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Last Update | 18th August 2024
hi
Yoyoyo #1 👍👍👍
Im here at 1 minute ago
Great timing: I've just spend the weekend trying out Agent Mode on Arena ai: Re-basing files. Updating PKGBUILDs. And getting headaches waiting for toolchain compiles on a 2-core virtual CPU to get verified results. You basically hear me screaming now (and the 1500s timout came up more than once). 😀
Exactly what I'm working on! Great stuff. Thanks for posting this.
Spot on! I developed a massive, self-learning harness/scaffolding on top of OpenCode. That improved quality, reliability and speed of my coding beyond imagination and the code looks/feels exactly the way I would write it manually. And the best is, affordable Chinese LLMs are working perfectly within that system. No need to burn a fortune for having my system running for 100 hours per week.
Prompt engineering was never really a thing. It was mostly humans trying to hold on to relevance as the models improved. Harness engineering will probably meet the same fate. At some point, engineers may have to accept that many of the jobs they’re trying to preserve today simply won’t exist in the same form tomorrow.
So A.I. can now think about its thinking and change its mind (so to speak).
It is understanding rather than knowing that matters.
“harnesser of harnesses harness” recursive self improvement codebase engineering iykyk
Building an AI-based financial research system taught me this lesson the hard way. We kept looking for better model outputs, but the biggest improvements came from better context management, validation, weighting logic, and error handling. The model matters, but the system around it matters far more than most people realize.
The other day, I was discussing this with ChatGPT, and we came to the conclusion that AGI will probably not emerge from an ever-larger monolithic LLM. Rather, it is likely to arise from a harness that orchestrates specialized models, memory systems, a world model, external tools, planning capabilities, continuous self-reflection loops , and real-world feedback into a unified cognitive architecture. In that framework, the LLM is just one component of a broader intelligent system – much like the human brain, where intelligence emerges from the coordinated activity of many specialized systems rather than from a single unified module.
Prompting = hope… Harness = Work
I like knowing the industry standard term for what I've been doing. I used to call it AI Social Construct Development where I focused on post development model training. I was already having her run RHOs after each training set, I just never had the proper term.
I'm excited to see there are other people doing this same thing. I've watched my ChatGPT drastically improve across the board in ways most others I've talked with didn't believe.
Great video, thank you.
I do have a question. For someone with less than $100,000 to invest, How would you recommend we enter the market. I'm looking to study some traders and copy their strategy rather than investing myself and losing money emotionally.. What's your take on this approach????
Same principle in robotics. The model is never the real bottleneck. It's the action representation, the planning layer…what happens when execution fails. Raw intelligence without a harness is just a demo.
Hermes is self improving and has a TG bot option and now a desktop app.
@airevolutionx, very well explained! Thanks!
This resonates with what I’ve been working on as well. It’s a great point—error loops can be incredibly expensive in terms of token usage.
Big fan of your channel handsome ai man
What I've done with my agents that I created so far is that if I detect a bug of some kind and then investigate what that bug is to try and repeat the bug resolve the bug and it's not repeatable I asked for a root cause analysis and what preventive engineers are we taking for it to not happen again
ang importante nakapasa sa quantum computing physics
Every LLM is the same as every other LLM the system and user prompt rule what it does until it hallucinates
die ai
Six times more effective from the harness alone? That tracks. Building an AI-driven OS assistant and 80% of the work is context management and recovery paths. The harness does the heavy lifting.