Lost an afternoon to what looked like an off-by-one.
The loop was right. The input wasn't.
np.arange(1, 1.3, 0.1) returns 4 elements, not 3. Float accumulation drifts the last value past stop.
For evenly spaced floats, use np.linspace: specify the count, not the step.
Stop making users do mental gymnastics in your data pipelines.
I just renamed `is_not_historical_data` to `is_nrt`.
Every read used to force a double-negation.
Now the queries actually read like English.
Anyone telling you that you need to use Claude to run just about any OpenClaw spending a wacky $1000 or more per month is no expert you should trust.
Use @Grok. Great pricing and 4.3 is in another category. You wonโt be burning $1000 a month.
You will absolutely thank me.
Youโll really start to notice these limitations when youโre building an AI Agent (interacting directly with a model API) or when you're running a local model. In those cases, youโre dealing with a pure text predictor, and thatโs where the "math logic" can easily break down.
Tip for doing Mathematical Calculations:
Instead of asking an LLM to perform calculations directly on a dataset, ask it to write the Python code to do it for you. You can then run that code to get the actual results.
While apps like ChatGPT and Gemini now have built-in tools for searching the web or doing math, itโs easy to forget that the underlying model is still just predicting text.
A lot of things have become possible due to vibe coding.
Features that were earlier not worth investing time on can be shipped fast.
Itโs time when software would get more refined.
@googleaidevs Some serious prompting would have gone into it for sure otherwise we would have got another generic slop. Great if we can get prompts as well.
Spent an hour debugging a deployment flow.
Built it into a reusable skill.
Now the same problem solves itself in 30 seconds.
At scale: developers publish solutions as skills at a marketplace. AI agents discover and apply them instantly.
Knowledge compounds automatically.