No matter how advanced prompt engineering is, hallucinations stemming from the underlying architecture of LLMs (probabilistic next-token prediction models) cannot be completely eliminated. The essence ...
The conclusion for successfully implementing AI agents in practice is to design them based on controllable workflows and evaluation foundations, rather than relying on excessive autonomy.While ...
Semgrep Mandoline gives AI agents fast program analysis across 50 languages, so they spend tokens reasoning about code ...
This in-depth guide systematically unveils the full technical roadmap of TokenSpeed, spanning every critical phase from ...
Researchers have developed P4Struct, an open-source Python framework that unifies finite element analysis, topology ...
AI SAST combines program analysis, dataflow tracing, and LLM reasoning to find logic flaws rule-based SAST misses. Learn how it works and how to evaluate tools.
Simon Willison’s SQLite parse_number() experiment initially put Rust ahead of C, until a targeted parser rewrite reversed the ...
Researchers have developed AFLDL, a greybox protocol fuzzing framework that utilizes deep learning on runtime memory ...
SambaNova Systems said on November 11, 2026, that Z.ai’s open-weight GLM-5.3 model is available on its SambaCloud inference ...
Shahein Ockersz documented two firmware quirks in ZKTeco's ADMS HTTP push protocol and published a Python server that ...
Foundry in the real world: Turning LLM hunches into verified findings – Security Loop Series, Part 2
See how Foundry turns LLM hunches into verified findings on Langflow, MLflow, and PyGoat, using an evidence gate and an honest look at token cost.
September’s updates help you run agents on a schedule, load tool definitions on demand, and investigate behavior from traces. Routines and Tool Search are now GA; Insights enters public preview, and ...
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