251–260 of 266 posts
Its only a matter of time where folks write user stories and an LLM takes over for the first draft, then iterate from there. Btw, that speaks to how important it is to get clear business requirements for work.
Greptile ( https://www.greptile.com/ ) tries to do that, at least for bug tickets. I recall being annoyed by its suggestions (posted as Linear comments).
I think with MCPs and related tech, if Apple just internally went back to the drawing board and integrated the concept of MCPs directly into iOS (via the "Apple Intelligence" umbrella) and seamlessly integrated it into the App Store and apps, they will win the mobile race for this. Being Apple, they would have to come up with something novel like they did with push (where you have _one_ OS process running that delega…
Do you really think Apple can catch up with and then surpass all these SOTA AI labs? They bet big and got distracted on VR. It was obviously the wrong choice at the time, and even more so now. They're going to have to abandon all that VR crap and pivot hard to AI to try and catch up. I think the more likely case is they can't catch up now and will just have to end up licensing Gemini from Google/Google paying them to…
As others have pointed out, if that’s what App Intents are, have they started to integrate this as part of Apple Intelligence?
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Well I had half a mind to not tell them to see what they’d say, but I also was excited to show everyone so they can also be empowered with it. One of them said “yeah I was wondering cuz you never write that much” - as a leader, I actually don’t set a good example of how to leave quality JIRA comments. And my view with all these things is that I have to lead by example, not by orders. With the help of these kinds of t…
Notice they commented on the quantity, not the quality? I don't think it's good leadership to unleash drivel on an organisation, have people waste time reading and perhaps replying to it, thinking it's something important and thoughtful coming from atonse. Good thing you told them though, now they can ignore it.
Also, a lot of the kinds of comments are things like, when you combine a bunch of tickets, leaving comments on the cancelled tickets to show why they were cancelled.
In the past, that info simply wouldn’t be there.
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Well I had half a mind to not tell them to see what they’d say, but I also was excited to show everyone so they can also be empowered with it. One of them said “yeah I was wondering cuz you never write that much” - as a leader, I actually don’t set a good example of how to leave quality JIRA comments. And my view with all these things is that I have to lead by example, not by orders. With the help of these kinds of t…
Someone please shoot me if my PM ever gets this idea in his head of using LLM slop to spam tickets with en masse. There's nothing I hate more than people sending me their AI messages, be it in a ticket or a PR or even on Slack. I'm forced to engage and spend effort on something it took them all of 3 seconds to generate without even proofreading what they're sending me says. The amount of times I've had to ask 11 clar…
I’m not remotely interested in throwing random slop in there.
In fact, we did try a year ago to have AI help write our tickets and it was very clear that they were AI generated. There was way too much nonsense in there that wasn’t relevant to our product.
So we don’t do that.
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I'd love a _tip jar_ MCP, where the LLM vendor can automatically tip my website for using its content/feature/service in a query's response. Even if the amount is absolutely minuscule, in aggregate, this might make up for ad revenue losses.
Not that exactly, but I just saw this on twitter a few minutes ago from Stripe: https://x.com/jeff_weinstein/status/1918029261430255626
It is really cool to witness the velocity of MCP adoption.
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Greptile ( https://www.greptile.com/ ) tries to do that, at least for bug tickets. I recall being annoyed by its suggestions (posted as Linear comments).
Co-founder of Greptile - that was a bad feature that we since deprecated to focus entirely on AI code reviews
Remote MCP servers are still in a strange space. Anthropic updated the MCP spec about a month ago with a new Streamable HTTP transport, but it doesn't appear that Claude supports that transport yet. When I hooked up our remote MCP server, Claude sends a GET request to the endpoint. According to the spec, clients that want to support both transports should first attempt to POST an InitializeRequest to the server URL.…
Here's my tool for Desktop: https://github.com/kordless/EvolveMCP
The integrations feel so rag-ish. It talks, tells you it’s going to use a tool, searches, talks about what it found… Hope one day it will be practical to do nightly finetunes of a model per company with all core corporate data stores. This could create a seamless native model experience that knows about (almost) everything you’re doing.
I worked at a startup where the CEO swore up and down that real-time fine-tuning was the future — that models would continuously update with company data. It sounded cool until you remember: That’s not how LLMs work. It’s not efficient. It’s not flexible. And it’s not even necessary — we already have RAG.
Pipedreams make good pitch decks. But they break when you hit production.
It's a fucking pipedream, this. That's not how LLMs work, it's not efficient, it's not useful (we have RAG for reference augmentation), and it’s not even desirable unless you want your model overfitting on stale, internal narratives every night.
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There were initial difficulties in finetuning that made it less appealing early on, and that's snowballed a bit into having more of a focus on RAG. Some of the issues still exist, of course: * Finetuning takes time and compute; for one-off queries using in-context learning is vastly more efficient (i.e., look it up with RAG). * Early results with finetuning had trouble reliably memorizing information. We've got a muc…
So is a good summary: 1. If you have a large corpus of valuable data not available to the corporations, you can benefit from fine tuning using this data. 2. Otherwise just use RAG.
Fine-tuning makes sense when you need behavioral shifts (style, tone, bias) or are training on data unavailable at runtime.
RAG excels when you want factual augmentation without retraining the whole damn brain.
It's not either/or — it's about cost, latency, use case, and update cycles. But hey, binaries are easier to pitch on a slide.