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Facebook is honestly the least interesting crawler misbehaving right now. The real shift is GPTBot, ClaudeBot, PerplexityBot and a dozen other AI crawlers that don't even identify …

One underappreciated angle: the dot-com boom created new distribution channels that were broadly accessible. Anyone could put up a website and reach people. The AI boom is quietly …

The irony is that while AI slop is gaming Google, the AI models themselves (ChatGPT, Claude, Perplexity) are surprisingly resistant to it. We tested ~150 B2B tools and the correlat…

The explicit ads angle is only half the story. Even without paid placements, these models already have implicit recommendations baked in. We ran queries across ChatGPT, Claude, and…

The JS rendering point is critical. Even though bots like GPTBot technically have headless capabilities, they often fall back to text-only extraction for non-priority pages to save…

One angle thats underappreciated here is discovery. Even if the SoR survives as the backend, the interface layer is shifting to AI agents and assistants. We've been looking at how …

Interesting that you have "GEO ready" baked into the boilerplate. Curious what that covers specifically -- are you doing structured data / schema markup for LLM retrieval, or somet…

This is a great dataset. The 'cross-domain causality leap' is something we see constantly in brand monitoring—e.g. an LLM seeing a pricing page for 'Product A' and a feature list f…

Distinguishing 'AI Research' (crawling) from 'AI Referral' (user clicks) is the hardest part. Most agents (OAI-SearchBot, ClaudeBot) declare themselves in UA, but the actual click-…

Very cool to see a local-first approach to agent memory. The shift away from hosted vector DBs for single-agent use cases makes total sense. Are you using `sqlite-vec` under the ho…

This matches our internal data too. The biggest shift we're seeing is that 'Zero-Click' searches on Google are becoming 'Multi-Turn' conversations on AI. Users don't just search fo…

The 'Vector DB vs Keyword Search' section caught my eye. In your testing for RAG pipelines, where do you draw the line? We've found keyword search (BM25) often beats semantic searc…

Interesting tool. We've been looking at this space (GEO) as well. One big challenge we've hit is the lack of determinism. Unlike Google where rank #1 is fairly stable, ChatGPT/Gemi…

The 'without SEO bias' point is the most interesting part here. We're already seeing users trust LLM synthesis more than direct search precisely because it (theoretically) filters …

Signal aggregation is definitely the right mental model. We've found that tracking 'Share of Model' over time (e.g. how often a brand appears in the top 3 recommendations for a cat…

This aligns with what we're seeing too. LLMs often distrust new domains but will happily cite the same content if it appears on a platform they already trust (like Reddit, Medium, …

This is a really interesting application of LLMs. The lack of "repeatable, traceable results" is indeed a huge issue for any serious use case (we see this constantly in enterprise …

The "Virtual Personality Engine" / style transfer is a killer feature. One of the biggest issues with LLM-generated content (whether for voice, text, or even brand answers) is the …

The shift to zero-click discovery is definitely real. We've been tracking this "AI visibility" metric internally too (using a mix of prompt injection and search monitoring) and fou…

the "exploit the user's agentic LLM" angle is underappreciated imo. we already see prompt injection attacks in the wild -- hidden text on web pages that tells the agent to do thing…

This extends further than most people realize. If agents are the primary consumers of your product surface, then the entire discoverability layer shifts too. Right now Google index…

Hey HN, OP here. I’ve been seeing the "Jira vs. Linear" holy war play out on my timeline for months. It usually boils down to anecdotal "Jira is slow" vs. "Linear doesn't scale" ar…