Avelios raises €30M Series B led by Sequoia for hospital data system overhaul - Related to y-combinator-backed, sequoia, underrated, emerges, hospital
3 underrated (HBO) Max movies you should watch this weekend (February 7-9)
Table of Contents Table of Contents Love & Basketball (2000) Final Destination 2 (2003) Triple 9 (2016).
This weekend is one of the few cases where the arts take a backseat to sports. Super Bowl LIX will be played on Sunday, February 9. The Kansas City Chiefs and the Philadelphia Eagles will play for the right to call themselves champions. Over 115 million viewers are expected to watch Super Bowl LIX. More people will watch when you consider that the Big Game draws big crowds around one television.
The Super Bowl is Sunday night at 6:30 [website] ET. Until then, what should you watch? Max has an excellent lineup of prestige dramas, hilarious comedies, action thrillers, and frightening horrors. While the Max homepage is a great resource, many movies are hidden within each genre. The three movies below represent under-the-radar picks that can be equally as satisfying as the noteworthy titles.
We also have guides to the best movies on Netflix, the best movies on Hulu, the best movies on Amazon Prime Video, the best movies on Max, and the best movies on Disney+.
Love & Basketball involves — you guessed it — love and basketball. It sounds like the premise of a Lifetime picture, but Love & Basketball is in a different class than a made-for-TV film. Directed by Gina Prince-Bythewood, Love & Basketball is a heartfelt and captivating look at a relationship from childhood through adulthood.
Childhood friends Monica (Sanaa Lathan) and Quincy (Omar Epps) love basketball more than life itself. Quincy’s father, Zeke (Dennis Haysbert), plays professionally, so Quincy plans to follow in his footsteps. Monica must forge her own path since women’s professional basketball does not yet exist in the United States. As they become teens and young adults, Monica and Quincy fall in and out of love. Are the two willing to sacrifice their dreams to be with each other?
Final Destination 2 (2003) - premonition of a deadly pileup, caused by a semi carrying logs.
After a 14-year hiatus, the Final Destination franchise returns in 2025 with Final Destination Bloodlines. These movies revolve around a select group of people who avoid death after one person has a premonition and warns them about the impending disaster. Essentially, the survivors “cheat” death. However, Death takes no prisoners and comes back to kill each survivor horrifically.
Final Destination 2 is not the top movie of the franchise. However, the opening premonition is arguably the high point of the series. College student Kimberly Corman (A. J. Cook) has a premonition about a deadly car crash on the highway caused by a log truck. Unfortunately for Kimberly and the unlucky survivors, the worst is yet to come.
Triple 9 is the perfect “Dumpuary” movie. It’s an entertaining crime thriller with an overqualified cast. It’s not Heat. It’s more in the Den of Thieves neighborhood, which isn’t a bad place to be. An Atlanta heist crew — including corrupt cops Marcus Belmont (Anthony Mackie) and Franco Rodriguez (Clifton Collins Jr.), former SEALs Michael Atwood (Chiwetel Ejiofor) and Russell Welch (Norman Reedus), and Russell’s brother Gabe (Aaron Paul) — successfully pull off a robbery in broad daylight.
Unhappy with the results, their crime boss, Irina Vlaslov (Kate Winslet), orders the crew to steal from the Department of Homeland Security. Left with no choice, the men plan a Triple 9, or an “officer down” scenario. They pick Marcus’ new partner Chris Allen (Casey Affleck) as their target. However, lies and deception from all parties threaten to foil the heist.
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Table of Contents Table of Contents The Replacements (2000) The Waterboy (1998) My All American (2015).
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Avelios raises €30M Series B led by Sequoia for hospital data system overhaul
Munich healthtech startup Avelios has raised €30 million Series A (€30m) funding led by Sequoia to advance its efforts in developing its hospital data operating system.
Most hospitals run on data centres developed in the ‘90s, if not earlier. Data is unstructured; interoperability is non-existent; UIs are anything but user-friendly.
The consequences, in terms of both inefficiencies and patient outcomes, are severe. Yet despite decades of technological development, hospital information systems (HIS) remain undisrupted because they are incredibly difficult to build.
The founders of Avelios have first-hand experience with these challenges.
During the height of COVID, Dr Sebastian Krammer spent valuable time counting patients by hand, then reporting the results to authorities via fax.
When he and Nicolas Jakob, a software engineer and deep learning expert, tried to expand their promising research on classifying skin conditions with AI, they quickly discovered hospitals’ antiquated systems couldn’t provide the needed data.
So they teamed up with Christian Albrecht, a McKinsey alum and previous co-founder with Nicolas, to solve the problem.
Instead of “treating the symptoms” by building on top of outdated legacy software, they decided to take the hard path of “treating the cause” and tackle the problem at its core by building a completely new HIS.
Over the last couple of years, Avelios has built a full hospital information system that inclu:
Documentation such as EHR, medication, and operating room management.
Administration: billing, staff, clinic management.
They took a modular approach so they could land in hospitals with one offering before expanding to the full operating system.
. Avelios epitomises “the right team, with the right product, at the right time.”.
Fortuitously, one of the leading HIS providers showcased they would sunset their product in 2027, creating an urgent need for more than 1,000 hospitals to replace their systems.
Further, regulators in Germany and elsewhere responded to problems revealed by COVID-19 with both fines for failing to digitise and funding for modernisation — Germany’s Hospital Future Act, for example, dedicates €[website] billion to this.
Additionally, demand for AI exploded, spotlighting the difficulties of accessing data and integrating with legacy hospital systems — exactly the challenges that Avelios was designed to solve.
As a result, the startup has already won contracts with several of Germany’s largest public and private [website] statement by Sequoia on its website shared:
“When we met the team, we were deeply impressed by their ambition, the depth and breadth of the product, the end-people they’d won and their revenue growth—and more importantly, that they achieved all of this with just Pre-Seed funding. Fortunately for us, Christian, Nicolas, and Sebastian agreed to a partnership, and we are proud to lead this Series A round as they and their fast-growing team work toward their goal of revolutionising healthcare."
With Avelios, doctors and staff will have the system of record and AI tools they need to make improved, more informed decisions. They’ll be able to focus on why they got into medicine in the first place—helping people.
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Y-Combinator-backed Model ML emerges from stealth with $12M funding
AI fintech platform Model ML today emerged from stealth with $12 million in funding.
Model ML is founded by brothers Chaz and Arnie Englander – serial entrepreneurs who transformed the sharing economy with Fat Llama (Hygglo) and instant delivery with Fancy (GoPuff) backed by Y-Combinator three times, with two successful acquisitions from their previous companies.
The enterprise aims to redefine how professionals worldwide harness data and drive innovation.
Initially built for financial services but quickly moving into consulting and strategy teams, Model ML helps professionals make high-stake decisions faster and smarter by removing the grunt work.
The ability to bring all -value tasks that drain resources, hinder growth, and cost billions annually.
Despite AI's promise, traditional tools have failed to make an impact because 're not designed for the siloed systems and processes in play. They can't meet the demands for precision, compliance, and security, nor do they truly understand the challenges professionals face.
Model ML's platform is akin to building a bespoke AI brain for each organisation, and each AI agentic system is custom-built for individual clients.
The platform slots into existing workflows, only pulling and processing data from a corporation's bank of trusted data.
Using the platform's voice-first interface on a mobile or desktop, professionals can query the data and insights and automate the creation of spreadsheets, reports, and dashboards instantly. This represents a transformation in human-computer interaction where you'll simply speak to your device to accomplish what once required hours of manual work.
While in stealth, Model ML went live with over 20 end-consumers, including some of the largest financial institutions in the world. Early end-consumers study improved efficiency, with some rewriting their business strategies based on the platform's impact.
Chaz Englander, co-founder and CEO of Model ML, stated: "Traditional software requires manual data gathering for emails, financial models, and presentations—but that's changing.
Model ML has rebuilt these applications from the ground up on top of advanced AI agentic systems, eliminating the need for manual data collection.
Led by YC and LocalGlobe, the round also included angel investments from some of the biggest names in banking and private equity.
Ziv Reichert, Partner at LocalGlobe, noted:
"We're rapidly moving toward a world where any single worker can do far more, far faster. Less than a year in, Model ML is already transforming the way the organisations it serves operate—a testament to Chaz and Arnie's approach to building: an obsession with the customer and a relentless drive for velocity."
Model ML was founded in the UK but headquartered in New York, with offices in Hong Kong and London, where its engineering team is based. The organization will use the funding to scale its product, expand its customer base, and continue transforming financial workflows.
Lead image: Model ML. Photo: uncredited.
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Market Impact Analysis
Market Growth Trend
| 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|
| 12.0% | 14.4% | 15.2% | 16.8% | 17.8% | 18.3% | 18.5% |
Quarterly Growth Rate
| Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 |
|---|---|---|---|
| 16.8% | 17.5% | 18.2% | 18.5% |
Market Segments and Growth Drivers
| Segment | Market Share | Growth Rate |
|---|---|---|
| Digital Transformation | 31% | 22.5% |
| IoT Solutions | 24% | 19.8% |
| Blockchain | 13% | 24.9% |
| AR/VR Applications | 18% | 29.5% |
| Other Innovations | 14% | 15.7% |
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity:
Competitive Landscape Analysis
| Company | Market Share |
|---|---|
| Amazon Web Services | 16.3% |
| Microsoft Azure | 14.7% |
| Google Cloud | 9.8% |
| IBM Digital | 8.5% |
| Salesforce | 7.9% |
Future Outlook and Predictions
The Underrated Movies Should landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:
Year-by-Year Technology Evolution
Based on current trajectory and expert analyses, we can project the following development timeline:
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:
Innovation Trigger
- Generative AI for specialized domains
- Blockchain for supply chain verification
Peak of Inflated Expectations
- Digital twins for business processes
- Quantum-resistant cryptography
Trough of Disillusionment
- Consumer AR/VR applications
- General-purpose blockchain
Slope of Enlightenment
- AI-driven analytics
- Edge computing
Plateau of Productivity
- Cloud infrastructure
- Mobile applications
Technology Evolution Timeline
- Technology adoption accelerating across industries
- digital transformation initiatives becoming mainstream
- Significant transformation of business processes through advanced technologies
- new digital business models emerging
- Fundamental shifts in how technology integrates with business and society
- emergence of new technology paradigms
Expert Perspectives
Leading experts in the digital innovation sector provide diverse perspectives on how the landscape will evolve over the coming years:
"Technology transformation will continue to accelerate, creating both challenges and opportunities."
— Industry Expert
"Organizations must balance innovation with practical implementation to achieve meaningful results."
— Technology Analyst
"The most successful adopters will focus on business outcomes rather than technology for its own sake."
— Research Director
Areas of Expert Consensus
- Acceleration of Innovation: The pace of technological evolution will continue to increase
- Practical Integration: Focus will shift from proof-of-concept to operational deployment
- Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
- Regulatory Influence: Regulatory frameworks will increasingly shape technology development
Short-Term Outlook (1-2 Years)
In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing digital innovation challenges:
- Technology adoption accelerating across industries
- digital transformation initiatives becoming mainstream
These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.
Mid-Term Outlook (3-5 Years)
As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:
- Significant transformation of business processes through advanced technologies
- new digital business models emerging
This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.
Long-Term Outlook (5+ Years)
Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:
- Fundamental shifts in how technology integrates with business and society
- emergence of new technology paradigms
These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.
Key Risk Factors and Uncertainties
Several critical factors could significantly impact the trajectory of digital innovation evolution:
Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.
Alternative Future Scenarios
The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:
Optimistic Scenario
Rapid adoption of advanced technologies with significant business impact
Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.
Probability: 25-30%
Base Case Scenario
Measured implementation with incremental improvements
Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.
Probability: 50-60%
Conservative Scenario
Technical and organizational barriers limiting effective adoption
Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.
Probability: 15-20%
Scenario Comparison Matrix
| Factor | Optimistic | Base Case | Conservative |
|---|---|---|---|
| Implementation Timeline | Accelerated | Steady | Delayed |
| Market Adoption | Widespread | Selective | Limited |
| Technology Evolution | Rapid | Progressive | Incremental |
| Regulatory Environment | Supportive | Balanced | Restrictive |
| Business Impact | Transformative | Significant | Modest |
Transformational Impact
Technology becoming increasingly embedded in all aspects of business operations. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.
The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.
Implementation Challenges
Technical complexity and organizational readiness remain key challenges. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.
Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.
Key Innovations to Watch
Artificial intelligence, distributed systems, and automation technologies leading innovation. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.
Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.
Technical Glossary
Key technical terms and definitions to help understand the technologies discussed in this article.
Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.
API beginner
How APIs enable communication between different software systemsinterface intermediate
fintech intermediate
platform intermediate
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