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I should not trust their "vibe-coded productivity/token cost saving hacks" but I should trust yours? Save 30% token costs when using Claude Code, Codex, Op

I should not trust their "vibe-coded productivity/token cost saving hacks" but I should trust yours? Save 30% token costs when using Claude Code, Codex, OpenCode for free - with open source, local semantic search. Works for small and large codebases and monorepos! Enterprise-ready and fully compliant via Ollama and SQLite-vec. Releases v0.0.42 Latest last month Why should I trust that what you're peddling isn't snakeoil?

icantevenhold 6d ago
Only way to find out is to do some testing yourself i think. I’m using less tokens with Lumen but I also use a bunch of other tokens hacks/skills; it’s hard to measure the impact exactly but it feels significant
huflungdung 6d ago
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aeneas_ory 6d ago
I literally say you should take benchmarks with a grain of salt :) > Of course, it's always dependent on statistical noise + host system load, and running sufficiently large benchmarks is simply too expensive, so take em with a grain of salt. And the savings listed are coming from a benchmark harness that implements different OSS bugs one time with and one without lumen - in those cases the % saved are reproducible (caveat: it was on older models, Opus 4.6 I believe). Also I explain WHY it saves tokens - because the model doesn’t have to brute force different terms until it finds the match it needs, but uses semantic „distance“ so the embedding does it for the model.
- lucaprata 6d ago
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deleted 6d ago
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bustermellotron 5d ago
I just went through a lot of benchmarking and the only thing that seemed better than rg was chunkhound, which sounds similar to this project. Actually a small Jina embedding model actually did better than voyage AI, but took a long time to index. Also chunkhound doesn’t work well with worktrees. In the end, I decided to stick with rg.
iharnoor 5d ago
Half of what we see or consume is hype, so we should view everything with nuance. Sometimes truth lies in the middle.

icantevenhold 6d ago

Only way to find out is to do some testing yourself i think. I’m using less tokens with Lumen but I also use a bunch of other tokens hacks/skills; it’s hard to measure the impact exactly but it feels significant

huflungdung 6d ago

[dead]

aeneas_ory 6d ago

I literally say you should take benchmarks with a grain of salt :) > Of course, it's always dependent on statistical noise + host system load, and running sufficiently large benchmarks is simply too expensive, so take em with a grain of salt. And the savings listed are coming from a benchmark harness that implements different OSS bugs one time with and one without lumen - in those cases the % saved are reproducible (caveat: it was on older models, Opus 4.6 I believe). Also I explain WHY it saves tokens - because the model doesn’t have to brute force different terms until it finds the match it needs, but uses semantic „distance“ so the embedding does it for the model.

lucaprata 6d ago
[flagged]

deleted 6d ago

[deleted]

bustermellotron 5d ago

I just went through a lot of benchmarking and the only thing that seemed better than rg was chunkhound, which sounds similar to this project. Actually a small Jina embedding model actually did better than voyage AI, but took a long time to index. Also chunkhound doesn’t work well with worktrees. In the end, I decided to stick with rg.

iharnoor 5d ago

Half of what we see or consume is hype, so we should view everything with nuance. Sometimes truth lies in the middle.