[flagged]
how many datacenters / computers are there running millions of hours of computer games? Why is escaping reality and damaging climate with compute better than using LLMs?
Build a Basic AI Agent from Scratch: Long Task Planning
31–40 of 60 posts
Re: Build a Basic AI Agent from Scratch: Long Task Planning
#32[flagged]
Re: Build a Basic AI Agent from Scratch: Long Task Planning
#33I've tried most form of planning - from the basic AGENTS.md guide to keeping ./dev/ plan files, todo list tools, sqlite db with both minimal and extensive tracking, etc. None of them have been worth it. A year ago the models needed to be reminded. Today they can follow a plan from text alone. This is my experience from working on a project alone - in teams ... i actually think the same lesson holds in the new AI para…
Did you read the article?
It's not about enhancing Claude. This article is about creating your own agent, and giving it the ability to create plans and tasks list for its or.
The way Claude code creates plans and tasks list for itself.
The article is about creating that in your own harness for things not using claude code, like say a custom LLM integration in your own web app.
Re: Build a Basic AI Agent from Scratch: Long Task Planning
#34Earlier quoted context omitted.
> great Rust is (which it is) They just took undefined behaviour and called it unsafe. Theyve not really solved anything. Even their own std lib has security bugs in unsafe code. And their only ever retort is "there are thousands of these bugs a day in c code"... Let's wait until rust gets used seriously in the systems and embedded space first, no point comparing c to minnows like rust when it comes to total cves.
Security and safety are two different things.
Clutching at straws, as is typical of a rustacean
Re: Build a Basic AI Agent from Scratch: Long Task Planning
#35Re: Build a Basic AI Agent from Scratch: Long Task Planning
#36I've tried most form of planning - from the basic AGENTS.md guide to keeping ./dev/ plan files, todo list tools, sqlite db with both minimal and extensive tracking, etc. None of them have been worth it. A year ago the models needed to be reminded. Today they can follow a plan from text alone. This is my experience from working on a project alone - in teams ... i actually think the same lesson holds in the new AI para…
Please don't take offense to this very dumb question: Why can't you do the planning ? Figure out what needs to be done , break it down into small tasks and then ask the agent to execute those small tasks? When we executed projects in the past, this is what I would do as a lead: figure out the overall software architecture and delegate the tasks to developers. This way I always knew how the system worked and could ext…
I could do the planning but I don't, for the same reason that I could write the source code but I don't, for the same reason that I could write the machine code but I don't.
Re: Build a Basic AI Agent from Scratch: Long Task Planning
#37> In my case, I asked it to migrate my static site from using Eleventy to Hugo
This blog is on medium so I guess the migration went sideways!
Joke aside, nice series of tutorials, don't let the haters get to you. I think with the current token panic it might get handy soon
Re: Build a Basic AI Agent from Scratch: Long Task Planning
#38[flagged]
Re: Build a Basic AI Agent from Scratch: Long Task Planning
#39I've tried most form of planning - from the basic AGENTS.md guide to keeping ./dev/ plan files, todo list tools, sqlite db with both minimal and extensive tracking, etc. None of them have been worth it. A year ago the models needed to be reminded. Today they can follow a plan from text alone. This is my experience from working on a project alone - in teams ... i actually think the same lesson holds in the new AI para…
A while back I created a tool called Jobs [0] to help with this workflow. The pattern is:
1. Have a conversation with a smart model (Opus/Fable) about what you're building. Go back & forth until you've ironed out the important architectural choices (deciding what to build).
2. Ask the model to write up its plan in a Markdown doc, including a structured plan in YAML format (telling it to consult `job schema`).
3. Clear the context and tell a leaner model (Sonnet/Opus) to read the plan doc and then pick up the task via `job status`.
From there, the CLI helps the agent take the next step. I designed the `job` CLI through extensive iteration with agents, conducting user-centered design with the agents to make it as smooth and intuitive to them as possible.
When context gets full, you can pause, clear, and pick right back up. Using Jobs (or other tools like it), you can take on large, ambitious plans and keep the agents on-task the entire time.
[0]: https://github.com/bensyverson/jobsRe: Build a Basic AI Agent from Scratch: Long Task Planning
#40[flagged]
how many datacenters / computers are there running millions of hours of computer games? Why is escaping reality and damaging climate with compute better than using LLMs?
Because it's more fun?
Being serious this is a silly line of reasoning. Maybe they are both bad? It's like asking why is it bad to light a forest fire when there is a forest fire already burning.
I take issue with the cognitive dissonance too though. HN became very hostile to Bitcoin but took no issue with people gaming on PCs and consoles that were consuming more and more electricity each year. Now everyone is silent on all fronts because LLMs make their job easy and gives them something new and interesting to play with.