This is EVERYTHING you need to know about building a Company Brain in 28min.
From connecting all your tools into one intelligence layer to the new org chart around it.
This is the new way to work in the AI era.
Here's the full breakdown:
00:00 Introduction & AI Adoption
01:45 The Four Levels of AI Maturity
02:30 The Single Brain Concept
05:30 Building an AI-Powered Org
07:20 Specialist Agents & Agent Fleets
11:20 Workflows & Closed Loops
14:30 Real Agent Examples in Slack
18:00 AI ROI & Token Economics
20:45 Business-to-Agent Commerce
24:10 Action Steps & Future of Work
A2go retweeted
🚨 Jensen Huang, CEO of Nvidia:
"Every engineer is going to have and manage hundreds of agents."
The most valuable engineering skill of 2026 is not taught in any university.
No CS program teaches harness engineering.
No bootcamp teaches agent memory architecture.
No degree prepares you to build systems that survive production.
One builder mapped the entire thing out — free, step by step, no degree required.
This is the roadmap ↓
Bookmark this for the weekend.
Creator of Claude Code:
"At Anthropic, almost 100% of our engineers are running 100+ agents with self-improving loops
self-improving loops help agents become better with each run."
in a 1-hour podcast, Boris explains how they build agents loops from sratch.
Claude + loops + routines + dynamic workflows - that’s the secret.
Watch the talk, then read how to apply the same playbook to quant trading below.
In the 1920s, a Stanford psychologist tracked genius children for 50 years.
Malcolm Gladwell breaks down what he discovered:
Rich families → successful. Poor families → failures.
Not average. Failures. Genius-level IQs that produced nothing.
He spent 60 minutes at Microsoft explaining why we're wrong about success:
The psychologist was named Terman. He gave IQ tests to 250,000 California schoolchildren.
He identified the top 0.1%. Kids with IQs of 140 and above.
His hypothesis: these children would become the leaders of academia, industry, and politics.
He tracked them. And tracked them. For decades.
The results split into three groups.
The top 15% achieved real prominence. The middle group had average, moderately successful professional lives.
And the bottom group? By any measure, failures.
The difference wasn't personality. Wasn't habits. Wasn't work ethic.
It was simple: the successful geniuses came from wealthy households. The failures came from poor families.
Poverty is such a powerful constraint that it can reduce a one-in-a-billion brain to a lifetime of worse than mediocrity.
There's a concept called "capitalization rate."
It asks a simple question: what percentage of people who are capable of doing something actually end up doing that thing?
In inner city Memphis, only 1 in 6 kids with athletic scholarships actually go to college.
If our capitalization rate for sports in the inner city is 16%, imagine how low it must be for everything else.
Here's something stranger.
Gladwell read the birth dates of the 2007 Czech Junior Hockey Team:
January 3rd. January 3rd. January 12th. February 8th. February 10th. February 17th. February 20th. February 24th. March 5th. March 10th. March 26th...
11 of the 20 players were born in January, February, or March.
This isn't unique to the Czechs. Every elite hockey team in the world shows the same pattern. Every elite soccer team too.
Why?
The eligibility cutoff for youth leagues is January 1st.
When you're 10 years old, a kid born in January has 10 months of maturity on a kid born in October. That's 3 or 4 inches of height. The difference between clumsy and coordinated.
So we look at a group of 10 year olds, pick the "best" ones, give them special coaching, extra practice, more games.
We think we're identifying talent. We're just identifying the oldest.
Then we give the oldest more opportunities, and 10 years later they really are the best.
Self-fulfilling prophecy.
The capitalization rate for hockey talent born in the second half of the year? Close to zero.
We're leaving half of all potential hockey players on the table because of an arbitrary date on a calendar.
Kids born in the youngest cohort of their school class are 11% less likely to go to college.
11% of human potential squandered because we organize elementary school without reference to biological maturity.
Now here's the part about math.
Asian kids dramatically outperform Western kids in mathematics. The gap is enormous and consistent across decades of testing.
Some people say it's genetic. It's not.
It's attitudinal.
When Asian kids face a math problem, they believe effort will solve it.
When Western kids face a math problem, they believe the answer depends on innate ability they either have or don't.
Here's the proof.
The international math tests include a 120-question survey. It asks about study habits, parental support, attitudes.
It's so long most kids don't finish it.
A researcher named Erling Boe decided to rank countries by what percentage of survey questions their kids completed.
Then he compared it to the ranking of countries by math performance.
The correlation was 0.98.
In the history of social science, there has never been a correlation that high.
If you want to know how good a country is at math, you don't need to ask any math questions. Just make kids sit down and focus on a task for an extended period of time.
If they can do it, they're good at math.
Why do Asian cultures have this attitude?
Gladwell's theory: rice farming.
His European ancestors in medieval England worked about 1,000 hours a year. Dawn to noon, five days a week. Winters off. Lots of holidays.
A peasant in South China or Japan in the same period worked 3,000 hours a year.
Rice farming isn't just harder than wheat farming. It's a completely different relationship with work.
There's a Chinese proverb: "A man who works dawn to dusk 360 days a year will not go hungry."
His English ancestors would have said: "A man who works 175 days a year, dawn to 11, may or may not be hungry."
If your culture does that for a thousand years, it becomes part of your makeup.
When your kids sit down to face a calculus problem, that legacy of persistence translates perfectly.
Now consider distance running.
In Kenya, there are roughly a million schoolboys between 10 and 17 running 10 to 12 miles a day.
In the United States, that number is probably 5,000.
Our capitalization rate for distance running is less than 1%.
Kenya's is probably 95%.
The difference isn't genetic. The difference is what the culture values and where it spends its attention.
Here's the most fascinating finding.
30% of American entrepreneurs have been diagnosed with a profound learning disability.
Richard Branson is dyslexic. Charles Schwab is dyslexic. John Chambers can barely read his own email.
This isn't coincidence. Their entrepreneurialism is a direct function of their disability.
How do you succeed if you can't read or write from early childhood?
You learn to delegate. You become a great oral communicator. You become a problem solver because your entire life is one big problem. You learn to lead.
80% of dyslexic entrepreneurs were captain of a high school sports team. Versus 30% of non-dyslexic entrepreneurs.
By the time they enter the real world, they've spent their whole life practicing the four skills at the core of entrepreneurial success: delegation, oral communication, problem solving, and leadership.
Ask them what role dyslexia played in their success and they don't say it was an obstacle.
They say it's the reason they succeeded.
A disadvantage that became an advantage.
Here's what Gladwell wants you to understand:
When we see differences in success, our default explanation is differences in ability.
We forget how much poverty, stupidity, and attitude constrain what people can become.
We refuse to admit that our own arbitrary rules are leaving talent on the table.
We cling to naive beliefs that our meritocracies are fair.
The capitalization argument is liberating.
It says you don't look at a struggling group and conclude they're incapable. It says problems that look genetic or innate are often just failures of exploitation.
It says we can make a profound difference in how well people turn out.
If we choose to pay attention.
This 60 minute Microsoft talk will teach you more about success than every self-help book you've ever read combined.
Bookmark this & give it an hour today, no matter what.
A2go retweeted
This paper from Harvard and MIT quietly answers the most important AI question nobody benchmarks properly:
Can LLMs actually discover science, or are they just good at talking about it?
The paper is called “Evaluating Large Language Models in Scientific Discovery”, and instead of asking models trivia questions, it tests something much harder:
Can models form hypotheses, design experiments, interpret results, and update beliefs like real scientists?
Here’s what the authors did differently 👇
• They evaluate LLMs across the full discovery loop hypothesis → experiment → observation → revision
• Tasks span biology, chemistry, and physics, not toy puzzles
• Models must work with incomplete data, noisy results, and false leads
• Success is measured by scientific progress, not fluency or confidence
What they found is sobering.
LLMs are decent at suggesting hypotheses, but brittle at everything that follows.
✓ They overfit to surface patterns
✓ They struggle to abandon bad hypotheses even when evidence contradicts them
✓ They confuse correlation for causation
✓ They hallucinate explanations when experiments fail
✓ They optimize for plausibility, not truth
Most striking result:
`High benchmark scores do not correlate with scientific discovery ability.`
Some top models that dominate standard reasoning tests completely fail when forced to run iterative experiments and update theories.
Why this matters:
Real science is not one-shot reasoning.
It’s feedback, failure, revision, and restraint.
LLMs today:
• Talk like scientists
• Write like scientists
• But don’t think like scientists yet
The paper’s core takeaway:
Scientific intelligence is not language intelligence.
It requires memory, hypothesis tracking, causal reasoning, and the ability to say “I was wrong.”
Until models can reliably do that, claims about “AI scientists” are mostly premature.
This paper doesn’t hype AI. It defines the gap we still need to close.
And that’s exactly why it’s important.
A2go retweeted
🚨 Anthropic just dropped 12 FREE AI courses that make most “AI degrees” look outdated.
They quietly dropped these courses that teach you how to actually build with Claude in 2026:
• Make real API calls and ship tool-using agents
• Build and deploy full RAG pipelines
• Connect models to live tools and data with MCP
• Spin up production-grade MCP servers with logs + scaling
• Run Claude inside Amazon Bedrock and Google Vertex AI
• Automate dev work from the CLI with Claude Code
• Integrate GitHub, workflows, prompt scoring, multi-turn agents
This is the stack serious builders are learning while everyone else is still arguing about prompts.
If you’re not learning agent workflows and Model Context Protocol this year, you’re already behind.
A2go retweeted
Anthropic's own researchers just proved that using AI to learn new skills makes you 17% worse at them.
and the part nobody's reading is more important than the headline.
the paper is called "How AI Impacts Skill Formation." randomized experiment. 52 professional developers. real coding tasks with a Python library none of them had used before. half got an AI assistant. half didn't.
the AI group scored 17% lower on the skills evaluation.
Cohen's d of 0.738, p=0.010.
that's a real effect.
and here's what makes it sting: the AI group wasn't even faster.
no significant speed improvement. they learned less AND didn't save time.
but the viral framing of "AI bad for learning" misses what actually matters in this paper.
the researchers watched screen recordings of every single participant.
they identified 6 distinct patterns of how people use AI when learning something new.
3 of those patterns preserved learning. 3 destroyed it.
the gap between them is enormous. participants who only asked AI conceptual questions scored 86% on the evaluation.
participants who delegated everything to AI scored 24%.
same tool. same task. same time limit.
the difference was cognitive engagement.
the highest-scoring AI users actually outperformed some of the no-AI group. they asked "why does this work" instead of "write this for me."
they generated code then asked follow-up questions to understand it. they used AI as a thinking partner, not a replacement for thinking.
the lowest-scoring group did what most people do under deadline pressure: pasted the prompt, copied the output, moved on. they finished fastest.
they learned almost nothing.
and here's the finding that should concern every engineering manager alive: the biggest score gap was on debugging questions.
the skill you need most when supervising AI-generated code is the exact skill that atrophies fastest when you let AI do the work.
the control group made more errors during the task. they hit bugs.
they struggled with async concepts. they got frustrated. and that struggle is precisely what built their understanding.
errors aren't obstacles to learning.
they ARE learning.
removing them with AI removes the mechanism that creates competence.
participants in the AI group literally said afterward they wished they'd "paid more attention" and felt "lazy."
one wrote "there are still a lot of gaps in my understanding."
they could feel the hollowness of having completed something without understanding it.
that's not a productivity win. that's debt.
this paper isn't an argument against using AI. it's an argument against using AI unconsciously.
Anthropic publishing research showing their own product can inhibit skill formation is the kind of intellectual honesty the industry needs more of.
the practical takeaway is simple: if you're learning something new, use AI to ask questions, not to skip the work.
the struggle is the product.
A2go retweeted
This article breaks down how to become an AI engineer in 2026.
xitter.cf/rohit4verse/status/200…
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The carboxyl terminus (C-terminus or COOH end) of the last amino acid in a protein’s primary structure is the free carboxylic acid group (-COOH) at the extreme C-terminal end of the polypeptide chain, opposite the N-terminus.
In the completed chain, this terminal -COOH remains unmodified unless the protein undergoes post-translational processing (e.g., amidation, prenylation, or cleavage that generates a new C-terminus), so in most nascent polypeptides it exists as a negatively charged carboxylate (-COO⁻) at physiological pH.
It is chemically distinct from internal peptide bonds and serves as a key recognition site for sorting signals (e.g., PDZ-binding motifs, ER retention signals like -KDEL/-HDEL, or peroxisomal targeting -SKL), proteolytic processing, and interactions with chaperones, ubiquitin ligases, or scaffolding proteins.
Core principle: the C-terminal carboxyl group anchors the protein’s identity and fate—treat the endpoint of any linear system (biological, informational, or engineered) as a deliberate design feature, not an afterthought, because it often dictates stability, localization, interaction specificity, and ultimate function far more powerfully than its modest chemical footprint suggests.
A2go retweeted
Gemini 3 + Gamma + n8n is absolutely WILD
This combo is absolutely insane for turning meetings into professional decks automatically.
No manual notes. No "I'll send that over later." No remembering who needs what.
Just AI tools working together like a professional operations team.
Here's how it works:
→ Meeting ends, n8n trigger fires and pulls Fireflies transcript
→ Gemini 3 analyzes everything and creates professional presentation structure
→ Gamma API generates designer-quality deck with interactive link + PDF
→ Slack sends you preview with Approve/Reject buttons
→ Hit Approve, system emails deck to ALL participants with action items
→ They get PDF attachment + Gamma link before you even close Zoom
Perfect for founders and sales teams who want to look impossibly organized.
The power is in the combo:
n8n = zero-click automation that runs itself
Gemini = extracts what matters, structures it professionally
Gamma = designer-quality decks that look like you hired a team
While others are scrambling to remember action items, your deck is already in their inbox.
Close deals faster.
Look impossibly organized.
Never miss follow-up again.
Like, RT + reply with "GAMMA" and I'll DM the complete system
(Must be following so I can DM)
Skip this and keep manually building slide decks at 11pm.
A2go retweeted
Final testing is complete 😍
Paste your YouTube channel → Get instant content for every platform that ranks on ChatGPT, Perplexity, and Google.
While everyone else is manually writing 47 different posts, I'm generating everything in 3 minutes.
Blog posts
Social media
Email sequences
Video descriptions
All optimized for AI search
People trust AI results 22% more than Google.
If you're not showing up when they ask ChatGPT about your expertise, you're missing out.
Comment "TOOL" if you'd like to try it 👇
(This is about to change everything)
A2go retweeted
Introducing Meridian.
50% of searches are now powered by AI (Think ChatGPT and Gemini)
Meridian is the FIRST Visibility Engine to get you ranked #1 by AI.
Bold claim? Here's how 👇
A2go retweeted
Lindy + Perplexity + Nano Banana + Sora 2 is absolutely INSANE
This AI system replaces your entire marketing team and ships campaigns in hours instead of weeks.
No agencies. No freelancers. No $150K/year teams.
Just complete marketing campaigns from research to ready-to-launch creatives.
Here's how it works:
→ Research Agent uses Perplexity to analyze competitors and build strategy
→ Analysis Agent scans thousands of ads and trends to find high-ROI angles
→ Creative Agent uses Nano Banana + Sora 2/Veo 3 to generate hundreds of ad variants
→ System writes copy, creates visuals, and produces videos automatically
→ Everything gets organized in Airtable ready for testing
Campaigns ready in hours, not weeks.
Hundreds of variants to test immediately.
Runs 24/7 without touching it.
While competitors wait 3 weeks for agencies to deliver 3 mediocre ads, you'll have tested 47 variations and scaled the winners.
Perfect for founders, agencies, and marketing teams who need to move faster than humanly possible.
These are plug-and-play Lindy templates. No building. Just add your product and go.
Want all 3 AI agents?
Comment "CMO" + RT + Like
I'll DM you the complete Lindy AI CMO templates
(Must be following so I can DM)
Skip this and keep paying agencies $150K/year for work AI does in 2 hours.
A2go retweeted
This AI system researches prospects like a psychopath and writes to them like a best friend.
This combo is absolutely insane for outreach that actually books calls.
No SDRs. No manual research. No sales reps spending 3 hours per prospect.
Just AI tools working together like a professional sales team.
Here's how it works:
→ AI scrapes LinkedIn profiles + recent posts automatically
→ Perplexity researches their company across the entire internet
→ Claude analyzes pain points and writes hyper-personalized 3-email sequences
→ System references specific achievements like you personally studied them
→ Emails send in proper time zones with perfect deliverability rotation
→ Sequences stop automatically when prospects reply
Perfect for anyone tired of 2% reply rates and "hope this finds you well" templates.
The power is in the combo:
LinkedIn scraping = behavioral data most sales reps ignore
Perplexity = company intelligence that makes prospects think you know them
Claude = emails that feel like a best friend wrote them, not a robot
While others are manually researching 10 prospects per day, you're personalizing 100 per hour.
Test different angles.
Book more calls.
Scale immediately.
Like, RT + reply with "SYSTEM" and I'll DM the complete workflow
(Must be following so I can DM)
Skip this and keep manually copying LinkedIn bios into ChatGPT.
A2go retweeted
I compiled 50+ n8n automation templates you can copy & paste into your business or sell to other companies.
Just straight plug-and-play systems for:
– Lead gen
-Content creation
– Email outreach
– CRM updates
– AI workflows
Follow + Retweet + Reply “n8n” and I’ll send it.
A2go retweeted
"Introduction to Machine Learning Systems"
- FREE from MIT Press
- Authored by Harvard Professor
- 2048 Pages
To Get It Simply:
1. Retweet & Reply "ML"
2. Follow so that I will DM you.
A2go retweeted
I noticed something weird about the ads that get 10M+ views.
While most brands randomly animate everything hoping something works, viral ads follow a precise psychology pattern.
Here's what I discovered:
→ They never animate text (protects cognitive load - Daniel Kahneman's research)
→ They focus motion on ONE primary element (leverages attention theory from William James)
→ They use specific timing that triggers dopamine release (based on BF Skinner's variable reward schedules)
Most animated ads fail because they create visual chaos that overloads working memory.
The brands crushing it with animated ads use Notch's Animated Ads feature, where they're giving away a master prompt system built on decades of behavioral psychology research from Stanford and MIT.
No more guessing what motion will work.
No more paying $5K for animations that flop.
No more static ads getting buried in feeds.
I'm giving away their complete Animated Ads Master Prompt guide that reveals the exact animation psychology.
This is the same system top agencies use to create scroll-stopping animated creatives that convert.
Want the viral animation playbook?
Comment "NOTCH" + RT + Like
I'll DM you the master prompt guide
(Must be following so I can DM)
Skip this and keep wondering why your animated ads don't work.
A2go retweeted
“I will miss the creativity of teaching.”
On #WorldTeachersDay, check out this #ScienceWorkingLife essay from a retired professor emeritus on how she challenged students to think beyond facts—and how she learned to teach like a scientist. scim.ag/4nSP7OQ







































