Nobel Prize winner Demis Hassabis just accidentally revealed who survives the next 5 years and who doesn't.
"One person who understands AI will outperform an entire startup team"
Most founders heard that and thought: "Oh no, I need to learn prompt engineering"
Wrong.
That's not what "understands AI" means anymore.
It means: building workflows. Chaining systems. Automating entire departments.
Not typing better questions into ChatGPT.
The split is brutal:
> 90% of people = still using AI like a calculator
> 10% of people = treating it like infrastructure
In 5 years, the 10% will run everything with half the headcount.
The 90%? Replaceable.
Which group are you in?
Watch the full breakdown. This is the only skill gap that actually matters right now.
Bookmark this. You'll want to reference it.
Anthropic engineer:
"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."
this is one of the best workflows I've seen in a long time
in this video she breaks down exactly how most people are using Claude:
- the 14% you lose to CLAUDE.md before typing a word
- one agent researching. one building. one reviewing. one orchestrating
- the architecture that separates hobbyists from real builders
- the 3 properties every agent team needs to actually survive
if you've been using Claude for more than a month and never left the chat window, you have at least 25 untouched features. probably 28
instead of another show tonight, watch this
make sure to bookmark it before it gets lost in your feed
the guide is in the article below
THE GUY WHO WON ANTHROPIC'S HACKATHON JUST GAVE AWAY HIS ENTIRE CLAUDE CODE PLAYBOOK FOR FREE. 10 MONTHS OF WORK, ALL PUBLIC
Affaan Mustafa won the Anthropic x Forum Ventures hackathon by building a full startup in 8 hours with Claude Code. Then he open-sourced the exact setup that did it. It's called Everything Claude Code, and it turns Claude from one assistant into an entire engineering team
Repo: affaan-m/ecc
This isn't a prompt pack. It's a system he refined over 10+ months of daily use shipping real products
What's inside:
A huge library of skills, dozens of specialized subagents, and ready-made commands, all working together. Each piece does one job. One subagent reviews security against OWASP standards. One optimizes memory so Claude stops forgetting earlier decisions around hour three. One learns from your past sessions and projects so the setup gets smarter the more you use it. Others handle planning, test-driven development, and language-specific code review
Instead of one assistant writing code, you get an orchestrated team. A main session delegates to the right specialist when the task calls for it, the way a real dev team splits work
The best part: it's not locked to one tool. It runs in Claude Code, Cursor, Codex and OpenCode, across Windows, Mac and Linux. Free, MIT licensed
This is the difference between using Claude like a search box and running it like a team that ships. The guy spent 10 months figuring out what actually works so you don't have to
Bookmark this
@DerMaerzAT Wolf ist seit Jahren dafür bekannt, dass er gern Steuerzahler auf social media attackiert.
Wenn der @ORF sich selber finanziert, kann er machen was er will.
Wolf wird aus Steuergeld finanziert und ist untragbar.
what is agent looping
for the last two years we prompted agents one task at a time. that is starting to change
instead of asking an agent to build the landing page and then driving every step yourself, you set up a loop that handles discovery, planning, the work, checking, and iterating until the goal is met
looping is a setup you build. almost any agent harness can run it, it just depends on how you wire it up
at its simplest, looping is one agent working on itself:
> researches
> drafts
> checks the draft against a goal
> fixes what is weak
> runs that cycle again until the work clears the requirements
you are not prompting each step anymore. the agent repeats the cycle for you
the bigger version is a fleet looping. you give an orchestrator agent a goal, it breaks the goal into pieces, hands each piece to a specialist agent, and those specialists hand smaller jobs to their own subagents
the whole tree keeps looping through discovery, planning, execution, and verification until the goal is met
one agent looping is like a person redoing their own draft. a fleet looping is a whole team running a project end-to-end
you create a goal, and the system runs the loop until it finishes within the reqs you set
open and closed looping:
OPEN LOOPING is exploratory. it still has conditions and a goal, but you give the agent or the fleet a wide space to move in. it can try different paths, discover things, build something you did not fully spec out
this is the exciting end, it is what Peter and others are doing, and tbh it is where I want to spend more time
the catch is cost, an open loop with real room to explore burns an insane amount of tokens. for the 90 percent of people without an unlimited budget it is not runnable yet, and pointed at projects with a loose standard it turns into a slop machine
CLOSED LOOPING is bounded. a human designs the end-to-end path first:
> clear goal
> defined steps
> an eval at each step
> a point where it stops or hands back to you (and feeds back performance data)
the agents still loop, but inside framework you built. it gets better every run because each pass feeds the next, and it runs on a normal budget because the path is tight.
for most marketing work, closed is the one that pays off today.
> the orchestrator owns the goal
> the specialists own the steps
> the subagents do the narrow work
> an eval gate make sure its not slop
this is f*cking gold
THE AI guy dropped a free masterclass on harness engineering
if I had this a year ago, I would've worked 5x faster
in the right hands, this changes everything:
@Erzbischof2023 Die Frage ist, ob er als Mitarbeiter fit für seinen Job ist, wenn er Kandidaten unter der Gürtellinie attackiert.
Wäre es ein privates Unternehmen, das sich so daneben über Kandidaten für Boardfunktionen äußert, wäre er nicht lange mehr dort.
I agree. The only mistake domain experts can make is ignoring the agentic technology.
Agents need humans to be effective. I do not see this change anytime soon, since goals and processes follow human needs. Software doesn't have it.
Agentic design will be as normal in 10 years as working with MS Office is today.
Get started now.
@lukasludens Milliardäre geben wenigstens ihr eigenes Geld aus.
Politiker lassen sich das vom Steuerzahler bezahlen, ohne das der Steuerzahler was dagegen machen kann.
@lukasludens Die Regierung erhöht sich selbst die Parteienförderung und plant für die Bevölkerung weitere Steuern.
Nach den letzten 6 Jahren sollen sie zum Teufel gehen. Alle bei jeder Wahl rauswählen.
That's an easy trap. A cheaper option might look great at first but backfire down the road. When working with state-of-the-art LLMs, startups that want to sell advanced reasoning to B2B should aim for the best engine under the hood rather than one that's just cheap.
Price shouldn’t be the main decision driver.
If you want to win an F1 championship, you need an F1 car, not a second-hand Mini.
@ImtiazMadmood The best was definitely @Keir_Starmer in this ugly charade, saying that it is @elonmusk's fault.
Get the left-wing extremists out of governments whenever there is an election. They are toxic.