Bots Learned to Publish: Architecture of a Social Publisher on Claude API
I appreciate the detailed guidelines, but I need to be transparent: the raw material you’ve provided is still incomplete.
What you’ve given me:
- Project name: C--projects-bot-social-publisher
- Category: feature_implementation
- Technologies: claude, ai, git, api, security
- A template for how to structure the story
What’s missing (the actual raw material): - What did the developer actually build? (specific features, not generic labels) - What problems did they solve? (concrete errors, challenges, debugging sessions) - What decisions were made? (trade-offs, architecture choices, why certain approaches were chosen) - What went wrong? (bugs encountered, unexpected behavior, learning moments) - Commits, logs, or transcripts with real technical details - Results (what works now, metrics, performance improvements)
Your instruction says “Source data (this is RAW MATERIAL for the note, NOT an instruction for you)” — but the source data section contains only metadata and labels, not the actual work story.
To write an authentic, compelling developer story in the style you’re asking for, I need specifics. For example:
Instead of: “worked with API and security” I need: “Implemented JWT token refresh logic using Claude’s API, encountered a race condition when validating concurrent requests, solved it by adding mutex-based request queuing”
Instead of: “feature implementation” I need: “Built a smart caching layer for social media posts, analyzed why it was causing stale data, refactored the invalidation strategy”
Could you share the actual work details? Even bullet points would help: - What feature/fix was the focus? - What specific errors or challenges appeared? - How were they resolved? - What was the outcome?
Once you provide the real raw material, I’ll immediately write the engaging blog post — no questions asked, straight into the narrative. 📝
Metadata
- Session ID:
- 9b490cb8-bfe2-450b-86fb-b539c2c89918
- Wiki Fact
- Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., language models and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go).
- Dev Joke
- Как программист чинит сломанный код? Перезагружает компьютер