Engineering + Marketing @GauntletAI Cohort 3 GameDev hobbyist

Austin, TX
Joined January 2025
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My first job was a grocery bagger and I was so cracked you guys have no idea.
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You aren't experimenting enough. Within 24 hours you can go from a brand new idea to: • product built • landing pages/funnel built • paid campaigns on multiple platforms driving traffic
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When you've spent years avoiding PHP and Railway for this exact moment.
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Use AI to organize your X bookmarks: Ask Grok Bot/Codex/Cursor to: 1. Back up everything 2. Propose folders based on what I save 3. Reorganize after approval Then once a week, ask it to sort new bookmarks. Much cleaner.
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Your company isn't building nearly as much internal tooling as it could be. AI has made it easier than ever, and most companies haven't caught on yet.
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Matt retweeted
one day you wrote console.log("here") for the last time and didn't even know it
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Here's Opus 5.5 with a visual reading of Karpathy's latest tweet. Watch on 2x speed. (one prompt btw)
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
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I agree somewhat, HOWEVER: Plenty of "batteries_included" software will continue to be a preference for tons of people. Not everyone wants highly customized stuff, or to do the maintaining/new-feature work it requires.
Software is dead It's now the era of personal software and everyone just builds what they need whenever they need it I think
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Update: More and more Opus 5.5. Using plenty of Haiku 5.5 as cheap verifiers as well.
My workflow these days: Sol 6.1 - main driver Astra 6 - escalation/big planner ad hoc Opus 5.5 - all visual work, invoked through CLI by Codex I've moved over to Codex App 100% as well over the CLI. Way too convenient.
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Let your app choose the right model, tool, or action in near real-time with Decisions API, now available to all developers in public beta. The Decisions API makes decisions up to 10x faster than GPT-6 Luna through the Responses API.
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My workflow these days: Sol 6.1 - main driver Astra 6 - escalation/big planner ad hoc Opus 5.5 - all visual work, invoked through CLI by Codex I've moved over to Codex App 100% as well over the CLI. Way too convenient.
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If usage/cost was less of a concern I'd be using much more Astra/Fable, but it's honestly overkill for 95% of use cases.
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People outside of tech ask me why I’m so bullish on AI. Because it's taking down the barriers between Human imagination and reality.
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We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better: Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better: Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better: Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work! In summary: - As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding. - Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.
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OpenAI dev day wins best conference swag Limited edition OAI clear mod retro chromatic complete with secret dev mode, a custom empty cartridge, and a codex /skill to flash games onto it. Best day ever.
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You can finally scroll up in Codex CLI without losing your input bar. `npm install -g @openai/codex@alpha` Restart Codex after installing. (This is a prerelease)
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We all know the frontier labs are going to build native Jev equivalents, right? Cool technology!
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Oh hey look I'm still good at DraftKings
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GPT's Astra 6 is incredible but doesn't like using subagents by default. I found this in session-level runtime instructions: “Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work.”
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