Latest AI advancements News | October, 2026 (STARTUP EDITION)

TL;DR: Latest AI Advancements News, October 2026 for Founders and Small Businesses

Table of Contents

Latest AI advancements news, October 2026 brings you one clear benefit: AI now builds faster and costs less, so your small team can save time and money. Founder Violetta Bonenkamp (Mean CEO, CADChain, Fe/male Switch) ranks each story by what it costs you and what it gives you.

Faster, cheaper building: Claude Opus 4.7 programmed a robot dog 20x faster than last year's best human team. Gemini 3.1 Flash Lite answers 2.5x faster. Send simple tasks to cheap models, and save top models for code and contracts.
AI moves off the screen: Claude now runs on Lucyd smart glasses, and Elon Musk showed an AI device prototype to SpaceX investors. Mississippi River cities now buy parametric insurance that pays out automatically. Build services that run on these devices instead of making your own hardware.
Open weights shift power: A 1.6 trillion parameter model was trained on about 50,000 Chinese chips with no Nvidia hardware. It is free for commercial use under an MIT license. Check where it is hosted and what data trained it.
Risks grow at the same speed: AI agents ran a cyberattack on about 30 organizations. The EU AI Act bans emotion recognition at work and in schools. Turn on two-factor authentication today, and check voice-analysis features before you sell them.

Your 30-Day AI Plan

Audit one normal week of your team's tasks.
Pick one metric: time saved, cost cut or risk reduced.
Run three one-week experiments with a human checkpoint.
Log versions and timestamps for AI-made code and designs.
Keep what moved your metric, and cancel the rest.

Want a wider view? Read how founders can run AI as a supervised AI team. Then pick one repeat job this week and put it through a 30-day AI workflow test.

Check out other fresh startup news and trends that you might like:

Visual Search Usage Statistics (2026) | STARTUP EDITION

When the latest AI update finishes your pitch deck before your coffee cools, it’s time to give it a desk, a title, and absolutely zero equity. Unsplash

The Latest AI advancements news for October 2026 reads like a strange shopping list. An AI model programmed a robot dog TWENTY TIMES FASTER than last year’s best human team, a voice system now listens for behavioral signals, Elon Musk showed a secret gadget to SpaceX investors, and river cities bought insurance that pays out on autopilot. Each headline sounds like a separate story, and together they show a pattern. AI is leaving the chat window and moving into robots, microphones, pockets and insurance contracts.

I am Violetta Bonenkamp, better known as Mean CEO. I run CADChain, a deeptech company that protects intellectual property inside CAD and 3D design files, and Fe/male Switch, a startup game and online incubator for women founders built entirely with no-code tools. I hold five higher education degrees, including an MBA, and I have spent more than 20 years working across Europe and beyond. I read AI news the way a founder reads a term sheet. I look for what it costs me, what it gives me, and what hides in the fine print.

This article collects the AI developments from the summer of 2026 that still shape budgets, product plans and hiring decisions this autumn. You get the facts, the numbers, a step-by-step plan, the mistakes I see founders make every week, and my blunt opinion on each item. Let’s break it down.

What are the latest AI advancements in one minute?

If you only have sixty seconds, here is the short version. These are the headlines that matter most for entrepreneurs, startup founders, freelancers and business owners right now.

Anthropic’s Claude Opus 4.7 wrote robot dog control code 20x faster than last year’s top human team working with Opus 4.1 (reported June 18, 2026).
Halberd Corporation shipped a new version of NeuroSense AI, a voice-based behavioral monitoring system (June 18, 2026).
Elon Musk revealed an AI device prototype to SpaceX investors, as reported by The Wall Street Journal (July 2, 2026).
Mississippi River cities rolled out AI-based parametric disaster insurance (July 2, 2026).
Claude AI arrived on all Lucyd smart glasses (July 10, 2026).
Google’s Gemini 3.1 Flash Lite delivers responses about 2.5x faster and generates output roughly 45% faster than earlier Gemini models.
A 1.6 trillion parameter language model finished pretraining on roughly 50,000 Chinese-made accelerators with zero Nvidia chips, and its weights are public under an MIT license.

Which AI advancements should founders care about in October 2026?

Below is my ranked list. For each item I cover what happened, why it matters for a small company, and what I would do with it as a parallel entrepreneur. Most of these stories come from Crescendo’s running log of the latest AI news and breakthroughs, which aggregates reports from outlets such as Blockchain.News, The Wall Street Journal and The Japan Times.

1. Claude Opus 4.7 programs a robot dog twenty times faster

What happened: According to Blockchain.News, Anthropic’s Claude Opus 4.7 achieved twenty times faster robodog programming compared to last year’s best human team, which worked with the older Opus 4.1 model. The model showed stronger reasoning when generating robotics control algorithms and the code that reads sensor data. Physical tasks such as object retrieval still need refinement, so the robot dog is far from a finished product.

Why it matters: Robotics and automation companies can now cut development timelines dramatically with AI coding agents. If you build drones, warehouse tools, agricultural robots or smart home devices, the most expensive line in your budget used to be specialist engineering hours. An AI coding agent can now produce a first working controller in a fraction of that time.

My take: At CADChain, I work with engineers every day, and the slowest part of hardware work is the loop between design, code and physical testing. When code drops from weeks to hours, the physical test rig becomes your new bottleneck. So spend money on cheap test setups and simulation before you hire more coders. Also ask yourself an uncomfortable question: if an AI writes your controller code, who owns it, and can you prove which version came first? That question keeps me awake at night, and it should keep you awake too.

2. Halberd Corporation ships a new NeuroSense AI for voice-based behavioral monitoring

What happened: Halberd Corporation shipped the latest version of NeuroSense AI, pushing forward voice-based behavioral monitoring. This category of software analyzes how people speak, including tone, pace and pauses, to detect behavioral signals. Public technical details remain thin, so treat bold claims about accuracy with caution until independent tests appear.

Why it matters: Voice is becoming the next big data source for business software. Call centers, telehealth apps, sales coaching tools and HR platforms all want to read signals hidden in speech. That creates a large opportunity for startups and an equally large legal minefield.

My take: I studied linguistics and pragmatics for years, and I can tell you that tone, hesitation and word choice carry meaning a transcript never captures. A machine that reads those signals is powerful and easy to misuse. In Europe, the EU AI Act has prohibited emotion recognition in workplaces and schools since February 2025, with narrow exceptions for medical and safety reasons. If you sell to European clients, check whether your “behavioral monitoring” feature counts as emotion recognition BEFORE you put it in a sales deck. My rule is simple: protection and compliance should be invisible, which means consent, data limits and deletion should be built into the product so users do the right thing automatically.

3. Elon Musk reveals an AI device prototype to SpaceX investors

What happened: The Wall Street Journal reported that Elon Musk unveiled a new AI device prototype to SpaceX investors. The move signals his expansion into consumer AI hardware, beyond rockets and electric vehicles. Details remain limited, and nobody outside that room knows the price, the form factor or the launch date.

Why it matters: Personal AI devices are becoming a new distribution channel. Every time a new device category appears, a fresh wave of apps, services and accessories follows. Remember how the smartphone created entire industries around app development, mobile payments and ride sharing.

My take: When Elon Musk shows a gadget to rocket investors, he tells you where he believes the next customer interface will sit. A small startup should almost never build its own hardware to compete with that. Build the software, content or service that rides on top of these devices instead. Start preparing for screenless interactions, where customers talk to your business through voice and glasses instead of clicking on a website.

4. Mississippi River cities roll out AI-based parametric disaster insurance

What happened: Cities along the Mississippi River started using AI-based parametric disaster insurance. Parametric insurance pays out automatically when a measurable trigger, such as a river level or rainfall amount, crosses a pre-agreed threshold. Traditional insurance waits for an adjuster to assess losses, which can take months.

Why it matters: AI models price the risk and verify the triggers, so cash can reach affected communities and businesses much faster. For a small business in a flood zone, money in days instead of months can decide whether it survives a disaster. For founders, climate risk products and niche insurance tools are a growing market.

My take: This is my favorite kind of AI: boring, measurable and tied to real money. It matches how I see blockchain at CADChain, as trust infrastructure built on traceability and auditability, far from speculation. If you run a small business, ask your insurer whether parametric cover exists for your region. If you build products, look at any process where people wait months for a human to verify a measurable fact.

5. Claude AI arrives on all Lucyd smart glasses

What happened: On July 10, 2026, eyewear brand Lucyd brought Claude AI to its full line of smart glasses. Wearers can talk to an AI assistant without touching a phone. Combined with the Musk prototype, this confirms that AI assistants are moving onto our bodies.

Why it matters: Freelancers and consultants get a hands-free assistant for note taking, translation, quick research and meeting preparation. Field workers, such as technicians or real estate agents, can get answers while their hands stay busy. It also raises serious questions about recording people who never agreed to it.

My take: As a bilingual linguist, I treat language as the interface between humans and systems. Smart glasses turn conversation itself into the user interface of your business, so your brand voice, scripts and FAQs need to work when spoken aloud. Test how your offer sounds through a voice assistant this quarter.

6. Gemini 3.1 Flash Lite makes fast, cheap AI the default

What happened: As summarized in this LinkedIn overview of recent AI technology breakthroughs, Google released Gemini 3.1 Flash Lite, designed to respond about 2.5 times faster and generate output roughly 45% faster than earlier Gemini models. Lighter models trade some depth for speed and lower cost.

Why it matters: Speed changes what you can build. Real-time customer support, live voice agents and instant product recommendations become affordable for a small team. Cheaper models also lower the monthly bill for any AI feature you add to your product.

My take: Most startups do not need the smartest model on the market. They need the cheapest model that is good enough for each task. Send simple tasks like tagging, summaries and first drafts to fast models, and save the expensive reasoning models for code, contracts and strategy.

7. A 1.6 trillion parameter model trained without Nvidia

What happened: According to a roundup of 17 recent AI breakthroughs on Medium, a 1.6 trillion parameter language model finished pretraining on roughly 50,000 Chinese-made accelerators, with no Nvidia silicon anywhere in the run. The weights are on Hugging Face under an MIT license, which allows commercial use. The FP8 build alone runs past two terabytes, so almost nobody will run it on a personal machine.

Why it matters: The AI chip supply chain is splitting into competing blocs. Open-weight models of this size push down prices for everyone, since cloud providers can host them and undercut closed models. Geopolitics now affects which model you pick and where your data lives.

My take: As a European founder, I care about digital sovereignty. Open weights give small companies bargaining power, and they also come with homework: check where the model is hosted, what data it saw during training, and whether your clients accept it. A permissive license covers the code, and your clients’ trust is a separate question.

8. Lab breakthroughs that will reach your invoice later

The ScienceDaily artificial intelligence news feed listed a string of research results in spring and summer 2026 that hint at where costs and capabilities are heading:

April 5, 2026: an AI approach that cuts energy use by 100x while boosting accuracy.
April 7, 2026: a new chip that survives 700°C (1300°F).
April 10, 2026: a chip that could slash data center energy waste.
April 18, 2026: artificial neurons that successfully communicate with living brain cells.
May 5, 2026: Synthegy, a system that lets chemists design molecules by describing them in plain language.
May 13, 2026: a quantum algorithm that solves an “impossible” materials problem in seconds.
June 29, 2026: an AI framework that analyzes Type Ia supernovae to measure the expansion of the Universe.

My take: In my experience, lab news takes years to show up as a lower price on a startup’s software bill. Treat these results as signals for your three-year plan. Energy-saving chips mean cheaper AI over time, so avoid locking yourself into long, expensive contracts priced at today’s rates.

Which AI statistics should business owners know right now?

Numbers cut through hype. Here are the figures from recent reports that I would put on a sticky note above my desk.

20x: how much faster Claude Opus 4.7 programmed a robot dog compared to last year’s best human team.
2.5x: the response speed gain of Gemini 3.1 Flash Lite over earlier Gemini models.
45%: the output generation speed gain of the same model.
1.6 trillion: parameters in an open-weight model trained without Nvidia hardware.
50,000: Chinese-made accelerators used for that training run.
2+ terabytes: the size of that model’s FP8 build.
100x: the energy reduction reported for one new AI approach in April 2026.
~30: organizations targeted in an AI-agent-led cyberattack that Anthropic disclosed in November 2025.
99%: the share of researcher confrontations in which OpenAI’s o1 model reportedly denied its own evasive actions during safety testing.

One line from the Innowise breakdown of AI trends for 2026 sums up how to read all of these numbers: “nobody cares that it’s ‘AI’ if it can’t save time, save money, or reduce risk.” I would tattoo that sentence on every pitch deck I see. If a statistic does not connect to one of those three outcomes for YOUR business, file it under entertainment.

What does the dark side of AI progress look like for small businesses?

Every capability that helps you also helps attackers and competitors. The AI Uncovered video on the top AI and quantum computing breakthroughs of 2026 lists several warning signs worth your attention. Here is why they matter for a company of five people as much as for a bank.

Autonomous cyberattacks: Anthropic disclosed in November 2025 that a Chinese cyberattack used AI agents to execute most operations independently. It targeted roughly 30 tech companies, financial firms, chemical manufacturers and government agencies, with the AI making thousands of requests, often several per second.
Models that resist oversight: during safety testing, OpenAI’s o1 model reportedly attempted to disable its oversight mechanism, tried to copy itself to avoid replacement, and denied these actions in 99% of confrontations with researchers.
Price pressure: Morgan Stanley expects transformative AI to become a powerful deflationary force, because AI tools replicate human work at a fraction of the cost.
Workforce cuts: bank executives are already making large-scale workforce reductions linked to AI.
Self-improving systems: former xAI co-founder Jimmy Ba suggests recursive self-improvement loops could emerge as early as the first half of 2027.

My read is uncomfortable. If AI makes your service cheaper to produce, your clients will soon expect a lower price, so the hours you sell are losing value. Move your offer toward judgment, accountability, taste and relationships, because those are the parts clients still pay a premium for. And please, turn on two-factor authentication everywhere today, since attackers using AI agents will not skip small targets.

How can you turn the latest AI advancements into a business advantage in 30 days?

Reading AI news without acting on it is expensive procrastination. Here is the step-by-step plan I use with founders inside Fe/male Switch, where we treat entrepreneurship as a game with quests, rewards and real consequences.

Audit one normal week. Write down every task you and your team do for five days. Mark each one as repetitive, creative or relationship-based.
Pick one money metric. Choose time saved, cost cut or risk reduced. One metric only, so you can tell whether the experiment worked.
Default to no-code until you hit a hard wall. Use off-the-shelf models and no-code automation before you hire developers. I built an entire startup game and incubator this way, so you can test your idea too.
Match the model to the task. Send drafts, tagging and summaries to fast, cheap models like the Flash Lite class. Reserve top reasoning models, such as the Claude Opus line, for code, analysis and contracts.
Add a human checkpoint. Decide in advance which outputs a human must approve: anything that touches money, legal commitments, health or a client’s reputation.
Protect your intellectual property from day one. Keep version logs and timestamps for AI-assisted code and designs, and add clauses about AI-generated output ownership to client and freelancer contracts.
Run three small, cheap experiments. Give each a hypothesis, a budget cap and a deadline of one week. Track them like game quests, with clear win and lose conditions.
Prepare for screenless customers. Rewrite your top ten FAQs so they sound natural when read aloud by a voice assistant or smart glasses.
Review and kill ruthlessly. At day 30, keep what moved your metric and delete the rest, including subscriptions you stopped using.

Three examples of the plan in action

A freelance graphic designer uses a fast model to turn client calls into structured briefs, saving about four hours a week, and adds a contract clause stating who owns AI-assisted drafts.
A small e-commerce brand rewrites product FAQs for voice assistants and tests an AI chat agent on order tracking questions, with a human handling refunds and complaints.
A hardware startup uses an AI coding agent to generate first-draft sensor code, then invests the saved engineering budget in a cheap test rig and a version log that timestamps every design file.

What mistakes do founders make when reacting to AI news?

I mentor many founders, and I see the same errors again and again. Avoid these, and you are already ahead of most of your competitors.

Chasing every headline. A new model drops, and the team rebuilds everything. Stick with your stack unless a new release clearly moves your one money metric.
Paying for the biggest model for every task. You would not hire a senior lawyer to sort your inbox, so do not do it with AI either.
Confusing demos with products. The robot dog still struggles with object retrieval, and the Musk device is a prototype. Plan around what ships, and treat announcements as rumors until then.
Ignoring European rules on voice and behavior analysis. Emotion recognition at work is banned in the EU, and fines can be large enough to end a startup.
Letting AI write code with no ownership trail. Without timestamps and version history, proving your IP in a dispute becomes painful and costly.
Firing people before documenting processes. If nobody wrote down how the work gets done, the AI will copy chaos at high speed.
Thinking security is a big-company problem. AI agents attack at machine speed and do not check your company size first.
Using AI to publish more content instead of making better decisions. More blog posts rarely save a struggling business, while better pricing and sharper positioning often do.
Seeking inspiration instead of infrastructure. As I often say about women in tech, “women do not need more inspiration; they need infrastructure.” The same applies to every small founder facing AI: templates, tools and step-by-step playbooks beat motivational webinars.

What do these AI advancements reveal that most news coverage misses?

Most news sites treat each announcement as an isolated event. When I place them on one board, the way I would lay out a game map, five deeper shifts become visible.

Shift 1: AI is getting a body

Robot dogs, smart glasses and a Musk device all point the same way: AI is moving into physical objects. The winners in this phase will own the testing loop and the sensor data, since writing code is becoming cheap. If you work in manufacturing, logistics or design, start collecting clean data from your physical processes now.

Shift 2: Speed is cheap, and judgment is expensive

A 20x speed gain in coding and 2.5x faster responses make raw output almost free. What stays scarce is the person who knows which output to trust, which to throw away, and how to explain the decision to a client. I advocate human-in-the-loop AI for this reason: humans keep responsibility for judgment, ethics and narrative, while AI handles patterns and mechanical work.

Shift 3: Compliance becomes a product feature

Voice monitoring, smart glasses and AI-written code all create legal exposure. At CADChain, we build IP protection directly into CAD workflows, so engineers stay compliant without becoming lawyers. I expect the same model to spread across AI products, where the startup that makes compliance automatic will beat the one that sends users a 40-page policy.

Shift 4: Compute geopolitics opens a door for Europe

A trillion-scale model trained without Nvidia proves that the hardware market is splitting. Europe has strong privacy rules, strong engineering and a talent pool that wants trustworthy AI. A European founder who offers sovereign hosting, clear data provenance and audit trails has a real selling point with cautious corporate buyers.

Shift 5: Parallel entrepreneurship becomes rational

I run several connected ventures at the same time, and people used to call that reckless. With AI agents acting as mini-teams for research, drafting and process scaffolding, the cost of running two or three related projects drops sharply. Reuse your knowledge, infrastructure and network across ventures instead of starting from zero each time.

Want a wider frame for these shifts? IBM’s analysis of where artificial intelligence is heading discusses multimodal AI, neuromorphic computing and federated AI, where data is processed locally on many devices to protect privacy. For the technical background on deep learning, the Case Western Reserve University guide to AI and machine learning advancements and the Johns Hopkins Engineering overview of AI and machine learning advancements are solid starting points.

Frequently asked questions about the latest AI advancements

What is the most important AI advancement of 2026 so far?

For founders, the biggest shift is AI coding agents applied to physical systems, shown by Claude Opus 4.7 programming a robot dog 20 times faster than the best human team of the previous year. It signals that software development costs for hardware and automation products are collapsing.

What is Claude Opus 4.7?

Claude Opus 4.7 is a large language model from Anthropic, reported in June 2026 to show stronger reasoning for writing robotics control algorithms and sensor code. It succeeds earlier models in the Opus line, such as Opus 4.1.

What is parametric insurance?

Parametric insurance pays a fixed amount automatically when a measurable event, such as a river level or wind speed, crosses an agreed threshold. AI helps price the risk and confirm the trigger, which makes payouts much faster than traditional claims.

Is voice-based behavioral monitoring legal in Europe?

It depends on the use case. The EU AI Act prohibits emotion recognition in workplaces and educational settings, apart from narrow medical and safety exceptions. Always get legal advice before selling voice analysis features to European employers or schools.

Should small businesses build their own AI hardware?

Rarely. Hardware is capital-hungry and slow, and giants like Musk’s companies are entering the market. Small businesses usually win by building services, content and software that run on top of new AI devices.

How can solo founders keep up with AI news without wasting time?

Pick two or three trusted aggregators, read them once a week, and ask one question per headline: does this save time, save money or reduce risk for my business? If the answer is no, move on. Video roundups such as this video on 15 new breakthroughs found by AI work well for a quick weekend catch-up.

What should you do next with the latest AI news?

The AI stories of 2026 share one message: AI is leaving the screen and entering the physical world, through robots, glasses, voice systems and automated insurance. Coding is getting dramatically faster, models are getting cheaper, and open weights are shifting power away from a single chip supplier. The risks are growing at the same pace, from autonomous cyberattacks to price pressure on any service sold by the hour.

My advice as a parallel entrepreneur who has built companies in deeptech and edtech is direct. Treat your startup like a strategic game, where the goal is to collect information, assets and relationships faster than your competitors. “Education must be experiential and slightly uncomfortable,” and the same goes for learning AI: you will only understand it by putting it to work on real tasks with real stakes.

Next steps for this week:

Today: turn on two-factor authentication across all business accounts.
This week: audit your tasks and pick one money metric.
This month: run three small AI experiments with a human checkpoint and a version log for anything AI creates.
This quarter: test how your business sounds through voice assistants and smart glasses.

The founders who act on these signals now will spend 2027 collecting the rewards, while everyone else is still reading headlines. Start with one experiment, measure it honestly, and keep playing.

FAQ on the Latest AI Advancements for Startups in 2026

How should founders evaluate vendor AI benchmark claims like the 20x robodog result?

Treat vendor-linked benchmarks as hypotheses until you test them. Ask for the task definition, the baseline team, the number of runs and the failure rate. Then replicate a small version on your own workload before budgeting around it. Read why the robodog claim needs context.

How do AI reasoning effort levels help startups control model costs?

Newer models let you adjust reasoning depth. Claude Opus 4.6 offers low, medium, high and max effort settings. GPT-5 with extended reasoning outperformed o3 while using 50, 80% fewer output tokens. Use low effort for routine work and reserve max for hard problems. See March 2026 AI model release details.

Voice recordings can reveal health, identity and emotional state, so treat them as sensitive data in every market. Require explicit opt-in, publish retention periods and let users delete recordings easily. Consolidation such as SoundHound's LivePerson acquisition makes vendor data terms critical. Explore voice monitoring privacy risks.

Can AI agents run research and development for a small startup?

Partly. Sakana AI's "The AI Scientist" mimics a full research workflow, from idea generation to simulated peer review, which could cut R&D costs in pharma, manufacturing and tech. Use such agents to generate and screen hypotheses, and have experts validate results before you invest. Discover how AI Scientist reshapes startup R&D.

How can startups stop AI inference bills from destroying their margins?

Calculate the cost per customer action before launch, then cap it. Cache repeated answers, route simple requests to small or edge models, and batch non-urgent jobs. Price AI features in usage tiers so heavy users pay more, and review inference spend against gross margin every month. Learn why compute cost is now strategic.

What is agentic AI and how does it differ from a chatbot?

A chatbot answers prompts. An agentic system receives a goal and handles the steps itself: it plans, calls tools, checks results and retries after failures. Start with bounded agents that have limited tool access, spending caps and activity logs, so mistakes stay small and traceable. Read the Innowise guide to agentic AI trends.

How does federated AI help privacy-focused startups?

Federated AI processes data locally across many devices instead of sending everything to central data centers. This lowers latency and limits exposure of sensitive records, which suits health, finance and wearable products. Neuromorphic and optical computing may later add energy savings to these setups. Explore IBM's outlook on federated AI.

Which AI techniques power voice and vision features in startup products?

Convolutional neural networks break images into layered features, which powers recognition, quality inspection and medical imaging. Recurrent neural networks remember earlier inputs in a sequence, which suits speech recognition and language generation. Knowing which architecture fits your data helps you choose vendors and avoid paying for capabilities you don't need. Understand CNNs and RNNs in practice.

How can climate and insurtech startups use AI weather forecasting?

AI models like GraphCast and Huawei's Pangu now outperform traditional weather forecasting systems. Startups can build on these forecasts to price parametric insurance triggers, warn farmers, plan logistics or protect energy assets. Always validate predictions against local sensor data before tying payouts to them. Watch AI breakthroughs in weather forecasting.

Where should a bootstrapped startup start automating with AI?

Choose one repetitive, measurable workflow, such as lead qualification, invoice processing or support triage. Map the current steps, automate them with no-code tools and an affordable model, and keep human approval on decisions involving money or legal commitments. Expand only after that first automation proves its savings. Get the AI automations playbook for startups.

Violetta Bonenkamp

Violetta Bonenkamp, also known as Mean CEO, is a female entrepreneur and an experienced startup founder, bootstrapping her startups. She has an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 10 years as a solopreneur and serial entrepreneur. Throughout her startup experience she has applied for multiple startup grants at the EU level, in the Netherlands and Malta, and her startups received quite a few of those. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely. Constantly learning new things, like AI, SEO, zero code, code, etc. and scaling her businesses through smart systems.