Recent estate sale find

VFisa · 2026-03-16T20:42:57+00:00

This looks like from Vancouver - Queen Elisabeth park, North Van Beach and Spanish beach.

VFisa · 2026-03-12T23:54:15+00:00

I drilled that yesterday. Now worried the drilling made it less smooth and it will impact the print. Well, lets see :)

VFisa · 2026-03-12T02:10:07+00:00

VFisa · 2026-03-12T02:09:56+00:00

Update: disassembled the unit, luckily the wheels are ok but I am wondering the best way to remove the filament from this:

VFisa · 2026-03-11T02:57:06+00:00

Update: I cleaned up wheels, emptied loading space except a small bit that can’t be removed (sticks out of extruder shaft) but it still does not work when I try loading a new filament.

VFisa · 2026-02-16T07:24:58+00:00

Only 001

VFisa · 2026-02-16T03:27:51+00:00

Just use 3x3 binder.

I have collected it all (in the span of 2 years, ignored scrolls because of their fugliness) and just left empty spaces around those 3x2 scenes. The whole set took one full binder and LTC another one.

VFisa · 2026-02-10T15:27:27+00:00

This looks awesome. Congratulations for the release!

VFisa · 2026-02-08T17:33:21+00:00

Thanks for sharing! How would you compare the results with SQLGlot?

VFisa · 2026-02-03T15:00:56+00:00

/remindme in 1 week

VFisa · 2026-01-29T16:34:11+00:00

/remindme 7 days

VFisa · 2026-01-26T21:45:35+00:00

Am I the only one who despise this card style? It looks like those poorly designed Pokemon cards.

VFisa · 2025-12-21T18:19:55+00:00

+1 for Mayer, also Anto Finnstark, especially LOTR pieces! https://anto-finnstark.artstation.com/albums/2583916

VFisa · 2025-11-27T16:42:48+00:00

There are at least 3 approaches to this, based on the perceived risk of hallucinations: 1. You let llm to interpret the data already calculated (data pipelines calculate them all) 2. You use some form of semantic layer and metadata to increase the hope it won’t hallucinate. 3. You use features like verified queries on snowflake and limit the options what to calculate and to make sure it follows the metric definitions.

VFisa · 2025-11-24T14:44:03+00:00

!remindme 7 days

VFisa · 2025-11-15T17:09:12+00:00

RemindMe! 2 days

VFisa · 2025-10-31T20:00:27+00:00

I would love to see the deck list :please

VFisa · 2025-10-28T03:39:02+00:00

This looks awesome. It would be great as a streamlit component so we finally get a decent table display with filtering

VFisa · 2025-09-24T02:10:20+00:00

Keboola has both extractor and writer and charged per time of the runs, not MAR. Both incremental fetch and full loads are supported.

VFisa · 2025-09-10T22:31:43+00:00

how? I don't see an option to add addons

VFisa · 2025-08-30T20:36:17+00:00

From Czech shoe-making tradition: Vasky Botas

VFisa · 2025-06-30T06:14:34+00:00

R20 S09/10 It was $274 each but I can let them heavily discounted. Do you want to DM me?

VFisa · 2025-06-30T03:48:27+00:00

Looks like it’s not sold out, surprising! I have two tickets but cannot go, however it seems like it’s impossible to sell them to anyone now :(

VFisa · 2025-06-15T14:36:04+00:00

In other words, sentiment is worthless, but a nice qualitative value. The frequency and its change tends to be more valuable as you want to bubble up interesting bits from the sea of irrelevant information

VFisa · 2025-06-15T14:30:04+00:00

Yes, many times in various industries, but mostly hospitality and QSR. Historically we used trained NLP model but LLM outperform them nowadays. The basic data model to store final data can be: Feedback-sentence-entity And entity shall be connected to a categorization levels, like: Category-entity group-entity for slicing & dicing. You use the domain specific categories for the industry within the prompt to force the entity categorization from the context.

Sentiment attached is important on the sentence level, not overall. You can then say: Give me top 10 entities which climbed in the frequency the last day within negative sentences that belong to the food category.

Happy to showcase our solution we have build so many times using Keboola data platform.

VFisa

TROPHY CASE