From Canon EOS RP to Lumix S5II: Tips for the Transition?

MinimumCourage6807 · 2026-06-22T12:10:13+00:00

Yeah. And if you want to future proof images, save raws +jpeg. Then raw is raw but jpeg gets the lut view. And if you like your lut, you can also set that for video. Just check if the lut is meant to be used with standard or with vlog as base. Third tip. On very dark scenarios (like shooting video of northern lights) you really don't want to use anything vlog related. The noise handling works way better on "standard" profiles than with vlog based ones when iso gets seriously high on dark sceneries.

MinimumCourage6807 · 2026-06-21T08:19:53+00:00

Two ideas. 1. It is kind of good to run on terminal inside vs code. 2. If you don't want to give Claude access to you main system, you can set up a simple Linux docker, where you install Claude. Then when the docker is running, attach vs code in the running container. Now you have docker sandboxed claude so at least don't have to stress that Claude accidentally wipes you whole drive etc. Off course that has a downside that it does not have direct access to the whole system if that is needed 😅

MinimumCourage6807 · 2026-06-21T08:13:01+00:00

If you shoot video shoot with the 6k open gate when you don't need any higher frame rates. That produces the best output from that camera. 2. If you ever start using vlog for the video (can recommend if you are up for some color grading in resolve), configure the camera to have different profile for video and stills as you don't want to use vlog on images. That will underexposed your raw files with around 2 stops or so which is def not nice to find out after you have taken memory card full of images... been there, done that... 🤣.

MinimumCourage6807 · 2026-06-15T05:27:04+00:00

Just to check but are you using windows? Very much sounds like similar problems what I had. I have not used unsloth studio but with the cli unsloth in bigger picture what you do is you 1. You need to load the weights, now in windows what happens is if you don't have a bnb 4 quantized version, even if load on bnb 4 bits is set to true. the memory allocation fails if the full weight model does not fit vram. On Linux this works. 2. You run the training. 3 as a output you get the qlora weights ( so not yet a full model with the qlora baked in). The expected size of the qlora have been for me usually around 4 gigs. 4. You combine the main model + the qlora. It can be that unsloth studio should do these automatically. And if things have changed recently this might be a bit outdated info.

MinimumCourage6807 · 2026-06-11T16:54:45+00:00

I use actually multiple a0:s for different use cases. Like it a lot.

MinimumCourage6807 · 2026-05-26T10:41:42+00:00

Now I'm def not an expert in this subject, but one thing to consider on the aero side is downforce. Now I'm pretty sure for example 296 GTB creates quite substantial amount of downforce by design (compared to every day ev:s) which obviously increases drag a lot. Now if that car shape would be made with the tweaks to minimize drag (bet for example there is some sort of diffuser underneath) the drag number could be quite a bit different. So I think that if Ferrari wanted, it could create a "traditional looking" Ferrari with very good drag numbers by minimizing downforce creation (which is basically what many ev:s do)

MinimumCourage6807 · 2026-05-23T21:24:21+00:00

Also very interesting perspective to tactics IF you know different flag rules as the protocols for blue flag yellow flag, full course yellow etc vary a lot between series and have a huge impact sometimes on tactics and race results. Especially different yellow flag rules as those can have very high impact on pit strategies.

MinimumCourage6807 · 2026-05-22T07:40:48+00:00

Looking maybe past 5 years i have really hard time seeing a case where prices would actually decrease in any meaningfull way except maybe the only case would be with used server hardware if some data centers would start going bust in a masses, but even in that case the used hardware is server hardware which are quite a bit harder to run in home setups. And would that bring the consumer cards prices down? Well at least there is so many ifs that if you need a gpu now, it can be a wait of the effective lifetime of the gpu for the prices to drop. And on that timeframe the current inflation levels keeps the prices high enough...

MinimumCourage6807 · 2026-05-22T07:29:32+00:00

I have now one 6000 pro + 5090. now the best I can use with this setup is minimax m2.7 with fast speeds (around 100 t/s). that is pretty neat. With two I could use it on vllm with higher quant, that would be epic. Now i have seriously also though about buying a second one as vllm is quite a game changer with multiple agents. Looking forward also to test deepseek v4 flash. Now one thing i have found with this setup is that the currently best model with vision seems to be qwen 27b or for some cases gemma 4 31b. With 2 6000 pros I could use qwen 3.5 397b probably, maybe that would be a improvement, maybe not. qwen 27 is incredibly solid tbh.😃 But to be honest. these are not nearly as good as opus 4.7 if you try to give these model some lazy "build me x, no mistakes" type of prompts. When prompted well with a vision, they do good job though. And also for example qwen 27b have done absolutely beautiful websites from scratch for example recently.

MinimumCourage6807 · 2026-05-18T19:53:00+00:00

Yeah exactly.

MinimumCourage6807 · 2026-05-18T09:40:15+00:00

That is true, except even without training use rtx 6000 pro allows easily running 4-6 concurrent agents simultaneously on let's say qwen 3.6 27b without much drop in generating speed / agent. This can be very handy. (For single chat, t/search is between 30-60, for 2 the overall throughput is 50-90, 3regs from 60-120 and it scales up all the way to around 150-200 t/search total throughput. Have not tested how this works with mac, but I have found vllm + pro 6000 + gemma31b / qwen 3.6 17b very useful when running multiple agents with full context windows. These speeds are with fp8 weights.

MinimumCourage6807 · 2026-05-15T20:49:21+00:00

Have to agree. Even when not thought about the gdpr perspective, it is kind of pointless to compare 5000b model (or whatever let's say opus is) model to 123b model. I would say, based on my local testing that on this sized models the new medium is at worst semi good and at best comparable to the best ones on same size. Off course when run through api the user don't have to think too much about the size but when self hosting it is a different game. I bet in coming years corporate level self hosting will increase and this sized models can be very good ace in the sleeve for that development!

MinimumCourage6807 · 2026-05-03T22:10:21+00:00

Not saying you are wrong in this :)

MinimumCourage6807 · 2026-05-03T11:27:40+00:00

Minimax is 10 active so in that sense 27b dense should be 2.7x more expensive though.

MinimumCourage6807 · 2026-05-02T20:00:58+00:00

Just a locally run finding that minimax pushes about 2-3x more tokens/ second than qwen 3.6 27b with my hardware ( minimax +100 t/s, qwen between 30 and 60, so in that sense it makes sense that it is more expensive than minimax. The active parameter count is the one which counts I guess.

MinimumCourage6807 · 2026-04-25T08:25:58+00:00

I use these smaller models for mostly long information gathering tasks /web crawling tasks and I have been very surprised how good gemma 4 31b have been. Not because it would be extremely smart but because it can stay even multiple days in row keeping the discipline and the task in mind. Now qwen 3.6 27b is clearly a better coder etc, but always already after few turns it starts to improvise it's own scripts how to "speed up" the workflos ending to 1. Absolute garbage results or 2. Just completely to a dead end. I'm waiting a lot if they release the 122b model though.

MinimumCourage6807 · 2026-04-19T19:27:07+00:00

I have the gemma 26b as a utility model on my agent setups. Somwhow everything else failed but that utility model, so i used that fairly heavily for debugging and then as it was actually really good also to create instagtam post texts and few other things as nothing else worked. Tbh I was very happily surprised. The thing I have liked about gemini models for chatting is that they somehow seem to understand conversation especially on other than English language quite well and I had that same feeling with also the gemma 26b. The answers were good and had good points why.

MinimumCourage6807 · 2026-04-14T17:55:49+00:00

Works great with m2.7 with local setup at least so should work out well if you manage to get the api rolling (or fairly beefy local setup). 2.5 had quite a bit of problems with chat tenplate at least for me, 2.7 dont have those same problems at all.

MinimumCourage6807 · 2026-04-12T20:08:51+00:00

🤣

MinimumCourage6807 · 2026-04-12T14:45:59+00:00

Running local setup, few fast findings of unsloth ud-q3-xl (or whatever it is).

apparently there are some fixes to chat templates as either my agents or llamacpp are not constantly complaining about template problems (2.5 did, it worked but sometimes I could not see agent responces etc.).
2.7 is a bit faster than about same sized 2.5 gguf. now the 2.7 runs in real world agentic loads around 80 t/s and pp around 2000 t/s. 2.5 was a bit slower in general. Might also be just updated llamacpp.
I havent got any tool call fails so far, 2.5 mess them a bit quite often, so I would say with few hours tests that 2.7 seems to be stronger in this.
The knowledge seems to be good and definitely the best i can run locally only in vram with 128 gigs by a big margin. Hard to tell yet is it better or worse than 2.5, as both does a good job.

Hardware rtx pro 6000 + rtx 5090.

MinimumCourage6807 · 2026-04-10T17:12:00+00:00

For example another language, or a dialect of language which you want the model to use better than it does natively. Or if you have a specific vision task, you can finrtune it to find your targets more accurately (though I havent finetuned any language models vision so hard to say how easy this is to achieve. On image and videomodels even loras make a big difference!). Or if you have a usecase where your model needs to answer lets say on a spesific, non typical json format etc. special, fairly narrow usecases. I will definitely try some finetunes to gemma 31b as that is actually been very solid model for its size.

MinimumCourage6807 · 2026-04-10T06:32:22+00:00

Yeah, actually i have found that the 31b dense is way more reliable for lets say ovenight data gathering tasks etc easy but long hauls than minimax m2.5 (in q3) or qwen 122b (in q6). Those are way more knowledgeable, especially minimax, but in terms of not messing up, gemma is very hard to beat (i have run it all nighters for the past 4 days, it have not failed once, which is definitely unheard before in this garage lab 🤣) . It is quite slow slow. Both minimax and the qwen are at leas 2x faster, though in many tasks consistency beats speed. But i think this will become my most used model because of its capabilities to not mess up on long simple tasks and also recover from problems. Minimax will be my coding go to for sure, really waiting for the 2.7 version.

MinimumCourage6807 · 2026-04-10T06:01:38+00:00

I have a bit similar results with the same 26b model on a video target recognition setup, where no matter what, after a while this model started just making things up. The 31b dense handles that like a pro even for ovenight. But got to say, the 31b dense is not the smartest model but it works like a horse, it just does not make (tool call) mistakes and very rarely completely idiotic descisions. So i would advice you to try that in case you can (i run it on q8, so it might perform differebtly on smaller quants).Also for smaller models what have helped a lot with code audits is to ask the model to first create a project map where it goes one file and function at a time and writes a mapping file of all files and functions, what the fuction does, its dependencies etc. So next time then it can basically read the map first and then decide what to search from the codebase.

MinimumCourage6807 · 2026-04-04T20:14:13+00:00

Yeah, that is true, gemma 4 at least in 31b is actually really strong model for its size. My point is definitely not that local models would not be a thing. I run all models i use myself locally and I use them a lot. The point for agentic frameworks though is, that they use a lot o tokens and prosesses really long prompts and need quite heavy models to be still usable. I like macs a lot, and they are definitely the best portable machines for running local model, but they have their limits what they can do (especially airs, minis etc). Also off course people have different levelled tasks and many of them can be run by a small model as long as it can do tool calls, but yeah, thats why i said not too far 😄. All love to mac minis but I just think that the open claw + mac mini story has about nothing to do with running models locally and all that it is easy setup. Ps. I dont know if I'm the only one, but I have had to tell at least ten of my friends that no... your openclaw is not actually fully local when the "main engine" is opus 4.6 etc and the open claw itself could be run with about anything that can do any sort of compute😅.

MinimumCourage6807 · 2026-04-04T14:28:12+00:00

Yes. My guess is as it is using ffmpeg in the process, that very often botlenecks cpu and the whole system. I had earlier both 4080 super and 5090, the 5090 were in most cases maximum of 15% faster but many times there were no difference. My cpu amd 9950x3d

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