Artificial Intelligence (AI) Startup Reflection AI emerged from Friday (March 7) saying it focuses on building autonomous coding systems.
“In Reflection, we’re building a close autonomous system,” the company said in a LinkedIn post Friday. “We believe that solving autonomous coding will allow for more broadly super intelligence.”
A Bloomberg article linked by the Post on Friday said it raised $130 million, including a $25 million seed funding round led by Sequoia Capital and CRV and a $105 million Series A led by Lightspeed Venture Partners and CRV.
According to the report, the company was founded by two former research scientists, Alphabet’s AI unit Google Deepmind: Misha Laskin and Ioannis Antonoglou.
The purpose of reflection is not to co-pilots or assistants, but to build completely autonomous tools and develop tension, which is the smarter AI than most humans, the report says.
The company is already able to pay for customers in financial services, technology and other sectors with large coding teams, according to the report.
In a blog post Friday, Sequoia said that coding assistants have already helped developers get 10 times faster and more productive, while autonomous coding agents are the next leap.
“Reflection’s autonomous code agents are integrated directly into the organization’s codebase and engineering workflows, autonomously working on the right, fully end-to-end engineering tasks,” says Sequoia’s post. “They read, write, test, deploy code, handle all the infrastructure on your behalf, reducing the burden on the entire engineering workload from developers and teams.”
In a blog post Friday, CRV said it is building up technology reflections “may redefine industry.”
“Imagine a world where engineering teams can work on the backlog in days rather than months. Imagine code migrations happening seamlessly and cyber vulnerabilities will be fixed before they become important,” the CRV post said. “This is a global reflection that AI is building.”
Lightspeed said in a blog post Friday that an approach to expanding Reinforcement Learning (RL) on the autonomy of Reflection AI’s large-scale language model (LLMS) will bring AI closer to crowding.
“Their systems can autonomously plan, debug and execute complex programming tasks. They can advance the leap to position Reflective AI as the key player of the future in intelligent software engineering,” says Lightspeed Post.
