WideNames

You don't need a million-dollar office to look like a million-dollar company. You just need the right URL. ​> Small brands look big with the right name. > A generic name makes you a "vendor." > A premium name makes you the "industry leader." #BrandBuilding #startup #AI #Domains

@domain @atomHQ congrats, I have also Gohaste.com atom.com/name/GoHaste

Haste.com sold for 7 figures (24 month LTO) Thanks @atomHQ 🎉 Excited to see what the buyer builds!

DeployYourAI.com — a premium, action-driven domain built for the AI boom. If you’re building agents, SaaS, or automation tools, this name instantly communicates what you do #AI #ArtificialIntelligence #AIAgents #SaaS #Startup #BuildInPublic #TechDomains #Entrepreneur #ai

One man is building the future. • Tesla - how we move • SpaceX - how we leave Earth • Neuralink - how we think • Starlink - how we connect • xAI - how AI evolves • X - how we communicate • Robotaxi - autonomous transport • Boring Company - how cities move All of it. He’s not even 60. Terrifying or inspiring?

Wearsight.com 👁️ A sharp, futuristic name for wearables, AI vision, smart glasses & next-gen tech. Perfect for startups building the future of human + machine interaction. #domainforsale #brandable #startup #AI #wearables #techstartup #branding #smartglasses

San Francisco has hundreds of VCs. But do you know 10 who actually invest in your space? Probably not. That’s why we built Google Maps for venture capital. We call it OpenMap: 1️⃣ Go to OpenMap (link below) 2️⃣ Zoom into San Francisco 3️⃣ Filter by any vertical you want 4️⃣ See who’s active, where they’re based, and what they invest in Then go deeper: check their website, find warm intros, and understand your local ecosystem If you’re a founder in San Francisco, you should know this map. If you’re a VC in San Francisco, you should be on it. 🌉 Explore the San Francisco investor map here → openvc.app/to/recTlsUZPa9… PS: Not in SF? You’ll probably find your city there too.

UNFORTUNATELY, the formula to a healthy life is... 1. 8 hours of sleep. 2. Drink a healthy amount of water. 3. Eat real food. 4. Lifting heavy. 5. Going outside. 6. Walk or move your body every day. 7. Reducing alcohol. 8. Taking care of your hygiene. 9. Minding your business. 10. Consume the right content. 11. Learning something new. 12. Showing up even on bad days. 13. Building healthy relationships. 14. Help where you can help. 15. Save and spend in a healthy manner. 16. Practice daily gratitude or mindfulness. 17. Cultivate a sense of purpose.

@Aladey chokran

@WideNames There are some things in life you simply can’t always explain.

Don’t trust blindly in this industry... Some of the biggest players are also the biggest rule-breakers. And those who love giving lessons aren’t always the ones setting the best example. Stay fair.

Yesterday saw $711k in domain name sales including: $65,000 Jetpacks․com $19,888 HappyOyster․com $9,950 LedgerSupport․com $9,638 CoinGames․com $8,201 Meek․ai $7,888 Eternal․cloud $6,250 Fintainment․com Full list 👉 namebio.com/daily #Domains

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

Big thank you to @atomHQ for facilitating the sale of Genesis.ai for $400,000. Also a huge thanks to @darpanmunjal :) This one was completed via their new AtomEdge feature, with a reduced commission of 12.5% - great to see continued innovation improving outcomes for sellers. On to the next 🚀

Yesterday saw $750k in domain name sales including: $100,000 Choice․ai $49,995 HappyHorse․com $49,888 Oria․xyz $22,245 CourtBooking․com $8,500 TheHeritageClub․com $7,000 Fairground․xyz Choice․ai last sold for $8k and HappyHorse․com for $3,049 🔥 Full list 👉 namebio.com/daily #Domains

Yesterday saw $504k in domain name sales including: $16,169 LiveHigher․com $10,750 Hot․bet $9,400 0446․com $4,949 VoiceArena․com $4,000 TownWorks․com $3,000 Lucu․ai $3,000 HindiDay․com Full list 👉 namebio.com/daily #Domains

We just hit $1.5M ARR with GojiberryAI To celebrate, I put together a short document breaking down exactly how we got there. Feel free to share it, and tag a founder who could benefit from it 🙌

السلام عليكم ورحمة الله وبركاته If you think someone made a 10X sale (from $1,000 to $10,000) and that’s pure profit… you’re missing the bigger picture. Let’s break it down 👇 First: Platform fees range from 5% up to 30% Second: The acquisition cost of the domain Third: Research & validation cost (Is this domain actually worth it? Does it have real demand?) Fourth: Experimentation cost Buying dozens or even hundreds of domains just to land ONE sale… with no guarantees Fifth: Marketing cost (if you’re actively promoting) Sixth: Daily cost Time + mental effort + constant market monitoring waiting for a return that may or may not come The reality 👇 Big numbers ≠ real profit 10X on paper is not the same as actual profit 💰 Domaining = High Risk / High Reward And those who truly understand the game… know this well

Yesterday saw $842k in domain name sales including: $350,000 Free․ai $10,351 PDFEdit․com $10,000 Moissan․com $8,888 Inwi․ai $7,500 Lug․io $5,102 AIHorizon․com $4,999 Grove․LTD Full list 👉 namebio.com/daily #Domains

Yesterday saw $438k in domain name sales including: $19,995 Looma․ai $8,000 Berry․app $6,200 WinCloud․com $5,000 VibeCode․net $4,050 OpenWallet․ai $3,999 DefenseClaw․com $3,950 TheJournalist․com Full list 👉 namebio.com/daily #Domains

Just looked at search trends on Atom (last 60 days vs prior) AI still leads by a wide margin Agent, LLM, tech, health seem to be trending up Also seeing spikes in "open" and "claw" which is a good example of how data can be misread If your takeaway is “buy claw domains” there is a good chance you will be dropping most of them next year. Better to invest in names that outlast the hype.