Date of sighting: Oct 2023Location of sighting: USA (no other info given)Source: Dept of WarLink: https://www.war.gov/UFO/?releaseDate=Release+06&release=06#LLE-UAP-PR002-Unresolved-UAP-Report-Colorado-October-2023I was looking at the Dept of War website at…
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Background Movements for civil rights were a worldwide series of political movements for equality before the law that peaked in the 1960s. In many situations…
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Background The following document was obtained by The Black Vault through the Freedom of Information Act (FOIA), and was part of the “Stargate Collection” release.…
Read MoreFor Rupert Young ’95, SM ’95, his career in data science and cybersecurity began when his grandfather gifted him thousands of stamps: He built intricate databases to catalogue them, displaying the “precise eye” for detail and nuance that his MIT application essay said would make him a good engineer. Young is now chief product officer…
Even as a teenager, Laurie Stach ’06 says, she had a “crazy ambition to take on the world and solve problems.” At MIT, she realized she wasn’t the only one. “Adults always see these youth who are good at math and science and say, ‘You’re going to do great things—someday,’” says Stach. “Meanwhile, traditional education…
Understanding Codex hooks The post Put Your Own Logic Inside the Codex Agentic Loop appeared first on Towards Data Science.
Use Grok Build to create a production-ready data science workflow with EDA, scikit-learn, model training, FastAPI, API testing, and cloud deployment.
A practical guide to running compact, privacy-preserving language models on your own hardware for faster, cheaper, and more controllable AI-powered applications.
AI agents don’t just have a context problem—they have a context typing problem. When instructions, memory, retrieved evidence, and tool outputs are flattened into one string, their semantic boundaries can disappear. I built a lightweight, zero-dependency Python runtime that keeps those boundaries explicit, tracks provenance, and rejects invalid context transformations before they reach the model.…
Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acceptance metrics, and show what determines whether speculation pays off.…
Most LLM inference runtimes have no idea a physical deadline exists. This one refuses admission rather than miss a 33ms robot control cycle, evicts KV cache by meaning instead of age, and is written entirely in hand-written CUDA — no cuBLAS, no libtorch. The post Can an LLM Forget the Right Things? appeared first on…
Enterprise Document Intelligence [Vol.1 #M3] - The ten positions the series argues from, and the map of every article that argues them The post 10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong appeared first on Towards Data Science.
arXiv:2608.20384v1 Announce Type: new Abstract: Multimodal affect and behaviour classifiers that fuse heterogeneous text, audio, and visual streams must simultaneously achieve competitive accuracy and produce human-understandable explanations of the cues driving their decisions -- a dual objective that current high-capacity models, notably Transformers, only partially address. While Transformers attain strong predictive performance, their distributed representations…
