Funny coincidence - I visited that valley for the first time just last week, on my way to the Isle of Skye.
And as a further coincidence, I met Jimmy Saville about 25 years ago. I was in Leeds hospital after a heart operation, and this old and somewhat scruffy track suited guy just walks in to the ward and starts talking to me. I had no idea who he was. After he left, a nurse asked “did you speak to Jimmy?”. It was creepy and unnerving seeing first hand how he just got to roam around.
I can confirm, the graffiti-covered Saville residence has almost completely been demolished.
I am not sure it is inherent to LLM code generation as much as the training data and the tuning of the model. Emphasizing verbose code with lots of explicit explanation. Possibly the stuff you see in CS textbooks. And probably lots of vibe code style edits where the LLM fixes a bug, always adding further complexity to the code.
Funny thing is you could create measurable criterias explaining what is wrong with the code. Ie. function line count or cyclomatic complexity and then letting those guide the code generation.
Very true, with the right feedback loop AI would do a wonderful job of refactoring.
But if AI is the primary author and consumer of this code, that would be an unnecessary step. No need to clean it up for our feeble little human minds.
I was just interested in what this file actually does - and am finding it hard to grok, scrolling through on a mobile device!
I used to find Gary Marcus a good antidote to the AI hype, and followed his critique. But honestly, his more recent writings are clutching at straws. This article feels like desperation.
It’s a bit like saying that driving cars still requires human muscles to operate the controls, so human strength has ‘won’, when it is clearly the internal combustion engine that has created the speed advantage of the car.
Looks interesting. Quick question - one of the biggest challenges with agentic systems in non-deterministic behaviour. Does this framework do anything to address this? Does it help test and validate agent behaviour?
This is where the governance layer of Orloj fits in. You create policies and attach them to agents/tools which are all governed at runtime. These policies could be token guardrails, tool authority, etc. You can then check all of the traces of a task to have an audit trail for debugging (cli or UI). There are also human in the loop approval features that can be applied to make sure things are working correctly before proceeding on tasks.
Nice visualizations. I went the opposite way, showing how many times the timezones were adjusted for different regions, on map (both with and without DST).
My random claim to fame; I was the support act (juggler) for Norman Lovett (the red dwarf ships computer), for one night only in the Welsh town of Bangor.
Speaking of which I remember Chris Barrie (who played Rimmer) lamenting some of the filming of Red Dwarf and how he struggled to and gave up on hanging out with Craig Charles (Lister) and Danny John-Jules (Cat) because he'd be tired and ready for bed and they'd just be getting started. And then they'd show up sometimes straight from the clubs to shooting the next morning, or sometimes drunk still, or hungover.
Craig Charles nicked my lighter in Oscar's nightclub in Plymouth in roughly 1991. I wouldn't have minded but it was my Dad's Zippo (RAOC, 7th/11th Armoured Brigade). He asked for a light, wandered off with it and then vanished, whilst I was distracted ahem.
And Open Design (HN front page yesterday) is supported by “Six load-bearing ideas”
The similarities in the way these prompt libraries are documented doesn’t feel coincidental.
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