This is a great question. In the next verses, the disciples ask pretty much the same thing: "Who then can be saved?" and then Jesus explains to them:

    With men this is impossible, but with God all things are possible.

1. The general consensus is that there were more people. This is assumed in Genesis and it (annoyingly!) doesn't bother to explain it, as the audience at the time already assumed it. Also, the authors weren't interested in all the logistics and technicalities that we are today.

2. Cities referenced in Genesis were likely fortified settlements, rather than like modern cities.

The idea that people in Africa could only build simple huts is a myth that came from the colonial era. Africa had large cities, architecture and metallurgy while parts of Europe were still tribal.

If you're keen to learn more, there are some good books that explain this much better than a comment can, such as "How to Read the Bible for All Its Worth" by Fee & Stuart and "Genesis for Normal People" by Pete Enns. I haven't read it but "African Civilizations" by Graham Connah is probably the go-to book on how African cities and technologies were so much further ahead than traditional European/US narratives place them.

The best resource for these kinds of questions is probably "The Bible Project". They have a load of YouTube videos and podcasts that cover these kinds of questions.

Here's the `skills` package on NPM: https://www.npmjs.com/package/skills - it's MIT licensed but I can't find it on Github.

`skills` looks to be a wrapper around `add-skill`: https://github.com/vercel-labs/add-skill

From the docs, `add-skill` auto detects from 16 different potential paths to copy skills to in a repository (.claude/, .codex/, .Gemini/, etc).

`add-skill` also let's you install skills globally (~/). From the code, `skills` looks like it doesn't support global installs but under the hood it passes all args to add-skill, so you should be able to install skills globally or install multiple skills (even if the wrapper doesn't expect it).

Aside: although lots of agents have adopted SKILLS.md conventions, they're currently all using their own paths. There doesn't seem to be a consensus yet, like there is with AGENTS.md. There are even 3 generic paths: .agent/skills/, .agents/skills/ and just skills/

Comments URL: https://news.ycombinator.com/item?id=46608740

Points: 2

# Comments: 1

Here's an agent in 24 lines of PHP, written in 2023. But it relies on `llm` to do HTTP and JSON.

https://github.com/dave1010/hubcap

https://en.wikipedia.org/wiki/Dancing_Ledge

The quarry caves at Winspit are worth an explore if you're in the area - they've been used in the set for Dr Who, Blake's 7 and Andor.

https://en.wikipedia.org/wiki/Winspit

- require proof of age (ID) to install apps from unofficial sources on your phone or PC. Probably best to block this at both the OS and also popular VPN downloading sites like github.com and debian.org.

- require proof of age (ID) to unblock DNS provider IP addresses like 8.8.8.8 and 1.1.1.1 at your ISP.

- make sure children aren't using any other "privacy" tools that might be a slippery slope to installing a VPN.

This makes it so much easier for the parents too! The internet will be so safe that they won't even need to talk to their children about internet safety.

My tool collection [0] is inspired by yours, with a handful of differences. I'm only at 53 tools at the moment.

What I did differently:

Hosted on Cloudflare Pages. This gives you preview URLs for pull requests out the box. This might be possible with Github Pages but I haven't checked. I've used Vercel for similar projects in the past. Cloudflare seems to have the odd failed build that needs a kick from their dashboard.

Some tools can make use of Workers/Functions for backend processing and secrets. I try to keep these to a minimum but they're occasionally useful.

I have an AGENTS.md that's updated with a Github action to automatically pull in Claude-style Skills from the .skills directory. I blogged about this pattern and am still waiting for a standard to evolve [2].

I have a base stylesheet that I instruct agents to pull in. This gives a bit of consistency and also let's them use Tailwind, which they'd seem to love.

[0] https://tools.dave.engineer/

[1] https://github.com/dave1010/tools/tree/main/functions

[2] https://dave.engineer/blog/2025/11/skills-to-agents/

Comments URL: https://news.ycombinator.com/item?id=46154138

Points: 5

# Comments: 2

I think this is more about the cost and time saving of being able to use cheaper models. Sub-agents are effectively the same as parallelization and temporary context compaction. (The same as with human teams, delegation and organisational structures.)

We're starting to see benchmarks include stats of low/medium/high reasoning effort and how newer models can match or beat older ones with fewer reasoning tokens. What would be interesting is seeing more benchmarks for different sub-agent reasoning combinations too. Eg does Claude perform better when Opus can use 10,000 tokens of Sonnet or 100,000 tokens of Haiku? What's the best agent response you can get for $1?

Where I think we might see gains in _some_ types of tasks is with vast quantities of tiny models. I.e many LLMs that are under 4B parameters used as sub-agents. I wonder what GPT-5.1 Pro would be like if it could orchestrate 1000 drone-like workers.

There's a big section on deception. One example is Opus is fed news about Anthropic's safety team being disbanded but then hides that info from the user.

The risks are a bit scary, especially around CBRNs. Opus is still only ASL-3 (systems that substantially increase the risk of catastrophic misuse) and not quite at ASL-4 (uplifting a second-tier state-level bioweapons programme to the sophistication and success of a first-tier one), so I think we're fine...

I've never written a blog post about a model release before but decided to this time [1]. The system card has quite a few surprises, so I've highlighted some bits that stood out to me (and Claude, ChatGPT and Gemini).

[0] https://www.anthropic.com/claude-opus-4-5-system-card

[1] https://dave.engineer/blog/2025/11/claude-opus-4.5-system-ca...

It only worked because of your LLM tool. Standing on the shoulders of giants.

In my mind LLMs are just UNIX strong manipulation tools like `sed` or `awk`: you give them an input and command and they give you an output. This is especially true if you use something like `llm` [1].

It then seems logical that you can compose calls to LLMs, loop and branch and combine them with other functions.

[0] https://github.com/dave1010/hubcap

[1] https://github.com/simonw/llm

This isn't so clear though: https://docs.innate.bot/main/software/basic/connecting-to-ba...

> BASIC is accessible for free to all users of Innate robots for 300 cumulative hours - and probably more if you ask us.

Is BASIC used just to create the behaviours or to run them too? It sounds like this is an API you host that turns a behaviour like "pick up socks" into ROS2 motor commands for the robot. Are you open sourcing this too, so anyone can run the (presumably GPU heavy) backend?

Does the robot needs an internet connection to work?

Also, more importantly, what does it look like with googly eyes stuck on?

Author here. (If you can call me that. GPT-4 and Gemini did the bulk of the work)

This is a (slightly tongue in cheek) benchmark to test some LLMs. All open source and all the data is in the repo.

It makes use of the excellent `llm` Python package from Simon Willison.

I've only benchmarked a couple of local models but want to see what the smallest LLM is that will score above the estimated "human CEO" performance. How long before a sub-1B parameter model performs better than a tech giant CEO?

Strobe warning, especially after about 20 seconds.