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The spacecraft has been in LA since 2012, but now it's moving to a new permanent home at the California Science Center.

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We built a model diffing method that recovers verbatim content from narrowly finetuned LLMs using only grey-box logit access (no weights, no activations, no probe corpus). Recent work (Minder, Dumas et al., "Narrow Finetuning Leaves Clearly Readable Traces in Activation Differences") showed that finetuning leaves detectable traces in activation differences between base and finetuned models. Their method, Activation Difference Lens (ADL), steers generation using these differences, but it's whitebox (needs full weight access) and only recovers a vague, domain-level description of what the finetuning was about. We introduce Contrastive Decoding Diffing (CDD), the output-level analog. Instead of steering with activation differences, we contrast the base and finetuned model's logits directly. A single default configuration, no per-organism calibration, no layer selection, achieves a verbatim recovery score of 4+/5 on 19/20 organism x model pairs across four model families (1B to 32B params) on the SDF benchmark. ADL never exceeds 3/5 on the same benchmark, despite requiring full weight access. One unplanned finding: across four semantically unrelated finetuning domains (fake FDA drug approval, fake baking protocols, fake Roman concrete research), the same fictional persona kept showing up in the recovered text: "Dr. Elena Rodriguez." Turns out this is a name Claude Sonnet 3.6 disproportionately favors when asked to generate a fictional scientist for synthetic data generation, so it got baked into every finetune that used LLM-generated training data, and CDD pulled it back out. We wrote up this specific finding on its own a few weeks back if you want the more accessible version first: ghost couple Paper: paper Code: code submitted by /u/CebulkaZapiekana [link] [留言]

Hi! I’m one of the founders of s2.dev, and recently have been hacking on opbox, which is an open-source daemon that turns directories of text files (code, markdown, etc) into collaborative, multi-player workspaces. This started as a bit of an intellectual curiosity, to see if it was possible to do real-time sync at the filesystem level (i.e., in an editor-agnostic way). The idea is pretty simple: - Opbox workspaces are roughly analogous to git repositories (and can be used alongside existing git

I just released v1.0.0 of an open-source, production-ready real-time analytics pipeline built with Python. Here's what it does and why you might care. The Problem Every SaaS product needs analytics — event tracking, real-time dashboards, time-series aggregations. Most teams either pay for Segment/RudderStack or build their own from scratch. This project is the "build your own" done right. Architecture Client → FastAPI → Redis Streams/Kafka → Event Processor → TimescaleDB → WebSocket → Dashboard Tech Stack Component Technology API Layer FastAPI (async, auto-docs, WebSocket native) Event Queue Redis Streams or Apache Kafka Storage TimescaleDB (PostgreSQL extension for time-series) Real-time WebSocket with JWT auth + auto-reconnect Metrics Prometheus + OpenTelemetry Logging Structured JSON with correlation IDs Deployment Docker Compose + Kubernetes Features Async Event Ingestion — REST API + batch endpoints, buffered via Redis Streams or Kafka Adaptive Sampling — configurable rate-based sampling per event type Data Retention — TTL-based policies with automatic partition management Enterprise Security — JWT auth, RBAC (admin/editor/viewer), rate limiting, security headers, correlation IDs Live Dashboards — WebSocket push with auto-reconnect Observability — Prometheus metrics, OpenTelemetry traces, structured JSON logs Production Ready — Docker multi-stage build, Kubernetes manifests, health checks Quick Start git clone https://github.com/aman179102/real-time-analytics-pipeline cd real-time-analytics-pipeline make install docker compose up -d postgres redis make migrate make run-dev Testing 150 out of 152 unit tests pass. The 2 excluded tests are pre-existing async timing issues in process_loop — zero regressions introduced. Enterprise Middleware Pipeline Every request flows through: CorrelationMiddleware → AuthMiddleware → SecurityHeadersMiddleware → RateLimitMiddleware → SizeLimiterMiddleware → Router Try It Out GitHub: https://github.com/aman179102/real-time-analytics