salma-remyx/vqasynth_testing_evals_eval
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Salma Mayorquin PRO
salma-remyx
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reacted to TravisMuhlestein's post with 👍 about 7 hours ago
One of the more interesting questions emerging around AI-assisted software engineering isn't whether models can write code.
We've already crossed that threshold.
The harder question is:
Which programming languages are best suited for AI-generated software?
One lesson we've seen at GoDaddy is that AI-assisted development changes what we optimize for. It's no longer enough to generate code quickly—we also need systems that help AI continuously verify, refine, and improve what they produce.
I'm a huge Rust fan. It's extremely durable, and its lints and chatty compiler are exactly the kind of feedback loops LLMs devour. We're seeing systems running at one-tenth the cost with 10x the durability, all built through AI test-driven development with full documentation and backward/forward compatibility.
Rust offers an interesting answer.
Its ownership model, strict compiler, and memory safety guarantees create an environment where verification becomes part of the development loop rather than something added afterward.
For autonomous coding agents, that's a meaningful shift.
Instead of relying primarily on runtime failures or manual review, agents receive immediate compiler feedback that improves subsequent iterations. The compiler effectively becomes another participant in the reasoning loop.
This shifts the conversation beyond code generation.
It's about building engineering systems where generation, verification, and correction happen continuously.
As agentic development evolves, language design may become just as important as model capability.
Great technical write-up from the GoDaddy Engineering team.
🔗 https://www.godaddy.com/resources/news/rust-is-the-native-language-of-agentic-development
Curious whether others think AI-native development will influence programming language adoption—or whether models will simply adapt to the languages developers already use. repliedto their post about 8 hours ago
It's conference season, so you'll find an uptick in chatter around the research reproducibility crisis. Consider this a PSA on where the real challenges in working with research actually live.
After all, AI has made it way easier to release code and model artifacts alongside the preprints. And how many times do you really need to replicate the authors' exact configuration?
Downstream of that, as engineers evaluate candidate methods for improving THEIR systems, they rarely find a drop-in solution. More often, they're making tough tradeoffs in fidelity to the documented technique and the constraints of their deployment scenario.
They're swapping models or data indexing strategies. They have their own benchmarks to measure changes against. They're making principled reductions of a technique to respect some resource limit not considered in the source paper.
AI coding has made replication cheap when a paper provides starting point for your own experiments. But the work of adoption requires validation grounded in real-world outcomes.
So put these techniques to the test in your own system, and you'll understand a method's impact well before the survey paper drops in six months.
At Remyx AI, we're helping teams discover, implement, and validate what's next for their systems.
Get Outrider: https://github.com/remyxai/outrider repliedto their post about 9 hours ago
It's conference season, so you'll find an uptick in chatter around the research reproducibility crisis. Consider this a PSA on where the real challenges in working with research actually live.
After all, AI has made it way easier to release code and model artifacts alongside the preprints. And how many times do you really need to replicate the authors' exact configuration?
Downstream of that, as engineers evaluate candidate methods for improving THEIR systems, they rarely find a drop-in solution. More often, they're making tough tradeoffs in fidelity to the documented technique and the constraints of their deployment scenario.
They're swapping models or data indexing strategies. They have their own benchmarks to measure changes against. They're making principled reductions of a technique to respect some resource limit not considered in the source paper.
AI coding has made replication cheap when a paper provides starting point for your own experiments. But the work of adoption requires validation grounded in real-world outcomes.
So put these techniques to the test in your own system, and you'll understand a method's impact well before the survey paper drops in six months.
At Remyx AI, we're helping teams discover, implement, and validate what's next for their systems.
Get Outrider: https://github.com/remyxai/outriderOrganizations
