It is always better to prepare a game plan for a large market drop that never happens vs. not being prepared for a large market drop that does happen. #ContingencyPlanning# #RealisticExpectations# #DayByDay# #DataVsFear#

Do facial sculpting tools really work? Experts say yes, but to set realistic expectations. These devices provide a temporary reduction in facial puffiness and more defined appearance, among other things.

Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research. Frontend development — say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast! Backend development — say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents. Infrastructure. Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development. Research. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally. Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much. I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future posts. [Original text: ]

A pilot often starts with excitement, strong adoption, and increased productivity. But why do those results sometimes fall flat when organizations try to scale? GitHub’s Agentic Engineering System helps organizations understand the conditions that make scaling agents successful. This way, they can set realistic expectations and improve the odds of seeing similar results at scale. Learn more ⬇️

I haven't read the feed at all yet, but I'm actually super happy with this call. Great job by the team to finally just give it to us straight. A little bit of communication goes a long way, and I felt satisfied by everything I heard. Expectations need to be reset, past projections need to be let go. But they're grinding and moving toward their goals. Super happy with Ashok's statement, and the way they answered so many pressing questions head-on. Talking about why they're starting new cities before scaling any and why it's useful, regulatory stuff, operational lessons to learn, realistic expectations for growth and Optimus rollout. The stock blows and probably will for a while, but the thing is being done. Mountains are being moved. Also just fucking merge the fuckin companies so we don't have to talk about it anymore. Whatever.