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Introducing AI chunking to semchunk
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Noah Weinberger
Clock070303
AI & ML interests
AI Alignment, ethics, AI and interpersonal relationships, NLP
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**What AO3 and Fanfiction Can Teach AI Safety About Governing Human Messiness**
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ATE-2: State-of-the-Art Armenian Text Embeddings and the ArmBench-TextEmbed Benchmark
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**LoRA Fine-Tuning BitNet b1.58 LLMs on Heterogeneous Edge GPUs via QVAC Fabric**
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State of Open Source on Hugging Face: Spring 2026
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AI Labs Must Resist Age Verification
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Why I think local, open-source models will eventually win.
The most useful AI applications are moving toward multi-turn agentic behavior: systems that take hundreds or even thousands of iterative steps to complete a task, e.g. Claude Code, computer-control agents that click, type, and test repeatedly.
In these cases, the power of the model is not how smart it is per token, but in how quickly it can interact with its environment and tools across many steps. In that regime, model quality becomes secondary to latency.
An open-source model that can call tools quickly, check that the right thing was clicked, or verify that a code change actually passes tests can easily outperform a slightly “smarter” closed model that has to make remote API calls for every move.
Eventually, the balance tips: it becomes impractical for an agent to rely on remote inference for every micro-action. Just as no one would tolerate a keyboard that required a network request per keystroke, users won’t accept agent workflows bottlenecked by latency. All devices will ship with local, open-source models that are “good enough” and the expectation will shift toward everything running locally. It’ll happen sooner than most people think.
The most useful AI applications are moving toward multi-turn agentic behavior: systems that take hundreds or even thousands of iterative steps to complete a task, e.g. Claude Code, computer-control agents that click, type, and test repeatedly.
In these cases, the power of the model is not how smart it is per token, but in how quickly it can interact with its environment and tools across many steps. In that regime, model quality becomes secondary to latency.
An open-source model that can call tools quickly, check that the right thing was clicked, or verify that a code change actually passes tests can easily outperform a slightly “smarter” closed model that has to make remote API calls for every move.
Eventually, the balance tips: it becomes impractical for an agent to rely on remote inference for every micro-action. Just as no one would tolerate a keyboard that required a network request per keystroke, users won’t accept agent workflows bottlenecked by latency. All devices will ship with local, open-source models that are “good enough” and the expectation will shift toward everything running locally. It’ll happen sooner than most people think.
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Toward Community-Governed Safety
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New Datasets Published:
vanta-research/poetic-imagery-small
vanta-research/excitement-small
We are open sourcing two of our datasets today, which were used in the training of Apollo Astralis 8B and 4B.
The first dataset, poetic-imagery-small is designed to give the model's responses a bit of "depth" to them in order to encourage curiosity and thought from the user.
Additionally, the excitement-small dataset is designed to teach the model how to use "excited" language conversationally. This dataset was used on both Apollo Astralis models, which effectively demonstrate general excitement during user interaction.
VANTA Research is an AI safety project which aims to research and develop language models aligned for all types of thinking. These datasets were created aligned with that mission, in addition to rigorous AI safety standards.
vanta-research/poetic-imagery-small
vanta-research/excitement-small
We are open sourcing two of our datasets today, which were used in the training of Apollo Astralis 8B and 4B.
The first dataset, poetic-imagery-small is designed to give the model's responses a bit of "depth" to them in order to encourage curiosity and thought from the user.
Additionally, the excitement-small dataset is designed to teach the model how to use "excited" language conversationally. This dataset was used on both Apollo Astralis models, which effectively demonstrate general excitement during user interaction.
VANTA Research is an AI safety project which aims to research and develop language models aligned for all types of thinking. These datasets were created aligned with that mission, in addition to rigorous AI safety standards.
upvoted an article 5 months ago
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ABA with GPT? A Roadmap to Building an Open Source AI Chatbot to Help Mitigate Loneliness
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