Anamnesis

Anamnesis is a local-first developer tool designed to help developers resume work after interruptions by providing an AI-generated briefing of code changes, tests, intent, and the clearest next step. The tool consists of a CLI and VS Code extension, with core functionality built around collecting local workspace signals (Git state, file activity, test output, shell history) and optionally using GPT-5.6 to synthesize them into a structured briefing.

The project is self-reported as a hackathon submission by three university students. It includes a shared TypeScript codebase, privacy-focused design with redaction and opt-in inference, and supports checkpoints for comparing workspace states. The tool does not collect or process sensitive data beyond what is explicitly provided by the user in their local environment.

Key commercial due-diligence questions include: What is the actual developer adoption rate? How does Anamnesis differentiate from existing tools like Git-based context managers or IDE-integrated task tracking? Is there evidence of a sustainable business model or path to monetization?

The description states that Anamnesis is a local-first developer tool with a CLI and VS Code extension. It reconstructs working memory from the workspace you return to using:

• Git state and diffs
• Recent file activity
• Optional shell history
• Optional captured command output
• Conservative static analysis

When remote inference is explicitly enabled, GPT-5.6 turns this evidence into a concise Done / In progress / Broken / Next step briefing.

The tool collects local signals without sending raw data to an AI model directly. Instead, it sanitizes and persists data locally before any inference request.

It supports two modes:

1. Current State Only: no checkpoint; normal unplanned interruption
2. Checkpoint Comparison: an active checkpoint adds an intent note and local before/after comparison

Checkpoints are optional and stored locally in a workspace's ignored .anamnesis/ folder.

The system is built with a shared TypeScript core, including:

• Local signal engine
• AI briefing layer
• VS Code extension UI

Not evidenced: The exact nature of the AI briefing output or whether it includes structured data beyond what is described.

The description states that Anamnesis helps developers resume work after interruptions with an AI briefing of code changes, tests, intent, and the clearest next step. It positions itself as a solution to the problem of distractions and resumption lag, drawing on research by Guo et al. (2021) which identified reminder cues as one of the most effective methods for helping people get back on track.

The authors claim that Anamnesis is built around the idea that "a good developer tool is built to fit real human behaviour, not just to collect high-quality data." They emphasize frictionless design and the need to work even when users forget to prepare.

They also state that they learned how to build a local-first TypeScript product with privacy controls and AI integration during development. The positioning implies a focus on productivity enhancement for developers who face frequent interruptions.

Inferred: The tool may be positioned as a lightweight, privacy-conscious alternative to more invasive context-aware tools or IDE plugins that require cloud-based processing.

The description states that Anamnesis targets developers who experience distractions and interruptions—specifically mentioning interns and office workers. It addresses the pain point of never-ending distractions such as Slack pings, calls from managers, or fellow interns asking for coffee.

It is designed for developers working in environments where focus is easily broken and resumption of work is difficult.

The tool is built to be frictionless, meaning it works even when users forget to prepare checkpoints. This suggests a broad target audience that includes casual or unprepared users rather than power users who might already have established workflows.

Not evidenced: Specific customer segments beyond general developers, nor any indication of segmentation strategy or targeting within the developer community (e.g., enterprise vs. individual developers).

The description does not provide evidence of a business model or pricing structure. It is self-reported as a hackathon project with no mention of monetization, subscriptions, licensing, or commercial partnerships.

Not evidenced: Any indication of how the tool would be sold, whether it's free, paid, or offered through a freemium model.

The tool is built using:

• TypeScript
• Node.js
• VS Code extension API
• CLI interface
• OpenAI GPT-5.6 (used for AI briefing)
• Git integration
• Shell history support (PowerShell, zsh, bash)

Key technical features include:

• Shared SignalPayload contract to prevent inconsistent data shapes
• Local-first architecture with privacy controls
• Sanitization and redaction of sensitive information
• Deterministic static analysis focused on JS/TS refactor failures
• Checkpoint management with up to 10 checkpoints per workspace
• Structured AI output via schema validation

The system includes:

• Local signal engine collecting Git, file activity, test output, shell history, and static analysis
• AI briefing layer that consumes sanitized data and returns structured results
• VS Code extension UI with sidebar, timeline, and controls for managing checkpoints

Not evidenced: Any information about scalability, performance metrics, or deployment architecture beyond the local-first design.

The description states that this is a hackathon project submitted to the OpenAI 2026 hackathon on Devpost. It includes:

• 79 passing tests across multiple components
• A shared TypeScript codebase with CLI and VS Code extension
• Local-first design with privacy controls
• Support for checkpoints and comparison modes

However, there is no evidence of:

• Revenue or customer base
• Adoption metrics
• Usage statistics
• Product-market fit validation
• Market traction beyond the authors' own use

Not evidenced: Any indication of real-world usage or user feedback beyond internal testing.

The description does not provide information about competitors or the competitive landscape. It is self-reported as a hackathon project without references to existing tools in the market.

Inferred: Anamnesis likely competes with or complements:

• Git-based context managers
• IDE-integrated task tracking tools
• Developer productivity apps that aim to reduce context switching

Not evidenced: Any comparison with existing products, nor any indication of competitive advantages or differentiation strategies.

1. Lack of Traction: The project is described as a hackathon submission with no evidence of real-world adoption or revenue.
2. AI Dependency Risk: Reliance on GPT-5.6 for AI briefing introduces dependency risks related to API availability, cost, and model accuracy.
3. Limited Scope: The tool's deterministic static analysis is intentionally narrow, focusing only on JS/TS refactor issues, which may limit its utility across other languages or domains.
4. Privacy vs. Utility Trade-off: While privacy controls are emphasized, the effectiveness of AI-generated briefings without full code understanding remains uncertain.
5. Frictionless Design vs. Feature Depth: The emphasis on frictionless design might come at the cost of advanced features that could be valuable to power users.

1. What is the actual developer adoption rate or usage beyond your own testing?
2. How does Anamnesis compare to existing tools in terms of functionality and user experience?
3. Is there a plan for monetization or commercial viability beyond the hackathon?
4. What are the technical limitations of the AI briefing layer, particularly regarding accuracy and reliability?
5. How do you intend to scale the tool beyond its current local-first architecture?
6. Are there any plans to expand support for other programming languages or frameworks?
7. What is the long-term vision for Anamnesis, including potential partnerships or integrations?