matchagaucho

GPT 5.6 Luna at $0.20 per 1M is pretty good for 80% of enteprise applications.

What's the incentive here?

Open Responses API doesn't appear to support state management (yet)

This is where the quality magnification seems to be occurring.

Goal-driven loops can make good code great, or bad code worse.

How do we make subscribers become equally comfortable paying for AI usage? Tokens, credits, inference calls?

1:1 AI entertainment probably won't be a cold start experience.

Much like the "Choose Your Own Adventure" books of the 80's, consumers choose a baseline template, customize the characters, and interact at various points within the plot.

But this demonstrates we're a couple orders of magnitude away from generating 1:1 hyper-personalized entertainment and media for individuals, rather than the masses.

Leo knew and acknowledged his body inspirations (Bigsby and Rickenbacker), and considered his true IP to be in sound, pickups, mechanics, tremolo...

I really wish Warmoth or PRS could get some legal fee subsidizing to push back.

The hardware manager was cool and would let employees turn slabs of wood into Tele- and Strat-style bodies after hours.

When the Fender/German court ruling came down, my first thought was: Fender has had roughly 70 years with the Stratocaster design, and the broader industry has been making S-style guitars for decades.

Surely at some point a body shape becomes generic, right?

This is also the reason the stock has hit a 3-year low. Not because CRM can be replaced entirely. But because the seat count can be reduced 50%+.

Someone needs to spot when a linked list is better than a map. And the other needs to spot when clinical trial coding should happen before claims, audits, or patient outreach.

It’s a new frontier, and there are no absolutes. But I suspect the most durable AI systems will be built around highly composable, well-orchestrated agents.

Customer: “I don’t want to pay more than $100/mo for my website” Developer: “What are your goals?” Customer: “1M daily visits, 1,000 monthly signups.”

And we've spent the past 25 years offering serverless compute, auto-scaling, pay-as-you-go for AWS and Internet infrastructure. And the economics are still a hard sell.

The GPT models are highly steerable. So I suspect the "soul" is working as expected.

(for context, in OAI enterprise background agents, they have no personality. They just get 'er done)

Edge AI on iPhone, however... many potential applications around vision, hearing, interpreting your surroundings in real-time.

See Bezos' playbook for Amazon. They weren't profitable for years.

We're only using 1% of what these models will ultimately do when they're running 24/7 as utilities serving new economic models.

There just isn't enough compute right now to realize the larger monetization strategies.

Many teams remain anchored on equating AI with chat experiences, while a growing share of enterprise value is emerging from leasing compute clusters to run agentic workloads in containerized environments.

OpenAI has built a cloud-first architecture that supports this model. The desktop experience and applications are sexy, but enterprise usage will likely skew heavily toward asynchronous, background processing.

Distilled mini/nano models need regular reminders about their objectives.

As documented by Manus https://manus.im/blog/Context-Engineering-for-AI-Agents-Less...

The markup includes self-describing metadata and constantly reminds the GPT model of explicit context.

If coding agents already read AGENTS.md before making changes, they can also maintain a TECHNICAL_DEBT.md checklist.

Keep the loop intact: AGENTS.md ensures technical debt remains in context whenever changes are planned.

If I can run resume {session_id} within 30 days of a file’s latest change, there’s a strong chance I’ll continue evolving that story thread—or at least I’ve removed the friction if I choose to.

Whereas OpenAI won their contract on the ability to operationally enforce the red lines with their cloud-only deployment model.

That's not the case. OpenAI is advancing on many fronts; codex, vectorStore, embeddings, response API, containers, batch processing, voice-to-speech, image generation... the list goes on.

Eventually the culture will come around to: a) build new sh-- quickly with AI b) build a new productivity stack

A commercial RAG solution would use Query Expansion (QE) and examples to find nearest neighbors.

I still believe there's a mise en place step before doing the thing, when quality counts.