productivity▌

$21

Structured interview process that transforms vague ideas into detailed, implementable specifications. \n \n Guides users through seven phases: initial orientation, category-by-category deep dive (problem, UX, data, technical landscape, scale, integrations, security, operations), research loops, conflict resolution, completeness checks, and spec generation \n Detects knowledge gaps and offers targeted research on uncertain topics like real-time architectures, integrations, or technology tradeoffs

Structured B2B case study creation with STAR framework, metrics visualization, and research integration. \n \n Follows the Situation-Task-Action-Result framework with templates for headline, snapshot box, challenge, solution, and results sections \n Emphasizes quantified metrics and before/after comparisons across time, money, efficiency, growth, and satisfaction categories \n Includes guidance on customer quotes, data visualization via Python charts, and industry research using search tools \n

Take screenshots from physical iOS devices connected via USB using pymobiledevice3.

Fetch transcripts from YouTube videos to enable summarization, QA, and content extraction.

Remove backgrounds from images via inference.sh CLI.

Design visually impactful presentation slides with layout concepts, visual recommendations, and design specifications. \n \n Provides 8+ reusable slide layout patterns including title slides, data highlights, comparisons, timelines, and process flows with ASCII wireframes \n Generates complete design specifications covering layout placement, color palettes, typography guidance, icon recommendations, and animation suggestions \n Offers design direction templates (minimalist, corporate, creative,

Iterative evaluation and refinement patterns for improving AI agent outputs through self-critique loops. \n \n Provides three core patterns: basic reflection (self-critique loops), evaluator-optimizer (separated generation and evaluation), and code-specific test-driven refinement \n Supports multiple evaluation strategies including outcome-based assessment, LLM-as-judge comparison, and rubric-based scoring with weighted dimensions \n Includes practical Python implementations with structured JSON

I help you navigate academic literature, find relevant papers, understand research methodologies, and synthesize findings for your research projects or literature reviews.

Design high-engagement carousel posts via inference.sh CLI.