GoKawiil
Tech News
A technical blog post examines the common practice of AI agent designers asking large language models to output a numeric confidence or probability score alongside their answers. The author argues there is no sound basis for trusting these numbers, contrasting them with how a human forecaster's confidence is grounded in historical data and a consistent updating process.
Basis reports that AI agents have been used to produce mathematically verified software, including a Linux nftables compiler and, separately by MIT researchers, the xv6 operating-system kernel down to machine code. The company says LLMs are improving fast at theorem proving but that most programming languages still lack a formal semantics, limiting how broadly such verification can be applied.
A Ruby developer has archived the source code of all their Ruby gems, explaining in a blog post that they no longer wish to maintain them. They cite disillusionment with Rails creator David Heinemeier Hansson's recent direction and with Ruby creator Matz's push to reposition the language for consumption by LLM agents.
Developer Isuru Wijesiri has published 'Harness Engineering 101,' a 16-chapter book explaining how AI agents are built around stateless LLM APIs. The series uses a roughly 300-line toy agent written in Python to illustrate how so-called agent features are really patches applied to the conversation array sent to a model. The patterns are also demonstrated in an open-source coding agent called One Code, built on a project named pi.
A Fly.io team post examines VSCode's remote SSH extension, noting that rather than running lightweight shell commands like Emacs's Tramp does, it deploys a Bash script that downloads and installs a full agent, including a Node.js binary, on the remote machine. The authors say they are exploring integration with this remote-editing flow because so many developers now use VSCode forks paired with LLM code-generation agents.
A reader survey by Android Authority, prompted by its coverage of the OpenClaw tool, found that 43% of respondents would not give an AI agent full access to their computer for security reasons, while another 27% said they simply weren't interested. Only about 25% said they'd allow it if properly secured, and fewer than 4% expressed no concern at all.
Researchers introduced Procedural Graphs, a method that encodes step-by-step procedural knowledge as structured graphs rather than leaving agents to infer next actions from raw history. At each decision point, the system pinpoints where the agent is in the graph and generates situational guidance for its next move, while an LLM refiner compares failed and successful runs to update the graph's structure over time. Tests across several datasets, task types and LLMs show the approach outperforms memory-based agent baselines and can even fix flawed manually designed procedures.
TradingAgents, an open-source multi-agent LLM framework for financial trading, has shipped version 0.4.0. The update fixes look-ahead bias issues across macroeconomic (FRED), social sentiment, and decision-log memory data sources, adds clearer decision signals, restores CLI checkpoint resume functionality, grounds trader price data, and adds support for GPT-5.6 and GLM-5.3 models. This follows recent v0.3.1 and v0.3.0 releases that addressed similar correctness issues and expanded provider and data-vendor support.