THE GIST ▸ What it is: A 3,826-line system prompt steering Claude Fable 5 inside the Claude app, pulled from a public GitHub archive. ▸ What’s in it: Rules about safety, tone and restraint. ▸ Why it matters: it shows a frontier “AI” is far more an engineered rulebook than a mysterious mind. Before your […]
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Prompts shape every interaction with a large language model. Clear instructions produce focused, useful responses, while vague ones often lead to inconsistent results. This becomes harder when teams need the same task completed repeatedly in a fixed format, tone, or structure. Meta-prompting asks the model to design a reusable prompt, template, checklist, or workflow before […]
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Two short clips. One question: how alike do they look? Sounds trivial, it isn’t, and I learned that the slow way. My setup: one reference clip, eight others to rank against it, all waterfalls (more on why in a second). I figured this was an afternoon job, grab a model, compute a number, move on. […]
The post How to Measure Video Similarity: 6 Techniques I Tested (and the One I...
Most real-world classification problems are imbalanced. Fraud, disease, churn, and defects are rare by nature. Standard classifiers chase accuracy, so they quietly ignore the very class you care about. For years, SMOTE was the reflex fix that everyone reached for first. But SMOTE often fails on the messy, high-dimensional data that production systems actually see. […]
The post Handling...
LLMs are getting stronger every day, and building a RAG pipeline has never been easier. Knowing whether it actually works is not. Most teams ship a RAG system, see decent-looking answers, and call it done, until users hit hallucination, missing context, or irrelevant chunks. That’s where evaluation frameworks come in. RAGAS, TruLens, and DeepEval are […]
The post RAG Evaluation...
For twelve days, the best AI models on the planet existed and almost nobody could touch them. That ends now! GPT-5.6 Sol, Terra, and Luna go public today! The models are accessible by all users (no subscription required) This is the full breakdown of what’s on offer: three models, four prices, one precedent, and a […]
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AI agents are moving from one-time assistants to persistent workers that can repeat tasks, monitor changes, run checks, update workflows, and return with results. Instead of prompting an LLM once and deciding every next step manually, teams can now use AI agents that keep working (on a Loop) until a goal or stop condition is […]
The post Loop Engineering for AI Agents: How /loop is...
DeepSeek’s new DSpark module brings speculative decoding to DeepSeek-V4. It might look like a niche inference tweak, but in production it boosted per-user generation speed by 60 to 85 percent with no drop in model quality. What sets DSpark apart is that it tackles two longstanding problems at once, weak draft quality and the waste […]
The post DeepSeek DSpark: The Speculative Decoding...
A surge in AI adoption is forcing enterprises to expose sensitive data to new systems and access paths, creating security risks that traditional perimeter models can't contain.
In this episode, Todd Vancil, Vice President of Veeam's Securiti AI Sales Engineering Team, examines how securing the data itself — through classification, labeling, and governed access — becomes the...
Not long ago, the work of creating a competitive analysis took weeks of research, strategic frameworks and review. This service could cost $10,000 or more for the analysis plus strategy recommendations. Today with AI, the time (and cost) has shrunk to almost nothing.