Miguel P3ra1v

Futurism, Technology, Artificial Intelligence, Inference, Representation Learning, Tesla, Blockchain. Opinions are my own.

[Miguel P3ra1v @LordAstinus]

Tesla Is A Monopoly, We Just Don’t Know It Yet by Corey Recvlohe https://t.co/yNtHg1haTg

$TSLA #tesla

[Science girl @sciencegirl]

The hand signal for help

Teach it, share it for awareness

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[Jaana Dogan ヤナ ドガン @rakyll]

I'm not joking and this isn't funny. We have been trying to build distributed agent orchestrators at Google since last year. There are various options, not everyone is aligned... I gave Claude Code a description of the problem, it generated what we built last year in an hour.

[Mario Nawfal @MarioNawfal]

🇺🇸 SAN FRAN BLACKOUT – WAYMO FROZE, TESLA DROVE

Waymo’s robotaxis got a little too real last night - by completely shutting down when San Francisco’s power outage knocked out traffic lights.

Meanwhile, Teslas on FSD? Kept rolling. No drama, no headlines - just handling chaos like it’s a walk in the park.

This is what happens when you train your AI on billions of real-world miles instead of coddling it in a simulation padded with perfect data and wishful thinking.

Waymo bet on maps and order. Tesla bet on mess - and won. When the lights go out, the difference isn’t theoretical. It’s traffic.

Source: @Tesla_AI, @elonmusk

[Ronald van Loon @Ronald_vanLoon]

Revolutionary Magnetic Seal Stops Water, Acid, and Oil Leaks Instantly
by @IntEngineering

#Innovation #EmergingTech #Technology #Tech

cc: @karpathy @terenceleungsf @kuriharan

[Miguel P3ra1v @LordAstinus]

@carpathianzungu @pedrodri612 @Eugenetakesoff The same happened with MSFT, TSLA and META in XM platform before market opened (screenshots in CET). 60% down all of them.

[Tesla @Tesla]

With over $280 billion of total assets under management and over $1 billion in Tesla shares, the State Board of Administration of Florida Retirement System came out strongly in favor of our incentive proposal for Elon, designed to drive extraordinary growth, which the SBA describes as the “gold standard”

See the SBA’s full comments below

https://t.co/03zZqB7hr4

[🅰️nanto Mohammad @01Ananto]

Tesla FSD 14.1.3 reverses at a construction zone to allow pedestrians to pass. Amazing! @Tesla_AI @aelluswamy @wholemars @hletes

[Devin Olsen @DevinOlsenn]

Just did a 4 hour drive this morning testing FSD V14.1.4.
It was 100km (62 Miles) of driving all throughout multiple cities, including two costcos and countless destination parkings.
I did not have to intervene once the entire time.
THAT IS SO CRAZY - we are living in the future.
I look out at all of the other cars on the roads and I truly do not think people understand what is about to happen. FSD is going to change the world.
Incredible work @Tesla_AI

[TESLARATI @Teslarati]

🚨🚨 Morgan Stanley's Adam Jonas officially says his money is on Tesla in the autonomy race:

"I'm callin' it. Autonomous cars are solved. Do I mean six or seven 9's to the right of the decimal? No. Perfection? Never. But enough to pull the safety driver at scale in major metros. From our discussions with Tesla, it appears the only thing preventing Tesla from pulling the driver is its own abundance of caution."

[Tesla @Tesla]

It’s the little things

[Brett Winton @wintonARK]

First impression of FSD v14
It simply feels more precise and intentional than v13.
Kind of like driving a sport-tuned car after having driven a squishy/drifty luxury sedan.
It is punchier and whippier in ways that are confidence inspiring (on windy curves alongside traffic, in lane shifts etc.)
And then it pulls you into the parking spot.
🦾

[Qingqing Zhao @qingqing_zhao_]

We build world models grounded in physical reality, naturally generalizing across cars and robots 🤖

[Ashok Elluswamy @aelluswamy]

Wrote about some of the recent work from the excellent @Tesla_AI team. The same material was presented at the International Conference of Computer Vision this week.

The main purpose of sharing this content is to show the cutting-edge work that the team has been developing and attract more talented engineers to join the team.

Tesla is THE place to solve real-world robotics. The team is extremely talented, determined and motivated. The technology that is already developed is cutting-edge and the roadmap is exhilarating. The work done here will tremendously benefit all of humanity. Hence, we believe that this is the best place to work on AI on the entire planet currently. Join us and let's bring millions of intelligent, friendly and useful robots to life.
https://t.co/AWpzhZVNcM

[Tesla @Tesla]

We can synthesize new challenges (such as a pedestrian or a car cutting in)

[Tesla @Tesla]

To push self-driving into situations wilder than reality, we built a neural network world simulator that can create entirely synthetic worlds for the Tesla to drive in.

Video below is fully generated & not a real video

[Black Tesla @blktsla]

Ok @Tesla_AI what have you done here? I thought the word "sentient" was hyperbole but after experiencing this "car" find its way out of a multi-level garage like a human, I'm very impressed. I've been fortunate enough to be testing Tesla's early software over 10 years and this is by far the most profound moment in Tesla history imo, I think you've cracked the code! Great work all around. IT'S ALIVE! @aelluswamy @YunTaTsai1 @pduan

Full Video: https://t.co/pxanS6fe00

[TESLARATI @Teslarati]

https://t.co/VDhJVu9j7e

[Jukan @jukan05]

FT: Elon Musk’s xAI is developing a technology known as “world models”, which are artificial intelligence (AI) systems capable of understanding and designing physical environments.

To achieve this, xAI has hired former Nvidia specialists.

These world models are considered a more advanced form of AI than the large language models (LLMs) trained primarily on text data, and are expected to surpass the limitations of popular AI tools such as ChatGPT and xAI’s own Grok.

According to FT’s sources, xAI plans to apply world models first in the gaming sector.
The models could be used to automatically generate interactive 3D environments and potentially be applied to AI systems for robotics.

Some tech companies expect world models to become a next-generation core technology that could extend AI applications beyond software into physical products, such as humanoid robots.

Last month, Nvidia told the Financial Times that the market potential of world models could be nearly as large as the entire global economy.

Musk also reaffirmed his earlier goal by posting on X (formerly Twitter) that xAI will release a “great AI-generated game” by the end of next year.

[Elon Musk @elonmusk]

In the near future, your Tesla will drop you off at the store entrance and then go find a parking spot.

When you’re ready to exit the store, just tap Summon on your phone and the car will come to you.