> start generating profit, and then you can start optimizing your costs
Good thing they jumped on that as soon as they were profitable instead of burning cash. Oh wait...
I think a distinction to draw here is that Cloudflare had relatively large capital raises and were almost immediately profitable¹. They had the luxury of throwing away money. Judicious optimisation makes sense for scrappy start-ups, especially when trivial optimisations like these could easily be farmed off to an agent.
Minor nitpick: I believe Elixir and Erlang are strongly typed languages, but for a long time they have both been dynamically typed. With v1.20, Elixir is well on its way to transitioning to a gradually static typed language, and to-date has done so almost purely through inference (except for guards which can explicitly specify type information)
This is where fuzzing would be useful. We have an at-least-parity-bug-level oracle with the reference PostgreSQL implementation. Just build a generator of queries (both invalid and valid) and ensure the output matches. The yardstick is how many log10(queries) it can go on average before a discrepancy is found.
It’s 2026, so another basic technique that every team should add to their testing strategy (in addition to proven techniques like fuzzing) is agentic user simulation. Set up an AI agent with access to the product, and prompt it to use the thing in hundreds of realistic use cases, and to report any possible bugs. It will catch a lot of ‘blind spot’ bugs that were previously things that were only caught by humans.
Debug yourself of the AI hype^. Besides config, SQL implementations have a very limited textual API surface of accepting queries. Putting whole layers of UI and agents around it is really inefficient way of finding edge cases. I wouldn't call that basic, nor the most comprehensive option. Fuzzing at the query layer is far more computationally efficient and effective.
A truly stochastic method is more likely to hit against edge cases, rather than an agent that tends to towards idiomatic solutions and that is trained against a corpus of existing software, and burns millions of tokens/watts spinning its wheels.
^ There's plenty of business value to be found in agentic AI without reaching for it for every solution. I'd even posit agentic AI is even better when paired with focused old-fashioned squishy-brained software engineering in the loop.
you don't have to compare whether stochastic fuzzing or agentic testing is the best. You can just do both of them. They both have enough ROI to be worth the trouble. That's why I said "in addition to".
The article cites another study by Richard Weller:
> Sure enough, when he exposed volunteers to the equivalent of 30 minutes of summer sunlight without sunscreen, their nitric oxide levels went up and their blood pressure went down.
I can't find information on the methods for this particular study. So I'm curious if he just set up UV lights on a timer and sat his subjects under them. That's something anyone can set up in their home office if they live somewhere gloomy. Instead of taking a vitamin D pill, turn the timer switch on for 30 minutes of a properly calibrated and positioned low-dose UV light (and out of direct line of sight to anyone not under it)
> turn the timer switch on for 30 minutes of a properly calibrated and positioned low-dose UV light (and out of direct line of sight to anyone not under it)
Gosh. Or just go outside! It has the added benefit of being somewhat enjoyable.
Or gosh, just read my comment! I said in somewhat gloomy places. Sometimes people are working when the 10 minutes of sunlight comes out. Often that's not enough time to get down the elevator and get outside before the sun hides behinds the clouds again, even if you can take the daytime break.
Elixir is not just expressive, it's highly conventional. I've found best practice code usually converges on the same idiomatic patterns, and well written codebases look very similar to each other in style
> I feel like most would just pay for the thing that is already setup and ready for them
Nothing stopping turnkey OSS AI hardware being productised, including niceties like opt-in automated updates. If the trend continues of models becoming smaller and more capable for everyday use, it also derisks against obsolescence.
The difference with biological brains is that the 'weights' (or synaptic action potentials) are updated with greater frequency. If one were reaching to make some kind of analogy to consciousness, this update frequency could be considered the 'resolution' of consciousness.
Good thing they jumped on that as soon as they were profitable instead of burning cash. Oh wait...
I think a distinction to draw here is that Cloudflare had relatively large capital raises and were almost immediately profitable¹. They had the luxury of throwing away money. Judicious optimisation makes sense for scrappy start-ups, especially when trivial optimisations like these could easily be farmed off to an agent.
¹ https://timeline.www.cloudflare.com/