Thanks @datadoghq for your startup program. We (myself and @Electricaliee ) are getting our jetson orin nanos onto our dashboard now and discovering a ton of potential optimizations for @offlinebase
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When are there going to be price/duration pricing for LLMs? Mercury 3 is running at 1000 tok/s $0.70 per million output 16.667 min to one million output tokens (uninterrupted token streaming) Vs Haiku 5.5 runs at 232 tok/s (according to @ArtificialAnlys) $0.50 per million output tokens ~72 min to one million output tokens So you’re [let’s call it] investing your $ into tokens, 5x faster (omitting price). Of course, this isn’t a big deal because you would ideally get to your outcome faster and still spend the same amount of tokens (if not less because Mercury 3 is reportedly smarter than Haiku 5.5).
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Working on using @liquidai d1 to help you moderate your, your kids, your employees, etc. before things get sent to AI models, infra, etc. All done locally! Working on this and more @offlinebase
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@alexandr_wang @elonmusk @sama I think every personal agent should have a locally running moderation model on the consumer’s device that blocks specific things from going to your guys’ infra to protect both the consumer and protect yall from liability!
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Was blessed to win the latino x AI hackathon last week at Columbia Business school w @GilFoss and my boy Felix
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I wonder if there is any demand for data center and edge compute live streams? It would be cool to hear the data center whirr as you send a prompt, also to get more transparency I think I may set up a live stream for my jetson orin nanos and raspberry pis that are running models
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Actual “decision tree”. @Raspberry_Pi 5 running @liquidai d1 600m Being powered by solar With a diy tree connected to Figuring out how to productize this for < $125
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I have d1 600m from @liquidai running on a @Raspberry_Pi 5 (2gb ram) powered by a solar panel in my apt.
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Insane work by @reflection_ai
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Try deepseek v4.1 Flash 1017 here on tryingopen[.]com for free @deepseek_ai
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Congrats on Beam, @zkwentz and the @reflection_ai team!
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: reflection.ai/beam
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Tesla demo was insane
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Had a good time going to the Open by Design event hosted by @reflection_ai @nvidiaai @togethercompute with @electricaliee If you’re interested in local, edge, open source ai, let’s chat!
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I think people forgot about this @CoreWeave @SemiAnalysis_
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Try out @liquidai new LFM 2.5 24B model routed by their D1 decision model, made by us @offlinebase. Website in comments. @EthanCastr
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If anyone wants to try @Muse Spark Open for free with no logins tryingopen[.]com
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Insane!
Today we’re releasing Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs: → 816B total / 42B active parameters → 1M token context window → Natively multimodal across text, image, video, audio
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