emilio andere @gpuemi
cofounder/ceo @wafer_ai. math @uchicago wafer.ai/manifesto san francisco Joined December 2015-
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@moby763canary21 this just takes me to a temporary chat
please codex desktop app let me have multiple tabs open :(
@joshkmartinez yeah for different agents running i want to have them open at the same time in different tabs. instead of having to click through each in the sidebar.
It's hard to overstate how valuable @wafer_ai's stability has been. Previous providers caused latency spikes and eval aberrations. Wafer has eliminated a class of issues we used to lose sleep over. If something goes awry, they're the most responsive partner we've worked with.
This is an awesome customer story (I’m a proud @brilliantorg investor and a huge supporter of their mission). We heard the same themes over and over again when speaking to @wafer_ai customers.
realtime inference? wafer.
Wafer beat Cerebras on latency for @ycombinator's AI Office Hours. GLM-5.2 on Wafer averaged 379 ms versus 674 ms for Gemma 4 31B on Cerebras. that's 44% lower latency with a much larger model. YC wanted people to get startup advice from AI versions of its partners at
ultra fast inference? wafer.
Wafer beat Cerebras on latency for @ycombinator's AI Office Hours. GLM-5.2 on Wafer averaged 379 ms versus 674 ms for Gemma 4 31B on Cerebras. that's 44% lower latency with a much larger model. YC wanted people to get startup advice from AI versions of its partners at
ultra fast inference? wafer
Wafer beat Cerebras on latency for @ycombinator's AI Office Hours. GLM-5.2 on Wafer averaged 379 ms versus 674 ms for Gemma 4 31B on Cerebras. that's 44% lower latency with a much larger model. YC wanted people to get startup advice from AI versions of its partners at
realtime inference? wafer
Wafer beat Cerebras on latency for @ycombinator's AI Office Hours. GLM-5.2 on Wafer averaged 379 ms versus 674 ms for Gemma 4 31B on Cerebras. that's 44% lower latency with a much larger model. YC wanted people to get startup advice from AI versions of its partners at
Wafer beat Cerebras on latency for @ycombinator's AI Office Hours. GLM-5.2 on Wafer averaged 379 ms versus 674 ms for Gemma 4 31B on Cerebras. that's 44% lower latency with a much larger model. YC wanted people to get startup advice from AI versions of its partners at conversational speed. after testing lightweight Gemma and OpenAI models, they moved to a dedicated Wafer endpoint. Wafer agents tuned the serving setup for YC’s request rate, cache usage, and prompt and response lengths. users spent 2.5 minutes longer talking to its AI partners on Wafer compared to other providers. read how YC built the experience and landed on Wafer 🧵 link in thread
Ever since we moved our tutor to Wafer, the responses feel near-instant. We didn’t expect things to get this fast so soon. Thanks to @gpuemi and the @wafer_ai team! Love using a product built by a founder who grew up on Brilliant :)
i grew up in Mexico, where it wasn’t always easy to find really high-quality educational resources that pushed me intellectually. i knew pretty early that i wanted to eventually go to one of the best universities i could in the US, and i spent a lot of time trying to get sharper
It's been such a game-changer. We used to spend a lot of effort making the ~5s response times feel fast -- multiple layers of caching, loading animations, restructuring our prompts, etc. Switching to Wafer meant we could spend that time on the core parts of the learning experience instead!
A great learning experience has a sense of flow. Having a tutor take 5-6s to respond kills that. Switching to @wafer_ai for inference made Brilliant’s tutoring sessions feel fluid and fun again, and every session metric improved along with that.
i grew up in Mexico, where it wasn’t always easy to find really high-quality educational resources that pushed me intellectually. i knew pretty early that i wanted to eventually go to one of the best universities i could in the US, and i spent a lot of time trying to get sharper
This is an awesome customer story (I’m a proud @brilliantorg investor and a huge supporter of their mission). We heard the same themes over and over again when speaking to @wafer_ai customers.
i grew up in Mexico, where it wasn’t always easy to find really high-quality educational resources that pushed me intellectually. i knew pretty early that i wanted to eventually go to one of the best universities i could in the US, and i spent a lot of time trying to get sharper
Brilliant's Story: wafer.ai/cases/brilliant
i grew up in Mexico, where it wasn’t always easy to find really high-quality educational resources that pushed me intellectually. i knew pretty early that i wanted to eventually go to one of the best universities i could in the US, and i spent a lot of time trying to get sharper on my own. @brilliantorg was a very meaningful part of that. i still remember being in early high school and absolutely spamming their logic courses. i loved them. they genuinely made me better at thinking, especially around math and logic, and helped build a lot of the skills that eventually got me where i wanted to go. so there is something very surreal about @wafer_ai now serving Brilliant :) their AI tutor, Koji, needs to respond incredibly quickly while students are working through math and coding problems. before Wafer, Brilliant was speculatively prefetching AI-generated responses so students wouldn’t have to wait. on our dedicated GLM-5.2 endpoint, they’re now getting ~250ms time to first token and 300+ output tok/s, 3x the throughput of their previous provider! that let them remove the prefetching layer entirely and cut inference costs by 50%. it’s hard to describe how gratifying it is to help power a product that helped me become a better thinker when i was a kid. very full circle moment. 🧵 Brilliant’s full story in thread.
@gpusteve @brilliantorg childhood dream come true 🥹🫶 @brilliantorg
@brilliantorg 3x’d their tokens per second and cut inference costs by 50%, by making its AI tutor fast enough to answer in real time instead of speculatively prefetching answers. To keep Koji, their AI tutor, responsive during math and coding lessons, the Brilliant team was speculatively prefetching AI-generated responses so students wouldn’t have to wait. On Wafer's dedicated GLM-5.2 endpoint, Brilliant reached ~250 ms time to first token and 300+ output tokens per second. This was a 3x throughput increase over their previous provider. That speed let Koji answer learners in real time, so the Brilliant team could stop pre-generating responses and remove the prefetching layer. Read how Brilliant made the switch! 🧵 Link in Thread
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