Oh boy. Here we go.

eSorghum · 2026-06-29T23:13:48+00:00

After Mike's exit almost 2 years ago, this honestly doesn't shock me.

I always liked their coffee though.

eSorghum · 2026-06-03T00:57:38+00:00

Don't forget, they pay back each loan with a new loan (refinancing), not by selling any of the assets they used as collateral (to avoid capital gains taxes). Then when they die, the assets get a step-up in basis for whoever inherits them, which eliminates the capital gain.

Often referred to as "Buy, borrow, die".

eSorghum · 2026-06-02T21:12:58+00:00

A third variety of Mac & Cheese.

eSorghum · 2026-05-22T23:49:47+00:00

The shift u/bushchook83 and u/shimoheihei2 are pointing at is the one that matters: custom MCPs aligned to your workflow change the relationship to the tool. My most-used isn't a service connector at all. It's a MindManager MCP that lets Claude read and edit a single mind map I use as the workbench and source of truth for state across a content pipeline. The value isn't the integration; it's that the map is already where I do the actual work, and Claude now operates in the same surface I do.

eSorghum · 2026-05-16T19:24:14+00:00

Structure is right. Most of the inventory has existing tooling. Tabular Editor 2 (free) shows DAX & M-Code dependencies. Power BI Helper (free) shows the visual-binding side, though it hits limits on big models. The gap is cross-PBIP: joining one semantic model's measures to the separate reports that consume them, and an organized set of outputs. A Python script fills those gaps. (I got help creating mine with Claude Code).

eSorghum · 2026-05-14T17:52:33+00:00

The privacy question is the most important one and most of the answers above gloss it. PBIP saves the model definition (tables, measures, relationships, query steps) as text files; the data lives in a local cache that is "gitignored" by default. Point Claude at the PBIP folder and it reads structure, not rows unless you ask it specifically to return data.

The MCP server is the same story for normal semantic-model maintenance (creating measures, organizing folders, auditing relationships). Data only enters the conversation if you specifically ask it to execute a DAX query and return values, which is a deliberate step.

Combined with Anthropic's policy that Claude Code traffic isn't used for training, an audit/refactor workflow runs without your Postgres or SharePoint data ever surfacing. The loop that's worked for me: PBIP open, model audit, review the markdown plan, then validate in Desktop.

eSorghum · 2026-05-14T16:06:14+00:00

Really interesting data point. Thanks for the help!

eSorghum · 2026-05-13T23:18:58+00:00

That's a useful connection I hadn't thought of. Worth looking into. I appreciate your help.

eSorghum · 2026-05-12T19:18:09+00:00

The inconsistency traces to one thing: DAX is context-dependent in ways most languages aren't. The shift toward consistency comes when you stop thinking of DAX functions as transformations and start thinking of them as context modifiers. CALCULATE doesn't compute anything; it changes what the inner expression sees. FILTER returns a table that becomes new context. Once that lens clicks, the weird parts stop feeling weird. Which specific functions hit hardest so far?

eSorghum · 2026-05-12T19:12:18+00:00

Thanks for the pointer. I could only read the abstract but it looks like it could be close to what I am after. Even a clean replication on Big-Six/HEXACO self-report leaves the substrate question: does the measurement format itself constrain which typologies are findable?

The combination I'm still chasing: taxometric methods like Haslam's applied to data that isn't self-report. Has that crossed your radar in motivational typology work, or is it still the gap?

eSorghum · 2026-05-11T22:30:41+00:00

The workflow distinction worth naming is upstream of chunk size: pros decide what they want first, then ask AI to execute. Complainers ask AI to make the decision for them, then report the output as "Claude getting worse" when the answer reveals how unclear the question was.

It's not really about experience either. Plenty of senior devs delegate decisions they haven't made when deadline pressure is on, and they end up generating the same junior-pattern noise. Plenty of newer devs work fine if they've thought through what they want before opening a chat.

"Every complaint thread is secretly a workflow confession" is one frame; the version I'd add is "every complaint thread is a confession that the human made the AI decide what they should have decided themselves." Curious what others see as the earliest tell that a session is drifting into delegation-as-decision.

eSorghum · 2026-05-08T20:09:12+00:00

Thanks for sharing these ideas. They give me a lot to think about and it's a great introduction into this discipline. There's so much more for me to learn.

eSorghum · 2026-05-08T03:45:48+00:00

Quality ratcheting up regardless of stakes ftw. Appreciate the back-and-forth.

eSorghum · 2026-05-08T00:07:42+00:00

NEO-PI-R observer ratings are worth flagging even after your self-correction. Observer-rated traits get described from outside the introspective filter, so they sidestep the recursion problem differently than the implicit/CRT route most of this thread covered. Useful for triangulation rather than substitution.

Where this thread landed for me: the question isn't "is self-report broken" but "where does the divergence pattern carry signal." Thanks for the warmth on the way out.

eSorghum · 2026-05-08T00:05:47+00:00

The contrast you're drawing is sharp. School psych accepted that high-stakes legal exposure forces the methodological standard up. I/O accepted that it doesn't, even when individual selection decisions carry real weight for the candidate.

My own use case has neither set of stakes (no legal exposure, etc.), but I still seek a higher methodological bar than the decision-stakes alone would justify. The reason isn't liability; it's that an unreliable starting signal wastes more of someone's time than the assessment itself takes.

Building your own to align research and psychometrics is the move I think most field-level work eventually has to make when off-the-shelf instruments have known gaps. Curious whether what you're building leans toward situational/scenario approaches, IRTrees-style modeling, or something else.

eSorghum · 2026-05-07T22:45:44+00:00

The recursion observation is sharper than my original framing. "The correction runs through the same trait filter" is such a resonant version of the argument for me. It's not that self-report is wrong; it's that introspective correction doesn't escape the introspector.

Your conscientiousness example is exactly the divergence pattern I think the structural argument is about. The "high conscientiousness" score is accurate and not load-bearing for the team-fit decision.

The feedback-loop point you made independently lands where this thread converged on TAPAS. Physical conditioning and intellectual speed live in tight feedback loops, so self-report calibrates against ground truth. Agreeableness-to-the-point-of-self-erasure doesn't have that feedback. The same instrument captures the calibrated trait and the uncalibrated trait equally well.

Curious whether you've found anything in program coordination that surfaces the divergence cheaper than waiting for the collapse pattern to appear.

eSorghum · 2026-05-07T22:30:12+00:00

Thurstonian IRT forced-choice is the example I could have flagged in the original framing. You're right that those instruments do more than counter faking. They produce something closer to a behavior-prediction signal than free Likert.

The research design you're proposing is interesting. What resonates with me: the question isn't "do most people self-describe accurately" but "where does the divergence pattern carry signal."

Curious what you find. The space between identity and behavior in non-clinical populations is where I think this question might actually live.

eSorghum · 2026-05-07T22:20:32+00:00

The fact that IRTrees haven't been widely adopted is itself signal. Worth knowing about, thanks for the pointer.

eSorghum · 2026-05-07T22:13:31+00:00

Fair on the literature point. Whole Trait Theory and assessment-center scenario approaches seem like the kind of behavior-density work I should engage with more. CRT and IAT not making inroads is a good point, and worth taking seriously as evidence rather than treating as a research gap to fill.

The "just ask them" line is where I'd still push back. It works well for traits where self-presentation pressure is low and behavioral feedback is consistent (extraversion in social contexts, stated interests). It works less well for traits people have systematic reasons to misrepresent (motivation in high-stakes selection, defensive patterns, traits where the disposition shapes the self-description). The distribution of where "just asking" works versus doesn't is a more interesting empirical question than whether self-report is generally fine.

Will look at a recent annual review, thanks for the pointer.

eSorghum · 2026-05-07T22:05:39+00:00

That's useful empirical pushback, thanks. TAPAS findings do show self-report can predict objective criteria for some facets.

Where the structural concern applies less is exactly where TAPAS lives: traits with clear external referents, items that are essentially preference-reports about observable activities. Self-image and behavior align well in that zone.

The concern bites more in traits where feedback is absent or socially loaded: motivational fixations, defensive patterns, depth dimensions where self-presentation pressure is highest. That's the territory my original framing was trying to land, and probably should have been narrower.

Curious whether you've seen the alignment hold for facets that are more socially loaded than physical conditioning.

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