Nate King

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Nate King

Nate King

@natebking

Product/Marketing/Strategy. Hobbyist trader and investor. I know what I don't know, eager to learn more. Proud husband, father, and dog-father.

SF Bay Area Katılım Haziran 2011
3.9K Takip Edilen952 Takipçiler
Cynthia
Cynthia@yescynfria·
everyone shut up and look at this logo I saw today
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Nate King
Nate King@natebking·
@argofowl awesome - gonna share this with my team on Monday
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🥔🥔🥔
🥔🥔🥔@argofowl·
introducing namethatui.com a dictionary for ui things you can see but can't name made it because i'm primarily a designer, and my biggest resistance was always knowing what things are called when prompting my agents it learns as people use it: every search teaches the site new words, and the built-in pocket dictionary grows with it give it a try and let me know what you think can't find something? dm me and i'll add it i want this to be the lowest resistance resource you have you can just build things
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John (main)
John (main)@johnonmain·
The lack of seasons in SF is actually evil. Months slide past and nothing changes— perhaps different trees bloom, some new flowers pop up in the park, and the sun sets earlier— but nothing really seems to change. It's fun at first... you can go for a jog outside every day without worry, you can wear the same types of outfits week after week (pants, with a nice shirt and light jacket). But as time goes by it's eerie. Your life just races bay, with no visual or physical punctuation. There's no leaves on the ground, no crisp fall days, no scorching summer nights where you can wear shorts and a t shirt until midnight, no thunderstorms. It's spring. It's purgatory. Forever and ever.
PoIiMath@politicalmath

I live in one of the red zones in Tennessee and I can tell you that this map is bullshit

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CR1337
CR1337@CR1337·
In order to prevent stores from evading taxes, every receipt in Taiwan is automatically a lottery ticket, too, which can win up to $300k, turning customers into voluntary tax auditors:
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Peter Gostev
Peter Gostev@petergostev·
We are now in a position where a tiny proportion of the population uses Fable or soon GPT-5.6, while everyone else's experience of AI is 8-30b-model level - Google's AI Overviews, Meta AI, ChatGPT free tier, maybe MS Copilot at best. People outside of tech must be completely baffled how this is supposed to take their job, and annoyed that hundreds of billions are being poured into it.
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Blockhead
Blockhead@BlockheadNYC·
I shit on AI a lot but this close up of the AI image trump posted of people at the 250th anniversary fair is HIGH ART.
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Antonio García Martínez (agm.eth)
The new business model that pays for the Internet in a world of AI agents instead of humans browsing. Will take a while as most humans are still using AI as a siloed browser experience, but as soon as they start using AI like everyone in tech, the model will flip and quickly.
Cloudflare@Cloudflare

We're opening the waitlist for our Monetization Gateway, which will allow you to charge for any web page, dataset, API, or MCP tool behind Cloudflare. The charges will settle in stablecoins over the x402 open protocol. cfl.re/4eUFdt6

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Nate King
Nate King@natebking·
@kanavtwt Why is Claude creating lib files I don’t want to talk politics
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kanav
kanav@kanavtwt·
Day 1 of vibecoding
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Nate King
Nate King@natebking·
@gokulr Same findings! Claude is the coworker I get along with really well, but is prone to getting over excited (delivering headline findings then walking them back) or making errors. Codex has the personality of wallpaper but can execute ruthlessly.
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Gokul Rajaram
Gokul Rajaram@gokulr·
After using Claude Code (Opus 4.7/4.8) and Codex (GPT-5.5) incessantly for the past several weeks, my verdict is that (like every human) their greatest strength is also their greatest weakness. tl;dr Use Claude Code + Opus 4.7/8 for brainstorming and planning. Use Codex + GPT 5.5 for execution and building. Use both to adversarially review each other's plans / design docs. CC is really creative and a great brainstorming partner. However, this creativity makes it hallucinate when executing. Codex is an incredible, focused, fast executor and builder. However, this makes it poor at generating new, creative options. (I have friends at both OpenAI and Anthropic who agree with the above and use the "other" lab's product for precisely the use cases that their product is not good at).
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jon drake
jon drake@DrakeGatsby·
“Comparison is the thief of joy” that’s a pretty good saying. But I’ve heard better
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Nate King
Nate King@natebking·
People don’t want your book they want a skill.md file.
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austin petersmith
austin petersmith@awwstn·
The Certifiably Insane Way to Build an AI Agent: 1. choose a category where mistake tolerance is roughly the same as it is in self-driving cars. we chose "email-based scheduling assistant." many people want this product, but they immediately fire him if he screws up an interaction with a prospect, a candidate, or a potential investor 2. you learn that the edge cases are too complex and too frequent to be solvable. ours: managing timezones for people who travel (and change travel plans) constantly. knowing when NOT to respond, when to text the customer on the side to verify something, when to follow up, which sub-calendar to use, when to bend the rules on availability, when we can schedule that one type of call during your commute but not the other type of call. sharing your availabilities without compromising your privacy. and on and on. 3. the product doesn't feel viable, but you don't want to give up. you spend hours in a hot tub in Marin with a friend who makes self-driving cars. you make a plan to do it the way they did: hold the steering wheel. you go home and build a human-in-the-loop platform and hire contractors to serve as a backstop and catch mistakes before they happen (and to help design a map of what a world-class EA would do in every weird scenario). you decide trust is the currency in your category, so it must be the thing you won't compromise on. the product must succeed at any scheduling request, no matter how complicated. 4. you instantly feel an overwhelming market pull. so you keep going, growing that team to 75 people working 24/7 to support the nonstop scheduling needs of your customers. tons of engineering time goes to scaling the human platform instead of building the product. 5. you try to raise a Series A and investors say you are insane. your gross margins are extremely negative. they believe this is a problem worth solving, but they don't believe it is as hard to solve as you say. they want AI, not humans. your competitors put "NO HUMANS IN THE LOOP" on their landing pages to call you out. you keep going. 6. you work day and night building the harness that can meet the quality standard your customers have come to expect. you create a massive synthetic gold dataset. audit it, and clean it, label it. repeat. then, experiments. fine-tuning. RL. ACE. DSPy. sub-agents. sub agents for your sub-agents. rebuild the harness. throw more tokens at the problem. 7. some weeks you make big progress. some weeks your evals climb a single basis point, but that's better than nothing. more experiments. more tokens. john coogan said the hot trend in 2026 will be dogged pursuits. that pushes you to continue the pursuit, doggedly. 8. then, one day, you realize you are scheduling thousands of meetings a day and approaching 50% autopilot with no increase in churn or complaints. you put 150 customers in a full self-driving experiment, and they use the product MORE than they were using it when they had the human backstop. you can really start to let go of the steering wheel. 9. you don't know yet if this was a hill worth climbing, but you are nonetheless stoked that you can see the top. you have created a proprietary map of what to do in a million different situations. nobody else has that map, and the models keep getting better at following maps. your plan was to bet on trust, and your product can be trusted. today was the first day Howie crossed 50% autopilot:
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Gixx86 👾
Gixx86 👾@gixx_eightysix·
@mnolangray I think it boils down to shitty, rushed contractor work, coupled with lack of knowledge before the project began.
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