Yep! Can’t share the base model, but it already works incredibly well. You can:
– describe a specific style for it to write in, and/or
– show it examples of the style you want Available through the chat interface that we offer today.
LLMS
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Base Model Enables Custom Writing Styles Via Chat
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HyperWrite New Model Rollout Soon A/B Testing
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The new HyperWrite model is just so damn good If you’re lucky enough to get it in our A/B tests, you’ll see what I mean Broader rollout soon
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Comparing AI Model Performance and Prompt Engineering Techniques
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Sorry to disagree, but those models are far weaker than the leading models mentioned. The outputs won’t even be remotely as useful. Try the “no fingers” trick.
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Build Your Ideas Using LLMs as Your Development Partner
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Decide what you want to build and just go for it. Ask LLMs for help when you need it. This is the way.
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Claude excels at generation and self-prompting capabilities
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Claude is incredible at this. Very valuable. Especially if you’re using Claude for generation as well. It knows how to prompt itself quite well 🙂
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Using Claude Haiku for cost-effective prompt stuffing
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If it’s under 200k tokens and I can afford it, I’m using Claude Haiku and stuffing it all in the prompt.
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Groq Low Latency Enables Amazing User Experiences and Use Cases
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"Groq has shown that low latency can open up really interesting use cases, really interesting user experiences. You can go see [for yourselves] – they tweet all the time with what people are building on Groq, and it's amazing. They're definitely showing that there is a better
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AI Development Stack: Local Setup with Cloud Compute
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My couch. MacBook Pro. Runpod for compute. Axolotl for training. OpenAI Playground and Anthropic Console for prompt engineering. Sometimes PromptKnit.
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Complete AI Development Stack: Runpod, Axolotl, and Prompt Engineering
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My couch. MacBook Pro. Runpod for compute. Axolotl for training. OpenAI Playground and Anthropic Console for prompt engineering. Sometimes PromptKnit.
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AI Models Learn Faster With Less Training Data
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Doubt it. As models get smarter, less data will be needed to teach them new things. So if today, it takes millions of examples to teach it a new language, a couple years from now, it may only need a few thousand.