Stillcue

The description states that Stillcue is a tool that:

• Reads working state from dialogue logs in real time.
• Detects four psychological states (flow, confusion, frustration, fatigue) using lightweight signals and an LLM.
• Proactively offers a clean restart when the system detects frustration or fatigue.
• Operates locally on user machines without databases or frameworks.
• Uses a Node.js process to tail Codex CLI session logs and compute signals like Jaccard similarity, negation density, message-length collapse, burst tempo, context dumping, session duration, and novelty ratio.
• Delivers interventions only at task boundaries (after replies complete) via a quiet localhost page with a notification system.

It is described as a "watcher", a "two-layer judgment" system, and an "observation room".

Inference Stillcue is a local, lightweight, state-aware AI conversation assistant designed to improve human-AI collaboration by detecting working states and offering clean restarts.

The author positions Stillcue as:

• A psychological practice for AI collaboration.
• A tool that recognizes the “human side of the loop” in AI interactions.
• A way to honor flow, detect frustration, and offer a clean restart when needed.
• A shift from optimizing what the AI produces to optimizing the process between human and AI.

The project evolves from:

• A personal frustration with long AI conversations.
• An idea rooted in affect-detection research (AutoTutor, D'Mello, Graesser et al.).
• A desire to build a tool that supports working state awareness rather than just output optimization.

Inference Stillcue positions itself as a tool for improving the human-AI interaction process, not just the output quality — a niche in the broader AI tools space.

The description does not explicitly name target customers or define an ideal customer profile (ICP). It implies:

• Developers working with AI models like Codex.
• Users who engage in long, iterative conversations with AI.
• People who experience frustration or fatigue during AI interactions.

Inference The ICP likely includes developers, researchers, or knowledge workers who use AI tools for extended problem-solving sessions and are sensitive to the psychological dynamics of those interactions.

No evidence of a business model or pricing strategy is provided. The project is described as a personal build, not a commercial product.

Inference There is no evidence of a business model or pricing structure. The tool appears to be a prototype or personal project.

The author describes:

• A simple architecture: three parts (watcher, two-layer judgment, observation room).
• No frameworks or databases.
• Node.js process tailing Codex CLI logs.
• Lightweight signals computed on each user turn.
• LLM-based state judgment with confidence thresholds.
• Interventions only at task boundaries.

Inference The technical design is minimalistic and focused on local execution with no external dependencies — a strong signal of a prototype or proof-of-concept.

No evidence of traction, adoption, or user base is provided. The project is described as a single-person build submitted to a hackathon.

Inference There is no evidence of traction or maturity beyond the author’s own development.

The description does not reference any competitors. It focuses on the psychological layer of AI collaboration, which is a novel angle but not clearly positioned against existing tools.

Inference The competitive context is unclear. It may be a niche or emerging space with no direct competitors mentioned.

Key risks and red flags include:

• No traction or adoption evidence.
• Single-person development.
• Unproven concept in a crowded AI tools space.
• Risk of over-intervention (e.g., “one wrong interruption ends the relationship”).
• No commercial viability or monetization strategy.

Inference The project is unproven in terms of real-world adoption and may not scale beyond the author’s own use case.

1. What evidence do you have that people actually experience the working states you’re detecting?
2. How do you plan to validate or test the effectiveness of your interventions?
3. Have you tested this with users outside of yourself?
4. What is your path to commercialization or productization?
5. How do you intend to scale beyond a single developer’s use case?