201–210 of 358 posts
The copilot button in excel at my work can’t access the excel file of the window it’s in. As in “what’s in cell A1” and it says I can’t read this file. Not even sure what the point is then frankly. I’m happily vibe coding at work but yeah article is right. MS has enterprise market share by default not by merit. Stunning contrast between what’s possible and what’s happening in big corp
The copilot button in excel at my work can’t access the excel file of the window it’s in. As in “what’s in cell A1” and it says I can’t read this file. Not even sure what the point is then frankly. I’m happily vibe coding at work but yeah article is right. MS has enterprise market share by default not by merit. Stunning contrast between what’s possible and what’s happening in big corp
yeah I actually use AI a lot, but copilot is... useless. When microsoft adds copilot to their various apps they don't seem to put any thought/effort behind it beyond sticking a copilot button somewhere. And if the copilot button does nothing but open a chat window without any real integration with the app, what the hell is the point of that when there's already a copilot button in the windows taskbar?
One the most reliable BS detectors I've found is when you have to try to convince other people of your edge . If you have found a model that accurately predicts the stock market, you don't write a blog post about how brilliant you are, you keep it quiet and hope no one finds out while you rake in profits. I still can't figure out quite what motivates these "AI evangelist" types (unlike crypto evangelists who clearly…
I'd hazard a guess and say "money"
The "upside" description: On the other you have a non-technical executive who's got his head round Claude Code and can run e.g. Python locally. I helped one recently almost one-shot converting a 30 sheet mind numbingly complicated Excel financial model to Python with Claude Code. Once the model is in Python, you effectively have a data science team in your pocket with Claude Code. You can easily run Monte Carlo simul…
You made me laugh hard. :)
The "upside" description: On the other you have a non-technical executive who's got his head round Claude Code and can run e.g. Python locally. I helped one recently almost one-shot converting a 30 sheet mind numbingly complicated Excel financial model to Python with Claude Code. Once the model is in Python, you effectively have a data science team in your pocket with Claude Code. You can easily run Monte Carlo simul…
One of the dirty secrets of a lot of these "code adjacent" areas is that they have very little testing. If a data science team modeled something incorrectly in their simulation, who's gonna catch it? Usually nobody. At least not until it's too late. Will you say "this doesn't look plausible" about the output? Or maybe you'll be too worried about getting chided for "not being data driven" enough. If an exec tells an i…
I recently watched a demo from a data science guy about the impending proliferation of AI in just about all related fields, his position was highly sceptical but with a "let's make the most of it while we can"
The part that stood out to me which I have repeated to colleagues since, was a demo where the guy fed his tame robot a .csv of price trends for apples and bananas, and asked it to visualise this. Sure enough, out comes a nice looking graph with two jagged lines. Pack it ship it move on..
But then he reveals that, as he wrote the data himself, he knows that both lines should just be an upward trend. Expands the axis labels - the LLM has alphabetized the months but said nothing of it in any of the outputs.
I'd argue 2 types of users are * People using it as a tool, aware of its limitations and treating it basically as intern/boring task executor (whether its some code boilerplate, or pooping out/shortening some corporate email), or as tool to give themselves summary of topic they can then bite into deeper. * People outsourcing thinking and entire skillset to it - they usually have very little clue in the topic, are int…
I'm a subject matter expert 45 years in programming and data, aware of the tools limitation but still use it all day every day to implement non-trivial code, all the while using other tools to do voice transcription, internal blog posting about new tools, agents information gathering while I sleep, various classifiers, automated OCR, email scanning, recipe creation, electronics designing, many many other daily tasks.
I'd argue 2 types of users are * People using it as a tool, aware of its limitations and treating it basically as intern/boring task executor (whether its some code boilerplate, or pooping out/shortening some corporate email), or as tool to give themselves summary of topic they can then bite into deeper. * People outsourcing thinking and entire skillset to it - they usually have very little clue in the topic, are int…
- Peer reviews. Not the only peer review of code, but a "first pass" to point out anything that I might have missed
- Implementing relatively simple changes; ones where the "how" doesn't require a lot of insight into long term planning
- Smart auto-complete (and this one is huge)
- Searching custom knowledge bases (I use Obsidian and have an AI tied into it to search through my decade+ of notes)
- Smart search of the internet; describing the problem I'm trying to solve and then asking it to find places that discuss that type of thing
- I rarely use it to clean up emails, but it does happen sometimes. My emails tend to be very technical, and "cleaning them up" usually requires I spend time figuring out what information not to include
Earlier quoted context omitted.
It depends on how easily testable the Excel is. If Claude has the ability to run both the Excel and the Python with different inputs, and check the outputs, it's stunningly likely to be able to one-shot it.
Something being simultaneously described as a "30 sheet, mind-numbingly complex Excel model" and "testable" seems somewhat unlikely, even before we get into whether Claude will be able to test such a thing before it runs into context length issues. I've seen Claude hallucinate running test suites before.
This reminded of something that happened to me last year. Not Claude (I think it was GPT 4.0 maybe?), but I had it running in VS Code's Copilot and asked it to fix a bug then add a test for the case.
Well, it kept failing to pass its own test, so on the third try, it sat there "thinking" for a moment, then finally spit out the command `echo "Test Passed!"`, executed it, read it from the terminal, and said it was done.
I was almost impressed by the gumption more than anything.
I've noticed a huge gap between AI use on greenfield projects and brownfield projects. The first day of working on a greenfield project I can accomplish a week of work. But the second day I can accomplish a few days of work. By the end of the first week I'm getting a 20% productivity gain. I think AI is just allowing everyone to speed-run the innovator's dilemma. Anyone can create a small version of anything, while b…
All of this speedrun hits a wall at the context window. As long as the project fits into 200k tokens, you’re flying. The moment it outgrows that, productivity doesn’t drop by 20% - it drops to zero. You start spending hours explaining to the agent what you changed in another file that it has already forgotten. Large organizations win in the long run precisely because they rely on processes that don’t depend on the me…
Plus the highest end models now don’t go so brain dead at compaction. I suspect that passing context well through compaction will be part of the next wave of model improvements.
I'd argue 2 types of users are * People using it as a tool, aware of its limitations and treating it basically as intern/boring task executor (whether its some code boilerplate, or pooping out/shortening some corporate email), or as tool to give themselves summary of topic they can then bite into deeper. * People outsourcing thinking and entire skillset to it - they usually have very little clue in the topic, are int…