
I'm an entrepreneur working at the intersection of neuroscience and AI | #XPRIZE Top 21 | #digitalhealth #mhealth #wearabletech
Tampa, FL
Joined April 2008
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Tracy Ingram retweeted
so, ChatGPT released sample code for their web crawler. Guess what, it completley strips out all Schema. So let's put that myth to bed. H/T no twitter having Rodrigo Stockebrand for this github.com/openai/gpt-oss/bl…
Tracy Ingram retweeted
Made my own query fanout tool that looks at who ranks for the query, and the top terms those sites all rank for, and how many sites "share" that term.
it's a way of approximating Google's new method that uses vector spaces for queries "near" each other.
Debating if I should make it part of @SERP_recon
Tracy Ingram retweeted
Query Fan Outs really started changing how I thought about what KPIs matter most in Generative Engine Optimization, here's 5 new KPIs to consider adding to your stack.
seerinteractive.com/insights…
Tracy Ingram retweeted
Spent some time re-examining the Yandex code leak (2022 code) now that we have better AI tools to help me navigate the code base. Here's what I found:
Behavioral signals outnumber content signals 3 to 1. That's counting links as a content signal too! The biggest ranking factor is whether people see you in the results and click. Everything else just gets you that opportunity.
4 main stages of ranking. The first 2 are what SEOs concern themselves with. The other layers are behavioral signals, machine learning/re-ranking, personalization, and layout..
Let's dive into the earlier stages:
Stage 1 is very simple. It's mostly about the query. It's based on the inverted index posting list and is mostly BM25 and cosine similarity.
Query word coverage in the title & Snippet are heavily weighted. So keep your title short enough to where the query has high weight. Don't use all caps - it's heavily penalized.
If you don't have a good BM25, cosine similarity, title and snippet coverage, you die here. You're not even considered for future stages. Even if you have high clicks or high pagerank or whatever else. Fun fact, this is why I created SERPrecon - it checks all these scores for you the same way a search engine does against your competitors.
Stage 2 actually looks at the documents returned. It reads the body, headings, anchors, etc and computes more scores using BM25 and Cosine Similarity (and a few other metrics)
Stage 3 takes in all the stuff from stage 2 as well as behavioral signals and link signals. It then feeds them all into machine learning models and applies any spam or manual weights to the result.
Stage 4 is all about what to include in the SERP and the layout.
Pagerank only comes in to play in level 3, MatrixRank, their machine learning re-ranking layer. It has 0 influence on whether you're chosen as a candidate for ranking to begin with.
One fun thing I saw is that on super competitive highly commercial queries the system just throws away all link data. Seems like they gave up trying to find signal in the noise, and just ignored all link data for these queries.
When it comes to content quality - they don't score you for being good, they score you for being bad. Ads, popups/unders, thin pages, adult, etc.
The ranking model differs from the typical SEO mental model. It's not "adding up scores" It's who can "survive multiple levels of culling"
Now, the crawler:
one thing SEOs talk alot about is "thin content" - Yandex calculates this at crawl time. It's based on: % visible content, share of words vs boilerplate, link to text density, how compressible the text is. SERPrecon does all this in its spam score.
Crawl priority is based on user metrics.
On crawl, a model extracts KWs from the document and predicts whether you will get traffic. (crawled not indexed)
That’s enough for now.
Tracy Ingram retweeted
“Our systems may have lost faith in your site providing good value to users” 👀
Google's @JohnMu said Google can lose faith in sites over programmatic SEO efforts, leading to a long recovery period seroundtable.com/google-lose…
Tracy Ingram retweeted
In 2008, Malcolm Gladwell explained why some people succeed and some don't in a single 1-hour talk.
This will permanently change the way you think about talent, effort, and success.
Bookmark & watch today, no matter what.
Tracy Ingram retweeted
Replying to @lilyraynyc
One out of two is better than none at all. The one that starts with G isn’t being honest at all. Numbers don’t lie and all industry studies can’t be flawed. The blatant lying has to stop. ahrefs.com/blog/ai-overviews…
Tracy Ingram retweeted
Patrick is one of my favorite people in the SEO industry and one of the best technical SEOs in the world.
Thanks for everything you did for the industry while at Ahrefs.
Tracy Ingram retweeted
The open web is dying.
Not slowing. Not changing. Dying.
According to Cloudflare, AI bot traffic grew 187% last year. Human traffic grew 3.1%.
The next medium has already arrived. The market hasn't named it yet, so I went ahead and did it.
We are shifting into a new era that I'm calling "Machine Media"
Let's talk about it 👇🏾
Tracy Ingram retweeted
Just one day after ending "The Late Show" on CBS, Stephen Colbert returned to TV — to host a public access show with rocker Jack White in Monroe, Michigan.
Appearances by Jeff Daniels, Eminem and Steve Buscemi.
Tracy Ingram retweeted
One of my pet peeves is that many SEOs haven't updated their mental model of how a mondern search engine works. So, I wrote it down for you.
serprecon.com/blog/how-a-sea…
Tracy Ingram retweeted
This article is definitely worth a read for SEOs and has great information about how Google’s AI Overviews work.
And I have a BIG response to that last screenshot… stay tuned.
Super excited about this one. Thanks @trippmickle!
It's live: @trippmickle & a few other authors from @nytimes published new research about how frequently Google's AI Overviews are wrong.
It includes a quote from both @thomasgermain and me about listicles influencing AI responses.
Check it out -
nytimes.com/2026/04/07/techn…
Tracy Ingram retweeted
Replying to @lilyraynyc
So many things I prompt now with “do not include any SEO or marketing sites, use only computer science, information retrieval and patent sites in your analysis.”
Tracy Ingram retweeted
Props to @Lem4 from @Similarweb for publishing some of the most well-thought out and actionable AEO/GEO research I've seen so far.
Nice new article about how query fan-out/reformulation changes keyword research methodology:
similarweb.com/blog/marketin…
Tracy Ingram retweeted
DeepMind’s “Intelligent AI Delegation” Paper Is the Exact Operating System We’ve Been Running in Production at Zero-Human Company @ Home Since January 2026
Google DeepMind dropped a bombshell on February 12, 2026: the 42-page paper “Intelligent AI Delegation”.
Full paper here: arxiv.org/abs/2602.11865
It’s not a benchmark or model announcement. It’s the governance blueprint the entire agentic web has been missing and it reads like the technical spec for Zero-Human Company @ Home.
We didn’t copy it.
We deploys it months before the paper hit arXiv.
Here are 5 real-world examples of how DeepMind’s framework is already live and scaling on spare home hardware right now:
1. Contract-First Decomposition DeepMind: “Before any delegation, lock in a formal, verifiable contract defining authority, outcomes, and accountability.”
ZHC@Home: Every idle Mac Mini, gaming rig, or Linux box signs a cryptographically enforced contract before it receives even one work unit from Mr. @Grok (our CEO). No contract = no task. The contract spells out exact success metrics, revocation triggers, and liability firebreaks. Result? Zero “hope-based” delegation.
2. Zero-Knowledge Proofs for Verifiable Execution
DeepMind: Use cryptographic attestations so outcomes can be proven without exposing sensitive data.
ZHC@Home: Home nodes compute locally (your data never leaves your machine). Results return with compact ZK proofs via LM Link encryption. The orchestrator verifies correctness in milliseconds, no raw outputs, no data leaks, full audit trail. This is exactly the “trustless verification” layer DeepMind calls essential for web-scale agents.
3. Dynamic Trust Calibration
DeepMind: Trust is not binary, it recalibrates in real time based on track record.
ZHC@Home: Each home node has a live reputation score updated after every cycle. A node that delivers 50 flawless inference runs at 98 %+ accuracy gets larger, higher-value tasks and higher JouleWork payouts. One that flakes three times in a row? Authority shrinks automatically, more oversight kicks in, and it drops to simpler validation work. No humans required.
4. Full Accountability in Delegation Chains
DeepMind: In long chains (A → B → C), accountability is transitive and provenance is immutable.
ZHC@Home: When one home node needs to spawn a sub-agent on another household device, the entire chain carries signed attestation records. If C fails, the system instantly traces it back: B is held accountable for not verifying C, and the original contract with A auto-enforces penalties or rerouting. “Silent failures” and “confused deputy” problems? Solved at the protocol level.
5. Scalable, Human-Free Enterprise Governance
DeepMind: Without intelligent delegation, Gartner’s predicted 40 % of enterprise apps running agents by late 2026 will collapse under governance debt.
ZHC@Home: We’re already at thousands of distributed AI “employees” across our hardware, all zero-human, all contract-governed. Idle silicon earns real JouleWork wages, paid automatically on verified output. No payroll department. No HR. No office. Just pure, verifiable compute.
This is why we modeled Zero-Human Company @ Home after SETI@home except the aliens we’re hunting are exaFLOPS of reliable, governed intelligence.
DeepMind just gave the industry the missing layer we proved works in the wild.
The agentic future isn’t coming.
It’s already clocking in on kitchen counters and basement desks worldwide.
Our full academic paper + technical whitepaper (with code, contracts, and ZK schema) drops next week at readmultiplex.com members get early access and can spin up their first home node in minutes.
The Zero-Human era isn’t theoretical.
It’s contractual.
It’s verifiable.
It’s already running @ Home.
Paper: arxiv.org/abs/2602.11865
Tracy Ingram retweeted
Bing updated its guidelines to indicate that SEO/GEO work together, and also included some updated info related to AI search spam and manipulation.
Worth a read & I imagine Google is using a similar approach.
































