Developer_tools

The Problem (Pain Level: 9/10) “Why is our OpenAI bill so high this month?” - A common question haunting every team that has deployed LLMs to production. Current pain points: Cost black box: Hard to track where API costs are coming from Performance opacity: No metrics for response time, token usage, error rates Quality management: No way to monitor and evaluate LLM response quality Debugging hell: Difficult to identify performance degradation after prompt changes Security concerns: Can’t track if sensitive data is being sent to LLMs Real example: ...

The Problem (Pain Level: 8/10) “What did I do this week?” - This question haunts every developer when it’s time to write status reports. Current pain points: Time waste: Manually checking Jira, GitHub, Slack, Linear, Notion to compile updates Missing items: Busy weeks mean important work gets left out of reports Inconsistent formats: Each team member uses different reporting styles Context switching: Jumping between multiple tools to gather information Weekly repetition: 2-4 hours of manual work every single week Real example: ...

The Problem (Pain Level: 7/10) “I didn’t know how many tokens my prompt was, and my API call failed” - A daily frustration for LLM developers. Current pain points: Token blindness: Can’t see real-time token count while writing prompts Unpredictable costs: Hard to estimate costs until API call is made Model confusion: GPT-4, Claude, Gemini all have different tokenizers No version control: Difficult to track prompt change history Team collaboration friction: Hard to share prompts and get feedback Target Market Primary Target: LLM app developers, AI engineers, prompt engineers ...