Comment by LZ_Khan
6 days ago
I'm curious how the 6 months have looked from a non-programmer's perspective. What kind of co-working tools and similar optimizations have people from other fields experienced?
6 days ago
I'm curious how the 6 months have looked from a non-programmer's perspective. What kind of co-working tools and similar optimizations have people from other fields experienced?
I am an instructor who helps deliver an apprenticeship. My new boss has been in our industry for about 20 years and is one of the most respected people in our company. They've just joined us to teach and are off doing a two week course. On the first day she was told to let AI write all of her lesson plans, and then feed the lesson plans to AI to make her slides...
Hopefully she rejects all this out of hand, but if she doesn't it'll mean that none of our trainees get the benefit of her experience, who she is as a person, and what she has to pass onto them.
We have 6 monthly reviews as instructors where we are told the same thing. "How could you use AI for your teaching?"
They don't even feel the need to justify why this would be desirable, or is needed at all. It's just pure bandwagonning. Unbelievably, most of my coworkers are extremely positive about AI, although none of them have told me they use it for anything besides preparing their lessons for them — they just use it instead of having to think, or spend time preparing...the only important thing they do at work.
It makes no sense to me.
I’m teaching a class at a university in Japan (on AI-related issues, as it happens). I’ve been teaching for more than 40 years, but at 106 registered students this is by far the largest class I have ever taught. AI tools are very helpful for class management, such as keeping track of attendance and homework submissions.
I have to consciously avoid using AI for more cognitive tasks, though. It would be very tempting to have Claude, ChatGPT, or Gemini summarize, classify, and grade the students’ assignments, write individual feedback, prepare my lesson plans, etc. However, I know that my engagement with the material and with the students would suffer. I also want to show the students that they are learning together with me and with each other, not with bots.
I am semiretired and have a light teaching load that gives me plenty of time to prepare for class. I can see that full-time teachers might find it hard to resist the lure of offloading their thinking to AI.
I've been a teacher (most of the time a college professor) for...a long time. Nowadays, when preparing a new course, I definitely work with AI: "Here's what I want, and who my audience is - give me a course outline".
That gives me a starting point. Of course, I modify it. Maybe I bounce back and forth to the AI for further refinements and suggestions, but ultimately I have to be happy with the result.
When prepping the individual lessons, the biggest time saver is coming up with examples to illustrate particular points. I could do this alone, but sometimes that involves staring at a blank screen for a while. It is faster to ask the AI for suggestions, pick the one I like, and refine it further myself.
AI is a tool. Use it appropriately.
> AI is a tool. Use it appropriately
Yes, but no room is made for people who see no use for it. There is a forced-consensus that this technology is useful, which I have to combat against at work.
We teach in a very different environment, but your use sounds typical of my colleagues. "I ask it for suggestions and pick one", but nobody seems to wonder about what is lost when we shrink the horizon of what we will teach to the most likely outputs from a chatbot, one of which we will use.
Maybe this makes more sense in other fields. I have to prepare people to work in the shipping industry, in extremely dangerous roles where they will be operating heavy machinery, steering ships, driving cranes etc. The fact is that AI knows next to nothing about this field because an AI cannot experience handling a ship in rough weather, has never secured a boat to a ship's side with the rain and wind in its face.
Yet, when people are brought in to instruct our trainees, they are told to "tell AI what you want and pick one of the suggestions", in the best case, or just give over everything to the AI in the worst case. And nobody seems to be able to explain why this is a better way of working than sitting with a pen and paper, brainstorming some ideas for a lesson based on your real experiences, and then delivering it. The only justification I'm ever given is your one, "I pick from a list so I am really still in control", "it's quicker and I don't have to think as hard or as long", "it's better at making slides or writing good-sounding (to management and auditors) lesson plans". No-one ever seems to justify it by saying it is genuinely a better experience for the trainees.
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In pure maths:
- pre GPT-5.4: very limited use; some smart people got some mileage out of the models, but it always required serious work and a very suitable problem. Of course the models could solve homework problems, but that felt more like a downside to us who teach.
- since GPT-5.4 (Mar 2026): the "wow" release; suddenly answering MathOverflow-level problems that have previously been stumping experts. Still prone to hallucinations, but smart enough to use the built-in Python skill to verify its claims on small examples when possible. Probably a lot better at formula-heavy math than at the abstract "philosophical" kind.
- GPT-5.5: gave me a fascinating, significantly nontrivial and highly instructive "proof from the book" on an MO-hard problem that I'm in the process of writing up. Might have been luck and good prompting, though. Didn't really feel like a qualitative leap from 5.4, but I take quantitative any time. Still requires suitable problems, but it's much harder to rule out suitability from the get-go.
Claude and Gemini have been also-rans the whole time and still are. I use Claude for secretary-like tasks; occasionally it finds an easy proof too, but usually because I've missed something obvious.
Oh, and GPT, and to a lesser extent Claude, are great at hunting errors in maths. Probably 90% of my prompts so far have been for proofreading my writings.
I work at a company that deploys AI to enterprises
The average office worker is amazed at Copilot (not in the IDE - but the app bundled with Windows), and they mostly copy paste material into their enterprise provided ChatGPT / Gemini, and get tips from Facebook / Instagram on their top 5 best prompts for work productivity
Showing them agents that automate work at scale is a very magical experience
And then everyone that has to deal with their copy pasted output is too nice to say how bad it is and how much work it just offloads to the next person that’ll probably get frustrated and have an agent handle it.
Claude in Office was a tipping point for nontechnical folks around me. Everyone’s slides decks are immaculate now. Finance isn’t needing nearly as much BI help. It’s pretty impressive.
I find it really troubling finance are relying on LLMs (word generators!) for financial analysis - I mean I guess it means there will never be any annoying gaps in the data.
Depends on how it’s done.
I use it a lot now for knocking up grafana charts etc. It’s not so much that the LLM is feeding the numbers through. You can still use real tools to analyse and summarise the numbers, it’s just much quicker at driving them.
As ever with data analysis, two things will continue to be true. Real insights come from spotting something that looks off and digging into it deeper. Secondly, it’s really easy to connect data in a misleading way.
I’ve had a Claude analysis handed to me this morning including a summary list of actions we’re going to take next which falls into this very trap.
The insights you’ll get from your data will only be as deep as the curiosity of the person at the helm.
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Can I get Claude to view the slide decks for me so I don't waste my time?
Interesting. I don't have to use PowerPoint much, but I hate it when I do. I don't want the llm to write the words but I do want it to make things look nice. So does this work well now?
Claude for Powerpoint will generate legitimately beautiful decks for you. The chat app will create them as artifacts also.
My pipeline for this is vscode + prompts + markdown templates + GitHub copilot -> markdown docs -> pandoc to produce.docx -> copilot in word for “nice” formatting -> copilot in ppt for nice decks. LLMs all the way down.
I find it’s easier to version control and diff the .md artefacts, those remain my authoritative source.
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If you don't want an LLM to write the words, surely you also want to decide on the data and graphs to show by yourself? Isn't that 90% of a presentation? The "looking nice" part doesn't matter as much, it could be black text on a white background and it would be fine.
The important part is the presentation matching your presenting cadence, which is something LLM generated presentations never get right. I don't have a problem with people generating presentations, but most of the time they just end up reading whatever is on the screen when presenting.
With a little bit of work, it works very well. You can generate powerpoint directly with Codex or Claude Cowork. There is also Canva support for these tools and it has its own AI integration. Another useful tool in this space is the Gemini integration in Google slides.
If you are a bit technical, reveal.js is actually really nice for this. I one shotted a pdf export for that uses a headless browser. I've used that a few times now.
What works well for me is to take an existing presentation and then some raw input and generate a new presentation in the same style as the old one from the raw input. After that, I can go in and tweak individual slides.
Another thing I did recently was take somebody's existing pitch deck and fix it with a one line prompt: "this deck is a bit meh, pimp it!" that worked unreasonably well. I like using shitty prompts like that. Codex often manages to do the right thing if you don't overthink your prompts.
Classic deck of somebody that used way too much text and only bullets. It did a great job on that presenting the content in a more simple and better structured way. Pulling out key facts and highlighting those, simplifying text, etc. Doing that manually would have taken hours.
In business: using coworking tools to review and propose filing of emails; manage my files and folders; on a daily basis scour the intranet for interesting and relevant content.
Personal: my wife tutors in her native language to non-native primary and high school kids. They are all using these tools now generate fresh content for practice based on school lesson plans. The kids are improving much more quickly now than they were just a few months ago.
As someone who works somewhere where the intranet is a bit of a jungle: what tool do you use to scour the intranet?
Thanks!
Copilot Cowork in the M365 ecosystem. It inherits all the permissions from my account, has access to exchange to send me emails, and OneDrive to save each day’s summary for posterity and future refinement.
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My day job is not in the tech industry. I am an editor. Literally nothing has changed for me in the last four years.
As a former data scientist, I started to use code agent 3 monthes ago. Before that, I use chat completion on web. Now, I nearly do everything which outputs documents with code agent.
Can you give a sanitized example or a hypothetical scenario of what you mean by “output documents with code agents”? Thanks.
I’m not him, but I’ve started using them to do the analysis (SQL, Python etc.) and then output the report as Quarto HTML which can be hosted on GitHub Pages. It works well for this analysis style work.
Once I was going to send some figures to leadership so I checked the queries myself and not only had it done it correctly, but it had also included a lot of sanity checks with other places in the database which as a human I doubt I’d have had the time or inclination to do.
Even for modelling work it can be good to check your ETL queries, or write one itself and then check it etc.
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All the documents that were typed with a keyboard before, now can be created by code agents with properly designed and implemented prompts and skills.
I generate my blog with this method and you can refer to: https://blog.chuanxilu.net/en/
I am responsible for all the contents but the process of those essays and reports are first generated by prompts that embody my ideas, thoughts and facts I encountered.
I think Claude Cowork through the Microsoft thing which was copilot but is now named M365 (or something?) is likely creating every powerpoint resentation within our organisation at this point.
We have whatever AI is in teams transcribe every meeting, and it's scaringly good at it. It's also extremely good at sumerizing or finding things from pervious meetings when tasked. One disadvantage in this, is that I can see how stupid I sound on writing. I'll go "yeah, hmm, yeah, that's, yeah", but it really is pretty good.
I assume we're going to see a massive increase in AI with this Cowork inside the Microsoft client. We actually have a better tool available through a librechat where you can create and configure your own agents with the same filesystem access to your one drive, and a lot more tools and models than just Claude. Almost nobody has been capable of figuring out how to use it though, so they've been using the regular office365 copilot and it sucks so bad that a lot of people stopped beliving in AI.
It's ironic that Microsoft fumbling the ball on AI, but being very good at enterprise customers (especially non-IT) means that they'll likely be the company which is going to sell us AI tools that people will actually use. I have no idea why it's so hard for people to pick up the Librechat tool we're given access to through our equity fund. It's quite litterally a copy of ChatGPT where you can point-and-click configure an agent, but we're seeing that even employees who use a lot of ChatGPT privately don't use this tool professionally. Meanwhile everyone has been capable of using the Microsoft thing (that I personally think is less user friendly since you will need to add your configuration files to every promt).
"I have no idea why it's so hard for people to pick up the Librechat tool we're given access to through our equity fund"
That's because M365 is integrated with the whole Office/Exchange environment, especially in terms of security policies, etc. MS also guarantee that the data are private, this is very important for many companies both from the IP protection perspective and the liability to expose some users/customers data (think of GDPR regulations is Europe).
I don't know who is behind Liberchat, probably some good and friendly folks, but when it comes to privacy/security Microsoft has much more to loose and if shit happens it is easier to sue them than some random VC-financed company from the USA.
At work the tools handed to most are still essentially chatbots. Getting access to coding tools is an uphill battle because there isn’t really a good way to manage risk yet. Hard enough to keep a coding agent in check locally and ensure it does rm -rf anything. Scale that to thousands of people with limited skill and it doesn’t really work. So currently they just don’t.
That’s in a finance shop. I’d imagine it’s different in programming shops where handing people Claude code is a bit more plausible
Purely anecdotal, but in my team of 20 data analysts, we've seen a bunch of them become quite productive in producing tools and apps. These are analysts with mostly domain knowledge, and not so much programming knowledge - meaning that they knew the basics to write scripts, and wrangle data programmatically, but not enough to actually engage in software engineering.
Some of these are now contributors.
I also have a friend (beware, N=1 study) with zero prior programming knowledge that has released his first app.
They lag behind because we build for ourselves first. We are rolling out Claude to the biz team this week and they will get access to Cowork, which is still preview aiui.
Sales will be another big user of agent automations, for better or worse. Poor usage by Google to craft emails and slides for us is why the suits are getting an Anthropic sub. Stay human in the loop my friends!
for non-coders: local AI. a couple years ago you needed a dedicated GPU rig. now a 30B model fits on a laptop and runs offline.
I've always been a "power user", making little python programs and figuring out new ways to do things with seemingly unrelated systems. My knowledge is shallow, but very broad.
A year and a few jobs ago I was genuinely up against a wall I could not see breaking through, not if I wanted to ever sleep again. Hundreds of completely bespoke customers. Hideous archaic tooling. Two of us. It was bad times. So I started paying for Claude - desperation move, to try and vibe my way out. Honestly, it's been a little bit like having superpowers.
Not just code generation, which has been great, but gaining knowledge and understanding with incredible velocity - sort of like how RSS felt back in the day, or when Google stopped being worthless in the very end of the 20th C. When Wikipedia started.
So where am I now? Well, I ditched the hell job (I didn't really drink the koolaid of their "Enterprise Solution" anyway), and got a regular day job in my core competency. I guess I do a lot of what is called "vibe coding", all kinds of utilities, what I call my "extracurriculars". A graph view for Asciidoc in VSC to show includes, xrefs, partial includes. Graph view for everything actually - it's surprisingly insightful for PDM and config management. Analysis tools for sensor faults based on Python open source astronomy tools. All sorts of converters and aggregators and cleaners for a devil's piss bucket of enterprise systems. A bazillion new MapTools macros for gaming, making complex RPG systems nearly pushbutton. A little harvest of local LLM systems doing all sorts of things, like my "Reviewinator" for copy edit. I could type the rest of the day and wouldn't come close to the end of the list.
So, pretty amazing. Very interesting systems with what must be some N-dimensional geometry underlying, maybe a signal to an underlying principle of emergence. Who knows?
In the long term, it's going to be Enterprise Software that eats the big losses from these systems. For all sorts of reasons, but mostly because Enterprise is where software goes to die. It's all bespoke to hell, it's all ancient, no one is working there because they want to. So a domain expert, with AI assist and a little know how, is probably going to whip up a superior set of tools in a short enough time to make it really worthwhile. Watch that space: SAP, Siemens, Teamcenter, SalesForce. Watch their consulting revenue.