Presiden Prabowo Subianto mengumumkan telah meneken Peraturan Presiden (Perpres) Nomor 27 Tahun 2026 tentang Perlindungan Pekerja Transportasi Online. Prabowo memberi sinyal, perpres ini mengatur mulai dari jaminan sosial yang wajib diberikan ke mitra ojol hingga pemangkasan maksimal nilai potongan aplikasi ojol menjadi 8%.
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#Ojol #KomisiOjol #Katadatacoid #KalauBicaraPakaiData
Lembaga pemeringkat global MSCI Inc mendepak 18 saham dari indeks mereka. Sebanyak enam saham Indonesia dari daftar MSCI Global Indexes. Tiga di antaranya merupakan saham milik konglomerat Prajogo Pangestu yaitu PT Barito Renewables Energy Tbk (BREN), PT Chandra Asri Pacific Tbk (TPIA) dan PT Petrindo Jaya Kreasi Tbk (CUAN).
Selengkapnya: https://t.co/xsA81hRlfM
#MSCI #PrajogoPangestu #Katadatacoid #KalauBicaraPakaiData
People often ask whether China or the West is “winning.”
That’s the wrong question.
The more useful question is: how do different systems work, and what trade-offs do they make?
When you look at history, geopolitics, and economics together, you start to see repeating patterns — and those patterns matter far more than day-to-day noise.
How to succeed in strategy execution?
Sharing 12 tips to drive real results:
1. Get everyone on board
Build real buy-in.
Make sure every team member:
Understands and supports the plan.
2. Stay agile
Be ready to pivot.
Adapt quickly when things change.
3. Set clear goals
No confusion.
Everyone knows what success looks like.
4. Track performance
Measure what matters.
Use data to keep progress visible.
5. Design roles around strategy
Make sure people know their part-and own it.
6. Build accountability
Assign ownership.
No one drops the ball.
7. Cascade objectives
Break big goals into smaller, team-level targets.
Keep everyone aligned from top to bottom.
8. Learn and improve
Review, reflect, and adjust.
Every cycle is a chance to get better.
9. Communicate clearly
Share updates, wins, and roadblocks.
No one’s left guessing.
10. Decentralize decisions
Empower teams to act fast.
Don’t bottleneck at the top.
11. Focus resources
Put your best people and tools where they matter most.
12. Manage change
Support your team through transitions.
Stay steady even when things shift.
The key is to:
A) Keep learning, keep adapting.
B) Connect strategy to action at every step.
C) Make ownership and communication non-negotiable.
If you nail these, your strategy won’t just sit on paper:
It will drive real results.
P.S. Which step does your team need to work on most?
♻️ Share so others can master execution too.
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Most engineers have seen this formula.
P(A|B) = P(B|A) × P(A) / P(B)
Almost none can explain what it actually does.
Here's Bayes' Theorem in plain English, and where it's hiding inside systems you use every day.
The core idea in one sentence:
Bayes' Theorem updates your belief about something after seeing new evidence.
That's it. Four terms:
Prior → what you believed before the evidence
Likelihood → how probable the evidence is, given your hypothesis
Evidence → how common the evidence is overall
Posterior → your updated belief after seeing the evidence
A concrete example:
Say 40% of all emails are spam (your prior).
You see a new email containing the word "lottery."
10% of spam emails contain "lottery." Only 1% of legitimate emails do.
Plug into Bayes:
P(spam | "lottery") = (0.10 × 0.40) / P("lottery") ≈ 87%
The word "lottery" updated your belief from 40% → 87%.
That's Bayes in action. Prior belief + new evidence = updated belief.
Where it lives in AI:
1/ Spam filters
The Naive Bayes classifier, the algorithm behind most spam filters - applies this exact calculation word by word across an entire email. Each word shifts the probability up or down. It's called "naive" because it assumes each word is independent of the others, which isn't realistic, but works remarkably well in practice.
2/ Medical diagnosis AI
A patient has symptom X. What's the probability of disease Y? Bayes updates the base rate (how common the disease is) with the likelihood of seeing that symptom in patients who have it. Same formula, different domain.
3/ Your LLM's uncertainty
Modern language models don't just predict the next token, they assign a probability to every possible token. The sampling process (temperature, top-p) is directly working with those probability distributions. Bayesian reasoning is embedded in every response your model generates.
The insight most engineers miss:
Bayes doesn't give you certainty. It gives you a rational way to update uncertainty.
That's exactly why it's foundational to AI - real-world systems are never certain. They're always working with incomplete, noisy, probabilistic information.
Every model that learns from data is, at its core, doing some version of this:
Start with a belief. See evidence. Update the belief.
That's Bayes. That's machine learning.
Stop telling AI: “make my resume”
Stop telling AI: “improve this CV”
Stop telling AI: “write summary”
You’re using a powerful tool like a basic template builder.
AI works best when you give:
• role
• job target
• constraints
• achievements context
• output format
Here are 10 powerful resume prompts you can copy-paste 🧵👇