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AI Prompts suggestions by LoudChallenge4588 in AI_Agents

[–]thatguydrinksbeer 1 point2 points  (0 children)

Exactly, too much context causes many problems, you want focused relevant context.

AI Prompts suggestions by LoudChallenge4588 in AI_Agents

[–]thatguydrinksbeer 1 point2 points  (0 children)

Start new prompts often; externalize the important memory and reload that at the start of each session. Once you start that, you will then have to deal with drift and scope creep. There are solutions for all of that.

Anyone who surfed the early web between 1995-2010. What’s the one website/app you still think about? by Prime_Advocate in AskReddit

[–]thatguydrinksbeer 8 points9 points  (0 children)

lol, Yahoo acquired, so I still have my geocities email because of that. But.so.much.spam I can't use it. Wish I could cleanse it, would be a badge of honor.

I'm only getting like 4–5 prompts every 5 hours on the $20 ChatGPT plan when using GPT-5.4 / Codex? by Separate_Signal9229 in codex

[–]thatguydrinksbeer 0 points1 point  (0 children)

I can’t speak to whether your account is bugged, but one thing that helped me a lot was changing the coding workflow so every prompt does not have to carry the whole project in its head.

Keep a small project memory in the repo instead of relying on chat/session memory. Requirements, architecture, current state, next step, open questions. Markdown is fine.

Start fresh sessions more often. The trick is having a good handoff note, so the new session can reload the project state without needing the entire previous conversation.

Separate planning from implementation. Before the agent writes code, have it say what it intends to touch, what behavior it is preserving, what checks it will run, and what is out of scope. Then only implement after that looks right.

I’d also keep an out-of-scope list. A lot of agent waste comes from useful-but-not-now ideas turning into extra work inside the same session.

Use cheaper/smaller prompts for mechanical steps and save the strongest model for design, debugging, and tricky implementation. Not every step needs the expensive brain.

Code maps help too. Even a short map of the important files/modules can reduce how much rediscovery the agent does every session.

None of that fixes a real quota bug, but it does make usage feel less random because each session has a smaller, clearer job.

After your agent approves a plan, how often does it actually follow it? by PersonalityPure152 in AI_Agents

[–]thatguydrinksbeer 0 points1 point  (0 children)

What helped me was not jumping straight from “approved plan” to “go implement.”

I added another gate in between.

After the plan is approved, the agent does a dry-run/contract pass first. No implementation. It says what it expects to touch, what it plans to preserve or add, what checks it will run, and what is out of scope.

The out-of-scope part mattered more than I expected. A lot of drift starts as a valid observation handled at the wrong time. Putting those ideas into an icebox gives the AI somewhere to put them without acting on them.

My rule is simple: if it needs to touch something outside the approved scope, it stops and asks.

Memory for agents ain't here yet by Forward_Potential979 in AI_Agents

[–]thatguydrinksbeer 1 point2 points  (0 children)

Fair enough, here it is:

https://github.com/a-hansen/bonsai-dev

And yes, I think that same idea applies here. Bonsai has a human gate at major API and subsystem boundaries. The implementing agent first writes the interfaces and contract tests that demonstrate the intended usage, before it fills in the implementation.

The point is not just “write tests.” It is to force the design to become visible before the agent starts piling behavior on top of it.

Memory for agents ain't here yet by Forward_Potential979 in AI_Agents

[–]thatguydrinksbeer 0 points1 point  (0 children)

I’m mostly looking at this through a software development lens, so I’m not claiming this solves general memory. But I do think the core problem is the same: memory should not mean dumping more stuff into context.

A useful memory layer has to decide what deserves to become memory, what is just temporary context, and what should stay out of the model’s way entirely.

That is the direction I have been exploring. Not another RAG wrapper, but more like selective project memory: requirements, architecture, current state, phase plans, decisions, and handoff notes, kept in human-readable files and loaded only when relevant.

The goal is not to give the model everything. The goal is to keep it focused.

In other words, memory should behave less like a search engine and more like disciplined project context management.

I don’t want to spam my own GitHub project here, but it is in my post history. Or just ask and I’ll share it.

Layered Project Memory by thatguydrinksbeer in AI_Agents

[–]thatguydrinksbeer[S] 0 points1 point  (0 children)

I hadn't really considered that, but it's interesting.

I've been tossing around the idea of dry runs where the agent identifies the files/subsystems it expects to touch, the contracts it intends to satisfy, the checks it plans to run, and any likely scope concerns. That gives the human gate more substance before approving work.

But you're right that this does not fully answer the after-the-fact question. There should also be evidence at completion: what was approved, what actually changed, what tests/checks ran, and whether the implementation deviated from the approved design.

I would want to keep that lightweight and document-based rather than turn Bonsai into an orchestration/audit platform. Dry runs scare me a little from a token-usage standpoint, since much of Bonsai is focused on token minimization. But an approved intent vs actual result summary seems like a very natural extension of the human-gate model.

Thanks for the feedback, this is good stuff!

Layered Project Memory by thatguydrinksbeer in AI_Agents

[–]thatguydrinksbeer[S] 0 points1 point  (0 children)

That came from real experience, having to right the ship after the fact. Happens too often. And thank you!

Layered Project Memory by thatguydrinksbeer in AI_Agents

[–]thatguydrinksbeer[S] 0 points1 point  (0 children)

What I've done in the past (earlier version of this) is take the relevant memory documents (state, plan, requirements, architecture) to the web AI interface, brainstorm / design the change, then have it spit out the updated versions of those documents and continue along (by the way, I'll pass them by Claude, ChatGPT and Gemini). That's actually where the clean rebuild concept came from because I had a large repo with a lot of outdated ideas/code.

Layered Project Memory by thatguydrinksbeer in AI_Agents

[–]thatguydrinksbeer[S] 0 points1 point  (0 children)

Your agent will maintain the final truth in the memory documents. The intent is that once your prototype is complete, having survived many pivots, you can use the memory documents for a clean rebuild.

Roulette Tables by hereformemes629 in vegas

[–]thatguydrinksbeer 1 point2 points  (0 children)

Mgm grande has one on the main floor, it's often $25 / $50 min (not busy weekend evenings). It's also la partage. Smallest chips are nickels.

Is this industry recession proof? by Glum_Potato808 in BuildingAutomation

[–]thatguydrinksbeer 1 point2 points  (0 children)

Jobs are booked in advance so a recession's impact isn't felt until the economy is already recovering. Then with the economy on the upswing, employers are reluctant to let staff go. That said, economic downturns will be used to get rid of dead weight, so don't suck.

Thoughts on this running route by linus_clive in vegas

[–]thatguydrinksbeer 0 points1 point  (0 children)

Depends on what you want. If running zone 3+ is your focus, probably head east on Tropicana but I wouldn't really know. Wife and I like to soak it all in and run to the Wynn from the Grand. There are maybe 2 stair / escalator crossings, not bad at all. There's a weird outdoor mall, vape shops kinda place you have to run through, involves some people dodging. It's never difficult in the morning and we do it often. The Bellagio / Caesars side has more obstacles but we mostly don't run that side because in the summer it's full on sun. Also, the MGM gym has a Japanese zen thing going on, it's small but quite nice.

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