Tag: ai content creation

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  Google is Hiring Engineers to Create AI Content in Discover

  Google is opening new horizons in the world of personalized content by hiring engineers to develop and implement artificial intelligence into its Discover service. This strategic decision aims to create a dynamic, ever-changing stream of information that will meet users’ deepest interests. If you’ve ever wondered how to make 100 unique pieces from one source, Google seems to have found the answer in AI.

  According to job postings, the Discover Feed Recommendation team will “recommend AI-generated content (AIGC) to meet user interests.” This is not just a recommendation, but an entire system capable of circumventing traditional algorithms and offering something completely new, unique to each user.

  How Google Plans to Transform Discover with AI

  Job postings reveal Google’s ambitious plans to transform Discover into a “proactive, hyper-personalized concierge service.” This means that the platform will not just show you content, but also anticipate your needs, offering information that will help you achieve your goals.

  Roles and Responsibilities of New Specialists

  • Staff ML Engineer: This role involves quality control, topic discovery, and content understanding for the Discover team’s AI-generated content. Tasks include building agent flows that generate content for “proactive surfaces” like Discover and Notifications. Special attention is paid to fact-checking, relevance, coherence, and tone, as well as feedback mechanisms for finding and delivering engaging stories worldwide.
  • Senior Staff ML Software Engineer: Similar work, but at a higher level, with an emphasis on the quality of AI-generated content used across all major Google platforms.
  • Principal UX (Director+): This leadership design position aims to transform Discover into a “concierge experience” that helps users progress towards their goals. The salary for this position ranges from $275,000 to $382,000 USD plus bonuses and stock. The specialist will work with YouTube, Shopping, Chrome, and Android teams, scaling AI formats in the feed while keeping the human voice and sources at the forefront.

  “Our goal is to help people dive deeper into their interests and move from inspiration to action,” one job posting states.

  This is not just content repurposing; it’s its complete repackaging and adaptation to individual needs, almost like creating “video DNA” for each user.

  Google is already actively integrating AI into Discover. In October 2023 (presumed typo correction in the original), AI previews for trending topics were launched in the US, South Korea, and India, each linking to content on the internet. This is an excellent example of how video uniqueness and articles can help bypass anti-plagiarism and attract new audiences.

  Features already available or being tested:

  • Discover Customization: Since August, users can customize their feed, indicating what they want to see more or less of.
  • “Dive Deeper” Button: In September, Robbie Stein, VP of Product at Google Search, announced testing a button that opens a brief overview of a topic with links to related stories, starting with videos. This is a kind of “video matryoshka,” where many small pieces of content, adapted for TikTok, Shorts, and Reels, can be obtained from one large piece of content.

  The Discover Experience team, responsible for the user-facing part of the feed, including AI integration, serves over 500 million active users daily, making Discover one of the largest products in the world.

  
  

  Search Console Report and Control over AI Content

  Search Console already provides reports on when links to your site appear in Discover’s generative AI features. This allows webmasters to track the effectiveness of their pages in Google’s new recommendation “matrix.”

  • The report shows impressions of links to your site in Discover’s generative AI features, grouped by page, country, and date.
  • The “Pages” view lists the canonical URLs that provided the information shown in these features.

  It’s important to note that the inclusion of your site in Discover’s generative AI features is regulated through Search generative AI control, which also covers AI Overviews and AI Mode. Excluding a site from these features prevents its links and content from appearing in generative AI Search.

  Frequently Asked Questions

  What is content repurposing in simple terms?

  Content repurposing is the process of transforming existing content into new formats or for new audiences. For example, turning a webinar into 50 short clips for social media or adapting an article into an infographic. This allows you to scale content and get more return on invested resources.

  Should I order uniqueness or create new content?

  It depends on your goals and budget. Video uniqueization and other content can be significantly cheaper and faster than creating new content from scratch, especially if you already have high-quality source material. For bypassing ad bans or for generating variations for A/B tests, uniqueization is a powerful tool.

  How does AI help in bypassing anti-plagiarism?

  AI can analyze text or video, identify key elements, and rephrase them, change the structure, background, voice timbre, creating new, unique versions. This allows you to get a uniqueization package that passes originality checks while maintaining the main meaning and message.

  Conclusion

  Google is actively investing in the development of AI-generated content for Discover, striving to create the most personalized and proactive user experience. This opens up new opportunities for content creators in the field of repurposing and uniqueization. Monitor Search Console reports and experiment with adapting your content to make the most of these innovations. Start adapting your content for different social networks today to be at the forefront of this digital transformation!

  12.10.2026

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  Melius: From Ramp Engineers to $25 Million for AI Video Generation

  In a world where speed and efficiency play a key role, Melius, a platform for creating advertising campaigns, images, and videos using artificial intelligence, is making a real breakthrough. The company announced that it has raised $25 million in investments, which underscores the enormous potential of AI video generation in the modern industry. This significant financial influx includes a $20 million Series A round led by CRV and an initial $5 million round from General Catalyst. Founded by former Ramp engineers, Melius demonstrates impressive growth, achieving over $1 million in annual recurring revenue just two months after emerging from stealth mode in July.

  Melius Raises $25 Million: Revolutionizing AI Ad Generation

  From Idea to Scaling: How Melius Reinvented Itself

  Melius co-founder Juwon Kim admits that the company’s journey was not smooth. Initially, over a year ago, the team, consisting of Juwon Kim, Yang Kim, and Arnav Ramu (all three former Ramp engineers), focused on creating a tool to optimize advertising spending.

  However, as Kim notes, after six months, it became clear that the initial idea “had no prospects.”

  “We completely destroyed the entire codebase; we burned it all down,” Kim told TechCrunch.

  This decisive step allowed Melius to reorient itself to a much larger task: developing tools for generating creative assets themselves and advertising campaigns. This marked the beginning of a new era for the company, creating an “agent lab for creative work.”

  Melius in the Competitive Landscape of AI Production

  The market for AI creative generation is rapidly evolving. Among Melius’s competitors are projects such as Higgsfield, which was valued at $5.4 billion in August and demonstrates annual revenue of over $700 million. There are also smaller players, such as Krea and Flora AI. Juwon Kim sees no problem with this, emphasizing the enormous potential of the market.

  • Higgsfield: Valuation of $5.4 billion, annual revenue of $700+ million.
  • Krea: A young startup in the field of AI generation.
  • Flora AI: Another player creating creatives with AI.

  “There are a lot of players out there, and it’s pretty exciting,” Kim noted. “It means there are customers to win, and there’s demand in this space.” This confirms our position that the cost of one AI creative today is significantly lower than traditional production, which opens up huge opportunities for mass video generation by neural networks.

  
  

  From YouTube Dream to AI Avatars: The Founder’s Personal Motivation

  Interestingly, behind the decision of the former Ramp engineers to engage in advertising generation is Juwon Kim’s personal story. He describes himself as a social media influencer and dreamed of creating short videos since childhood. “I tried my best to make vlogs,” Kim recalls. “My father was great at FinalCut Pro, and I wasn’t.”

  These early attempts were unsuccessful, but his passion for digital media remained. Now, with Melius, Kim is building the tool he always dreamed of: a platform where anyone, from an experienced creative director to a novice, can bring their ideas to life using video generation from text.

  This will allow content creation to be scaled in ways previously impossible, making subscription-based neuro-production accessible to everyone looking for AI efficiency versus real UGC.

  Frequently Asked Questions

  Question	Answer
  What is Melius and what services does it offer?	Melius is an AI platform for generating advertising campaigns, images, and videos. It allows users to create creative assets using artificial intelligence, which significantly simplifies and speeds up the content production process.
  How much investment has Melius attracted?	Melius has attracted a total of $25 million: $20 million in a Series A round and $5 million in an initial round.
  Who founded Melius?	Melius was founded by former Ramp engineers: Juwon Kim, Yang Kim, and Arnav Ramu.
  What is Melius’s annual revenue?	Melius achieved over $1 million in annual recurring revenue just two months after emerging from stealth mode in July.
  Does Melius have competitors in the AI ad generation market?	Yes, Melius has competitors such as Higgsfield, Krea, and Flora AI. However, Melius founders are confident that the market is large enough for multiple players.

  Conclusion

  The story of Melius is a vivid example of how flexibility and foresight can lead to success in the rapidly changing world of technology. By abandoning its initial idea, the team created a platform that not only attracts significant investment but also promises to revolutionize AI video for advertising. We see how neural networks are transforming content creation, making AI conveyor 500 videos per day possible and offering new opportunities for scaling creative processes.

  If you want to know where to order AI UGC for a crypto project or how to generate 100 videos using neural networks, Melius demonstrates that the future is already here. Join this neuro-revolution and contact us to find out how AI can scale your content production!

  07.10.2026

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  Vibecoding: Real Project Incomes and Myths About Millions

  Talks of millions from vibecoding are everywhere: a million in 17 days, a million by October, a million from one prompt. I decided to investigate: how many projects have truly crossed the threshold of $100,000 in monthly revenue or $1,000,000 in total income (including exit value). The picture turned out to be much more modest than expected. If we exclude projects whose product itself is an AI solution, very few startups based specifically on vibecoding remain.

  In this analysis, I do not conflate ARR (Annual Recurring Revenue) and actual revenue. ARR is a projection of the current month multiplied by twelve, not money earned. Where a source indicates ARR, I note it and recalculate the monthly revenue separately. The order of projects in the table below is determined not by revenue size, but by the reliability of financial data confirmation.

  Projects that have crossed the $100,000/month or $1 million total income threshold

  The list includes projects with public reports of revenue from $100,000 per month or cumulative income from $1,000,000.

  1. Medvi: $401 million in the first year

     Telemedicine specializing in GLP-1 weight loss drugs. Founder Matthew Gallagher launched the project in September 2024 with an investment of about $20,000. According to publications, the first year brought $401 million in revenue, about 250,000 clients, and $65 million in profit with a 16% margin. Staff: two people (Gallagher himself and his brother Elliot), the rest are contractors.

     • Vibecoding: Code, website, and agents were written using Claude, Grok, and ChatGPT. Visuals for advertising — Midjourney and Runway. Client calls — ElevenLabs.
     • Business essence: The main part of the business — doctors, pharmacy, prescription writing, and legal framework — is outsourced through CareValidate, OpenLoop Health, and LegalZoom. Gallagher focused on assembly and traffic.

  2. Base44: sold for $80 million

     An application builder from text description, created by Maor Shlomo. Launched in early 2025. In May, the company showed $189,000 in monthly profit. In June, Wix announced its acquisition for $80 million in cash. At the time of the deal, the project had 250,000 users. Shlomo was the sole owner, but at the time of sale, eight people worked for the company, who received $25 million from the deal amount as retention.

     • Vibecoding: Shlomo deliberately built the codebase so that most of it was generated by models, and used prompt routing between different models. This is one of the purest examples where the product itself is vibecoding and built using the same method.
     • Confirmation: Financial performance is confirmed by the deal document, not a tweet.

  3. Peec AI: $4M ARR (up to $833,000/month)

     A Berlin-based company that tracks how a brand is represented in AI search (ChatGPT, Perplexity, Gemini). Founder Marius Meiners. The first prototype was built on v0 from Vercel in about a day and a half. The product was then developed in a traditional way by a team. In November 2025, the company announced a Series A of $21 million, with a total of $29 million raised. At that time, Peec AI had 1300+ connected companies and ARR of about $4 million (approximately $333,000 per month). Later, ARR grew to $10 million (about $833,000 per month).

     • Vibecoding: Used to quickly create a prototype and test demand, but the main product was built traditionally.

  4. Wave AI: $7M ARR

     An application for recording, transcribing, and retelling audio, focused on casual conversations, not meetings. Author Josh Moorer, former Uber GM for New York, not a programmer. He built the first working prototype in Swift in a day using ChatGPT. The project reached $7 million ARR with $3 million in annual profit and about 4000 paid subscribers.

     • Vibecoding: Prototype created with AI.
     • Feature: High costs for accessing the best models to ensure transcription quality, which is the key product.

  Projects that did not reach the threshold, but are often mentioned

  These projects often appear in selections, but their financial performance does not meet the specified threshold.

  1. Cal AI: $2M monthly revenue, but the label is questionable

     An application for counting calories from a photo of food. Authors Zach Yadegari and Henry Langmack. Launched in May 2024. By July 2025 – 8.3 million downloads and, according to CNBC Make It, about $1.4 million in gross monthly profit, and $2 million in monthly revenue is also mentioned. It passes in terms of money, but not in terms of the vibecoding label. TechCrunch revealed that Yadegari wrote the code himself, starting programming at the age of ten. Anthropic and OpenAI models are only used for image recognition within the product.

  2. fly.pieter.com: $1M ARR, but peak $87,000/month

     A browser-based flight simulator by Pieter Levels. Basic prototype in 30 minutes. $1M ARR was announced in February 2025, 17 days after release. Peak monthly revenue was around $87,000. Monetization: paid aircraft models and items for $29.99. Levels’ method: “one prompt – one layer.” $1M ARR is an annual projection, not a million earned. The project did not reach the $100,000 monthly revenue threshold.

  3. Stanley: $1M ARR, but not revenue

     An AI content tool created by Vitaly Dodonov and Jon Hu in 14 days. The LinkedIn version was released in June 2025 and showed $200,000 ARR after six weeks. The team passed the $1M ARR mark by October 2025. “14-day app” refers to the first working cut, not the full product. The main value was added after launch. Again, this is ARR, not actual revenue.

  4. Lumoo: €700,000 ARR, about €58,000/month

     A platform for fashion and retail brands by Henrik Skagerlind Fast and Peter Törngren. Offers photorealistic visualizations and virtual try-on. According to the Lovable case, in nine months the product reached €700,000 ARR, connecting more than fifteen brands. The goal is €1M ARR by November 2025. Monthly revenue is about €58,000, which is below the threshold. The project’s success is due to the founders’ industry connections, not a generated interface.

  5. Plinq: $456,000 ARR, about $38,000/month

     A background and safety check app by Sabine Matos. Built entirely on Lovable. 6,000 ARR was reported, R3 million raised, and over 10,000 users. 6,000 ARR is about $500 a month, and that’s in the best interpretation. The case is interesting because the app gathered an audience before it made money. Investments came after users appeared, not because the first version was generated by a tool.

     
     

  General Conclusions and Key Success Factors

  Analysis shows that project success is not limited to cheap, AI-generated code. There are several common traits among successful startups.

  Team Size at Launch

  Four out of five successful cases started with one or two people. This is not so much a credit to vibecoding as it is an indicator that a product can be built faster than a team can be formed.

  Infrastructure on Others’ Shoulders

  Successful projects actively used third-party services and APIs:

  • Medvi outsourced doctors, pharmacies, and lawyers.
  • Wave AI used third-party APIs for transcription.
  • Cal AI — for recognition and product database.
  • Lumoo integrated into existing brand workflows.

  Only what was not on the market had to be built from scratch.

  Money doesn’t come from cheap code

  No product won solely due to cheap or generated code. Money came from other factors:

  • Cal AI — through influencers.
  • Stanley — through distribution via LinkedIn.
  • Medvi — through contractors with licenses.
  • Wave AI — through expensive inference (model computations).
  • Plinq — through an audience that appeared before revenue.

  Code generation shortens the path to launch but is not the main source of income.

  Multimodel Verification Method

  Almost all teams in the selection use the trick: run one task through several models and compare the results. This is how Peec AI checked demand, Base44 routed prompts, and Levels assembled game layers, changing the model for each task. This allows for quick hypothesis testing and finding optimal solutions. You can test this approach in an evening: take your current prompt and run it through three different models to see the discrepancies and choose the best option.

  Frequently Asked Questions

  What CPM should be considered profitable for hypothesis testing?

  For hypothesis testing, not only CPM is important, but also the speed of data acquisition. If AI tools allow for quick creative generation and campaign launches, then even with a relatively high CPM, hypothesis testing is cheap due to reduced time and labor costs. A profitable CPM is one that provides sufficient data volume for decision-making with minimal launch costs. For example, if the price per one finished clip, generated by AI, allows launching 100 variants for the same budget as 10 variants from a human, it is profitable.

  What’s cheaper: banner advertising or bloggers?

  A direct price comparison is difficult, as the result is different. Banner advertising (how much does 1 million impressions via banner cost) can be cheaper in terms of CPM, for example, CPM cheaper than 50 rubles per 1000 impressions. But bloggers provide native integration and audience trust. For a budget alternative to blogger seeding, you can consider a mass-posting package price which will be lower, but the reach may be less targeted. Cheaper than influencers can only be your own traffic or very targeted advertising.

  Is it worth investing in AI for content creation?

  If the goal is to reduce content production costs and increase speed, then is AI worth it — definitely yes. For example, the cost of generating 100 AI videos will be many times lower than creating them manually. The cost of uniqueizing one video with AI also significantly reduces costs. However, it is important to remember that price vs result is a key factor. AI speeds up the process, but a quality result still requires human control and refinement.

  How to estimate the cost of AI video production?

  The price per minute of AI video with an avatar can vary. It is important to consider not only generation, but also how much it costs to cut 1 hour of stream into short clips using AI. This allows for a lot of content from one source. The cost of publishing 500 videos, if they are generated or cut by AI, will be significantly lower than manual work. What is cheaper: banner or target — this is a question of effectiveness, not just price, and AI can increase the effectiveness of any channel through personalization and scale.

  Conclusion

  Analysis shows that “millions on vibecoding” is more of a myth than a reality, if we consider the net income from generated code. The success of projects lies in the ability to quickly assemble an MVP, use third-party infrastructure, and scale the business through effective distribution and customer work, and not just through cheap code. The unit economics of these projects are based on a comprehensive approach. Before believing in another success story, always demand statistics screenshots and ask the main question: how much money will each ruble spent bring. For those who are ready to experiment, we recommend trying different models on the same task. Use promo code CTRLAI for a 20% discount on any plan at SYNTX.AI.

  07.10.2026

• 

  AI Industry: Slowdown or Strategic Move?

  In the world of artificial intelligence, a serious discussion is brewing: should we slow down the pace of development or continue to move forward without looking back? Against the backdrop of Anthropic CEO Dario Amodei’s plans to “pace the frontier” and Nvidia CEO Jensen Huang’s statements about the unnecessary nature of regulation, the question arises: how serious are the intentions of industry leaders like Amodei and OpenAI’s Sam Altman regarding a slowdown?

  Slowing Down AI Progress: Myth or Reality?

  On the latest TechCrunch Equity podcast, Kirsten Korosec, Sean O’Kane, and Anthony Ha discussed these conflicting opinions. Anthony Ha was surprised by how many industry leaders supported Amodei’s plan.

  However, Sean O’Kane noted that the plan looks vague and lacks specifics, which raises doubts about its feasibility.

  Lack of Regulation and Consumer Choice

  “The federal government right now is certainly not eager to broadly apply regulation, let alone in specific cases,” Sean O’Kane stated, emphasizing that existing laws are not always effective in a rapidly changing technological environment.

  He also noted that in the market for AI solutions, there is virtually no consumer choice that could serve as leverage over companies.

  “It doesn’t feel like consumer choice is heavily influencing this market in the sense that if one of these companies does something really bad, they’re going to see the consequences in terms of cancellations or anything,” O’Kane added.

  This means that even serious missteps by major players may not lead to significant financial losses due to the loyalty of corporate clients and huge investments.

  Jensen Huang and Nvidia’s Position

  Nvidia CEO Jensen Huang publicly spoke out against a slowdown, supporting Donald Trump’s view that fears about AI are a “hoax” and regulation is unnecessary.

  His position raises questions, given that Nvidia is one of the main beneficiaries of the rapid development of AI technologies, supplying the necessary equipment for rendering and training neural networks.

  Why is Nvidia Against a Slowdown?

  • Economic interests: The rapid growth of AI directly correlates with the increased demand for Nvidia’s graphics processors. A slowdown could negatively impact their revenues.
  • Role of the “adult in the room”: Sean O’Kane believes that Huang positions himself as the “adult in the room,” aiming to be a useful liaison between the industry and the administration. This allows him to influence decisions and shape the agenda.
• Lack of direct competition: Nvidia does not develop end-user AI models, but provides infrastructure, so the direct risks from “unsafe” AI are lower for them.

Specifics of “moderate development” proposals

Kirsten Coroset mentioned proposals for “moderate development,” which include:

1. Attracting independent third-party evaluators to monitor AI safety within companies such as Anthropic and OpenAI.
2. Coordination between major AI companies in democratic countries to develop common safety standards and restrictions.
3. International coordination of efforts to regulate AI.

However, as Coroset noted, these proposals suffer from a lack of specifics. It is unclear who will act as independent evaluators, what standards will be applied, and how international cooperation will be carried out.

This creates the impression that statements about a “slowdown” may be more declarative than practical.

AI pipeline: from text to video in seconds

While discussions about regulation continue, the AI video generation industry is demonstrating unprecedented growth rates. Companies like Synthesia and other market players offer solutions for mass video generation by neural networks, opening new horizons for business.

We see how neural networks allow the creation of AI commercials, animated AI characters, and even AI avatars with subtitles and AI voiceovers.

Advantages of AI production:

• Speed: An AI pipeline can produce hundreds of videos per day, which was impossible to scale – now possible.
• Cost savings: The cost of 1 minute of AI video is significantly lower than traditional production. This makes neuro-production by subscription accessible even for small businesses.
• Personalization: The ability to generate video from text and use mass generation API allows for the creation of unique content for each client.
• Efficiency: The efficiency of AI versus real UGC (User-Generated Content) can in some cases be higher, especially when dealing with large volumes of content.

These technologies are actively used in e-commerce, for AI UGC advertising, and even in crypto projects, where fast and effective communication with the audience is required.

The ability of neural networks to write scripts and edit videos significantly reduces the time and cost of content creation.

Frequently Asked Questions

What is AI content production?

AI content production is the process of creating video, audio, and text content using artificial intelligence technologies. This includes generating video from text, creating animated AI characters, AI voiceovers, and even neural networks writing scripts and editing.

How much does one AI creative cost?

The cost of an AI creative can vary depending on complexity, duration, and features used. However, in general, the cost of 1 minute of AI video is significantly lower than traditional production methods, making it accessible to a wide range of companies, including startups and projects in the cryptocurrency sector.

Where to order AI video for crypto?

For AI UGC for a crypto project, you can turn to specialized platforms and studios that offer services for mass video generation by neural networks. Many of them provide AI production by subscription and use mass generation API to create content tailored to the specific needs of the crypto industry.

Conclusion

The discussion about slowing down the development of the AI industry, while important for ensuring safety, faces pragmatic interests and a lack of clear implementation mechanisms. At the same time, AI video generation continues to develop rapidly, offering businesses unprecedented opportunities for content creation.

Instead of slowing down, we are likely to see increased competition and the emergence of new, even more effective solutions for AI production. Want to know how AI video for advertising can scale your business? Contact us to discuss the possibilities of an AI video factory for e-commerce and generating 100 videos for your marketing campaigns.