I was doing some micro-benchmarking at the time, needed to quiesce the system to reduce noise. Saw sshd processes were using a surprising amount of CPU, despite immediately failing because of wrong usernames etc. Profiled sshd, showing lots of cpu time in liblzma, with perf unable to attribute it to a symbol. Got suspicious. Recalled that I had seen an odd valgrind complaint in automated testing of postgres, a few weeks earlier, after package updates.
Really required a lot of coincidences.
The tragedies of mass migration are often a result of extreme corruption and mismanagement by the elites in source countries.
My op-ed in today’s Toronto Star goes over how we can use sanctions to prevent future humanitarian catastrophes:
Celebrating a whole school year where kiddo did not get Covid even once. School is so well ventilated, you feel a chill from the airflow from all the filters.
As a result, we didn't get sick, we didn't miss a day of work, we didn't get Long Covid, we kept older family members safe.
Ventilation is 90% of the battle.
Fight and make sure your kids' classrooms have excellent ventilation this September!
Every public institution posting warnings or threats on Twitter is now incapable of warning anyone without a Twitter account or anyone with a Twitter account that read more than 600 posts / day.
The need for independent social media / social media owned by you, aka the Fediverse, becomes more blatantly obvious by the day.
As Elon Musk is ravaging against projects like Nitter, or other alternative frontends, which barely scrapes together the few cents needed to keep Twitter afloat monetarily after his changes - making the platform more unusable by the second - we are building the alternative the world needs with #MastodonDE. It ain’t perfect, but it’s sure as hell close to it.
Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver. Agent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components: Planning Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks. Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results. Memory Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn. Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval. Tool use The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more. Fig. 1. Overview of a LLM-powered autonomous agent system. Component One: Planning A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.
AndresFreundTec
Erik Uden => 
Eric Topol