Continue Launches 1.0 with Open-Source IDE Extensions and a Hub that Empowers Developers to Build and Share Custom AI Code Assistants
$5M seed round raised from Heavybit, Y Combinator, and angels to meet developers where they are now
SAN FRANCISCO, CA, February 26, 2025 (EZ Newswire) -- Continue, the open-source AI code assistant platform, today announced the launch of Continue 1.0, a major milestone on its journey to empower developers with fully customizable AI code assistants. Continue enables developers to create, share, and use custom AI code assistants with open-source IDE extensions that can now seamlessly leverage a vibrant hub of models, context, and other building blocks. With hundreds of thousands of users, 20k+ GitHub stars, and a thriving Discord community of 10k+ developers, Continue is setting a new standard for open-source AI-enhanced development.
The release of Continue 1.0 includes a new hub that makes it frictionless to create AI code assistants with a registry for defining, managing, and sharing Continue building blocks. There are blocks published and maintained by verified partners like Claude 3.5 Sonnet from Anthropic, Codestral from Mistral, DeepSeek-R1 from Ollama, voyage-code-3 embeddings from Voyage AI, and MCP servers from Docker. Blocks and assistants may also be created and shared to the hub by individual developers, independent software vendors (ISVs), and other organizations.
Also included is the first major release of Continue's open-source extensions for VS Code and JetBrains. Developers can use these extensions with assistants and blocks from the hub via a free, solo tier. Organizations can take advantage of paid teams and enterprise tiers. Working with early enterprise users such as Siemens, Morningstar, and IONOS helped to shape the product. The hub provides engineering leaders with governance, security, and infrastructure control over AI code assistants within their organizations.
'Continue 1.0 is a huge leap forward in making AI-powered development truly customizable, private, and developer-first. The 'one-size-fits-all' AI code assistant will be a thing of the past. With this release, we're making it easier than ever for individual developers and teams to take full control over their AI coding experience through both our open-source community and our hub of building blocks for custom AI code assistants,' said Ty Dunn, co-founder of Continue.
Continue is built on the foundation of developer empowerment and data control. Unlike closed-source alternatives, Continue ensures that every developer has the power to decide how AI integrates into their coding environment. Key features of the teams and enterprise tiers on hub.continue.dev include:
Standardize development practices: Teams can establish custom AI code assistants that help developers align with shared development, review, and testing practices
Governance controls: Organizations can define and enforce policies around AI-assisted development, governing what blocks can be created, shared, and used within their teams
Private data plane deployment: Enterprises can deploy a data plane within their own infrastructure, ensuring that all code and analytics remain secure without exposing API keys or sensitive data
'Developers thrive when they have the freedom to build with the best tools available. Continue 1.0 amplifies every developer, team, and organization with the power to choose and customize AI code assistants to fit their unique workflows and preferences. This launch isn't just about AI helping developers write code—it's about making AI a natural, customizable extension of how they already work. Continue gives developers superpowers that amplify and enhance the way they already work. This is why Heavybit has been investing in developer-first startups for more than a decade,' said Jesse Robbins, General Partner at Heavybit and co-founder of Chef.
'At Mistral, our mission has always been to democratize artificial intelligence. Our partnership with Continue perfectly aligns with our vision of a developer-first ecosystem where AI code assistants are both secure and customizable. Whether you need local, on-prem, in your VPC, on the public cloud, or via serverless APIs, you can use Mistral models with Continue,' said Arthur Mensch, co-founder and CEO of Mistral AI. 'Together, we're building the future of AI-powered development on a foundation of openness and trust.'
'We believe that when it comes to AI coding tools, developers should be able to consume with confidence. In this period of rapid change, confidence requires openness, pluggability, and modularity. Now is the time to embrace the transparency and innovation of open source solutions. We believe in Continue's approach, and we're excited to partner with them and the community to define this ecosystem,' said Craig McLuckie, co-founder of Stacklok, Kubernetes, and the Cloud Native Computing Foundation
'At YC, we invest in teams that put developers first, and Continue's 1.0 launch is a perfect example of that philosophy in action,' said Garry Tan, CEO of Y Combinator. He continued 'By making it easy to create custom AI code assistants, they're giving developers the power to tailor their coding experience like never before. This is a major step forward in building an open ecosystem where innovation and democratization go hand in hand.'
Backed by Heavybit and Y Combinator, Continue has raised a total of $5 million in seed funding to create a developer ecosystem built on trust, privacy, and developer empowerment. With 1.0, Continue is not only revolutionizing the AI coding assistant landscape but also ensuring that developers everywhere have the tools they need to harness AI on their own terms.
To learn more about Continue, visit www.continue.dev. If you're interested in working at Continue, apply online.
About Continue
Continue enables developers to create, share, and use custom AI code assistants. Loved by hundreds of thousands of developers worldwide at organizations ranging from small startups to Fortune 500 companies, our open-source IDE extensions fit into existing workflows, while letting users leverage our vibrant hub of models, context, and tools. Backed by Heavybit, Y Combinator, and angels, including Julien Chaumond (co-founder of Hugging Face), Lisha Li (founder of Rosebud AI), and Florian Leibert (co-founder of Mesosphere), Continue was founded in 2023 and is based in San Francisco. For more information, visit https://www.continue.dev.
SOURCE: Continue
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Business Insider
7 hours ago
- Business Insider
The 10 most exciting AI agent startups at Y Combinator's Demo Day for its first-ever spring cohort
Christmas came early for Y Combinator. The storied Silicon Valley accelerator had AI agents startups at the top of their wish list — and the startup community delivered. When Y Combinator issued a callout earlier this year for more founders building so-called "agentic" AI — software that can essentially think and perform tasks on its own — entrepreneurs were all ears. Of the 144 companies in YC's spring 2025 batch, 70 build agents, according to the accelerator's startup directory. Many of the spring batch startups showed off their technology at YC's Demo Day on Wednesday. Each batch brings together a few hundred early-stage startups for a three-month sprint of product development and mentorship in San Francisco. Tens of thousands apply, and only a fraction make the cut. YC invests $500,000 in every company it selects, and its alumni roster includes success stories such as Stripe, Airbnb, DoorDash, Coinbase, and Instacart. 2025 marks the first time YC has run a spring cohort, which ran from April to June. For its inaugural spring batch, YC partners said they were especially keen on startups exploring new and creative ways to harness artificial intelligence. Many of the startups that made the cut apply agentic AI across industries — from cybersecurity to consumer fintech to healthcare. BI dug through YC's Spring 2025 cohort to find 10 AI agent startups revolutionizing their respective industries. To represent the spread of startups in the overall cohort, we picked a handful of the many startups YC categorized as B2B, two healthcare startups, and one each of fintech, consumer, and industrials. From technology that teaches old robots new tricks, to agents that help consumers apply for the best mortgage, here are some of the most exciting agentic startups in YC's inaugural spring batch.
Forbes
2 days ago
- Forbes
AI Agents Are Coming To Healthcare
A digital workforce is coming to healthcare. getty Y Combinator calls 2025 the 'year of AI agents,' and singled out healthcare as a key focus area. Bill Gates predicts that agents will 'upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons." Nvidia CEO Jensen Huang recently proclaimed that agents "present a trillion-dollar opportunity.' You get the point. Agents are all the rage throughout the tech industry. But what exactly are agents? What role might they play in healthcare? And what's holding them back from realizing their potential? Because early chatbots used decision trees and scripted responses, they struggled with in-depth conversations. With ChatGPT's release, millions of people discovered how to interact with large language models across an almost unlimited range of topics. Agents build on that foundation. They are LLMs enhanced with capabilities such as retrieval, memory, and tools—enabling them to carry out narrowly defined tasks without human supervision (e.g., booking a flight or responding to a customer service request). While copilots assist humans, agents take over tasks entirely. Agentic AI coordinates multiple task-specific agents to accomplish multi-step goals. Many healthcare provider organizations are struggling. Their workforces are strained and shorthanded. Margins are thin, labor costs are rising, and many processes are inefficient and wasteful. Meanwhile, patients often struggle to access timely, affordable, and effective care. Numerous industries, such as banking, travel, and personal finance, have improved productivity and service by doing more with fewer people. Agents may unlock similar opportunities in healthcare. Always on, scalable, and tireless, agents could automate a range of administrative and even clinical processes. That's why many view agents not just as tech, but as operational infrastructure and digital labor. As Luminai CEO Kesava Kirupa Dinakaran explained, 'When you think about how computers can drive value, it's by improving operations. At their core, healthcare organizations are all about operations.' The hope is that agents can expand access, lower costs, improve experiences, and enhance outcomes. A burgeoning wave of companies is racing to deliver AI agents that enable provider organizations to do more with less. Call centers are today's leading use case. Most patients still make appointments by phone, and many skip them due to scheduling hassles. Agents can manage inbound calls by conversing with patients and taking the next best actions (e.g., scheduling an appointment or making a referral). When agents cannot handle an end-to-end call, they can verify key details, summarize the conversations, and hand off to human staff. Assort Health Co-CEO Jeff Liu explained that his company aims to help healthcare organizations 'train the best operators they ever had.' By turning scheduling protocols into rules engines and plugging agents into EHRs, his co-CEO Jon Wang reported that their company automates most inbound calls—routing the rest to call center workers and nurses in priority order. Similarly, Hello Patient CEO Alex Cohen reports that his company's 'fully generative voice and SMS agents handle real-time front‑office calls and proactively re-engage patients, letting clinics boost access and fill schedules without adding headcount.' Agents are also handling outbound calls. Notable's agents prepare patients for clinic visits by verifying their insurance benefits, clarifying (and documenting) their reason for the visit, and checking them in for their appointments. Qventus' agents help optimize patients for procedures and surgeries by providing preparation instructions, sending appointment reminders, answering general care questions, and coordinating pre-admission testing. Infinitus deploys agents to perform health risk assessments and help patients access specialty therapies and rare disease programs. Some agents support patients between and after visits. Ambience Healthcare is developing agents that send follow-up instructions, medication reminders, and scheduling prompts based on visit notes. Hippocratic AI provides a constellation of agents for nurse-level clinical tasks, such as making post-discharge follow-up calls and closing care gaps. The company calls this 'super staffing' since many organizations lack the staff to do enough of this work on their own. Sword Health, a digital physical therapy platform, uses agents to onboard patients, provide customer service, and even help them return hardware. And Cedar recently launched an AI voice agent, Kora, to handle billing inquiries, explain charges, surface payment options, and connect patients with financial assistance. Agents are not limited to patient-facing roles—they're also streamlining the back office. For example, one of Luminai's agents reads incoming faxes and automatically triggers downstream workflows like refills and referrals. VoiceCare AI automates communication between provider organizations, insurers, and patients. Its CEO, Parag Jhaveri, reported that their agent, Joy, can wait on hold for more than 30 minutes, navigate phone trees, sustain multi-hour conversations, and take actions like updating claims and filing requests. Building a well-scripted, polished demo is easy. Delivering reliable performance on real-world healthcare tasks is much harder. Agents often score far short of human performance. For one, healthcare is complex—filled with edge cases, exceptions, and contextual nuance. As legendary software engineer Steven Sinofsky noted, automation is ultimately about handling exceptions, not the routine. Several technical barriers stand in the way. Healthcare data is deeply siloed and fragmented. Lisa Bari, Head of External Affairs at Innovaccer, warns against deploying agents without full contextual data. Also, while LLMs enable agents to handle a wide range of inputs, they can produce uncontrolled outputs. Longer conversations and more contextual data can reduce accuracy and increase latency. Moreover, error rates compound across multi-step processes. For example, an agent with 98% per step will complete a five-step task successfully only 90% of the time (0.98⁵). Developers use various strategies to make agents more reliable. As Sword Health product lead Rik Renard, RN, emphasized, "Evaluating agents' output against pre-specified criteria is essential for deploying reliable agents, yet few people discuss this.' Many agentic systems use specialized knowledge graphs to contextualize information and 'coordinating agents' to link multiple, narrow task-specific agents. Technical guardrails help ensure agents stay within scope and flag questionable output for human review. Still, picking the right use cases is critical. Notable Chief Medical Officer Dr. Aaron Neinstein told me that his company first deploys agents in low-risk areas (e.g., patient intake) to build trust before expanding into more complex workflows. Even with clear use cases, deployment remains hard and no shortcuts exist. As Cedar CEO Dr. Florian Otto summed it up, 'Agents must be built workflow by workflow and only deployed when they reliably work well.' Agents must also integrate with other tech systems—like EHRs and CRMs—to access contextual data and execute tasks. Most use native API integrations, though some interact through the same point-and-click interfaces that healthcare workers use. Ultimately, in an 'agentic economy,' agents must interact with one another—communicating information, transferring resources, collaborating, and tracking transactions. This will require persistent identity and seamless communication protocols, which developers are now building. Several companies, including Salesforce, Microsoft, and Innovaccer, have launched platforms to orchestrate multi-agent healthcare workflows. "Any sufficiently advanced technology is indistinguishable from magic.' Arthur C. Clark famously explained, "Any sufficiently advanced technology is indistinguishable from magic.' If you haven't interacted with an AI agent or tried a modern voice model, you should. This isn't your mother's old pharmacy's IVR system. The technology has crossed the uncanny valley — it feels like magic. But unlike magic, it's not infallible. In high-stakes situations, unreliable AI can cause real harm. As extreme examples, consider how the National Eating Disorder Association's chatbot 'Tessa' encouraged users with eating disorders to diet, or how a companion allegedly pushed a teenager to commit suicide. Agents, however, are more than chatbots. They are tireless digital workers who are always ready to complete specific tasks. When stitched together, they can form multi-agent systems—or 'agent swarms'— that handle complex, interdependent processes and behaviors. Yet, US and EU regulators have approved exceedingly few healthcare AI agents. Hardian Health's Dr. Hugh Harvey warns, "Health systems and clinicians using unregulated AI agents must accept all the risk.' Will they be willing? While regulatory approval is cumbersome, it may be necessary to speed adoption. Also, unlike magic, AI agents aren't plug-and-play. Implementing agents is a massive change-management undertaking. Patients will need to adjust their expectations and learn to interact differently with technology and healthcare. In his book Alchemy, Rory Sutherland explains that in our 'unrelenting quest' for greater efficiency, we often forget to ask 'whether people like efficiency as much as economic theory believes they do.' Take the 'doorman fallacy:' a hotel that replaces doormen with automatic doors may save money, while overlooking the other valuable functions doormen provide, such as hailing taxis, providing security, welcoming guests, and signaling the hotel's status. Similarly, healthcare workers often do far more than their simple job description. For example, if agents automate scheduling, who will reassuringly mention that 'everyone loves Dr. Smith' or that she tends to run late at the end of the day? Of course, healthcare workers aren't perfect or always available. Several company leaders I spoke with say patients prefer interacting with their agents over healthcare workers. But this remains to be seen. Outside healthcare, Klarna—the buy now, pay later company—recently walked back its ambitious efforts to replace two-thirds of its customer service workforce with AI agents. It turns out many customers still want to talk to real people. Agents will also reshape how healthcare workers do their jobs. By offloading drudgery, agents could empower some. Yet others may resent having to babysit new digital coworkers that could potentially replace them. Interestingly, one company CEO shared that executives and managers–not frontline staff–are often the most resistant. Perhaps they worry agents will shrink their teams and reduce their influence. Or, more likely, they may feel daunted by all that responsible deployment demands: surfacing tacit knowledge, defining ground truths, streamlining workflows, and retraining workers for new forms of human-AI collaboration. Infinitus CEO Ankit Jain explained his company 'sells outcomes, not technology.' It focuses on supporting change management, recognizing that 'organizations must be able to crawl before they walk and then run.' For agents to succeed, organizations must look inward. Technology is an amplifier. It can boost productivity or magnify inefficiency. Healthcare organizations, which strongly pull towards inertia, must actively reexamine their operations, rebalance their workforces, and reinforce their digital governance. Otherwise, poorly integrated agents could cause confusion and chaos. Optimizing workflows is essential. So is relieving downstream constraints. For example, a flawless appointment-scheduling agent is of limited value if doctors' schedules are always full. An outreach agent that flags patient needs is only helpful if there are enough nurses and clinicians available to respond. Taken together, these developments point toward a future that's both promising and uncertain. 'Technology is neither good nor bad; nor is it neutral.' Healthcare's digital history has taught us hard lessons: technology can help, but it cannot miraculously solve all problems. And tech doesn't work in isolation—lasting change depends on rethinking people, processes, and priorities, not just deploying tools. Roy Amara famously observed, 'We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.' That seems likely with AI agents. In the near term, agents will make existing workflows faster and cheaper—answering calls, managing intake, and making appointments. Next, they may improve those workflows—coordinating across channels, adding personalization, and responding with context. Eventually, they may enable entirely new approaches, with networks of agents operating semi-autonomously across systems. At the heart of this evolution is a core tension: leverage versus certainty. Agents promise a kind of abundance—tireless labor at negligible cost. But that leverage introduces risk. For now, they'll likely remain in administrative domains, where errors are less costly and rarely dangerous. Still, care delivery is also quite inefficient. Care models for both acute and chronic illness have barely changed in decades—and the clinician-patient encounter remains healthcare's choke point. Here, too, agents may help: handling triage, guiding protocol-driven decisions, even managing chronic conditions. Much of this is already technically feasible. But real progress will require much more: rigorous evaluation, regulatory clarity, updated business models, cultural acceptance, redesigned teams, and seamless escalation paths to human care. Melvin Kranzberg's First Law reminds us: 'Technology is neither good nor bad; nor is it neutral.' The promise of agents is real—but conditional. Their impact depends on how we design, deploy, and govern them. Will agents make care more personalized—or more transactional? Will they return time to clinicians—or reduce their autonomy and turn them into machine supervisors? Will they bring people closer together—or insert more distance? Will they relieve burden—or hollow out the human core of care? Agents are coming. What they become depends on us. I thank the following people for discussing this topic with me: Ray Chen and Jonathan Fullerton (Ambience Healthcare), Jeffery Liu and Jon Wang (Assort Health), Florian Otto (Cedar), Hugh Harvey (Hardian Health), Alex Cohen (HelloPatient), Rick Keating (Hippocratic AI), Ankit Jain (Infinitus), Abhinav Shashank and Lisa Bari (Innovaccer), Pankaj Gore (Insight Health), Kesava Kirupa Dinakaran (Luminai), Aaron Neinstein and Tushar Garg (Notable), David Atashroo (Qventus), Rouhaan Shahpurwala ( Rik Renard and Kevin Wong (Sword Health), Maria Gonzalez Manso (Tucuvi), Parag Jhaveri (VoiceCare AI), Sergei Polevikov (WellAI), and Stuart Winter-Tear.
TechCrunch
5 days ago
- TechCrunch
AI startups continue fueling San Francisco's office recovery
In Brief Early-stage AI startups are imbuing new life into San Francisco's Northern Waterfront, after years of 'for lease' signs dotting the post-pandemic landscape. According to the San Francisco Business Times, five AI-focused companies — four backed by Y Combinator — recently leased 23,900 square feet at the Waterfront Plaza complex. This is part of a citywide trend: AI firms are one of the few sectors expanding in San Francisco, accounting for 1.6 million square feet leased last year and now occupying 5 million total. (Unsurprisingly, OpenAI accounts for a sizable chunk of that overall figure.) Real estate services firm CBRE projects that these scrappy startups could reach 21 million in square feet within five years, potentially halving the city's current 35.8% vacancy rate and creating tens of thousands of jobs. 'It could fundamentally change the vibrancy of downtown,' CBRE analyst Colin Yasukochi tells the outlet. Waterfront Plaza is a five-building campus totaling 442,000 square feet; among its past tenants is WeWork.
