TechCrunch Disrupt 2026 Super Early Bird rates end in 1 week

12:21 AM IST · February 21, 2026

Super Early Bird pricing forTechCrunch Disrupt 2026ends February 27 at 11:59 p.m. PT. This is your final window to lock in the lowest ticket prices of the year. If Disrupt has been on your must-attend list, now is the time to act. Save up to $680 on your pass or secure up to 30% off with community passes.Register now before prices increase. From October 13–15, San Francisco’s Moscone West will become the global epicenter of tech. Disrupt is a curated, three-day experience built to maximize signal over noise — bringing together 10,000 founders, investors, operators, and tech leaders for 200+ expert-led sessions featuring 250+ influential voices. Across the ecosystem, past attendees consistently point to the same value: AtDisrupt, you’ll explore what’s next as300+ exhibiting startupsdebut new breakthroughs, feel the high-stakes energy of the intense startup pitch-off inStartup Battlefield 200, and engage incurated, high-impact networkingwith the people shaping the future of tech. The 2026 agenda drops soon. Visit theevent pagefor updates. Disrupt isn’t about wandering between sessions. It’s about intentional connections and curated experiences designed for how people actually grow in tech. Founders meet investors actively backing breakthrough ideas. VCs cut through the noise to discover startups aligned with their investment focus. Operators exchange real-world lessons on building, scaling, and shipping what’s next. Aspiring innovators get inspired with a front-row seat to tomorrow’s tech and invaluable insights from those shaping it. If you’re building, investing, or scaling in tech, Disrupt was built for you. Find your ticket match now and secure the lowest rate before it’s gone. Founders and investors can unlock specialized passes designed to support your goals: Founder Pass:Accelerate growth with the right insights, tools, and connections. Investor Pass:Discover standout startups and expand your portfolio through curated access. This deal ends in just one week. Lock yours in before Friday, February 27, at 11:59 p.m. PT.Register hereto saveup to $680on your TechCrunch Disrupt 2026 pass or save up to 30% with group passes.

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Adaption aims big with AutoScientist, an AI tool that helps models train themselves

For years, AI researchers have anticipated the moment when AI systems will be able to improve themselves better than humans could. With investors pouring money into a new generation of research-driven AI labs, there are more resources than ever available to pursue the goal. Now, one of those neolabs has taken a major step towards making it real. On Wednesday,Adaptionintroduced a new product calledAutoScientistthat helps models learn specific capabilities quickly by using an automated approach to conventional fine-tuning. The techniques are applicable to a wide range of fields, but the Adaptation team is particularly focused on the potential for speeding up and easing the process of training and fine-tuning a frontier-level AI model. According to co-founder and CEO Sara Hooker, who previously worked as VP of AI research at Cohere, AutoScientist represents a new way to approach the AI training process. “What’s super exciting about it is that it co-optimizes both the data and the model, and learns the best way to basically learn any capability,” Hooker told TechCrunch. “It suggests we can finally allow for successful frontier AI trainings outside of these labs” AutoScientist builds on the company’s existing data offering,Adaptive Data, which aims to make it easier to build high-quality datasets over time. AutoScientist, meanwhile, is designed to turn those continuously improving datasets into continuously improving AI models. “Our view at Adaption is that the whole stack should be completely adaptable, and should basically optimize on the fly to whatever task you have,” Hooker says. Of course, that approach will only be as good as the results. In its launch materials, Adaption boasts that AutoScientist has more than doubled win-rates across different models — impressive numbers, but difficult to put into context. Since the system is built to adapt models to specific tasks, conventional benchmarks like SWE-Bench or ARC-AGI aren’t applicable. Still, Adaption is confident that users will see the difference once they try AutoScientist out — so confident that the lab is making the tool free to use for the first 30 days after its release. “The same way that code generation unlocked a lot of tasks, this is going to unlock a lot of innovation at the frontier of different fields,” Hooker says.

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