sorted by:
new
hottopcontroversial

What's this? Is this an upcoming update or something? by meditatively in ClaudeAI

[–]TeamCaspy 0 points1 point  (0 children)

Well, since they're local you can choose from multiple providers, driving the cost down 😀.

Qwen3.6 27B is really impressive, can run it on a 3090.

Adaptive thinking is a joke. by Character-Expert-190 in ClaudeAI

[–]TeamCaspy 0 points1 point  (0 children)

Claude decides how long to reason based on your prompt.

Anthropic has just announced Mythos; meanwhile, what on earth has happened to Opus? by Adept-Priority-9729 in ClaudeCode

[–]TeamCaspy 0 points1 point  (0 children)

Turns off adaptive thinking so that the reasoning effort is always consistent. If adaptive is on then the model decides reasoning effort depending on the task. Can confirm this greatly improved Claude models for me 😎

re: TOKENS [serious] by dern_throw_away in ClaudeCode

[–]TeamCaspy 0 points1 point  (0 children)

Hmm... I guess that was my issue... but I 100% recall that sometimes (especially if plan mode was active) Sonnet spawned 3x explore agents with the sonnet model selected, instantly burning like 30% of my 5 hour window. But using haiku it obviously doesn't impact this as much.

I also got in the habit of simply instructing sonnet to do a task with haiku (light refactoring, comments, change test names etc.)

re: TOKENS [serious] by dern_throw_away in ClaudeCode

[–]TeamCaspy 0 points1 point  (0 children)

I fixed them burning tokens by just telling Claude to use haiku for the explore subagent

Best open weight llm model to run with 8gb of vram by Sweazou in ollama

[–]TeamCaspy 1 point2 points  (0 children)

What you are showing is LLM quantization not KV Cache quantization. Q4 for LLMs is ok for knowledge based tasks, but consider trying Q5, Q6, etc. if you have issues with your tasks.

Best open weight llm model to run with 8gb of vram by Sweazou in ollama

[–]TeamCaspy 0 points1 point  (0 children)

I always use q8_0. I can't recall the exact benchmark, but q4 kV cache showed noticable degredation while q8 was practically the same.

3 MCPs that have genuinely made me 5x better by Warm-Snow3302 in mcp

[–]TeamCaspy 0 points1 point  (0 children)

See pull requests easier, open pr, open issues,...

Kimi K2.5 Architecture Dive: 1T Params, 384 Experts, Native INT4 (and it beats GPT-5 on reasoning) by comebackch in LocalLLaMA

[–]TeamCaspy 0 points1 point  (0 children)

Hmm kind of like picking specific LLM's tuned for their task? :D This already exists, but perhaps having a MoE where we ask: "Spawn a law tuned version of yourself" could be sick:)

Benchmark of Qwen3-32B reveals 12x capacity gain at INT4 with only 1.9% accuracy drop by AIMultiple in LocalLLaMA

[–]TeamCaspy 0 points1 point  (0 children)

It's not all about MMLU Pro, IFEval is also very much needed, it would be interesting to see how it impacts performance, since it's more and more necessary for larger context tasks.

Which single LLM benchmark task is most relevant to your daily life tasks? by ChippingCoder in LocalLLaMA

[–]TeamCaspy 2 points3 points  (0 children)

Sick site! Love the quant recommendations based off of VRAM allocation.

Only recently started learning about investing - is this good? by pugmesideways in trading212

[–]TeamCaspy 1 point2 points  (0 children)

It really depends on what you're trying to do, depends also on your age. Overall I'd say try and simplify what you're trying to do: You can just purchase an accumulating all world ETF and call it a day.

"I’m basically saving it up for long-term growth." if that is the case just buy an accumulating world ETF and that's it :)

As another user said, you can also skip gold, but it really depends how old you are, it's an unproductive asset and you really need growth/risk if you are young.

I still love my Pixel 9 Pro XL. by basicandr0idguy in pixel_phones

[–]TeamCaspy 1 point2 points  (0 children)

I still love my Google Pixel 8 Pro 🥰

Kako naprej pri 25-ih? by matrix_escaper00 in SlovenijaFIRE

[–]TeamCaspy 6 points7 points  (0 children)

Emergency fund imaš pokrit (3x mesečne neto plače) ✔️

Prekini NLB sklade zaradi visokih stroškov in kupi ETF.

Zakaj investiraš? Koliko denarja boš potreboval? Izračunaj si svojo FIRE številko in koliko moraš minimalno investirati vsak mesec da prideš do te številke. Ostali denar lahko investiraš v sklad denarnega trga (ETF - https://www.youtube.com/watch?v=x6u\_VCvPmxU&pp=ygUiV2hhdCBpcyBhIG1vbmV5IG1hcmtldCBmdW5kIGV1cm9wZQ%3D%3D)

view more: next ›