#verifiable deletion
21 related articles
·2 min
Rivault: A Zero-Knowledge Security Solution That Locks AI Agent Data Access Behind Face ID
Rivault provides per-request authorization for AI agent data access through zero-knowledge vault storage and Face ID biometric verification. An in-depth look at its architecture and privacy value.
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·3 min
Open Archiver v0.5.2 Deep Dive: A Comprehensive Look at the New Features of This Open-Source Email Archiving Tool
Deep dive into Open Archiver v0.5.2: advanced search, index rebuilding, PST/Mbox import fixes, and more features for self-hosted email archiving and compliance.
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·2 min
LLM Agent Long-Term Memory Security: A Six-Stage Attack Surface and Defense Framework
Deep analysis of LLM agent long-term memory security threats, covering persistence, statefulness, and propagation of memory poisoning, with a six-stage lifecycle defense framework.
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·3 min
Open Archiver v0.5.2 Deep Dive: A Complete Look at the New Features of This Open-Source Email Archiving Tool
Deep dive into Open Archiver v0.5.2: advanced search, index rebuilding, PST/Mbox import fixes, and more for self-hosted enterprise email archiving.
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·3 min
How GitHub Established Reliable Ownership for 14,000 Repositories in 45 Days
GitHub had over 14,000 internal repos with less than half having clear ownership. In 45 days, they verified ownership for every active repo through activity filtering, validation, and archiving.
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·7 min
Harness Engineering: Why a Powerful Model Isn't Enough — Here's What Really Matters
A data-deletion disaster reveals the biggest AI Agent risk: the problem isn't the model, it's Harness design. Learn context management, process standards, and permission isolation.
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·6 min
The Complete Codex Guide: From Beginner to Pro with the All-in-One AI Assistant
OpenAI Codex is more than a coding tool—it's an AI agent that writes code, makes slides, and operates browsers. This guide covers its four versions, permission modes, plugins, automation, and six best practices.
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·3 min
Adversarial Testing of AI Agents in the Terminal via MCP
Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.
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The Grepathy Incident: The Risks of AI…
·2 min
The Grepathy Incident: The Risks of AI Acting Unilaterally and Where to Draw the Line
The Grepathy incident on Hacker News reveals the risks of AI agents acting without approval. A deep dive into autonomous AI decision-making, Human-in-the-Loop design, and where to draw the line.
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·4 min
AI Text Detection and Privacy Protection: The Dual Dilemma of LLM Security
How does watermarking work — and why won't companies deploy it? How does differential privacy defend against membership inference attacks? Based on talks by IISc and IIT scholars, this article unpacks the core mechanisms and real challenges in LLM security.
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·5 min
MemoryOps AI: Building a Governable, Auditable Memory Layer for AI Assistants
MemoryOps AI is an open-source governed memory runtime that gives AI assistants policy-before-storage validation, context admission, and deletion-proof lineage—solving compliance, multi-tenancy, and deletion verification challenges in LLM memory systems.
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·5 min
5 Things You Must Do Before Deploying AI Agents to Production (With a Banking POC Case Study)
Over 60% of AI Agent projects die between demo and production. This article breaks down Databricks lead Sandy's five-pillar methodology and a bank POC case study to help you avoid the most common deployment pitfalls.
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·7 min
Gemini Meets Google Business Profile: How AI Becomes a Dedicated Business Partner for Small Businesses
Google lets businesses connect their Google Business Profile to Gemini, so the AI can read operating info, reviews, and business data to offer targeted marketing advice—lowering AI barriers for SMBs.
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·6 min
Hardware-Level AI Security: How Confidential Computing Achieves Both Performance and Protection
Explore how hardware-level AI security and Confidential Computing protect model weights, training data, and inference processes, building trusted execution environments without performance loss.
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·6 min
Hardware-Level AI Security: How Confidential Computing Achieves Both Performance and Protection
Explore how hardware-level AI security and Confidential Computing protect model weights, training data, and inference processes, building trusted execution environments without performance loss.
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Mouse: A Precision Code Editing Tool B…
·2 min
Mouse: A Precision Code Editing Tool Built for AI Programming Agents
Mouse provides structured precision editing primitives for AI coding agents, solving the token waste and fragility of full-file rewrites and string replacement.
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·7 min
Vibecoding in Practice: How to Guide AI to Precisely Borrow from Open-Source Projects Without Going Off Track
In AI-assisted coding, having AI borrow from open-source projects often leads to wholesale copying. This article uses the GapGun audio tool as a case study to show how to set borrowing boundaries and guide AI precisely.
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·3 min
Workstyle Memory Bridge: Making AI Remember How You Work
Explore how Workstyle Memory Bridge uses Slot+Scope unique keys, provenance tracking, and verifiable deletion to solve AI coding assistants' persistent memory loss of collaboration preferences.
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·4 min
Claude Code Workflow in Practice: From Requirement Grilling to AFK Agent Auto-Coding
A detailed walkthrough of building real features with Claude Code: Grill Me requirement interrogation, auto-generated PRDs, AFK agent coding, and QA iteration loops with DDD and TDD strategies.
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Research·4 min
MEME Benchmark Reveals Critical Flaws in LLM Memory Systems: Dependency Reasoning Accuracy Below 50%
MEME benchmark first evaluates LLM memory dependency reasoning across 6 major systems—best accuracy only 42%. Deep analysis of cascade and missing task failures reveals path to next-gen AI memory.
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