You just articulated why I struggle to personally connect with Gemini. It feels so unrelatable and exhausting to read its output. I prefer to read Opus/Deepseek/GLM over Gemini, Qwen and the open source GPT models. Maybe it is RLHF that is creating my distaste from using it. (I pay for Gemini; I should be using it more... but the outputs just bug me and feel more work to get actionable insight.)
Wow, a bunch of NFT people used to say the same thing.
lmao, please explain to me why these companies should be valued at 200x revenue.. They are providing autocomplete APIs.
How come Google's valuation hasn't increased 100-200x, they provide foundation models + a ton more services as well and are profitable. None of this makes sense, its destined to fail.
I like your name, it suggests you're here for a good debate.
Let me start by conceding on the company value front; they should not have such value. I will also concede that these models lower your value of labor and quality of craft.
But what they give in return is the ability to scale your engineering impact to new highs - Talented engineers know which implementation patterns work better, how to build debuggable and growable systems. While each file in the code may be "worse" (by whichever metric you choose), the final product has more scope and faster delivery. You can likewise choose to hone in the scope and increase quality, if that's your angle.
LLMs aren't a blanket improvement - They come with tradeoffs.
(I had to create a new account, because HN doesn't like LLM haters (don't mess with the bag ig)
the em dashes in your reply scare me, but I'll assume you're a real person lol.
I think your opinion is valid, but tell that to the C Suite who's laid of 400k tech workers in the last 16 months in the USA. These tools don't seem to be used to empower high quality engineering, only to naively increase the bottom line by decreasing the number of engineers, and increasing workloads on those remaining.
Full disclosure, I haven't been laid off ever, but I see what's happening. I think when the trade-off is that your labor is worth a fraction of what it used to be and you're also expected to produce more, then that trade-off isn't worth it.
It would be a lot different if the signaling from business leaders was the reverse. If they believed these tools empowered labor's impact to a business, and planned on rewarding on that, it would be a different story. That's not what we are seeing, and they are very open about their plans for the future of our profession.
Automation can be good overall for society, but you also can't ignore the fact that basically all automation has decreased the value of the labor it replaced or subsidized.
This automation isn't necessarily adding value to society. I don't see any software being built that's increasing the quality of people's life, I don't see research being accelerated. There is no economic data to support this either. The economic gains are only reflected in the values of companies who are selling tokens, or have been able to decrease their employee-counts with token allowances.
All I see is people sharing CRUD apps on twitter, 50 clones of the same SaaS, ,people constantly complaining about how their favorite software/OS has more bugs, the cost of hardware and electricity going up and people literally going into psychosis. (I have a list of 70+ people on twitter that I've been adding too that are literally manic and borderline insane because of these tools).
But hey, at least your favorite AI evangelist from that podcast you loved can afford the $20,000/night resort this summer...
It's getting a lot easier to do this using sub-agents with tools in Claude. I have a fleet of Mastra agents (TypeScript). I use those agents inside my project as CLI tools to do repetitive tasks that gobble tokens such as scanning code, web search, library search, and even SourceGraph traversal.
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.
Related: as an actual magician, although no longer performing professionally, I was telling another magician friend the other day that IMHO, LLMs are the single greatest magic trick ever invented judging by pure deceptive power. Two reasons:
1. Great magic tricks exploit flaws in human perception and reasoning by seeming to be something they aren't. The best leverage more than one. By their nature, LLMs perfectly exploit the ways humans assess intelligence in themselves and others - knowledge recall, verbal agility, pattern recognition, confident articulation, etc. No other magic trick stacks so many parallel exploits at once.
2. But even the greatest magic tricks don't fool their inventors. David Copperfield doesn't suspect the lady may be floating by magic. Yet, some AI researchers believe the largest, most complex LLMs actually demonstrate emergent thinking and even consciousness. It's so deceptive it even fools people who know how it works. To me, that's a great fucking trick.
Also, just like how in centuries past, rulers/governments bet their entire Empires on the predictions of magicians / seers they consulted. Machine learning Engineers are the new seers and their models are their magic tricks. It seems like history really is a circle.
I love where you're going with this. In my experience it's not about a different persona, it's about constantly considering context that triggers, different activations enhance a different outcome. You can achieve the same thing, of course by switching to an agent with a separate persona, but you can also get it simply by injecting new context, or forcing the agent to consider something new. I feel like this concept gets cargo-culted a little bit.
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.
Thank you and I do like the mantra-agents concept as well and would love to explore adding something similar in the future such that you can quickly create subagents and assign tasks to them
The skepticism is understandable given the trajectory of GPTs and custom instructions, but there's a meaningful technical difference here: the Apps SDK is built on the Model Context Protocol (MCP), which is an open specification rather than a proprietary format.
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.
I've found myself more and more using local models rather than ChatGPT; it was pretty trivial to set up Ollama+Ollama-WebUI, which is shockingly good.
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)
Same here. I've found that I currently only want to use an LLM to solve relatively "dumb" problems (boilerplate generation, rubber-ducking, etc), and the locally-hosted stuff works great for that.
Also, I've found that GPT has become much less useful as it has gotten "safer." So often I'd ask "How do I do X?" only to be told "You shouldn't do X." That's a frustrating waste of time, so I cancelled by GPT-4 subscription and went fully self-hosted.
