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Alex Strick van Linschoten
mlops.systems
Third time’s a charm: setting up instrumentation with
Braintrust
,
Langfuse
and litellm\.
Braintrust
ended up not being as ergonomic as
Langfuse
so I switch over midway\.
mlops.systems
·
49w
It’s important to
instrument
your AI applications\! I hope this can more or less be taken as given just as you’d expect a
non-AI-infused
app to capture logs\. When you’re...
mlops.systems
·
49w
I’ve been working on a project called [
hinbox
](https://
github.com/strickvl/hinbox
) - a flexible entity extraction system designed to help historians and researchers build...
mlops.systems
·
50w
Error analysis to find
failure
modes
mlops.systems
·
51w
How to think about
evals
mlops.systems
·
52w
original
↗
mlops.systems
·
57w
original
↗
mlops.systems
·
61w
Building an MCP Server for
Beeminder
: Connecting AI
Assistants
to Personal Data
mlops.systems
·
64w
Tinbox
: an LLM-based
document
translation tool
mlops.systems
·
65w
Starting the
Hugging
Face Agents
course
mlops.systems
·
66w
AI Engineering Architecture and User
Feedback
mlops.systems
·
66w
Notes on ‘AI Engineering’
chapter
9: Inference
Optimisation
mlops.systems
·
66w
Dataset
Engineering: The Art and Science of Data
Preparation
mlops.systems
·
66w
Notes on ‘AI Engineering’ (Chip
Huyen
) chapter 7:
Finetuning
mlops.systems
·
68w
Notes on ‘AI Engineering’ (Chip
Huyen
)
chapter
6
mlops.systems
·
68w
Notes on ‘AI Engineering’ (Chip
Huyen
)
chapter
4
mlops.systems
·
68w
Notes on ‘AI Engineering’ (Chip
Huyen
)
chapter
3
mlops.systems
·
69w
Notes on ‘AI Engineering’ (Chip
Huyen
)
chapter
1
mlops.systems
·
69w
Final notes on ‘
Prompt
Engineering for LLMs’
mlops.systems
·
69w
Assembling
the Prompt: Notes on ‘Prompt Engineering for LLMs’
ch
6
mlops.systems
·
70w
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