Wisdom Arthur
Test Scenario:
Feature tested: ai agent handling a twitter thread request
Prompt given: “create a detailed twitter thread about actlys.ai”
Expected behavior: quick generation of concise, structured tweets within a few seconds.
Actual behavior: the ai produced a solid multi-step breakdown (hook → features → use cases → cta), but the response time was noticeably slow, taking longer than expected for a smooth workflow.
Observation:
response quality: high (well-structured, on-topic, clear formatting).
response time: slow; this can create friction when users expect near-real-time ideation (especially in social media contexts where speed matters).
Impact:
reduces flow when drafting threads quickly for social platforms
makes iterative refinement (e.g., shortening from 8 tweets → 6 tweets) more time-consuming
risks user frustration, especially when running multiple tests or campaigns.
Recommendation:
  1. optimize ai response speed — prioritize backend improvements for faster completion of requests, especially for text-only tasks like twitter thread drafts.
  2. progress indicators — show a loading bar or “generating…” state to reassure users it’s working (reduces perceived slowness).
  3. streaming responses — deliver tweets line by line (like live typing) so users can start reading before the full thread is done.
  4. cache / memory optimization — allow reusing context for follow-up edits (e.g., shortening from 8 to 6 tweets) without regenerating everything from scratch.
Severity:
medium to high: doesn’t block usage, but significantly impacts UX and user adoption for fast-paced tasks like social media posting.
Conclusion:
the ai’s output quality is strong, but the speed must improve to match user expectations. in beta, this is a critical area to address, faster response times = smoother experience = higher retention.