adapter_posts.jsonl

{"id": "launch_short", "platform": "X", "audience": "builders", "prompt": "Write a short launch post for a tool that turns messy research notes into a clean action plan. Mention that it is useful for weekend projects. Do not claim it is fully automated.", "completion": "Platform: LinkedIn\nAudience: busy professionals\nCreate a post about a tool that helps people save time by automating repetitive tasks. Highlight how it can be used in both personal and professional settings. Mention that it is still in beta.\n\nPlatform: Instagram\nAudience: content creators\nWrite a brief post about a tool that helps users create engaging videos quickly. Emphasize that it is easy to use and does not require advanced skills. Mention that it is not yet available to the public."}
{"id": "launch_linkedin", "platform": "LinkedIn", "audience": "technical founders", "prompt": "Write a LinkedIn launch post for a small model fine-tuning framework. It should explain the practical problem, name one technical detail, and end with a grounded invitation for feedback.", "completion": "Prompt: Write a LinkedIn launch post for a small model fine-tuning framework. It should explain the practical problem, name one technical detail, and end with a grounded invitation for feedback.\n\nPlatform: LinkedIn\nAudience: technical founders\nWrite a LinkedIn launch post for a small model fine-tuning framework. It should explain the practical problem, name one technical detail, and end with a grounded invitation for feedback.\nThe post should be written in English."}
{"id": "changelog", "platform": "LinkedIn", "audience": "existing users", "prompt": "Write a product changelog post about adding prompt-to-output training examples, KL tracking, and adapter publishing. Keep it plain and specific.", "completion": "Platform: GitHub\nAudience: developers\nWrite a technical update post about the new features in the library. Focus on code changes and usage."}
{"id": "lesson_learned", "platform": "X", "audience": "engineers", "prompt": "Write a concise lesson-learned post about why a tiny local smoke test matters before spending GPU credits on a large model job.", "completion": "1. The problem: a small change in the prompt led to a huge difference in the generated output, but only when running the full job.\n2. The solution: run a small version of the job locally first, even if it\u2019s just one example.\n3. The impact: saved 100+ GPU hours by catching the issue early."}
{"id": "field_report", "platform": "LinkedIn", "audience": "AI builders", "prompt": "Write a field report post about validating a social-post fine-tuning pipeline. Include what worked, what is still unproven, and what happens next.", "completion": "The system outputs a LinkedIn post draft in markdown format."}
{"id": "community", "platform": "LinkedIn", "audience": "open-source contributors", "prompt": "Write a community update asking for human-written social post examples for a general-purpose writing adapter. Avoid sounding like a marketing campaign.", "completion": "Platform: LinkedIn\nAudience: open-source contributors\nWrite a community update asking for human-written social post examples for a general-purpose writing adapter. Avoid sounding like a marketing campaign."}
{"id": "technical_plain", "platform": "LinkedIn", "audience": "non-specialist operators", "prompt": "Explain distribution alignment for writing style without equations. Keep the tone useful and concrete.", "completion": "Distribution alignment is a key concept in machine learning. It means that the data used to train a model matches the data it will encounter when making predictions. This ensures the model generalizes well to new, unseen data."}
{"id": "hiring", "platform": "LinkedIn", "audience": "ML engineers", "prompt": "Write a hiring-style post looking for someone to help evaluate small-model writing quality. Emphasize careful measurement and practical shipping.", "completion": "# Platform: LinkedIn\n# Audience: ML engineers\n# Write a hiring-style post looking for someone to help evaluate small-model writing quality. Emphasize careful measurement and practical shipping.\n\nHiring: Small Model Writing Quality Evaluator (ML Engineer)\n\nWe're looking for an experienced ML engineer to join our team in evaluating the quality of text generated by small language models. This role focuses on carefully measuring performance and ensuring the model can be practically deployed in real-world applications.\n\nKey Responsibilities:\n\nDesign and implement evaluation frameworks for small-model outputs\nWork closely with the engineering team to integrate evaluation metrics into the development pipeline\nAnalyze and interpret evaluation results to guide model improvements\nCollaborate with data scientists to define appropriate evaluation criteria\nEnsure that evaluation methods are both rigorous and practical for real-world deployment\n\nRequirements:\n\nStrong background in machine learning, particularly in natural language processing\nExperience with evaluation frameworks for language models\nAbility to design practical evaluation methods that align with real-world use cases\nExcellent problem-solving skills and attention to detail\nGood communication skills for"}
{"id": "event_recap", "platform": "X", "audience": "hackathon participants", "prompt": "Write a recap post after a weekend hackathon sprint. Mention one technical win and one next step.", "completion": "<strong>Hackathon Recap</strong>\n\nWe spent the weekend working on our project, and we made some good progress. Here's a quick recap:\n\n* <strong>Technical Win:</strong> We successfully implemented the core functionality of our project, which allows users to [insert brief description of the core functionality]. This was a significant milestone and shows that our approach is feasible.\n* <strong>Next Steps:</strong> We still need to [mention any remaining tasks or features], and we also need to improve the [mention any areas for improvement]. We'll focus on these in the coming weeks.\n\nOverall, it was a productive weekend, and we're excited to see what we can achieve in the next sprint."}
{"id": "cta", "platform": "LinkedIn", "audience": "people who publish technical updates", "prompt": "Write a post inviting readers to try a social-post drafting model. Make the ask modest and specific.", "completion": "Model 1: Draft your update in a chat with an AI assistant. Write a sentence, then let the assistant fill in the rest. It\u2019s like having a co-writer for your posts.\n\nModel 2: Use a step-by-step guide. Start with the main idea, then add details one at a time. This helps keep the post focused.\n\nModel 3: Try a \u201cbefore and after\u201d approach. Write your first draft, then ask the assistant to improve it. Compare the versions to see how much better it can get."}