What secretly eats your local LLMs' speed as your context fills up - Part 3
Article summary
Quick briefing — cleaned from the original RSS feed
It’s been 3 weeks since the first article of this series. A lot has changed for me. In the first article there was a lot of excitement, because I found evidence, data and something new. The whole thing seemed a lucky coincidence. Over time I had the chance to revise my way to identify that situation - not only that. How do you prevent - or at least recognize - when a model will degrade for a given context? My answer: there's no rule, no magic formula - it has to be grounded in data. You cannot…
1Key Takeaways
- It’s been 3 weeks since the first article of this series.
- In the first article there was a lot of excitement, because I found evidence, data and something new.
- The whole thing seemed a lucky coincidence.
- Over time I had the chance to revise my way to identify that situation - not only that.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that it’s been 3 weeks since the first article of this series.
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