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Adaptive Recall - Adaptive Memory for AI Applications

▲ 20 points • by abratabia • 4mo ago • HN discussion ↗

Pangram verdict · v3.3

We believe that this document is fully AI-generated

99 %

AI likelihood · overall

AI
0% human-written 100% AI-generated
SEGMENTS · HUMAN 0 of 2
SEGMENTS · AI 2 of 2
WORD COUNT 343
PEAK AI % 99% · §2
Analyzed
Jul 12
backend: pangram/v3.3
Segments scanned
2 windows
avg 172 words each
Distribution
0 / 100%
human / AI fraction
Verdict
AI
Pangram v3.3

Article text · 343 words · 2 segments analyzed

Human AI-generated
§1 AI · 99%

Patent Pending Store, recall, and forget with a memory system that learns from every interaction. Retrieval quality improves automatically over time, powered by cognitive science and machine learning. Get Started Free

Standard Memory API +Store text with vector embeddings +Search by cosine similarity +Filter by metadata tags -Single retrieval strategy -Static results, no learning -No quality monitoring -Memories never evolve -No knowledge graph Adaptive Recall +Four retrieval strategies running in parallel +ACT-R cognitive scoring from 30 years of research +Automatic knowledge graph construction +Memory lifecycle with confidence evolution +Evidence-gated parameter learning +Self-verification of retrieval quality +Curiosity-driven knowledge gap detection +ML pipeline that trains on your usage patterns

Adaptive Retrieval Four search strategies run in parallel: vector similarity, temporal recency, full-text keyword, and knowledge graph traversal. The system learns which strategies to prioritize for each type of query. Learn more → Cognitive Scoring Results are ranked using ACT-R activation modeling from cognitive science. Recency, access frequency, entity connections, and validated confidence all factor into which memories surface first. Learn more → Knowledge Graph Entities and relationships are extracted automatically from stored memories. The graph becomes a retrieval pathway, finding relevant information through connections rather than just text similarity. Learn more → Memory Lifecycle Memories are not static rows in a database. They progress through stages, gain or lose confidence based on corroborating evidence, and fade naturally when no longer accessed. Learn more → Self-Improving System The system trains ML models on your usage data, validates every parameter change against real query history, and monitors its own retrieval quality.

§2 AI · 99%

It gets better the more you use it. Learn more → Simple API Eight tools: store, recall, update, forget, graph, status, snapshot, feedback. Works over MCP for Claude Code and other CLI tools, or plain HTTP REST for any application. Bearer token auth, JSON in and out. Learn more →

Start Building with Adaptive Recall Sign Up FreeNo credit card required. 500 memories free.

Adaptive Recall is built and maintained by AI Apps API, the company behind a family of AI tools and custom agent systems.