Building AI Agents, sharing cool stuff happening in #Elixir, #Python, #MachineLearning
Lichess is a real-life example of a 138x developer.
www.youtube.com/watch?v=7VSV...
Lichess is a real-life example of a 138x developer.
www.youtube.com/watch?v=7VSV...
Expect 2025 to be the year LLMs get smaller and faster. Its a matter of time before we get GPT-4 level models that can run on local devices.
venturebeat.com/ai/meta-laun...
Expect 2025 to be the year LLMs get smaller and faster. Its a matter of time before we get GPT-4 level models that can run on local devices.
venturebeat.com/ai/meta-laun...
On device models for knowledge retrieval and tasks will be used more frequently than large models for complex reasoning and the best of these models will be open source.
On device models for knowledge retrieval and tasks will be used more frequently than large models for complex reasoning and the best of these models will be open source.
Elixir = a functional language with fault tolerance
Flame = Spins up VMs to execute functions and powers them down when done
Oban = job orchestrator that handles fault tolerance
LiveView = real-time UI tracking
Elixir = a functional language with fault tolerance
Flame = Spins up VMs to execute functions and powers them down when done
Oban = job orchestrator that handles fault tolerance
LiveView = real-time UI tracking
Feels like Threads in the early days. Hopeful it’s a place people keep coming back to. That never happened with Threads
Feels like Threads in the early days. Hopeful it’s a place people keep coming back to. That never happened with Threads