Working 100 hours a week is characteristic of jobs where you “work” very little, but need to always be on call (think: investment bankers). Fields that require deep creative work or technical precision usually get 4 productive hours each day. Agents moved SWE from (2) to (1)
Once you internalize the auto-research concept and use /goal with it, you unlock ridiculously fast app improvement on anything that has a numeric rubric.
We’re all still too rooted in the “labor is expensive” world.
Agentic workflow is totally different from the coding flow I remember from even a year ago. More like working a hard puzzle- giving it time to work in my head to execute a better decision.
I thought running parallel agents in separate worktrees would let me get more done while waiting.
In practice I’m finding it’s more helpful for giving the work time to breathe.
I can try something, then step away, tackle something else, give my subconscious time to sit on it.
The AI ponzi scheme goes like this:
Everyone is generating all these long ass docs and then passing them off for others to read
Then the person receiving is like, wtf this is way too long, and hands that into an AI to read and summarize
Then they are generating a long ass response back
and this cycle goes like that forever. and we call this work now 😅
The token lords watch this from their towers nodding and grinning.
How to ragebait a VC
- Which podcast gave you that opinion?
- How would you add value if you found a way to be valuable?
- You look like you make all of the 2 but none of the 20.
- Sorry, we aren't raising from you right now.
- Which AI model did you use to vibe code your firm's website?
- What startup role would you take if they'd have you?
Latest news in MCP, MCP Apps & Agent Discovery
This week: Anthropic code leak, organic MCP discovery & the future of chatbot/agent UI
In our next vid we'll be sure to cover all the announcements out of Google I/O
01:30 – MCP app ecosystem growth: 1,100+ ChatGPT apps, Anthropic doubles
04:00 – ChatGPT opens the floodgates vs Anthropic stays selective
06:30 – How MCP apps work today: the iframe layer explained
09:00 – Where MCP app UIs are heading: AI-assembled generative components
11:30 – Brian Chesky on TBPN: why chatbots aren't the right interface
14:30 – Conway: Anthropic's leaked always-on agent and external packages
18:00 – Building agents in the Anthropic stack: the monetization play
21:00 – OpenAI & Anthropic partner with McKinsey, Bain, Capgemini
24:30 – Live Artifacts in Claude: persistent dashboards powered by connectors
27:00 – Claude organic discovery is live: real prompt examples
30:00 – How ranking works: registry, tools, and descriptions
33:30 – Agent Discovery Optimization vs SEO, AEO, GEO
36:00 – Closing thoughts and what to expect next week
Today is May 25th, 2026.
This is the first time I remember reading an LLM-produced public artifact which is obviously professionally relevant and which is sufficiently complete that I do not perceive the lack of a human author materially compromising its utility to me.
The Graveyard Shift changes everything: instead of managing AI agents during the day, let them process your GitHub issues overnight so you start your morning with a review of completed work.
https://t.co/FYe5jvphqT
Being a language lawyer, and an adept at syntax juggling, are no longer valuable skills. The agents will subsume those skills and perform them better than any human could. The human skill that will survive is the deep problem-solving that comes from holding a dynamic model of a system in your mind.
@dharmesh The harness (and its use) of course are necessary for connecting the engine to the road, but where in that value chain is significant _competitive value_ to be found?
@harjtaggar Seeing this firsthand. 13 years in and the biggest advantage isn’t experience anymore — it’s how recently you’ve updated your mental model of what’s possible.
The half-life of AI knowledge is maybe 3 months right now.
AI is embarrassing a lot of senior engineers. A junior who touched the frontier yesterday often has better instincts for what’s possible than someone experienced who last touched it six months ago.
@halvarflake I don’t get this concern . Agentic development is making me to think bigger and more expansively. I mean, I have to push the ai to push me to do that, but when it does, I’m operating at a much higher level.
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.