Zest Raises $1.8 Million To Build AI-Powered Restaurant Discovery Platform

Innovative Approach To Restaurant Discovery

Restaurant discovery startup Zest is using transaction data and artificial intelligence to generate dining recommendations based on users’ actual spending habits rather than reviews or saved wishlists. Founded in November 2024, the company aims to help users discover restaurants through verified dining activity and personalized recommendations.

Strategic Funding And Early Adoption

Founded in November 2024, Zest has rapidly captured market attention with $1.8 million in pre‐seed funding from notable investors, including Alexis Ohanian via 776 and Steve Jang at Kindred Ventures. The platform, which has been in beta since inception, expanded its user base steadily from a circle of friends and family to a broader audience, garnering over 100,000 visits in a matter of weeks post-launch.

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Data-Driven Personalization In Action

Unlike other apps that simply compile dining wishlists, Zest’s distinctive advantage lies in its reliance on verifiable transaction data. By linking a user’s credit card to the platform, Zest imports verified dining transactions to create a personalized map of favorite eateries. This transparent method extends beyond curated posts, instead offering recommendations based on the frequency and monetary investment users commit to their chosen spots.

Leveraging Trusted Financial Partnerships

Zest integrates data through Plaid, a leading financial services provider trusted by major banks and fintech innovators. This partnership ensures that only dining-related transactions are extracted, improving the accuracy of its personalized mapping while preserving user privacy and data integrity.

Curating The Authentic Dining Experience

Co-founder Mario Gomez-Hall emphasizes the platform’s focus on genuine dining experiences over ostentatious social sharing. “It’s about uncovering your regular spots, the dependable ‘hole in the wall’ you love, not just the high-end, Michelin-rated restaurants,” he explains. With the combined technical expertise of co-founder Alex Moller, whose background includes Apple and other tech giants, Zest is poised to set a new standard in authentic dining exploration.

Expanding The Culinary Landscape

Alongside transaction-based recommendations, Zest analyses more than 80 million reviews from multiple sources, including Michelin and Reddit. The startup is also introducing new features that allow users to save personal notes about restaurants and share recommendations. A new “Fresh Picks” feature will highlight recently discovered restaurants in a format similar to Spotify’s Discovery Weekly.

Beyond Restaurants: A Vision For Urban Exploration

The company plans to expand beyond restaurant recommendations and explore additional categories of local experiences, including shopping and nightlife. According to Gomez-Hall, the long-term goal is to build a broader discovery platform focused on helping users navigate cities through personalized recommendations.

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Meta Unveils Muse Wonder Models In A Bid For Frontier AI Leadership

by THEFUTURE.TEAM

October 13, 2026

Meta has introduced two new AI models, Muse Wonder and Muse Wonder Full, signalling a more ambitious move into frontier AI. The releases build on Muse Spark but target more demanding tasks, including coding, while placing greater emphasis on long-term memory and personalised assistance.

Larger Models Target More Demanding Tasks

Unlike Muse Spark, which was designed for low latency and lightweight use, Muse Wonder and Wonder Full are significantly larger and, according to Meta, more capable. The company reports stronger benchmark performance and improvements on demanding workloads, including coding.

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According to Meta’s model card, both models achieved benchmark results comparable to Fable 5.1 and GPT-6 Astra in the company’s internal testing. Meta described the release as “the next step on our scaling ladder and a major leap toward personal superintelligence” in its announcement.

Pricing Reflects Meta’s Frontier Ambitions

The models’ pricing places them in the premium AI segment. Through the Meta Model API, Muse Wonder will cost $4 per million input tokens and $20 per million output tokens, putting it in a similar pricing tier to GPT-6 Sol and Opus 5.5.

Muse Wonder Full, by contrast, will not be offered through the API. Meta says it will be exclusive to Muse Code subscribers, tying access to its most advanced model to a premium product. Both models will also power the Muse agent.

Long-Term Memory Takes Centre Stage

Meta is increasingly positioning its AI strategy around assistants that can support users over time rather than respond only to individual prompts. A central part of that approach is what the company calls “active memory”: the ability to retain, organise and synthesise information provided by users across extended periods.

Meta points to the MemoryBench benchmark, which evaluates systems designed for continual learning and memory construction. The company says Muse Wonder now outperforms Fable, Opus and Astra on this measure.

“We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” research scientist Kavya Kopparapu said in a launch video.

New Models Follow Meta’s AI Reorganisation

The launch is also significant within Meta’s broader AI strategy. It is the company’s first frontier-model release since Scale AI founder Alexandr Wang reorganised its AI operations in August 2025. Meta has since introduced Muse Spark and the Muse agent, while making fewer high-profile model announcements than competitors such as OpenAI and Anthropic.

The new models were trained at Meta’s Prometheus data centre in central Ohio, which came online earlier in 2026, according to the company. The facility highlights the growing importance of infrastructure in the race to develop advanced AI systems, where performance depends not only on algorithms and data but also on computing capacity, data-centre investment and access to capital.

As competition intensifies, Muse Wonder and Wonder Full represent a broader bet on larger models, persistent memory and premium AI products. Their longer-term significance will depend on how their reported capabilities translate into real-world performance and whether users find enough value to pay for access.

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