Since Gemini 3.1 Pro, it's all benchmaxxed models. I recently tried Argon 4 and it failed to use tool correctly so much it's not worth bothering about Google's models anymore.
I have a completely opposite experience. DS4.1 and Mimo v2.6 take too long to finish a task, not worth it.
These investors are not that smart
All FF did was building a dataset.
https://arxiv.org/pdf/2402.03300 DeepseekMath was published many months before O1-preview.
DeepSeek invented the whole reasoning paradigm and keep pushing for innovation. I hope they get the success they deserve.
This is “google brain”
Funny to think about this when he got fired releasing a well-received product. On the other hand, Google released a subpar antigravity 2.0, full of bugs, almost unusable. The tech lead of Antigravity went on X to claim…
So are my DSP/Audio ML career, I spent decades acquiring the expertise. Now at 40, I don't know what to do if I lost my current job. Very sad, but from economic POV, I also don't need junior engineers in my team anymore.
Nawh, they trained on test since Llama 2, no wonder.
On semantic VAD, I recommend https://github.com/pipecat-ai/smart-turn
I hope SpaceX succeeds
Nice read!
Has been a life changer for me.
See: https://arxiv.org/pdf/2411.03923
Same. I went to this thread and saw similar talking points brought up. Dangerous time for the US ahead.
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Bad investment IMHO. Mistral was started by people who cheated on benchmarks with their Llama 1. It showed as they had the head start but fell far behind Gemini, DeepSeek and Qwen teams.
You can optimize prompt with MIPROv2 without examples (set the max number of examples to 0)
My go to framework. I wish we can use global metrics in DSPy, for examples, F1 score over the whole evaluation set (instead of a single query at the moment). The recent async support has been life saver.
I’m disappointed. Nobel price for neural network architects that failed the test of time..
Well more deserving than the author of Restricted Boltzmann machine.
True, that's a yellow flag for me.
I had a few problems with DSPy: * Multi-hop reasoning rarely works with real data in my case. * Impossible to define advanced metrics over the whole dataset. * No async support
Since Gemini 3.1 Pro, it's all benchmaxxed models. I recently tried Argon 4 and it failed to use tool correctly so much it's not worth bothering about Google's models anymore.
I have a completely opposite experience. DS4.1 and Mimo v2.6 take too long to finish a task, not worth it.
These investors are not that smart
All FF did was building a dataset.
https://arxiv.org/pdf/2402.03300 DeepseekMath was published many months before O1-preview.
DeepSeek invented the whole reasoning paradigm and keep pushing for innovation. I hope they get the success they deserve.
This is “google brain”
Funny to think about this when he got fired releasing a well-received product. On the other hand, Google released a subpar antigravity 2.0, full of bugs, almost unusable. The tech lead of Antigravity went on X to claim…
So are my DSP/Audio ML career, I spent decades acquiring the expertise. Now at 40, I don't know what to do if I lost my current job. Very sad, but from economic POV, I also don't need junior engineers in my team anymore.
Nawh, they trained on test since Llama 2, no wonder.
On semantic VAD, I recommend https://github.com/pipecat-ai/smart-turn
I hope SpaceX succeeds
Nice read!
Has been a life changer for me.
See: https://arxiv.org/pdf/2411.03923
Same. I went to this thread and saw similar talking points brought up. Dangerous time for the US ahead.
[flagged]
[flagged]
Bad investment IMHO. Mistral was started by people who cheated on benchmarks with their Llama 1. It showed as they had the head start but fell far behind Gemini, DeepSeek and Qwen teams.
You can optimize prompt with MIPROv2 without examples (set the max number of examples to 0)
My go to framework. I wish we can use global metrics in DSPy, for examples, F1 score over the whole evaluation set (instead of a single query at the moment). The recent async support has been life saver.
I’m disappointed. Nobel price for neural network architects that failed the test of time..
Well more deserving than the author of Restricted Boltzmann machine.
True, that's a yellow flag for me.
I had a few problems with DSPy: * Multi-hop reasoning rarely works with real data in my case. * Impossible to define advanced metrics over the whole dataset. * No async support