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    Elvis Saravia Stresses Varying Quality of AI Harnesses

    Tweet argues good AI harnesses solve context engineering via specific optimizations.

    EL
    4 Sources, 28d ago, first seen 28d ago

    TLDR

    Creator and AI researcher Elvis Saravia shared thoughts on AI harnesses in a recent post. He pointed out that not every harness performs the same and rejected the idea of a single approach fitting all needs. The tweet explains that effective harnesses tackle context engineering issues directly. Saravia highlighted how attention to elements including system prompts, caching, and tool calling allows certain harnesses to differentiate themselves from others. He described harness engineering as an important area of focus. The post comes from someone involved in AI education and tools.

    Combined views

    24.7K

    4 Sources, first seen 28d ago

    Combined views

    24.7K

    4 Sources, first seen 28d ago

    213 likes
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    213 likes
    63 comments
    105 saves
    27 reposts
    63 comments
    105 saves
    27 reposts

    Sentiment

    Positive96.7%3.3%Negative

    Summary

    Sentiment

    Positive96.7%3.3%Negative

    Many accounts welcomed building minimal custom harnesses because it turns AI coding from magic into engineering and reveals real bottlenecks, failure modes, and control points.

    Based on 35 sentiment-bearing replies from 30 accounts across 2 conversations.

    Summary

    Many accounts welcomed building minimal custom harnesses because it turns AI coding from magic into engineering and reveals real bottlenecks, failure modes, and control points.

    Based on 35 sentiment-bearing replies from 30 accounts across 2 conversations.

    4 Sources

    @omarsar0Not all harnesses are created equal. And there is no one-size-fits-all. A good harness should solve your context engineering problems. Understanding and optimizing lower-level details like system prompts, caching, and effective tool calling is how the harness stands out. This is why harness engineering is an important skill for AI engineers today. Start by building your own tiny/minimal harness (in your favorite language) with a basic agent loop, system prompt, and tool calling capabilities. Test it on a use case, verify results, and iterate on it. In no time, you realize that anyone can build a quite powerful harness. But it's important to understand those lower-level details first. Understand the role they play and how to tune them. These skills can transfer easily to existing, more production-ready harnesses when you get there. At that point, you are better equipped to modify and tune that harness for your tasks. This is not about just learning fundamentals. It's having a good grasp of the knobs you can tune to get the most out of your agent harnesses.

    4 Sources

    @omarsar0Not all harnesses are created equal. And there is no one-size-fits-all. A good harness should solve your context engineering problems. Understanding and optimizing lower-level details like system prompts, caching, and effective tool calling is how the harness stands out. This is why harness engineering is an important skill for AI engineers today. Start by building your own tiny/minimal harness (in your favorite language) with a basic agent loop, system prompt, and tool calling capabilities. Test it on a use case, verify results, and iterate on it. In no time, you realize that anyone can build a quite powerful harness. But it's important to understand those lower-level details first. Understand the role they play and how to tune them. These skills can transfer easily to existing, more production-ready harnesses when you get there. At that point, you are better equipped to modify and tune that harness for your tasks. This is not about just learning fundamentals. It's having a good grasp of the knobs you can tune to get the most out of your agent harnesses.