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13.01.2026 17:08
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What’s more concerning is that anybody would want a *heated* bed
LLMs can do incredible things. This ain’t it though.
I just had to talk to a chatbot at a doctors office and I think I developed a heart condition in frustration.
They replaced the normal intake form with a thousand chatbot turns.
Everyone is all hyped about LLMs and here I just want SAM 3
Bring back capes
The only thing I don’t like about MCP is that clients make me beg to include tools in the plan. Like, just give me a checkbox to say you MUST schedule a tool call in the plan.
I have! But I don’t have the patience for spec driven development most of the time, even though I know it would help.
AWS has to contend with Google absolutely killing it with dev tools and libraries for AI workloads. AWS has traditionally struggled with making tools that developers actually want to use.
AWS might pull ahead in because of market share, but I’ll be surprised if they come out ahead in capabilities.
I feel like a lot of teams start loose and fast with Firestore and then find themselves in a pickle as soon as they start growing a team.
What fields does this collection have? Who knows, better iterate over every single item just to figure that out ☠️
No, the only answer is a strict contract
I can already imagine a persons whole job being chasing down teams trying to standardize field names
I literally cannot work with Firestore without seed data in json. I lose track of fields almost immediately, otherwise.
How does Firestore scale to large teams? Do you end up with a ton of spaghetti checking if slight variations of field names exist?
When the commits devolve to literal begging for a green check mark
Had a dream that a rust app was silently crashing every time I tested it. That’s it. That was the dream. Over and over.
September 8th, the good PNW weather begins.
I desire cold
I think the response to 4o being shut down alarmed people. It put a spotlight on the psychological impact chatbots have on some folks.
These things are more relatable every day
To suggest you can’t learn from something just because it’s not always 100% correct demonstrates an unfortunate character flaw.
I learn from my peers, but I don’t go expecting them to be know everything. Part of the learning process is discovering the correct answer together through trial and error
segoe ui bold, if you will
Windows Phone UI was inspired. And the colors of the Lumia series were amazing. I used to get so many comments on my cyan phone.
If Microsoft tries another handheld device, I hope they’re bold on UX again.
.NET Aspire makes working with Dapr 1000% more comfortable
Not perfect but it kind of works. Speaker detection of multi-user webcam input.
My favorite thing about wandering into Applied AI as a focus is that in 200 lines of code you can make something profoundly useful. Like, the dopamine efficiency is off the charts, especially in CV.
Trying out Dapr Agents today. I tried Dapr+Aspire about a year ago for this purpose but they didn’t have a dedicated concept of an agent.
“I ain’t reading all that” as a service is actually useful though.
Oh and there’s this docs.litellm.ai/docs/provide... which has a sample for tool calling
Another way would be MCP servers. Anthropic has a platform agnostic spec for tools that you can check out here. github.com/modelcontext...
They have an SDK also that lets you interface with these OSS servers that facilitate things like browsing or any domain specific task.
It’s kind of jumping into the deep end, but I think the full stack sample from this tool is great. github.com/GoogleCloudP.... It’ll replicate DeepResearch using the Google Search tool. The ADK docs show how to use LiteLLM for agents which is an abstraction for basically any inference provider
Stay skeptical. They’re very useful, but it’s almost as if as soon as you put too much trust in them, they demonstrate their limitations.
Very fun tool to try to keep up with though. It’s all moving very fast.