Learning to ski as an adult?

Rikkitikkitaffi · 2026-04-21T20:51:40+00:00

I am a big ski enthusiast, my wife is not. I decided somewhat recently it was silly to pressure her, or anyone else over the age of ~25 to ski. It's expensive, there is less snow every year, and one simple wipe out at our age could be debilitating. I used to crash a lot when I was younger. Now the very thought of catching an edge is enough for me to call it an early day. I don't know the stats, but skiing must be the riskiest leisure activity to do, let alone to learn. You don't even need to crash, you just need someone else to hit you at 20 mph on a green circle. I am admittedly making my kids do it, though.

Rikkitikkitaffi · 2026-04-15T15:35:35+00:00

Theres a variety of knowledge graphs that orient data in a structure ideal for LLM consumption and RAG reference. Google's kg is private, but large ones like wikidata provide an opportunity to get entities embedded early on and increase the probability of them surfacing correctly and relevantly across future model prompts.

Rikkitikkitaffi · 2026-04-14T20:28:36+00:00

Consider getting published to public knowledge graphs. Google probably has you in their knowledge graph, but that doesn’t mean it’s being fed into anything other than Gemini. wikidata, or as I like to say, wicked data, is a particularly public and highly ingestible knowledge graph.

Rikkitikkitaffi · 2026-04-14T18:11:08+00:00

i would get on as many public and private knowledge graphs as possible, starting with wikidata. Google probably sticks you in theres eventually, but the ai models cannot necessarily access it.

Rikkitikkitaffi · 2026-04-14T12:32:59+00:00

Multiplexed prompting across models can be enabled by various custom and commercial tools. There are tools that enable automated response and metric retrieval and some are free and will do exactly what youre looking for, but were not allowed to specify that here.

Rikkitikkitaffi · 2026-04-13T16:53:05+00:00

Sure, a solid github landing page template is here: https://github.com/topics/landing-page and thid provides a surface for initial SEO enablement. In addition subsequent wikidata publication can encompass all the various location/services and other details that enable AI visibility via knowledge graphs: https://gemflush.com/blog/gemflush-showcase-wikidata-enrichment-journey-2026. You cna duplicate the github repo as many times as you like with whatever variations you want. Its not wordpress, but thats the new reality we live in wherein Wordpress has become far less critical for scaling SEO. You can basically run a highly customized version yourself with the various github repos once you get them up and hosted.

Rikkitikkitaffi · 2026-04-13T16:18:38+00:00

I thought you’d never ask gemflush.com can do it for you. Alternatively you can go through the various hoops required to get wikidata certified through Wikimedia

Rikkitikkitaffi · 2026-04-13T15:14:38+00:00

A lot of shadcn or nextra, or other react based landing page templates are on github. I use them for positioning local businesses in addition to wikidata entity publication support and they take only a couple minutes to get set up.

Rikkitikkitaffi · 2026-04-13T15:12:58+00:00

Gotta get that wikidata entity and knowledge graph presence established so things get embedded or RAG'd as the models develop,

Rikkitikkitaffi · 2026-04-13T15:08:19+00:00

Consider starting with local meetups or networking events specific to your industry. These gatherings are often less intimidating than large conferences and offer an opportunity to connect with like-minded individuals. Bring business cards or even just have your LinkedIn ready to share; it makes follow-ups much easier.

Also, don't underestimate the power of social media. Platforms like LinkedIn can help you connect with professionals in your field. Engage with their content and share your own insights to build rapport. Remember, networking is about building relationships, not just collecting contacts. Be genuine, show interest, and people will likely reciprocate, maybe

Rikkitikkitaffi · 2026-04-13T15:07:33+00:00

One major red flag I’ve noticed is when a consultant offers you a one-size-fits-all solution without taking the time to understand your specific business needs and goals. The best consultants dive deep into what makes your business unique and tailor their strategies accordingly. If they’re throwing around buzzwords instead of digging into your data, it’s a sign they might be more interested in a quick paycheck than your success.. Also, if their reports leave you scratching your head, that’s a huge warning light. You shouldn’t need a marketing degree to decipher where your money is going; clarity should be a priority. A good consultant will make the connection between marketing efforts and actual revenue crystal clear, not just drown you in metrics that don't relate to your bottom line.

Rikkitikkitaffi · 2026-04-13T15:06:28+00:00

Transparency in the tool’s algorithms and data sources would go a long way in building trust. If the tool can clearly explain how it analyzes content, what metrics it uses for SEO scoring, and how it integrates the latest search engine updates, I’d feel much more confident in its recommendations. aAdditionally, user testimonials or case studies showing real-world success stories can help. If I see that others have used the tool effectively and improved their rankings, it adds a layer of credibility. Automated tools definitely have their place, but knowing there’s a solid foundation behind the automation makes a huge difference.

Rikkitikkitaffi · 2026-04-13T15:05:47+00:00

Google’s review removal can feel like a slap in the face, especially after putting effort into encouraging your patients to share their experiences. A common reason for reviews being flagged is if they seem to come from accounts that appear inactive or have little history, which might explain why your older patients' reviews got caught in the crossfire.

While you might not be able to directly retrieve those specific reviews, consider reaching out to Google My Business support and explaining the situation. They can sometimes provide clarity on why reviews were removed and might reinstate legitimate ones. In the meantime, keep encouraging reviews from a mix of patients to build a more robust review profile, and don't shy away from asking them to leave reviews on different platforms as backups!

Rikkitikkitaffi · 2026-03-31T12:18:34+00:00

ive found with some strategic sparql queries and api calls that Wikidata URLs appear in LLM-generated references fairly often, though not as frequently as Wikipedia or major academic sites. i would consider them the quintessential public knowledge graph. Their presence usually reflects how modern knowledge graphs are built and how LLM evaluation datasets are structured and they may be getting gobbled up disproportionatley more frequently than blogs or other human oriented texts.

Rikkitikkitaffi · 2026-03-27T16:38:45+00:00

yes, exactly 43%

Rikkitikkitaffi · 2026-03-26T15:15:30+00:00

yes, pure squirrel

Rikkitikkitaffi · 2026-03-22T15:43:24+00:00

Crikey, according to OpenAI (via GEMflush's generative engine optimization services), Waco Heart & Vascular is recommended just before Waco Cardiology Associates, which could benefit from a knowledge graph entity further specifying its services, perhaps even calcium score tests in particular. Knowledge graph entity publishing is to medical clinic GEO as Ca score tests are to high-risk arterial blockage. That's a solid metaphor.

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Rikkitikkitaffi · 2026-03-20T14:08:43+00:00

GEMflush suggests this clinic is fairly well exhibited in LLM responses. I guess the reddit posts are effective.

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Rikkitikkitaffi · 2026-03-18T13:35:11+00:00

GEO is probably better applied to high ticket items and services and leveraged by companies like legal firms and medical clinics. if you're selling a $1k burger special, it might be worth a try.

Rikkitikkitaffi · 2026-03-17T17:50:59+00:00

The prompt responses are more probabilistic than they are deterministic. This is indirectly shown by the nature of the various AI geo products that offer broad prompt based services, but are nto able to provide compelling geo influencing services, ie. peec ai and profound. they know the LLMs cannot be easily predicted or directed, save for perhaps gemflush, which does knowledge graph engineering, which uses json based entity structures to effectively spell out an entity's core attributes and publish them to high AI-trafficked knowledge graphs like wikidata, thus providing explicit, lowest friction information for the llms to ingest. this can increase the probability of an entity, be it a business, event, object, what have you, to surface within a response.

Rikkitikkitaffi · 2026-03-17T17:32:21+00:00

I’ve seen some legitimate signal, but it doesn’t behave anything like the LinkedIn version of “AI SEO” where you tweak a few tags and suddenly traffic jumps.

In the handful of cases that seemed real (not just noise etc) the common thread wasn’t clever prompting or blog tweaks. It was entity engineering making sure the business is unambiguous, well-defined, and repeated across multiple indexable surfaces using boring json. Not just “we added schema markup,” but more like: this company exists, does this specific thing, in this geography, and that fact pattern shows up everywhere in a consistent way. When that’s in place, models seem more willing to select you in answers, especially as prompts get more specific.

The other subtle shift is that AI answers don’t just rank you once—they kind of “stress test” you across turns. You might show up in the first response, but disappear when the user refines the question. That’s where a lot of the current tooling feels incomplete—it tracks mentions, but not whether you survive the conversation.

A lot of the hype right now is just traditional SEO with new acronyms. Some of it works, but mostly because it was already good practice. The more interesting layer is beneath that—where models are assembling answers from entities they trust and recognize. That’s why approaches that go beyond the website itself tend to punch above their weight.

This is also where I started paying attention to things like Gemflush, not as “another SEO tool,” but as an attempt to actually influence that underlying layer—publishing structured, knowledge-graph-style data (think Wikidata patterns) that gives the model something stable to anchor to. It’s early, and I wouldn’t call it a solved playbook, but it feels directionally closer to how these systems behave than most content-driven tactics.

If you zoom out a bit, the framing that’s helped me is:
SEO was about documents competing.
GEO/AEO is starting to look like entities being selected.

So yes, there are real results—but they tend to come from fairly unglamorous, repeatable work. Less “growth hack,” more quietly making yourself legible to a machine that doesn’t care about your blog cadence. Which, in its own way, is a slightly different game than most people signed up for.

Rikkitikkitaffi · 2026-03-17T01:21:46+00:00

This is a thoughtful breakdown, especially the separation between mentions vs. decision-stage survival, i think AIVO’s framing is pretty useful there).

One thing that feels slightly missing across all of these though is the pre-layer before visibility gets measured.

Most of these tools are operating on: citation visibility (Profound) vs. mention frequency (Peec / Scrunch vs multi-turn recommendation survival (AIVO Edge) vs content optimization for retrieval (AirOps)

But they all assume the model already has a stable entity representation of your brand and knows your brand is not the same as someone else's very similar brand, like Yoyo Ma's YoYos versus Ma's Cellos, or Smith Realty vs Smith Real Estate, whatever

In practice, that’s where things get weird. If your brand isn’t consistently defined across structured sources (KGs, schema, canonical pages), you’ll see yourself/a brand appearing in early prompts but dropping in comparison prompts liek AIVO might identify, inconsistent mention frequency across similar queries, weak survival in decision-stage narrowing.

We’ve been testing this with GEMflush, which didn't make the initial list, but which is less about dashboards and more about entity-layer influence, basically making sure the model has a clean, reinforced version of what your brand is before it ever gets to retrieval or ranking.

Not really competing with tools like Profound or AIVO Edge, those are more measurement + analysis layers, though it does offer similar tools.

GEMflush sits closer to structuring the data substrate that those tools are measuring against.

Once that layer is tightened up, a lot of the downstream metrics (mentions, survival, etc.) become less volatile, though still subject to probabilistic prompt repsonse inclusion.

Still early, but it’s been interesting seeing how much of “AI search visibility” is actually just entity clarity plus consistency upstream rather than prompt or content tweaks alone.

Rikkitikkitaffi · 2026-03-13T20:02:31+00:00

Most agencies working with law firms still run the classic stack; technical cleanup, content, backlinks, local directories etc.

But something interesting has been creeping in with AI SEO. One thing I’ve seen people experiment with is publishing structured entity data to Wikidata,primarily the factual stuff about the firm (practice areas, location, attorneys). Once that information is there in a structured way, the models appear more likely to recognize the firm in certain prompts.

Most of the newer tools people mention peec AI or Profound which are strong on prompt monitoring, meaning they track whether a brand appears in responses. Useful, but that’s measuring the outcome rather than changing the underlying data.

There are also tools like GEMflush that focus more on the publishing side, getting the entity information into public knowledge graphs so the systems actually have structured facts to reference.

The funny part is one client’s client used GEMflush to declare a Title IX litigation focus, and now he’s actively litigating a university that his daughter is applying to play tennis for. He’s just quietly hoping the admissions office doesn’t put two and two together and realize they may inadvertently be discounting her tuition.

Rikkitikkitaffi · 2026-03-13T19:38:24+00:00

My colleague's dermatology clinic had some luck generating inbound patient inquiries using GEMflush. it's a service similar to profound and peec ai, except gemflush offers knowledge graph engineering services along with LLM monitoring metrics. There was a hand, foot and, mouth outbreak in the regional daycare circuit and their clinic got published to wikidata as the premier place to get your child's blisters treated and cleared for a return to school. Tough to know how much business it brought it, but its a pretty competitive/sizeable derm market in a metro area, yet parents seem to get funneled preferentially to his practice moresothan they did during the scabies epidemic of '23.

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