LSBot is down impacting students worldwide.
However many engineering hours Railway saved with vibe coding, we’ve now paid 100x the price for that convenience in outages, incident response, and key rotation.
Domain expertise more important than ever.
Chris Lee retweeted
the right way to use model capabilities is not to ship 10x more features to prod
it's to spend more time understanding your users, trying experiments, building prototypes, learning about things you don't understand so that you can ship things that actually work
It’s an amazing time to be a Rubyist
look at how the Ruby OGs @chadfowler, @obie, @cooperx86, @dhh, @tobi are talking and thinking about AI
that's all I'm saying
Great roadmap for learning/specialization.
Some more tales from the road. Met with a couple dozen technology leaders this week across banking, media, information services, insurance, and consulting to discuss agents in the enterprise.
Some of the biggest trends right now:
* Cyber! Everyone nervous about the growing rate of vulnerabilities coming at them from AI, and the implications of the OpenAI Hugging Face incident. The conversation is not as existential as it is in Silicon Valley, but still highly concerned and pragmatic about what to do about it operationally in their environments. Lots of new discoveries due to AI, and still hard to keep up with all the changes they have to execute now.
* Model battles persist. Most companies are deploying multiple frontier models within their enterprise. Too hard to standardize on anything and seeing different preferences across their teams and use cases. But the dollars are still concentrated on just a few vendors. Open weights still in infancy at scale in most of these organizations, often due to lack of domestic “frontier” OSS options. Plenty of appetite for more options here, but so far few places to go.
* Agent security and identity. Somewhat tied to Hugging Face, there’s much more awareness to the new challenges around agent security and identity management in a world when agents are trying to get into every system they can. In a perfect world enterprises could setup identities for all their agents and control what they’re doing, but of course sometimes the agent needs to act exactly as the user as well.
* Process reengineering. Most companies realizing that the big upside of agents is when they can change the actual workflow itself to get the full gains from AI. Far more ROI when companies can adjust their workflows to support agents changing how the work happens instead of just layering on agents into the existing flow. But the big question is who can actually tackle driving these changes, where does that live, etc. Best lessons were still around embedded FDEs in the functions.
* Ruthless adjusting of architectures. Most companies had examples of changing systems out multiple times just in the past year or two with different vendors. I probably haven’t heard “we tried X and it didn’t work so have gone with Y” more than in today’s environment. The lesson here is that because innovation is happening so fast, no one hangs around until a vendor gets something right, they just move on to the next one.
* Evals! Still very early for most companies to have a good grasp of evals of their workflows. A few customers out of a couple dozen called this out - huge opportunity right now for enterprises to have a good sense of how their work actually happens and how well AI is doing against it.
* Legacy systems still a hurdle. As always, legacy systems still remain a mainstay issue that holds back enterprises from rapid adoption of AI in enterprises. Data is fragmented across legacy environments that weren’t built for an agentic world. Companies spending a lot of time just cleaning up these old platforms.
Many more topics, but these tend to be some of the more top of mind items at the moment in the enterprise.
You can see this most tangibly in sports: the advance moves are only possible when combining or adjusting fundamental moves.
Chris Lee retweeted
The jobs apocalypse is postponed. An AI jobs boom is here
According to @TheEconomist, AI is actually proving to be a net job creator in the US, easily generating over 1M new positions (from data center construction to AI engineering) to offset back-office layoffs.
While routine admin and customer service roles face real disruption, the overall labor market is proving resilient.
economist.com/finance-and-ec…
Chris Lee retweeted
Beware people obsessed with outcomes instead of building outcome machines. Its worse than ever with AI, but these people existed before. Short term results above all else, etc. Don't fall into the trap. Invest in building strong fundamentals, invisible supports, and outcomes flow like water. An outcome machine.
Chris Lee retweeted
"There's a satisfaction [to onboarding a junior eng] that you can't get from talking to Claude."
When will more of the industry re-discover a big reason we hired interns and new grads? It wasn't because they were productive on day one. And yet they help teams from day one...
Chris Lee retweeted
Satya’s post is worth reading closely because it gets at the real AI question for companies.
Who captures the learning?
His argument is that companies are becoming a new kind of learning system.
People bring judgment, taste, relationships, context and ambition. AI brings scale, memory, reasoning and execution. The value comes from building a loop where the company gets smarter every time work happens.
The important asset is the learning system around the model.
That system is built from the record of how work actually gets done. Workflow traces show the path people take. Corrections reveal judgment. Accepted outputs show what good looks like. Rejected approaches sharpen the standard.
Private evaluations, domain-specific context and institutional memory give that learning structure.
Over time, the company starts to retain more of what used to disappear inside meetings, edits, comments, decisions and individual experience.
That is the learning loop Satya is pointing at.
The judgment that once lived in a few people’s heads can become part of how the company operates.
Chris Lee retweeted
It was the best of times, it was the worst of times.
This has quietly been a miracle month in medicine.
In the last 5 weeks we’ve got news on:
- retatrutide, the triple agonist GLP-1 from Lilly, basically melting fat and body-wide inflammation at record levels
- RevMed’s new pancreatic cancer drug showing unprecedented abilities to extend life
- small trial of a one-and-done PCSK9 gene editing therapy for slashing LDL cholesterol
- Mayo’s AI-assisted radiology showing vastly improved cancer detection
- this new therapy for metastatic solid tumors
This stuff is at varying levels of evidence. Retatrutide is ~100% on its way, other stuff needs more clinical trial data. But put it together and we’re maybe on the verge of majorly reducing the mortality of heart disease and cancer, the two leading causes of death in America.
Chris Lee retweeted
Q: How are job postings for software engineers rising rapidly despite AI agents automating coding?
A: Because there’s far more code to manage than ever before. We’re already seeing a 14x YoY increase in GitHub commits, and it’s accelerating.
AI has dramatically lowered the cost of writing code, so it’s now being used across far more businesses, applications, and use cases.
We’re at the beginning of a massive productivity boom driven by the proliferation of bespoke software throughout the entire economy.
Coding has been AI’s breakout use case this year. The fact that it’s increased demand for software engineers — rather than decreased it — should call into question the entire “AI will cause mass job loss” narrative.
Chris Lee retweeted
Levie’s Law of AI Psychosis:
The farther away you are from the actual work the more confident you are that humans are no longer needed
I like it
CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI.
So when they play with AI, they see the happy path results, often not considering the next 10 or 20 things that have to happen to get sustainable results from agents.
“Look I made this awesome product prototype”. Yes but you didn’t have to review the code before it went into production and fix a bunch of issues.
“Look I generated a contract”. Yes but you didn’t verify all the terms before it goes out to the counterparty and didn’t have to wire up all the past contracts to work with.
The best thing you can do as a CEO is to use AI a *ton* to figure out the real implications of agents in the enterprise, and come out the other side with an appreciation for both the upside and the real work that goes into them.
Chris Lee retweeted
You just patched last month’s Nginx vulnerability that was actively exploited in the wild?
It’s already time for a fresh 0-day RCE.
The whole world is basically “pwned-by-default”, patching vulnerabilities before they’re exploited feels like a Sisyphean task... 🫠
Introducing nginx-poolslip, a fresh RCE for the the latest nginx release 1.31.0.
nginx-rift has been patched, but our security agent Vega has found a new 0 day.
We will release the full technical writeup with ASLR bypass 30 days after the patch on nebusec.ai.
LSBot uses Railway, so all LSBot is down at the moment.
Google Cloud has blocked our account, making some Railway services unavailable. We have escalated this directly with Google. The Railway Platform team has since confirmed access to Google Cloud and is working on restoring access to all workloads.
We have access to some of our Google Cloud–hosted infrastructure and are working to restore the rest of the service. We apologize for the disruption.
Chris Lee retweeted
how installing an npm package feels nowadays
🚨 BREAKING: Socket is investigating an active npm supply chain attack compromising hundreds of packages in the @antv ecosystem.
The malicious publish wave appears tied to Mini Shai-Hulud and packages connected to the npm maintainer account atool.
