Media is too big
VIEW IN TELEGRAM
The One File That Stops AI Agents From Wasting Your Tokens
How many AI agents can you juggle at once?
AI agents give you an undeniable speed advantage on a lot of tasks. Over time I started spinning up multiple agents simultaneously, each working on a different project.
One's trying to hunt down a bug in a client's system. Another's editing a promo site for a new event for a different client. A third is spinning up a local dev environment for a new project.
And I'm switching between them, adding context, unblocking blockers and conflicts, reviewing output, giving the go-ahead or queuing the next task. Sounds pretty productive, right?
Except it has the opposite effect too - constant and frequent context switching, trying to hold the whole stack in your head while processing it all pretty fast, gives you the same feeling you get at a hard deadline when your ass is on fire, you can't keep up with anything and you just keep paddling to stay afloat.
And yet paradoxically, you get everything done and then some - more than you planned - but it doesn't feel that way. Apparently my cognitive hardware isn't used to operating in these conditions yet and still interprets it the old-fashioned way.
One task at a time - works great. But how do you deal with the fact that the AI is doing the task and you're just watching it? Sit there doing nothing waiting for the next iteration? No way, if there's time, might as well knock out other things in parallel! And that's how it starts spiraling.
You get this too? How do you deal?
<written by a human being>
AI agents give you an undeniable speed advantage on a lot of tasks. Over time I started spinning up multiple agents simultaneously, each working on a different project.
One's trying to hunt down a bug in a client's system. Another's editing a promo site for a new event for a different client. A third is spinning up a local dev environment for a new project.
And I'm switching between them, adding context, unblocking blockers and conflicts, reviewing output, giving the go-ahead or queuing the next task. Sounds pretty productive, right?
Except it has the opposite effect too - constant and frequent context switching, trying to hold the whole stack in your head while processing it all pretty fast, gives you the same feeling you get at a hard deadline when your ass is on fire, you can't keep up with anything and you just keep paddling to stay afloat.
And yet paradoxically, you get everything done and then some - more than you planned - but it doesn't feel that way. Apparently my cognitive hardware isn't used to operating in these conditions yet and still interprets it the old-fashioned way.
One task at a time - works great. But how do you deal with the fact that the AI is doing the task and you're just watching it? Sit there doing nothing waiting for the next iteration? No way, if there's time, might as well knock out other things in parallel! And that's how it starts spiraling.
You get this too? How do you deal?
<written by a human being>
I don't usually talk about new models, but today I'll join the trend, since I've already had a chance to get my hands on the new Opus - and I really liked it!
Literally from a single prompt it solved tasks - and did so faster, more precisely, without any hassle, deviations, or back-and-forth questions.
One of my automations broke and I asked it to fix it. Not only did it do that on the first try, but it also dug into the root cause - which turned out to be two consecutive system crashes that had corrupted the automation script file. On top of that, it kindly warned me that this isn't normal and suggested checking the system for serious errors. The previous Opus wasn't that perceptive.
Today I have several coding sessions ahead, and I'll be putting the new model through its paces in real battle conditions, for its intended purpose. If anything stands out beyond the noticeably improved thoughtfulness, I'll definitely share.
In the meantime, we're being promised that the new model has become more honest and accurate in its assessments. That was genuinely a problem before - it would give completely unrealistic timelines, like saying 2 days for a task it would finish in 20 minutes. And apparently it should be less of a yes-man now, and instead think harder about whether an action actually makes sense before doing it.
And the cherry on top - optimized token consumption, meaning the model got smarter without supposedly eating through more limits than the previous one. But all of that will show in practice. Let's go check it out!
Media is too big
VIEW IN TELEGRAM
Stop Re-Explaining Your Project to Claude Every Session
<written by a human being>
My impressions of the new Claude Opus 4.8 are very positive. Just a couple of posts ago I was writing about how running multiple agents simultaneously causes cognitive overload due to constant context-switching and the simple fact that agents demand your attention.
So yesterday I fired up the first task and it went off to do it. Purposefully, deeply - and it became pretty clear pretty fast that my help wasn't needed there. It didn't get stuck on obvious little things, made decisions (which, by the way, I thought were the right ones).
And I realized I could calmly spin up another agent in parallel. Did that, and it went off to work too. A few minutes passed - out of habit I started checking on both of them, but both were so busy that I had no choice but to launch a third!
I also thought that in this mode they'd burn through all the 5-hour limits pretty fast, but I was wrong. All three delivered results, working for 10–15 minutes and spending around 15% of the limit, while completing the expected task.
Then there was one interesting case where I gave it a task involving client database remapping - something that implied a multi-stage pipeline: exporting raw data from different sources and several stages of processing it into its final form. However, the session token for exporting one of the sources had expired, and there was no way to download the data from there without my help. The previous version would've stopped immediately and reported the blocker. But 4.8 saw an opportunity to keep going - it first downloaded data from the available source, ran it through processing, and only then asked me for the token.
That really impressed me. The new model keeps pushing toward the result, navigating around obstacles, and doesn't bother the operator until there's a real blocker. All in all, I'm more than satisfied so far. I don't see any reason to use 4.7 anymore.
🔥1
<written by a human being>
My endless dev-loop on the video editing system continues, but now I've actually got real hope. The fresh Opus model, the moment it saw the project, started tearing our development approach from the previous model to shreds and proposing other solutions.
And right here you can already feel that promised honesty - it actually says what looks questionable, instead of just nodding along, meekly accepting my decisions and getting on with executing them.
The agent now runs longer and clearly tries to deliver a result that moves things further, closer to the end goal.
Second case - my home accounting. We're untangling a knot of data spanning many years of keeping it, filling the gaps, consolidating a ton of sources, including bank exports, databases from old programs I used to run, transaction history on the blockchain. And of course it doesn't immediately work to match, say, transfers - the balance doesn't come out to zero.
But Opus, on its very first pass through the current analysis cycle, suggested updating the approach: first classify a taxonomy of the unmatched transactions, and only then glue them into final chains as separate clusters. And as it turned out, this approach actually worked and moved us forward way more effectively than all the previous days of work on the old algorithm.
From the observations - you can now actually hand it complex tasks to reason through. Before, I tried to decompose them so I wouldn't overload the agent's context, which would inevitably forget something from it. And now I get the feeling that the full context gets taken into account, and all at once.
Two days in - the flight's beautiful. Off to test Max effort. By the way, the latest version of the Claude app got voice input - finally!
🔥1
<written by a human being>
People ask me how to set up a proper workflow with AI. When you're steeped in it every single day, you barely even notice how it becomes part of the routine - open VSCode, launch an agent from the terminal with the --dangerously-skip-permissions flag, point it at the instructions, brief it on the task at hand, and respond to occasional HITL callbacks.
But for someone who's far from technical work, all of this feels unfamiliar, alien, not particularly meaningful, confusing, and complicated. Let's untangle it. I don't want to make this technically heavy, so I'll pull out the 20% of effort that delivers 80% of the results.
First - work with coding agents instead of classic chat. The thing is, in coding mode you can do everything you can do in chat mode, but it gives you a whole lot more leverage when working on any kind of project, even if that project has nothing to do with development or writing code.
Second - create a project working folder and launch your coding agent from that folder. This way it always has the ability to pull together the full context of what we're working with. And that "project" can be your entire business or personal life - it doesn't have to be some atomic scope dedicated to a narrow topic.
Third - agent instructions: essentially a text file that briefly and densely describes what we're working on in this project (folder), with pointers to individual files that deepen the context when needed.
If you switch to this mode of working - a coding agent, launched from a project folder, with instructions - you'll already be getting a massive advantage from using AI.
🔥2
Business case: migrating a database to a new system with AI
My client's ERP system was built on top of no-code tools - Bubble, Directual, and n8n. It ran on that stack for three years and was originally conceived as a prototype information system for testing hypotheses around partial business automation.
And now, finally, the client decided to build their own code-based system to replace the old one - the next evolutionary step. But of course, nobody's throwing away years worth of accumulated data - we're doing a migration.
The task is obviously non-trivial: the databases are different and require, among other things, different identifier mapping. On top of that, the developers slightly changed the data structure, which naturally requires post-processing of the exported tables.
The first exports I did manually, because Directual doesn't let you export all data through the API for free - every request costs money. But exporting tables through the interface - no problem. To automate this, I armed an AI agent with Playwright CLI, which let me automate that tedious process.
Next comes post-processing, remapping, and all the other joys of working with data. That's a job for Python - just the way we like it. The scripts were naturally written by Claude Code, refactored a few times, and validated against several data snapshots.
Now I run the export with remapping and post-processing literally from a single prompt, and from there the AI agent orchestrates the entire migration pipeline - from export through post-processing to final validation.
<written by a human being>
My client's ERP system was built on top of no-code tools - Bubble, Directual, and n8n. It ran on that stack for three years and was originally conceived as a prototype information system for testing hypotheses around partial business automation.
And now, finally, the client decided to build their own code-based system to replace the old one - the next evolutionary step. But of course, nobody's throwing away years worth of accumulated data - we're doing a migration.
The task is obviously non-trivial: the databases are different and require, among other things, different identifier mapping. On top of that, the developers slightly changed the data structure, which naturally requires post-processing of the exported tables.
The first exports I did manually, because Directual doesn't let you export all data through the API for free - every request costs money. But exporting tables through the interface - no problem. To automate this, I armed an AI agent with Playwright CLI, which let me automate that tedious process.
Next comes post-processing, remapping, and all the other joys of working with data. That's a job for Python - just the way we like it. The scripts were naturally written by Claude Code, refactored a few times, and validated against several data snapshots.
Now I run the export with remapping and post-processing literally from a single prompt, and from there the AI agent orchestrates the entire migration pipeline - from export through post-processing to final validation.
🔥3
Business case: AI project manager
Lately I've been using AI agents as a project manager. In any reasonably complex project, a backlog of tasks piles up pretty fast - and it needs to get cleared.
Assuming there's actual task management discipline in place, a proper task tracking system, and the capacity to execute - AI lets you knock out all those pain points in one shot. All you need to do is give the agent the right context (point a coding agent at the project folder with the instructions) and sic it on the project management system's API (MCP and/or CLI).
And then the AI magic kicks in. In the morning you can open a chat with the agent and ask what's on today's task agenda, what needs to get done. It'll go through everything in the backlog and active statuses and tell you what's on fire and what can wait.
For task management discipline itself, there's no better assistant - the AI agent never gets lazy about writing detailed context for tasks: what needs to be done, what the Definition of Done looks like, and generally keeping to the conventions that human workers find pretty tedious to follow.
To create a task, just ask the agent - give it a quick rundown of the core idea or reason the task exists. And if everything is set up properly (conventions and task management rules baked into the instructions), the task will land in the right project, in the right status, assigned to the right person, with all the details they need so there are zero questions about what's what.
Not even going to get into the fact that a good chunk of these tasks could probably be handled by the AI agent itself - given the right context and access to the right places. Project managers, your time may be up.
<written by a human being>
Lately I've been using AI agents as a project manager. In any reasonably complex project, a backlog of tasks piles up pretty fast - and it needs to get cleared.
Assuming there's actual task management discipline in place, a proper task tracking system, and the capacity to execute - AI lets you knock out all those pain points in one shot. All you need to do is give the agent the right context (point a coding agent at the project folder with the instructions) and sic it on the project management system's API (MCP and/or CLI).
And then the AI magic kicks in. In the morning you can open a chat with the agent and ask what's on today's task agenda, what needs to get done. It'll go through everything in the backlog and active statuses and tell you what's on fire and what can wait.
For task management discipline itself, there's no better assistant - the AI agent never gets lazy about writing detailed context for tasks: what needs to be done, what the Definition of Done looks like, and generally keeping to the conventions that human workers find pretty tedious to follow.
To create a task, just ask the agent - give it a quick rundown of the core idea or reason the task exists. And if everything is set up properly (conventions and task management rules baked into the instructions), the task will land in the right project, in the right status, assigned to the right person, with all the details they need so there are zero questions about what's what.
Not even going to get into the fact that a good chunk of these tasks could probably be handled by the AI agent itself - given the right context and access to the right places. Project managers, your time may be up.
Media is too big
VIEW IN TELEGRAM
Bubble Is a Black Box to AI - Here's the Loophole
Business case: migrating a service to corporate infrastructure
I've talked about Plane before - an open-source project management system. For speed of deployment, I initially set it up on my own VPS, which I already had running and used for my vibe-coding experiments.
But once the corporate infrastructure - a paid hosting environment - was ready, it was time to move Plane from my personal VPS to the corporate one. Without the right knowledge, this would've easily taken me a week: finding the information, drafting a migration plan, making backups, thinking through the migration, spinning up the server, setting up the required environment, deploying the tooling, testing, transferring the data, verifying correctness, switching DNS, stabilizing everything, and finally cleaning up and shutting down the old server.
Looking at that list, I realize a week might actually be optimistic. Obviously - unless you're a seasoned DevOps engineer, which is exactly what a modern AI agent is. Because with it, I did everything listed above in a single session. Not counting one additional session where we scoped out the migration procedure as a task.
What the agent needs:
1. Access to the source VPS - ideally full access so nothing gets stuck, though it can be scoped to just the container where the migrated system lives
2. Access to the target VPS, same deal
3. Access to edit the DNS records for the domain tied to the service - this I prefer to keep under my own control and not hand to agents (yet)
That's it. After that, with a well-structured plan (use your Superpowers), the agent can execute the entire procedure end-to-end in a single session. In my case, I even gave the agent access to configure the VPS itself - meaning it took care of spinning up the right instance in the right configuration, and everything worked on the first try.
Twenty-four hours later, once we confirmed the new VPS was running stable and clean, the agent shut down the system on the source VPS and freed up the space. Plane now lives on corporate infrastructure.
<written by a human being>
I've talked about Plane before - an open-source project management system. For speed of deployment, I initially set it up on my own VPS, which I already had running and used for my vibe-coding experiments.
But once the corporate infrastructure - a paid hosting environment - was ready, it was time to move Plane from my personal VPS to the corporate one. Without the right knowledge, this would've easily taken me a week: finding the information, drafting a migration plan, making backups, thinking through the migration, spinning up the server, setting up the required environment, deploying the tooling, testing, transferring the data, verifying correctness, switching DNS, stabilizing everything, and finally cleaning up and shutting down the old server.
Looking at that list, I realize a week might actually be optimistic. Obviously - unless you're a seasoned DevOps engineer, which is exactly what a modern AI agent is. Because with it, I did everything listed above in a single session. Not counting one additional session where we scoped out the migration procedure as a task.
What the agent needs:
1. Access to the source VPS - ideally full access so nothing gets stuck, though it can be scoped to just the container where the migrated system lives
2. Access to the target VPS, same deal
3. Access to edit the DNS records for the domain tied to the service - this I prefer to keep under my own control and not hand to agents (yet)
That's it. After that, with a well-structured plan (use your Superpowers), the agent can execute the entire procedure end-to-end in a single session. In my case, I even gave the agent access to configure the VPS itself - meaning it took care of spinning up the right instance in the right configuration, and everything worked on the first try.
Twenty-four hours later, once we confirmed the new VPS was running stable and clean, the agent shut down the system on the source VPS and freed up the space. Plane now lives on corporate infrastructure.