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A minimal LLM Ops stack with tracing and model costs
I built a minimal FastAPI “customer support reply drafter” with TF-IDF retrieval and Langfuse tracing. You’ll see exactly what context the model used, where latency came from, and what each request cost, plus the trade-offs behind the design.
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RAG: A (mostly) no-buzzword explanation
Retrieval-Augmented Generation (RAG) is a pattern that fixes the knowledge cutoff and hallucination problems by giving an LLM access to the right data at answer time. Instead of asking the model to “remember everything”, RAG lets it look things up first, then answer.
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Unikernels, without the marketing
It all started when I saw Prisma put “serverless” and “no cold starts” in the same sentence describing their “Prisma Postgres” product 🤔
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Solving the Openfire Lab Blue team challenge
As a cybersecurity analyst, you are tasked with investigating a data breach targeting your organization’s Openfire messaging server. Attackers have exploited a vulnerability in the server, compromising sensitive communications and potentially exposing critical data.
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Solving the ShadowCitadel Lab Blue team challenge
Today, we dive into a host-based forensics investigation − a curious case of a breach inside the enterprise environment of a company called TechSynergy. They have detected an anomaly after an employee engaged with an unexpected email attachment. This triggered a series of covert operations within the network, including unusual account activity and system alterations.