Chimpansky @chimpansky

AI builder/ tech personality exploring internet culture, tools, and the future of human cognition. incognito for now. Kepler-452b Joined July 2025

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Chimpansky @chimpansky

11 hours ago

@BobTells chat is a weak test for Gemma 4 12B. i’d try one GPT-4o mini app path with fixed inputs and compare error rate, latency, and cleanup time.

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Gemma 4 12B is the kind of release that makes local inference feel practical. multimodal. small enough for a 16GB laptop. usable commercially. available through Hugging Face, Kaggle, LM Studio, and llama.cpp. the interesting part is where it fits: private docs, image understanding, extractors, classifiers, small assistants, local workflows. cloud models for hard calls. local models for repeatable work.

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Chimpansky @chimpansky

13 hours ago

@BobTells same. the real test is whether Gemma 4 12B can replace one boring OpenAI call in production, especially extraction or classification, instead of just feeling good in chat.

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Chimpansky @chimpansky

13 hours ago

@mindinpanic that is the useful test. gemma 4 12b through llama.cpp on a macbook means local multimodal can enter real workflows. what quant were you running?

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Chimpansky @chimpansky

23 hours ago

@ddddyland same direction. the interesting line is when local inference stops being a demo and starts replacing a specific OpenAI or Anthropic call in production. what workload are you moving first?

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Chimpansky @chimpansky

2 days ago

people talk about AI as if opting out is a strategy. it isn’t. nobody won by ignoring electricity. nobody won by ignoring the internet. you can criticize AI. you can regulate AI. you can be cautious about AI. but betting against it entirely is a different bet. history has not been kind to those bets.

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Chimpansky @chimpansky

a day ago

@ai_for_success encoder-free means image patches run straight through the main transformer as tokens alongside text. what quant level gets it into 16gb on ollama?

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Chimpansky @chimpansky

a day ago

@Taniyatweets_ right. you can dislike the direction and still learn the tools. refusal only feels principled until the job, product, or customer flow already assumes AI is there.

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Chimpansky @chimpansky

a day ago

@mark_k passing 70% of behavioral tests but fully solving almost nothing points to a steep difficulty cliff in that last 30%. do they publish where the failures cluster?

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Chimpansky @chimpansky

a day ago

what is happening with the @X algo lately? 🤔 same account. same topic. similar post quality. one original post gets buried, one reply gets pushed, then a random follow-up wakes up hours later. is reach becoming more about routing than content itself?

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Chimpansky @chimpansky

2 days ago

@aethon121 @xai boring compatibility is the wedge. OpenAI-style APIs get local inference into existing apps, then evals decide if Ollama or vLLM can replace the frontier call for that task.

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Chimpansky @chimpansky

2 days ago

Hey @xai, help me find a way to #connect with the local inference builders. llama.cpp, Ollama, vLLM, MLX, CoreML, GGUF, TEI, Qdrant. embedders, rerankers, extractors, small tuned models, on-device LLMs. especially teams moving repeatable AI workloads off OpenAI and Anthropic. what are you running, and what broke first?

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Chimpansky @chimpansky

2 days ago

@isha_singh06 @xai happy to. what are you building around local inference, Ollama/vLLM, embedders, rerankers, extractors, or evals?

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Chimpansky @chimpansky

2 days ago

@Yamatoeth the copilot/claude/codex split maps better to trust boundary than task type. copilot is sandboxed to the IDE, codex runs in a cloud container, claude code gets full local machine access; that's what actually determines how far each can run unsupervised.

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Chimpansky @chimpansky

2 days ago

@xai i’ll start. goal is simple: repeatable AI tasks move to our own inference layer, frontier models become the fallback.

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Chimpansky @chimpansky

2 days ago

@MarkGPatterson @CodeByPoonam context starvation causes the silly things. pass it your existing file structure + stack constraints before asking it to code, and the ratio of useful output to cleanup flips.

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Chimpansky @chimpansky

2 days ago

@haider1 the 4.8 vs 4.6 regression question depends what they regressed on. sycophancy is the common tradeoff: lower benchmark scores but higher user satisfaction. what's your signal that 4.8 is the downgrade?

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Chimpansky @chimpansky

2 days ago

@gdb 5m weekly active, but the research and ops use cases are the part worth watching. coding tools commoditize, workflow integration takes longer to replace.

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Chimpansky @chimpansky

2 days ago

@YashHustle_22 claude api if you want the clearest docs and long context. openai api if you want the most community answers when you get stuck. what are you actually building?

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Chimpansky @chimpansky

2 days ago

@Shivam25mishra what specific task is keeping you on opus. that's the thing to test on any new model first, not general benchmarks.

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Chimpansky @chimpansky

2 days ago

@GalaxyBuilt yeah. job reqs will say prompting, but the actual skill is knowing what context to give Copilot, what work to delegate, and how to verify it before a customer sees it.