Code mode yields a 99.2% cost reduction in our systems

A maxim I’ve arrived at working w llm every day is “put deterministic things in code, non deterministic in llm”. I do wish the harnesses would be more helpful in this regard. For example I’ve seen tons of scheduled jobs that people wrote in Claude/copilot/etc that could easily have been scripts. They aren’t scripts because the author doesn’t know how to script and they stop at the point the llm does what they want. It isn’t hard to then tell the llm “make a script to do all of the tool calls this prompt needs and then pass the result back to the llm for this non-deterministic part”.

I am still a bit surprised that this is not more widely used. Normal inference + tool calls is effectively composition via serialized natural language, whereas Code Mode / Programmatic Tool Calling is using purpose built languages (aka programming languages) for composition and concurrency. It somewhat feels obvious to me that the latter is way more token and latency efficient.

The article is so painful to read.

We ultimately switched to this approach with great success for our own product. We stumbled on its success a little by chance.

We have a visual designer and for LLMs to interact with it we originally built a tool per manipulation operation type (e.g. add, edit, delete item - for various item types).

We actually already had a JavaScript API with corresponding .d.ts file for scripting inside our product and one of our clients asked that we also expose this as an MCP tool. I figured sure, should be quite quick and easy, and with Claude Code's help I managed to do it in a single afternoon.

We then found that the LLMs way preferred reaching for this tool, managing with it to get their tasks done with fewer mistakes along the way and in much smaller time frames.

After seeing this and doing some more validation (I've also read that Cloudflare article), we ditched the other tools completely and made a cheat sheet for the LLMs on how to use our API.

Because our API returns decent error messages including stack traces, even if the script fails the LLMs have no trouble making another script to fix their mistake and carrying on from where the error occurred.

In hindsight it really was hardly surprising as LLMs are already aiming to be as good as possible at coding and with JS being so popular I imagine it's particularly good at it.

The problem is that when the LLM writes ad-hoc code how can you trust it?

For example in my Liveclip MCP server I'm working on (not released yet) I have table manipulation type tools

So for example a couple days ago Claude made a combined ranking score for my Youtube Shorts analytics -- it did all these tool calls in the same turn

First it normalized the % values into numbers

{ "destination_col": "G", "key": "cinemasleepstories_temp_2026071702", "pattern": "%", "replacement": "", "source": { "col": "E", "row_start": 1, "row_end": 6 } }

Then it made the composite score

{ "dest_col_start": "H", "dest_row_end": 6, "dest_row_start": 1, "expr": "round((likes+1)(followers+1)stayed, 1)", "source_key": "cinemasleepstories_temp_2026071702", "sources": [ { "col": "C", "name": "likes", "row_offset": 0 }, { "col": "D", "name": "followers", "row_offset": 0 }, { "col": "G", "name": "stayed", "row_offset": 0 } ] }

And then it set the new headers

{ "headers": { "G": "Stayed (numeric)", "H": "Composite Score" }, "key": "cinemasleepstories_temp_2026071702" }

If Claude just said "let me write some pandas against this CSV" the workflow would be a lot more iffy and generally uncomfortable/ephemeral

Cool but isn’t everyone already doing this? Surprised this is what I saw at top of HN this morning.

I could be misreading this, but looking at the numbers, it seems that--despite the claim "we didn't estimate this"--basically everything in this is an estimate. It's a little hard to tell though, because the generated prose seems to occasionally contradict or repeat itself.

Token savings are great, but I wonder whether the biggest long-term benefit is latency reduction rather then API cost,Faster feedback loops can easily outweigh a few cents saved per request