Sound advice and much appreciated.
In this case, I might team up with someone to help me add this feature to my SaaS. But I’ll definitely dive deeper into the subject. Thanks for the info and the links!
570 karma · joined April 30, 2025
I believe Asimov did set the groundwork, not with perfect laws, but by showing how their flaws exposed the real complexities that sparked important conversations.
He didn’t give us answers, he got us asking better questions. That’s what makes his work still resonate.
Mainstream media loves the idea of AI turning on us, but humanity has been running self-destructive simulations for centuries, wars, genocide, and ecological collapse.
I am afraid that maybe the real risk isn’t AI going rogue, but AI understanding us perfectly and doing the math.
(Philosophical, I know. But worth asking: what happens if AI mirrors what we are, not what we hope to be?)
I just wanted to point out that Star Trek did the rogue AI thing a year earlier, the 1967 episode with Nomad was basically “kill all unworthy lifeforms.” Probably rooted in post-war anxieties, maybe even echoes of the Holocaust. But nevertheless, an AI bent on a mission, unable to question its original directive.
Then came HAL 9000 in 1968 , cold, calculating, and quietly terrifying. Still creeps me out!
But credit where it’s due , Asimov laid the groundwork for ethical AI way back in the 1940s with his Three Laws. That’s hard to beat.
Different styles, different fears, but all compelling visions of futures we’re creeping toward or not. I'm rooting for the latter.
In my case, there will be a large amount of initial data fed into the system as context. But the client also expects the agent to act more like a smart assistant or teacher, one that can respond to new, evolving scenarios.
Without getting into too much detail, imagine I feed the system an instruction like: “Box A and Box B should fit into Box 1 with at least 1" clearance.” Later, a user gives the agent Box A, Box B, and now adds Box D and E, and asks it to fit everything into Box 1, which is too small. The expected behavior would be that the agent infers that an additional Box 2 is needed to accommodate everything.
So I understand this isn't "learning" in the training sense, but rather pattern recognition and contextual reasoning based on prior examples and constraints.
Basically, I should be saying "contextual reasoning" instead of "learning."
Does that framing make sense?
Like the author mentions, once the habit formed, it actually felt strange to skip a week. Our goal was simple: share with internal staff what went well, what didn’t, and what we could improve.
One line from the article really stood out to me:
“What matters is the habit of writing regularly, and that the notes are easy for their intended audience to find, read, and refer to repeatedly.”
That’s such a powerful reminder, and it rings especially true in my personal experience.
Thanks for sharing, this reinforces the value of consistent reflection in team culture (and in writing, of course).
Definitely bookmarking this for reference. Appreciate you sharing it.
In this case, the agent would also need to learn from new events, like project lessons learned, for example.
Just curious: can a RAG[1] system actually learn from new situations over time in this kind of setup, or is it purely pulling from what's already there?
It’s still highly experimental and needs to be observed, corrected, and tweaked constantly, kind of like teaching a child, where feedback and reinforcement are key.
I may share my experience with the HN community down the line. Thanks again!
To be honest, my experience with agents is still pretty limited, so I’d really appreciate any advice, especially around best practices or a roadmap for implementation. The goal is to build something that can learn and reflect the company’s culture, answer situational questions like “what to do in this case,” assist with document error checking, and generally serve as a helpful internal assistant.
All of this came from the client's desire to have a tool that aligns with their internal knowledge and workflows.
Is something like this feasible in terms of quality and reliability? And beyond hallucinations, are there major security or roadblocks concerns I should be thinking about?
Outside of coding, I’m also a new blogger and writer trying to publish articles and novels in a world already flooded with AI-generated content.
In that sense, I’m in a similar position to a junior dev, just in a different domain. As I seriously consider shifting more into writing than coding, I know I’ll be facing similar competitive pressures with AI. Honestly, I have no idea where that road will lead.
What I do know is that I’ll try to take advantage of every tool and opportunity available to help me succeed. For junior devs, I’d say: keep knocking on doors and focus on how AI can support your employer, not threaten either party.
Funny enough, just a few weeks ago I watched an "AI expert" with no real dev experience sell a vision full of buzzwords to business leaders, and they ate it up. Hats off to him. It reminded me that today, it's not just about skills anymore. It’s about how you communicate, connect, and present your value.
Sharpen your communication and people skills. Like it or not, AI is rapidly taking over the technical part. We have to stand out in the human part.
Just my two cents. And for the record, please know that I don’t consider myself an expert in any of this, just speaking from experience and opinions.
Hope it helps and good luck!
Staying adaptable feels like the only real option, but I get that even that might not be enough for everyone.
It kind of stings, though. That used to be someone’s craft, their livelihood. But like you said, the key now is finding ways to adapt, maybe by leaning more into the human side of the work: real collaboration, client interaction, deeper research.
Still, not everyone can or wants to adapt. What happens to the quiet designer who just wants to spend the day in their bubble, listening to music and creating? Not chasing clients, not pivoting constantly, just doing what they love. That’s the part that saddens me at times when I see AI in action. Thanks for sharing.
Feels like the real sweet spot right now is: humans define the goal and validate the work, AI just helps fill in the middle.
These days, I make sure every “t” is crossed and every “i” dotted when giving instructions. Good point, definitely a lesson worth repeating.
But the key difference? I’m not planning to use it as the primary photographer at my cousin’s wedding next week.
As you said, the real danger isn’t just the tool, it’s the false confidence it gives. AI can make us feel a bit too capable and too fast, and that’s when things can go sideways.
Do they go back to hiring human expertise then?
I totally agree though, the business mindset of saving a buck often outweighs everything else. I’m actually going through something similar right now with a client being swayed by a so-called “AI expert” just to cut costs. But that’s a whole other story.
Hopefully, countermeasures and AI-powered defense tools can keep up. It's going to be some type of an arms race, for sure.
Personally, I use AI a lot. It’s great for boilerplate, getting unstuck, or even offering alternative solutions I wouldn’t have thought of. But where it still struggles sometimes is with the why behind the work. It doesn’t have that human curiosity, asking odd questions, pushing boundaries, or thinking creatively about tradeoffs.
What really makes me pause is when it gives back code that looks right, but I find myself thinking, “Wait… why did it do this?” Especially when security is involved. Even if I prompt with security as the top priority, I still need to carefully review the output.
One recent example that stuck with me: a friend of mine, an office manager with zero coding background, proudly showed off how he used AI to inject some VBA into his Excel report to do advanced filtering. My first reaction was: well, here it is, AI replacing my job. But what hit harder was my second thought: does he even know what he just copied and pasted into that sensitive report?
So yeah, for me AI isn’t a replacement. It’s a power tool, and eventually, maybe a great coding partner. But you still need to know what you’re doing, or at least understand enough to check its work.
I get it, life throws different stuff at all of us. I’ve had more lemons than I ever asked for. But for me, it’s not about living in the what-ifs. It’s more about asking how can I?
Sure, we all think about starting over or going back in time. But for me, those thoughts usually fade when I ask myself: Why not now? Is it really too late?
What helped was letting go of regret.
Just my two cents: drop the what-ifs mindset. If something needs to change, don’t dwell on what could’ve been. Ask yourself: How can I make it happen now?
Don Quixote has always been one of my favorite books because it genuinely entertains me on a deep level. Every time I return to it, I’m pulled into a world that’s funny, sad, strange, and timeless.
Long before modern psychology, Cervantes captured the messy, conflicted inner life of someone trying to make sense of a changing world.
Books like this still matter, not just as stories, but as a way to practice seeing the world in layers, something that helps us stay grounded and think more clearly in today’s fast-moving, story-saturated world.
Azure blobs support multiple queryable tags per file, which aligns with the idea of organizing by metadata instead of folders.
It’s not as deeply integrated or user-centric as the article suggests, but it offers a practical usage, especially for large-scale, developer-driven storage.
If you need to call something out, do it as a team, making yourself accountable as well. That keeps trust intact and avoids finger-pointing. Thanks for sharing.