<!-- SC_OFF --><div class="md"><p>I keep running into the same annoying gap.</p> <p>An AI workflow looks solid in the first run. The happy path works. Logs are clean enough that
<!-- SC_OFF --><div class="md"><p>Every time one of our agents made a bad call in production, we had the same problem. Logs showed what happened. Nothing showed why it decided to
<!-- SC_OFF --><div class="md"><p>we've been building an open source red teaming tool for AI agents. wanted to share what we keep finding because i don't think enough people are
<table> <tr><td> <a href="https://www.reddit.com/r/OpenAI/comments/1sw0vcf/stanford_researchers_fed_a_language_model_a_dna/"> <img alt="Stanford researchers fed a language model a
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s1ckkm/why_llms_sound_right_but_fail_to_actually_do/"> <img alt="Why LLMs sound right but fail to actually
<!-- SC_OFF --><div class="md"><p>One thing we’ve been noticing lately is that a surprisingly large percentage of day-to-day AI workflows no longer seem to require frontier-scale
<!-- SC_OFF --><div class="md"><p>Been heads down building graphify since April 5th. The idea: instead of stuffing your entire codebase into context every session, build a
<!-- SC_OFF --><div class="md"><p>One recurring issue with domain-specific agents is that overly defensive refusal behavior can make them much less useful once the workflow gets
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