MiroFish: The AI Swarm Engine Predicting Human Behavior in a Digital Sandbox
In an era where understanding human behavior is more valuable than ever, a new open-source innovation is pushing the boundaries of… Continue reading on Medium »
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Detecting collusion through multi-agent interpretability
TL;DR Prior work has shown that linear probes are effective at detecting deception in singular LLM agents. Our work extends this use to multi-agent settings, where we aggregate the activations of groups of interacting agents in order to detect collusion. We propose five probing techniques, underpinned by the distributed anomaly detection taxonomy, and train and evaluate them on NARCBench - a novel open-source three tier collusion benchmark Paper | Code Introducing the problem LLM agents are being increasingly deployed in multi-agent settings (e.g., software engineering through agentic coding or financial analysis of a stock) and with this poses a significant safety risk through potential covert coordination. Agents has been shown to try to steer outcomes/suppress information for their own
The MCP Security Baseline Problem
When npm audit was introduced in 2018, it did something deceptively simple: it made security a default part of the developer workflow. You run npm install , you get a vulnerability report. Not because you remembered to check — because the tooling assumed you should always know. The MCP ecosystem has no equivalent. And that gap is becoming a serious problem. The Baseline That npm Built The Node.js ecosystem spent years building a security infrastructure that developers now take for granted: npm audit — vulnerability scanning on every install Snyk — static analysis, dependency scanning, PR checks Dependabot — automated PRs to update vulnerable dependencies GitHub's dependency graph — passive vulnerability monitoring ESLint security plugins — lint-time detection of dangerous patterns None of
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Obsolescence without hostility: optimization, uniformity, and the erosion of human meaning in a post-AI world
Most contemporary discussions of artificial intelligence focus on misalignment, loss of control, or catastrophic harm. This paper examines a different and comparatively neglected possibility: that advanced AI may erode the social conditions under which human meaning has historically been generated, without conflict, coercion, or displacement. The central question is not whether AI dominates humanity, but whether human participation remains causally significant once AI systems outperform humans across core instrumental domains. The argument is conditional and long-horizon in scope. It proceeds from the observation that existing limits on AI superiority are primarily technological and economic rather than principled. If these constraints are progressively overcome, and AI systems come to out
I built an AI data extraction engine and E2E encrypted SMS router in Rust
I recently launched SendStackr. The original itch came from my time working at an agency that managed multiple Upwork accounts and a "phone farming" operation (around 50 physical devices). We needed to centralize inbound emails and forward SMS (mostly for 2FA/OTPs), but the existing tools were either prohibitively expensive, forced us to set up individual webhooks per device, or timed out when trying to pipe heavy unstructured data through standard no-code wrappers. To solve this, I built my own routing and extraction engine. Instead of relying on standard APIs, I wrote the backend entirely in Rust, building custom SMTP and IMAP servers to ingest the emails natively. It uses Graph RAG to read unstructured PDFs, raw HTML emails, and webhooks, extracting the exact variables needed and return
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