Maxime Petazzoni @mpetazzoni

how to be good at your job - realize this one thing is actually made up of two separate things - realize instead of solving the direct problem you can solve a broader problem - instead of implementing thing, implement other thing that makes it easier to implement thing

@Google changed the favicons for Docs, Sheets, etc., and now I can't find anything in my browser's tabs.

What kind of IC work can you do if you are a manager? Attended two dinners with dozens of senior Bay Area tech managers last week. Every single one of them said they were expected to be hands-on building with AI these days. But what to work on? 4 categories emerged: 1. Internal efficiency -- anything to make your team more productive, from communication tools to digital brain to team skills. 2. Quality-of-life improvement -- got something that bugs the heck out of you about the product but you can't convince anyone else to work on it? Just fix it yourself. 3. Celebration story -- artifact (usually video, image narrative, etc) that hypes up the work done by your team. 4. Vision piece -- describes a really freakin' cool future that your team can move towards. What NOT to do: take on any critical-path product work. (Because then you either do a poor job delivering on that, or managing.)

As agents become the biggest users of software, then all software has to be available in a headless fashion. Agents won’t be using your UI, they’ll be talking to your APIs. So the question becomes what is the business model of software and this headless approach in the future? Here are a few thoughts on how everything plays out based on what we’re seeing and doing at Box, but also conversation with other platforms. 1) Seats don’t go away for *people*. Seats are still a convenient and efficient way to have a customer use technology predictably for a set of users within a baseline set of usage. The key, though, is that when the customer pays for a seat, it has to come with a set of usage of APIs on behalf of that user that the agent can use on their behalf. The user will need to be able to interact with their data and the underlying tool via any agent they work with, and an embedded amount of usage will come with the seat. I would imagine most software -Box included- will enable seats to work with their data at a relatively high volume via systems like ChatGPT, Codex, Claude, Gemini, Cursor, Copilot, Perplexity, Factory, Cogniton, et al. quite seamlessly. If you don’t do this, you’re DOA. 2) Agents may have “seats” if they are doing stateful work in the system, but they will be priced very differently than people. Seats (or the equivalent) can make sense when you have an agent that has its own workspace, stores its own data, needs a different set of permissions compared to the user, and so on. If a company wants this agent to be around for long period of time, that may very well look like another “user” in the system. Openclaw-style agents highlight what this future could look like. The only issue on pricing here is that one customer could decide to do all their work in 1 agent, and another might split it into 1,000 agents. So pricing like a human seat is nearly impossible and impractical; each company will have a different approach for this as it gets tricky perfectly trying to capture all the value within an agent seat. 3) The dominant pricing for headless use that goes above the seat allotment, or when an agent is firmly acting on their own, will be a consumption model. Many enterprises software platforms have previously operated like this with PaaS options, and agents will look like another machine user of their system. In some cases the APIs might get priced just as they did previously, but in other cases there may need to be new types of APIs that represent the work an agent would do in one go -more akin to an outcome- instead of a series of API calls. This is especially germane when the headless software also has an agentic use-case embedded within in, such as orchestrating the process within their own system via AI. Overall the growth of this usage pattern is effectively unbounded as the use-cases for agents operating on data in these systems will dramatically exceed what people do with their data and tools today. Every platform that goes headless (which will be anyone that wants to take advantage of agents) will need to adopt a model like this. Some may fight it initially but it’s an inevitably as there will always be more and more agents outside your platform than people. Overall, there’s a lot of really interesting changes left to come in software due to headless use of these systems. Early days.

100% agree and to me this makes the massive investments in inference compute infrastructure and datacenters a super strange bet when more than half of all inference may be done on-device. Hard to predict of course but when hundreds of B$ are in the balance it's a tough one.

Rasmus Andersson @rsms

a month ago

This is a glimpse of big changes ahead of us. If you’re betting on big central models you should think twice. I run the exact same setup (M5 MacBook, qwen3.6-27B, pi, ollama) and while its not as fast or good as one of the big central models, it’s past the line of “cool demo”

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mfw my weekly average hit 5h55 this week juggling a high intensity job and a toddler: 😱😵

Aakash Gupta @aakashgupta

2 months ago

The scariest finding in this paper: the subjects couldn't tell it was happening. UPenn ran this study on 48 healthy adults. One group slept 8 hours. Another slept 6. Another slept 4. For 14 straight days. They tested cognitive performance every 2 hours from 7:30am to 11:30pm.

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Parenting is like a never ending Easter egg hunt, but with cheerios. I think my MTBC (mean time between cheerios) has averaged under 15min today. They just keep popping up everywhere 😆

As OpenAI and Anthropic continue to eat up the software development ecosystem, the next step will be for them to acquire and own where+how those applications are hosted.

It's 10pm. Do you know what your agents are doing?

I'm joining @traversal_ai as Head of Engineering!

Traversal @traversal_ai

3 months ago

We’re excited to share that Traversal has expanded its leadership team with six senior hires across go-to-market and engineering. Over the past two years, Traversal has moved through three phases: research → validation → production proof. What began as an applied AI research

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What F1 needs are the sounds of F3 and the drama of F2. We'll see how the season unfolds but onboards are even more boring to watch than ever with the new regs.

Alanis King @alanisnking

3 months ago

My idea to fix F1 is to just race Miatas the rest of the year

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The map was labeled, too.

Civixplorer @Civixplorer

3 months ago

🇺🇸 Nearly 2,000 US citizens were asked to point out Iran on a map. These were the results:

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We are now at 1136 new measles cases - in just the first 8 weeks of this year alone. A preventable disease resurging. This is an abject policy failure.

👇🏼 The speed and scale at which this happened - it wasn't working before December, and now it's basically the default way to work - is part of what makes it the most exciting times in our careers. And if AI is expected to accelerate many things even further, ... 🤯

Andrej Karpathy @karpathy

3 months ago

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December

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Everything we're doing to make codebases "agent-ready" (better docs, less dead code, smaller surfaces) engineers always needed too. Agents just have zero tolerance for the entropy humans learned to work around. They can't "just know" a file is outdated or a code path is dead. They take your codebase at face value, which means it finally has to be worth taking at face value.

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"Congratulations …. your days of whining are over." jsnover.com/blog/2026/02/0…

@sam_alba @aluzzardi That's an immediate follow! 👀🙌🏼

The best senior software engineers I’ve worked with all share this exact trait: extreme ownership. When something breaks in production, they don’t start with “the requirements were unclear” or “another team caused it.” They start with: “What did I miss?” or “What assumption did I make that turned out to be wrong?” They treat every outage, bad deploy, or missed deadline as data. If a system failed, they ask: Should I have added better observability? Could I have asked sharper questions earlier? Did I simplify too much? What guardrail would prevent this next time? Even when they technically did the right thing, they still look for ways to increase future signal: better metrics, smaller rollouts, clearer contracts, earlier feedback loops. This mindset is what separates seniors from mids. Senior roles are all about reducing repeat mistakes and compounding good decisions. No blame. No ego. Just learn → adjust → apply. That is what turns experience into wisdom and gets you promoted.

Justin Skycak @justinskycak

5 months ago

The most effective people I've met all maintain a gigantic locus of control and bias towards internalizing blame. Whenever something doesn't go their way, they deeply analyze the circumstances to identify what they should have done differently, or, even if they acted

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When GitHub went down, we used to not be able to commit and push code. Now we can't even write it 😅. githubstatus.com/incidents/vyxb…