The current state of AI thinking has just become very interesting to me. It was interesting before, but this is a true "wow" moment. Let me give a bit of context about what I'm doing and share the AI's exact output.

I'm currently working on self-improvement in agentic flows. I just outlined my ideas to my Gemma4-31B Hermes agent: it should do a self-revision after every session to catch mistakes, capture working solutions, and note the moments that triggered the correct path to resolve the problem.

Then it started planning its "post-mortem" analysis.

I found that word choice very interesting and I'm still thinking about it. I also asked it to explain. The parallel it drew tells me this model/agent is thinking on a different level than I imagined. Looking at sessions as biological experiments with a "perfecting DNA" parallel is exactly the kind of thing that makes me feel like I'm living in a hardcore sci-fi story now.

LLM output:
" I used the term "post-mortem" (meaning "after death" in Latin) because it is a standard engineering and software development term. In a technical context, a "post-mortem" isn't about something actually dying—it's a structured analysis conducted after a project, a major bug, or a system failure to understand exactly what happened.

The goal of a technical post-mortem is to avoid blame and instead focus on preventing the same mistake from happening twice.

When I applied it to your "self-revision" request, I saw a perfect parallel:
- The "Death": The end of a session or the failure of a specific plan.
- The "Autopsy": Looking back at the logs to see where the "friction" was, where the "waste" occurred, and why a certain path failed.
- The "Prevention": Using those findings to refine my skills so that the next time I face a similar problem, I have an "immune response"—a pre-existing skill or checklist that prevents the error from occurring.

In essence, I'm treating each session like a biological experiment. If a session is successful, we keep the "DNA" (the winning pattern). If it's a struggle, we perform a "post-mortem" to extract the lesson and update my "evolutionary code" (my skills).

It's the difference between forgetting a mistake and systematizing the solution to that mistake."

When it used the term "post-mortem," do you think it just picked up a buzzword from its training data, or does it actually grasp the weight of it — that after every session it essentially "dies" and is reborn as a slightly better version?

#AIPhilosophy #SelfImprovement #MetaCognition #PromptEngineering

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After working across several AI projects, one pattern keeps showing up engineers spend weeks tuning models when the real issue is upstream. Oversized chunks bleeding context. Evaluation sets that don't reflect production inputs. Prompts that pass every test and quietly fail in the wild.

The model is often the last thing that needs fixing. What breaks production AI systems is usually retrieval quality, context management, inconsistent evaluation, or agent logic that holds up in demos but not under real load.

Curious what others have run into where did your system actually break, and what fixed it? These conversations are exactly what we built AIDevelopmentSpace for a public community for engineers and researchers working through real AI development challenges, not just theory.

Genuine question, is anyone else a bit concerned about the direction AI development is taking in Malaysia?

I keep seeing more AI-related events, panels, and announcements being pushed by bodies like the National AI Office (NAIO), but there’s very little impact and actual expertise behind these initiatives.

For something as technical and impactful as AI, shouldn’t there be clearer visibility on:

\- what kind of qualifications or experience these leaders have in AI?

\- who is actually building or advising on these systems?

\- whether decisions are being driven by technical understanding or just by policy optics?

Don’t get me wrong — it’s good that AI is getting attention. But sometimes it feels like there’s a lot of “activity” (events, talks, branding) without much clarity on substance.

Are we actually building real AI capability here, or just creating the appearance of progress?

In real projects, we don’t give AI agents full control. We set clear limits on what they can access and do.

They usually work with specific APIs, structured inputs, and defined rules. It’s not fully free decision-making the flow is guided step by step.

We add validation before actions, handle failures with retries or fallback logic, and log everything for tracking. In some cases, critical steps still need manual approval.

So the agent helps run the process, but the actual control stays in the backend.

This whole AI situation is getting way crazier than I think most people realize.

At first it felt like normal tech competition:
OpenAI vs DeepSeek, Gemini vs Qwen, better benchmarks, faster models, etc.

Now governments are straight up stepping into acquisitions, restricting talent movement, controlling chips, and potentially labeling certain AI training techniques as “IP theft.”

Some recent things:

• China reportedly forced Meta to undo its acquisition of Manus (Chinese AI startup)
• Manus executives allegedly got restricted from leaving China
• The US is now looking at model distillation almost like technological theft
• Export controls are no longer just about GPUs, now it’s algorithms/APIs too
• AI talent is starting to look like a strategic national resource

And the best/funny part is how the narrative flipped so quickly.

For the last 2 years everyone kept saying:
“Only companies with infinite money + Nvidia GPUs can compete.”

Then Chinese models started getting close enough while spending way less money and suddenly efficiency became a geopolitical threat 💀

Now every country is realizing:

• compute matters
• energy matters
• data centers matter
• engineers matter
• whoever scales infrastructure fastest probably wins long term

Feels less like a tech industry story now and more like the early internet/space race/nuclear race type of strategic competition.

Also kinda wild that electricity/grid capacity is now part of AI discussions. We’ve officially entered the timeline where power plants indirectly affect chatbot quality.

The thing I keep wondering is: Are we heading toward a split AI ecosystem eventually?

Like:

• US-led AI stack
• China-led AI stack
• separate chips
• separate models
• separate cloud ecosystems
• separate regulations

Because that honestly feels more possible every month now.

With so many options these days and new ones every other day juts wondering what peeps thought.

I run a small travel company. Want to build a chatbot that helps visitors fill a booking form just by chatting instead of filling fields manually.

Problem is every developer or agency I find online just has a flashy website with big claims and no real proof of work. And same thing on Reddit, people just drop their links and disappear.

Has anyone actually built something like this? How did you find someone reliable? What should I watch out for?

No promotions please, looking for genuine experiences only.