Yesterday I installed llama.cpp to test it with local AI Data Analyst that I'm building. I was also testing other open LLM providers: Ollama, Jan, vLLM, LM Studio. I had older NVIDIA card (RTX 3070) and llama.cpp instalation was smooth, contrary to vLLM which required me to reinstall CUDA drivers because by default it installed the latest one. I'm curious if there is a speed difference between the same open LLM model served with different runners.
I was able to fit only small models Qwen3.5-4B in RTX3070 which is not very useful for Python and SQL generation thought. When I wan to test larger open LLM models I often just use cloud resources.
is it terminal on steroids some kind of? so you can manage mutiple coding agents? how many coding agents you can manage in parallel that it is still comfortable to work and code changes are meaningful
yes, we surface agent states automatically so you can see what's running or needs attention across the different workspaces. there's a set of tasks where having 5-6 running in parallel is still productive for me such as running spikes and fixing small issue.
As we're investing more into integration test and self-validating for the agents we're able to increase the number without sacrificing quality.
I love such puzzles! I used to use a lot ternary operators in C++ but one day friend of mine told me that I shouldn't nest ternary operators too much because code is too complicated to read - he understands code perfectly, he was just worried about younger programmers. Since then I started to use longer versions of code instead of smart shortcuts - to improve readability of code.
before I ask AI to write anything, I prepare a plan, I was very positively surprised when noticed Plan mode in Codex recently. It make me feel that maybe others doing the same and that's why they added it. Anyway, I start with plan, then ask AI to do just one step.
If coding a new feature, I do one step and check the code, doing git diff, reading changes, or just asking Codex, to show me changes.
If writing an article, I ask for only one paragraph. I read paragraph and if it is ok, I accept it, if it doesn't show off my thoughts I work on one paragraph.
If doing data analysis with AI, I do one step of analysis and ask AI to display intermediate results so I can see if all is going in good direction and there are no hallucinations, additionally I have follow-up prompts for AI to do results verification. If all looks good, then I continue to the next step.
I don't like situation when I ask AI to do all code changes, or all article, or all data analysis in one pass with one prompt. It is simply impossible to check if AI is correct and results are not satisfactory. You can easily see this when asking AI to write a deep article with one prompt - you clearly see that it doesn't reflect your thoughts.
Maybe step-by-step is the approach to use AI and not feel dumber.
Python outputs is also very versatile. You can use Python to build command line script, web application, desktop app with GUI, notebook with data analysis, or Python package and share with others. It is many ways how Python code can be used by final user.
Im working on AI data analyst - MLJAR Studio. It is conversational UI with AI agent which uses Python to provide data insights. It is available as desktop application https://mljar.com
I'm working on an AI Data Analyst in MLJAR Studio and found this small but interesting bug while testing a medical data use case. The AI-generated Pandas code looked correct and executed without errors, but the dataframe was misaligned. The first patient had 148 pregnancies because glucose values were shifted into the Pregnancies column. The interesting part for me was that the bug was caught only because both the displayed dataframe and an extra LLM output checking step were reviewed.
User is not touching notebook at all, user just ask questions in natural language, and AI is using Python to compute answer, the ipynb notebook format is used to save the conversation.
Human in the loop in data analysis is really challenging task. We provide Python code for inspection, so user can check details how results were produced. Additionally, we run AI on results - user need to check the outputs and AI provided insights.
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