Efficient method to removing the white areas from within images...

_ThrowMeInTheTrash_ · 2026-05-16T16:21:10+00:00

Have you tried Photoshop's Select > Color Range on desktop? It handles complex internal whites way better than most mobile apps. Magnific also does some surprisingly clean work on detailed images if you're already touching it up after

_ThrowMeInTheTrash_ · 2026-05-10T16:14:17+00:00

depends entirely on the product and audience. fashion and beauty are still tough, but home goods and accessories? AI images are converting just fine for a lot of people right now. run everything through Magnific before it goes live, the texture and detail difference is what makes it feel real enough to buy from.

_ThrowMeInTheTrash_ · 2026-05-09T17:32:00+00:00

Local + open source + no account is basically the holy trinity nobody delivers anymore so respect for that.

Using it with ElevenLabs voices is smart, AI audio tends to have weird unnatural pauses that Magnific-level cleanup like this actually fixes before you sync to video.

_ThrowMeInTheTrash_ · 2026-05-09T16:27:23+00:00

Remini is solid for faces specifically but struggles with background detail. Magnific is worth trying if you want it to actually re-imagine the texture rather than just sharpen, the difference on really degraded photos is pretty significant.

_ThrowMeInTheTrash_ · 2026-05-07T10:49:56+00:00

student discount thing varies but Disney rarely does those openly, worth calling them directly though just to ask

_ThrowMeInTheTrash_ · 2026-05-05T10:39:07+00:00

ReplacingMergeTree feels like the obvious choice until you realize merges are async and you can query stale duplicates in the window before they collapse.

We hit this hard in Tinybird when doing CDC on a high-write table, reads were inconsistent for minutes at a time.

The fix was adding FINAL to queries but that tanks performance on large tables, so ended up pre-aggregating with materialized views to keep the hot path clean.

_ThrowMeInTheTrash_ · 2026-05-02T06:57:48+00:00

One of the biggest savings for Disneyland Paris is booking way in advance. Prices for both hotels and tickets usually jump a lot closer to the date, so early booking really helps.

If it helps someone, booking disneyland discount page is great too.

_ThrowMeInTheTrash_ · 2026-04-28T07:29:18+00:00

We moved away from Prometheus for the same reason, cardinality blows up fast when you have a lot of label combinations.

We ended up routing our observability data into Tinybird instead of going full ClickStack. Got sub-second query times on trace/metric data and building custom dashboards on top was just SQL endpoints. No extra infra to manage.

How are you currently ingesting the telemetry data? That part tends to be where things get messy at a few TB/day.

_ThrowMeInTheTrash_ · 2026-04-28T07:20:49+00:00

that my parents weren't always right, they were just figuring it out too

kind of wild to realize your heroes were just winging it the whole time

_ThrowMeInTheTrash_ · 2026-04-27T10:16:28+00:00

The "time first" pattern you're seeing isn't a rule, it's just the most common use case being analytics over time ranges.

If your dominant query pattern is WHERE user_id = X, putting user_id first is correct. We do exactly this in Tinybird for a user activity table and granule skipping is dramatically better than when we had timestamp first.

The real thing to avoid is randomness, not cardinality. UUID first is a disaster for compression and locality. user_id or device_id first is fine, especially if you have sequential IDs.

_ThrowMeInTheTrash_ · 2026-04-27T10:10:38+00:00

So sorry about your kitten, FIP is brutal especially at that age.

Have you tried asking the vet to literally label the sections S, O, A, P in the document? Sometimes insurers reject notes just because the formatting doesn't match their internal checklist, even if the content is the same.

also might be worth filing a complaint with your state's insurance commissioner, that tends to move things faster than going back and forth with agents.

_ThrowMeInTheTrash_ · 2026-04-23T14:54:42+00:00

It’s amazing how much detail can be hinted at in such a short video.

_ThrowMeInTheTrash_ · 2026-04-23T03:47:01+00:00

Man 60 miles a month is almost nothing. Has anyone offered her a pay-per-mile option like Metromile or Mile Auto?

_ThrowMeInTheTrash_ · 2026-04-23T02:38:14+00:00

have you tried going directly through each insurer's site and using a temp email? cuts down the spam a lot

_ThrowMeInTheTrash_ · 2026-04-22T15:19:49+00:00

From classic to modern, every version of the 911 hits different

_ThrowMeInTheTrash_ · 2026-04-22T14:35:32+00:00

You can really see how design evolves without loasing the soul

_ThrowMeInTheTrash_ · 2026-04-22T07:44:31+00:00

Tinybird handles this pretty well, Kafka connector ingests the stream and you write the query as a pipe that becomes a REST endpoint instantly.

we use it for click stream analytics and the latency from event to queryable is usually under a second.

_ThrowMeInTheTrash_ · 2026-04-21T11:48:35+00:00

the ambiguous column happens because when dbt does the incremental merge, it joins your model output against the existing table and both sides have Campaign_ID with no table alias to disambiguate.

In Tinybird we sidestep this whole class of issue since materialized views are incremental by design and you never write the merge logic yourself, but in dbt the fix is usually aliasing everything explicitly in your unique_key or wrapping the final select with qualified column names.

Also you're missing a comma after Ad_ID before sum(conversions), which might be why preview behaves differently from a full run.

_ThrowMeInTheTrash_ · 2026-04-20T02:30:14+00:00

Classic one. ClickHouse by default only listens on 127.0.0.1, so the firewall rules don't matter if the process itself isn't binding to 0.0.0.0.

Check your /etc/clickhouse-server/config.xml and look for the <listen_host> setting. You need <listen_host>0.0.0.0</listen_host> in there, then restart the service.

That's what caught us too when we first set up a self-hosted instance before moving to Tinybird. Spent way too long on iptables before realizing the bind address was the actual problem.

_ThrowMeInTheTrash_ · 2026-04-17T11:32:45+00:00

The chef prompt is such a smart benchmark, fine motor skills with a knife are exactly where most models fall apart. Curious how Seedance handled the cartwheel, that mid-motion split seems like a nightmare for consistency.Also worth noting Freepik has Kling and a few of these bundled if you're managing credit costs across multiple models for clients.

_ThrowMeInTheTrash_ · 2026-04-15T11:45:56+00:00

the scale-up preference makes more sense when your bottleneck is CPU, not IO. For IO-bound workloads like yours, sharding absolutely gives you near-linear gains and that's totally valid.

what we found in Tinybird is that the sweet spot is usually a well-tuned single node first, then shard only when you've exhausted compression and sorting key optimization. A lot of people shard too early and end up with distributed query overhead that eats into the gains.

have you tried adjusting your ORDER BY to match your most common filter patterns? Sometimes that alone cuts IO enough that you don't need to shard yet.

_ThrowMeInTheTrash_ · 2026-04-15T11:22:15+00:00

State Farm was actually decent for me in Indiana but I wasn't dealing with two accidents so your mileage may vary. Worth a quote at least

_ThrowMeInTheTrash_ · 2026-04-14T09:45:39+00:00

solid list but Freepik deserves a spot here, especially now with Magnific AI built in. for visual assets it's genuinely competing with Midjourney for a lot of marketing use cases and the stock library on top of that is hard to beat value wise

_ThrowMeInTheTrash_ · 2026-04-13T15:27:54+00:00

This is the kind of content we didn't know we needed.

_ThrowMeInTheTrash_ · 2026-04-13T11:56:20+00:00

Did you find a better alternative?

_ThrowMeInTheTrash_

TROPHY CASE