Building "Trax", Software for Hotels. Try this daily puzzle game I built? https://xitter.cf/t.co/pdMHVPZQkJ Software Dev. prev. @BranchInsurance @sanctucompu @commercejs
Florida
Joined August 2013
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John Raymon retweeted
This is my dream situation.
I want to be able to talk through interfaces, changes, ideas, consequences and think through things. I get so derailed asking a question and coming back in 5 minutes
DeepSeek V4.1 Flash 1017 runs at 1,521+ tok/s on Fireworks Ultrafast.
Fast enough to ship Tetris in 11 seconds, 7x faster than Claude Opus 5.5.
Join the Ultrafast waitlist: bit.ly/3UA0wKq
John Raymon retweeted
I have discovered the the BEST way to get 3D modeling results from LLMs
The app LiDAR scans the room while taking hundreds of high quality photos.
Then a workflow + skill I've created models out every item recursively checking the model against the photo at the exact spot + angle where the photo was captured
The result is a a dimensionally accurate and high quality model of your room
All in my latest video on how to do it youtube.com/watch?v=JGQCcCzu…
John Raymon retweeted
If your company refers to engineers as the IT department, you have nothing to worry about. You were already breached last week.
Has anyone reverse-engineered Apple’s tvOS?
I imagine a RE'd version w/o native Apple ecosystem integration wouldn't be as good as the real thing and would lose its purpose.
I’d love to run tvOS natively on a Vizio TV and still control it via the iPhone/iPad remote.
John Raymon retweeted
Kalau kamu buka halaman dv-g3b-emudhra.we-have-a-pro… dan tidak muncul error seperti ini (ERR_CERT_REVOKED) maka browser kamu perlu segera di-update.
private keys from 8 intermediate CA certs have been compromised (eMudhra)
Happy "Update everything" day to all my pager-carrying friends
emudhra.we-have-a-problem.co…
John Raymon retweeted
My dear friends, I’d like to show you my latest toy project
Introducing Handwired: turn your regular JavaScript code into a neural net!
- no training
- exact outputs vs the JS program
- fully differentiable
All runnable in a browser, without GPU
John Raymon retweeted
What if Spotlight actually understood the meaning inside your files?
DigUp is an open-source macOS menu bar app powered by EmbeddingGemma 2 that brings 100% local, multimodal semantic search to your Mac.
Instead of searching for file names, you can use natural language to:
📸 Find specific scenes in your photos
⏳ Jump to the exact timestamp in audio or video
💻 Search through your codebase by intent
Everything runs locally in a single vector space.
John Raymon retweeted
If you are reasonably optimistic about your future and have done sufficiently difficult things in life - you learn early on that stress often overinflates the severity of the problems you are FEELING.
Things do get better, and time is an excellent salve for everything.
Knowing this, one might be tempted to "skip forward" to the times where everything is "better". The problem with this is that all sufficiently ambitious projects have a string of problems that reveal themselves only when a previous problem is "better".
Thus, thinking this way guarantees a life where you are fast-forwarded to your death bed.
You are in the good old days; even if it doesn't feel like it.
John Raymon retweeted
On work vs. rest, I personally live by @naval's quote from his JRE episode.
"The right way to work is like a lion. You and I are not like cows. We’re not meant to graze all day, right? We’re meant to hunt like lions. We’re closer to carnivores in our omnivorous development than we are to herbivores.
As a modern knowledge worker athlete, as an intellectual athlete, you want to function like an athlete. Which means you train hard, then you sprint, then you rest, then you reassess. You get a feedback loop, then you train some more, then you sprint again, then you rest, then you reassess."
I have found this quote (and entire episode) to be life changing in many ways.
John Raymon retweeted
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.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
John Raymon retweeted
Btw, I've never reached farther than 80 using this approach.
Found that if I'm bad at something and want to get good at it (coding, writing, working out) it helps to start at 100 and count backwards. I expect to have 100 bad attempts. So the first attempt is 100, the second is 99, the third is 98. Seems to rewire my expectations, reduce frustration and allow me to practice in peace.
John Raymon retweeted
I am once again, with @poteto, advocating you to do the hardest thing possible justinjackson.ca/hard-thing
John Raymon retweeted
I work in the AI industry. I burn about 1.5 billion tokens a week. Between personal and business I have 8 AI subscriptions. And I still think I am falling behind the accelerating rate of change. The last three months alone have been a quantum jump in capabilities. Nobody outside the industry understands how insane this is going to be. Like a whole other industrial revolution, but compressed into 10 years. Nobody is ready for the societal implications. It's going to be a 10.0 earthquake plus an F5 hurricane all at once, globally.
John Raymon retweeted
if you have a non-traditional background, this is exactly why applied AI should be very, very interesting to you. applied AI, as it exists today, has literally only been around for like 12 months.
there are VERY few people that have a head start on you and the field continues to evolve.
it is very realistic to find yourself a little niche that you can really own in just a few months time.
Here's an empowering thought for you:
Most people who you think are better than you aren't better than you. They've just been doing the one thing longer than you have. Sports aside (you'll likely never be a pro athlete regardlesss of how much you practice), you can rise to the top 5% of any field if you give it enough time.
John Raymon retweeted
It's actually INSANE how prescient this was.
They made this in 2011! This is basically what it's like to use computers now. It's barely exaggerated
John Raymon retweeted
Agreed. If you’re a product engineer, you do a bit of data science, design and PM. You ship backend, frontend, whatever moves the needle, not just “mobile.” The hard part is being ruthless about not shipping slop and killing experiments that don’t work.
If you want to know who survives AI, look at what happened to Hollywood in the 1970s.
Movie Studios used to enforce tight “shooting ratios”; usually 5:1, maybe 10:1 TOPS. As in, for every 5 feet of film shot, at least 1 foot had to end up in the final cut (35mm was expensive!)
In the 70s, there was a rapid elimination of production constraints. Improvements in film cost and camera portability made the old studio rules…obsolete. Why not take multiple angles, roll simultaneously? Set could have 3, 5, even 10 cameras rolling at once. Shooting ratios *exploded*, 50:1, even 100:1 (Apocalypse Now)!
Of course, it also *radically* changed the workflow. Editing use to be the quiet, thankless job in the studio years (30s-50s). Suddenly, they became the most important people on the totem pole!
How do you find the masterpiece hiding in the (literal miles) of film? A ton of the footage was…trash, of course. But as each individual camera because *less* important, it opened up directors to take on significantly higher risks.
Now, don’t get me wrong. Insane shooting ratios were still *kinda* a niche in the 70s. More traditional directors stuck with only slightly inflated shooting ratios (15:1, 30:1 common); it was the more experimental pioneers (Coppola, Scorsese, etc) that hit the big 100:1 numbers. The huge ratios were still crazy.
Until they weren’t.
You can probably guess what happened in the early 2000s. Digital cameras reduced the cost of individual footage to a complete non-constraint. Effectively free, don’t even cut on set, just keep the camera rolling.
Suddenly…*EVERYONE* starts shooting 100:1. Entire new forms of content get created, like Reality TV, where ratios can hit 500:1! Massive shift of human labor to the editing side.
I’d say, we’re currently in the “1970s Hollywood” of AI. It can be easy (and cheap!) to produce things (code, tools, etc), but YOU have to be the ruthless editor. It’s the “miles of film” problem all over again. Hollywood solved it by throwing massively more resources on the editing side, and editing became a “constantly talking with the director about vision/direction” job, instead of a “hide in the back office and do your work” job.
It’s a hunch, but I’d bet that filmmakers closer to the experimental 1970s “ratio explosion” directors handled the later 2000s digital shift *much* better, because the underlying principles were the same. They’d certainly be ahead of the curve predicting it, and (hopefully) were less surprised when the digital ratios did hit.
I’m not saying AI is exactly the same problem. But it’s a good idea to look at parallels in history where rapid elimination of constraints changed an industry, and how that industry handled it 5, 10, even 50 years later!
When something starts to get cheap…helps to imagine what would happen when it’s free!
