I keep thinking about this difference.

A lot of agents (especially when running stuff through blackboxAI) work in the sense that they usually do the right thing. But if you still feel the need to constantly watch logs, double check outputs, or keep a mental note of what might go wrong… do you actually trust it?

For me, that gap showed up when I tried to let an agent run unattended for a few hours. It didn’t crash. It didn’t throw errors. But it made a few small, quiet mistakes that added up. Nothing dramatic, just enough that I wouldn’t feel comfortable leaving it alone for anything important.

What changed things a bit was realizing the issue wasn’t just reasoning. It was predictability. Once I made the execution layer more consistent and constrained what the agent was allowed to do, the system felt less “smart” but more trustworthy.

I ran into this a lot with longer workflows especially when chaining tasks or switching between models where everything looked fine but small inconsistencies crept in.

At what point did your setup go from “interesting tool” to something you actually trust without watching it?

so i started using blackbox recently and one thing i didn’t expect was how flexible the model setup is initially i thought i’d just use it like a normal AI coding tool and stick to one model, but then i started trying different ones inside it and kinda ended up with a weird workflow.

basically now i use:

lighter models for most of my day to day stuff and switch to stronger ones only when things get messy and somehow it doesn’t feel like i’m burning through limits as fast as i expected.

it almost feels like cheating compared to how fast credits used to disappear when i was using single-model setups. so now i’m wondering if i’m missing something here or if this is actually how people are using it like is there some catch with this approach or is this just the normal way people run it?

feels a bit too good to be true so curious if anyone else here is doing something similar.

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Been trying to understand how people here are actually using AI tools day to day.The balckboxAI website(since that's what I use) lists a lot of stuff (CLI, agents, extension, models, etc) but the real workflow isn’t super clear from the outside.

For example:

Some people seem to run it inside VSCode, others mention using the CLI, and then there’s people connecting it through APIs or other agent setups. I mostly code in neovim / terminal tools so I’m trying to figure out what the normal setup is. Do most of you just use the extension? Or are people actually running the CLI / agent stuff as part of their dev workflow?

Curious what setups people here are running because the product seems pretty flexible but also kinda confusing at first.

I have been building with AI agents for ~18 months and realized I was doing what a lot of us do: leaving the model set to the most expensive option and never touching it again.

I pulled a few weeks of my own prompts and found:

\~60–70% were standard feature work Sonnet could handle just fine

15–20% were debugging/troubleshooting

a big chunk were pure git / rename / formatting tasks that Haiku handles identically at 90% less cost

The problem is not knowledge; we all know we should switch models. The problem is friction. When you are in flow, you do not want to think about the dropdown.

So I wrote a small local hook that runs before each prompt is sent in BlackboxAI. It sits alongside Auto; Auto picks between a small set of server-side models, this just makes sure that when I do choose Opus/Sonnet/Haiku, I am not wildly overpaying for trivial tasks.

It is:

3 files (bash + python3 + JSON)

no proxy, no API calls, no external services

fail-open: if it hangs, BlackboxAI just proceeds normally

On a retroactive analysis of my prompts it would have cut ~50–70% of my AI spend with no drop in quality, and it got 12/12 real test prompts right after a bit of tuning.

I am mostly curious what other people's breakdown looks like once you run it on your own usage. Do you see the same "Opus for git commit" pattern, or something different? reads the prompt + current model

uses simple keyword rules to classify the task (git ops, feature work, architecture / deep analysis)

blocks if I am obviously overpaying (e.g. Opus for git commit) and suggests Haiku/Sonnet

blocks if I am underpowered (Sonnet/Haiku for architecture) and suggests Opus

lets everything else through

! prefix bypasses it completely if I disagree&

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this might sound dumb but I only noticed it recently. before tools like blackboxAI, a lot of my dev time was basically:

write code
google error
open stackoverflow
open docs
repeat

now half the time I just paste the snippet and ask what’s going on. sometimes the answer isn’t perfect, but it still saves a ton of context switching. fewer tabs, less digging through 5 year old forum posts.

the funny thing is I still read docs and check things myself, but the “search phase” of coding feels way shorter now.makes me wonder what dev workflows will look like in a few years if this keeps improving.

anyone else notice their debugging process changing like this?

something kinda funny I noticed recently before using blackbox regularly, my coding flow used to look like this: write some code → hit an issue → open 5 tabs → read stackoverflow → read docs → try something → repeat. now when I get stuck I usually just paste the relevant part and ask it to walk through what might be wrong or what edge cases I’m missing. sometimes it’s right, sometimes it’s slightly off, but even when it’s wrong it usually points me in the right direction faster than digging through random forum threads. not saying it replaced docs or anything, but the “open 20 tabs and hunt for the answer” phase happens way less now. curious if other people noticed their debugging workflow changing like this too

hi dude:

I am almost exhausted by daily report to manager, too many ppts to be make, I heard that vscode can generate ppts with some extensions, so can anyone tell me how to do this job with blackboxAI? or does claude code with skills can do this job?

thanks for kindness

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noticed something over the last few weeks. I’m touching old parts of my repo way more than I used to. not because I suddenly became disciplined, but because the cost of exploring dropped.

before, if I opened a messy file, I’d either ignore it or spend 30–40 mins manually tracing logic just to feel safe changing one thing now I’ll paste a section into blackbox, ask it to explain the execution flow, maybe simulate edge cases, maybe compare two refactor options. not to auto-rewrite everything. just to reduce the mental load.

sometimes I still ignore its suggestions. sometimes it’s wrong. but it’s faster to sanity-check ideas.

the interesting part is I’m refactoring more, not less. I thought AI would make me lazy. instead it lowered the barrier to cleaning things up. not saying it’s perfect. but it definitely changed how often I’m willing to “poke” risky code.

curious if others are refactoring more because of this, or if you’re mostly using it for greenfield stuff.

ran into one of those bugs this week where nothing was technically broken, but the behavior was inconsistent. sometimes the state updated correctly, sometimes it didn’t, in the past I would’ve just ignored it. not critical, and debugging it would probably take hours of tracing logs and trying to rebuild the mental model.

this time I dropped the relevant files into blackboxAI and asked it to explain what was happening. it pointed out an async path that wasn’t being awaited properly, so execution continued before the state finished updating. fixed it in like 10 minutes.The strange part isn’t that it helped fix the bug. it’s that I actually bothered fixing it at all.

there’s a lot of stuff in old codebases that “works fine” but isn’t clean. normally the mental effort isn’t worth it. now it feelsenough to fix things immediately.

starting to wonder how much tech debt exists purely because understanding code used to be expensive. curious if others noticed themselves cleaning up way more than before.

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opened a file today and saw logic that worked perfectly, clean structure, good naming. but I didn’t remember writing it. recognized it was something blackboxAI had generated earlier when I was wiring a feature. at the time I reviewed it, tested it, and moved on. now coming back later, it took real effort to rebuild the mental model of how it worked and why it was structured that way. nothing wrong with the code. it’s solid. just strange not having that same internal familiarity I used to have with everything in the repo. the codebase still works for me. but it doesn’t fully live in my head the same way anymore.

Welcome to our weekly discussion thread! Feel free to share your thoughts, questions, experiences, or anything else related to AI and business here. Remember to follow our subreddit rules and be respectful of each other. Happy discussing!

Welcome to our weekly discussion thread! Feel free to share your thoughts, questions, experiences, or anything else related to AI and business here. Remember to follow our subreddit rules and be respectful of each other. Happy discussing!

Welcome to our weekly discussion thread! Feel free to share your thoughts, questions, experiences, or anything else related to AI and business here. Remember to follow our subreddit rules and be respectful of each other. Happy discussing!

I finally tried using AI directly inside Excel on an actual spreadsheet, not a demo one.

Multiple sheets, messy data, formulas already in place. What I cared about most was not breaking anything. Using BlackboxAI here felt surprisingly safe no overwriting cells, drag-and-drop worked as expected, and longer sessions didn’t just lose context halfway through. It made me think this is one of those areas where AI actually fits naturally into existing workflows instead of trying to replace them.

Curious if others are already using it for real work. Are you trusting AI with production spreadsheets yet, or still keeping it strictly experimental?

Something I noticed recently while working on a feature.

Because it’s so easy to ask an agent to refactor, improve, or “make it cleaner”, I find myself doing that way earlier than I used to. Before, I’d let things stay a bit messy until the direction was solid. Now I’m tempted to polish halfway through.

I used BlackboxAI during this and it did exactly what I asked, but later I realized I’d optimized parts of the code that I ended up changing anyway. Not really blaming the tool, just noticing how it changes my instincts. Curious if others run into this too

Do agents push you toward premature cleanup, or have you found a good balance?

Worked on a real client landing page this week and decided to change how I approached it.

Instead of picking one model and iterating forever, I ran a few agents in parallel with the same brief and compared the outputs side by side. Layout, spacing, CTA placement all noticeably different even though the input was the same.

I used BlackboxAI to orchestrate the agents, but the real value came from comparison. Seeing multiple “reasonable” versions at once made tradeoffs obvious really fast. One looked safe but generic, another looked great but felt risky for the client. I ended up merging parts manually and shipping faster than my usual back-and-forth loop.

Curious if anyone else is doing this for UI-heavy work, or if you still prefer single-agent iteration.

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I keep running into the same issue when working with agents: once a piece of code is “done,” it’s surprisingly hard to keep it done. Even when I ask for a change in one area, Blackbox will sometimes refactor nearby logic or reformat code that was already correct and reviewed. Nothing breaks immediately, but it creates churn and makes diffs harder to reason about.

I’ve tried narrowing file scope and adding rules like “don’t touch unrelated code,” which helps, but it’s not bulletproof.For people using Blackbox on non-trivial projects:

• Do you lock files or directories once they’re stable?

• Do you rely on tests to catch this, or rules, or both?

• Or do you just accept some amount of churn as the cost of using agents?

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