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Key characteristics of LLMs: Scale: LLMs are huge in terms of parameters (the internal weights that encode language patterns). For instance, OpenAIs GPT-4 is one of the largest publicly known LLMs its rumored to have on the order of trillions of parameters, and it can process up to 25,000 words of text in one go.
It was paramount for TestDome to have a partner to help them easily scale across borders and take many kinds of payments from many different places, but there weren’t many options available to them at the time. So candidates usually won’t use a service like ChatGPT during a test. Now, Stripe offers it. Braintree offers it. He laughed.
Why can’t we escape hands-on cloud operations work to unlock softwaredevelopment nirvana (aka frictionless, faster development and deployment processes)? Put another way: you could make your software stack serve both specific business needs and general-purpose platform tooling. We’re talking to you, ChatGPT.)
We brought together some of the best AI founders and leaders—from those scaling the underlying foundation models to those building products that could transform entire industries—for a series of conversations on where we are, where we’re going, and the big open questions in the field. You have this perverse economy of scale.
Over the past couple of years and maybe the past 12 months, in particular, with the launch of ChatGPT and GPT-4, you can really see the potential of the platform in the same light as the personal computer or the smartphone. You’re building for things other than softwaredevelopment.
In 2025, foundation models or generative AIs like GPT-4, Claude, Gemini, and open-source LLaMA are reshaping AI research, softwaredevelopment, and SaaS products. These transformer-based neural networks excel at tasks from creative writing to code completion and chat. According to Scale AIs SEAL leaderboard, Llama 3.1
Fine-tuning can also involve alignment with human preferences, such as Reinforcement Learning from Human Feedback (RLHF), to make the models outputs more helpful and safe (this was crucial in making ChatGPT produce more user-aligned responses). Its worth noting that preprocessing at the scale of LLM data is a big data challenge in itself.
But in working with these enterprises in their corporate l&d teams, and seeing the inefficiencies in the workflows, and and then coming from a sales background, seeing existing operating systems for rev ops, or even DevOps for softwaredevelopment, or marketing, ops, so on and so on. What was gaps in their operations?
Theyve taken the tech world by storm for example, OpenAIs ChatGPT (built on an LLM) reached 100 million users just two months after launch, becoming the fastest-growing app in history. Well also look at some popular LLM examples (like ChatGPT and Google Bard), their benefits and limitations, and what the future might hold.
B) The Generative AI Boom: SaaS That Creates for You Unless youve been living under a rock, youve probably heard about ChatGPT, DALLE, and other AI content generators. In 2025, hackers are using AI to automate and scale attacks , making old security defenses useless. 3⃣ Easier Scaling & Future-Proofing Businesses evolve.
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