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Asere Baloi that is a comment…

So nothing to do with the Department of War Paul?

That’s a tough disconnect, but it is also the reality of a career in IT. Curriculums do not always keep up with industry. By graduation, some of what they learned may be behind what employers are looking for. Ideally, colleges and universities should be much more forward-thinking and faster at adapting to emerging technologies, frameworks, tools, and ways of working during their program. Students also need to understand that formal education is only the beginning. To stay competitive, they need to supplement their coursework with practical experience, current technical skills, internships, personal projects, open-source contributions, bootcamps - whatever helps them bridge the gap between school and the real world. Generative AI really only became mainstream about three and a half years ago, and it has already changed expectations in IT and other areas. I sincerely hope colleges & universities are not just reacting to this shift, but actively preparing students for what comes next.

Muchas gracias por compartirlo !!!

>jin-guh 🤦‍♂️

Why not just download codex and use the same subscription? Unless you moved to Claude Code before GPT 5.5 came out (Like Me), Codex is still currently better at coding than Opus.

Exactly yes

Sakib Ziad both, actually I ask same question to llms, so instead waiting for someone replay the post, now days llms do. So speed and development become faster than ever

Wow, totally awesome!!! 😍

STACK

Interesting! Workflow fatigue is becoming a bigger problem than model quality.

I think we are entering the “adoption race.” This brand is better than that one. I’m switching to this platform. This stack replaces my previous stack. But maybe the deeper question is not which AI tool makes everything easier. The question is: easier for what? If the craft is weak, AI only helps us produce weak work faster. The real value is not just in replacing tools. It is in understanding what kind of knowledge, judgment, and responsibility those tools are meant to support.

Geographic anchoring may take care of logistical routing but at the same time erase a patient's biological identity by defaulting to Western clinical baselines by increasing genetic and biological blind spots. Medical AI safety requires decoupling genetics from location, prompting for both the physical location of the patient and their specific ethnic health predispositions. This problem is already existing example where patient of different ethinicty vists a GP in a different geograhical location My view is that Medical AI would be more efficent on regional flavour rather than one solution fits all

The Meat Puppets must be satiated . . . . . . . . . . . . Says who?

Meanwhile stack overflows revenue has increased manifold because they've been feeding to the LMs instead 🫡

hey the biggest leap we need in ai right now isnt just better capabilities its moving from controlling model behavior to real consequence controlonce these systems can reason across modalities and act in the real world we have to ask the hard questions what evidence drove the decision what actually changed can it be reversed and most importantly who is accountable when something goes wrongsafety cant stay abstract it needs to become operational with clear auditability and human oversight especially as we push into agents and roboticsthis controllability gap feels like the next major bottleneck what do you think is the most important layer we should be building right now to make ai truly safe in the wild

The workflow-fatigue point is real, but I’d be careful with the “one workspace for everything” conclusion. Centralizing tools can reduce friction, but it can also blur the reason different models are valuable in the first place. Different models have different strengths: reasoning, coding, UX, retrieval, image generation, long-context analysis, structured outputs, or synthesis. If everything is pushed through one workspace, the risk is that orchestration becomes easier while evaluation gets weaker. The hard part is not just accessing multiple models. It is knowing which model should handle which step, how outputs should be compared, and how QC happens across the workflow. The future is not only “one platform.” It is governed orchestration: routing the right task to the right model, then validating the output before it becomes execution.

The LLM’s won’t have anything to train on. Hmmm…

Well said—declaring "RIP ChatGPT" misses the mark, like swapping out a screwdriver and calling carpentry dead. The real magic is how teams stitch together these AI models into cohesive, value-generating systems that actually do the work, not just talk about it. That’s exactly where platforms like https://www.chat-data.com/ shine. Instead of chasing the latest model hype, Chat Data lets you design robust workflows, link multiple agents, and automate complex processes across text, voice, and much more. It’s about building a reliable AI-powered machine shop, not just showing off shiny new tools.

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