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Vercel Domains just got a full rebuild: • Search without logging in • Instant, streaming search results • At-cost pricing (up to 50% lower) • Private search, with no logging • Support for 500+ TLDs • Multi-domain checkout Try it now at

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925,365 次观看 • 11 个月前 •via X (Twitter)

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Vercel Domains now supports AI search. Go beyond keywords, describe what you’re looking for. Try it now at

Vercel Domains now supports AI search. Go beyond keywords, describe what you’re looking for. Try it now at

Vercel Developers

93,974 次观看 • 10 个月前

this is wild! so new apple apps pop up in search in the full name when you start to type them but if you click the suggestion, it will be buried under 10 other apps.. however if i just search my app “lovelee” without full keyword match it pops up instant. see what i mean:

this is wild! so new apple apps pop up in search in the full name when you start to type them but if you click the suggestion, it will be buried under 10 other apps.. however if i just search my app “lovelee” without full keyword match it pops up instant. see what i mean:

jack friks

118,958 次观看 • 11 个月前

Search is now updated across Box. This foundational upgrade changes what enterprise search can actually do. 4 to 10x faster results. File indexing reduced from 15 minutes to seconds. And a stronger foundation for AI agents to perform deep-document search across your enterprise content. All at any scale, including folders of 1 billion files, as in this example.

Search is now updated across Box. This foundational upgrade changes what enterprise search can actually do. 4 to 10x faster results. File indexing reduced from 15 minutes to seconds. And a stronger foundation for AI agents to perform deep-document search across your enterprise content. All at any scale, including folders of 1 billion files, as in this example.

Box

116,897 次观看 • 3 个月前

🫡 Check out the latest updates in #Binance Wallet! • Flash Buy supports stablecoin payments with saved preferences • Earn search optimization improves multi-type search in-app • Access Topic Rush directly from the widget for a faster experience • Enhanced social media content previews are now available on web 👉 Try it out: 💬 Share your feedback below!

🫡 Check out the latest updates in #Binance Wallet! • Flash Buy supports stablecoin payments with saved preferences • Earn search optimization improves multi-type search in-app • Access Topic Rush directly from the widget for a faster experience • Enhanced social media content previews are now available on web 👉 Try it out: 💬 Share your feedback below!

Binance Wallet

38,580 次观看 • 4 个月前

Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

Akshay 🚀

215,726 次观看 • 7 个月前

cutting the video on purpose just to get people to come for her and call her a racist and a nazi for no reason at all is SO weird? maybe try logging off this app u need it

cutting the video on purpose just to get people to come for her and call her a racist and a nazi for no reason at all is SO weird? maybe try logging off this app u need it

sara, occasionaly

68,915 次观看 • 1 年前

I got sick of wasting time trying to find single word domain names for side projects so I built a command line TUI to search them it worked well and I got swipe.md and some other decent ones but the speed at which good ones get registered is crazy

I got sick of wasting time trying to find single word domain names for side projects so I built a command line TUI to search them it worked well and I got swipe.md and some other decent ones but the speed at which good ones get registered is crazy

Ian Nuttall

53,908 次观看 • 1 个月前

Ling-3.0-flash, the new MoE model from Ant Ling, is now free in Nous Portal for the next week! At 124B parameters and 5.1B active, it's quick to run and built for agent workloads: coding, search, research, and tool use. Try it free today at

Ling-3.0-flash, the new MoE model from Ant Ling, is now free in Nous Portal for the next week! At 124B parameters and 5.1B active, it's quick to run and built for agent workloads: coding, search, research, and tool use. Try it free today at

Nous Research

73,793 次观看 • 1 个月前

iMessage search is still broken in 2025. So I fixed it myself. It doesn't even work most of the time. When it does, it's slow, misses messages constantly, and only matches exact words. Search "dinner" and you won't find "let's grab food." iMessage Intelligence fixes all of this while also being insanely fast. Here's how it works: → Full text search index on your messages for keyword matching → On-device ML (MiniLM via CoreML) generates semantic embeddings. Your Mac generates these embeddings in under a minute, then syncs to your phone → sqlite-vec stores vectors locally for similarity search → Hybrid results: keyword hits appear in <10ms, semantic matches merge in <60ms It also has advanced filters iMessage doesn't have: sort options, date ranges, conversation filters, sent vs received, groups vs 1-on-1. Search runs 100% on-device. No cloud processing. Sign up for the waitlist:

iMessage search is still broken in 2025. So I fixed it myself. It doesn't even work most of the time. When it does, it's slow, misses messages constantly, and only matches exact words. Search "dinner" and you won't find "let's grab food." iMessage Intelligence fixes all of this while also being insanely fast. Here's how it works: → Full text search index on your messages for keyword matching → On-device ML (MiniLM via CoreML) generates semantic embeddings. Your Mac generates these embeddings in under a minute, then syncs to your phone → sqlite-vec stores vectors locally for similarity search → Hybrid results: keyword hits appear in <10ms, semantic matches merge in <60ms It also has advanced filters iMessage doesn't have: sort options, date ranges, conversation filters, sent vs received, groups vs 1-on-1. Search runs 100% on-device. No cloud processing. Sign up for the waitlist:

Seif Abdelaziz

97,261 次观看 • 9 个月前

Introducing Snowflake on v0. • Ask questions about your data • Build data-driven Next.js apps • Deploy securely: compute in Snowflake, auth with Vercel Your trusted data, moving at the speed of AI.

Introducing Snowflake on v0. • Ask questions about your data • Build data-driven Next.js apps • Deploy securely: compute in Snowflake, auth with Vercel Your trusted data, moving at the speed of AI.

Vercel

35,163 次观看 • 10 个月前

We are releasing instant pricing today on our website!! Just upload a STEP and see an instant price! Now you don’t have to wait for me to quote you manually and I can finally go to bed 🙃 Try it out at More exciting news coming soon 👀👀

We are releasing instant pricing today on our website!! Just upload a STEP and see an instant price! Now you don’t have to wait for me to quote you manually and I can finally go to bed 🙃 Try it out at More exciting news coming soon 👀👀

Timmy Seto

11,050 次观看 • 1 年前

YouTube is a massive asset for $GOOGL in the AI economy. It sits at the intersection of search, education, entertainment & commerce so Google can bake AI into the ecosystem with no behavior change. YouTube is Google’s compounding advantage.

YouTube is a massive asset for $GOOGL in the AI economy. It sits at the intersection of search, education, entertainment & commerce so Google can bake AI into the ecosystem with no behavior change. YouTube is Google’s compounding advantage.

Shay Boloor

53,252 次观看 • 8 个月前

Learn how we built our interactive 3D badge for Vercel Ship with React Three Fiber.

Learn how we built our interactive 3D badge for Vercel Ship with React Three Fiber.

Vercel

275,070 次观看 • 2 年前

🔎 How I Search 29+ Million Public Entities for FREE Downloading leaked datasets just to search them is the fastest way to waste hours A lot of investigators still do this the hard way: 1. Download the leak. 2. Unzip it. 3. Click through thousands of files manually. That gets old fast. The Library of Leaks, built by (DDoSecrets), works more like a search engine for leak data. You can search for: • Name, • Company • Email address • Phone number • Address across multiple collections at once, instead of digging through raw archives one folder at a time. It currently indexes documents, emails, government records, corporate files, BlueLeaks, and the Kazakhstan Ministry of Energy files. When I'm profiling a company or person, I usually check whether they appear in datasets like this early in the process. It can save hours. Link: P.S. ♻️ Repost if you found this helpful.

🔎 How I Search 29+ Million Public Entities for FREE Downloading leaked datasets just to search them is the fastest way to waste hours A lot of investigators still do this the hard way: 1. Download the leak. 2. Unzip it. 3. Click through thousands of files manually. That gets old fast. The Library of Leaks, built by (DDoSecrets), works more like a search engine for leak data. You can search for: • Name, • Company • Email address • Phone number • Address across multiple collections at once, instead of digging through raw archives one folder at a time. It currently indexes documents, emails, government records, corporate files, BlueLeaks, and the Kazakhstan Ministry of Energy files. When I'm profiling a company or person, I usually check whether they appear in datasets like this early in the process. It can save hours. Link: P.S. ♻️ Repost if you found this helpful.

CyberSudo

11,982 次观看 • 25 天前

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

171,340 次观看 • 1 年前

𝗥𝗘𝗦𝗖𝗨𝗘 𝗢𝗣𝗘𝗥𝗔𝗧𝗜𝗢𝗡 𝗔𝗕𝗔𝗡𝗗𝗢𝗡𝗘𝗗 The emergency services team have abandoned the search and rescue operation for the bakkie that was swept away attempting to cross the flooded Tswinga Bridge in the Vhembe District, Limpopo on Saturday afternoon. A Bakkie with Somali nationals inside was carrying stock when it tried to cross the waterlogged bridge en route to deliver stock to a local supermarket when the incident occurred. Police search-and-rescue divers responded swiftly to begin a search operation. However, the operation has since been abandoned and will resume on Sunday. Some stock items were spotted floating at the river back at a pond downstream, not too far from where the car was swept away. #LimpopoChronicle

𝗥𝗘𝗦𝗖𝗨𝗘 𝗢𝗣𝗘𝗥𝗔𝗧𝗜𝗢𝗡 𝗔𝗕𝗔𝗡𝗗𝗢𝗡𝗘𝗗 The emergency services team have abandoned the search and rescue operation for the bakkie that was swept away attempting to cross the flooded Tswinga Bridge in the Vhembe District, Limpopo on Saturday afternoon. A Bakkie with Somali nationals inside was carrying stock when it tried to cross the waterlogged bridge en route to deliver stock to a local supermarket when the incident occurred. Police search-and-rescue divers responded swiftly to begin a search operation. However, the operation has since been abandoned and will resume on Sunday. Some stock items were spotted floating at the river back at a pond downstream, not too far from where the car was swept away. #LimpopoChronicle

Limpopo Chronicle

81,148 次观看 • 8 个月前

V2U equipped with artificial intelligence shot down by Wild Hornets anti-aircraft drones 💥 The “Posipaky” unit destroyed a loitering munition with an AI system for autonomous search, recognition, and target engagement. It has a payload of up to 3.5 kg, a cruising speed of up to 60 km/h, and the ability to operate in electronic warfare conditions without constant communication with the operator.

V2U equipped with artificial intelligence shot down by Wild Hornets anti-aircraft drones 💥 The “Posipaky” unit destroyed a loitering munition with an AI system for autonomous search, recognition, and target engagement. It has a payload of up to 3.5 kg, a cruising speed of up to 60 km/h, and the ability to operate in electronic warfare conditions without constant communication with the operator.

Wild Hornets

12,050 次观看 • 6 个月前