LAST DAY TO APPLY TO BREAK IN. there is no better place to build a startup than sf. but some of the worlds most talented founders struggle because they lack access to its network and resources; and thus, the thousands of hours they pour into their projects go unnoticed. a

We use LLMs for everyday tasks—research, writing, coding, decision-making. They remember our conversations, adapt to our needs and preferences. Naturally, we trust them more with repeated use. But this growing trust might be masking a hidden risk: what if their beliefs are

APPLICATIONS NOW OPEN! free housing in SF for 3 weeks this december. build your startup on @dedaluslabs and we'll help you break in. link in the comments.

Over the past few months at @dedaluslabs, we’ve noticed how many talented founders from around the world are being held back by one thing: location. There is no better place in the world to build an AI startup than San Francisco. SF is home to all the major AI labs, top talent,

HUGE

Build wings for your agents

We just raised an $11M seed to help developers build complex agents across any model and any tool in 5 lines of code. Our round was led by @KindredVentures (@stevejang) and @saga_ventures (@maxaltman, @braveben, @itsthomson). At @DedalusLabs, we believe the future of AI agents

Dedalus Labs (@dedaluslabs) is building the infrastructure layer for agentic AI—the simplest way to build and deploy complex agents across any model and any tool. Congrats on the launch, @itsCathyDi & @WindsorNguyen! https://t.co/vxjzXtA6tA

@PrincetonHCI at #UIST2025 ✨

it’s been just one month since @dedaluslabs hosted @ycombinator’s first-ever overnight hackathon… here’s the recap 🪽 take a look to see what all the teams built! ft. our friends @convex @Vapi_AI @browser_use @onkernel @_interfere @nozomioai @agentmail_to @autumnpricing

The GAIA game is over, and Alita is the final answer. Alita takes the top spot in GAIA, outperforming OpenAI Deep Research and Manus. Many general-purpose agents rely heavily on large-scale, manually predefined tools and workflows. However, we believe that for general AI

Using LLMs to build AI scientists is all the rage now (e.g., Google’s AI co-scientist [1] and Sakana’s Fully Automated Scientist [2]), but how much do we understand about their core scientific abilities? We know how LLMs can be vastly useful (solving complex math problems) yet

Ever feel lost in jargon while watching technical videos - and stuck trying to find clear, timely help? Check out our work at #CHI2025 on how real-time AI systems can bridge knowledge gaps without overwhelming users!