matchagaucho

7 ms·

1.
by matchagaucho 24d ago
We're mostly paying for AI delivery, performance and SLAs at this point. GPT 5.6 Luna at $0.20 per 1M is pretty good for 80% of enteprise applications.
2.
by matchagaucho 1mo ago
Right? Should we switch from direct OpenAI API integration to OpenRouter? What's the incentive here? Open Responses API doesn't appear to support state management (yet)
3.
by matchagaucho 1mo ago
"Okay. I'll create a loop and goal so it doesn't stop until it's checked that everything works." This is where the quality magnification seems to be occurring. Goal-driven loops can make good code great, or bad co
4.
by matchagaucho 1mo ago
Fortunately, SaaS vendors have already conditioned users to accept usage limits within seat-based plans: “Upgrade to Pro for 50 GB of storage.” How do we make subscribers become equally comfortable paying for AI usage? Tokens, credits, inf
5.
by matchagaucho 2mo ago
Ultimately, most people don't have ideas for the kinds of personalized entertainment they want 1:1 AI entertainment probably won't be a cold start experience. Much like the "Choose Your Own Adventure" books of the 80&#
6.
by matchagaucho 2mo ago
It's difficult to think in exponentials. 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.
7.
by matchagaucho 3mo ago
The 1950s were a different era. Industrial and functional designs and were not protected. Leo knew and acknowledged his body inspirations (Bigsby and Rickenbacker), and considered his true IP to be in sound, pickups, mechanics, tremolo... I
8.
by matchagaucho 3mo ago
It would be odd to try this as copyrightable in the U.S, where there’s a pretty clear distinction between art added to a guitar, like PRS bird inlays, and the core body shape.
9.
by matchagaucho 3mo ago
Similar work experience, I was with a CBS-owned music company that had a CNC machine with some old Fender Telecaster and Stratocaster body templates. The hardware manager was cool and would let employees turn slabs of wood into Tele- and St
10.
by matchagaucho 3mo ago
There's another dimension to the Salesforce CRM "build" argument; which is to reduce your 25 seats down to 5, and expend Eng resources to building "agents" to automate many recurring data-entry CRM tasks. This is al
11.
by matchagaucho 4mo ago
I'd like to believe that stable state ends in a pair-programming structure, with a systems thinker/engineer and a domain expert. Someone needs to spot when a linked list is better than a map. And the other needs to spot when clini
12.
by matchagaucho 4mo ago
In agentic design, I keep coming back to the idea of “how easily we can compose elements” , along with Fowler’s OOP and refactoring principles. It’s a new frontier, and there are no absolutes. But I suspect the most durable AI systems will
13.
by matchagaucho 5mo ago
Same debate as the dot-com era. 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 serv
14.
by matchagaucho 5mo ago
I would love to see a GPT model running on an OpenClaw SOUL.md. 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
15.
by matchagaucho 5mo ago
More likely people will delegate their agents to run in the cloud. Edge AI on iPhone, however... many potential applications around vision, hearing, interpreting your surroundings in real-time.
16.
by matchagaucho 5mo ago
Profit is not the goal in large transformational tech cycles. See Bezos' playbook for Amazon. They weren't profitable for years.
17.
by matchagaucho 5mo ago
"previous killer apps" - exactly. That's the point. Everyone is anchored in AI as being the next desktop app. It's not. We're only using 1% of what these models will ultimately do when they're running 24/7
18.
by matchagaucho 5mo ago
As someone working in the enterprise space with OAI, this still feels like we're in the top of the first inning. Many teams remain anchored on equating AI with chat experiences, while a growing share of enterprise value is emerging fro
19.
by matchagaucho 6mo ago
Some redundancy also helps to keep a running todo list on the context tip, in the event of compacting or truncation. Distilled mini/nano models need regular reminders about their objectives. As documented by Manus https://ma
20.
by matchagaucho 6mo ago
Keyboard response feels 10x slower in ChatGPT Projects (possibly for reasons other than react state).
21.
by matchagaucho 6mo ago
Not to mention LLMs love XML. The markup includes self-describing metadata and constantly reminds the GPT model of explicit context.
22.
by matchagaucho 6mo ago
Agents can propose refactoring just as readily as humans. 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
23.
by matchagaucho 7mo ago
For me, it’s about preserving optionality. 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.
24.
by matchagaucho 7mo ago
The OpenAI PR implies that Anthropic had a "usage-policy" clause with no actual enforcement. Whereas OpenAI won their contract on the ability to operationally enforce the red lines with their cloud-only deployment model.
25.
by matchagaucho 7mo ago
These articles are largely based on a false equivalence of LLM=moat. That's not the case. OpenAI is advancing on many fronts; codex, vectorStore, embeddings, response API, containers, batch processing, voice-to-speech, image generation
26.
by matchagaucho 7mo ago
Launch an internal hackathon. Everyone must use the latest Gemini coding models. Vote for the top 5 Chat/Productivity tools. Eventually the culture will come around to: a) build new sh-- quickly with AI b) build a new productivity st
27.
by matchagaucho 7mo ago
Large models in general. A semantic query for "fake articles", without examples, is a wildcard search. A commercial RAG solution would use Query Expansion (QE) and examples to find nearest neighbors.
28.
by matchagaucho 7mo ago
Results from a one-shot approach quickly converge on the default “none found” outcome when reasoning isn’t grounded in a paper corpus via proper RAG tooling.
29.
by matchagaucho 8mo ago
"If I had six hours to chop down a tree, I’d spend the first four sharpening the axe." I still believe there's a mise en place step before doing the thing , when quality counts.
30.
by matchagaucho 8mo ago
Credit to the early AI coding startups. They masterfully forked Microsoft VS Code and integrated frontier LLMs into a familiar IDE. Instant audience. But it was only a matter of time before: a) Microsoft reclaimed its IDE b) Frontier model

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