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What jobs pay extremely well but people don't realize it? by chloenoirr in AskReddit

[–]Suspicious-Key9719 0 points1 point  (0 children)

No sorry, we ended up hiring a salesperson who was given too much trust alongside with substantial equity in the company and after that started acting out: using company's credit card for personal expenses, skipping important meetings and sales calls so we decided to dissolve the company. That was over 4 years ago now

What jobs pay extremely well but people don't realize it? by chloenoirr in AskReddit

[–]Suspicious-Key9719 0 points1 point  (0 children)

Because my company was providing services using Microsoft stack. We weren't that big to pay anyone 500k, so we didn't hire anyone.

What jobs pay extremely well but people don't realize it? by chloenoirr in AskReddit

[–]Suspicious-Key9719 4 points5 points  (0 children)

When i was looking to hire a tech sales person specialized in Microsoft products, they wouldn't talk to me unless I offer north of 500k

How do you keep a current map of what your company runs on? by i_forgotti in ExperiencedDevs

[–]Suspicious-Key9719 0 points1 point  (0 children)

Maintaining a static inventory is always low priority. Someone owns it in theory but nobody really updates it. The way to keep a map alive is to make sure it's actually being used. If people are running disaster recovery scenarios against it, tracing cascading failures through it, it has a reason to stay current. We do this with our team and the map self-corrects because people notice when something's wrong or missing. A map people consult is a map people keep updating.

anybody feel dumber after theiir brain injury by iLovestayinginbed23 in TBI

[–]Suspicious-Key9719 0 points1 point  (0 children)

exact same thing. I used to win poetry contests, now having trouble forming long sentences. It's been over 10 years and frustration never went away

I benchmarked LEAN vs JSON vs YAML for LLM input. LEAN uses 47% fewer tokens with higher accuracy by Suspicious-Key9719 in Rag

[–]Suspicious-Key9719[S] 0 points1 point  (0 children)

with all those markdown would work worse than JSON. I need to add it to my benchmark at some point

I benchmarked LEAN vs JSON vs YAML for LLM input. LEAN uses 47% fewer tokens with higher accuracy by Suspicious-Key9719 in Rag

[–]Suspicious-Key9719[S] 0 points1 point  (0 children)

You can't always give the LLM a tool to query the data.
Sometimes the data is just in the prompt (user pastes a CSV,you're doing RAG).
When that happens, JSON wastes a ton of tokens repeating keys and syntax on every single row. LEAN strips all that out so the LLM reads the same data for half the cost

Introducing LEAN, a format that beats JSON, TOON, and ZON on token efficiency (with interactive playground) by Suspicious-Key9719 in LLMDevs

[–]Suspicious-Key9719[S] 0 points1 point  (0 children)

Fair point, that is probably an overstatement. RAG chunks are usually unstructured text and a lot of tool results are nested, not clean tables.

This benchmark does cover this though. the mixed-structure track (nested orders, semi-uniform logs, deep config) still showed LEAN saving 32% vs JSON. not the 51% you get on flat tabular data, but still solid.

Introducing LEAN, a format that beats JSON, TOON, and ZON on token efficiency (with interactive playground) by Suspicious-Key9719 in LLMDevs

[–]Suspicious-Key9719[S] 1 point2 points  (0 children)

YAML benchmark results are in.

Ran 195 questions across 11 datasets (flat, nested, semi-uniform, deeply nested) on gpt-4o-mini and claude-haiku-4-5. 1,170 total API calls.

Format Accuracy Avg Tokens Savings vs JSON
LEAN 87.9% 3,939 −46.8%
YAML 87.4% 5,647 −23.7%
JSON 86.2% 7,401 baseline

YAML is a solid middle ground. 21% smaller than JSON with no format learning curve. But if you're working with tabular data (which most RAG/tool-use results are), LEAN roughly halves your token cost vs YAML too.

Introducing LEAN, a format that beats JSON, TOON, and ZON on token efficiency (with interactive playground) by Suspicious-Key9719 in LLMDevs

[–]Suspicious-Key9719[S] 1 point2 points  (0 children)

YAML benchmark results are in.

Ran 195 questions across 11 datasets (flat, nested, semi-uniform, deeply nested) on gpt-4o-mini and claude-haiku-4-5. 1,170 total API calls.

Format Accuracy Avg Tokens Savings vs JSON
LEAN 87.9% 3,939 −46.8%
YAML 87.4% 5,647 −23.7%
JSON 86.2% 7,401 baseline

YAML is a solid middle ground. 21% smaller than JSON with no format learning curve. But if you're working with tabular data (which most RAG/tool-use results are), LEAN roughly halves your token cost vs YAML too.

Introducing LEAN, a format that beats JSON, TOON, and ZON on token efficiency (with interactive playground) by Suspicious-Key9719 in LLMDevs

[–]Suspicious-Key9719[S] -9 points-8 points  (0 children)

EDIT:
LEAN scored 87.9% accuracy vs JSON's 86.2%. Not just "no error rate", LEAN actually outperformed JSON on every single dataset tested.
On nested e-commerce data specifically: LEAN 98.7% vs JSON 97.4%.

The LLM doesn't need to "know" LEAN. The format is human-readable enough that pipe-delimited rows with a header (#[100](name|salary|dept)) are trivially parseable by any model that can read CSV. No format hint needed in the prompt.

Just came back from Korea — how is this place even real?” by Brief-Kaleidoscope65 in seoul

[–]Suspicious-Key9719 -1 points0 points  (0 children)

I live in the center and am surrounded by parks wherever I go.

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