For a time I had to wake up at 6AM for work and it was a period where I struggled the most with my ADHD symptoms.
I hope there is more awareness in the future about the importance of sleep for neurodivergent people. This could help with the social jet lag most of us feel with having to follow a schedule that simply does not work for us.
I was formally diagnosed as a kid but didn’t do the drug route due to my father not liking these new drugs. I’m in my 40’s now and only started enforcing a sleep routine 10ish years ago. Eg. Bedtime at 11 and get up when alarm goes off at 7:30. And the effect on me was astounding. Everything just levelled out. I still like/need a coffee in the morning.
I wish it was as easy as that. I naturally fall asleep later than the average person and forcing myself to sleep early does not permanently solve the underlying problem.
I have been using OpenCode for most work related stuff now. It works well with my ChatGpt Pro subscription as well as with our locally hosted Qwen3.8.
Is it worthwhile to spend time and effort into setting up and switching to Pi? I always see people praising Pi online but I am curious to know from people who switched over from OpenCode why they did so and what I am missing.
Yes, I switched from OpenCode to Pi and never looked back.
What's great with Pi is that it's very easy to extend, because it knows its own doc. So you tell it "implement a plugin that does this" and it does it directly in its own folder.
That means I have a plan mode that works exactly the way I want, I have a hook that cleans up added comments after each change, I can ask "pull this github PR and assess the comments", etc.
How does this work in practice? You type /plan and it runs something that got linked into pi, or an interpreter that can run stuff and also print back out to the terminal?
I've been avoiding investing in these kinds of tools and workflows partially because I don't understand the UX (and partially because I worry UX and tooling will change so fast that I won't get a positive ROI). Your hook sounds like a script, but your plan mode sounds like an interactive TUI, and "pull this PR and assess" sounds like a reverse proxy tool or something?
I can definitely see why the custom plan mode is useful vs just calling a script or asking the model to do something, but what's the draw to building comment cleanup and github API access into the harness vs running the comment cleanup as part of CI, or just asking the model to pull a PR and review it using gh/the web UI/the github api (with no special harness handling)? Does the model get updated with changes you make to files it already read or recently wrote?
The way I normally use coding agents is by giving them a very large task specification in a fresh session with some kind of verifiable exit criteria, and sending them off + staying out of their way. Normally I would just use other sessions or run scripts to do these things because the coding agents I've used queue any /command I give them (very frustrating when you have to wait 30+m just to check usage!) while the model is working, and in my sessions the model is working >90% of the time, so I usually have other terminal windows or applications open anyway.
Pi is like archlinux of agentic harnesses; it's tinkering for the sake of tinkering. I tried it for a few weeks but didn't think it was worth the effort.
I feel like the models work better in the more minimalist harness. It's totally anecdotal but I feel like the models get lost whenever I run them in OpenCode. Sort of like the "10x"-engineer who overengineers everything.
I' ve heard so. But I'd still consider my opinion anecdotal because I haven't done any research into the subject (or measured it myself) really. All I've done is tried both harnesses.
The Gemini Flash models makes perfect sense to me coming from a company like Google. Google AI Mode for search is a product I really find useful. It makes sense that Google focused on smaller, faster yet smart enough models that wouldn't break your bank on inference. It plays well into their product ecosystem.
Google AI Mode consistently gets me consistently good results and good speeds. It really changes what "googling" is for me.
I similarly have a weird affinity for Gemini that I can't really articulate. I used Gemini's free chat and found it great for exploring technical topics (and random one-off general walking-around-questions) and appreciated its speed, tone and accuracy. I spent a month playing with Gemini CLI / Antigravity and found it also an effective coding agent, at least for my workflow (entirely in the loop development and review). I also was really surprised that I could just paste it images of a project I was working on and have it immediately understand what it was looking at -- which I've come to learn is considered a unique strong point for Gemini. I've been playing with GPT5.6 for about a month and it's definitely powerful but I honestly think I'll go back to Gemini. There's something kind of charming about working with an AI that not only is particularly good at web search and information gathering, but also one that doesn't feel like some superhuman overengineering freak when it comes to code.
Well, Google is probably the only one among frontier model providers in the US that doesn't have a massive financial pressure to deliver business results ASAP (and this focus on agent coding and long-running agentic tasks), so Google is able to focus more on encoding deep scientific, cultural and historical knowledge to its systems. I'd assume DeepMind's focus on the scientific core also played a role in the tone and approach Gemini models take for explanations and Q&As.
I find that GPT models and Claude tend to talk in strong slangs and in-group jargon, but love Gemini's massive general knowledge corpus — reminds me of Richard Feynman from his lectures.
I like Gemini (I'm just a dumb person without knowledge of 'benchmarks' or how x compares to y) while understanding that shoving it into Google auto summaries has been a bad idea and produces inaccurate results
> Cannabis dependency really can sneak up on people, and for some it’s quite difficult to get off it, with real physical and emotional symptoms.
Throughout my university years, many of my peers smoked cannabis on a daily basis, often several times a day. Most of them seemed oblivious to the fact that they might have developed a dependence; whenever anyone tried to talk to them about it, they dismissed it, claiming cannabis is completely benign and nothing to worry about. Today, I personally know that at least a couple of them developed serious mental and physical problems, and to this day, they are unable to cope with life without being high.
There should be more awareness about safe cannabis use and its risks rather than framing it as a "healthier" alternative to other drugs, with people usually saying "at least I am not doing X, I am just smoking weed".
> Today, I personally know that at least a couple of them developed serious mental and physical problems, and to this day, they are unable to cope with life without being high.
Perhaps the cannabis helps with the problems, and stops your friends feeling worse.
That’s pretty silly whataboutism. The two aren’t mutually exclusive, don’t trade off, have both risks and benefits, and can be used or prescribed responsibly or irresponsibly. What are you getting at?
I do not think this is a valid argument. Especially coming from the Indian government, who has at many times cited security and safety issues to curb internet freedom.[1][2]
AI does make me more productive. At least until the stage of getting my idea to the "working prototype stage". But in my personal experience, no one has been realistically able to get to the 10x level that a lot of people claim to have achieved with LLMs.
Yes, you do produce more code. But LoC produced is never a healthy metric. Reviewing the LLM generated code, polishing the result and getting it to production-level quality still very much requires a human-in-the-loop with dedicated time and effort.
On the other hand, people who vibe code and claims to be 10x productive, who produces numerous PRs with large diffs usually bog down the overall productivity of teams by requiring tenuous code reviews.
Some of us are forced to fast-track this review process so as to not slow down these "star developers" which leads to the slow erosion in overall code quality which in my opinion would more than offset the productivity gains from using the AI tools in the first place.
I've spent 30 years seeing the junk many human developers deliver, so I've had 30 years to figure out how we build systems around teams to make broken output coalesce into something reliable.
A lot of people just don't realise how bad the output of the average developer is, nor how many teams successfully ship with developers below average.
To me, that's a large part of why I'm happy to use LLMs extensively. Some things need smart developers. A whole lot of things can be solved with ceremony and guardrails around developers who'd struggle to reliably solve fizzbuzz without help.
Did you also notice the evolution of average developers over time? I mean, if you take code from a developer ten years ago and compare it with their output now, you can see improvement.
I assume that over time, the output improves because of the effort and time the developer invests in themselves. However, LLMs might reduce that effort to zero — we just don't know how developers will look after ten years of using LLMs now.
Still, if you have 30 years of experience in the industry, you should be able to imagine what the real output might be.
> Did you also notice the evolution of average developers over time? I mean, if you take code from a developer ten years ago and compare it with their output now, you can see improvement.
This makes little sense to me. Yes, individual developers gets better. I've seen little to no evidence that the average developer has gotten better.
> However, LLMs might reduce that effort to zero — we just don't know how developers will look after ten years of using LLMs now.
It might reduce that effort to zero from the same people who have always invested the bare minimum of effort to hold down a job. Most of them don't advance today either, and most of them will deliver vastly better results if they lean heavily on LLMs. On the high end, what I see experienced developers do with LLMs involves a whole lot of learning, and will continue to involve a whole lot of learning for many years, just like with any other tool.
After 30 years in front of the desktop, we are processing dopamine differently.
When I speak about 10 years from now, I’m referring to who will become an average developer if we replace the real coding experience learning curve with LLMs from day one.
I also hear a lot of tool analogies — tractors for developers, etc. But every tool, without an exception, provides replicable results. In the case of LLMs, however, repeatable results are highly questionable, so it seems premature to me to treat LLMs in the same way as any other tool.
Right, I've seen a lot of facile comparisons to calculators.
It may be true that a cohort of teachers were wrong (on more than one level) when they chastised students with "you need to learn this because you won't always have a calculator"... However calculators have some essential qualities which LLM's don't, and if calculators lacked those qualities we wouldn't be using them the way we do.
In particular, being able to trust (and verify) that it'll do a well-defined, predictable, and repeatable task that can be wrapped into a strong abstraction.
> if we replace the real coding experience learning curve with LLMs from day one.
People will learn different things. They will still learn. Most developers I've hired over the years do not know assembly. Many do not know a low-level language like C. That is a downside if they need to write assembly, but most of them never do (and incidentally, Opus knows x86 assembly better than me, knows gdb better than me; it's still not good at writing large assembly programs). It does not make them worse developers in most respects, and by the time they have 30 years experience the things they learn instead will likely be far more useful than many of the things I've spent years learning.
> But every tool, without an exception, provides replicable results.
This is just sheer nonsense, and if you genuinely believe this, it suggests to me a lack of exposure to the real world.
> However, LLMs might reduce that effort to zero — we just don't know how developers will look after ten years of using LLMs now.
LLMs might help the new joiner produce code on the level of an average developer faster. But, at the same time, if LLMs are really trained on all open source repositories without any selection, that level might be limited.
It looks like the overwhelming majority of projects on Github, does not really follow stable growth tendencies. In all fairness, as these were the smaller projects, their developers might have never intended to demonstrate best practices, or make the project sustainable on the long-term.
This is all fine, experimentation and learning are very welcome in open source. But, with 83,9% of the projects (in my study) falling into this category, LLM might pick them up as demonstrating overwhelmingly popular best practices.
In the worst case, this might even lead to actual best practices being drowned out, over time.
> if you take code from a developer ten years ago and compare it with their output now, you can see improvement.
really? it depends on the type of development, but ten years ago the coder profession had already long gone mainstream and massified, with a lot of people just attracted by a convenient career rather than vocation. mediocrity was already the baseline ("agile" mentality to at the very least cope with that mediocrity and turnover churn was already at its peak) and on the other extreme coder narcissism was already en vogue.
the tools, resources, environments have indoubtedly improved a lot, though at the cost of overhead, overcomplexity. higher abstraction levels help but promote detachment from the fundamentals.
so specific areas and high end teams have probably improved, but i'd say average code quality has actually diminished, and keeps doing so. if it weren't for qa, monitoring, auditing and mitigation processes it would by now be catastrophic. cue in agents and vibe coding ...
as an old school coder that nowadays only codes for fun i see llm tools as an incredibly interesting and game changing tool for the profane, but that a professional coder might cede control to an agent (as opposed to use it for prospection or menial work) makes me already cringe, and i'm unable to wrap my head around vibe coding.
You don't have to trust it. You can review its output. Sure, that takes more effort than vibe coding, but it can very often be significantly less effort than writing the code yourself.
Also consider that "writing code" is only one thing you can do with it. I use it to help me track down bugs, plan features, verify algorithms that I've written, etc.
Many of us are literally being forced to use it at work by people who haven't written a line of code in years (VPs, directors, etc) and decided to play around with it during a weekend and blew their minds.
I could say the same about every web app in the world... they fail every single day, in obvious, preventable ways. Don't look into the javascript console as you browse unless you want a horror show. Yet here we all are, using all these websites, depending on them in many cases for our livelihoods.
I don't trust it completely but I still use it. Trust but verify.
I've had some funny conversations -- Me:"Why did you choose to do X to solve the problem?" ... It:"Oh I should totally not have done that, I'll do Y instead".
But it's far from being so unreliable that it's not useful.
I find that if I ask an LLM to explain what its reasoning was, it comes up with some post-hoc justification that has nothing to do with what it was actually thinking. Most likely token predictor, etc etc.
As far as I understand, any reasoning tokens for previous answers are generally not kept in the context for follow-up questions, so the model can't even really introspect on its previous chain of thought.
I mostly find it useful for learning myself or for questioning a strange result. It usually works well for either of those. As you said, I'm probably not getting it's actual reasoning from any reasoning tokens but never thought that was happening anyway. It's just a way of interrogating the current situation in the current context.
It providing a different result is exactly because it's now looking at the existing solution and generating from there.
I guess I should have used ‘completely trust’ instead of ‘trust’ in my original comment. I was referring to the subset of developers who call themselves vibe coders.
I certainly wouldn't use a compiler that "screws up" 1% of the time; that's the perfect amount where it's extremely common where everything I use it for will have major issues but also so laborious to find amongst the 99% of correct output that I might as well not use it in the first place.
Which is ironically, the exact case those of us who don't find LLM-assisted coding "worth it" make.
LLMs screw up far more than 1% of the time. They screw up routinely, far more than a professionally trained human would, and in ways that would have said human declared mentally ill.
> enabling programmers around the world to be far more productive
I know a lot of us feel this way, but why isn't there more evidence of it than our feelings? Where's the explosion of FOSS projects and businesses? And why do studies keep coming out showing decreased productivity? Why aren't there oodles of studies showing increases of productivity?
I like kicking back and letting claude do my job but I've yet to see evidence of this increased productivity. Objectively speaking, "I" seem to be "writing" the same amount of code as I was before, just with less cognitive effort.
It's like everyone forgot that "lines of code" as a productivity metric was a running joke for a decade-plus. The real bottleneck in our work isn't producing boilerplate code, it's producing more or less the right kind of code for the problem at hand, and LLMs, having no real underlying ability to reason, are just not very good at it.
I haven’t done web development since 2002 except for some copy and paste work. My development is purely back end and AWS IAC. I have completely vibe coded three internal web admin sites that had about 10 pages of functionality and authentication with Amazon Cognito. I didn’t look at a line of code. I just told it what I wanted and the changes based on the UX I wanted
not questioning the cost of adopting new tech is so foolish it boggles my mind that so many nominally intelligent people just close their eyes and take a bite without wondering whether that's really fudge on their sundae or something fecal.
Pure ideology, as a certain sniffing slav would say
FTFY: “There’s this incredible new technology that allows evil megacorporations to get richer and control the world while destroying the beauty of the Web.”
I stopped using PowerPoint for presentations long back. What I use now is LaTeX with the beamer class to make classy professional looking presentations. This looks like another good alternative to try out.
You might also like "marp", which works as a VS Code plugin for markdown. Much simpler & less control than Beamer, but does allow equations (which it seems the linked presenta.cc does not, or not yet) and a bit more off-line focused perhaps?
I hope there is more awareness in the future about the importance of sleep for neurodivergent people. This could help with the social jet lag most of us feel with having to follow a schedule that simply does not work for us.
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