u/annakows

I tried rolling out an Agentic Analytics pipeline last quarter to handle ad-hoc SQL queries, but less than 10% of my small team's pilot queries made it into production without hallucinating join conditions.

Interesting, gartner predicts 40% of enterprise software will adopt task-specific agents by late 2026, yet running agents on unmodeled schemas is a mess. When we plugged LLMs directly into Snowflake, we ended up with four conflicting definitions of active churn. Moving metric logic downstream into a governed semantic layer on cube helped stabilize query generation, though multi-step agent reasoning still tripled our token costs. and mst industry coverage of Agentic Analytics centers on model capabilities, but the real bottleneck is that over half of enterprise data is not structured for agent consumption. for teams currently running AI agents on live data, how are you enforcing metric governance without breaking ad-hoc exploration?

recently migrated my team's core revenue definitions to the dbt semantic layer, six month or so have passed. was hoping to end weekly arguments between finance and marketing.

yes, such semantic layer fixed standard dashboard consistency across 12 core models, meanwhile, connecting internal LLM agents has exposed real architectural limits present

because we needed sub second query performance for live agent interactions; we ended up testing self hosted cube dev setup upstream of snowflake to do catching and dynamic sql rewriting too. for now it seems to solve latency issues but running two separate modeling layers quickly created a bit of sync headache for 3 of our analytical engineers

and I have a question, for teams feeding metric context to llms how do you work with complex ad-hoc calculations that fall outside your predefined YAML schemas?

In landscape archaeology, one of the most persistent diagnostic hazards in my experience is to differ pre-modern ritual alignments from some undocumented mid-20th-century physical science test arrays

​so last season, during a non-invasive survey on a Cambro-Ordovician quartzite scree ridge (gołoborze) in the Świętokrzyskie region, me and a couple of my colleagues surveyed a line of five low stone cairns that had circulated since 1990s in regional media as a "bronze age solar alignment":

​first, the cairns were built entirely of local quartzite blocks heavily colonized by crustose lichen (Rhizocarpon geographicum).

​lichenometry then confirmed the surface stones had sat undisturbed for roughly 50 to 60 years, which is well plausible

​the alignment matched a precise 42-degree azimuth along the natural fault fracture, ok

​we opened a 50 cm test trench beneath Cairn #3 to check for prehistoric stratigraphy, and really, zero cultural horizons there so no ceramic sherds, no charcoal, no bone fragments as well (underwhelming, I know)

instead, at 40 cm depth there were other findings, so we did not leave with zero result

​first, 6 mm galvanized steel grounding rod embedded in an unreinforced pour of sulfur-cured industrial cement. ​the rod was somehow clamped to a high-purity electrolytic copper plate set directly into a bedrock fissure, with an insulated lead tracking deeper into the scree

​afterwards cross-referencing with university records did indeed mention the site was Station KW-3, an experimental field outpost set up in 1969 by Rev. Prof. Włodzimierz Sedlak’s bioelectronics group (KUL). seems they were logging natural piezoelectric discharge and spontaneous galvanic potential gradients across quartzite seams during barometric cold fronts also measuring a continuous, subterranean analog current directly through the materials like said stone matrix. the researchers likely deliberately constructed the surface markers from loose scree blocks so forestry maintenance would leave the grounding junctions undisturbed.

​this has to be looked into deeper but anyways I wonder how many undocumented "megalithic monuments" or "ritual cairns" like what media and people there call or think of them, all across Central and Eastern Europe, may be actually Cold War geophysical or telemetry grounding points that were permanently camouflaged by the terrain?

​

​Took the CAIT and old SAT. Visual-spatial and figure weights sit comfortably in the 135–140 range. Spatial relational recognition feels instantaneous—my brain handles static geometric constraints with zero perceived load.

And ​the moment a task introduces discrete, step-by-step procedural manipulation under unbuffered time constraints (e.g., mental multi-stage transformations, reverse digit span past 7, or tracking alternating rule inversions), my working memory buffer collapses.

​What I’ve noticed testing myself on sequential tracking:

​Static mapping: I can resolve complex overlapping nomograms, spatial geometry, and layout grids in one glance.

​Procedural lockout: The second a task requires holding a 5-bit serial state while executing an offset rule—like tracking a shifting signal return or calculating mechanical stepping-relay cycles in sequence, for example the original baseline gets dumped from RAM to make room for the transformation operator.

​Analog current vs discrete steps: If I have a continuous tactile reference (let's say a pencil edge, mechanical counter, or paper crease), sequential logic is effortless. And if I have to hold the states entirely unshielded in my head, performance drops to average (nearly ~100–105 WMI).

​Does anyone else test with a severe spatial-vs-working-memory split where continuous/analog relational processing works fine, but discrete sequential recursion causes an immediate buffer overflow? Is this just a classic bottleneck in phonological loop bandwidth, or is mental state retention inherently unstable without external physical anchors? 

Is text to SQL just a commodity layer that increases metric drift?

I see colleagues and industries still obsess over llms generating ANSI SQL while in production the raw syntax generation may easily be the easiest pipeline part. I find bottleneck to be in context engineering, maybe in ontology too. To have antonomous agent querying arr / churn\_rate to apply my data teams precise joints to use active user definitions, filters, rather than hallucinating against raw warehouse labels as it often tends to do

When I connect ai agents or bi tools to Dita warehouses directly (snowflake, databricks) I faced an awful architectural dillema...

To lock warehouse down with rigid views (which bottlenecks my team!)

or

To let clients query raw schemas and then I end up with 15 conflicting definitions of same core CPI

That started to annoy me a lot and I thought if there was any way out, perhaps, moving from gen3 (cloud warehouse-native bi) to gen 4 that people now sometimes talk about, or ai native analytic layers. In such architecture upstream semantic layers like cube dev sit in code (version-controlled by git)

That exposes SQL-first semantic model that seems to remain extensible at query time and seems to allow downstream clients / internal dashboard / embedded app or LLM querying via model context protocol to run adhoc calculations on top of governed dimensions. Would it measure without breaking central source of truth? Probably

But how do you work with this tension between centralized governance and client level query flexibility?

A lot of games treat interpretation like my homework which also comes after the plot itself. I would like examples where interpreting in the process changes everything.

How you move, what the character notices, even the rules of the game (or what I as a player believe the rules to be)

I don't mean ambiguous endings in themselves; what interests me is : meaning making as a mechanic

Which games do something like this and do it well?

For my new essay I had to go through early academic computing history a bit, and a curious thing appears in 1974–1975 where I found the title (title itself) of a doc titled "Principles of Tangible Computing". Asking my dean he said it circulated informally across labs including MIT, Bell Labs, and even allegedly at several Soviet research institutes.

as far as I understand from the surrounding context, the paper argued against the complete pivot to binary digital architectures, and advocated instead for continuous-variable, mechanical, and paper-based computation. finding info on it it seems sections of the circulated drafts were encrypted using classical easy ciphers (such as Caesar, Atbash, and Baudot strings),

I learned that by late 1975, funding for these alternative tangible computing projects across both Western and Eastern institutions gone to zero, evaporated. Then several researchers left academia entirely or had their work quietly re-filed into theoretical obscurity (maybe you can name some of them, interesting what you can find)... And is this simply natural market consolidation around microprocessors, or more like a coordinated institutional effort to standardize computing architecture?

when you change your screenshots, what do you watch besides conversion rate? I know there are different approaches.

i used to treat screenshot testing like a landing page test: did installs go up, yes/no.

With time i started thinking that can miss, awfully so

a screenshot can improve installs and still attract worse-fit users. especially if screenshot one promises speed, automation, “ai,” money saved, or some perfect outcome the product only partly delivers.

the scary version is: page conversion improves,install volume improves, trial starts improve

BUT then reviews start saying it's misleading, or not what they expected, "way too limited," or "subscription trap"

that is still a screenshot result in my experience, but it is just a delayed one.

lately i try to read review language after bigger store-page changes, again, multiple ways to do it. i’ve used for that some appfollow and alternatives , so for me it became a bit easier to filter by version/country and see themes, but the same idea works manually at smaller volume.

the question i would ask before is "did more people install?"

But now I am interested in did the right people install with the right expectation?..

Also super curious how other people measure this. do you look at review text after screenshot changes, or mostly app store connect numbers?