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i'm joining the team at @ExaAILabs!
search is one of the most important pieces of the entire AI stack and Exa is building the best search engine in the world.
excited to get to work!
Charles retweeted
this API is incredibly useful.
i don't think i'm going to rely on any app to recommend podcasts anymore.
i'll just have my agent search thousands of episodes, transcripts included, and recommend what i should listen to.
Charles retweeted
Introducing Exa Monitors V2
Get alerts across the largest index of web + proprietary data
- 130B+ web pages
- 211M businesses
- 1.3B products
- 1B people
Most monitors just rerun search, missing results. Exa runs entity recognition on the whole web. Now 7x cheaper at $2/1k runs
Charles retweeted
We're excited to announce Exa 3.0!
A huge number of improvements:
- Bigger, better embeddings model driving core search
- Higher quality highlights with further reduced token counts
- Lower latency, cheaper search type "Lite"
- Better reranking, particularly across long documents
- Even better real-time news, sports, weather, and market data
- Laundry list of other stuff
Let us know what you think! 🫡
Introducing Exa 3.0: our largest ever improvement to search quality.
Exa is now #1 on every benchmark we tested, from public evals to verticals like coding and GTM intelligence.
It's the result of multiple research breakthroughs 🧵
Charles retweeted
Exa has released Exa 3.0, scoring 80 on the Artificial Analysis Search Index in Dynamic Highlights mode, tying with Perplexity Search for the #1 spot
@ExaAILabs has released a new Dynamic Highlights mode to its Search API. Given a search query and its results, Dynamic Highlights determines how much text to return from each page within a shared budget
We benchmarked two Exa search types with Dynamic Highlights ahead of release: an upgraded auto mode, which balances search quality and speed, and a new lite mode, optimized for lower cost and latency. Both were evaluated using our Stirrup agent harness and GPT-5.6 Luna (medium) as the agent model
Key benchmarking results for Exa Search with Dynamic Highlights:
➤ Ties for #1 on the Artificial Analysis Search Index: Exa Search (auto, Dynamic Highlights) scores 80, level with Perplexity Search (medium) in the #1 spot, while Exa Search (lite, Dynamic Highlights) scores 79. On DeepSearchQA, auto (85.6 F1) and lite (84.9 F1) rank 1st and 2nd respectively, 4 to 5 points ahead of Perplexity Search (medium, 80.8)
➤ Reduces search calls and token usage: Exa Search (auto, Dynamic Highlights) uses 7.6 searches per task, the fewest of any Search API on the leaderboard, down from 9.4 with standard highlights. Tokens per task fall 47%, from 299k to 157k
➤ Reduces cost per task by 21%: Exa Search (auto, Dynamic Highlights) costs $0.066 per task, compared to $0.083 with standard highlights. Lite costs $0.044 per task, with both sitting on the cost vs. capability Pareto frontier. Exa Search is priced at $7 per 1k searches for auto and $4 per 1k searches for lite
Charles retweeted
Exclusive: Instinct is using Exa for web search
We gave Instinct URLs for nonexistent pages on a website we administer. Since the requests were for fake pages and the timing matched our conversation with Instinct, the requests couldn’t plausibly have come from anything else.
In total, it made about 12 requests over an hour and a half. We checked Cloudflare analytics and the server’s raw request logs to get the full picture.
A few things stood out:
- Instinct used Exa to fetch pages. Every direct fetch arrived with the user agent Mozilla/5.0 (compatible; ExaUser/1.0; +crawler.exa.ai/exauser), from a different IP each time.
- Exa signs its requests (Web Bot Auth, Signature-Agent: "crawler.exa.ai"). But nothing in the requests identifies Instinct. They are indistinguishable from those of any other product built on Exa.
- In one test, after the fetch returned a 404, Instinct loaded the page in its browser without being asked.
- Instinct’s browser had no self-identifying features. It looked like standard Chrome on desktop Linux from a residential IP address, with no bot user agent or signature.
We directly asked it to use the browser in two other tests. All three browser visits came from the same residential IP over about 80 minutes. The browser kept its cache between visits, consistent with a persistent browser behind a residential proxy. We can’t say whether that IP is stable over longer periods or shared with other users, so it’s not a reliable way to identify Instinct.
This presents unique challenges for marketers hoping to track Instinct traffic to their site. Unlike ChatGPT, Claude, or Meta, Instinct doesn’t send its own user agent or signature. Its fetches look like generic Exa traffic, and its browser visits look like a normal user’s.
All of this means you can’t track, allow, or block Instinct specifically. The only reason we could link these requests to Instinct was that we controlled the URLs and the timing. For normal traffic, you don’t have those luxuries.
Link to it on sunbeam: sunbeam.ai/thoughts/instinct…
Charles retweeted
10x a year for 3 years? I've seen a lot of growth, and that is truly exceptional. It's not unusual for startups to grow 10x in a year, but to keep it up for 3 years is close to unheard of. That's 1000x.
Exa just passed $100m in annualized revenue.
We've grown almost exactly 10x per year for 3 years. We now handle 2 billion requests a month from 200k+ teams. And it’s still so early:
We predict agents will search 1000x more than humans by 2030.
Agents don't search like humans. They write long, complex queries and want all the world's data, not just the web. We've spent 5 years building a search engine designed for them.
We now serve personal agents, AI labs, coding agents, GTM agents, really any agent. Exa gives these agents access to 100s of billions of web documents and tons of proprietary data.
Our customers are increasingly noticing that information is the bottleneck to agent performance. While the world seems close to perfect intelligence with models like Fable and Astra, we are very far from perfect information.
To our small but legendary team, thank you for getting us here, from engineering to research to GTM to ops.
To our customers, it is an honor to serve you. We have some exciting releases coming soon and can't wait to share them 🫡
Charles retweeted
Exa just passed $100m in annualized revenue.
We've grown almost exactly 10x per year for 3 years. We now handle 2 billion requests a month from 200k+ teams. And it’s still so early:
We predict agents will search 1000x more than humans by 2030.
Agents don't search like humans. They write long, complex queries and want all the world's data, not just the web. We've spent 5 years building a search engine designed for them.
We now serve personal agents, AI labs, coding agents, GTM agents, really any agent. Exa gives these agents access to 100s of billions of web documents and tons of proprietary data.
Our customers are increasingly noticing that information is the bottleneck to agent performance. While the world seems close to perfect intelligence with models like Fable and Astra, we are very far from perfect information.
To our small but legendary team, thank you for getting us here, from engineering to research to GTM to ops.
To our customers, it is an honor to serve you. We have some exciting releases coming soon and can't wait to share them 🫡
Charles retweeted
We sat down with @3blue1brown to discuss our research vision of "Attention over all the world's information."
Plus why knowledge should not live in model weights, the compression trick behind cheaper retrieval, and why we hire through work trials.
1:20 – What is “attention over everything”?
4:46 – A knowledge graph of the whole web
11:32 – How do you evaluate search for AI agents?
15:13 – Why not put everything in the weights?
22:55 – Why Exa hires through work trials
25:15 – The compression trick behind cheaper retrieval
27:40 – Dealing with misinformation
Charles retweeted
If you think LLM benchmarks are a shitshow, you should learn about the web search benchmarks space 😅
ATLAS is a solid step towards advancing it!
We built ATLAS, a web search benchmark for how agents actually search today.
Existing agentic search benchmarks are often outdated, contaminated, or saturated. ATLAS is designed to separate strong search from weak search.
Learn more: exa.ai/blog/atlas-benchmark
Charles retweeted
We built ATLAS, a web search benchmark for how agents actually search today.
Existing agentic search benchmarks are often outdated, contaminated, or saturated. ATLAS is designed to separate strong search from weak search.
Learn more: exa.ai/blog/atlas-benchmark
Charles retweeted
We just added hundreds of millions of places to our index.
For each entity we add, we extract meaning and structure and add to an index of hundreds of billions.
Let us know if there are more types of data you'd like your agents to have access to!
Charles retweeted
after building agentic design systems for multi-million dollar startups, I want to start sharing everything I’ve learned
starting with free skills for your coding agent:
design.how View article
Charles retweeted
Introducing Agent Ultra - a step change in deep research
Agent Ultra orchestrates swarms of agents to perform exhaustive research, build comprehensive lists, and answer questions requiring thousands of sources.
In both evals and vibes, it's state of the art
Charles retweeted
Jev with Exa is INSANE.
> Jev without websearch confidently gives wrong outputs
> Jev with websearch is literally much more accurate
Try Jev (with Exa websearch) for free 👇