AI Consultant 789Labs | Ex-Microsoft AI 🧑💻 🌉 👾
Joined November 2014
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🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
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Julien retweeted
Be extremely intentional with your friendships — not acquaintances, not business partners, actual friends
Try to pick high-integrity people — the right people
Invest time in those relationships
Cut or downgrade folks who are transactional or who don’t show up for you
Reallocate that time and energy to the right people who do
It's ok to be cutthroat about this, as life is the memories you create with your friends and family.
Invest in making moments.
Julien retweeted
Why are we calling every new kernel release a Megakernel. This is a well written CUDA graph engine with persistent kernels and PDL.
The author didn’t even call this a megakernel
github.com/vllm-project/vllm…
BREAKING NEWS🚨: NVIDIA has just achieved 423 tok/s/user 🚀 on the 2.8T Parameter Kimi K3 model via a new upstream vLLM optimization. 1.68x Faster on the Same Hardware!
This was done by NVIDIA CentML's Benjamin, who wrote NVIDIA's new decode-specialized kernels in Cute DSL via aggressive Megakernel fusion. This is a 12,000 LoC Megakernel and is the future of advanced AI-assisted kernel generation. NVIDIA is already working on DeepSeekv4.1 and other models for vLLM too.
Someone should give this NVIDIA 10x engineer a raise for this great new major feature!
Julien retweeted
When making money is no longer your goal, that is when you can start being truly effective.
Julien retweeted
Every person that moves to SF goes to a bunch of tech events in the first two weeks and realizes it's a complete waste of time.
Julien retweeted
Some observations about the "total" automation of software engineering:
1. There are more software engineers now than in 2025, and demand is growing.
2. Software engineers have never worked harder than they do now.
3. They largely don't write code anymore, agents write ~100% of the code.
4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption.
This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
Julien retweeted
I’m literally that weirdo who wrote (all by hand) a 15K word blog post on:
- why one cant treat models as oracles
- how models gaslight you all the time
- what it takes to build confidence in the model’s output
copyconstruct.medium.com/how…
So yeah sure, I believe in magical woo-woo
Julien retweeted
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
Julien retweeted
I'm simply not going to have it output 100k loc
Code is literally useless. Code in fact is a liability. Having more code is actually worse overall. I am trying to solve problems with the least amount of code possible. This is very, very important
Julien retweeted
Character AI has gone bankrupt.
The AI startup announced its Chapter 11 filing through a late-night newswire press release
Making my way through this piece and enjoying every bit of it. Some 🔥 lines in there.
"Treating debugging as an open-ended problem where an agent is free to conjure any hypothesis under the sun after reading some cherry-picked part of the code is tantamount to giving an agent enough rope to hang itself."
Julien retweeted
Introducing Microsoft-Decision-1, our new model for fast decision-making.
It delivers top performance on structured decision tasks, outperforming both LLMs and other decision models in latency and quality.
We’re already testing it across Microsoft for everything from incident response and quality control to scientific discovery.
Julien retweeted
These are some very important observations from Satya.
As models and harnesses commoditize, intent, verification and evidence will gate how quickly SI permeates enterprises and businesses.
Importantly, you need to implement systems that separate who does the work from those who govern, measure and sign off on it.
This is a new kind of “harness” that will emerge over the next few years. It may become a new system of record over time. One whose entire role is to track “change” and map it back to SOPs, policies, laws and the like.
Julien retweeted
Thank you, @POTUS, for this incredible honor today. I'm humbled to stand alongside so many giants of American innovation and looking forward for what we can continue to do together to advance technology and drive American prosperity.
.@POTUS: "For more than three decades, @satyanadella has been a key figure in the transformation of Microsoft — revolutionizing the company and empowering businesses, students, and consumers from all over the world with some of the most capable software ever built."
Julien retweeted
ok it's official... AI-written unit tests and integration tests are empirically proven to be unhelpful. you should tell your agents to stop writing tests by themselves
on the deepswe eval set, banning sonnet 5.5 high from writing any tests actually resulted in slightly higher success rate (non stat-sig), with less time and token spent (stat-sig). this is pretty hard empirical evidence
across the tests that were written by the "tests allowed" baseline arm, 65% of them were unit tests, 35% were integration tests, neither bucket resulted in any improvement compared to not writing any tests at all
i also picked a random sample of 44 tasks subset where i completely disabled executing even existing tests - it also did not affect success rate at all
i spot checked many tests written in the baseline arm, and my intuition is that most tests are simply a repetition of the implementation
agent-written tests do not add any value because both the implementation and the tests were simply the agent's interpretation of our intent. the tests aren't any more accurate than the implementation itself
i suspect we can still extract some value from unit tests and integration tests if we describe them ourselves when we believe we can articulate our intent better through test cases than through requirements. but i have not proven this yet
also worth noting, during deepswe eval the agent wrote almost no e2e tests (only 17, compared to 3000+ unit/integration tests written). so this analysis does not prove nor disprove the value of e2e tests - i will do another eval specifically for that
