Overall, it's allowed me to maintain more consistent workflows as I'm less dependent on Opus. Now that Mastra has introduced the concept of Workspaces, which allow for more agentic development, this approach has become even more powerful.
Overall, it's allowed me to maintain more consistent workflows as I'm less dependent on Opus. Now that Mastra has introduced the concept of Workspaces, which allow for more agentic development, this approach has become even more powerful.
You'd have to be specific what you mean by AGI: all three letters mean a different thing to different people, and sometimes use the whole means something not present in the letters.
> If you could only give it texts and info and concepts up to Year X, well before Discovery Y, could we then see if it could prompt its way to that discovery?
To a limited degree.
Some developments can come from combining existing ideas and seeing what they imply.
Other things, like everything to do with relativity and quantum mechanics, would have required experiments. I don't think any of the relevant experiments had been done prior to this cut-off date, but I'm not absolutely sure of that.
You might be able to get such an LLM to develop all the maths and geometry for general relativity, and yet find the AI still tells you that the perihelion shift of Mercury is a sign of the planet Vulcan rather than of a curved spacetime: https://en.wikipedia.org/wiki/Vulcan_(hypothetical_planet)
I find it helpful to even change the persona of the same agent “the prompt” or the model the agent is using. These variations always help but I found having multiple different agents with different LLMs in the backend works better
I personally have moved to a pattern where i use mastra-agents in my project to achieve this. I've slowly shifted the bulk of the code research and web research to my internal tools (built with small typescript agents).. I can now really easily bounce between different tools such as claude, codex, opencode and my coding tools are spending more time orchestrating work than doing the work themselves.
MCP standardizes how LLM clients connect to external tools—defining wire formats, authentication flows, and metadata schemas. This means apps you build aren't inherently ChatGPT-specific; they're MCP servers that could work with any MCP-compatible client. The protocol is transport-agnostic and self-describing, with official Python and TypeScript SDKs already available.
That said, the "build our platform" criticism isn't entirely off base. While the protocol is open, practical adoption still depends heavily on ChatGPT's distribution and whether other LLM providers actually implement MCP clients. The real test will be whether this becomes a genuine cross-platform standard or just another way to contribute to OpenAI's ecosystem.
The technical primitives (tool discovery, structured content return, embedded UI resources) are solid and address real integration problems. Whether it succeeds likely depends more on ecosystem dynamics than technical merit.
Well played sir! Nice shot man! :D
I'm so tired of arguing with ChatGPT (or what was Bard) to even get simple things done. SOLAR-10B or Mistral works just fine for my use cases, and I've wired up a direct connection to Fireworks/OpenRouter/Together for the occasion I need anything more than what will run on my local hardware. (mixtral MOE, 70B code/chat models)
I also hang out on a few Discord servers: - Nous Research - TogetherAI / Fireworks / Openrouter - LangChain - TheBloke AI - Mistral AI
These, along with a couple of newsletters, basically keep a pulse on things.
Using these things will fry your brain's ability to think through hard solutions. It will give you a disease we haven't even named yet. Your brain will atrophy. Do you want your competency to be correlated 1:1 to the quality and quantity of tokens you can afford (or be loaned!!)?
Their main purpose is to convince C-suite suits that they don't need you, or they should be justified in paying you less.This will of course backfire on them, but in the meantime, why give them the training data, why give them the revenue??
I'd bet anything these new models / agentic-tools are designed to optimize for token consumption. They need the revenue BADLY. These companies are valued at 200 X Revenue.. Google IPO'd at 10-11 x lmfao . Wtf are we even doing? Can't wait to watch it crash and burn :) Soon!