Richard Tong

[Richard Tong @tongrj]

That's vision.

[X Freeze @XFreeze]

Jensen Huang just told the story of how Elon Musk became NVIDIA’s very first customer for their powerful AI supercomputer - when literally nobody else in the world wanted it

“When I announced this thing, nobody wanted to buy it. Not one purchase order

Except for Elon
He was at the 2015 event. Told me his company could really use it

I got excited… until he said it was a non-profit company

All the blood drained out of my face

In 2016, I personally boxed it up and delivered the first one to a tiny room in San Francisco

It was OpenAI
Just a handful of researchers - Pieter Abbeel, Ilya Sutskever, and the team were all there”

Elon backed AI when almost no one else believed

[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.

[Matthew Berman @MatthewBerman]

MIT Researchers destroyed context window limits.

10m+ token prompts are now possible by moving context out of the model and into code environments.

Full breakdown below.

[Richard Tong @tongrj]

Check out my latest article: Vibe Proving, Doubting Geniuses, and the Future of Mathematical Truth https://t.co/uZhBHFj958 via @LinkedIn

[Richard Tong @tongrj]

Check out my latest article: The New Era: Designing Nature, Not Just Deciphering It https://t.co/w1Kb2Bx02E via @LinkedIn

[PBS News @NewsHour]

President Donald Trump issued an executive order late Thursday night that instructs the Corporation for Public Broadcasting (CPB) to halt all direct federal funding to the nation’s two major public broadcasting networks – the Public Broadcasting Service (PBS) and National Public Radio (NPR).

The move will directly affect the roughly 1,500 public media stations nationwide, as well as national programs like the PBS News Hour. Trump also directed CPB – a congressionally chartered, private, nonprofit corporation which provides more than $500 million to local PBS and NPR stations every year – to eliminate indirect government sources of financing for the networks.

Patricia Harrison, the president of CPB, said in a statement that the corporation is “not a federal executive agency subject to the president’s authority. Congress directly authorized and funded CPB to be a private nonprofit corporation wholly independent of the federal government.”

CPB filed a lawsuit against the Trump administration earlier this week after the White House fired three of the corporation’s board members. The executive order is also expected to be challenged in court.

PBS CEO Paula Kerger called Trump’s executive order “blatantly unlawful,” while NPR CEO Katherine Maher said her organization “will vigorously defend our right to provide essential news, information and life-saving services to the American public. We will challenge this executive order using all means available.”

The order came on the heels of reports the White House planned to engage Congress in an effort to rescind already distributed funds from the public media networks. It’s unclear at this time if the rescission plan will move forward following Trump’s executive order. Both NPR and PBS are engaged in campaigns to encourage their supporters to lobby Congress to protect federal funding.

The Trump administration argues that taxpayers should not be supporting media that he argues carries a liberal bias. The president has referred to the news media as “the enemy of the people,” filed lawsuits against CBS News and ABC News, and pushed The Associated Press out of the press pool whose job is to cover his administration.

[Min Choi @minchoi]

2. Gemini 2.5 Pro MAX + Cursor "feels close to AGI"
https://t.co/bavBRXsPlr

[Min Choi @minchoi]

Gemini 2.5 Pro has really changed the LLM game.

People are coming up with wild use cases. There's a shift.

10 examples + use cases:

1. Vibe code morphing particles

[Richard Tong @tongrj]

@PeteHegseth How about Dept. of Peace

[Richard Tong @tongrj]

@_KarenHao @skdh R&D is the pillar of the economic foundation and future growth, so these federal cuts to basic research? Total bummer. We should be pumping resources into it, not pulling back!

[Karen Hao @_KarenHao]

For decades, the US government has painstakingly kept American science #1 globally—and every facet of American life has improved because of it. The internet? Flu shot? Ozempic? All grew out of federally-funded research. Now all that's being dismantled. 1/ https://t.co/biY4lV8M5A

[Richard Tong @tongrj]

@moritzkremb Builder

[Avthar @avthar]

VECTOR DATABASES ARE THE WRONG ABSTRACTION. Here’s a better way: introducing pgai Vectorizer, a new open-source PostgreSQL tool that automatically creates and syncs embeddings with source data, just like a database index.

❌ Why vector databases fail
Vector databases treat embeddings as independent data, divorced from the source data from which embeddings are created, rather than what they truly are: derived data.

This pitfall means that many AI projects that start out as simple vector search implementations inevitably evolve into a complex orchestra of monitoring, synchronization, and firefighting.

😓 Keeping embeddings in-sync is hard
In an attempt to avoid stale embeddings, engineering teams have to build and maintain a maze of ETL pipelines, juggle multiple databases (vector DB, metadata store, lexical search), and manage complex queuing systems for updates.

Add monitoring for data drift, alert systems for stale results, and validation checks across systems - and you have a brittle infrastructure that inevitably breaks down, leading to stale embeddings and wasted engineering hours.

What if you could just use Postgres instead?

✅ Pgai Vectorizer: Vector embeddings as database indexes
Pgai Vectorizer treats embeddings like database indexes. It automatically creates, updates, and maintains embeddings as your data changes. Just like an index, the database handles all the complexity: syncing, versioning, and cleanup happen automatically.

This means no manual tracking, zero maintenance burden, and the freedom to rapidly experiment with different embedding models and chunking strategies without building new pipelines.

🤔Why did we build pgai Vectorizer?
Our team at @timescaledb built pgai Vectorizer because many developers regard PostgreSQL as the “Swiss army knife” of databases, as it can handle everything from vectors and text data to JSON documents.

We think an “everything database” like PostgreSQL is the solution to eliminate the nightmare of managing multiple databases, making it the ideal home for vectorizers and the foundation for AI applications.

⚙️How does pgai Vectorizer work?
Check out the code snippet below –  it takes just 6 lines of SQL to put your embedding creation pipeline on autopilot with pgai Vectorizer!

Under the hood, pgai Vectorizer checks for modifications to the source table (inserts, updates, and deletes) and asynchronously creates and updates vector embeddings in an external worker.

🧑‍💻 Sounds exciting! How can I get started?
Pgai Vectorizer is open-source under the PostgreSQL license and available for free to use on any PostgreSQL database. You can find installation instructions on the pgai GitHub repository (see end of post). It’s also available as a managed service in Timescale’s PostgreSQL cloud platform.

📚Learn more
[1] Pgai github repo: https://t.co/hut1MxuwPZ
[1] Technical explainer post: https://t.co/A9hOz482Rg

Share this post with your followers to let them know about pgai Vectorizer and comment your reactions and questions.

[Richard Tong @tongrj]

@julianhquevedo Mind-blown!

[Julian Quevedo @julianhquevedo]

oasis is here! it's an interactive diffusion transformer that predicts the next frame autoregressively. here, we used it to create one of the first immersive, generative worlds. and the future possibilities for interactive video are so, so exciting.

[TCEA @TCEA]

Check out these quick videos to enhance your #teaching strategies!

https://t.co/KDQBSH2iIC via @cultofpedagogy
#educoach #edutwitter #k12

[edutopia @edutopia]

Our brains are wired to forget, but there are research-backed strategies you can use to make your teaching stick.

Here are some of them:

🧵 1/12

[madison @dearmadisonblue]

rewriting languages like markov algorithms are basically logic programming, so this type of thing might be a good complement to generative image models ("neurosymbolic programming"). especially since this thing can condition on the grid becoming a certain state eg specific pixels

[Richard Tong @tongrj]

IMHO the biggest threat to humanity is not AI, nuclear weapons, generic engineering or even global warming. It is greed, tribalism, and hatred from ignorance. We all know “absolute power corrupts absolutely.” Therefore, the biggest threat of AI is that th…https://t.co/GNWDwb2CAJ