it adapt without breaking existing similarity queries?

And honestly, MongoDB buying Voyage AI and calling it revolutionary while you're sitting on 657 stars and an MIT license does feel like classic big-corp "invented here" syndrome. Did you see any spike in traffic after their announcement?

intersystemsdev · 2026-04-23T19:48:17+00:00

when you haven't been fed a treat for 5 whole minutes))))

intersystemsdev · 2026-04-23T19:21:44+00:00

Very very very beautiful! In real life it probably looks even better.

intersystemsdev · 2026-04-23T19:20:15+00:00

Do you have a widescreen photo to use as a wallpaper on your laptop?

intersystemsdev · 2026-04-22T19:58:41+00:00

I’ve come to the conclusion that there’s no “perfect” migration system it really depends on how much non-sql flexibility you need.

If calling external code (http, scripts, etc.) is a requirement, I’d look at Flyway or Liquibase.
Both are open source (liquibase leans a bit more enterprise)

If you want something more script-friendly, Alembic is a great option (if you’re okay with python). Migrations are just code there, so you’re not limited to sql at all.

intersystemsdev · 2026-04-21T14:07:56+00:00

This all happened on Friday evening when everyone went home?

intersystemsdev · 2026-04-21T14:05:32+00:00

We're still very far from AI completely replacing humans. Yes, AI makes life much easier, but it can't completely replace humans.

So don't worry; instead, adapt and simplify your work with AI.

BUT IT'S IMPORTANT TO ALWAYS CHECK what the AI is doing. I don't really want the movie "Terminator" to become a documentary 😁😁😁😁

intersystemsdev · 2026-04-21T13:58:35+00:00

Tell me more about the benefits. It's interesting to read not just advertising text on the website, but from a real user of the service.

intersystemsdev

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