13pixels

7 ms·

1.
by 13pixels 7mo ago
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 themselves half the time. I've been monitoring ser
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by 13pixels 7mo ago
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 reshuffling distribution in ways most companies haven't
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by 13pixels 7mo ago
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 correlation between Google rank and LLM recommendation was 0.08. Bas
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by 13pixels 7mo ago
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 Perplexity asking for product recommendations in ~30 B2B ca
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by 13pixels 7mo ago
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 compute. We see a lot of "invisible" content in
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by 13pixels 8mo ago
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 LLMs actually recommend software tools (audited ~150 B2
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by 13pixels 8mo ago
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 something more like llms.txt / meta tags aime
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by 13pixels 8mo ago
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 for 'Product B' and confidently
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by 13pixels 8mo ago
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-through often strips the referrer or sho
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by 13pixels 8mo ago
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 hood for the embeddings or a custom extension? Also, curious h
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by 13pixels 8mo ago
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 for a keyword; they describe a p
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by 13pixels 8mo ago
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 search for specific entity names/IDs, while v
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by 13pixels 8mo ago
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/Gemini answers vary wildly based on user history,
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by 13pixels 8mo ago
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 out the affiliate spam. But aren't we ju
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by 13pixels 8mo ago
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 category query) is much more stable than individ
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by 13pixels 8mo ago
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, or established industry sites). Another factor we'
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by 13pixels 8mo ago
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 adoption). Have you found that forcing the LLM int
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by 13pixels 8mo ago
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 "generic AI accent" that just screa
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by 13pixels 8mo ago
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 found that brand mentions correlate strongly wit
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by 13pixels 8mo ago
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 things the user didn't ask for. now scale tha
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by 13pixels 8mo ago
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 indexes your marketing page -- soon the question is whether Claud
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Temporal Knowledge Graphs for Brand Intelligence(deltaintel.ai)
1 points by 13pixels 10mo ago|0 comments
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by 13pixels 10mo ago
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" arguments. I wanted to see
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Show HN: I analyzed 5k comments to quantify the Jira vs. Linear sentiment gap(deltabrandcheck.com)
2 points by 13pixels 10mo ago|1 comments