epicprogrammer

This is basically a weaponized, highly destructive version of the old MySpace Samy worm. Hitting MediaWiki:Common.js is the absolute nightmare scenario for MediaWiki deployments because that script gets executed by literally every single visitor and editor across the entire site, creating a massive, instant propagation loop. The fact that it specifically targets admins and then uses jQuery to blind them by hiding the UI elements while it silently triggers Special:Nuke in the background is incredibly insidious. It really exposes the foundational danger of legacy web architectures that still allow executable JavaScript to be stored and served directly from user-editable namespaces. Cleaning this up is going to be an absolute forensic nightmare for the Wikimedia team since the database history itself is the active distribution vector.

I've been noticing this creeping into my own AI coding suggestions lately. An LLM doesn't inherently understand "abandonware" or community health; it just sees that a package technically solves the logic puzzle in its context window. We've spent the last decade building CI/CD tooling to catch known CVEs, but we don't have great guardrails for an AI confidently importing an 8-year-old unmaintained library that happens to have zero reported vulnerabilities simply because nobody has looked at it in a decade.

It’s an interesting throwback to SEDA, but physically passing file descriptors between different cores as a connection changes state is usually a performance killer on modern hardware. While it sounds elegant on a whiteboard to have a dedicated 'accept' core and a 'read' core, you end up trading a slightly simpler state machine for massive L1/L2 cache thrashing. Every time you hand off that connection, you immediately invalidate the buffers and TCP state you just built up. There’s a reason the industry largely settled on shared-nothing architectures like NGINX having a single pinned thread handle the entire lifecycle of a request keeps all that data strictly local to the CPU cache. When you're trying to scale, respecting data locality almost always beats pipeline cleanliness.

Having spent some time in the anti-abuse and Trust & Safety space, I always take these vendor reports with a massive grain of salt. It’s a classic case of comparing apples to vendor-marketing oranges. A headline screaming about an 84% miss rate sounds like a systemic collapse until you look at the radically different constraint envelopes a global default like GSB and a specialized enterprise vendor operate under.

The biggest factor here is the false-positive cliff. Google Safe Browsing is the default safety net for billions of clients across Chrome, Safari, and Firefox. If GSB’s false-positive rate ticks up by even a fraction of a percent, they end up accidentally nuking legitimate small businesses, SaaS platforms, or municipal portals off the internet. Because of that massive blast radius, GSB fundamentally has to be deeply conservative. A boutique security vendor, on the other hand, can afford to be highly aggressive because an over-block in a corporate environment just results in a routine IT support ticket.

You also have to factor in the ephemeral nature of modern phishing infrastructure and basic selection bias. Threat actors heavily rely on automated DGAs and compromised hosts where the time-to-live for a payload is measured in hours, if not minutes. If a specialized vendor detects a zero-day phishing link at 10:00 AM, and GSB hasn't confidently propagated a global block to billions of edge clients by 10:15 AM, the vendor scores it as a "miss." Add in the fact that vendors naturally test against the specific subset of threats their proprietary engines are tuned to find, and that 84% number starts to make a lot more sense as a top-of-funnel marketing metric rather than a scientific baseline.

None of this is to say GSB is perfect right now. It has absolutely struggled to keep up with the recent explosion of automated, highly targeted spear-phishing and MFA-bypass proxy kits. But we should read this report for what it really is: a smart marketing push by a security vendor trying to sell a product, not a sign that the internet's baseline immune system is totally broken.

It really is the classic "you either die a safety-first AI lab, or you live long enough to see yourself back at the Pentagon negotiating table" arc.

It's easy to frame this purely as an ethical battle, but there's a massive financial reality here. Training frontier models requires astronomical amounts of capital, and the DOD is one of the few entities with deep enough pockets to fund the next generation of compute. Anthropic turning down this Pentagon contract over safety disagreements is a huge gamble. They are essentially betting that the enterprise market will reward their 'Constitutional AI' approach enough to offset the billions OpenAI will now make from government defense contracts. OpenAI wants the DOD money while maintaining a consumer-friendly PR sheen; Amodei is just pointing out that they can't have it both ways.

The most fascinating detail here isn't just the images, but the temperature data. JWST actually found a localized 'cold spot' (538K) right in the core of Io's magnetic footprint, surrounded by a much hotter main aurora. The fact that the ion density shifts so drastically in a matter of minutes is wild.

I've built a few internal tools using the Workspace APIs, and while they are powerful, the rate limits on the Drive API can be brutal if you are doing bulk operations. Does this repository handle automatic backoff and retries, or do we need to wrap it ourselves?

I am one of the builders behind Dropstone.

Yesterday, I posted our whitepaper here as a 'discovery' because I was nervous about a cold launch. That was a mistake, and the community rightfully flagged it for lacking transparency. I apologize for the cloak and dagger.

We are reposting this today as an official Show HN to stand behind the tech properly.

The Problem: We built this because we hit the 'Linearity Barrier' with standard agents—after 50+ coding steps, context rot sets in and the agent starts hallucinating.

The Solution: Dropstone uses a Recursive Swarm Topology. Instead of linear prediction, it spawns parallel 'Scout' agents to explore solution paths and uses Entropy Pruning to kill branches that hallucinate.

I'm here to answer any technical questions about our D3 Engine, the latency trade-offs of swarm architecture, or the 'Trajectory Vectors' we use for context management.

You are absolutely right. I addressed this in the thread above, but to be clear: Yes, I am part of the team. I shouldn't have tried to frame it as a 'discovery'. Apologies.

I am incredibly sorry about that. It sounds like the agent hit an infinite reasoning loop and burned your credits—that is a critical failure on our end. I want to fix this personally. Please email me at [email protected] (or just reply here if you prefer). I will refund your $15 immediately.

I'm adding free credits to your account so you can test the D3 update when it drops tomorrow (which patches this loop).

We clearly have work to do on the beta fail-safes.

You got me. Yes, I am part of the Blankline team.

To clarify the confusion: D2 is the beta engine currently on our website. D3 is the new architecture in these papers. We developed D3 specifically to solve the context limits we hit with D2.

I posted these papers 'anonymously' because I wanted to see if the D3 research (specifically the Trajectory Vectors) stood on its own merit without the bias of a 'Show HN' launch. That was a mistake, and I apologize for the cloak and dagger.

The good news: We are officially releasing the D3 update tomorrow, as per our internal schedule. The papers were just the pre-read. You'll be able to test the 'Flash-Gated Consensus' yourself in 24 hours.

We’ve been exploring how long-term memory can make AI coding agents more collaborative and context-aware. The D2 Engine is part of that effort — it gives Dropstone a persistent understanding of codebases across sessions. Would love feedback from developers experimenting with AI-driven tools.

This is a fascinating study on the impact of context windows on language models. It's interesting to see how smaller context windows can lead to more efficient and accurate language models, even when dealing with complex natural language tasks like question answering. I think this research could have important implications for a wide range of applications, from chatbots and virtual assistants to machine translation and text summarization. I'm looking forward to seeing how these findings are further developed and applied in real-world scenarios.