> "The models are finally ready. Costs of inference are getting optimized with open models, and even on-device models. And, when I first experienced OpenClaw earlier this year, I had the epiphany that it isn’t the models that matter, but the harnesses, loops, and context which will lead to so many new opportunities ahead. Just as we’re learning in enterprise, we can finally invent new products that allow users to do things more naturally, using simple language to express their needs."
Seems like a wild move from the Devin team. Very curious about the metrics going into their "estimator" and also that they compared it to human estimates as a measure of success.
This is a bit of a tangent but can you explain the Western gaming studios analogy a bit further? It's not something I'm familiar with but am curious (I get the idea and broad parallel, but actually would love to better understand the details).
I wondered about this as well, but could also imagine AI tools (perhaps at the application layer and not root-LLM layer) helping to mitigate this - that's a world I've been exploring.
Don't think we're there yet, but it feels like there's a future dream of everyone being able to build and maintain custom software with AI, and not having to make the tradeoff of creativity vs. components that's historically been true with low/no code.
That said, where we are today, completely agree with what you're saying. LLMs like to generate code, and in my experience are pretty bad at iterating on or maintaining the prolific code they've created.
I found this exploration super interesting, especially the points around when it's okay for design to feel "generic" (when speed matters above all else) versus when design needs to feel "good" (squishy word, but in this case more thoughtful, unique, and deliberate).
> The most effective way to build software and get massive adoption is no longer high quality mainline apps but via building blocks that enable and encourage others to build quantity over quality.
I read the first sentence and immediately got excited. Building blocks, but not in the traditional sense of no / low code where you trade off creative flexibility for constrained preexisting blocks. Instead, it's building blocks that enable more people to build higher quality application experiences. Not slop, not copy/paste components, but something creative.
> "Quantum computers promise to solve otherwise impossible problems, including examples in chemistry, drug discovery, and energy. However, large-scale cryptographically relevant quantum computers (CRQCs) will also be able to break current, widely used public-key cryptography that protects things like people’s confidential information. Governments and others, including Google, have been preparing for this security challenge for many years. With continued scientific and technological progress, CRQCs are getting closer to reality, requiring a transition to PQC, which is why we recently introduced our 2029 migration timeline."
Is this as wild a news as I think it is? I'm surprised I haven't yet seen more reactions to the 2029 migration timeline plan (proposed?).
> "The real promise of this era is not that everyone shapes and customizes their own tools with malleable systems. It is that agents become capable enough that we no longer need to."
I thought the conclusion here was super interesting, and personally have mixed feelings. On the one hand, love the vision of high-quality tools being available near-instantly in this new AI-native software era.
On the other, I think there's a loss in turning everyone from builders into consumers. Yes, not every person wants to build every tool all the time. But I also think there's something inherently so empowering and delightful in making it possible - albeit not necessary - to build something you've imagined.
We play with legos not because we can't get pre-made toy houses or cars or such, we do it because it's delightful in its own right.
> "Then there's Rocky. He's the alien co-lead, and he's not CGI. Neal Scanlan, the creature designer who built the Porgs for Star Wars, spent a full year on this character. Over 300 designs before they landed on the final look. Rocky is a thin, hollow shell, 3D-printed from a digital sculpture, then hand-painted in see-through layers so light passes through him like skin. His arms pop off and swap out depending on the scene: one set has a closed fist for walking, another has tiny motorized fingers strong enough to pick up objects. Five puppeteers (nicknamed the "Rockyteers") operated him in every scene. James Ortiz, an award-winning puppet designer from New York theater, voiced Rocky and controlled him on set. When Scanlan met him, he told Ortiz, "You're Frank Oz, and I'm making Yoda for you." Every reaction Gosling gives to the alien is to something physically in front of him."
The level of detail and puppets / tech / etc. is so impressive.
As someone who just recently got into baking sourdough and was literally discussing earlier today some of the challenges around different starters, conditions, and lots of bad / misleading advice online, this is fun to see.
As a bit of an aside, I've found my starter is a lot more resilient than the internet would have me believe. I managed to bring it back to life from the brink after accidentally pouring boiling water directly on it. Fingers crossed no more near-death experiences for it again!
I also find King Arthur's guides and recipes super helpful; recently, I've started popping them into ChatGPT and requesting modifications (e.g. volume, ingredient alterations based on my learnings or needs, altitude adjustments etc.).
If you refresh are you still seeing it? I just got a 404 but am now able to access on refresh.
Copy/pasting below for easier reading in case you still have issues:
An AI Agent Broke Into McKinsey’s Internal Chatbot and Accessed Millions of Records in Just 2 HoursA red-team experiment found an AI agent could autonomously exploit a vulnerability in McKinsey’s internal chatbot platform, exposing millions of conversations before the issue was patched.
A security startup said their autonomous AI agent was able to break into McKinsey’s internal generative-AI platform in roughly two hours, gaining access to tens of millions of chatbot conversations and hundreds of thousands of files tied to corporate consulting work.
Researchers at red-team security firm CodeWall targeted McKinsey as part of a controlled test designed to simulate how modern hackers might use AI agents to probe corporate infrastructure. The experiment ultimately allowed the system to obtain full read-and-write access to the company’s AI chatbot database, according to a report by The Register.
CodeWall’s AI agent identified a vulnerability in Lilli, McKinsey’s proprietary generative-AI platform introduced in 2023 and now widely used across the firm. The chatbot has become a central tool inside the consulting giant. About 72 percent of McKinsey’s employees—more than 40,000 people—use Lilli, generating over 500,000 prompts every month, according to The Register.
Within two hours of launching the automated test, the researchers said their AI agent had accessed 46.5 million chatbot messages covering topics such as corporate strategy, mergers and acquisitions, and client engagements. The system also exposed 728,000 files containing confidential client data, 57,000 user accounts, and 95 system prompts that govern how the chatbot behaves, The Register reported.
Because the vulnerability allowed both reading and writing data, an attacker could theoretically manipulate the chatbot’s internal prompts, quietly altering how it responds to consultants across the company. That means someone exploiting the flaw could potentially poison the advice generated by the system without deploying new code or triggering standard security alerts.
“No deployment needed. No code change,” the researchers wrote in their blog post. “Just a single UPDATE statement wrapped in a single HTTP call.”
How the AI Agent Broke In
The attack began when CodeWall’s AI agent identified publicly exposed API documentation tied to Lilli. The documentation included 22 endpoints that required no authentication, one of which logged user search queries.
While analyzing the system, the agent discovered a classic flaw: The software was taking information from users and plugging it directly into its internal database without checking it first—known as SQL injection. That’s like a building security desk automatically letting anyone make their own keycards to get in.
CodeWall disclosed the vulnerability chain to McKinsey on March 1. By the following day, the consulting firm had patched the exposed endpoints, taken the development environment offline, and restricted access to the API documentation, The Register reported.
“Our investigation, supported by a leading third-party forensics firm, identified no evidence that client data or client confidential information were accessed by this researcher or any other unauthorized third party,” a McKinsey spokesperson told The Register. “McKinsey’s cybersecurity systems are robust, and we have no higher priority than the protection of client data and information we have been entrusted with.”
The Autonomous Cybersecurity Threat
For CodeWall’s CEO, Paul Price, the bigger concern is not this specific vulnerability but the speed and autonomy of the attack itself. The AI agent that conducted the probe operated without human guidance, Price said.
“We used a specific AI research agent to autonomously select the target,” he told The Register. “Hackers will be using the same technology and strategies to attack indiscriminately.”
That shift could enable cybercriminals to conduct machine-speed intrusions, automating reconnaissance, vulnerability discovery, and exploitation at a scale traditional attackers couldn’t achieve. And as companies increasingly deploy internal AI systems like McKinsey’s Lilli, those platforms may become some of the most valuable, and vulnerable, targets.
Super interesting take...is this something you've seen / experienced before?
I had sort of assumed best intent but probably good to be a little more skeptical / critical but also narcissism or psychopathy seems like a pretty significant deal if true.
I think how one might've learned even a couple years ago vs. how one might learn now are somewhat different.
For me, and I consider myself still learning / non-expert, having projects I wanted to build combined with looking for ways to learn the fundamentals (mostly free online courses + books) and then leveraging AI to get unblocked and coached has helped. Of course, take AI answers with a grain of salt.
Harvard's CS50 MOOC is a good "learn how to think about programming" that quickfire introduces you to a lot of fundamentals and challenges.
If you want more structure and more courses, Frontend Masters has a ton of learning paths that are great as well.
I've also heard good things about the Odin project but have not personally tried it out.
Memory management is one of the most challenging parts of working with Claude Code; too little effort or too much, and you waste tokens and Claude gets confused.
> "We attempted to use CLAUDE.md and continue to do so. Our root-level CLAUDE.md helps communicate some of the rules of our repo, such as approaching changes via test-driven development (TDD), as well as tribal knowledge our team has internalized. However, we don’t want to overload it with information about every area of the codebase, given context window constraints and our desire to avoid confusing Claude with irrelevant details."
Having issues with Claude [dot] md seems to be a common experience, and leveraging rules and having a background agent analyze each session is a clever approach here that works well. I've found this to be incredibly helpful.
> "I like to think about shipping better code in terms of technical debt. We take on technical debt as the result of trade-offs: doing things "the right way" would take too long, so we work within the time constraints we are under and cross our fingers that our project will survive long enough to pay down the debt later on.
The best mitigation for technical debt is to avoid taking it on in the first place."
This is actually something I've been thinking about a lot, to the point that I'm now building agentic experiences to try to help make it easier to create and maintain better code and iterate in an AI-centric way while building software.
> "Embrace the compound engineering loop"
Agreed! I feel like making it easier for AI agents to create high-quality code that's easy to test, iterate on, and maintain (without adding tech debt) is crucial here.
This part resonated a lot, personally.