Hotpot helps people create with AI.
Professional graphics, photos, and writing are inaccessible to many people, especially those in developing nations. We can change this with smarter software. Our mission is to make graphic design, image editing, and media creation 10x faster and more affordable.
For professionals, our goal is to spark creativity and automate drudgery. For casual users, the goal is to make content creation as simple as texting a friend.
Our product philosophy is straightforward: software should be like Frappuccinos. You could buy the equipment and ingredients then spend five minutes every day mixing the perfect Frappuccino for 10% of what you pay Starbucks.
But you don’t. Instead, you happily plop down $5 and walk out the door with a smile on your face and a Frappuccino in hand. Because the convenience is worth $5.
The process is frictionless. (Pardon the VC-speak.) Order. Pay. Leave. You can create a Starbucks account; you can pay anonymously; or you could pay as Spiderman if you're so inclined.
Software isn’t like this. For security and business model reasons, most SaaS and software providers require accounts and often monthly charges. Even if you only intend to use them once or twice per year. They want a relationship when all you want is a Frappuccino. (You heartless commitment-phobe! Get help.) Even worse, some companies charge the equivalent of $100 for a single Frappuccino.
If you're a VC and reaching for an inhaler because this post doesn't contain enough buzzwords, fear not: software-as-a-transaction (SaaT) is the model we're proposing. As opposed to the software-as-a-sucker (SaaS) model in fashion today. Didn't know SaaS stands for software-as-a-sucker? You're welcome. This term was coined at the dawn of the SaaS boom by the prestigious research firm, Albee Esse Research Institute.
We may need to require subscriptions someday, but we will do our best to dodge this day the same way this guy hilariously dodges Ann Coulter in the shopping mall. For as long as possible, we will endeavor (free vocabulary apps FTW!) to let you collaborate with others, edit designs, and check out without requiring an account. In short, we will endeavor (repeating verbs because investors wouldn't pay for the premium version) to put users first and investors second. Unless you're an investor (who isn't today), in which case, our strategy is to prioritize investors first and users second in FILO order.
P.S. If you read this far, reward yourself with this secret video of Taylor Swift and Kanye collaborating on a new song. The drop date is in 3 weeks so you'll be among the first to see it.
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Lab Philosophy
Our lab philosophy is to address real-world use cases and advance AI models without Internet-scale datasets, employing a two-tier approach.
Labs like ours have long believed the future of AI will mirror the computing industry, where supercomputers tackle the most complex cases, but smartphones serve billions of people. While supermodels have dominated since GPT-3, the smart model paradigm is finally shifting from fringe to credible. Our first tier focuses on architecture and dataset research to meet the inference demands of pragmatic use cases.
This entails both creating base models and extending existing ones. In protein structure prediction, for example, we develop proprietary models to investigate sub-optimal dataset and architecture choices in current approaches. For radiology, we explore how models like Gemma 4 could enable a future where edge devices in rural and underserved communities act as an AI Radiologist, helping overworked general practitioners flag suspicious cases of potential lung cancer.
The second tier concentrates on system research. Our hypothesis is that certain problems are now constrained by model judgment, not model intellect. This research investigates how to construct systems for injecting the relevant priorities and facts for frontier models to reason over, then leading them toward solutions in an iterative cycle similar to PIs leading a lab or CEOs leading a company.
Our mindset is shaped by the observation that raw intellect and job performance are not perfectly correlated, much as raw physical abilities and athletic success are not perfectly correlated. Tom Brady, for example, holds many football records yet was dismissed early on due to ordinary physical skills. His success stemmed in large part from understanding the opponent and situation, and executing the right play at the right time.
Office tasks exhibit comparable patterns. Grandmasters rarely become top investors while the best CEOs and investors routinely hire people with higher IQs than themselves. What matters in business is the right information at the right time. The same principle applies broadly, reflecting how people specialize after college -- not to deepen raw cognitive abilities but to acquire domain knowledge for solving real-world problems. Specialization trains human experts on what to learn and when to apply this knowledge. In short, specialization yields judgment.
We have applied our systems research to develop a drug discovery platform for rare diseases, targeting both novel therapies and drug repurposing strategies. We are currently testing the platform against a rare cancer type.
Unable to publish commercial research, we established HotpotBio to advance science in other ways. We draw inspiration from open source, where ephemeral teams innovate by attracting talent across organizational boundaries. Since biomedicine is characterized by sparse data and evolving facts, the field presents a high-impact opportunity for validating hypotheses aligned with our lab vision. Furthermore, ML research resembles biomedical research more than most realize.
Just as findings in one patient may not generalize to others due to genetic and lifestyle differences, ML findings -- even on core parameters like learning rate -- may not generalize due to architecture and training differences. In both, generalizability is far weaker than in physics or mathematics.
Both biomedical and ML research are constrained by data quality. Central to our effort is rethinking biomedical datasets and training approaches in clinical reasoning, oncology, neuroimmunology, drug development, and other specialty areas.
While data errors are tolerable for general ML models, uncommon variants in biomedicine may drive pathology. Training on imprecise medical information may cause misdiagnosis, clinical errors, or drug candidates with elevated toxicity. Complicating matters, evolving medical facts may invalidate training data and model knowledge. What was true last year may be false today. For instance, in April 2024 the U.S. Preventive Services Task Force revised its longstanding guidance and now urges biennial mammograms starting at age 40 -- down from the previous benchmark of 50 -- for average-risk women, citing rising breast-cancer incidence in younger patients.
Partners
The Hotpot API and services are designed for enterprise-level scalability and reliability, suitable for bursty workloads and sustained high-volume traffic.
To ensure we adopt best practices and state-of-the-art technologies, Hotpot is proud to partner with leaders in cloud infrastructure and machine learning.
Hotpot?
The rationale for the name is rooted in our vision and passion for food.
Similar to the meal, our platform offers the ingredients for simple, high-quality personalization. We altered the spelling to reflect the fusion of two distinct concepts, AI and personalization, and our motivation to invent a new software category.
