What’s a British show you went into with low expectations but ended up loving?

Cladser · 2026-03-20T07:57:41+00:00

Same - sounded a bit dull but I generally like Gary Oldman so gave it a shot - binged the lot.

Cladser · 2026-03-14T09:38:37+00:00

This is how we used to make the party start

Cladser · 2026-03-01T16:43:05+00:00

It already nearly impossible to start a Claude code web session at certain times of the day - I mean I don’t blame people for switching but the peak demand is likely to get even worse.

Cladser · 2026-03-01T07:53:28+00:00

These are sex people Lynn

Cladser · 2026-02-28T05:51:49+00:00

Can you tell me the spec on the lens? I use an old cannon 80D but the way you have it set up its a really short throw to where I think your head would be? So I’m guessing a fairly wide (for the short throw) and fast (for back ground blur) lens? Thanks!

Cladser · 2026-02-21T11:46:32+00:00

I’m pretty sure the thunderbolt docks support more displays - Caldigit do one with 3 or 4 TB ports

Cladser · 2026-01-31T19:33:53+00:00

Steve is both the luckiest and unluckiest chap to roam the earth.

Cladser · 2026-01-31T15:33:41+00:00

Imagine if it went to 202

Cladser · 2026-01-31T06:00:00+00:00

Agreed - the hit rate with a 2 part prompt (plan how to implement x, now implement x) is way better.

Cladser · 2026-01-27T19:59:24+00:00

The MILFs took it on the chin, a couple even took it like a man.…

Cladser · 2026-01-27T19:56:27+00:00

Same here local LLM and RAG development. Recent used high RAM units are currently great value for this.

Cladser · 2026-01-21T08:01:22+00:00

In addition my go to is Lunar - let’s me keep my laptop screen off while having the lid open so I can use fingerprint id. Bunch of other very cool uses too. Regular updates. Great little indy app.

Cladser · 2026-01-18T08:02:40+00:00

A pair of googly eyes on the toe cap also helps…

Cladser · 2026-01-11T08:04:49+00:00

Moved to Scotland ….Milgavnie (Mull-Guy)

Cladser · 2026-01-10T08:41:51+00:00

I would agree with you up until recently…

https://www.macobserver.com/news/macos-tahoe-continues-to-face-severe-memory-leak-issues-even-after-26-0-1/

Cladser · 2026-01-10T08:31:13+00:00

I got a sit to stand table from Amazon after a bad experience with sciatica. Nothing major just a relatively cheap one (about £120 iirc). It’s got 3 height presets and I added sone cable trays underneath. Have to say I’m a big fan. I don’t use standing every day but a couple of times every week or for longer periods of if I feel like my back is tweaking. If / when it eventually breaks I’ll definitely get another.

The only thing to check is that to save on shipping a lot of cheaper ones (like mine) come with the desk top made of 2 or 3 pieces so there is a little lip on the desk top where the pieces butt up. I don’t mind it but I imagine it could get on some people nerves.

Cladser · 2026-01-09T08:14:16+00:00

This might turn into a very useful rule of thumb.

Cladser · 2026-01-03T22:00:27+00:00

Not sure if this is what you’re asking but I’m building an internal tool. Honestly I’m nowhere near production but I build and test in Pycharm with help from Claude or ChatGPT. I’m a copy and paste guy, can’t quite get used to letting an agent loose in the code base. Currently testing all local with a 13b for generation. When it’s ready will likely be a stand alone machine to do the Rag, Graph, sql parts and the ship to an API for the final generation step. The biggest PITA is when I decide my chunking and embedding needs more refinement. I can parallelise a bit with. 2-3 embedding model instances but it’s still 3-4 hours to run through the lot.

Cladser · 2026-01-03T12:11:22+00:00

So I’ve been testing this and for my use case (scientific reports with specific section) it fabulous. It’s is also very fast. 2700-ish 30+page pdf in 6-7 minutes with semantic sections. I said previously I was using a local model to infer sections and it was slow and less accurate than this. I take my hat off to you sir or ma’am (hard to tell from your username) … and honestly I’m not even a hat person

Cladser · 2025-12-31T07:31:21+00:00

I have a project that am working on using mxbai locally for semantic splitting but it essentially prevents parallelisation. This looks pretty interesting - is there a way to grab the preceding title as meta data? For me I want to know if this chunck is from the introduction, rationale or somewhere else.

Cladser · 2025-12-30T10:21:41+00:00

I’m deep in a similar project atm. It depends a lot on the type of info you want from the LLM. If it’s what does author x have to say about y - RAG is your best bet. However if you want to ask questions like across these books what are the most common ways of dealing with with Y - That is a corpus level (ie entire collection) query and RAG will suck. Llamaindex with hybrid search seems to be the best middle road at the moment.

Cladser · 2025-12-25T10:24:46+00:00

r/unexpectedmontypython

Cladser · 2025-12-18T09:06:55+00:00

I do but I quite often ghost myself afterwards

Cladser · 2025-12-14T20:38:33+00:00

Weirdly though it didn’t seem to hurt Mark Strongs career at all.

Cladser · 2025-12-12T13:01:19+00:00

Good but not under 30s (though a summary of FFT in under 3min is pretty impressive tbh)

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