bb'ロン @bbronste

The fallacy of this is that more creates more. More hours, more hiring, more something. And it is true in a sense. If you put in more work, more work will happen. But I think for most startups, the leverage is really in how differently you approach the problem, how well you cultivate your team, and the strategy. Any large company can outspend you on hours. They have thousands or tens of thousands more people, spending more hours. If hours worked were the metric, every large company and government organization would always win and do the best work. More hours, better output. This thinking is often representative of younger founders, where the startup becomes their identity and life. They have a hard time doing anything else, and cannot understand that your work is not the person that is you. But activities outside of work can grow you as a person too and make you do better work. I’ve never worked this way. As a designer, I always saw the need to take a step back, to take a break. At times, I might work 12 hours or 16 hours, or whatever amount was needed, but it wasn’t the norm. You just can't grind design, you need inspiration. But taking that step away from the work, would give me more perspective, inspiration and I could approach the problem differently or I could just see the solution. Grinding is never good for any creative problem, and startups or creating new products are often mostly about creative problem solving. Grinding works ok for email jobs, or where you just executing on very clear playbook. With Linear, we’ve never worked this way. We work reasonable hours, 5 days a week. All of us founders have families. Many of our employees have families. I personally stop every evening, spend time with the family, cook dinner for the family, eat dinner together, and focus on things outside of work. Sometimes I work in the late evenings or weekends, but to me the pride is that I don’t need to. Company should be succesful without it. My goal is to build a company that is sustainable in the long term, and doesn’t require heroics or personal sacrifices every single day. There are times when our team is heroic. Launches, incidents, some other work that just needs to be done. They will work late into the night because they know it is the right thing. But we don’t require that every day or every week, and the more this happens, the more I think it is a failure of our company and leadership. The team and the leaders should always keep a reserve to use when something is needed. Our thinking was also that quality, which we value, doesn’t emerge from working more or stressing people more. It emerges when you create the conditions for it to emerge. Often it is the appreciation, space, time, and how the person feels. A person who is rested will do better work. I wouldn’t attribute much of our success to working a lot. The success came from having clear thinking, ideas, and focus to do the right things. I sometimes wish we could move the culture more toward a Zen master. Real mastery is not exerting the most effort. It is achieving the outcome with the least necessary effort.

Harry Stebbings @HarryStebbings

a week ago

"If you are not working 7 days per week, you are going to lose". Corgi Insurance is the most intense workplace culture in startups. - The company works 7 days per week. - Founder (@nico_laqua) lives and sleeps in the office. - He built a cafe in the office because there was

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✝️ Pope Leo XIV releases 42,000-word encyclical covering humanity’s struggle with technology: The Chicago-born pope released his first papal encyclical calling out the dangers of artificial intelligence and urging the safeguarding of the human person. “We must remember that humanity flourishes not despite limitations, but often through them,” the pope writes. The “technological transformation” of our age, he says, is having a profound impact on both truth—through the ability of AI to mimic and distort reality and shape communications—and conditions of work, where the pope urged an economy that treats humans with dignity. The encyclical, Magnifica Humanitas, is here: vatican.va/content/leo-xi…

Pope Leo XIV @Pontifex

2 weeks ago

Today, among the goods that are universally intended for everyone, we must also include new forms of property, such as patents, algorithms, digital platforms, technological infrastructure and data. In a context where the wealth of nations depends increasingly on knowledge and

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- clean up bucket. remove heavy .gguf files

#todays_to_do - adjust billing budget. overshot current by 37.5% - find issue with inference server. after image decoding , cpu usage drops by appx ~90%, log of "...prompt processing done..." is issued (by e2-machine-type based llama-server. for c4, this log is not issued). ...

I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out. I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really). It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely. The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture. We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying. I worry.

Your greatest weapon is your mind, train it to see oppprtunities, not obstacles.

Me designing data intensive applications

Raven @raven_brah

a month ago

Ride now, ride! Ride for ruin and the world's ending!

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Birds chirp an hour before dawn and the frequency opens up the stomata of the plants to breathe. The frequency is common in classical music. Play classical to your plants for MUCH larger crops. Its called sonicbloom.

Ifediche @esther_stan

2 months ago

Teach me something new .

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Open source projects like transformers are drowning in AI agent PRs, so we auto-merged everything to see what would happen and share the results. tl;dr: if 100s of agents want to fix something, it’s probably broken. Agent PRs on transformers have quadrupled over the past quarter. We classified and validated 1k PRs (42% features, 39% bugs, 13% docs). The quality distribution is skewed toward noise. But the bug fixes cluster around a small number of hotspots: tokenizer handling, model loading, dtype mismatches, multimodal pipelines. I.e. an underlying problem. When 28 PRs independently flag the same area, that is signal regardless of whether any individual fix is correct. One issue generated 39 near-identical PRs in a day. Each applied the same decorator pattern to a different model file. A maintainer would do the same cognitive work 39 times, so a single combined PR replaces all of that work. We built tooling to cluster, deduplicate, and merge these contributions at scale, then ran an experiment: bulk-merge hundreds of agent PRs into a fork, benchmark it, and see what breaks. Nothing broke. Zero delta across three models on arc_challenge, gsm8k, and hellaswag. The contributors are not adversarial. They lack the context to evaluate whether the agent's output is correct. Check out this blog post, where we dive deep on this pipeline: huggingface.co/spaces/hugging…

Addiction is proof that you are capable of intense devotion. You just have a false god

A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work. His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing. In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen. Here's the framework that has been quoted by every serious scientist for the last 40 years. His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired. He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow. The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one. The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed. The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else. The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices. He finished the lecture with a line I have never been able to shake. He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day. The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword. Hamming died in 1998. He gave his final lecture a few weeks before. He was 82. The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.

This is where we are right now. And i’m not gonna lie it feels pretty magical 🧚‍♀️ Qwen3.6 27B running inside of Pi coding agent via Llama.cpp on the MacBook Pro For non-trivial tasks on the @huggingface codebases, this feels very, very close to hitting the latest Opus in Claude Code, or whatever shiny monopolistic closed source API of the day is. In full airplane mode. Most people haven’t realized this yet. If you have, it means you have a huge headstart to what I call the second revolution of AI. Powerful local models for efficiency, security, privacy, sovereignty 🔥

reason for reading about qwen chat templates, to possibly use in vs code

Frank O. Jaker @frankojaker

2 months ago

you really need to build your own infra, not just harness, for AI agents. can’t be integrating 10 fast changing/unchanging unreliable services you can’t control. each llm update means adjusting something.

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Today, we’re introducing Skills in @GoogleChrome, a new way to build one-click workflows for your most frequently used AI prompts — like asking for ingredient substitutions to make a recipe vegan, generating side-by-side shopping comparisons across multiple tabs, or scanning long docs to get the info you need quickly. When you write a prompt that you want to use again, you can save it as a Skill directly from your chat history. The next time you need it, select your saved Skill in Gemini in Chrome by typing forward slash ( / ) or clicking the plus sign ( + ) button, and your Skill will run on the page you’re viewing, along with any other tabs you select.

I’m a millennial so my retirement plan is societal collapse

Young engineers in the Gaza Strip are converting organic waste into cooking gas and liquid fertilisers, helping farmers cope with shortages amid Israel’s war.

“The idolatry of profit that plunders the earth’s resources”

Pope Leo XIV @Pontifex

2 months ago

Death is always lurking. We see it in violence, in the wounds of the world, in the cry of pain that rises from every corner because of the abuses that crush the weakest among us, because of the idolatry of profit that plunders the earth’s resources, because of the violence of war

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The older I get, the more I realize being in a hurry is a terrible way to live your life.

Google’s Gemma 4 E2B running on-device on iPhone 17 Pro Gemma 4 is built from the same research as Gemini 3, has image understanding capabilities and can reason if needed Running at ~40tk/s with MLX optimized for Apple Silicon