Agent-skills-eval – Test whether Agent Skills improve outputs
https://github.com/darkrishabh/agent-skills-eval
#HackerNews #AgentSkills #Eval #Testing #Skills #Outputs #AIResearch #MachineLearning
Agent-skills-eval – Test whether Agent Skills improve outputs
https://github.com/darkrishabh/agent-skills-eval
#HackerNews #AgentSkills #Eval #Testing #Skills #Outputs #AIResearch #MachineLearning
Cory House has a Full-Day Hands-On Workshop July 22nd at Nebraska.Code().
Learn more about 'Coding Effectively with AI' here:
https://nebraskacode.amegala.com/
#Editor #CLI #AIModels #ConfigTechniques #Outputs #MCPServers #AI #CodeReviewWorkflows #ContextManagement #CoryHouse #PromptingTechniques #TechWorkshop #ArtificialIntelligence #Tech
LLM Structured Outputs Handbook
https://nanonets.com/cookbooks/structured-llm-outputs
#HackerNews #LLM #Structured #Outputs #Handbook #LLM #Handbook #Structured #Outputs #Machine #Learning #AI #Resources
#!/bin/bash
# disconnect every existing connection
IFS=$'\n' read -r -d '' -a outputs < <( pw-link -o && printf '\0' )
IFS=$'\n' read -r -d '' -a inputs < <( pw-link -i && printf '\0' )
# pw-link -d out in
i=0; while [ $i -lt ${#outputs[@]} ]; do
j=0; while [ $j -lt ${#inputs[@]} ]; do
pw-link -d ${outputs[$i]} ${inputs[$j]} 2> /dev/null
((j++));
done
((i++)); done
I'm thinking about making time-lapse videos of my prints and I'm curious if the Prusa XL Buddy board has outputs or open-collector lines that can be managed with gcode to control a camera remote. 🤔 Has anyone tackled something similar or has insights on this?
#Design #Approaches
Design for meaningful outcomes · People don’t want outputs; they want outcomes https://ilo.im/157zga
_____
#Business #Customers #ProductDesign #UxDesign #UiDesign #WebDesign #Mindsets #Outcomes #Outputs
The thing about this working from home debate that I don't like is that it is being top down implemented.
Different people have different work-life needs & patterns.
#flexibility is key. Success measured by #outputs.
#WFH #Meta https://mastodon.social/@the_verge/111014123691436367
The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text. As LLMs increasingly powerconversational agents used by the general public world-wide, the synthetic personality traits embedded in these models, by virtue of training on large amounts of human data, is becoming increasingly important. Since personality is a key factor determining the effectiveness of communication, we present a novel and comprehensive psychometrically valid and reliable methodology for administering and validating personality tests on widely-used LLMs, as well as for shaping personality in the generated text of such LLMs. Applying this method to 18 LLMs, we found: 1) personality measurements in the outputs of some LLMs under specific prompting configurations are reliable and valid; 2) evidence of reliability and validity of synthetic LLM personality is stronger for larger and instruction fine-tuned models; and 3) personality in LLM outputs can be shaped along desired dimensions to mimic specific human personality profiles. We discuss the application and ethical implications of the measurement and shaping method, in particular regarding responsible AI.