
CEO @CryptoMize, a Digital Conglomerate with presence in 3 Continents, serving Governments, Politicians, MNCs, Celebrities, Family Offices, Monarchies and HNIs
New Delhi, India
Joined December 2009
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Lithvik Mukesh Sharma retweeted
The surreal appearance of the great eared nightjar. Pretty much a dragon bird.
[📷 Jenisha Aggarwal]
Lithvik Mukesh Sharma retweeted
Google accidentally revealed how its AI search systems choose which sites to recommend and send traffic to.
Now that none of it is much of a secret anymore, let’s talk about it.
With the new Google Search rolling out as we speak, it has never been more important to understand how to maximize value from this marketing channel.
If you want to see where your site stands across Google, ChatGPT, Claude, Perplexity and broader AI search, you can do so for free here:
rightcited.com/
Let’s start from the beginning.
Metehan Yesilyurt, who previously went viral for his analysis of Perplexity’s ranking factors, recently broke down Google AI ranking factors in a fascinating blog post.
A lot of what he found also lines up with what SEO Stuff (seo-stuff.com/ai-seo) has been doing all year to get customers more traffic and sales.
Specifically with the done-for-you package:
seo-stuff.com/gold-plan-pack…
Basically, Google sells much of the underlying retrieval infrastructure through Google Cloud Discovery Engine, also called Vertex AI Search.
By analyzing what Discovery Engine exposes, Yesilyurt was able to show how Google’s AI retrieval and ranking stack works.
The seven major signals he identified were Base Ranking, Gecko embedding similarity, Jetstream cross-attention relevance, BM25 keyword matching, predicted click-through rate, freshness and Boost/Bury rules.
In simpler terms, Google is combining traditional ranking signals with semantic similarity, deeper contextual relevance, keyword matching, engagement predictions, recency and manual business logic.
Discovery Engine exposes a maximum chunk size of 500 tokens, roughly 375 words, while ancestor headings can travel with each chunk and tables, images and page layouts can all be parsed with Gemini-enhanced understanding.
That means important ideas need to make sense inside relatively small, clearly labeled sections.
If your content is one massive wall of text, Google has a much harder job extracting the exact information it needs.
This is also why I keep talking about question-based headings, direct answers, clear comparisons and structured pages.
Additionally, Discovery Engine shows Google processing structured data using separate Searchable, Indexable and Retrievable settings.
Those can affect whether information can be found, used for filtering or ordering and surfaced by the model. Mind you, they are independent.
So a field can potentially influence retrieval or ranking without necessarily being displayed in exactly the same way.
That gives us a pretty significant clue about why structured data continues to matter as Google becomes more AI-driven.
Discovery Engine also reveals a four-stage pipeline: Prepare, Retrieve, Signal and Serve.
Google first interprets the query, handles things like synonyms and intent, then retrieves and chunks relevant content, applies ranking signals and finally uses Gemini to generate the response with grounding and safety rules.
Traditional Search, AI Overviews and AI Mode can be thought of as different configurations built on much of the same underlying retrieval infrastructure.
So what does all of this mean for businesses?
You need to optimize for several layers at once.
Your content needs strong semantic alignment so Google clearly understands what the page is about, enough contextual detail to handle nuanced searches such as comparisons and “best for” queries, and clean chunk-level structure so useful answers can be extracted without digging through hundreds of words of filler.
That means clear definitions, direct answers, comparison language, question-based headings, concise sections, factual claims, TLDR summaries, clean HTML and useful tables where appropriate.
This is exactly why SEO Stuff content is structured the way it is.
The done-for-you package combines 10 long-form, search-focused articles with three DR50+ contextual authority placements:
seo-stuff.com/ai-seo
The content is designed around semantic relevance, keyword alignment, structured sections, comparisons, freshness and extractability, while the authority placements help strengthen the underlying authority of the pages and domain.
The Premium Content Bundle takes the same idea much further:
seo-stuff.com/premium-conten…
It includes 60 long-form articles designed to build broader category coverage, stronger semantic associations and more opportunities for Google to retrieve useful sections across the different questions customers ask.
Then there is the Premium Authority Bundle:
seo-stuff.com/premium-backli…
It adds three contextual DR50+ authority placements designed to strengthen search authority and reinforce the company’s identity and category across the web.
And if you want to see where your site stands across Google, ChatGPT, Claude, Perplexity and broader AI search, you can do so for free here:
seo-stuff.com/free-audit
Anthropic just released a new Claude model.
For businesses trying to show up in broader AI search, there is one interesting new improvement to take note of.
It goes without saying that Claude does not search the web every time newer information exists - so what does it do?
Well, Anthropic has documented how developers can make Haiku search more reliably.
Let’s go through it.
By the way, you can see whether Claude, ChatGPT, Google AI, Perplexity and Grok are recommending your business right now here. It’s free:
rightcited.com/
The new model Haiku 5.5’s reliable knowledge cutoff is June 2026.
Its published system prompt says that when Claude’s existing knowledge may have been superseded, it should point users toward web search.
But Anthropic’s Haiku-specific prompting guide adds an important wrinkle - when Haiku is given a search tool, it can still skip a search that would uncover newer information.
Anthropic says this happens most often at low effort and with long system prompts.
Their recommendation?
Give Haiku today’s date and explicitly remind it that things like prices, versions, rules, records and anything “latest” may have changed.
In Anthropic’s testing, that increased searching on questions whose answers had changed.
On questions that did NOT need search, it caused unnecessary searches in only 0–3% of attempts.
That has a pretty important implication for businesses.
AI visibility partly depends on being there when the model decides it needs current information.
If you changed your pricing, launched a product, added an integration, opened a new location or published a new customer result after the model’s knowledge cutoff, current public sources become much more important.
That means maintaining clear, discoverable pages around pricing, products + services, locations, integrations, case studies, comparisons, FAQs, etc.
Oh, and obviously the questions customers actually ask before buying.
Anthropic’s web-search documentation says Claude searches for current prices, recent information and details about organizations or products that may have changed.
So traditional search visibility still matters to the broader AI-search strategy.
That is where SEO Stuff's done-for-you package comes in:
seo-stuff.com/ai-seo
Your important pages need to be public, crawlable, clearly written and relevant to the searches buyers make.
Third-party coverage can add another layer.
Press, reviews, industry publications and other credible mentions create additional public sources a web-connected Claude experience may encounter while researching your company.
All of which is to say, Claude has knowledge from training.
Current facts can require web search.
Whether that search happens is conditional.
And when Claude does search, your business needs something useful to find.
Be discoverable.
Be current.
Be specific.
Answer buyer questions.
Give AI systems evidence they can actually use.
That is exactly what SEO Stuff is built around:
seo-stuff.com
Lithvik Mukesh Sharma retweeted
This might be the only prompt you need to build a proper Jev decision layer.
paste it into Claude and watch it architect the whole thing.
save this one.
I stopped collecting random inspo. These are the only UI + vibecoding resources I keep opening.
1. EvilCharts — animated React charts for dashboards
evilcharts.com
2. Rewamp UI — free open-source components, blocks, and landing pages
rewampui.com
3. Refero — real product UI patterns and screens for reference
styles.refero.design
4. Bencho — AI product UI components for faster builds
bencho.dev
5. Inspora — design inspiration for modern product UI
inspora.design
6. Neuform — design-to-code resources for AI UI work
neuform.ai
7. Open Design — DESIGN.md resources so AI builds with clearer UI rules
open-design.ai
Save this before your next build 👀
Lithvik Mukesh Sharma retweeted
COMPLETE SEO 2026 CHECKLIST :What Google Wants Now
============================
[01] SEO STRATEGY
- Search intent
- Keyword research
- SERP analysis
- Competitor analysis
- Topic clusters
- Keyword-to-page mapping
- Conversion-focused SEO
[02] TECHNICAL SEO
- Crawlability
- Indexability
- Robots.txt
- XML sitemaps
- Canonicals
- Redirects
- Status codes
- JavaScript SEO
- Duplicate content
- Orphan pages
- Crawl depth
- Site architecture
- HTTPS
[03] PERFORMANCE
- Core Web Vitals
- LCP
- INP
- CLS
- Page speed
- Image optimization
- JavaScript optimization
- Caching
- CDN
- Server performance
[04] ON-PAGE SEO
- Title tags
- Meta descriptions
- H1-H6 structure
- URLs
- Internal linking
- Image alt text
- Breadcrumbs
- Entity optimization
- Semantic relevance
[05] CONTENT SEO
- Search intent
- First-hand experience
- Original research
- Expert insights
- Content depth
- Content freshness
- Content pruning
- Content decay
- Cannibalization
- Topical authority
[06] ENTITY SEO
- Brand entities
- Author entities
- Organization entities
- Entity relationships
- Knowledge Graph signals
- sameAs
- Entity-based internal linking
[07] STRUCTURED DATA
- Organization
- LocalBusiness
- Product
- Article
- Breadcrumb
- Review
- Event
- Video
- FAQ (where eligible)
[08] LOCAL SEO
- Google Business Profile
- Categories
- Services
- Location pages
- NAP consistency
- Reviews
- Citations
- Local links
- Map Pack
- Local competitors
[09] OFF-PAGE SEO
- Quality backlinks
- Digital PR
- Brand mentions
- Link reclamation
- Unlinked mentions
- Competitor link gaps
- Industry authority
[10] E-COMMERCE SEO
- Category pages
- Product pages
- Faceted navigation
- Product schema
- Merchant Center
- Product feeds
- Reviews
- Variants
- Internal linking
[11] INTERNATIONAL SEO
- Hreflang
- Country targeting
- Language targeting
- Localized URLs
- Localized content
- Regional keyword research
[12] PROGRAMMATIC SEO
- Page templates
- Database-driven pages
- Search-demand validation
- Unique page value
- Internal linking
- Indexation control
- Thin-page prevention
[13] AEO
- Direct answers
- Question-based content
- Answer-first formatting
- Featured snippets
- Entity clarity
- Structured information
- Citation-worthy facts
[14] GEO / AI SEARCH
- Google AI Overviews
- Google AI Mode
- ChatGPT
- Gemini
- Perplexity
- Bing / Copilot
- AI citations
- Brand mentions
- Retrieval-friendly content
- AI crawler accessibility
[15] ANALYTICS
- Google Search Console
- GA4
- Organic conversions
- Revenue attribution
- Query performance
- Page performance
- CTR
- Indexation monitoring
- Brand vs non-brand
[16] SEO TESTING
- Title testing
- Internal-link testing
- Content updates
- Template testing
- Schema testing
- CTR experiments
- Page consolidation
[17] SEO AUTOMATION
- GSC API
- GA4 data
- Automated crawls
- Rank tracking
- Internal-link automation
- Cannibalization detection
- Indexation monitoring
- AI-assisted analysis
[18] BUSINESS SEO
- Qualified traffic
- Leads
- Calls
- Revenue
- CAC
- Pipeline value
- Organic conversion rate
- Assisted conversions
#SEO
Lithvik Mukesh Sharma retweeted
Here's a list of literally everything you need to know about SEO:
1. Technical SEO
-> Crawlability
-> Indexability
-> XML sitemaps
-> Robots.txt
-> Canonical tags
-> Site architecture / URL structure
-> HTTPS / SSL
-> Core Web Vitals (LCP, INP, CLS)
-> Page speed optimization
-> Mobile-friendliness / responsive design
-> Structured data / schema markup (JSON-LD)
-> Hreflang / international targeting
-> Pagination handling
-> JavaScript rendering / SSR vs CSR
-> Duplicate content management
-> Redirect management (301, 302, chains)
-> HTTP status codes / error handling (404, 5xx)
-> Log file analysis
-> Crawl budget optimization
-> Faceted navigation control
-> CDN / hosting performance
-> Image optimization (compression, WebP, lazy loading)
-> Orphan page detection
2. On-Page SEO
-> Title tags
-> Meta descriptions
-> Header hierarchy (H1–H6)
-> Keyword placement / density
-> Content optimization
-> Internal linking
-> Anchor text optimization
-> Image alt text
-> URL slugs
-> Content freshness / updating
-> Semantic HTML
-> Featured snippet optimization
-> Table of contents / jump links
-> Outbound linking strategy
-> Content depth / comprehensiveness
-> Readability
3. Off-Page SEO
-> Link building
-> Digital PR
-> Guest posting
-> Broken link building
-> Sneaky backlinks
-> HARO / journalist outreach
-> Unlinked brand mention reclamation
-> Tiered link building
-> Link buying / selling
-> Social syndication
-> Backlink profile analysis
-> Spam link discovery
-> Anchor text distribution
-> Link velocity
-> Brand mentions / citations
-> Influencer collaboration
-> Podcast / interview placements
-> Resource page links
-> Private blog networks
-> Skyscraper technique
4. Keyword Research
-> Search intent classification (informational, navigational, commercial, transactional)
-> Search volume analysis
-> Keyword difficulty scoring
-> Long-tail keywords
-> Semantic / LSI keywords
-> Keyword clustering / topic grouping
-> Competitor keyword gap analysis
-> Seasonal / trending keywords
-> Question-based keywords (PAA mining)
-> Zero-volume keywords
-> Keyword cannibalization detection
-> SERP feature analysis per keyword
5. Content Strategy
-> Topical authority building
-> Content hubs / pillar pages / clusters
-> Editorial calendars
-> Content gap analysis
-> Content pruning / consolidation
-> Content refreshing
-> Content formats (guides, listicles, comparisons, tools)
-> Programmatic SEO
-> User-generated content
-> Author bios / entity building
-> Information gain / original data
-> Content briefs / SOP creation
-> Newsjacking
6. Local SEO
-> Google Business Profile optimization
-> NAP consistency (Name, Address, Phone)
-> Local citations / directories
-> Review generation & management
-> Local link building
-> Location pages / service area pages
-> Local pack / map pack ranking
-> Geo-targeted content
-> Local schema markup
-> Proximity / relevance / prominence signals
7. GEO / AI Search Optimization
-> LLM citation optimization (ChatGPT, Claude, Perplexity, Gemini)
-> AI Overviews optimization
-> Consensus manufacturing
-> Parasite SEO
-> Entity optimization / knowledge graph presence
-> Structured, extractable content formatting
-> Brand mention frequency across the web
-> llms.txt
-> Statistics / quotable data inclusion
-> Wikipedia / Wikidata presence
-> Reddit / forum visibility (LLM training sources)
-> Answer-first content structure
8. E-commerce SEO
-> Product page optimization
-> Collection page optimization
-> Product schema (price, reviews, availability)
-> Faceted navigation SEO
-> Out-of-stock page handling
-> Product review optimization
-> Merchant Center / Google Shopping feed
-> Duplicate product description handling
-> Internal search optimization
-> Competitor data extraction
9. SERP Features
-> Featured snippets
-> People Also Ask
-> Knowledge panels
-> Image packs
-> Video carousels
-> Sitelinks
-> FAQ / How-to rich results
-> Review stars
-> Top stories
-> Local pack
Lithvik Mukesh Sharma retweeted
Rankings and clicks don't tell the full #SEO story anymore.
68% of searches end with an AI answer, per one study.
The 2027 reporting guide covers AI citations, Share of Model and how to tie it all back to revenue: searchengineland.com/seo-rep…
Lithvik Mukesh Sharma retweeted
🚨 BOMBAZO EN GITHUB
Un desarrollador chino acaba de lanzar una herramienta GRATUITA capaz de crear vídeos automáticamente para TikTok, Reels y YouTube Shorts.
Se llama MoneyPrinterTurbo.
Y ya ha superado las 100.000 estrellas en GitHub.
Funciona así:
1. Introduces un tema o una palabra clave
2. La lA genera el guion, la narración y los subtítulos
3. Busca el material visual, edita el vídeo y te entrega el resultado final
Todo ocurre en un único flujo de trabajo.
Guárdate esta herramienta.
Estoy seguro de que puedes sacarle muchísimo partido.
Repo abajo 👇
Lithvik Mukesh Sharma retweeted
Published today, Caffeine and Cardiovascular Disease:
A Scientific Statement from the American Heart Association.
Caffeine is rapidly absorbed and metabolized primarily by the liver enzyme CYP1A2, and by antagonizing adenosine receptors it increases sympathetic activity, leading to transient increases in blood pressure, heart rate, and alertness.
📷 Summary of the metabolism of caffeine and its immediate effects on tissues
✍🏼 @YepunaHB @MarilynCornelis @LarssonSC @gregorymmarcus
View the paper and supplemental resources here: bit.ly/4yxJbRC
Lithvik Mukesh Sharma retweeted
Someone just built shadcn for maps.
It's called mapcn. Beautiful, ready-to-use map components for React with zero config and one command setup.
100% Open Source. 7k stars. MIT.
These are top 10 UI libraries right now, which you can easily use through the @shadcn registry
1. Shadcn Blocks → shadcnblocks.com/
2. React Bits → reactbits.dev/
3. Shadcn Studio → shadcnstudio.com/
4. Tailark → tailark.com/
5. Aceternity UI → ui.aceternity.com/
6. Shadcn Space → shadcnspace.com/
7. Magic UI → magicui.design/
8. Shadcn UI Kit → shadcnuikit.com/
9. Skiper UI → skiper-ui.com/
10. UI Layouts → ui-layouts.com/
Lithvik Mukesh Sharma retweeted
Media server for anime and manga
github.com/5rahim/seanime/
Lithvik Mukesh Sharma retweeted
Collection of publicly available IPTV channels from all over the world
Lithvik Mukesh Sharma retweeted
iptv: Collection of publicly available IPTV channels from all over the world
GitHub: github.com/iptv-org/iptv
Channels: iptv-org.github.io/ (38,065 channel(s))
Lithvik Mukesh Sharma retweeted
Most people still think AI in SEO means “using ChatGPT to write blogs.”
Anna York and I are here to change the script.
But that’s just surface-level.
The real game is engineering SEO for AI search.
Here’s the shift 👇
Traditional SEO mindset:
“How do I publish more content to rank?”
New SEO mindset:
“How do I design content so AI chooses and cites it?”
Traditional SEO = volume, backlinks, keyword density.
New SEO = structure, authority, retrievability.
Old SEO ends at the SERP.
New SEO starts where users actually search:
↳ AI Overviews. ChatGPT. Gemini. Perplexity. Claude.
If your brand isn’t surfacing in those AI answers, you’re invisible.
It doesn’t matter if you “ranked top 3” last quarter.
Because the new front page isn’t page one of Google.
It’s the AI-generated answer.
That means shifting your SEO approach to the new stack:
⟶ GEO – Generative Engine Optimization (train AI to cite you)
⟶ AEO – Answer Engine Optimization (Google AI Overviews)
⟶ AIO – AI Integration Optimization (structure content for models)
⟶ SXO – Search Experience Optimization (credibility + UX + conversions)
When you get it right:
✓ You’re cited in AI results
✓ Your brand becomes the “default answer”
✓ Trust compounds across channels
When you don’t:
⟶ Rankings won’t save you
⟶ Traffic collapses overnight
⟶ Competitors own your AI visibility
SEO isn’t about chasing clicks anymore.
👉 It’s about being retrieved, cited, and trusted by AI.
That’s the new game.
And it’s already happening.
P.S. If you want a high-resoultion of this copy, just comment "LLM SEO" and I'll send it over asap.
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♻️ Repost & Follow me, @AnnaYork404, for more AI + SEO insights 🙋
Lithvik Mukesh Sharma retweeted
This is amazing - a periodic table showing every element and how it’s used.
Chemistry really is everywhere. 🔬✨
What’s your favorite element?
Lithvik Mukesh Sharma retweeted
Google focuses on showing relevant results to match what users are looking for. Our study found that text relevance is the top factor linked to higher SEO rankings ⬇️
social.semrush.com/46GnPEx.













































