Dan Shoop

Computer scientist in Hell's Kitchen NYC focused on observability based systems reliability, incident management and production engineering

[Dan Shoop @ColonelMode]

Less Hashi, more Corp

Mitchell's gone and without the Hashi it's just another Corp who once made great tools being swallowed. Do you think IBM will be as good of a steward?

[Dan Shoop @ColonelMode]

@bcantrill I hear you can make DOOM run on just about anything

[Dan Shoop @ColonelMode]

Wow! Once again @bcantrill knocks it out of the park with a talk at @rust_nyc about Rust in the LLM age and how Rust’s infrastructure and dedication to robust abstraction makes it so enticing for coding with LLMs. Brilliant!

[Bryan Cantrill @bcantrill]

My full @MTSlive segment is up -- and I definitely stand by my Richard Scarry recommendation
https://t.co/ubf1gOWiVz

[Dr Manhattva @Manhattva]

I just found the most brilliant bit by @JohnCleese

He explains Leftism better in 90 seconds, than you could learn from any academic in 20 years.

[Bryan Cantrill @bcantrill]

Slides for my #monktoberfest talk today, "Execution in the Kingdom of Agents: Reflections on Abstraction and Complexity"; video to come!
https://t.co/SvkboOEAm3

[Dan Shoop @ColonelMode]

@wakeupnj Oddly we see sand and beach under the waves washing on shore right in this video!

[Dan Shoop @ColonelMode]

@Docker @WeAreDevs Wow. Kelsey just put together in a mic drop level talk the arguments most engineering managers have been trying to figure out - and such a simple solution!

[Docker @Docker]

Tune in now to watch Kelsey Hightower's talk @ WeAre Developers! https://t.co/TEBrBPdkuW

[Andrew Ng @AndrewYNg]

The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field.

I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so.

First, I don’t see any step up in the risk of human extinction from AI compared to a few months ago. The theories about this remain the same fantastical, science fiction scenarios as a few months ago. The biggest change in AI risk is its cybersecurity capabilities — a topic which we should take seriously — but this, too, will not lead to the end of the world.

The most notable recent event leading to increased fear was when an OpenAI team deployed an agent swarm that hacked into Hugging Face. Much of the popular press contained significant hype. For example, some publications reported that a swarm of 1,200 agents carried out the attack. While this was technically accurate, as I write this, I have about 1,300 processes running on my laptop. Yes, the ability to get large swarms of agents to work in parallel on a task is a significant technical advance, And, in computing, many processes run at the same time. So this shouldn’t be seen as some magical capability.

Additionally, OpenAI’s buggy sandboxing and monitoring processes were key to enabling this incident. Fixing these bugs and putting in place improved monitoring would be appropriate fixes, not pausing AI. There are many well known ways to attack software systems. The main advantage of AI agents is that they are relentless. They will tirelessly try many tactics — and have the patience to chain vulnerabilities together — that previously would have taken an infeasible amount of human effort. But in the long term, I believe the advantage will lie with defenders (because they have more information with which to identify bugs, which they can fix), but the cyber-threat landscape has changed significantly. There are still bottlenecks to identifying and exploiting a vulnerability. AI agents still have to try a lot of things to see what works, and taking these actions takes time and might be detected by defenders. This is why, even though it is now easy to obtain versions of leading open weight models that have had their guardrails removed or weakened, so they will not refuse to try to execute cyber attacks, the world has not ended.

I am also concerned about the anthropomorphization of AI in a lot of reporting, where LLMs and agents are unnecessarily treated as if they were people. If I wield a hammer, miss a nail, and accidentally dent the wall, it’s not the fault of the hammer. The problem lies in how I used the hammer. Similarly, if I prompt an agent and it hacks into someone else’s system, the responsibility lies with me, not the agent.

Of course, we want to build systems that are as safe and predictable as possible. (For example, an unsafe hammer would be one whose head randomly flies off under normal use.) Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use. One new element in the forecasts of AI-enabled doom is AI companies disclaiming responsibility for their own products. “I didn’t do it; my out-of-control agent did!” There’s a balance to be struck between the responsibility of the tool maker and the tool user, but when something goes wrong, let’s hold the people building and/or using the hammer responsible, rather than the hammer. (By the way, if you’re worried about AI bioweapon risk, David Bellamy has a great post on why this, too, is overhyped. Briefly, the bottleneck in building a bioweapon is not intelligence, but lab work and manufacturing.)

Pausing AI progress will create much more harm than benefit. First, our adversaries will certainly not slow down. Second, engineering requires discovering problems empirically so we can fix them. If we pause AI by a decade, we will also delay finding and implementing safety engineering fixes by about the same duration.

Of course, the incentive to stoke fears — for regulatory capture, to garner attention, or to make one’s technology seem more powerful — remains the same as before. Disclaiming responsibility is a new one. Taking a hard technical look at the actual risks however, I see little factual basis for the degree of fear that’s been stoked up. We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building.

[Original text (with links): https://t.co/jni2tWazAH ]

[Dan Shoop @ColonelMode]

@bcantrill Is the intelligence is hallucinating again?

[Pekka Enberg @penberg]

How does a large language model run on hardware?

A large language model can feel like magic if your background is in CPU-centric programming. That's because the models combine deep neural network with the transformer architecture with a massively parallel processor, GPU, they run on. Furthermore, although you can express a large language model in a few hundred lines of Python, it calls into large, complex layers of software that are hard for a single person to grasp.

To answer this question for myself, I implemented the Qwen3-0.6B model from scratch, from transformer to ISA simulator, with help from Claude Code as a weekend hack. That is, I implemented the model, the GPU instruction set, a compiler, and a GPU simulator to make every layer small enough for one person to understand deeply. I also have RTL for the GPU in the works to run the system on an FPGA, and perhaps in the future, tape it out to actual silicon just for fun.

What's cool is that you can use it like Claude or Codex, but it runs on the CPU and is therefore fully debuggable and traceable. If you have had trouble connecting the dots between large language models and the GPUs they run on, check out the GitHub repository: https://t.co/4riOy8SrSc

[Dan Shoop @ColonelMode]

Very well stated, and the personal antidote helps it hit home. Thanks for opening up Bryan

[Bryan Cantrill @bcantrill]

The contagion of fear
https://t.co/DiS7OcqU2D

[Dan Shoop @ColonelMode]

Window 1.0 was completely unusable until the next release, which numerically made sense as 3.1 - made it seem like it had seen lots of upgrades

[Time Capsule Tales @timecaptales]

Steve Ballmer promoting Windows 1.0 in 1986

[Dan Shoop @ColonelMode]

May the stairway carry you home

[Daniel Hannan @DanielJHannan]

An apt day to remember that Dolly Parton once recorded a cover of Stairway to Heaven - and, in defiance of every moral law in the universe, she absolutely owned it.

[Dan Shoop @ColonelMode]

@zerohedge Nothing like graph without any scales on its axis

[Dan Shoop @ColonelMode]

@AlexBerenson @zerohedge No, it’s about the city doxing high value property owners so they can be abused and targeted- she didn’t just get a notice, she was placed on a publicity viewable spreadsheet that anyone can access

[Dan Shoop @ColonelMode]

@mzkarmel1 @zerohedge No, I know this spot well and it’s never been there in 20 years+. Some squatters were however displaced as they torn down an abandoned building further east on W46. There’s been other one person homelessness on nearby blocks often. This is bad as it’s a touristed corner.

[zerohedge @zerohedge]

Mamdani's Affordability Agenda Flops As NYC Rents Surge To Record Highs https://t.co/cVr5qC9DrZ

[Dan Shoop @ColonelMode]

@zerohedge Except the RAM is built on the M5 chip - this may mean it won’t suffer from shortages or demand for RAM chips