Every week I try to balance regional updates with the bigger global shifts shaping tech, but this time the Balkans didn’t give us much to work with. AI, on the other hand, delivered more than enough. Lovable is sprinting toward 8 million users, Upwork just showed why humans and AI are stronger together, and Google is basically turning Search into a personal shopping assistant. Three different angles, one clear theme: AI is moving fast, and it’s not waiting for anyone. So even without local headlines, there’s plenty worth paying attention to. Let’s jump in.

I read Google’s announcement and honestly, it feels like it was written for engineers. It is dense and technical, but it matters, because if you want to lead in the AI world or even understand where this whole space is headed, this update is a big deal. Not for the code, but for the implications. So here is the simple version.

The real problem today is not building an AI agent. Anyone can do that and get it to work on their laptop. The moment real users touch it, things fall apart. It breaks, it hallucinates, or it racks up a five-figure API bill because someone joked around and asked it to reverse-engineer a bestselling book. So the challenge is not building agents. It is making them work for thousands of people at the same time.

This new launch tackles that problem. Now AI gets a real memory, from remembering what you said five minutes ago to what you said last week. It can track thousands of conversations and stop reintroducing itself like a goldfish. It also knows when it fails and automatically looks for another way to fix the problem. Security is stronger, so it protects you from attacks and limits what your agent can touch. And you can finally see everything in real time, including costs and the reasoning behind every decision.

This update is less about making AI smarter and more about making it reliable. Something stable enough to trust with real work. It moves AI closer to feeling like a utility that simply works in the background. And now you know the version that actually makes sense before you dive into the technical one yourself.

If you want the full detailed release, here it is.

Lovable, the Stockholm startup that launched just a year ago, is closing in on 8 million users after jumping from 2.3 million earlier this year. People are building around 100,000 new projects every day, and the company has already crossed 100 million dollars in annual recurring revenue. It has raised 228 million dollars so far, including a 200 million dollar round this summer at a 1.8 billion dollar valuation, with talk that new investors are considering a 5 billion dollar mark.

The momentum is strong even though traffic across vibe coding platforms has cooled. Lovable’s web visits were down about 40 percent in September. Even so, the platform is finding its place with larger companies. More than half of the Fortune 500 are said to be using it for prototyping and creative work, while individuals continue to run with it. An 11 year old in Lisbon built a Facebook clone for school using Lovable, and a pair of Swedish founders are making 700 thousand dollars a year from a startup built on the platform.

Lovable’s story began when CEO Anton Osika created GPT Engineer, an open source project that went viral and showed him the potential of helping people who do not know how to code. Since then, the team has added security checks and is hiring more engineers to make building on Lovable safer than writing code by hand. Despite competition from companies like OpenAI and Anthropic, Osika says there is room for more than one winner and that the real goal is unlocking human creativity.

The founders of Cursor, the fast growing AI coding tool, are now billionaires after raising 2.3 billion dollars at a 29.3 billion dollar valuation. Forbes estimates that each of the four founders, Michael Truell, Aman Sanger, Sualeh Asif and Arvid Lunnemark, holds roughly 4.5 percent of the company, giving each stake a value of at least 1.3 billion dollars. Cursor supports millions of developers and more than fifty thousand enterprise teams at companies like Nvidia, Adobe, Uber and Shopify, and the company says its annualized revenue has passed 1 billion dollars.

Cursor’s rise has been extraordinary. The team founded Anysphere in 2022 and quickly became one of the fastest growing startups in the sector after jumping from 1 million dollars in annual recurring revenue in 2023 to 100 million dollars a year later. The company has raised more than 3.3 billion dollars from top investors including Accel, Thrive Capital, Coatue, Andreessen Horowitz and DST Global. One founder, Arvid Lunnemark, left in October to start a new company focused on safer AI systems.

Cursor lets engineers use models from OpenAI, Anthropic, Google and xAI to write and edit large chunks of code, fix bugs and automate repetitive work. In October, the team also introduced its own model, Composer, which can generate and edit code faster while reducing dependence on third party models. The founders come from strong math and programming backgrounds, including International Math Olympiad medals and early work at companies like Octant and Google. Their story fits a larger pattern in the AI boom as young founders continue turning breakthrough tools into billion dollar companies at record speed.

Over the next year, Roku predicts that 100% of the streaming audience will see ads. For growth marketers in 2026, CTV will remain an important “safe space” as AI creates widespread disruption in the search and social channels. Plus, easier access to self-serve CTV ad buying tools and targeting options will lead to a surge in locally-targeted streaming campaigns.

Read our guide to find out why growth marketers should make sure CTV is part of their 2026 media mix.

Learn more.

A new Upwork study cuts through the noise and shows something most AI debates ignore. When AI agents work alone, even on simple tasks, they fail more often than you’d expect. But when you plug in a human expert for short feedback loops, success rates jump by as much as 70 percent. The research looked at more than 300 real jobs across writing, data science, engineering and marketing, and the pattern was the same. AI can push fast, but it still needs someone to guide the direction.

This tells us something important about the future of work. The sweet spot isn’t replacing people, but pairing AI with humans who understand judgment, context and quality. Coding tasks and structured data work already benefit the most, which is why coding agents look so impressive. But creative projects, translation and marketing still depend on human nuance. Those quick expert reviews turn AI from “almost there” to “done right” in a way benchmarks never capture.

For businesses, this shift changes how teams are built. The goal stops being full automation and becomes smart collaboration. A human who spends twenty minutes steering an agent can deliver work that would have taken days before. This unlocks new roles around oversight, workflow design and quality control. The winners will be the companies that stop waiting for perfect autonomy and instead invest in systems where people and AI amplify each other.

Google researchers introduced a new quantum optimization algorithm called Decoded Quantum Interferometry, or DQI. They say it can solve certain problems exponentially faster than classical computers. In a Nature paper released in October and discussed publicly this week, the team reported that some problem types could be solved with only a few million quantum operations rather than the staggering 10^23 steps needed on classical machines. The approach targets hard optimization tasks found in areas like route planning and drug discovery.

The core idea behind DQI is to turn optimization problems into decoding problems, similar to the error correction techniques used in DVDs and QR codes. This lets the algorithm use powerful tools from coding theory. Researchers say this works particularly well for problems with a strong algebraic structure. Experts have noted the significance of this step, although results are mixed for broader applications. In some unstructured or generic optimization cases, classical algorithms still match or come close to DQI’s performance.

Alongside the research, Google shared a five stage roadmap for building practical quantum applications. It highlights the gap between proving theoretical advantages and turning them into real world use cases. DQI itself cannot run on today’s hardware, which has only hundreds of qubits instead of the millions required for fault tolerant quantum computing. Even so, Google believes the first practical quantum applications could arrive within the next five years.

AI is reshaping how companies work by shifting focus from job titles to tasks. Instead of rigid departmental handoffs, workflows are now built around clear steps that can be handled by either humans or AI agents. This approach removes bottlenecks, speeds up delivery and improves the customer experience. In many cases, an AI agent can review data, follow policies and complete tasks faster than an entire department, while humans step in only when judgment or escalation is needed.

This new structure makes roles fluid. The same agent might handle customer questions today, run demand analysis tomorrow and forecast sales the next day. It all depends on the skills and access defined for each task. The result is a more accountable and transparent system where every action is logged and measured by real outcomes like speed, accuracy, cost and customer satisfaction. Companies across support, marketing and logistics are already seeing approvals that once took weeks shrink down to hours.

As AI takes on more operational work, the skills companies value are changing. Hard skills still matter, but soft skills and systems thinking become the real edge. People who know how to collaborate with AI, break work into steps, question results and make clear decisions will accelerate teams dramatically. This shift transforms hiring, management and culture. Businesses that embrace task based workflows gain speed, flexibility and a far more efficient way to coordinate work from end to end.

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