Owen Lawlor @owlDiscourse

🚨#BREAKING: A 28-year-old confirms he has spent the last 10 YEARS of his life interviewing World War II combat veterans to keep their stories alive... ...in fact, for the last 10 years, he has interviewed World War 2 veterans EVERY SINGLE DAY He started as a teenager, ditching school to ride his BIKE to the local retirement home, walking up to the front desk and asking to, "meet some World War II heroes." His name is Rishi Sharma. He's crossed all 50 states and half the world. He's slept in his car and lived on gas-station food to afford it. He asks these men for hours of their memories, and then he hands the entire recording to their families... ...FOR FREE So that 200 years from now, a great-great-grandchild will know not just their hero's name, but how he laughed, how he cried, and what he sacrificed. Rishi has no military family, his parents immigrated here from India. He does it out of pure gratitude. In his words: "My parents were given the opportunity to immigrate and raise a family because of veterans like these. It's a debt of love I'll spend my entire life trying to repay..." As one 100-year-old Marine who stormed Iwo Jima told him, remembering the flag going up: "The hair on my arms still stands up when I think about how beautiful it was." THAT is America. 250 years of ordinary people doing extraordinary things... God bless our veterans. 🇺🇸🇺🇸

Love this!!!

鈴森はるか 『haruka suzumori』 🇯🇵 @harukaawake

2 weeks ago

🇯🇵🇺🇸 Happy Independence Day to our friends in the United States! This song is very popular here in Japan and many artists often cover it. I particularly love this one.

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All right Google, I’ll give this one to you. This is pretty great.

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A lot to take in here

Marc Andreessen 🇺🇸 @pmarca

3 weeks ago

Self recommending.

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@AlfieJCarter SALES

Just realized I saw this while scrolling... how ironic

Massimo @Rainmaker1973

4 weeks ago

You'll never look at your phone again after watching this

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@AlfieJCarter agents

In twelve months, EVERY company will be running a Company Brain. The teams who build it this year will spend the next year compounding. Everyone else is going to play catch up. Here's what it actually is. You connect your Slack, your GitHub, HubSpot, all your tools into one intelligence layer, then build the org chart around it: a main brain up top, a fleet commander running the agent fleet, specialist sub-agents handling execution. The reason it works is change management basically disappears. Your team already lives in Slack. You're just adding agents to the room they're already in. You NEED to start building yours now. In a year this will stop being an advantage and will become table stakes.

ericosiu @ericosiu

2 months ago

x.com/i/article/2056…

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AMD CEO Lisa Su just killed Nvidia’s $4,000 AI box with a $1,499 lunchbox. She walked on stage, held it in one hand, and ran a 235 billion parameter model live. No data center. No cloud. No rented GPU. The chip inside is something nobody saw coming. AMD’s Ryzen AI Max+ 395 is the first x86 silicon where CPU and GPU share the same 128GB of memory. That single trick lets a desktop run models that used to need a server rack. Out of those 128GB, Linux hands the GPU 110GB to play with. For context, an RTX 5090 gives you 32GB. A 4090 gives you 24. This box gives you more than three times either of them, in a chassis the size of a thick paperback. The benchmark that broke the room: this chip beat an Nvidia RTX 5080 by more than 3x on DeepSeek R1 inference. A $1,499 lunchbox outrunning a $1,000 discrete graphics card on a real AI workload. Nvidia spent a decade convincing the world you needed their hardware for serious AI. AMD just put that on a desk for half the price. Here is what nobody is telling you. A heavy AI user right now pays $200 for Claude Code Max, $200 for ChatGPT Pro, $20 for Cursor, $20 for Gemini. That is $5,280 a year leaving your account. The box pays itself off in 9 months and then runs free for the rest of its life. Install Ollama. Pull Qwen3 235B. Point Claude Code at localhost. Same interface you already use, except now nothing leaves your machine, nothing costs per request, and no company throttles your usage at 3am when you finally have time to build. This is the moment every AI subscription becomes optional. Lawyers stop fearing OpenAI leaks. Developers stop watching the token meter. Founders stop renting H100s for prototypes that never ship because the bill scared them. The first thousand people to figure this out will own the next two years of private AI consulting. Save this, and read the full breakdown article below you are watching the next shift hit before everyone else does.

AdiiX @adiix_official

a month ago

x.com/i/article/2066…

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How many IRL?

Ian of the Baskervilles @IBaskervilles

a month ago

@MondoARG @KTB_500 @infantrydort @stuartscheller @PeteHegseth @DeptofWar x.com/phlerp/status/…

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@MondoARG @TradCatGeorge @KTB_500 @infantrydort @stuartscheller @PeteHegseth @DeptofWar How have they failed, exactly?

Karpathy said something you'll regret ignoring: "Remove yourself as the bottleneck. Maximize your leverage. Put in very few tokens, and a huge amount of stuff happens on your behalf." Loop engineering is the exact thing that does that. In a hand-run session, the operator handles two things: - deciding what the agent runs next - and checking its output before the next step Both are manual, and both decide how far the agent gets on its own without the operator. Loop engineering moves both steps into the system. A core operating structure surrounds the loop, and the diagram below depicts it. - A schedule decides what to run - Loop is the maker that produces the work - A separate checker agent grades the output - A file on disk holds the state they both read. The loop runs until either done, max iterations, or an exhausted budget. Here are some practical engineering considerations: 1) A model grading its own output justifies what it already did instead of catching where it failed. That's why a separate checker's findings return to the maker as the next instruction. And the cycle repeats until the checker finds nothing left to fix. 2) A loop with no stop condition burns tokens, and the cost climbs fast once sub-agents and long runs add up. That's why the exit must be set before the loop runs, not while it is running. A simple exit could be: ↳ fix only the major issues, run one final pass, and stop after two loops, with "all tests pass and lint clean" as the rule that ends it. 3) State has to live on disk, not in context. The model forgets everything between runs, so an MD file or a knowledge graph holds what is done and what is still open. Each run reads it and writes back to it, which lets a loop pick up again after days. 4) The lower the verification bar, the safer the loop. Boring, repetitive checks like a stale version string or a missing test are trivial to verify, so a loop runs them with little risk while the operator is away. Judgment-heavy work is loopable too, but only as far as the checker can confirm the result. Let's look at how an unattended loop fails in two ways. 1) It reports done when nothing is actually verified. The separate checker exists to prevent it, but it merges code faster than anyone reads it, so over weeks, the team stops understanding its own codebase while every check stays green. Green tests say the code passed the tests, not that anyone knows what shipped. Someone still has to read what the loop merges. 2) The checker keeps a running loop honest, but it only catches failures inside a run. The harness around the loop, like the prompts, tools, and checks wrapped around the model, still drifts and breaks in production as models change. That repair loop is usually run by hand based on observability traces. My co-founder wrote a detailed walkthrough (with code) on making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it cannot recur. Read it below.

Akshay 🚀 @akshay_pachaar

a month ago

x.com/i/article/2063…

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Ramp is solving a very difficult problem. Token utilization for intelligence gained is the mystery black box of our times...and they have taken a good first stab at solving the problem

Eric Glyman @eglyman

2 months ago

Today, Ramp raised $750M at a $44B valuation. Last time we grew this fast, we were 1/20th the size. For 2000 years, business was built on two pillars. Today, a third: intelligence. It’s your least governed cost. It’s also your single greatest opportunity.

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Hadamard thought in image space

a16z @a16z

2 months ago

World Labs CEO Dr. Fei-Fei Li: "The world is not made of words." "Language models have given machines an extraordinary command of concepts, vocabulary, and reasoning, but the physical world, virtual or real, runs on a different substrate." "Where language models learn the

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@Jason x.com/DrClownPhD

Unreal, but not surprising these days to hear this.

Today I’m launching AI IQ — frontier AI models, scored on the human IQ scale. Instead of endless leaderboard tables, AI IQ shows: • Where models land on the IQ bell curve • How frontier IQ is changing over time • How models compare on IQ and EQ • What intelligence costs in practice GPT-5.5, Claude Opus 4.7, Gemini 3.1, Grok 4.3, Kimi K2.6, Qwen3.6, DeepSeek V4, Muse Spark, and more. Link in the first reply. Curious which chart surprises you most.

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