2026-04-23 05:24:40
SpaceX didn't buy Cursor outright—they structured a $60 billion call option with a $10 billion breakup fee. This financial maneuvering protects SpaceX's massive upcoming IPO while giving xAI the proprietary developer telemetry it desperately needs to compete with OpenAI and Anthropic. For builders, this partnership threatens IDE model neutrality, jeopardizes strict enterprise data privacy agreements, and introduces severe geopolitical compliance liabilities due to Cursor's reliance on the Chinese base model Kimi 2.5.
2026-04-23 05:10:31
Berlin, Germany, April 22, 2026 — Coinlocally today launched 10 new tokenized stock pairs on its trading platform and introduced a zero-fee trading campaign for all newly-listed stock pairs. The new listings include widely recognized companies such as Tesla, Amazon, Apple, NVIDIA, and Alphabet.
Starting on April 14, users can trade TSLAX, COINX, AMZNX, AAPLX, NVDAX, GOOGLX, MCDX, HOODX, METAX, and CRCLX against USDT with zero trading fees through May 14, 2026. This new group of listings gives users exposure to some of the most closely Marco watched names across technology, consumer internet, and digital finance, while keeping that access within Coinlocally’s existing trading environment.
Tokenized real-world assets (RWAs) continue to grow across the digital asset market, with more than $26 billion in distributed on-chain value. At the same time, interest in tokenized equities has been building as more companies look at blockchain-based versions of traditional financial products. Coinlocally’s new listings arrive as tokenized stocks begin to attract wider attention from both crypto platforms and traditional market infrastructure players.
“We want users to be able to access newly-listed tokenized stock markets without extra cost during the launch period,” said Sam Baumann, COO at Coinlocally. “Listing these pairs with zero-fee trading is a practical way to make the product easier to try and more accessible to a wider range of traders.”
The rollout reflects Coinlocally’s broader strategy of connecting traditional market exposure with digital asset trading. The platform supports more than 600 digital assets across spot, margin, and futures markets, with tools for both retail and professional users. The new tokenized stock pairs expand that offering by bringing another set of familiar market names onto the platform.
Coinlocally has also been building out a wider product ecosystem beyond its main trading markets. In addition to spot and derivatives trading, the platform offers services such as P2P trading, Earn, Launchpad, and educational resources aimed at users with different levels of experience. Within that broader mix, the new stock pairs give users another way to access tokenized versions of traditional assets without leaving the platform.
Users can visit Coinlocally’s trading platform to explore the newly listed tokenized stock pairs and start trading with zero fees.
Founded in 2020, Coinlocally is a global fintech and digital asset exchange offering secure, fast, and transparent access to cryptocurrency and forex markets. With high liquidity and advanced trading tools, including spot, futures, bot trading, grid strategies, and copy trading, the platform serves both beginners and professional traders worldwide.
Coinlocally’s mission is to bridge traditional finance with the emerging world of decentralized finance, empowering users with greater control of their assets through a compliance-driven, seamless transition from centralized (CEX) to decentralized (DEX) trading and broader Web3 innovation.
For more information, users can visit coinlocally.com or follow Coinlocally on Telegram or X.
:::tip This story was published as a press release by Blockmanwire under HackerNoon’s Business Blogging Program
:::
Disclaimer:
This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR
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2026-04-23 04:00:47
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2026-04-23 03:26:18
There’s a moment every engineer who builds chatbots eventually confronts: the system works perfectly in the demo environment, and then real users show up.
Real users don’t speak in clean, grammatically correct sentences. They interrupt themselves. They abbreviate. They ask two questions in one message and forget they already told the bot their account number twenty seconds ago. They expect the system to remember context from a conversation they had three weeks back, because a human would.
That gap between designed behavior and real-world behavior has been Vijay Kumar Sridharan’s professional preoccupation for the better part of fifteen years. He’s currently a Vice President of Software Engineering at Goldman Sachs, where he leads Conversational AI initiatives at the scale you’d expect from one of the world’s largest financial institutions. But the work that shaped how he thinks about these problems started long before Goldman — specifically, during a pandemic-era crisis at OneMain Financial that forced him to build AI systems that actually held up.
When engineers talk about what makes chatbots difficult, the conversation usually turns to natural language understanding, intent classification, entity extraction — the standard NLP stack. These are hard problems. But Vijay’s view is that they’re not the hardest ones.
The hard problem is that most chatbots are built to handle the company’s information architecture, not the user’s mental model. You end up with a system that’s organized the way the product team thinks about their product. And the user has no idea how the product team thinks about anything. They just have a problem they need help with.
This distinction — between a bot that is technically capable and one that is actually useful — sits at the center of a lot of his published research. In his work on LLM-based chatbot development, he’s argued that the arrival of large language models has shifted the bottleneck. The NLP problem is largely solved for most enterprise use cases. What hasn’t caught up is the systems thinking: how do you structure a conversational AI system so that it serves the user’s journey rather than just answering inputs in isolation?
It sounds abstract until you see what it means in practice.
In 2020, Vijay was leading engineering at OneMain Financial, the largest personal installment loan company in the United States. When COVID-19 hit, branches restricted operations, call centers were overwhelmed, and customers still needed to manage their loans — deferring payments, understanding options, getting answers — around the clock.
The chatbot infrastructure was nowhere near ready for what was now being asked of it. Two prior development efforts had already stalled. Vijay was tasked with not just resuming those efforts, but delivering a production-grade multi-channel solution: mobile app, web interface, and SMS — simultaneously.
What we inherited was a set of failed approaches and a lot of speculation about why they failed. One of the first things we did was go back through the user interaction logs from the earlier attempts. The patterns were actually pretty clear. The bots were failing on anything outside a narrow set of expected phrasings. Customer intent was ambiguous, and the system had no graceful way to handle that ambiguity. It would just error out or give a generic fallback.
The solution required rethinking the NLP pipeline from the intent layer up. Vijay’s team built classification models that were trained not just on clean examples but deliberately on edge cases — the misspellings, abbreviations, and fragmented inputs that showed up in actual logs. They built escalation paths that felt like assistance rather than dead ends. And they designed context management that could maintain relevant session state across the kinds of interruptions and tangents that real users introduce.
The key architectural decision was around failure handling. Most chatbots, when they hit an intent they can’t classify confidently, either give a generic “I don’t understand” response or loop the user back to the main menu. Both feel like walls. Vijay’s team instead built graduated confidence thresholds — if the model was uncertain, the system would ask a clarifying question that moved the conversation forward rather than resetting it. The user experienced a bump, not a dead stop.
The operational impact was significant — call volume shifted, response times dropped, and the system absorbed peak pandemic demand that would have been unmanageable for human agents alone. But the metric that mattered most internally wasn’t about efficiency. It was adoption. Users kept coming back to it. That doesn’t happen with a system that frustrates people the first time they try it.
If the chatbot work was about redesigning a system under time pressure, the project that followed was about something different: the conviction to challenge an assumption that had already been accepted as fact.
OneMain needed a way for customers to submit check payments through their mobile devices, and for branch staff to process checks and documents via tablets. The project had been in development for seven months and was effectively stuck. Senior engineers had concluded that certain constraints in the existing architecture made the solution technically unfeasible.
Vijay disagrees with that framing now, and he disagreed with it then.
‘Technically unfeasible’ usually means ‘unfeasible given our current assumptions.’ When I dug into what had actually been tried, the team had been trying to build the solution on top of the existing architecture rather than alongside it. That was the constraint. Not the technology — the design choice.
He redesigned the solution from a different starting point — building alongside the existing architecture rather than on top of it. The result, known internally as CheckCapture, shipped and hit its performance targets. The document upload integration that accompanied it, spanning Kofax Total Agility, Mobius, and ELF (Electronic Loan Folder), handled the broader workflow of digital document ingestion that the mobile solution unlocked. Payment processing that had taken days was now measured in hours.
The company recognized the work. The mental model it reinforced mattered more to Vijay than the recognition. When a problem is declared unsolvable, the first question should be: which assumptions are making it so?
Alongside his engineering work, Vijay has built a sustained research track that engages questions he doesn’t have room to fully address inside a single product sprint.
His published work spans several interconnected themes: the evolution of NLP and LLMs in customer service contexts, the architecture of adaptive chatbot systems, the design of IVR (interactive voice response) systems using modern AI, and the specific challenges of deploying large language models in regulated financial environments.
The adaptive intelligence work is the thread he returns to most. His core argument is that most current chatbot systems are stateless in a way that fundamentally limits their usefulness — every conversation starts from zero, every user is a stranger, every session loses the context of what came before.
There’s a meaningful difference between a system that can answer your question and a system that knows you well enough to anticipate it. The first is a search engine with a conversational interface. The second is something closer to a trusted assistant.
The architecture required to bridge that gap involves persistent user modeling — building and maintaining representations of individual user preferences, communication styles, and behavioral patterns across sessions. The technical challenges are real: privacy requirements, computational cost, the difficulty of inferring stable preferences from noisy data. None of these are unsolved, but they require deliberate design decisions that most organizations haven’t yet made.
His research on IVR systems addresses a slightly different dimension of the same problem. IVR is a technology most people have experienced as frustrating — the phone menus that require you to navigate a tree of options designed around the company’s internal structure rather than any human’s actual request. Vijay’s work explores how LLM-based speech understanding can replace that rigid structure with something more responsive to how people actually speak when they call for help.
This research has been recognized by the engineering community. He holds Senior Membership in IEEE — a peer-evaluated designation that requires demonstrated contributions to the field. He’s a member of IAENG and has served as a judge for the Globee Customer Excellence Awards, evaluating submissions from across the fintech and enterprise software sector.
Being asked to evaluate others’ work at that level is its own kind of signal. It means the field trusts your judgment.
Vijay joined Goldman Sachs in 2022. The scale and the regulatory environment are different from what he’d navigated before, and that difference turns out to matter enormously for how you build AI systems.
At a consumer lending company, the consequences of a bad chatbot interaction are real but bounded. At a major investment bank, the surface area of what can go wrong is substantially larger — and the regulatory scrutiny is correspondingly more intense. The conversational AI systems Vijay oversees have to operate not just under technical performance constraints but under compliance frameworks that touch every interaction.
In finance, the cost of being confidently wrong is much higher than in, say, a retail recommendation system. If a chatbot suggests the wrong product or provides inaccurate information about a financial instrument, that’s not just a bad user experience. It’s a regulatory and trust issue.
This has shaped his thinking on a specific problem that the broader AI industry hasn’t fully reckoned with yet: LLMs are very good at sounding right even when they aren’t. The fluency that makes them impressive in demos is the same quality that makes them dangerous in production environments where accuracy is non-negotiable.
His approach involves building what he calls verification layers — mechanisms that sit between the model’s output and what actually reaches the user. These aren’t simple rule filters. They’re architecture decisions about where in the pipeline you introduce checks, what kinds of outputs you treat as high-risk, and how the system degrades gracefully when confidence is low rather than projecting false certainty.
The model being capable is necessary but not sufficient. The question is how the system behaves when the model reaches the edge of what it knows. That’s where most production deployments either earn trust or lose it.
It’s a problem that financial services is being forced to solve before other industries, because the stakes demand it. The solutions Vijay is developing there will almost certainly find their way into other high-stakes domains — healthcare, legal, insurance — as those industries start deploying LLMs at the same scale.
There’s a version of the current AI moment that gets told a lot: the models are extraordinary, the capabilities are expanding fast, the applications are everywhere.
Vijay doesn’t disagree with any of that. But he thinks it misses something.
The story about how good the models are is true. What’s less discussed is how much of the value of these systems depends on the plumbing around them. The retrieval architecture. The context management. The escalation logic. The way the system knows when it doesn’t know. That’s not as exciting to talk about as the model capabilities, but it’s often what determines whether a production deployment actually works.
His research and his engineering work are both aimed, in different ways, at making that plumbing better. Not just more capable, but more reliable — because in the industries where these systems are being deployed now, the bar for reliability is high.
The gap between a chatbot that works in controlled conditions and one that works for real users, at scale, in a financially consequential context — that’s the gap he’s been working on since long before the current AI cycle began. The tools are better now. The gap hasn’t closed.
Ask Vijay what progress looks like, and he gives a very specific answer.
A user calls or chats in with a problem. The system understands what they actually need, not just what they literally typed. It has context on who they are and what’s happened before. It either resolves the issue cleanly, or it gets them to the right person without friction. And the user comes away with the feeling that they were heard.
That’s the standard. It’s not exotic. Most people who’ve worked in customer service would recognize it immediately. The version of that experience that AI systems are currently able to deliver — even the best ones — still falls short of it in meaningful ways.
That gap, more than any specific product or paper or award, is what’s still driving the work.
I’ve been at this long enough to be skeptical of hype and long enough to believe the fundamental problem is worth solving. The financial services industry serves a lot of people who don’t have great alternatives. If the technology can genuinely help — not just technically function, but genuinely help — that matters.
It’s a straightforward statement. But it’s also the kind of thing you only say with conviction if you’ve spent years watching the distance between those two things up close.
Vijay Kumar Sridharan is a Vice President of Software Engineering at Goldman Sachs, where he leads Conversational AI initiatives. He is an IEEE Senior Member, a published researcher in large language models, adaptive chatbot systems, and AI-driven IVR. His prior work at OneMain Financial helped define the company’s digital transformation strategy across chatbot deployment, mobile payment processing, and document automation.
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:::tip This story was distributed as a release by Sanya Kapoor under HackerNoon’s Business Blogging Program.
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2026-04-23 03:16:28
Anthropic's Project Glasswing and Claude Mythos just proved what many of us in enterprise security have quietly feared for years: the attacker's advantage in the AI era isn't theoretical anymore. It's here. And the question isn't whether we should be alarmed. It's whether alarm is enough.
Let me be direct. When I read that Claude Mythos Preview autonomously discovered a 27-year-old vulnerability in OpenBSD, a system practically synonymous with security hardening, without any human guidance after the initial prompt, I didn't feel wonder. I felt the specific dread of someone who has spent years building enterprise data security programs and knows exactly how long patching backlogs sit untouched.
This is not a capability preview. It is a before-and-after moment. And I think the industry is only halfway processing what it means.
1000s zero-days found in weeks
<1% of findings patched at launch
27 yrs oldest hidden bug surfaced
Anthropic was unusually candid about something that deserves more attention: they did not explicitly train Mythos Preview to be a vulnerability discovery engine. These capabilities emerged as a downstream consequence of general improvements in code, reasoning, and autonomy. The same model that helps you write better Python found root-level remote code execution bugs in FreeBSD and critical flaws in every major OS and browser.
That "emergent" framing matters because it means we cannot treat this as an isolated security tool. The next capable model from any frontier lab, whether Anthropic, OpenAI, Google, or a state-sponsored program, will carry these capabilities whether its developers intend it to or not. The window to prepare is not years wide. It may be months.
"The window between a vulnerability being discovered and being exploited has collapsed. What once took months now happens in minutes with AI." - CrowdStrike, Project Glasswing partner
Project Glasswing itself is a genuine and commendable initiative. Bringing AWS, Apple, Cisco, CrowdStrike, Google, Microsoft, and NVIDIA into a coordinated defensive effort with $100M in model credits and $4M in donations to open-source security foundations is exactly the kind of pre-competitive industry coordination we rarely see. But I want to be honest about what it is and what it isn't.
Here is the uncomfortable truth I keep returning to: at the time Anthropic made its announcement, fewer than 1% of the vulnerabilities Mythos had found were patched. Let that settle. The model surfaces thousands of critical flaws across infrastructure the entire internet depends on, and our remediation capacity, largely volunteer-driven open-source maintainers working at human speed, cannot absorb the volume.
This is the Glasswing Paradox. The thing that can see everything cannot fix anything. Vulnerability discovery has always been supply-constrained. AI just eliminated that constraint entirely, without touching the demand side of the equation, which is skilled humans who understand the code well enough to safely remediate what gets found.
From an enterprise data security product perspective, this reshapes the problem statement fundamentally. We have spent years building programs around mean-time-to-detect. We optimized for finding things faster. Now the detection problem is largely solved at the code level, and the bottleneck has shifted entirely to prioritization and remediation velocity. Your CISO's job description just changed without anyone updating the job description.
First: treat open-source dependencies differently starting today. Not eventually. Today. Mythos surfaced ancient vulnerabilities in projects maintained by tiny volunteer teams. If your enterprise depends on OpenBSD, FreeBSD, or any of the hundreds of libraries the Glasswing consortium is scanning, you need direct line of sight into your dependency graph and your patch lag on each node. SBOMs are table stakes. What matters is having a prioritization framework that accounts for the new discovery rate.
Second: prepare for AI-augmented adversaries now, not when you see evidence of it in the wild. Anthropic itself disclosed last year the first documented case of a cyberattack largely executed by AI. A Chinese state-sponsored group used AI agents to autonomously infiltrate roughly 30 global targets, with AI handling the majority of tactical operations independently. The capability that Glasswing is trying to get defenders using first is already in adversarial hands in some form. The asymmetry we need to close is time, not access.
Third, and this is the piece I see least discussed: we need to start treating AI models themselves as part of our threat surface modeling. Not just as tools we use, but as systems that hold credentials, consume APIs, write to production environments, and take autonomous actions. The same autonomy that makes Mythos remarkable at finding bugs makes any sufficiently capable model running in your environment an entity whose permissions, reach, and failure modes have to be governed like any other privileged principal in your stack.
Anthropic has chosen, deliberately, not to release Mythos generally. They are betting that keeping it restricted to trusted partners gives defenders more runway than attackers. I think that's the right call, and I respect the transparency with which they've explained the dual-use calculus. But it is a bet, not a guarantee. Competitors, both domestic and state-sponsored, may not make the same choice. A model that costs billions to train will face enormous pressure toward monetization.
Project Glasswing is a starting gun, not a finish line. The $4M donated to the Apache Software Foundation and OpenSSF through the Linux Foundation is meaningful and symbolically important. But sustaining the human expertise needed to actually fix what AI will keep finding requires the industry to treat open-source maintainers as critical infrastructure workers, with compensation, tooling, and organizational support that reflects that status.
We are right now in the gap between the discovery capability arriving and the remediation capability catching up. What we do in this window will define the security posture of enterprise systems for the next decade. AI just solved the discovery problem. Nobody has solved the fixing problem yet. That's the half that matters.
2026-04-23 03:01:29
The 12% of agents that make it to production and deliver meaningful ROI don't share a common model or a common framework. They started where failure was tolerable, instrumented everything from day one, designed for reversibility before capability, and expanded autonomy incrementally as they earned confidence in the system's behavior. The opportunity in 2026 isn't building smarter agents. The opportunity lies in closing the 68 percent gap between adoption and production. It is not an AI problem but it is a governance problem, an organizational design problem, and an instrumentation problem. The intern doesn't need to be smarter. The intern needs a manager.
