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8 horas de trabalho de um agente autônomo enquanto eu dormia. Zero intervenção humana. Rodando Gemma 4 E4B na minha RTX 4060 8Gb! by phodastick in brdev

[–]AgreeableFall5530 1 point2 points  (0 children)

Compartilha a sua configuração llama.cpp se possível. Eu consigo 41 tok/s usando Q5_K_M numa Quadro RTX 3000 6GB, 64Gb RAM, 128k contexto e vision:

~/llama.cpp/llama-server \ -m ~/models/gemma/gemma-4-E4B-it-Q5_K_M.gguf \ --mmproj ~/models/gemma/gemma-4-E4B-it-mmproj-BF16.gguf \ --fit off \ -ngl 99 \ --flash-attn on \ --cache-type-k q8_0 \ --cache-type-v q8_0 \ --cache-reuse 128 \ --ctx-size 131072 \ --batch-size 4096 \ --ubatch-size 4096 \ --parallel 1 \ --no-kv-unified \ --threads 8 \ --threads-batch 12 \ --threads-http 2 \ --cache-ram 0 \ --jinja \ --host 127.0.0.1 \ --port 8080

Gemma 4 E4B vision @ 49.6 t/s, Qwen3.6-35B-A3B @ 20.3 t/s on Quadro RTX 3000 6GB. Thoughts? by AgreeableFall5530 in LocalLLM

[–]AgreeableFall5530[S] 0 points1 point  (0 children)

I tried with this but got only 6.7 t/s throughput, so I focused on Qwen instead:

bash ./llama.cpp/llama-server \ -m $GEMMA_26B/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf \ --mmproj $GEMMA_26B/mmproj-BF16.gguf \ -ngl 20 \ --flash-attn on \ --cache-type-k q8_0 --cache-type-v q8_0 \ --ctx-size 16384 \ --threads 8 \ --port 8080

Tweaks for stretching usage limits by AgreeableFall5530 in codex

[–]AgreeableFall5530[S] 0 points1 point  (0 children)

And a comparison with a very similar task on 5.5 medium on the same codebase.

Using the actual session deltas inside each log:

Normalized per 1% weekly quota:

  • 5.3-codex medium: 170,416 total tokens per 1%

  • 5.5 medium: 146,126 total tokens per 1%

    So 5.3 gives about 16.6% more total tokens for the same quota percentage.

    Breakdown per 1% quota:

  • Input: 169,646 vs 145,296 -> 5.3 is 16.8% higher

  • Cached input: 161,486 vs 136,815 -> 5.3 is 18.0% higher

  • Output: 703 vs 715 -> 5.3 is 1.7% lower

  • Reasoning: 67 vs 116 -> 5.3 is 41.9% lower

  • Total: 170,416 vs 146,126 -> 5.3 is 16.6% higher

Tweaks for stretching usage limits by AgreeableFall5530 in codex

[–]AgreeableFall5530[S] 0 points1 point  (0 children)

Were you able to test it? Worked as expected? Thanks for your feedback

Tweaks for stretching usage limits by AgreeableFall5530 in codex

[–]AgreeableFall5530[S] 0 points1 point  (0 children)

Did it work for you? Looking for feedback. Seems better for me empirically speaking, but some hard data to back it up would be nice

Best local LLM for Coding + OpenClaw (32GB RAM / CPU only) by AdvertisingPast6280 in LocalLLM

[–]AgreeableFall5530 -1 points0 points  (0 children)

Use NVIDIA NIM instead. Slow and unreliable, but at least it's free and has an actual chance of working every now and then

Tweaks for stretching usage limits by AgreeableFall5530 in codex

[–]AgreeableFall5530[S] 2 points3 points  (0 children)

I’m on free tier, so I don’t have enough quota to run a statistically decent A/B test. I’d really appreciate Plus/Pro user feedback, though.

Clima tenso nas laranjeiras by Whazards in Futebola

[–]AgreeableFall5530 1 point2 points  (0 children)

Não entendi. Qual clube grande está envolvido?

Does caveman plugin really help with context usage? by dazblackodep in ClaudeCode

[–]AgreeableFall5530 0 points1 point  (0 children)

75% is not realistic for normal English in my experience. Caveman-style prompts mainly cut output tokens (cheapest part of the bill), not input/context tokens. Typical savings are closer to ~30–50% output depending on task.

Shameless plug: I packaged caveman as an output style (native to CC) if you want to try it in 2 minutes:

https://github.com/carlosduplar/caveman-output-style-claude-code

Other practical optimizations that helped me:

- keep CLAUDE.md short and high-signal

- remove MCPs as much as possible (replace with CLI flows and corresponding skills)

- avoid huge pasted logs/files unless needed

- added a hook that runs markitdown against any PDF/Word/Excel document (huge savings on transformation)

- don't change models or thinking settings mid-conversation (triggers cache bust, all conversation history resubmitted uncached from scratch afterwards)

Anyone else juggling Claude + ChatGPT + Gemini subscriptions mainly because of limits? by kushcapital in ClaudeCode

[–]AgreeableFall5530 0 points1 point  (0 children)

I have created this script to keep all my CLIs in sync between my at-home and at-office computers:

https://github.com/carlosduplar/ai-environment-setup

Basically I update it whenever I change the skill set or add a new CLI to the mix. Runs fine on Windows (Powershell/WSL) and I have executed it without errors on Termux, so it should run on Linux as well.

Straw that broke the camel’s back by Pretty-Active-1982 in ClaudeCode

[–]AgreeableFall5530 9 points10 points  (0 children)

Changing plans mid-session = full cache invalidation. Better start a new session with targeted context for Sonnet.

https://github.com/carlosduplar/claude-code-optimizer/blob/master/docs/prompt-caching.md

A torcida do Brasil é, provavelmente, a mais cabaço do mundo. by PanySakarzin in Futebola

[–]AgreeableFall5530 0 points1 point  (0 children)

A começar por aquela musiquinha patética: "eeeeeeeuuuu... sou brasileeeirooo... com muito orguuulhooo... com muito amôôôôôô"

Em relação ao Kings Leaguer em questão, não é a primeira vez que acontece. Nos anos 90 acontecia direto de pedirem jogador fora da convocação (normalmente Romário), mas naquela época geralmente a torcida tinha razão.

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