hrpnk

clef from cloudflare runs on llama.cpp - being locked-in to strands cli would be a bummer and will slow down adoption.

Since it's a LoRa on Qwen, I assume this is runnable via llama.cpp. Pity that the PEFT/LoRa->GGUF translation is left to the user. Anyone got past:

    $ uv run --with transformers==5.19.0 convert_lora_to_gguf.py ~/Downloads/lora --dry-run --verbose
    [...]
      File "/Users/user/repos/llama.cpp/conversion/base.py", line 630, in map_tensor_name
    raise ValueError(f"Can not map tensor {name!r}")
    ValueError: Can not map tensor 'layers.0.linear_attn.in_proj_a.weight'

Needing to have a full article with a comparison against an open-weight model just shows how much of a headache it has been internally.

If you are forced to ID check with the bank via a 3rd party, the only way is to ask for removal of data after the ID check. Do you see other ways?

I wished OpenAI and Anthropic IPOd already. They just try to outcompete each other by finding the most splashy headlines undercutting prices in unhealthy and unsustainable means long-term.

Even if the trigger was spoofed, how come there is no secure channel that the govt provides to receive the data? Was this one also compromised?

What would the company need to do to comply with licensing? Release the studio, shared library, and server-side components as AGPL?

Even the simplest router that differentiates models between plan & execute will ensure that this is consistently followed. Folks have too high FOMO to choose themselves.

- write/update tickets & collect prompt queues for when it's back up

- read & respond to customer feedback

The smell is about folks being attached to the same model because they're used to it. Just like any UI change in an app that one uses everyday is annoying.

What's often missing is the actual engineering. If you have evals for your use case, you use these to adjust to the new model. DSPy has great prompt engineering constructs it ships with.

The smell is all about vibing, where a model feels better because the structure of the answers is more familiar to a person, instead of engineering where given constraints and input/outputs one is using/bending the system to fulfil the requirements.

I love how there is a "Please do not discontinue gemini-2.0-flash[-lite], 2.5 is NOT an equivalent" from Feb 20th. Getting too attached to models is a smell.

They highlight the cache write price now much more in the guide. Did it increase vs. prior generations?