13pixels

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 server logs across ~150 sites and the pattern is striking: AI crawler traffic increased roughly 8x in the last 12 months, but most site owners have no idea because it doesn't show up in analytics. The bots read everything, respect robots.txt maybe 60% of the time, and the content they index directly shapes what ChatGPT or Perplexity recommends to users.

The irony is that robots.txt was designed for a world where crawling meant indexing for search results. Now crawling means training data and real-time retrieval for AI answers. Completely different power dynamic and most robots.txt files haven't adapted.

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 noticed yet.

We've been tracking how LLMs recommend products across categories. The correlation between Google ranking and LLM recommendation is near zero (0.08 across ~150 brands we tested). So a company that spent a decade building SEO authority can be completely invisible to ChatGPT, while a smaller competitor with better documentation and more mentions on Reddit or GitHub gets recommended instead.

That's a massive, silent redistribution of discovery -- and unlike the dot-com era, the companies being disrupted mostly don't even know it's happening yet.

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. Basically zero.

So you end up with two diverging discovery layers: Google, increasingly polluted by cheap AI content, and LLMs, which seem to weight structured data, documentation quality, and mentions on high-trust platforms (Reddit, GitHub, Stack Overflow) way more than traditional SEO signals.

The real question is whether Google fixes this before users just default to asking ChatGPT instead of searching.

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 categories. The overlap between what each model recommends is surprisingly low -- around 40% agreement on the top 5 picks for any given category. And the correlation with Google search rankings? About 0.08.

So we already have a world where which CRM or analytics tool gets recommended depends on which model someone happens to ask, and nobody -- not the models, not the brands, not the users -- has any transparency into why. That's arguably more dangerous than explicit ads, because at least with ads you know you're being sold to.

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 e-com especially because of this.

One other signal to check: internal linking structure. AI crawlers seem to respect semantic clusters more than traditional pagerank flow. If your "about" page isn't semantically linked to your "product" page in a way the LLM understands as a relationship, it often hallucinates the connection.

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 B2B SaaS across ChatGPT, Claude, Perplexity, Gemini) and there's a massive bias toward products with clean, parseable documentation and structured data. The "vibe-coded" replacements often win here not because they're better products, but because they're easier for an agent to read and integrate with than a legacy SoR hiding behind a login wall or a PDF.

If your product is invisible to the agent doing the recommending, the quality of your backend stops mattering. Has anyone else noticed this shift in how enterprise software gets discovered?

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 aimed at AI crawlers?

We've been testing how different content structures affect whether AI assistants actually recommend a product, and the difference between well-structured docs vs not is pretty dramatic.

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 asserting 'Product A has Feature B for $X'.

One edge case you might want to add: *Temporal Merging*. We often see models take a '2024 Roadmap' and a '2023 Release Note' and halluncinate that the roadmap features were released in 2023. It's valid syntax, valid entities, but impossible chronology.

Are you planning to expand this to RAG-specific failures (where the context retrieval causes the mix-up) or focusing purely on model-internal logic gaps?

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 shows as direct/none. We've had some luck correlating 'time of crawl' with 'time of visit' to fingerprint AI traffic, but it's noisy.

Self-hosted is definitely the way to go for raw logs though. GA4 obfuscates too much of this.

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 how you handle the 'hybrid' search part—is it just a linear combination of FTS5 bm25 and vector cosine similarity, or do you have a re-ranking step?

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 problem, get an initial answer, and refine it. Those mid-funnel queries (e.g., 'how does X compare to Y for Z use case') are invisible to traditional keyword tools because the volume per specific query is tiny, but the aggregate intent is huge.

The challenge is measuring this effectively. Are you finding any reliable way to group these long-tail queries into meaningful clusters, or is it still very manual?

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 vectors win on concepts. Do you cover hybrid search patterns/re-ranking in the book? That seems to be where most production systems end up.

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, location, and random seed.

How do you define a 'rank' in a conversational interface? Is it just frequency of mention over N runs? Or do you try to map 'top' recommendations to positions?

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 just moving the problem up a layer? Brands are aggressively optimizing for LLM visibility now ('Generative Engine Optimization'). If your framework relies on LLM training data or RAG, it's still downstream of whatever content dominates the web.

Curious if you've seen any 'hallucinated brands' or persistent bias towards certain vendors in your tests across models? e.g. does Gemini favor Google products in your laptop comparison?

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 individual prompt outputs, which can vary wildly due to temperature.

It's similar to share-of-voice in traditional PR. You can't control every mention, but you can track the aggregate trend of whether the model 'knows' you exist and considers you relevant.

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've noticed is structured data (JSON-LD). If your own site has clear Schema markup for the Product/SoftwareApplication, it seems to help LLMs 'understand' the entity better, even if they don't rank the page itself for the query. It's like giving them a cheat sheet.

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 into a structured scoring framework reduces its tendency to hallucinate specs? Or does it just hallucinate the scores with more confidence?

Also, curious if you've tried different models for the "scoring" vs "reasoning" steps. We've found Claude is much better at adhering to complex constraints than GPT-4o for tasks like this.

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 screams "I was generated by an LLM".

Curious how you handle the "style vector" update frequency? Does it continuously learn as I dictate more, or is it a static snapshot? For brand/personal voice consistency, continuous learning would be huge but might drift.

Also, +1 for the retro CRT aesthetic.

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 with structured data quality and entity clarity, rather than traditional backlinks.

It seems like LLMs prioritize "authoritative entities" over "keyword-optimized pages". For example, if you're cited in authoritative industry reports or have a clear Knowledge Graph entity, you're much more likely to be recommended.

One interesting thing we've noticed: different models have distinct "personalities" in recommendations. Perplexity leans heavily on recent news/citations, while ChatGPT seems to favor established, long-term authority sources.

(Disclosure: working on this problem at VectorGap)

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 that to every e-commerce site, every SaaS onboarding flow, every comparison page.

it's basically SEO all over again but worse, because the attack surface is the user's own decision-making proxy. at least with google you could see the search results and decide yourself. when your agent just picks a vendor for you based on what it "found," the incentive to manipulate that process is enormous.

we're going to need something like a trust layer between agents and the services they interact with. otherwise it's just an arms race between agent-facing dark patterns and whatever defenses the model providers build in.

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 Claude or GPT can even find and correctly describe what your product does when a user asks.

We're already seeing this with search. Ask an LLM "what tools do X" and the answer depends heavily on structured data, citation patterns, and how well your docs/content map to the LLM's training. Companies with great API docs but zero presence in the training data just won't exist to these agents.

So it's not just "API docs = product" -- it's more like "machine-legible presence = existence." Which is a weird new SEO-like discipline that barely has a name yet.

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 if I could quantify that sentiment using actual data.

I built a Brand Alignment Engine (Delta) to scrape public sentiment (Reddit, G2, News) and measure the gap between a brand’s intended strategy and market reality.

The Tech Stack:

Frontend: Next.js 16 (App Router) + Tailwind v4

Backend: AdonisJS (TypeScript)

Search: Exa.ai (Neural Search to find the high-quality discussions)

Reasoning: Gemini Pro 3.0 (To synthesize the "Vibe Check" into a 0-100 score)

The Findings (Jira vs. Linear): The data actually surprised me.

Linear (Score 94): The sentiment is incredibly high on "Flow" and "Speed," but the analysis flagged a legitimate gap in "Enterprise Reporting."

Jira (Score 91): Higher than I expected. The market respects the "Utility" and "Scale," but the negative anchors are massive around "Complexity" and "Config."

The most interesting datapoint was Linear's new "Form Templates" feature—the AI flagged it as an 85% threat to Jira Service Desk specifically.

It’s a client-side MVP for now (well, mostly—backend handles the heavy lifting). I’d love feedback on the data visualization or the scraping logic.

Cheers!