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The coastal city is expanding its startup ecosystem, GCCs, AI work, and infrastructure to keep talent and investment closer to home.
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Vijay Shekhar Sharma offered Indian founders ₹1–2 crore of his own money to build their own AI models. The cheque is a rounding error against the number he put on screen. But the provocation behind it may be the most useful thing anyone said at Cypher 2026.
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The Chinese chipmaker plans to launch server memory based on the new architecture by year-end, following the mass production of its fifth-generation DRAM platform and LPDDR6 chips.
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In a controlled experiment, Anthropic researchers demonstrated that the Gemma4-26B-A4B model could run inference across 15 inexpensive, off-brand Android TV boxes.
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Character.AI filed for Chapter 11 bankruptcy and expects the sale process to conclude by late November or early December.
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The company is trying to control expenses while reinventing its business over the next three to five years.
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The company plans to make Maia its primary inference system for its products and services.
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Deal wins rose 27% year-on-year to $3.3 billion, while HCLSoftware revenue fell 11.2% in constant currency during the quarter.
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The Bengaluru-based startup is looking to expand its AI research, enterprise deployments and presence in global markets.
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Meta said Muse Wonder has the strongest safeguards to date against cyber misuse, but retains access to advanced cyber capabilities for defensive use.
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Journalists, entrepreneurs, and even some public relations professionals will tell you that the PR process is broken (buy me a drink sometime, and I’ll give you my rant). But could AI actually make things better? Honeyjar, which will be attending this week’sTechCrunch Disruptas part of theStartup Battlefield 200, wants to use AI to make the expertise and judgment of the best PR pros available to everyone, including founders who have no idea where to start. The startup’s founders, CEO Michelle Masek and CCO Nadia Jamshidi, certainly fit the bill when it comes to PR expertise. They’ve both spent decades in the field, doing in-house communications and working at agencies. Masek recently served as senior vice president of marketing and communications for Ev Williams’ venture firm,Obvious Ventures. As Masek described the challenges involved in “helping with the why and when of comms and marketing for 110 portfolio companies” (the startups her firm had invested in), I flashed back to my own brief stint leading marketing at anearly-stage VC firm, where founders were constantly asking for help getting into TechCrunch. So I wasn’t surprised that Masek found herself asking, “Is there a way to scale the senior-level expertise into AI [in a way] that actually helps users do the work of comms in the proactive and skilled way that we do?” Honeyjar is her answer to that question. It automates processes like writing announcements, finding the best journalists to pitch, and identifying the most promising speaking opportunities. But Jamshidi told me in a follow-up email that Honeyjar’s vision goes beyond getting press coverage and beyond specific AI tools. “Honeyjar can find reporters, build media lists, scan news and social, track coverage, and surface opportunities,” she said. “Those capabilities are important, but they’re just one piece of what we’re building. The bigger idea is bringing the whole communications workflow together: signals, context, strategy, judgment, timing, and execution.” And it’s not just for founders. Masek said she’s come to realize there are “millions and millions of unassigned communicators responsible for the work of comms.” “They don’t have the title of comms, but they’re marketers, investors, authors, creators, school superintendents, sports team leaders,” she added. “It’s incredibly horizontal, this need to nail strategic communications. It’s always passed around, but the problems are very often not solved.” Nor is Honeyjar trying to replace PR professionals. Masek said she and Jamshidi built the tool they wanted to use themselves. She argued that Honeyjar can make PR firms and in-house comms teams more effective, while allowing individual PR practitioners to “take on that third client without any scaling cost.” When Honeyjar opened its waitlist, it received 10,000 signups in the first 24 hours, said Masek. Beta testers include Nextdoor, tech startup Coco Robotics, investor Bloomberg Beta, and agencies Mission North and Full Picture. That sounds promising for companies looking to get more attention, but it could be a nightmare for journalists. We receive more pitches than we can respond to, many of them already written by AI chatbots. When I raised my fears about getting inundated with even more PR slop, Masek noted that while Honeyjar can draft a pitch email or a social media post in the customer’s voice, the customer actually has to hit send or publish. “We never want a world where the human is removed from the loop,” she said. And that’s also where judgment is key. Masek said she and Jamshidi tried to “infuse and encode Honeyjar with our wisdom.” Through a broader initiative called the Comms Lab, they’re now training the product on the judgment of other PR pros as well. “For us, judgment is knowing what to do, what not to do, when to act, and how to adapt to the situation,” Jamshidi added. “That comes from understanding a company’s goals and priorities, what’s happening inside and outside the organization, and knowing what to say, to whom, where, and when.” In some cases, Masek said Honeyjar might even tell a founder that they’re simply not ready for coverage. “We hear from reporters all the time who want better pitches, who want to hear from experts directly,” she said. “We actually see a future where there are more credible pitches in your inbox from sources who you actually want to hear from.”

Meta’s Superintelligence Lab releaseda pair of new state-of-the-art modelson Monday, Muse Wonder and Muse Wonder Full, representing the company’s first attempt at a frontier model. Where Muse Spark aimed to be a lightweight model that could perform at low latency, the Wonder models are larger and said to be more capable, with higher benchmark scores and better results on complex tasks like coding. According to Meta’s testing (presented inthe model card), Wonder and Wonder Full achieve comparable benchmark scores to Fable 5.1 and GPT-6 Astra. “Together, they mark the next step on our scaling ladder and a major leap toward personal superintelligence,” Meta’s blog post about the models reads. That scale is reflected in the pricing: Accessed through the Meta Model API, Wonder will cost $4 per million input tokens and $20 per million output tokens, making it roughly equivalent in cost to GPT-6 Sol or Opus 5.5. Notably, Wonder Full will only be available to Muse Code subscribers and will not be served through the API. Both models will also be used to power the Muse agent. In keeping with Meta’s interest in developing personal assistants that can perform tasks over a long period, the company placed particular emphasis on the new models’ ability to retain and synthesize information given by the user, a property Meta calls “active memory.” To achieve better recall, Meta highlightedthe MemoryBench benchmark, which challenges continual learning and memory construction frameworks. According to the company’s testing, Muse Wonder now scores higher on MemoryBench than Fable, Opus, or Astra. “We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” said research scientist Kavya Kopparapu in a video accompanying the launch. The new models are the first frontier model release since Scale AI founder Alexandr Wang restructured Meta’s AI effortsin August of last year. Since then, Meta has released its lightweight Muse Spark model andMuse agentbut has avoided the kind of state-of-the-art model releases OpenAI and Anthropic are known for. According to the post, the models were trained at Meta’sPrometheus data center in central Ohio, which came online earlier this year.
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