🛠️ Log and trace your AI's decisions securely with DecisionTrace, ensuring transparency and integrity in every choice it makes.
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Updated
Apr 12, 2026 - Python
🛠️ Log and trace your AI's decisions securely with DecisionTrace, ensuring transparency and integrity in every choice it makes.
Backend API for FrameForge built with FastAPI, Motor, and MongoDB. It receives prompts, orchestrates a multi-agent AI pipeline, reconstructs thread context, consumes OpenRouter via streaming, separates explanation from React code, persists the conversation, and exposes endpoints that the frontend consumes in real time.
Query JSON with SQL-like syntax. A readable jq alternative that generates pure jq under the hood. Table, CSV, YAML output. Interactive REPL. Pipes from curl, streams NDJSON logs.
Privacy-first personal data engine — schema-driven ingestion, canonical entity modeling, and local-first data pipelines
Infer robust Pydantic v2 models from messy, evolving JSON streams
Reference tooling for ICEP: schema validation, event-log utilities, example artifacts.
Structured JSON logging for ML experiments: schema-validated events, run context, pluggable sinks (stdout/file/Logstash, optional GCP), redaction, buffering, and summary emission.
Lightweight Python Market-data CLI to normalize & store top-of-book, then replay with timing for execution backtests
3-node Elasticsearch demo with Kibana + Cerebro on a single VM. Includes a tiny Python ingester for Simple English Wikipedia (Cirrus dumps), and health checks.
FastAPI Request Response Logger in ndjson.
You have directories containing data files and specification files. The specification files describe the structure of the data files. Write an app that reads format definitions from specification files. Use these definitions to convert the parsed files to NDJSON files.
A lightweight, high-performance Python library for parsing jsonl files.
ndjson with the same interface as the builtin json module
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