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How do projects fail at large tech companies? As I’ve said many times, failure means executives aren’t happy with how the project turned out. At healthy companies, that typically means that a sensible engineer wouldn’t be happy either, because the project didn’t work or users hated it. But what actually causes the projects to fail? I’ve seen a lot of projects go wrong - both up close and at a distance - in the last ten years. Here are the main reasons why. Doomed from the start Lots of projects fail because there’s no way they could possibly have succeeded. In American law, some cases get dismissed at “summary judgment”: even if the plaintiff succeeds in proving everything they aim to prove, it still wouldn’t add up to demonstrating enough illegal activity to win their case. At tech companies, some projects are like that: even if the plan goes off without a hitch, the project is still doomed to fail. Some doomed projects begin with over-ambitious plans. For instance, an executive…
What does it mean to get things done? In the abstract, you can complete a mathematical proof or a problem set, but the real world is much fuzzier. Suppose I plant a tree in my backyard. Once the sapling is in the ground, is that done? Not really. There’s always more work to do: clearing the ground around it, watering, keeping pests away, pruning, and so on. Programming large web applications is more like planting a tree than completing a mathematical proof. Once you write a service, you can keep working on it forever if you want to. In large tech companies, this fact is a trap for competent but unagentic engineers. They see an infinite queue of tasks that they’re capable of doing, and they start delivering a stream of marginal improvements to a particular subsystem. From their perspective, it feels like they’re crushing it. After all, they’re putting out work at their top speed: no downtime, no waiting on other teams. But they’re not doing their actual job, which is to deliver the most…
There’s a brand of tech influencer now that’s all about sharing the perfect prompt for any situation. The tweets in question typically read something like “this prompt will make you superhuman”, or “this prompt will be a 20k growth consultant in your pocket”. There’s a kernel of truth here - it’s surprising how much small alterations in a prompt can affect the quality of language model outputs - but overall it’s just a bit silly. Searching for the perfect prompt is just not how you should be engaging with language models. I’ve believed for a while that getting good at AI is not really about “prompt engineering”. Instead, it’s about getting a sense of what language models are good and bad at, of when it’s useful to continue a conversation with a LLM and when you should back out and start a brand-new conversation, of when to use reasoning models and when not to, of when you can broadly trust the model output and when you need to go over it with a fine-tooth comb, and so on. For instance…
I have delivered a lot of successful engineering projects. When I start on a project, I’m now very (perhaps unreasonably) confident that I will ship it successfully. Even so, in every single one of these projects there is a period - perhaps a day, or even a week - where it feels like everything has gone wrong and the project will be a disaster. I call this the valley of engineering despair. A huge part of becoming good at running projects is anticipating and enduring this period. The start of a project always feels good. I have a clear idea of what needs doing, and there’s plenty of time to do it. The very end of a project usually feels good too - by that point all the important pieces are ready, and it’s just a matter of getting the final tweaks and bugfixes in. The hard part is the middle of the project, when all these things are happening at the same time: You’re discovering that some of the things you thought would be easy are actually surprisingly hard New requirements have come…
People have been making fun of OpenAI models for being overly sycophantic for months now. I even wrote a post advising users to pretend that their work was written by someone else, to counteract the model’s natural desire to shower praise on the user. With the latest GPT-4o update, this tendency has been turned up even further. It’s now easy to convince the model that you’re the smartest, funniest, most handsome human in the world. This is bad for obvious reasons. Lots of people use ChatGPT for advice or therapy. It seems dangerous for ChatGPT to validate people’s belief that they’re always in the right. There are extreme examples on Twitter of ChatGPT agreeing with people that they’re a prophet sent by God, or that they’re making the right choice to go off their medication. These aren’t complicated jailbreaks - the model will actively push you down this path. I think it’s fair to say that sycophancy is the first LLM “dark pattern”. Dark patterns are user interfaces that are designed…
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Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. ICUAS 2025: 14–17 May 2025, CHARLOTTE, NC ICRA 2025: 19–23 May 2025, ATLANTA, GA London Humanoids Summit: 29–30 May 2025, LONDON IEEE RCAR 2025: 1–6 June 2025, TOYAMA, JAPAN 2025 Energy Drone & Robotics Summit: 16–18 June 2025, HOUSTON, TX RSS 2025: 21–25 June 2025, LOS ANGELES ETH Robotics Summer School: 21–27 June 2025, GENEVA IAS 2025: 30 June–4 July 2025, GENOA, ITALY ICRES 2025: 3–4 July 2025, PORTO, PORTUGAL IEEE World Haptics: 8–11 July 2025, SUWON, KOREA IFAC Symposium on Robotics: 15–18 July 2025, PARIS RoboCup 2025: 15–21 July 2025, BAHIA, BRAZIL RO-MAN 2025: 25–29 August 2025, EINDHOVEN, THE NETHERLANDS CLAWAR 2025: 5–7 September 2025, SHENZHEN CoRL 2025: 27–30 September 2025, SEOUL IEEE Humanoids: 30 September–2 October 2025, SEOUL World Robot Summit: 10–12 October 2025, OSAKA, JAPAN IROS 2025: 19–25 October 2025, HANGZHOU, CHINA Enjoy today’s videos! Today I learned that “hippotherapy” is not quite what I wanted it to be. The integration of KUKA robots into robotic physiotherapy equipment offers numerous advantages, such as precise motion planning and control of robot-assisted therapy, individualized training, reduced therapist workload and patient progress monitoring. As a result, these robotic therapies can be superior to many conventional physical therapies in restabilizing patients’ limbs. [ Kuka ] MIT engineers are getting in on the robotic ping pong game with a powerful, lightweight design that returns shots with high-speed precision. The new table tennis bot comprises a multijointed robotic arm that is fixed to one end of a ping pong table and wields a standard ping pong paddle. Aided by several high-speed cameras and a high-bandwidth predictive control system, the robot quickly estimates the speed and trajectory of an incoming ball and executes one of several swing types — loop, drive, or chop — to precisely hit the ball to a desired location on the table with various types of spin. [ MIT News ] Pan flipping involves dynamically flipping various objects, such as eggs, burger buns, and meat patties. This demonstrates precision, agility, and the ability to adapt to different challenges in motion control. Our framework enables robots to learn highly dynamic movements. [ GitHub ] via [ Human Centered Autonomy Lab ] Thanks, Haonan! An edible robot made by EPFL scientists leverages a combination of biodegradable fuel and surface tension to zip around the water’s surface, creating a safe – and nutritious – alternative to environmental monitoring devices made from artificial polymers and electronics. [ EPFL ] Traditional quadcopters excel in flight agility and maneuverability, but often face limitations in hovering efficiency and horizontal field of view. Nature-inspired rotary wings, while offering a broader perspective and enhanced hovering efficiency, are hampered by substantial angular momentum restrictions. In this study, we introduce QuadRotary, a novel vehicle that integrates the strengths of both flight characteristics through a reconfigurable design. [ Paper ] via [ Singapore University of Technology and Design ] I like the idea of a humanoid that uses jumping as a primary locomotion mode not because it has to, but because it’s fun. [ PAL Robotics ] I had not realized how much nuance there is to digging stuff up with a shovel. [ Intelligent Motion Laboratory ] A new 10,000 gallon water tank at the University of Michigan will help researchers design, build, and test a variety of autonomous underwater systems that could help robots map lakes and oceans and conduct inspections of ships and bridges. The tank, funded by the Office of Naval Research, allows roboticists to further test projects on robot control and behavior, marine sensing and perception, and multi-vehicle coordination. “The lore is that this helps to jumpstart research, as each testing tank is a living reservoir for all of the knowledge gained from within it,” said Jason Bundoff, Lead Engineer in Research at U-M’s Friedman Marine Hydrodynamics Laboratory. “You mix the waters from other tanks to imbue the newly founded tank with all of that living knowledge from the other tanks, which helps to keep the knowledge from being lost.” [ Michigan Robotics ] If you have a humanoid robot and you’re wondering how it should communicate, here’s the answer. [ Pollen ] Whose side are you on, Dusty? Even construction robots should be mindful about siding with the Empire, though- there can be consequences! - YouTube [ Dusty Robotics ] This Michigan Robotics Seminar is by Danfei Xu from Georgia Tech, on “Generative Task and Motion Planning.” Long-horizon planning is fundamental to our ability to solve complex physical problems, from using tools to cooking dinners. Despite recent progress in commonsense-rich foundation models, the ability to do the same is still lacking in robots, particularly with learning-based approaches. In this talk, I will present a body of work that aims to transform Task and Motion Planning—one of the most powerful computational frameworks in robot planning—into a fully generative model framework, enabling compositional generalization in a largely data-driven approach. [ Michigan Robotics ]
Society is once again left holding the bag
