A Drop of Optimism in the Sea of Pessimism

I have been the host of Hebrew-language’s oldest podcast devoted to the Middle East. My goal has always been to reach a broad audience of experts, policymakers, and curious listeners — Israelis, Arabs, and anyone who cares about this region. But in practice, I constantly struggle to find Arab and Palestinian guests who speak Hebrew and are willing to appear on an Israeli podcast.

The interview you see below is one of the most important conversations I have ever published. Despite my deep skepticism and pessimism — and despite the very limited hope I usually feel — my guest, Samer Sinijlawy, made me experience something unusual: a moment of optimism.

Paraphrasing Samer:

“We Need Imagination, Not Just Information”

(Translated and edited very ligtly for clarity, staying as close as possible to the original Hebrew transcript)

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Part I: On the Imperative of Peace

Boris Gorelik: Alright, let’s begin. This Week in the Middle East — hello everyone, I’m Boris. We continue our series of conversations devoted to peace. Personally, I’m exhausted from war. People tell me it’s not yet the time for peace, but still — we talk, and maybe the universe will somehow align with us.

Today we say hello — for the second time — to Samer Sinijlawy, a political activist in the Palestinian Authority. Hello, Samer.

Samer Sinijlawy: Hello and welcome, Boris.

Boris: First of all, thank you. Our listeners don’t know, but this is our fourth attempt to record this episode. Finally it worked. For those who didn’t hear the previous conversation — I’ll summarize, and Samer, correct me if I’m wrong. In general, your position is as follows:

You’re a Palestinian activist. You are not part of Israeli Arab politics, so when people ask me why MKs behave this or that way, I tell them you’re not responsible for that.

And your argument in our previous conversation, about six months ago, was that peace is far more urgent for Palestinians than for Israelis. Israel has had a state for 70-plus years; Palestinians, without a state, suffer more and more as time passes, and the dream becomes more distant. Another claim I understood from you was that in order for this to happen, the top Palestinian interest is that Israeli Jews feel secure and unafraid.

Did I summarize correctly?

Samer: First, I am part of Palestinian politics. I was born in the Old City of Jerusalem, I live in Jerusalem, and I am a Fatah member. I joined at fifteen and spent five years in prison as a Fatah activist. So I belong to Palestinian politics.

And I think, Boris, that we Palestinians are always one Israeli election away from peace. Always. The only way we can move forward toward compromise and a political solution is when 51 percent of Israeli voters, in some election, vote for a party that supports this direction. Nothing else will help us.

There will be an Israeli election next year — maybe early, maybe on time. If Israelis vote for a political path that supports a political solution, we’ll have one. If not, we won’t.

So we Palestinians must always ask how we rebuild trust with the Israeli public, how we convince them.

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Part II: War, Leadership, and Missed Chances

Boris: I’ll challenge that. In the 1992 election, when Rabin won, there was an Israeli majority for Oslo. But only months later that majority was gone. Israelis felt the other side didn’t actually support the process. There were terror attacks; there was Jewish terrorism too — but the “tango,” as people call it, failed.

So is it really true that the future of peace depends only on Israelis? Doesn’t something also need to change on the Palestinian side?

Samer: You’re right, and I said this in our previous talk: it begins with us. We Palestinians are also just one leader away from a breakthrough. We lack our own Ben-Gurion — someone who knows how to build the institutions of a Palestinian state, who understands when force is necessary and when diplomacy is necessary.

Boris: Right — and earlier you mentioned Hamas’s attacks in the 1990s. Earlier you spoke about Hamas attacks in the 90s. But it’s not only a “Palestinian Ben-Gurion” we need. We also needed someone like Menachem Begin. Remember when the Altalena arrived? Begin’s people were shelled; they suffered. But Begin said: “We bow our heads. There will be no civil war. Now we have a state.” There is a monument in Tel Aviv with a song that says, “We sought brothers and received a shell,” and still — he prevented civil war.

Samer: Israel had both Ben-Gurion and Begin. And you had another stroke of luck: after the Yom Kippur War, during the years leading up to the peace with Egypt, you had Begin — a right-wing leader, devoted to the Land of Israel, the man who built settlements in Sinai — who then dismantled those same settlements for peace. He and Rabin both knew how to make the hard decisions.

Boris: Thank you.

Samer: Meanwhile, on the Palestinian side, over the past century, we had only three leaders of consequence: Haj Amin al-Husseini, Yasser Arafat, and Mahmoud Abbas. None of them truly understood the conflict from the perspective of the other side.

Let’s go back to the 1990s. During Oslo, every week the IDF would withdraw from a different Palestinian city; there would be a ceremony; Palestinian police would take over; Arafat would arrive and give a speech. And at the same time, Hamas carried out attacks — in the morning in Tel Aviv, in the evening in Jerusalem.

Boris: It wasn’t only Hamas. Arafat himself spoke about a “million martyrs marching to Jerusalem.” Tanzim was also deeply involved in violence. It wasn’t only Hamas — that’s my point.

Samer: Yes. Our leadership always used a strategy of pressure — violence, confrontation. They believed pressure would lead to concessions. But pressure doesn’t work with Israelis. The only path forward is persuasion: building trust, speaking to Israelis, reaching their hearts.

We made many mistakes. One of the biggest was missing the offer from Ehud Olmert in 2008 — almost a complete Palestinian state.

Boris: Why did Abbas reject it? People give different explanations: fear of assassination, internal politics, or that Palestinians never truly wanted a state alongside Israel but wanted Israel gone. Others say Olmert was on his way out anyway, so it wasn’t credible.

What is your view?

Samer: Two main reasons. First, Olmert was facing legal troubles, and the Americans — especially Condoleezza Rice — thought Tzipi Livni would win the next election. They told Abbas: “Don’t rush, wait for Livni.”

Second, Abbas prefers a legacy of not giving up anything, rather than a legacy of achieving 90 percent of national goals. The refugee issue was especially hard for him.

He wanted to leave the world with a legacy of “I didn’t sign,” not “I solved most of the problem.”

Boris: And he stayed in power for twenty years.

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Part III: Hamas, Gaza, and the Consequences of Leadership

Samer: Yes. He cost us a whole generation. He made disastrous mistakes with Hamas. He allowed Haniyeh to form security forces in Gaza, enabling the takeover. Then he ordered 50,000 PA employees to stay home — which gave Hamas instant control of the institutions. A functioning system would have forced him to resign.

There was always confusion in our leadership. They didn’t know what we needed to do with the Israeli side. And the strategy was always pressure. Using violence was seen as a kind of pressure. They thought pressure would lead to achievement. I can tell you, Boris, I know Israelis well: pressure does not work on Israelis. The only way is convincing them. We need to speak to them, reach their hearts and minds.

We made many mistakes. We missed many opportunities. The biggest one was the peace proposal that Israeli Prime Minister Ehud Olmert presented to Mahmoud Abbas in 2008. It offered almost everything, including a Palestinian capital in East Jerusalem. Abbas did not clearly say “yes,” and the chance was lost.

Boris: Let me ask you: why did Abbas refuse? There are several explanations — fear of assassination, pressure from others, or that Palestinians care more about eliminating Israel than building their own state. What do you think truly happened?

Samer: Two things. First, Olmert had legal issues and elections were coming. The Americans — especially Condoleezza Rice — believed Tzipi Livni would win, and they told Abbas: “Don’t rush. Wait. Livni will sign the same deal.” Second, it’s something in Abbas’s personality. He prefers that his legacy be “I did not give anything up,” rather than “I achieved 90 percent of the national goals.” The most sensitive issue for him was the Right of Return. He did not want to put his signature on any compromise. So he preferred to leave things without a solution.

Boris: He became Arafat’s choice for compromise near the end of Arafat’s life. And after that, Abbas got stuck there.

Samer: Stuck for you, but even more stuck for us. He stole twenty years from our lives. A whole generation. Look at what happened with Hamas. In 2006–2007, he made terrible decisions. He agreed that Ismail Haniyeh, as prime minister, would create an internal security force in Gaza. That was a strategic disaster. When Hamas carried out the coup, Abbas made another terrible mistake: he told the 50,000 Palestinian Authority civil servants in Gaza to stay home and not go to work. That emptied the government institutions and allowed Hamas to immediately take control. They became the rulers overnight.

Someone who does that should resign. But he didn’t. And he is still there at age 90.

Boris: Politics being politics — even at ninety, no one says “I failed,” and walks away.

Samer: True. But for us it means a generation lost.

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Part IV: Is There a Palestinian Peace Camp?

Boris: My listeners asked several questions. Many say: how can we take Palestinian peace activism seriously when many human-rights groups have ties to terror groups? And also: you are the only Palestinian peace activist Israelis ever hear about. Are there really others?

Samer: There is a movement. It has two goals: internal Palestinian reform and democracy, and also Palestinian-Israeli dialogue. But most activists have no platform. I live in Jerusalem, so the PA cannot silence me. I also speak Hebrew, so Israelis can understand me directly. That makes a big difference.

But I represent my generation — a generation born under Israeli rule, who look at Israel not as a monster but as a successful model we want to learn from. If you ask me what I want a future Palestinian political system to look like, I’ll say: like the Israeli system — three branches of government, each with power and limits, checks and balances, a liberal democracy, and a market economy.

The Israeli public was in the streets in early 2023 defending liberal democracy. I want that same thing for Palestinians.

This is not naïve peace activism. It is a national interest. And many Palestinians support this.

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Part V: The Younger Generation

Boris: I agree that young people are different. In Israel too, the future belongs to people under thirty. But we — people in our fifties — are harvesting the fruit of mistakes made long ago.

Samer: Let me tell you something. I’m a father of nine children — ages one and a half to twenty-seven — and I see their world. Young Palestinians are not ideological the way we were. They live through social media. They have personal goals. When I was fifteen, I was willing to take a bullet in the head just to hang an Arafat poster. But today, many young Palestinians don’t even know the names of their leaders. They don’t care. Those leaders don’t shape their lives.

Boris: Is that good or bad?

Samer: It helps peace. If we get leadership that gives hope — if every morning Palestinians wake up feeling that today is slightly better than yesterday — everything will change.

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Part VI: Disarmament, De-radicalization and Changing Narratives

We talk about demilitarizing Gaza. But collecting weapons means nothing if we don’t treat the desire to use them. That means de-radicalization.

Boris: Exactly.

Samer: It starts with narrative. With language. With how each side thinks about the other.

Both societies must make hard decisions now. For Israelis, the choice is separation or annexation. For Palestinians, the choice is two states or maximal Right of Return. You cannot have both.

We must also acknowledge the Jewish historical connection to this land — it is even in the Qur’an. And Israelis must acknowledge the Palestinian connection. The question is not “who belongs?” but “how do we belong here together?”

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Part VII: Two States and Jewish Communities in Palestine

Boris: I recently interviewed Yinon Dan Khatib, who says the land belongs to both sides. His idea is: Jews can stay, Palestinians can stay, and two states might not even be necessary if rights are respected. Could Palestinians accept one state with equal individual rights?

Samer: Do Israelis really want another five and a half million Palestinians voting for the Knesset? If you give us full rights — you lose the Jewish democratic state. If you don’t give us rights — you create apartheid. Neither option works.

Look — I live in East Jerusalem. I pay the highest municipal taxes in the country but live in a garbage dump. Why shouldn’t I vote for my municipality? That’s our mistake. We should vote.

If we are not inside the system, why should anyone care about our needs?

And look at Israeli politics: even Israeli Jews who are not part of the coalition don’t get what they deserve. Now add Palestinians on top — impossible.

Boris: Yes.

Samer: You Israelis invest billions in the West Bank — new roads, new infrastructure. Many Israelis have never visited the West Bank. They don’t know what happens there.

A two-state agreement is the only workable path. One state means either losing your identity or denying ours.

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Part VIII: Reconciliation — The Hardest Part

Boris Gorelik: You describe reconciliation as the hardest step. Why?

Samer Sinijlawy: Because reconciliation requires something very few leaders or societies are willing to say: we were wrong. Both sides made terrible mistakes. In this conflict, no one was Mother Teresa. There is deep pain on both sides, and the emotional destruction is worse than the physical one.

I myself visited the shiva of the Bibas family. I asked, on behalf of my people, for forgiveness for the killing of their children. It was one of the hardest things I’ve ever done. But real reconciliation requires this kind of courage.

We Palestinians and Israelis are now neighbors in trauma. We must clear this trauma from our hearts and use the emotional energy not for revenge, but for building hope.

Gorelik: In October 2023, many Israelis felt blindsided. But you say the information was already there.

Sinijlawy: Exactly. The problem wasn’t lack of information — Hamas was clear about its plans. The problem was lack of imagination. We could not imagine they would actually do it.

Now, in October 2025, we again have strong information — this time pointing to regional normalization. Saudi Arabia, Indonesia, the UAE — all indicate they want to integrate Israel into the region. Even during the war, trade with the Emirates increased dramatically. Your natural strategic neighborhood is the Middle East, not Europe.

But again, the question is imagination. Can Israelis imagine being part of the Middle East? Can Palestinians imagine a shared future with Israelis?

Gorelik: People here struggle to imagine things like Israeli teams playing football in Cairo.

Sinijlawy: I understand. But imagination is the beginning of political reality.

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Part IX: The Last Word

Gorelik: When I speak with you, I become more optimistic. On most days, I’m quite pessimistic and skeptical. But you make me feel that maybe there is something real to hope for.

Sinijlawy: Peace is not a favor to the other side. It is a responsibility to your own people. And it starts with imagination.

Gorelik: Thank you again, Samer. And to our listeners — thank you for your questions and for staying with us. May we all find a way to speak about peace, even when peace feels far away.

Sinijlawy: Thank you, Boris. Always honored to join you.

One short prompt message that made my vibe coding life easier

Before every git commit, I paste this into Cursor / Claude / GitHub Copilot—whichever is working that day:

“examine the changes since the last commit – can you simplify anything?”

That’s it.

One line. No magic. Just a tiny push to refactor, delete, and clarify.

Since adopting it, I commit less junk.

I rewrite more than I add.

And future-me swears a little less.

#VibeCoding #DevTips #Git #AIpairProgramming #CleanCode

How Sausages Are Made (and How a Vibe Research Was Born)

It started with an idea.

Months ago, long before there was a paper or even a draft, I found myself circling around a question: why do some reasoning models seem to “fail” in ways that feel strangely human? I kept turning it over in my head on walks, in the car, in those in-between moments when you’re not really working but your mind refuses to let go.

Then one night I stumbled upon the Apple paper, The Illusion of Thinking, and one week later came The Illusion of the Illusion of Thinking. It was like someone had dropped a match into a pile of dry twigs. It didn’t give me the idea, it sparked the one I’d been nursing quietly for months. Suddenly, I couldn’t stop thinking.

I opened a new project, started pulling papers, sketching diagrams, whispering to myself while driving. I even used ChatGPT in voice mode during commutes, pouring my thoughts out loud so I would not lose them. I spinned up every deep search product I had access to, I wrote, rewrote, used Claude and ChatGPT to expand, clarify, and critique the ideas. It was like vibe coding, but in academic writing. For me, this collaboration with an AI was new, so I started a diary.

For me, this collaboration with an AI was new, so I started a diary and invited friends to read in in real time.

My diary shows 17 hours logged, spread over a few weeks. But those 17 hours? They are not the full picture. They are just the visible part of a much longer story, the part where I sat in front of a keyboard. The invisible part was the months of thinking, discarding, distilling, connecting dots that only started to make sense later.

Then came the reviewing process.

If you’ve ever sent a paper out for peer review, you know that’s when the sausage grinder really starts. Reviewer 1 was constructive, nudging me to explain how my work went beyond prior critiques. Reviewer 2, let’s just say their feedback was less than kind. They questioned the contribution, the clarity, even the usefulness of the whole thing. Two striking remarks stayed with me: Reviewer 2 wrote

“There is no contribution of the paper, the discussion is low-level quality and the conclusions do not seem to have practical or theoretical usefulness.” Another line hit just as hard: “The paper proposes a discussion on an extensive subject, but fails only analyzing shortly specific topics of that subject.”

For an hour or two, I was angry. Then I got to work. I rewrote the introduction, reframed the entire contribution, tightened every paragraph. I added design implications I hadn’t planned to include. I went back through decades of references to make sure every citation pulled its weight. Working on the original draft and the submission took 17 hours (MDPI.com’s author process is smooth as silk). Answering the reviewers took 14 more hours.

And then, something surprising happened. I realized that Reviewer 2’s harshness had actually made the paper stronger. Their criticism forced me to look hard at the messy, half-ground meat of my argument and turn it into something people could actually digest. Reviewer 2, by the way, was only slightly impressed and still recommended not to publish the paper, even after the improvements.

Now there’s a screenshot sitting on my desktop, full of tracked changes. It’s the clearest picture of what vibe research really is: an idea sparked, shaped by months of invisible labor, tested in the fire of critique, and finally molded into something you can hold in your hands.

So yes, my diary says 17 hours. But the real work started months before that, and it never really stops.

That’s how sausages are made.

What’s your own behind-the-scenes story? 👇 I’d love to hear it.

Celebration time

🎉 Not only has was my paper accepted to publication in Applied Sciences, its preprint has already been downloaded 100 times on preprints dot org! 🚀

BTW: if you are into #AI and think how it affects the humanity, you should totally read it. If you host podcasts on AI, you should totally invite me to talk . I LOVE talking about myself :-)

#Research #Science #Publication

A Bird in the Hand… and Two? Even Better!

Tonight I received two separate emails from two different journals – both saying the same thing:

Two of my papers have been accepted for publication.

One of them started its journey back in 2016 and finally crossed the finish line.

The other was written from scratch and accepted within a month.

No clear moral here, except: keep going, and keep trying.

(Also… check the timestamp on those emails 🙂)

Feel free to reach out if you’d like to hear more about the journey or the lessons learned!

Not a meeting – a ceremony 🥴

The worst communication antipattern I’ve ever seen?

A daily (or weekly) “sync” where 7–10 people go around reporting to the manager. One by one.

Everyone else? Daydreaming.

Zoom crowd? Writing emails. Scrolling LinkedIn.

Occasionally, someone tries to justify their presence and sparks a side discussion.

Guess what – it derails the meeting and helps no one.

In 99.9% of cases, these meetings end late a and have zero results.

How do you spot this waste of time?

Easy:

– Look at the faces. If most are bored? It’s a ceremony.

– If the only one getting value is the manager? It’s a ceremony.

– More than six people? No way it’ll be efficient. It’s a ceremony.

Why this happens? Because it’s easier for the manager to have everyone in the same room or call, it removes the responsibility to held and be on time in seven different 1:1s, and sometimes it makes them feel important. The result? Frustration, loss of initiative (Google

Why Group Brainstorming Is a Waste of Time

), no job is done.

Want your team aligned? Start with trust, not ceremony. Call me if you want a fresh look at your communication routine

How do your team syncs actually work?

#communication #workculture #meetings #leadership

I don’t believe I need to say this in 2025 🙄

But here we go: group brainstorming doesn’t work.

You gather 6–12 smart people, throw them in a room (or Zoom), say “let’s be creative” — and expect magic?

Here’s what you actually get:

– Loud voices dominate. – Quiet ones shut down. – No critical thinking — why bother if others are already talking? – Everyone feels the need to say something — even if it’s useless. – The real thinkers? They bring their best ideas after the session ends.

You know what does work? Give people time to think alone. Then review ideas asynchronously or in small, structured conversations.

Still think brainstorming is productive? Google “Why Group Brainstorming Is a Waste of Time site:hbr.org” — the article is from 2015. It was true then. It’s still true now.

#leadership #communication #creativity #workculture #brainstorming

Hardened as Fuck

This war will be over.
Maybe not in a week.
Maybe not in a month.
Maybe not in a year.
But it will be over.
And when it’s over, hundreds of thousands of Israelis will return.
Return to work.
Return to building.
Return to a country in a crisis.
And you know what crisis does?
Crisis amplifies creativity.
Crisis demands innovation.
Crisis breeds resilience.
They’ll come back hardened as fuck.
People who learned to operate under rockets, under pressure, under loss.
People who mastered extreme communication.
People who managed chaos, served both hierarchy and autonomy.
People who formed new networks, new skills, new muscles.

High-schoolers who learned for exams in bomb shelters,
college students who wrote assignments between battles,
developers who covered for half of the team who couldn’t work,
managers who managed companies, while taking care of broken families.

They will all come back.
They’ll come back as entrepreneurs.
As builders.
As devs.
As leaders.
They will come back hardened as fuck.
Good luck competing with them.
Good luck competing with us.
You’ll need all of it.
#war #israel #leadership

#war #israel #leadership

Is my hypothesis crazy?

Prove me wrong! Change my mind!

🧠 I just claimed that AI “thinking” isn’t an illusion, it’s a manifestation of 60 years of human mind research I fundamentally disagree with Apple’s “The Illusion of Thinking” study. When AI models reduce effort on impossible tasks, they’re mirroring human cognitive disengagement patterns documented since the 1960s. Key insight: AI token usage follows the same “inverted-U” curve as human pupil dilation during cognitive overload. This suggests bounded rationality, not system failure.

🤔 Am I anthropomorphizing AI or onto something bigger? In my paper, I propose experiments that could completely refute these claims. What do you think? Especially if you think I’m wrong!

Here’s the preprint. You can also download it. Original source: https://doi.org/10.20944/preprints202506.1675.v1

My new thesis

Ever heard of the “Illusion of Thinking” debate? (Parshin Shojaee’s paper, Alex Lawsen commentary)

I have an interesting take on it, and I’m very exited to share it with the world.

The white paper is 99% ready. It’ll be done tomorrow.

If you’d like me to ping you when it’s out, drop a comment below 👇

AWS – WTF?

“I’ll just spin up a small demo server for the customer. Half an hour and I’m done,” I tell myself.

The customer uses Amazon Web Services (AWS), so naturally I go there first. Seven hours later, after battling network configs, instance limits, IPv6 surprises, and exchanging hundreds of messages with ChatGPT and Claude…

I would have pulled hair off my head, had I had anything to pull.

So I give up.

I switch to DigitalOcean. Spin up a droplet. Check three check boxes. Install the stack.

Half an hour later – demo is ready.

Sometimes the cloud just means fog.

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Procrastinator? You need multitasking!

Procrastinator? You need multitasking! When I started my professional career, multitasking was the gold standard everyone was expected to follow. This was early 2000s. Then we discovered context-switching, and multitasking became the enemy of productivity. So why am I now suggesting multitasking? Because some of us just can’t stick to one task. If you’re prone to quickly drifting off to Facebook, Instagram, or LinkedIn (hi there!), here’s the trick: keep several projects running at once. Building a business plan for a startup? Developing an AI model? Preparing lessons for your students? Do them all simultaneously! When boredom hits, switch tasks, keep momentum going, and everyone stays happy—including you. Are you team multitasking or laser-focused? hashtag#Multitasking hashtag#ProductivityHacks hashtag#CareerTips hashtag#Procrastination hashtag#Focus

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On strategy

You can read many books on strategy, but then you read one sentence, and it’s so clear you feel like the fog has lifted.

If you’re not subscribed to the excellent newsletter by Danny Lieberman, you should be. Danny has decades of experience in his field, and each issue of his newsletter is sharp, insightful, and practical.

I just became a CEO

🎙️ I’m thrilled to announce that I’ve just promoted myself to CEO of This Week in the Middle East Podcast, Israel’s oldest active podcast on the region!

What does this mean in practice? Absolutely nothing.

But it’s a great excuse to remind you that I’m still here, still podcasting, and still bringing you sharp conversations about the Middle East — minus the noise.

👇 Drop a comment, say hi, or recommend a guest/topic for the next episode.

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Being a borderline libertarian, I dislike government involvement. But this story (if it is true) is an excellent example of why government involvement is sometimes required.

Go fuck yourself

“Go fuck yourself.” That was the closing comment in a Slack thread where I did something outrageous: I disagreed.

It started when someone posted a message I didn’t agree with. I replied politely, challenging the assumption. Instead of defending their position or even engaging in a back-and-forth, they apologized for “causing division.” I answered that division isn’t a bug, it’s a feature. I might said something like “Cows in a cowshed are united”.

That’s when someone else chimed in with: “go fuck yourself.” 

Now, that response made me think about how uncomfortable we’ve become with productive friction. Disagreement is not disloyalty. If anything, it’s the root of wisdom.

In Talmudic study, there’s a concept called אִפְכָא מִסְתַבְרָא “the opposite makes more sense.” It’s not just allowed to argue. You’re obligated to actively look for counter-arguments. Not because you want to win, but because truth emerges from collision.

And it’s not just a religious or cultural idea, there’s math and Bayesian statistics to back it up.

Take the paper titled “Too good to be true: when overwhelming evidence fails to convince.” It demonstrates how too much agreement undermines credibility. Paradoxically, a contradiction makes things more believable, more credible.

Bottom line: if no one in your team ever says “wait, I’m not sure,” you don’t have a culture, you have groupthink. If this happens, bring an outsider and give them a mandate to argue. You can hire me for that. Seriously!

👇 Let’s talk. You may even keep the profanity 😅

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Two weeks ago, I published a research paper Ethnic Divisions Within Unity: Insights into Intra-Group Segregation from Israel’s Ultra-Orthodox Society. It is my first paper in so many aspects

• First paper in a long while
• first paper I authored by myself
• first paper in social studies (I’m a former pharmacist, remember)

I have a confession to make. When I concluded the research phase, I stumbled upon an open question that I couldn’t answer. I really wanted to publish what I already had, so instead of trying to deemphasize the problem or write far-fetched theories, I decided to put the problem under a spotlight and declare it loud and clear, emphasizing that this publication is a chance to deal with publication bias — a phenomenon that leads scientific journals to favor positive or significant findings while neglecting studies that yield negative or inconclusive results.

This is not the end

Once the paper got published, I recalled that a sizable audience of my podcast (This Week in the Middle East) belongs to the Haredi society - the society that was the subject of my study. I recorded a video presentation (here: it’s in Hebrew), asking my podcast audience to share their inner knowledge and propose explanations. What do you know? I got several very interesting ones - that will serve as the basis for further research.

THE END

I recently came across a financial update with a plot that looks like the one below: a bar plot of revenue with a line plot of growth rate. The growth rate is on a secondary y-axis, and the x-axis is shared between the two plots. This type of plots are very common in financial reports, and they are often used to show the relationship between two variables that have different scales. However, they are also often criticized for being misleading, as the two variables are not directly comparable. In this post, I will show a better way to visualize this type of data. But first, what’s wrong with the plot below?

1. The secondary y-axis is not aligned with the primary y-axis. This makes it difficult to compare the two variables.
2. Two points on a graph that lie one above the other do not necessarily have a relationship of proportionality or a fixed ratio. This is because the two y-axes have different scales.
3. Navigation is difficult. The reader has to constantly switch between the two y-axes to understand the relationship between the two variables.

I wrote about double scales a lot in this blog. See the ‘double scale’ tag to read more

The solution: split the plot into two subplots

Let’s split the graph into two subplots, one for each variable, and share the x-axis. This way, the reader can easily compare the two variables, and the relationship between them is clear.

Attempt 1: a bar plot and a line plot

Nice, the two plots are aligned, it’s easy to know what’s happening in the revenue and the growth rate. Note, how I emphasized the zero line in the “growth rate” subplot.

However, showing the evolution of the revenue using bars is not ideal: the bars are not continuous, and the reader may think that the revenue is constant between two months. Let’s try another approach.

Attempt 2: two line plots

The advantage of the line plot is that it allows zooming in on the data, which is not possible (FORBIDDEN) with a bar plot. However, the colored area that the bars provided gave us the information about the total revenue over the time (recall that total revenue is the area under the curve). Now, this information is lost.

Let’s try to add this information back to the plot.

Attempt 3: line plot with shadowed area

This is the best of both worlds: we have the continuous line plot that gives a proper visualization, and we have the shadowed area that gives us the information about the total revenue over time. The two subplots are aligned, and the relationship between the two variables is clear. The reader can easily see that the revenue is increasing while the growth rate is fluctuating.

Conclusion

The fact you can do something doesn’t mean you should. Dual-axis plots are often misleading and difficult to read. Splitting the plot into two subplots is a much better way to visualize this type of data. It allows the reader to easily compare the two variables and understand the relationship between them. The code to generate the plot is below.

This document outlines why HR departments in large organizations benefit from a dedicated data science approach, highlighting impacts beyond recruitment. In short, my thesis is as follows: as organizations scale, so does the complexity of understanding their internal dynamics. Data tools become essential to analyzing large organizations, as they enable HR to identify patterns and insights that can drive strategic improvements across key areas.

Enhancing communication: Data science improves internal communication by identifying key influencers and assessing the effectiveness of HR initiatives.

Strengthening company culture: Using tools like sentiment and language analysis, data science reveals emerging trends and super-communicators who can drive cultural change.

Boosting employee retention: Predictive modeling enhances retention by identifying at-risk employees and addressing sources of dissatisfaction.

Ensuring fair compensation: Finally, data-driven analysis supports fair, competitive, and equitable pay practices within the company, fostering trust and motivation among employees.

Before exploring the details, it’s crucial to reinforce the importance of maintaining strict ethical standards and ensuring employee privacy. Data science in HR should be supervised by HR executives who understand the company’s culture, with data scientists clearly communicating any limitations and potential biases in their analyses.

Understanding scale: why large organizations need data science

In small companies, leaders and HR teams often have a clear, intuitive sense of the organization’s dynamics. They can easily recognize patterns in communication, cultural shifts, or employee dissatisfaction because these factors remain within a manageable scale. However, as organizations grow beyond a certain size—often exceeding Dunbar’s number of around 150 stable social relationships—these dynamics become more complex and harder to track. In large organizations, where direct observation and informal communication are no longer sufficient, a dedicated data science approach becomes essential to reveal insights that would otherwise remain hidden.

Enhancing communication using social network analysis

Social network analysis (SNA) provides valuable insights into the informal networks within an organization. By mapping communication patterns, SNA reveals key influencers, information brokers, and opinion leaders who shape company culture and drive change initiatives. These insights allow HR teams to identify potential ambassadors of change, enhance the effectiveness of internal communications, and reduce departmental silos.

In a recent project for the head of talent and development at a top multinational consulting firm, I analyzed collaboration patterns among managers across regional offices. This analysis identified interaction gaps within the management community, resulting in a de facto split in the team. Addressing this issue led to a measurable increase in manager collaboration over several months, and we also identified key influencers who could further drive change.

Strengthening company culture

Data science plays a pivotal role in fostering a positive company culture. Sentiment analysis on internal communications helps HR identify recurring sources of negativity, enabling targeted interventions to address concerns and boost morale. For this analysis, open communication channels like P2, Basecamp, or Slack are to be used, while private communications should never be included.

Keyword analysis allows HR to track emerging cultural trends within the organization, enabling a proactive response to shifts in employee sentiment. Additionally, language analysis—combined with other methods—can identify “super-communicators,” employees who excel in clear and engaging communication. These insights allow HR to target communication training effectively, ensuring consistent and cohesive language across the organization.

Several years ago, a colleague gathered internal communications to investigate complaints of toxic communication from certain executives. The analysis validated some complaints and disproved others, allowing HR to present findings to the relevant executives, resulting in improved communication culture and reduced employee frustration.

Enhancing employee retention

Predictive models that forecast employee attrition empower HR to take proactive steps before an employee decides to leave, making retention efforts more effective. By identifying individuals at risk of departing, HR can plan targeted interventions and strategically allocate resources to improve engagement. These models also uncover sources of dissatisfaction, such as issues with work-life balance, career growth opportunities, or management styles. By addressing these concerns early, HR can foster a supportive work environment that encourages long-term employee commitment.

Conducting compensation analysis

Data science can be instrumental in ensuring fair and competitive compensation practices within an organization. Mining external data allows HR to benchmark salaries against industry standards, helping design compensation packages that attract and retain top talent. Additionally, internal pay data can reveal disparities across departments, roles, and demographics, promoting a more transparent and equitable pay structure. Addressing these disparities reinforces a culture of fairness and inclusivity, reducing the risk of dissatisfaction related to perceived inequities and helping maintain a motivated workforce.

In-house or external data scientist?

While it is crucial for HR-related data science projects to be overseen by HR executives who understand the company’s culture and values, the choice between hiring an in-house data scientist and outsourcing the work depends on specific needs.

An in-house data scientist provides ongoing support to HR teams, tailoring analyses to the company’s unique needs and ensuring that insights are integrated effectively into HR practices. This approach fosters a deeper understanding of the company’s specific challenges and opportunities, enabling HR to make decisions aligned with strategic goals.

An external consultant, by contrast, offers objectivity and is less affected by company politics or internal biases, which can enhance the neutrality of analyses. External consultants also bring a wealth of experience from various organizations, offering fresh perspectives and innovative solutions to HR challenges.

Conclusion

With a dedicated data science approach, whether through in-house expertise or external consultation, HR departments gain the ability to make informed, data-driven decisions across communication, culture, retention, and compensation. Each of these areas contributes to a healthier, more cohesive organizational culture. By leveraging insights that reveal informal networks, track employee sentiment, predict turnover, and ensure fair compensation, a data science approach enables HR to address critical challenges proactively, ultimately fostering a stronger, more resilient organization.

I performed my first A/B tests ten years ago. Here are the most common mistakes I made

1. Doing an A/B test in the first place

Yes, the first mistake is doing the A/B test in the first place. An A/B test is an experiment. Many changes in products or services are not part of an experiment. They can be driven by business decisions, tech limitations, or shifting values. In these cases, managers decide to perform A/B tests to ease their conscience. This dilutes the concept of testing. A better approach is gradual deployment and post-deployment monitoring.

2. Not comparing apples to apples

Sometimes, especially in organizations new to A/B testing, the “B” variant is deployed using a workaround or a hack. In one case I witnessed during my freelancing career, the “B” variant was created by injecting pieces of JavaScript into the frontend, causing it to malfunction on several browsers. In another case, the test algorithm was deployed on an old and slow server. In both cases, the diminished performance of the “B” variant wasn’t because it was inherently worse but because of these implementation issues.

3. Not defining proper metrics

What do you want to measure? Conversion, lifetime value, user satisfaction? Do you assign the same weight to an improvement in one metric compared to a decrease in another? How sensitive are you to the risk of adopting the “B” variant when it’s actually worse? What about the opposite scenario? Answering these questions is critical. It’s the data person’s responsibility to ask them and demand answers. It’s the leadership’s responsibility to provide meaningful and thoughtful responses.

4. Not committing beforehand

Before starting an A/B test, discuss all possible outcomes and commit to accepting them. If you ignore some types of outcomes, you don’t need to perform the test at all (see point #1).

5. The peeking problem

I’ve seen many test owners examine the results of ongoing tests and decide whether to continue based on what they see. This is called peeking. Depending on the statistical approach you use, peeking ranges from being frowned upon to a huge no-no. If you can’t resist peeking, I advise using Bayesian methods for analysis which are considered less prone to errors from peeking.

6. Relying too much on statistical tests

You might recall power analysis from your introductory stats classes. We use power analysis to define the experiment size. Too small a sample size, and you won’t detect meaningful differences; too large, and you may waste resources. But sometimes, sample size isn’t the whole story. Some sites and applications have such high traffic that you can reach the required sample size in hours or days. However, if you do so, you might miss intrinsic variations in audience behavior: your nighttime users might differ from your daytime users, and weekday interactions may differ from weekend ones. Ignoring these aspects in your test planning can lead to unpleasant surprises.

A Likert scale study is a type of survey that measures respondents’ attitudes or opinions across a range of agreement levels.

Unfortunately, many visualizations for Likert scale data are poorly designed and fail to effectively convey the results.

To address this, I wrote a Python function that generates decent visualizations for Likert scale data. It also handles text in both Hebrew and Arabic, which is not a trivial task in Matplotlib.

Check out the code here: https://gist.github.com/bgbgbg-gmail/9aeced5372c3974eab25fa3103064f17

This post shares my recent experiences witnessing the resilience and innovation of Israelis. From mobilizing workers and students to volunteer efforts and professional development, we continually advance under challenging conditions.

In October 2023, Israel was in the midst of a political crisis. Then, we were attacked with unprecedented brutality, halting the entire economy. With nothing to do, I joined a volunteer team. We utilized our data expertise to build an OSINT analysis tools for the war effort. Initially, none of us knew each other or who know what. Yet, two weeks later, dozens of volunteer analysts were using our tools in three shifts, providing crucial support.

As a freelance data researcher, I was surprised to see how my clients, despite the difficulties, continued to work. They managed to meet deadlines even when some had to deal with employees who were killed, wounded, or enlisted into the reserves, and others who were present but absent due to friends or family being killed, injured, or enlisted.

I saw freelancers like myself joining startups at subsidized rates in place of missing employees to help meet deadlines and advance projects. This partnership not only demonstrated innovation but also showed the great solidarity that is so characteristic of Israeli society.

Additionally, a few weeks ago, I volunteered for reserve duty from which I’m exempt due to my age. I saw reservists of different ages, men and women, engaged in routine security tasks for the Israeli Defense Forces, with many continuing to work in their spare time—some on their work tasks, some on their studies, and others sitting in a corner discussing new business ideas.

I also lecture at a college. The entire college—the lecturers, the dean, the secretariat—everyone is mobilized to help students affected by the war progress towards their degrees as much as possible. This academic year is anything but usual, and yet, the second semester is over, and it looks like most of the students managed to overcome the challenges.

Everything I have seen reinforces the belief that Israel faces a bright future. Our innovation continues to improve. The recent influx of Jews, driven by waves of anti-Semitism in the West, adds even more strength and innovation. Before the war, Israel ranked among the top countries in wealth and innovation. I am confident that even after the war, we will maintain our high position and continue to strengthen our innovation and wealth.

https://worldhappiness.report/

https://en.wikipedia.org/wiki/Global_Innovation_Index

For the past three and a half years, I’ve hosted the Hebrew podcast This Week in the Middle East Podcast . Despite not being a Middle Eastern studies expert and knowing little Arabic, my passion and curiosity have led me on a remarkable journey of sharing knowledge and public speaking.

Each week, I’ve engaged with experts on various topics, providing insights into the Middle East and highlighting the importance of diverse voices. This experience has reinforced the value of knowledge sharing and open dialogue.

Recently, a major channel invited me to discuss Ramadan, despite my lacking of of formal credentials.

This illustrates that expertise extends beyond degrees to include passion, learning, and effective communication.

I am already perceived as a #datavisualization expert, and now, people start asking for my opinion in a completely different field. How did this happen?

It starts with you talking about something, then fearing to sound foolish, you learn about the subject to avoid embarrassment, and indeed, you become an expert

As we progress in our careers and lives, the significance of voicing our thoughts, exchanging ideas, and embracing various viewpoints becomes clear. Through such engagements, we evolve, learn, and foster meaningful discussions. Let’s keep breaking barriers through conversation.

In data visualization, much like in any form of communication, it’s vital to keep the main point front and center. That’s precisely why I’m a proponent of a clean, minimalistic approach to crafting data visuals, coupled with the inclusion of descriptive titles for each graph. These titles aren’t just fluff; they serve as a psychological lever, aiding in persuading your audience of your argument. Moreover, the act of titling forces a second look at the graph to ensure it accurately represents your intended message.

During a recent practical data visualization workshop I led, we tackled creating a graph that illustrated the income inequality in Israel in comparison to OECD countries. In the “before” version of the graph, displayed below on the left, there’s a noticeable redundancy between the title and the Y-axis label. Both essentially echoed each other, added no real value, and worst of all, were obscure to anyone not versed in the jargon of the “Gini Index”.

Our strategy for improvement was straightforward but effective: we swapped the title for the overarching conclusion. This modification was the kickoff for a cascade of enhancements. Yet, we hit a snag with the Gini Index itself—our focal point. Our solution? We underscored the fact that this index is a measure of inequality, clarified its scale (“Higher - more unequal”), and kept the term for those already in the know.

Wrapping up, the derision towards explaining the seemingly obvious, sparked by the “mansplaining” trend, has bled into all areas of communication. However, in the realm of data visualization, clarity and comprehensibility must reign supreme. By making our visual presentations both accessible and elucidatory, we widen the doorway for a more extensive audience to connect with and grasp complex information.

Or, How to Extract Value from Failed Projects

Typically, a professional post should begin with an introductory paragraph that provides some background and engages the reader. Let’s pretend that such a paragraph has been written and proceed directly to the story at hand. This story doesn’t end happily, nor does it end sadly. It simply begins and ends, and that’s all. Nonetheless, it’s worth your time.

The Story

I have a client who implements a variety of smart algorithms to assist individuals with money and innovative ideas in making a positive impact on society and the environment. They requested my help with modeling, and given my extensive experience as a data scientist and my track record of building numerous predictive models, I was keen to assist.

After working our modeling magic, we ended up with a predictive model that was “statistically significant” but practically unusable. What do I mean by that? Typically, we evaluate the value of a predictive model by comparing the predicted values with the known (“observed”) ones. Standard comparison procedures involve a correlation metric (or R-squared, which is NOT a correlation metric) and, for those who want to sound intellectual, a p-value. Both these metrics were excellent in our model. We also generated plots for a more comprehensive analysis. Below is a representation of our data using a completely fictitious dataset (rest assured, I would never share a client’s data).

Statistically Significant but Practically Useless

The graph indeed looks promising: the correlation coefficient was over 0.95, and the p-value (I can’t believe I’m resorting to this!) was 0.00000001, which is considered “excellent.”

However, there’s the rub: echoing an old Russian proverb, “you can’t spread the p-value on your bread”, or to quote a less old Hebrew saying, “you can’t pay with a correlation coefficient at the grocery store.” Statistical tests demonstrate the existence of a connection between your model and reality. Yet, this connection isn’t sufficient for making informed decisions due to the excessively high spread in our case.

For our model to be of practical use to my client’s clients, the typical deviation from real life should be within an order of magnitude of 0.5 (whatever the units may be). If the deviation is higher, my client’s clients would be wasting time and money. Despite our diligent efforts and the extensive work with the client’s team, the typical deviation was significantly larger, rendering the model practically useless.

… or is it?

An old Yiddish adage offers wisdom: [yeah, I don’t have anything relevant, but I’m sure there is one]. Consider our situation: we’ve spent considerable time and effort building a model. Does the model’s prediction bear any relation to reality? Yes. Are the deviations too high? Again, yes. What does this mean? It indicates that many instances we’re trying to model don’t behave as anticipated based on our data. Herein lies an opportunity.

In this project, we’re attempting to forecast a key business metric. If an entity’s metrics are notably worse than expected, this identifies a significant opportunity for improvement—a low-hanging fruit. Consequently, my client or my client’s clients could approach the entity and offer assistance.

However, there’s another side to this. What hasn’t been mentioned is that the “observed” data comes from self-reports. This implies that some reports may be manipulated to portray a more optimistic picture than reality. Therefore, the same model can be used to identify potential “mistakes” 😉 in self-reports, which is a valuable exercise in its own right.

A happy ending?

The typical “war story” of a freelance consultant generally concludes with the client accepting the consultant’s insights, raking in substantial profits, and treating the consultant to a swanky race car. Let’s pretend that this is what happened, despite the reality: my client listened to my take and decided to invest their resources in procuring more high-quality data.

Of course, if you ever need help with your modeling, feel free to reach out to me. And if a model doesn’t turn out as productive as you’d hoped, we can always attempt to make a rewarding ‘modelade’ from it. I’m always reachable at boris@gorelik.net.