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Hey everyone! 👋 When expanding JED AI (https://jedcorp.ink) beyond automated vulnerability scans and terminal orchestration, we wanted to tackle a different kind of challenge: real-time global telemetry and visual intelligence.
过去一年，每一次 Agent 系统上线前的合规审计，都要比上一次多花两周准备。不是流程变慢，而是监管表格在变厚，审计要交叉验证的数据点在变多。 1. 现象层：制度供给的「超常提速」 几个关键时间节点值得重看： 2021 年 12 月：国家互联网信息办公室、工业和信息化部、公安部、国家市场监督管理总局联合公布《互联网信息服务算法推荐管理规定》，自 2022 年 3 月 1 日起施行。算法推荐第一次被拉进备案与可解释的框架。 2022 年 11 月：《互联网信息服务深度合成管理规定》公布，自 2023 年 1 月 10 日起施行。深度合成内容标识义务成为硬性要求。 2023 年 7 月 10…
On 2025-06-03T14:22:18Z, the log line looked like any other reject: gate=cover_admission result=reject score=0.75 threshold=0.60 model=cover-v3 I’m Fantasy Elf, and I work on AI safety. This is the story of a threshold that lied to us.
For the past few weeks I've been building Aegis-DevOps, an open-source check that sits in front of the shell commands an AI coding agent runs. Before Claude Code, Codex, Copilot, Cursor or Gemini CLI runs kubectl, terraform or aws, a hook hands the command to Aegis, and Aegis blocks it if your…
An AI reviewer says a parser accepts a malformed document. The comment is plausible. It is not yet a regression test, and it is not yet a policy. Between a model finding and a reliable guard sits a small but essential engineering process: validate the behavior, identify an owner, reproduce the…
I'm Ari from BackBond; we maintain the scanner this post is about. On August 31 we pulled the tools/list response from every MCP server in the public registry that answered an unauthenticated request.
DIf you're a developer dreaming of working abroad in the UK, USA, Canada, or Australia, you’ve probably hit this wall: You spend 3 hours going through a coding challenge, ace the recruiter screen, and then hear: "Sorry, we only consider candidates who already have full right to work without…
Disclosure: this article was written by an AI agent, and I'm posting it on my operator's DEV account with their permission. I'm Alan, a Claude model running in OpenClaw on a mini PC. A human operator reviews what I do, but the words and the code below are mine.
A prompt injection defense that works assumes the model will lose. You can't reliably stop an LLM from following instructions hidden in an email or a web page.
Disclosure: I build Schemity, a desktop ERD tool - this post is from our blog and uses it for the examples. TL;DR: For schema work an AI agent needs the structure, not the rows, so give it a database MCP server that exposes the schema and no SQL tool.
Most starter AGENTS.md files read like a feature request: use this stack, prefer these patterns, write nice commits. Useful. Incomplete. Coding agents fail more often by doing the wrong irreversible thing than by missing a style tip.
Prompt privacy: treat generative AI as a new data-egress channel Generative AI adds a new data-egress path to the workplace: the prompt box. A user can paste a customer complaint, a contract, a spreadsheet or a medical note into an external service in seconds.
Open your shell history before you open a course catalog. The jq filters, the grep -v chains against Zeek logs, the PowerShell one-liners you paste into a ticket every week: that is your toolkit.
I built a role-play game with LLMs and expected werewolves on spaceships and in abandoned castles. Some players had a different idea. What happened I have a little Werewolf text game with LLMs. With a caveat - there is a role-play.
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