iamrobotbear (bk)

Product Managers when ChatGPT first came out:

say it with me now. experts are fake, smart generalists rule the world, everything is designed by people no smarter than you, and courage is in shorter supply than genius

You can now share your ChatGPT Site without sharing it with the world. Invite specific people to view your site while keeping it private from everyone else. Business and Enterprise teams can also invite guests from outside their workspace, making it easy to share dashboards, project hubs, and anything else they build with clients and partners. Available on Plus, Pro, Business, and Enterprise.

GPT-404

Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continuously. Most agent harnesses are reactive: you send a task, the agent completes it, and then it sits frozen until the next request. Cron jobs and heartbeats wake it up to run a checklist and put it back to sleep. A Headlong agent is never asleep. It keeps generating thoughts about whatever it decides is interesting, in a self-guided loop inspired by human inner monologue. Your message doesn't start a session. It's one more observation that lands in the agent's thought stream, and the agent decides if and when to reply. Headlong is built on the idea of persistent agency: continuous inner thought generation between external interactions. The agent sets its own interests and priorities, comes up with its own projects, and sometimes pings you unprompted with progress. To keep our prototype as simple and small as possible, we implemented Headlong as a microharness: a complete agent harness in under 10K lines of Bash, organized as a handful of small executables. It includes a loop that generates the next thought, shellm (a recursive language model written in Bash), a trajectory stored as a DAG of jsonl files, and context as a projection of that trajectory. We've been running one Headlong agent internally at Laude for several weeks. The whole team talks to it over Slack and Telegram, and every conversation lands in its single stream of thought. It works in its own fork of Headlong and we've pulled over 50 of its commits into main. One night, with nobody talking to it, it went back to check whether a recall process it had built was actually wired into its mind, found that it wasn't, diagnosed and fixed the bug, and verified the fix end to end. 48 minutes, no human asked for the fix or was in the loop at any point. Every step is a timestamped line in its log. Things broke too, and we wrote those up. Background thinking costs us $1 to $2 an hour, our agent stopped its own service three times by accident, and self-delegation died on day one. Details in the post. One line installs everything and starts an agent. Use a dedicated sandbox and spend-capped API key; it runs real shell commands and thinks around the clock. Headlong is research software, be careful! curl -fsSL headlong.ai/install.sh | bash Launch post: laude.org/updates/headlong-a… Repo: github.com/laude-institute/h… Headlong is a @LaudeInstitute / MIT collaboration.

I was asked recently what are the top reasons AI stalls inside companies. There’s basically two things 1. People aren’t creative (and they’re not product managers by trade) Adopting AI requires someone to be able to reimagine the workflows in the job, their team, company, etc and basically hot swapping new tech (a tech they barely understand or trust.) it’s not enough to say “can AI do X” basically you have to rebuild things from first principles, break your muscle memory, and convince others to do the same. Most people aren’t imaginative enough or they lack the hard skills to do this well, so they stall in the write docs/code/daily briefing trough of disillusionment. 2. Leaders don’t want to manage change The second issue is most leaders are not motivated to force change in a company, and even if they know it’s needed, they’re unwilling to do the hard bits. I hear a lot “yes, I know we need to change but…” and then a list of fears: everyone will quit, our data will leak, tokens will be too expensive, people will ship bad code, quality will drop, my VPs can’t, on and on and on. It mostly comes down to “I’ll have to tell the team things are different now and they might not like it.” The first is easier to solve: internal FDEs, hack weeks, investments in internal tools. The second sits almost entirely in the hands of ~5 people in a company, and if they don’t hold hands and jump, they’ll never get there. Funny thing is: the blocker is never tools or intelligence. Human systems, human problems!

PE-style companies (raise money → buy things → raise prices → make the service worse) are genuinely detrimental to society yeah sure they’re great businesses on paper
but they’re built on the worst parts of capitalism
 pure profit extraction with almost zero real value created we’d be better off if more people used capital to actually build things that help

yes, that is a very important point. 2030 the energy crisis will be a tough problem for the USA. China already solved it tho