Claude Integrations

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.

I'm more excited I can run now a custom site, hook an MCP for it, and have all the cool intelligence I had to pay for SaaS without having to integrate to them plus govern my data, it's a massive win. I just see AI assistant coding replicating current SaaS services that I can run internally. If my shop was a specific stack, I could aim to have all my supporting apps in that specific stack using AI assistant coding, simplifying operations, and being able to hook up MCP's to get intelligence from all of them.

Truly, OSS should be more interesting in the next decade for this alone.

MCP is yet another interface for an existing SaaS (like UI and APIs), but now magically "agent enabled". And $$$ of course

On one hand, yes this is very cool for a whole host of personal uses. On the other hand giving any company this level of access to as many different personal data sources as are out there scares the shit out of me.

I’d feel a lot better if we had something resembling a comprehensive data privacy law in the United States because I don’t want it to basically be the Wild West for anyone handling whatever personal info doesn’t get covered under HIPAA.

I agree with your overall message - rapid growth appears to encourage competition and forces companies to put their best foot forward.

However, unfortunately, I cannot shower much praise on Claude 3.7. And if you (or anyone) asks why - 3.7 seems much better than 3.5, surely? - Then I’m moderately sure that you use Claude much more for coding than for any kind of conversation. In my opinion, even 3.5 Haiku (which is available for free during high loads) is better than 3.7 Sonnet.

Here’s a simple test. Try asking 3.7 to intuitively explain anything technical - say, mass dominated vs spring dominated oscillations. I’m a mechanical engineer who studied this stuff and I could not understand 3.7’s analogies.

I understand that coders are the largest single group of Claude’s users, but Claude went from being my most used app to being used only after both chatgpt and Gemini, something that I absolutely regret.

None of those reports are any good though. Maybe for shallow research, but I haven't found them deep. Can you share what kind of research you have been trying there where it has done a great job of actual deep research.

That iterative research process is exactly how I use Google Deep Research since it has a 20/day rate limit. Research a problem, notice some off hand assumption or remark the report made, and fire off another research run asking about it. It depends on what you work on; in my use case I often have to do hours of research for 30 minutes of work like when integrating a bunch of different vendors’ APIs or pouring over datasheets for EE, so it’s worth firing off research and then working on something else for 10-20 minutes (it helps that the Gemini app fires off a push notification when the report is done - Anthropic please do this! Even for requests made from the web app).

As for long research times, one thing I’ve been using it for is historical research on old books. Gemini DeepResearch was the first one able to properly explain the nuances of identifying a chimeral first edition Origin of Species after taking half an hour and reading 400 sources. It went into all the important details like spelling errors and the properties of chimeral FY2** copies found in various libraries around the world.

I disagree. They can walk and chew gum, do both things at once. And this practical stuff is very important.

I've been using the Atlassian MCP for nearly a month now, and it's completely changed (and eliminated) the feeling of having an overwhelming backlog.

I can have it do things like "find all the tickets related to profile editing and combine them into one epic" where it works perfectly. Or "help me prioritize the 15 tickets assigned to me this sprint" and it'll actually go through and suggest "maybe you can do these two tickets first since they seem smaller, then do this big one" – i haven't hooked it up to my calendar yet.

But I'd love for it to suggest things like "do this one ticket that requires a lot of heads down time on wednesday since you don't have any meetings. I can create a block on your calendar so that nobody will schedule a meeting then"

Those are all superhuman things that can be done with MCP and a smart model.

I've defined rules in cursor that say "when I ask you to mark something ready for test, change the status and assign it to <x person>, and leave a comment summarizing the changes"

If you look at my JIRA comments now, you'd wonder how I had so much time to write such thorough comments. I don't, Cursor and whatever model is doing it for me.

It's been an absolute game changer. MCP is going to be what the App store was to mobile. Yes you can get by without it, but actually hooking into all your daily tool is when this stuff gets insanely valuable in a practical sense.

Building integrations is a more predictable way of developing a smaller competitive advantage versus research. I think most of the leading AI companies are adopting a multi-arm strategy of research + product/ecosystem development to balance their risks.

why is finetuning talked about so much less than RAG? is it not viable at all?

I would love to do this on my codebase after every commit

Permissoning: OAuth Data protection: Local LLMs

Definitely my experience. I manage context like a hawk, be it with Claude-as-Google-replacement or LLM integrations into systems. Too little and the results are off. Too much and the results are off.

Not sure what Anthropic and co can do about that, but integrations feel like a step in the wrong direction. Whenever I've tried tool use, it was orders of magnitude more expensive and generally inferior to a simple model call with curated context from SerpApi and such.

This is my concern as well. How successful is it in selecting the correct tool out of hundreds or thousands?

Different to what this integration is pushing, the LLMs usage in production based products where high accuracy is a requirement (99%), you have to give a very limited tool set to get any degree of success.

There's a niche for the kitchen sink approach. It's a type of search engine.

Throw in all context --> ask it what is important for problem XYZ --> curate what it tells you, and feed that to another model to actually solve XYZ

This has been my experience as well. The moment you turn internet access on, Kagi Assistant starts outputting garbage. Turn it off and you're all good.

you hit the nail on the head. my experience with prompting LLMs is that providing extra context that isn’t explicitly needed leads to “distracted” outputs

I mean, to be honest, they gotta do both to achieve what they’re aiming for.

A truly useful AI assistant has context on my last 100,000 emails - and also recalls the details of each individual one perfectly, without confusion or hallucination.

Obviously I’m setting a high bar here; I guess what I’m saying is “yes, and”

That's a tough pill to swallow when your company valuation is a $62B based on the premise that you're building a bot capable of transcendent thought, ready to disrupt every vertical in existence.

Tackling individual use-cases is supposed to be something for third party "ecosystem" companies to go after, not the mothership itself.