As a researcher who has to use LaTeX, I used to use Overleaf, but lately I've been configuring it locally in VS Code. The configuration process on Mac is very simple. Considering there are so many free LLMs available now, I still won't subscribe to ChatGPT.
I really doubt this marketing approach is effective. Isn't this just shooting themselves in the foot? My actual experience with Cursor has been: their design is excellent and the UX is great—it handles frontend work reasonably well. But as soon as you go deeper, it becomes very prone to serious bugs. While the addition of Claude's new models has helped somewhat, the results are still not as good as Google's Antigravity (despite its poor UX and numerous bugs). What's worse, even with this much-hyped Claude model, you can easily blow through the $20 subscription limit in just a few days. Maybe they're betting on models becoming 10x better and 10x cheaper, but that seems unlikely to happen anytime soon.
Hitting my head into buggy apps made by these AI companies and seeing them all be amazed in parallel that skills/MCP would be necessary for real work has me pretty relaxed about ‘our jobs’.
OpenAIs business-model floundering, degenerating inline to ads soon (lol), shows what can be done with infini-LLM, infini-capital, and all the smarts & connections on Earth… broadly speaking, I think the geniuses at Google who invented a lot of this shizz understand it and were leveraging it appropriately before ChatGPT blew up.
We use mcp at work. Due to some typo the model ran absolutely random queries on our database most of the cases. We had initially kept ot open ended but after that, we wrote custom tools that took an input, gave an output and that was strictly mentioned in the prompt. Only then did it work fine.
I can't comment on this matter because I don't know the details. However, based on my personal experience consuming Adafruit products and their generous open-source approach, I personally trust Adafruit very much.
I'm rewriting glicol (https://glicol.org/) with no std, and embassy-rs + 2350 is my go-to choice. Highly recommand this stack if you're planning to start working with embedded systems in 2026.
I was about to check out the GitHub activity summary like last year, but then I realized that a lot of things weren't actually pushed to GitHub this year. So I used Vibe-coded to take a look:
Relevant to this discussion - my project Glicol (https://glicol.org) addresses this space. Currently working on a no_std rewrite, demo coming next year :)
https://github.com/deepseek-ai/DeepSeek-OCR-2/blob/main/Deep...
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