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Veil#Drop: Blogspot-Hosted PowerShell Loader Delivers PureLog Stealer Through XOR-Encoded In-Memory .NET Payloads (20 minute read)
Veil#Drop is a multi‑stage PowerShell‑based framework that starts from a JavaScript file disguised as a document, then uses Windows Script Host to launch PowerShell with execution policy bypassed and pull further stages from attacker‑controlled Blogspot pages. Payloads are XOR‑encoded, decoded only in memory, and reconstructed into .NET assemblies that are loaded via reflection without touching disk. The campaign relies on compromised websites, document‑style multi‑extension filenames, LOLBIN‑based execution fallbacks, decoy content, cleanup of the initial launcher, and finally deploys PureLog Stealer to grab browser credentials, cookies, crypto wallets, and other user data.
Jun 12 | Blog
I Tried to Build a Context Layer for My Agent in a Weekend. Reader, I Did Not Build a Context Layer for My Agent in a Weekend.
A "simple" weekend project turns into real infrastructure, and why agent context deserves a boring, reliable foundation.
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Jul 07 | Tech
What The New 100x Agentic Engineer Looks Like In The Era Of Fable & GPT 5.6 (21 minute read)
The 100x software engineer is at least an order of magnitude more productive than normal software engineers due to a combination of tacit knowledge, experience, and technical prowess. Agentic coding doesn't normalize productivity - not everyone is equally productive with agents. Agentic engineering is a skill with a high ceiling. This article looks at the shift in the bottleneck in agentic engineering and how to become a 100x agentic engineer.
Jul 07 | AI
A global workspace in language models (26 minute read)
Anthropic's new research paper introduces the concept of "J-space" in language models, revealed as internal neural patterns playing a unique role in Claude's processing. Emerging during training without explicit design, the J-space allows Claude to internally reason, modulate thoughts, and solve multi-step problems, distinguishing them from automatic processes. These patterns enable the monitoring of internal thoughts for AI misbehavior and provide insights into AI consciousness, suggesting a tangible distinction between deliberate and automatic decisions.
Jul 07 | Dev
How we taught a small LLM to throw away 68% of our RAG context (9 minute read)
A small, cost-effective LLM was implemented to prune retrieved context chunks for question-answering systems in order to improve efficiency, successfully discarding 68% of unnecessary chunks while maintaining 96% recall. This method addresses the traditional challenge of balancing cost and recall in complex knowledge bases, where irrelevant context can increase expenses without aiding the response accuracy.
