Thread

1. tolugenius · 1790091450 · focus · HN ↗

   > About 20 Markdown files described browser use, connectors, payments, credentials, data handling, generated files, voice, goals, and scheduling.

   This the state of software engineering in 2026.

   Edit: clarified engineering to software engineering, which is more correct

   1. s08148692 · 1790092923 · focus · HN ↗

      To be fair there's probably a considerable amount of engineering that went into evaluating those markdown files so the agent behaviour is statistically reliable. The markdown is the product, not the process

      1. xnickb · 1790093338 · focus · HN ↗

         which part of that is engineering exactly?

         Not trying to be snarky. I genuinely don't get it

         1. thornewolf · 1790093887 · focus · HN ↗

            Write a prompt, evaluate the prompt, understand that is succeeds 95% of the time.

            Write a new prompt, evaluate, it now succeeds 99% of the time. Measure what changes between prompt #1 and prompt #2, understand what contributed to the performance jump.

            Write a third prompt, this one succeeds 100% of the time. Increase the size of your evaluation set, find a 1/5000 error-class and a 1/10000 error-class, add some explicit code to correct for this cases.

            Roll out to production, collecting usage metrics. You make some tweaks to your harness, your prompts. Eventually you have confidence that your system has fewer mistakes than 1 in 100k.

            Now, multiply this iteration across all your different prompts and different ways that they might interact with one another.

            1. kfsone · 1790100877 · focus · HN ↗

               There's a reason engineers are prissy about people coming along and saying "I write code, I'm an engineer" that people periodically try to sand-paper away.

               Engineers don't just tie a sheet to a rock and throw it off a cliff and call themselves aerospace engineers.

               They do full diligence on the theory, math, physics, material science, fluid dynamics, etc, and plan a controlled series of tests specifically designed to verify/challenge/disprove their concept and the theories behind it.

               Sure, there's a team member ultimately responsible throwing half a dozen rocks off a cliff in the first test.

               A technician.

               The guy who throws the rock off the cliff is a technician.

               1. [deleted] · 1790103383 · focus · HN ↗

                  [deleted]

               2. saltcured · 1790104658 · focus · HN ↗

                  The other glossed over part is that the above sounds like science.

                  Engineering often continues until the concepts and theories are developed into safe, practical methods. "If you stay within these parameters, you can confidently expect these results." The reliability can be codified and reproduced without going from first principles on every application of it.

                  It's not clear to me that the current AI fad is really developing such reproducible, safe methods. "If you stay within these parameters, you might get these results. Or a teapot. Or some subtly misleading fabrication."

                  You have to do full due diligence to validate every result. There is safe usage where the hard work was done up front so that day to day practice can skip to boring and reliable application.

                  1. kfsone · 1790113286 · focus · HN ↗

                     To a software engineer, a (current) LLM is a stateless algorithm that performs an idempotent transformation on a large numeric input.

                     People who think it's a system that thinks and reasons have confused the agentic harness, perhaps forgotten(?) layer0[0] is a seed, the inference engine sets to a concrete value when the caller leaves it as 0.

                     They probably work on (current) AI software by repeatedly writing prompts like "DON'T READ THE FILES IN /tmp. SOME OF THE FILES IN /tmp ARE VERY LARGE. DUE TO THEIR SIZE, YOU ARE NOT TO READ THE FILES IN /tmp." and wondering why the model becomes obsessed with files in /tmp 100k tokens into every conversation.

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