3 ms·

Comment retracted. My bad, missed some details.

by mhitza 8mo ago

selcuka 8mo ago
I think your comment is a bit unfair. > no reasoning comparison Benchmarks against reasoning models: https://www.inceptionlabs.ai/blog/introducing-mercury-2 https://www.inceptionlabs.ai/blog/introducing-mercury-2 > no demo https://chat.inceptionlabs.ai/ https://chat.inceptionlabs.ai/ > no info on numbers of parameters for the model This is a closed model. Do other providers publish the number of parameters for their models? > testimonials that don't actually read like something used in production Fair point.
- mhitza 8mo ago
  You are right edited my post (twice actually). Missed the chat first time around (though its hard to see it as a reasoning model when chain of thought is hidden, or not obvious. I guess this is the new normal), and also missed the reasoning table because text is pretty small on mobile and I thought its another speed benchmark.
  - selcuka 8mo ago
    I tried their chat demo again, and if you set reasoning effort to "High", you sometimes see the chain of thought before the answer (click the "Thought for n seconds" text to expand it). That being said, the chain is pretty basic. It's possible that they don't disclose the full follow-up prompt list.
- volodia 8mo ago
  Just to clarify one point: Mercury (the original v1, non-reasoning model) is already used in production in mainstream IDEs like Zed: https://zed.dev/blog/edit-prediction-providers https://zed.dev/blog/edit-prediction-providers Mercury v1 focused on autocomplete and next-edit prediction. Mercury 2 extends that into reasoning and agent-style workflows, and we have editor integrations available (docs linked from the blog). I’d encourage folks to try the models!
pants2 8mo ago
Reading such obvious LLM-isms in the announcement just makes me cringe a bit too, ex. > We optimize for speed users actually feel: responsiveness in the moments users experience — p95 latency under high concurrency, consistent turn-to-turn behavior, and stable throughput when systems get busy.

Comment retracted. My bad, missed some details.

by mhitza 8mo ago

Comment retracted. My bad, missed some details.

I think your comment is a bit unfair. > no reasoning comparison Benchmarks against reasoning models: https://www.inceptionlabs.ai/blog/introducing-mercury-2 https://www.inceptionlabs.ai/blog/introducing-mercury-2 > no demo https://chat.inceptionlabs.ai/ https://chat.inceptionlabs.ai/ > no info on numbers of parameters for the model This is a closed model. Do other providers publish the number of parameters for their models? > testimonials that don't actually read like something used in production Fair point.

mhitza 8mo ago
You are right edited my post (twice actually). Missed the chat first time around (though its hard to see it as a reasoning model when chain of thought is hidden, or not obvious. I guess this is the new normal), and also missed the reasoning table because text is pretty small on mobile and I thought its another speed benchmark.
- selcuka 8mo ago
  I tried their chat demo again, and if you set reasoning effort to "High", you sometimes see the chain of thought before the answer (click the "Thought for n seconds" text to expand it). That being said, the chain is pretty basic. It's possible that they don't disclose the full follow-up prompt list.
volodia 8mo ago
Just to clarify one point: Mercury (the original v1, non-reasoning model) is already used in production in mainstream IDEs like Zed: https://zed.dev/blog/edit-prediction-providers https://zed.dev/blog/edit-prediction-providers Mercury v1 focused on autocomplete and next-edit prediction. Mercury 2 extends that into reasoning and agent-style workflows, and we have editor integrations available (docs linked from the blog). I’d encourage folks to try the models!

pants2 8mo ago

Reading such obvious LLM-isms in the announcement just makes me cringe a bit too, ex. > We optimize for speed users actually feel: responsiveness in the moments users experience — p95 latency under high concurrency, consistent turn-to-turn behavior, and stable throughput when systems get busy.