this post was submitted on 17 Jun 2026
170 points (97.2% liked)
Technology
85494 readers
4329 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 3 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
My guess is the next big thing to come out is, we can probably squeeze a lot more reliability out of smaller models. But their workflows, context, validations, etc will need to be very tightly optimized.
I can see harnesses coming with their own highly specialized lightweight models in the future. Some for very efficiently converting a basic prompt into chain-of-thought steps. Some for very efficiently determining relevant parts of a repository. Some for… a lot of highly specialized stuff. Then the harness would orchestrate these under the hood, reducing the cognitive load placed on any larger generalized LLMs. Those “larger generalized LLMs” could be something like 12b parameters.
Hopefully, soon after, we can start benchmarking how much different harnesses and augmentations improve baseline model performance. Ideally, in the long run, with a deeper understanding of how to tailor harness to workload and produce more procedural determinism. Then we can start configuring harnesses like data pipelines and run them through higher-level orchestration like Airflow too.