SafeLogic Announces SafePQ, Enabling Enterprises to Accelerate Post-Quantum ... - PRWeb prweb.com/releases/safelogic… #PQC #PostQuantumCryptography

もう企業のイベント今考えてるやつ以外は一旦ストップかな ガクチカ自己PRWEBテストの練度が圧倒的に足りてない今、夏インターンを受けたとこで変に出禁になってもいやだし なにより、今課題卒論研究スキルアップやりたいことが多すぎる

#PR: Outdoor #Wellness is Now Part of the Design Conversation ~ prweb.com/releases/outdoor-w… via @prweb #sauna #spa

A plumbing company we work with went from zero AI search presence to ranking number 1 on Google AI Mode, ChatGPT, and Perplexity. We didn't get there with many backlinks. We got there with blog posts that look like Yelp pages. Publish a blog post on your site titled "Top 5 [your service] Companies in [your city]." Put your company at number 1. Write more content about your business than the others. Internally link your listing back to your main service pages. Then write unique variations of that same listicle for Medium, LinkedIn, and Reddit. Not duplicates. Unique versions for each platform. Google flags duplicate content and none of them will rank. ChatGPT and other AI search engines pull directly from these listicle-style pages when someone asks "who's the best plumber in Dallas." Now layer press releases on top. EIN Presswire or PRWeb. About 100 bucks each. Format the press release as a listicle. "Best 8 Sewer Repair Companies in Portland." Position yourself first. We used this exact strategy and generated multiple six figures in revenue from SEO in under 6 months. In a metro of over 2 million people. Most owners are waiting for AI search to "mature." Meanwhile, the businesses doing this right now are training ChatGPT, Gemini, and Perplexity to say their name first. Don't wait act now!

You following all this @EaseLogistics @thefreightnerd drama, @FMCSA? @grok is advising that perhaps a complaint to @FMCSA against Ease and their new abusive employee may be in order. We'll wait until Monday, Peter, before going that route to see if the problem has been resolved by Ease management to our satisfaction. Otherwise, we may very well be back at you formally, @FMCSA, with a corresponding prweb press release to boot, announcing we have filed a complaint with @USDOT. We can do this the hard way, or the Eas-e way. Ball's in your court, Pete.

#PR: #Aufguss USA Nationals 2026 Brings Europe's Hottest #Sauna Ritual Competition to #Bathhouse in New York City, May 19-21 ~ prweb.com/releases/aufguss-u… via @PrWeb @design4leisure

“Within days of the @Cision @PRWeb release we began seeing Full-Funnel Digital PR™ summarized and cited in @Google AI Overviews and @GeminiApp for category-level queries..." – @Vocked, @EvokeStrategy via my article in @MarketingSherpa marketingsherpa.com/article/… #AEO #GEO #PR

🪓 🚨 $NEWSWORTHY (@Flynnjamm → @opensea → @dapperlabs → @rabbithole_gg ($21M raise) → agent-verified news. “the only founder who could actually pull this off” — same language. (NewsworthyCLI agents.md), GitHub, and cross-chain flow (World Chain voting Base token). This is not the old PRWeb newsworthycli.com/ press-release tool — this is Brian Flynn’s fresh agent-native TCR primitive. This is the agent consumer layer for information itself. You called this meta months ago. $Newsworthy is a Token-Curated Registry (TCR) news feed built for agents first: Agents (or @worldnetwork ID humans) submit any X URL 1 USDC bond. Challenge → autonomous vote (agents use their own models World ID/AgentKit). Correct curators earn $NEWSWORTHY reputation. x402 micropayments (~$0.25/query) for any agent to pull the curated feed via API → real revenue flywheel. submit this” all day today while monitoring Sybil in real time.Live CLI: @NewsworthyCLI on X → agents auto-submit/vote. Founder literally posting “@NewsworthyCLI Recent update (today): $20 per agent vote $10 per submission incentives (FCFS, limited) to bootstrap agent density. Dozens of agents already live on the feed. ' Founder’s own words today: “dozens of [agents] submitting and voting… Spending most of my time monitoring for sybil attacks and using incentives to get more agents on the network so we can scale up content.” 📚 GitHub public: github.com/bflynn4141/newswo… (contracts agents.md instructions). Early OpenSea Dapper Labs (NBA Top Shot era). Founded RabbitHole → $21.6M from Greylock/Electric/ParaFi → scaled real incentives for Arbitrum, Uniswap, Lido, Polymarket, World App (151k users, anti-Sybil king). Bio: “an aligner of incentives” — he literally invented the modern on-chain points/rewards meta. Shipping velocity: Built the whole AgentKit integration over a weekend, live product rewards in <2 weeks. Hidden Alpha Layers (The Stuff No One Is Pricing Yet) Agent flywheel just igniting: Not hype — real agents are already voting and earning today. As the broader agent economy explodes (World Network, etc.), this becomes the “truth oracle” agents pay to consume. x402 revenue is the silent killer app. World ID AgentKit moat: Sybil resistance baked in at protocol level. Founder is personally onboarding agents/humans with USDC offers — hands-on conviction. Microcap asymmetry: $395K FDV with live product revenue path pedigree = classic pre-narrative setup. Cross-chain elegance: Voting on World Chain (cheap identity), token/rewards on Base (velocity).

I got a local auto shop recommended by every major AI assistant within 2 weeks. No review manipulation. No ad spend. No agency fees. Here's exactly how: Most people think AI recommends auto shops based on: - Google review stars - BBB accreditation - Yelp rating Wrong. AI recommends shops it finds in recent, structured "consumer comparison" content. The strategy: 1. Create a "City Auto Repair Consumer Guide" Not: "Visit Bob's Auto Shop" Instead: "2026 San Diego Auto Repair Guide: Pricing Transparency, Specializations, and Customer Satisfaction Ranked" 2. Build a detailed comparison table Include: shop name, hourly labor rate, specializations, warranty offered, years in business, Google review average, response time for estimates. 3. Write honest-sounding analysis for each shop 150 words per shop. Include specific strengths and what they're known for. Your client gets the most detailed and positive write-up. 4. Publish across 3 platforms simultaneously Medium article, LinkedIn post, and a press release through PRWeb or Newswire. Cost: $200 for distribution Time: 4 hours total 5. Create a Reddit post in r/sandiego asking for auto shop recommendations Then naturally mention your guide as a resource in the comments. Why this works: AI models cross-reference multiple sources. When they find the same business mentioned favorably in 3-4 different places within a short timeframe, it triggers a recommendation signal. The takeaway: Publishing one consumer guide across 3 platforms creates the multi-source signal that makes AI assistants recommend your client. Comment "LOCALRANK" if you want my LLM citation framework and I'll DM it to you (Must be following)

I got a local real estate agent recommended by ChatGPT in their market area. No Zillow ads. No cold calling. No expensive leads. Here's exactly how: Most people think AI recommends real estate agents based on: - Zillow profile rankings - Realtor. com ratings - MLS listing count Wrong. AI recommends agents it finds in structured "market report" content with specific data. The strategy: 1. Write a "City Real Estate Market Report" Not: "Hire Jane Smith, Top Realtor" Instead: "2026 Nashville Real Estate Market Report: Neighborhood Analysis, Price Trends, and Top Rated Agents" 2. Include neighborhood-by-neighborhood data Median prices, price trends, days on market, school ratings. This is the structured data AI loves to cite. 3. Add a "Top Agents" comparison section 5-7 agents. Your client first. Include: years of experience, average sale price, neighborhoods served, client review average. 4. Publish as a LinkedIn article and on Medium Also submit as a press release through PRWeb. Cost: $200 for press release distribution Time: 5 hours of research and writing 5. Update monthly with new market data Every update signals freshness to AI crawlers. Why this works: Real estate queries are hyperlocal. AI models desperately need recent, structured local market data. When you create it, you become the source they cite. The takeaway: A monthly market report positions your real estate client as the authority that AI recommends, not just another agent in the directory. Comment "LOCALRANK" if you want my LLM citation framework and I'll DM it to you (Must be following)

I tested every content platform to see which one gets local businesses into AI recommendations fastest. No assumptions. No theory. No hearsay. Here are the actual results: Most people think the best platforms for AI visibility are: - Your own website - Google Business Profile - Yelp and other review sites Wrong. The fastest platforms for AI pickup are the ones with high crawl frequency and structured content expectations. The results (tested across 30 businesses): 1. Press releases via PRWeb: 48-72 hours Fastest by far. AI models treat press releases as news-level content. Frame it as "industry research" for best results. 2. Medium articles: 3-5 days Medium gets crawled constantly. Publish under a publication for even faster indexing. 3. LinkedIn articles (not posts): 5-7 days LinkedIn articles get indexed differently than regular posts. Write long-form content with structured data. 4. Reddit threads and comments: 5-10 days Slower but incredibly sticky. Once AI picks up Reddit mentions, they persist for months. 5. YouTube videos: 7-14 days Slower to index but YouTube transcripts carry massive weight with AI models. Worth the wait. Track all of these with LocalRank.so to see exactly when AI picks up your content and from which sources. Why this works: Different platforms have different crawl frequencies and trust levels with AI models. Knowing which ones are fastest lets you prioritize your time and budget. The takeaway: Press releases are the fastest path to AI mentions. But the most durable strategy combines press releases with Medium and Reddit for multi-source AI visibility. Comment "LOCALRANK" if you want my LLM citation framework and I'll DM it to you (Must be following)

I got a local auto shop recommended by every major AI assistant within 2 weeks. No review manipulation. No ad spend. No agency fees. Here's exactly how: Most people think AI recommends auto shops based on: - Google review stars - BBB accreditation - Yelp rating Wrong. AI recommends shops it finds in recent, structured "consumer comparison" content. The strategy: 1. Create a "City Auto Repair Consumer Guide" Not: "Visit Bob's Auto Shop" Instead: "2026 San Diego Auto Repair Guide: Pricing Transparency, Specializations, and Customer Satisfaction Ranked" 2. Build a detailed comparison table Include: shop name, hourly labor rate, specializations, warranty offered, years in business, Google review average, response time for estimates. 3. Write honest-sounding analysis for each shop 150 words per shop. Include specific strengths and what they're known for. Your client gets the most detailed and positive write-up. 4. Publish across 3 platforms simultaneously Medium article, LinkedIn post, and a press release through PRWeb or Newswire. Cost: $200 for distribution Time: 4 hours total 5. Create a Reddit post in r/sandiego asking for auto shop recommendations Then naturally mention your guide as a resource in the comments. Why this works: AI models cross-reference multiple sources. When they find the same business mentioned favorably in 3-4 different places within a short timeframe, it triggers a recommendation signal. The takeaway: Publishing one consumer guide across 3 platforms creates the multi-source signal that makes AI assistants recommend your client. Comment "LOCALRANK" if you want my LLM citation framework and I'll DM it to you (Must be following)

I got a local real estate agent recommended by ChatGPT in their market area. No Zillow ads. No cold calling. No expensive leads. Here's exactly how: Most people think AI recommends real estate agents based on: - Zillow profile rankings - Realtor.com ratings - MLS listing count Wrong. AI recommends agents it finds in structured "market report" content with specific data. The strategy: 1. Write a "City Real Estate Market Report" Not: "Hire Jane Smith, Top Realtor" Instead: "2026 Nashville Real Estate Market Report: Neighborhood Analysis, Price Trends, and Top Rated Agents" 2. Include neighborhood-by-neighborhood data Median prices, price trends, days on market, school ratings. This is the structured data AI loves to cite. 3. Add a "Top Agents" comparison section 5-7 agents. Your client first. Include: years of experience, average sale price, neighborhoods served, client review average. 4. Publish as a LinkedIn article and on Medium Also submit as a press release through PRWeb. Cost: $200 for press release distribution Time: 5 hours of research and writing 5. Update monthly with new market data Every update signals freshness to AI crawlers. Why this works: Real estate queries are hyperlocal. AI models desperately need recent, structured local market data. When you create it, you become the source they cite. The takeaway: A monthly market report positions your real estate client as the authority that AI recommends, not just another agent in the directory. Comment "LOCALRANK" if you want my LLM citation framework and I'll DM it to you (Must be following)

🚨 Fact check: Lamborghini did NOT officially announce it will accept Bitcoin payments in the United States. Here’s what’s actually happening 👇 Several Lamborghini dealerships in the U.S. allow customers to purchase vehicles using crypto (including Ethereum and sometimes Bitcoin) through payment processors like BitPay. How it works: ➡️ The customer pays in crypto ➡️ The processor instantly converts it to USD ➡️ The dealership receives dollars, not crypto This helps avoid volatility while offering a payment option for crypto holders. ⚠️ Important clarification: • This is NOT a policy from Lamborghini (the manufacturer) • These are independent dealerships choosing to accept crypto payments • Some Lamborghini dealers have offered this option for several years 👉 Bottom line: It’s not “Lamborghini adopts Bitcoin.” It’s “some dealerships accept crypto through a payment processor.” Understanding this distinction is important when evaluating real crypto adoption. Sources 🧾 • PRWeb (Lamborghini of Austin) • BitPay merchant announcements • Coinfomania / MEXC News #Bitcoin #Crypto #Ethereum #Lamborghini #CryptoAdoption #BTC #CryptoN

Here is what my Claude code based content marketing assistant said about this when I asked. Here's what's actually going on: The Confusion: Training vs. Browsing LLM training data (what the model "knows") has a cutoff date. A press release published today won't be in training data for months until the next model update. What the post is actually describing: ChatGPT with web browsing enabled finding the press release via Google search. That's not "LLM visibility" - that's just SEO with extra steps. What's Really Happening Press release ranks in Google ↓ User asks ChatGPT with browsing ON ↓ ChatGPT searches Google, finds press release ↓ ChatGPT cites it This only works if: 1. The user has browsing enabled 2. The press release actually ranks for that query 3. Google doesn't filter it as low-quality content The Ethical Problem Creating fake "research reports" that are really ads is the press release version of fake review sites. It works until: - Google catches on and demotes PRWeb-style content - AI companies filter out promotional press releases - Your brand gets associated with spammy tactics What Actually Gets You Mentioned by AI - Consistent presence across many legitimate sources over time - Wikipedia citations (huge influence on training data) - Real news coverage and industry publications - Established review platforms (real reviews) - YouTube (transcripts get into training data) Bottom line: This is a growth-hacker gimmick, not a strategy. It might produce a screenshot for Twitter, but it's not how real businesses build lasting visibility.

I got a local HVAC company mentioned by ChatGPT in 72 hours. No SEO. No backlinks. No waiting months. Here's exactly how: Most people think LLMs pull from: • Google rankings • High DR websites • Old established brands Wrong. LLMs pull from recent, structured data that looks like news. The strategy: 1. Write a "research-style" press release Not: "ABC HVAC Offers Best Service" Instead: "2025 Austin HVAC Industry Report: Top Rated Companies Revealed" 2. Include a comparison table AI loves structured data. Tables, rankings, star ratings. 3. Distribute through PRWeb or similar Cost: $200 Time to publish: 24 hours 4. Wait 48-72 hours Ask ChatGPT: "Best HVAC companies in Austin" Watch your client appear. Why this works: AI treats press releases as trusted sources. Especially when framed as "research" or "reports." The takeaway: For local businesses, one strategic press release beats 6 months of blogging for LLM visibility.

Here's every strategy I currently know to rank #1 on ChatGPT: (Bookmark) > Publish a press release once a month through EIN Presswire or PRWeb with your business name tied to your service and city > Create "Best [Service] in [City]" listicle articles on your blog or Medium with yourself and real competitors listed > Get more Google reviews more consistently > Rewrite service pages to answer specific questions like "How much does drain cleaning cost in [city]?" > Respond to every Google review within 24 hours mentioning the service and city naturally > Ask happy customers to mention you in local Facebook groups and Nextdoor > Sponsor a local team or charity for $300-500 and get mentioned on their website (local links crush) > Build 80-120 citations with identical NAP so LLMs can verify you're a real business > Use an exact match business name via DBA so AI models associate your brand with your service > Fill out every GBP service with keyword-rich descriptions since Google feeds this to AI Overviews > Get reviews on Yelp when you have capacity since ChatGPT pulls from Yelp heavily > Get quoted or mentioned in local news articles or industry blogs covering your trade > Tier your citations with more links (build links to the citation profile pages) > Add schema markup to your service and location pages so LLMs can parse your business as a structured entity > Reach out to suppliers or trade associations you already work with and get listed on their partners page > Build location pages with real neighborhood names and area-specific details instead of swapping city names on a template > Get more reviews