When your LLM pipeline silently returns zero

One Sunday morning the daily scan ran for a user of Loud Camel, a tool that helps academics promote their research and get cited. It came back clean: a couple dozen items scored, zero relevant, zero results delivered. That looked like the system telling me there were no good matches this week. It was the system screaming, with nothing logged.

The silent-but-deadly failure mode

Pardon the analogy. Silent failures in LLM pipelines work like the worst farts in an elevator: nothing audible, nothing on the surface, then you notice the room has emptied. The LLM call returned. The parser returned a Python dict. Every type check passed. The number returned was zero, and zero looked like the truth.

What actually went wrong

The model hit its max_tokens cap and the response was truncated mid-string. No closing brace, no closing fence. The JSON parser had a clever repair fallback: it scanned for key-value pairs regardless of nesting depth and reassembled them into a flat dict. The repair returned an object that was technically dict-shaped but contained the wrong keys, all from the truncated inner level of the structure. The consumer iterated, found nothing it recognized, defaulted every item to a score of zero. The dashboard showed zero relevant, the user got an empty scan, and the cost line read like everything was normal.

Two days later the same shape showed up in a different LLM call site. The model output truncated at a different limit, the parser returned a dict-shaped object with the wrong keys, the consumer produced zero results. The day after, a third call site failed the same way. Three places. One bug class. No alarms.

How to make a silent failure loud

Two cheap defenses, neither of which I had on Sunday morning.

First, the parser cannot be allowed to lie about shape. A truncated array should return None or the complete prefix, never an object. A truncated nested object should return only the outer-level keys that were complete, never the inner ones hoisted up. The fix is unit tests at the parser boundary that assert this shape contract. Zero LLM cost. Deterministic.

Second, the consumer must validate the shape before defaulting to zero. If the function expects a dict keyed by request IDs, it should check that the returned keys are request IDs and warn loudly if they are not. A single line that reads ‘scored 0 of N items, response shape unexpected’ would have turned a four-day silent outage into a four-minute fix.

Why this is the bug class to invest in

LLM call sites multiply faster than you can audit them. Every prompt change, every model change, every batch size change opens a new path to the same failure. Patching each call site after it bleeds is stop-gap engineering. The structural defense is to make the parser refuse to lie and the consumer refuse to be silent. Both run in tests, in milliseconds, with no token cost. Both would have caught all three of my outages before any user saw a zero.

Silent but deadly is funny once. It is not funny when a real user is waiting on an empty scan for a week.

Not a Bug but a Feature

A common reaction to data on research visibility goes something like: “Most papers go unread? The whole academic system is broken.” It’s an understandable response. But I think it gets the diagnosis wrong.

Science has always been social. Robert Merton, writing in the 1940s, identified communalism as one of the constitutive norms of science: findings are the common heritage of the scientific community, and the obligation to communicate them is built into what science is. A result locked in a desk drawer isn’t doing science. Bruno Latour put it more provocatively: a claim doesn’t really become a fact until other researchers take it up, cite it, build on it, argue with it. Circulation isn’t downstream of knowledge production — it’s part of it.

This is why I push back on the “broken system” framing. If I publish a paper and it moves no one — no reader, no citation, no conversation — did I actually contribute something to the field? Humans are social creatures. Science is a human endeavor. The need to find your audience isn’t a flaw in the system; it’s closer to the whole point.

Where things genuinely do go wrong is the Matthew effect, also Merton’s term: attention compounds. Established researchers get seen, which gets them cited, which gets them seen more. Early-career researchers, with networks still forming, fall on the wrong side of that feedback loop — not because their work is weaker, but because nobody knows it exists yet.

So the problem isn’t that visibility matters. The problem is that visibility is unequally distributed in ways that have little to do with the quality of the work. Lowering the cost of strategic outreach — helping good work find the people who should know about it — isn’t gaming the system. It’s leveling it.

References

Latour, Bruno. Science in Action: How to Follow Scientists and Engineers Through Society. Cambridge, MA: Harvard University Press, 1987.

Merton, Robert K. “The Matthew Effect in Science.” Science 159, no. 3810 (1968): 56–63.

Merton, Robert K. The Sociology of Science: Theoretical and Empirical Investigations. Chicago: University of Chicago Press, 1973.

Customers see your tunnel vision before you do

You cannot detect tunnel vision from inside the tunnel. The light at the end is right there, but you stopped looking up from the rails. I learned this last week when an early user caught two failures in my product that I had built, reviewed, and shipped.

What I shipped

An early user opened my product last week. Loud Camel is a tool that helps researchers get cited and recognized. The first paper it surfaced was attributed to the wrong author. The named researcher had not written that paper.

Then they flagged something heavier. The cold-email drafts the product writes for users imply the sender read the recipient’s paper. The sender did not. I built that flow. I reviewed those drafts. I shipped them anyway.

How I lost the star

When I started Loud Camel I told myself integrity was the north star. Every recommendation honest. Every email truthful. Then I spent four months deep in OpenAlex joins, email parsing, and pipeline plumbing. The star drifted out of my field of view. I was looking at the code.

This is the bug in founder cognition that scares me most. The thing I cared about the most became the thing I stopped checking. Not because I stopped caring. Because I stopped looking.

Why founders cannot audit themselves

I have tried the standard remedies. Weekly review of priorities. A pinned list of values on the wall. Asking myself whether I am building what I said I would build. None of it pulled me out. The frame you use to evaluate the work is the same frame that built the work. You cannot audit yourself from inside the tunnel.

What customers see that you cannot

The user who writes to say ‘this looks wrong’ pulls you out. The teammate who says ‘wait, are we sure?’ pulls you out. They see the product the way you wanted it seen. You see it the way you currently see it. The customer sees what you stopped seeing.

If you are building something, schedule the conversations that yank you back to the surface. Treat them as a check on whether you still recognize the product you wanted to make.

What I am changing

I am adding a validation step that confirms the attributed author actually appears in the paper’s author list before any recommendation is surfaced. I am rewriting the cold-email drafts so they do not pretend the sender read what the sender did not read. I am writing back to every user who flagged something and thanking them.

The next time the north star drifts, I want a user to notice before me. I would rather hear it from them at month four than ship past it for another four months alone.

LLMs sharpen the Matthew effect in citations

The Matthew effect is a 1968 observation by sociologist Robert K. Merton. In science, credit accrues to people who already have it. Two researchers do the same work; the famous one gets cited, the unknown one is footnoted if they are lucky. Merton took the phrase from the gospel of Matthew: “For unto every one that hath shall be given.” In citation data it shows up as a power law. A small number of papers collect most of the citations, and once a paper joins the famous tier, the rate at which it accrues new citations only rises.

A new line of work asks what happens to that dynamic when the tool suggesting citations is an LLM.

The experimental finding

Algaba and colleagues fed GPT-4, GPT-4o, and Claude 3.5 the abstracts of 166 ML papers from AAAI, NeurIPS, ICML, and ICLR, and asked each model to suggest references. The LLM-suggested references had much higher median citation counts than the papers’ own references, even after controlling for publication year, venue, title length, and author count. A follow-up scaled the test to ten thousand papers and around 275,000 generated references across domains. The bias toward already-highly-cited, shorter-titled, somewhat more recent work persisted, even though the suggestions looked semantically appropriate inside existing citation graphs.

What this means for a working researcher

LLMs are pattern matchers over a corpus where the Matthew effect was already baked in. The thing they are good at, returning the most plausible reference for an idea, is exactly the thing that surfaces the already-famous paper over the equally-valid lesser-known one. Wieczorek and co-authors call this the status-quo scenario for LLM use in literature search: existing inequalities reproduce, possibly faster.

The career-level evidence is not in yet. Nobody has shown that LLM use is, on its own, tilting hiring, tenure, or funding outcomes. But citations feed those decisions, and citations are the channel where the bias has now been measured.

Treat the first three references your LLM suggests as a starting list, not the final list.

P.S. Two centuries before the gospel of Matthew, the Book of Daniel (2:21) made the same point in Aramaic: יָהֵב חָכְמְתָא לְחַכִּימִין וּמַנְדְּעָא לְיָדְעֵי בִינָה. “He gives wisdom to the wise, and knowledge to those who know understanding.” The traditional reading is that wisdom flows to those who already have it. Maybe Merton should have called it the Daniel effect. ¯_(ツ)_/¯

References

Algaba, A., Mazijn, C., Holst, V., Tori, F., Wenmackers, S., & Ginis, V. (2025). Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias. In Proceedings of NAACL 2025, 6844-6853.

Algaba, A., Holst, V., Tori, F., Mobini, M., Verbeken, B., Wenmackers, S., & Ginis, V. (2025). How Deep Do Large Language Models Internalize Scientific Literature and Citation Practices? arXiv:2504.02767.

Baert, P., Dorschel, R., Hall, M., Higgins, I., McPherson, E., & Philip, S. (2025). Dialogues Towards Sociologies of Generative AI. Social Science Computer Review (online first).

Wieczorek, O., Steinhardt, I., Schmidt, R., Mauermeister, S., & Schneijderberg, C. (2024). The Bot Delusion: Large Language Models and Anticipated Consequences for Academics’ Publication and Citation Behavior. Futures 166: 103537.

An Illustrated Guide to Academic Publishing

A short story about how a paper is born — and why almost nobody will read it.

Meet a researcher. Smart. Curious. Slightly overcaffeinated.

This is you. Or someone like you. You went into research because you wanted to understand something the rest of the world hasn’t figured out yet. You probably didn’t go in for the money. You definitely didn’t go in for the email volume.

Your job, more or less, is to take ideas out of your head and put them into the heads of other people. The path between those two points is longer than anyone tells you on day one. Here is what it looks like.

It starts with a speck

Somewhere in there, an idea.

Every paper begins as a tiny speck — a hunch, a stray sentence in someone else’s discussion section, an experimental result that doesn’t quite fit the textbook.

At this stage, the idea is small enough to fit on the back of a napkin and not quite small enough to ignore. You decide to keep it.

Let’s zoom in

The speck, up close. Still mostly empty space.

Up close, the idea is even less impressive than it looked from across the room. It is small, it is fuzzy, and it is surrounded by an enormous quantity of ‘I’m not sure yet.’

That’s fine. Most things start that way. Now you go to work on it.

You read. You think. You read some more.

The speck grows a little. Reading helps.

You read papers. You read papers that cite those papers. You read papers that those papers tried to refute. You scribble in margins. You stare at the ceiling. You explain the idea to a friend who is too polite to interrupt.

Slowly, the speck gets bigger. Not because you added anything from outside — but because you finally understand what was already there.

Literature review. Proposal. Funding.

Bureaucracy arrives.

Now things turn administrative. You write a literature review that proves you are not the first person on the planet to have a thought. You write a proposal explaining what you would like to do and why somebody should pay for it.

Then you wait. The idea, meanwhile, keeps growing — partly because you keep thinking about it, partly because explaining it ten times to ten different review panels forces you to make it sharper.

Collect the data. Run the experiments. Ask for help.

The speck is now noticeably less speck-like.

Funding (finally) comes through, or you proceed without it. Either way, the real work starts: experiments that don’t work, code that doesn’t run, instruments that pick today, of all days, to break.

You ask for help. You email someone you’ve never met. You buy a colleague coffee in exchange for thirty minutes of their attention. You learn, perhaps for the first time, that research is mostly other people.

Draft. Review. Refine. Polish. Repeat.

Most of your head is now occupied by one idea.

You write a first draft. It is bad. You knew it would be bad, but it is bad in ways you did not predict. You rewrite. Then you rewrite the rewrite.

By now the idea has filled almost everything in your head. You catch yourself thinking about it in line at the supermarket. You think about it in the shower. Your friends have started to change the subject.

You submit.

There is no other thought.

When you finally click ‘submit,’ there is nothing else inside your head. The idea has taken up all the space. You refresh the submission portal. You refresh it again. You explain to family members what ‘desk reject’ means. They nod politely.

Then the reviewers reply.

They have remarks.

Reviewer 1 is generous. Reviewer 2 is not. Reviewer 3 appears to have read a different paper, possibly in a different field. You read their comments three times — once for content, once out of anger, and once to actually take notes.

You revise. You respond. You explain, in the most patient voice you can summon in writing, why their kind suggestion would in fact destroy the paper.

Accepted.

Pride. Quite a lot of it, actually.

The email arrives. You read it twice to make sure. You tell your partner. You tell your supervisor. You tell, with somewhat less success, the person at the next desk who has been watching you suffer for the past eighteen months.

This is you. Proud and happy.

Take the afternoon. You earned it. The paper is out. Your name is on it. Somewhere in a server in Amsterdam, a row has been added to a database.

Now zoom out.

Find yourself. Take your time.

Here is what almost nobody tells you. You are not the only person who just published. Roughly five million peer-reviewed papers go out into the world every year. Each one is somebody’s two-year speck. Each one represents somebody’s afternoon of pride.

Most of them are read by almost no one. Half of all published papers are cited fewer than three times. A large fraction are never cited at all. The median paper has roughly the impact of a tweet that nobody retweeted.

That is the part that hurts. The work was real. The idea was real. The result was real. The visibility was not.

Your research is good. But nobody knows it.

The problem isn’t the quality of the work. The problem is that ‘publish and wait’ stopped working sometime around when search engines started ranking by engagement and AI assistants started answering questions without showing their sources.

Citations, grants, collaborations, invitations to give talks — they all start with someone, somewhere, encountering your work and remembering it. That encounter no longer happens on its own.

We built Loud Camel for the people in that crowd. Once a month, we put together a short brief: who in your field has started working on something near your topic, which conversations are happening in places that LLMs and search engines actually read, which dormant contacts are worth a two-line reconnect. You decide what to send. We just make sure you have something to send.

loudcamel.com — reclaim the visibility your research deserves.

Where you debut probably decides where you stay

A 2018 paper from Albert-László Barabási’s group (Fraiberger, Sinatra and colleagues) maps the global art world as a single network. Barabási is the network scientist who introduced scale-free networks two decades ago and runs labs at Northeastern and Harvard; his book The Formula: The Universal Laws of Success is the readable distillation of this whole research line. If any of what follows surprises you, pick it up.

The team tracked 496,354 artists across 16,002 galleries and 7,568 museums between 1980 and 2016, drawing an edge between any two institutions whenever an artist exhibited at one and then at the other. The result is a dense Western core (MoMA, MET, Guggenheim, Tate, Pompidou) with a ring of regional clusters around it: Japanese, Brazilian, Australian, Eastern European. The links between those clusters and the core are thin.

What an artist’s first five shows predict

The authors then take only the first five exhibitions of each artist and use them to predict the next thirty. A model that respects those five does it accurately. A memoryless model fails.

Curators choose new artists by looking at the curators who chose them before. The first tier you land in becomes the reference set that does most of the later picking for you.

What this means outside the gallery world

The same shape probably reproduces in any career path that flows through institutions and gatekeepers. First lab. First publication. First conference. First podcast. Each has a core and a periphery, and the gap between them takes time to cross.

If you can afford to be patient about exactly one career choice, make it the first one.

I built the wrong dashboard for two weeks

When I worked at Automattic, on parts of WordPress.com and Jetpack, we used to say that counting things is hard. With time I realized the harder problem is one rung up: counting the right things is even harder. Most teams solve the first problem, define the metric carefully, and never notice the second. The metric they defined is not the one that mattered.

I walked into a clean version of this on my own product. I built an outreach tool last month. The first thing I did was sit and watch it work. Emails sent today, emails queued, emails waiting for the morning batch. The numbers moved when I clicked things. It felt productive.

Two weeks in, I was still sending email and I had no idea who had read any of it.

How to tell when a metric is the wrong half of the loop

The reason this is so easy to get wrong is structural. Anything I do inside my own software produces a clean record on the way out. I click Send, my code notes the click, the counter goes up. The action and the metric are in the same loop, on the same machine, written by the same people.

What happens to the email after that is on someone else’s screen. It might land in a folder. It might be skimmed. It might sit unread in a tab that stays open all afternoon. Each step adds latency, ambiguity, and another team’s instrumentation choices. By the time any of it makes it back to me, it lives in a different table, behind a different filter, on a different page. The path is longer and the data is less clean.

Most teams do not bother to bring it back at all. They are not lying. They are measuring what is easy.

The shortcut for spotting this is to ask, of any number on a dashboard, who created the event that made the number move. If the answer is “I did”, or “my team did”, or “my system did”, the metric is on the inside of the loop. If the answer is “the person we are trying to reach”, the metric is on the outside. Most dashboards are 90% inside-the-loop because that is where the data is cheap.

What I changed this week

This week I pulled some of the response signal up to where I was already looking. I did not invent a metric. I just stopped hiding the ones I had. The contact card now tells me when I last drafted to a person and when I last marked them as contacted, both in plain language above the action buttons. The admin view for outbound links shows whether a link has been clicked, is still waiting, or has expired, with relative timestamps, instead of leaving me to grep logs.

The interesting result is what stopped happening, not what started. I stopped sending followups to people I had already reached. I stopped sending followups to people I had just contacted. None of this came from new resolve. The right number sitting in the right place did most of the work.

Loud Camel news

The product I have been describing is Loud Camel, a tool that helps researchers get cited and recognized. The contact-card recency lines and the magic-link status panel both shipped this week, alongside a first cut of an admin-driven prospect outreach flow. Each is small. Together they move the screen closer to the one I should have built first.

Takeaway

Open the dashboard you check every morning. If most of what is on it is things you did, the screen is telling you about your week and not about the world. The fix is rarely a new metric. It is usually a number that already exists somewhere, moved one screen over to where the next decision happens.

Rules don’t change how people write. Contrasts do.

If you want someone to write, design, or build differently, stop writing longer rules. Produce the alternative they can compare against. The contrast does the work the rule could not. A new Columbia paper gives this principle a name and the data.

What the paper found

Li et al. (2026) studied scientists trying to translate research into social media posts. Standard advice told them to use relatable examples, walkthroughs, and personal language. Most ignored it or actively resisted. Abstract rules don’t tell anyone what “personal” looks like in their own field.

The researchers tried something else. Two versions of the same explanation, side by side. One warm, one clinical. Same science, different style.

Behavior changed. Even scientists hostile to “personal language” picked it up after seeing the dry version next to the warm one. One participant said: “I never would have thought to say this.” That line became the title of the paper.

Why contrasts work and rules don’t

Recognition is cheaper than recall. Spotting which version reads better is fast. Generating a “more relatable” version from a rule is hard work. The paper invokes a basic usability principle: design for recognition, not recall. Most writing advice violates this.

Preferences are also a continuum, not a binary. There is no correct amount of personal language. People can only locate themselves on the spectrum once they see both ends.

What to do instead

When a PM tells a designer “make it cleaner,” nothing happens. When the PM produces a quick alternative mock and asks “this versus yours, what do we lose,” something happens. Same for coaching reports, editing PRDs, and prompting LLMs. “Make it better” produces hedged variants. “Here is your draft and here is a tighter version, pick which reads truer” produces change.

Style guides are rules. Diffs are contrasts. The work is in producing the alternative.

The worst way to visualize geographic data

Country choropleth maps are the wrong chart for visit counts. Or for any kind of count. They encode magnitude as land area, but the question you actually want answered is “where are my users?”, not “which country owns the most square kilometers?” The two have almost nothing in common.

I recently launched Loud Camel, a tool that helps researchers get cited and recognized. I opened the Google Analytics geographic report and got the “before” map below.

Look at it for five seconds. Cover the title. What are the top five countries by active users?

You probably said the United States. Maybe Canada and Russia. The blue blobs are big and obvious.

Now find Israel.

You can’t. It is one or two pixels somewhere east of the Mediterranean. Israel is currently the country with the most active users on the site. It is the leader. The map hides the leader.

Why country maps mislead about counts

A choropleth fills each country with a color whose intensity encodes a value. In theory the reader compares colors. In practice the reader compares areas, because area is the loudest visual signal on the page.

A country with a huge area and a low value looks important. A country with a tiny area and a high value disappears. The reader’s eye correlates magnitude with land mass, not with the data.

This works only when geographic proximity matters: epidemiology, regional logistics, climate maps. Visit counts have no spatial structure. A user in Texas is not “close” to a user in Mexico in any way that matters to the data. The map adds noise, not insight.

Use a sorted bar chart instead

The same data, sorted from largest to smallest as a horizontal bar chart, looks less sophisticated, but reads in one second.

Bar length encodes the value directly. The reader’s eye runs top-down by magnitude. Israel is at the top because Israel has the most users. The United States is second because it has the second-most. There is nothing to interpret.

The numbers in the chart are illustrative; the ordering is not.

What’s with the camel?

It is my new service. If you have an academic career and want to advance it, talk to me.

The hardest part of being a solo founder wasn’t what I thought

The hardest part of running a one-person company is not the workload. It is choosing the right work.

What I expected

When I started, I assumed the hard part would be wearing every hat. CEO. CTO. CMO. VP Product. Four jobs in one chair, no one to delegate to, no one to escalate to.

All of that while working full time as a lecturer and researcher at a college.

The workload is real. It is not the bottleneck.

What is actually hard

A solo founder is one person, and that person has a default mode. Mine is building. I want to ship features, fix bugs, and tighten infrastructure until it hums.

Building feels productive. It is measurable. It is satisfying. At the end of a coding day the diff is real, the bug list is shorter, the system is leaner.

The work that pays the bills feels like distraction

Sales conversations. Watching the market. Learning what customers actually need. Talking to people instead of typing into a terminal.

This work has no diff at the end of the day. No ticket closed, no graph that moves on a dashboard you control. It feels soft, slow, like you are getting away with something.

It is also the work that decides whether the product survives.

The real lesson

A perfectly built product nobody wants is worse than a half-built one customers are pulling out of your hands.

The hardest part of being solo was not doing everything. It was resisting the role I am best at when another role needed me more.

Building is the comfortable seat. Talking to customers is the uncomfortable one. The discipline is to spend more time in the uncomfortable seat than feels natural.

A small weekly check

Each Friday I ask one question: did I spend more time this week with code or with customers?

If the answer is always code, that is the bug to fix.

AI Articles Overtook Human Articles. That Is Not Automatically Bad

More AI generated articles than human written articles is not automatically a decline. It can be a transition in media production, similar to printing press replacing manual copying and photography replacing painting for documentation.

What the Reddit chart claims

A post on r/dataisbeautiful presents a crossover point where AI generated articles overtake human written articles. The exact dataset details need scrutiny, but the high level signal is clear enough to discuss. Cheap content generation is scaling faster than manual writing output.

That pattern is unsurprising. When production cost drops by orders of magnitude, volume usually explodes. The same thing happened when printing moved reproduction from skilled scribes to press operators. The same thing happened when cameras made visual capture fast and repeatable.

Why volume growth is not the core problem

A larger supply of text does not force lower quality consumption. Distribution systems decide what people see. Ranking models, recommendation systems, editorial choices, and user habits decide which items get attention. The bottleneck moved from production to filtering.

In that environment, the right question is not whether AI text exists. The right question is whether readers can quickly identify what is useful, original, and trustworthy. If curation improves, higher volume can increase discovery. If curation fails, noise wins.

How this connects to zero-directionality

This framing aligns with our preprint, “Either Companionship or Death: Zero-Directionality and the Structural Disappearance of the Social Other” (https://www.preprints.org/manuscript/202603.0382). The core claim is that many digital interactions have crossed into zero-directionality. In these interactions, the social other is absent and the machine becomes the communicative counterpart.

Seen through that lens, the AI article crossover is not only a content story. It is a structural story about who is in the loop. When drafting, ranking, and recommendation shift toward human-machine loops without enough human mediation, the risk is substitution. When AI helps humans evaluate, compare, and connect with other humans, the result can be companionship rather than displacement.

A better frame for the next year

The historical analogy is practical. Printing did not kill books. It multiplied access and shifted value toward selection, editing, and distribution. Photography did not kill art. It changed what painting was for. AI writing is likely to do the same. Routine drafting and templated explainers become cheaper. Human judgment, synthesis, and social accountability become more valuable.

So I would not argue that overtaking is bad by default. I would argue that institutions and creators need better quality signals. Provenance labels, source transparency, reputation systems, and stronger editorial standards matter more now than before.

What would change my mind

The strongest counterargument is not philosophical. It is empirical. If we observe that domains with high AI article penetration show lower factual accuracy, lower source diversity, and lower reader trust over time, then the transition is harmful in practice. The same applies if search and social ranking systems consistently reward synthetic repetition over original reporting.

This is measurable. Track correction rates, citation quality, source overlap, and time to find a reliable answer. Compare domains with different AI adoption levels. If those metrics degrade and stay degraded, abundance is hurting knowledge ecosystems. If they improve, abundance is likely helping people find and learn faster.

That is why panic is not a strategy. Instrumentation is. The important work now is to define quality metrics, publish them openly, and hold platforms accountable for them.

Takeaway

When machine output surpasses human output, panic is understandable. Better filtering is the real leverage point. Build that, and abundance becomes useful instead of overwhelming.

Simplification and ultra-personalization: two responses to a harsh critique

I shipped a feature-rich landing page for my product. About 100 unique visitors came through. Two signed up. The signal was loud; I almost missed it. A friendly but blunt critique from an opinion leader in the field forced two changes I should have made earlier. One was to strip the public landing page until it had a single message. The other was to generate a different landing page for every prospect I plan to contact this week. Both ship today.

What I shipped first

Loud Camel, a tool that helps researchers get cited and recognized, went out with a landing page I was proud of. Three sections, several screenshots, multiple use-case framings, an explainer video, social proof. Every feature got airtime. Every audience got a paragraph. From the inside, the page felt thorough.

What the signal was

Around a hundred unique visitors hit that page during the soft launch. Two signed up. A two-percent conversion rate isn’t a disaster; for a paid SaaS landing page it’s defensible. For a free tool offering a fast first report, it’s a warning. Visitors who arrive curious and bounce without leaving an email are telling you something the analytics dashboard cannot quite spell out: they didn’t understand what to do, or they didn’t believe it would be worth the effort.

I noticed the number. I told myself the launch was soft, the audience was diffuse, the funnel needed time. I was wrong.

The critique

I asked an opinion leader in my field to look at the page. He took the time to do it properly. The note he sent back was direct: too much, too fast, no clear next step, the strongest claim buried in the third paragraph. The page was a writer’s gift to me, not a reader’s path to action.

That kind of critique only lands when you hear it from someone who has nothing to gain by being polite. I am very glad I asked.

Response one: strip the public page

The new public page contains less information than the old one. The headline states one outcome in plain words. The single CTA leads to one form. Anything that does not move a first-time visitor toward ‘is this for me, yes or no’ is gone. Some content moved to a separate page; most of it I cut.

Less material on the page is the easier change. Saying less is not the hard part.

Response two: ultra-personalize per prospect

The harder change is the one I’m running this week as an experiment. With guidance from a mentor in this process, I’m shipping a minimal landing page generated per prospect: their field in the headline, a single sentence promising one specific output, a single email input. Nothing else.

A personalized landing page for computational chemistry researchers. Same template, different field in the headline, different sample preview. The page is dynamically generated for each lead I contact this week. Google Analytics is wired through end-to-end. Each version shares its origin link only with one researcher; conversion gets tracked per page.

The bet: a stranger who arrives via a personal email from me is converted by relevance, not by features. If the headline names their field and the form takes ten seconds, I find out which copy works. If they bounce, I find out which copy doesn’t.

What I’m watching this week

Two metrics. First, signup rate per personalized page versus the simplified public page. Second, time-to-first-action: how fast a visitor who arrives leaves an email. Both numbers go into a Google Analytics dashboard I will read on Sunday.

The general lesson, ahead of the data: when 2% sign up, the page is doing half the work. Simplify until visitors can answer ‘is this for me?’ in under five seconds. If you can also tell them yes by name, do that.

When rigid blocs break, they break together

A rigid voting bloc is supposed to be the boring case. People vote the same way, election after election, because their identity tells them to. The interesting story, the textbook says, is what happens when the bloc weakens.

I want to argue the opposite. The interesting case is when a rigid bloc breaks — and it breaks at every address at once. That synchronized fracture is not the bloc weakening. It is the bloc working.

The puzzle

Israel’s ultra-Orthodox (Haredi) population is one of the most disciplined voting blocs in any democracy. Two parties — UTJ for Ashkenazi voters, Shas for Sephardi voters — have split that vote with 90-95% loyalty for decades. Cross-ethnic voting (a Sephardi voting UTJ, or an Ashkenazi voting Shas) is so rare it is treated as measurement noise.

Then, in the March 2021 election, that noise jumped to 12-19% of Sephardi voters in five different cities, all at once. By the next election it was gone again. The voters did not move house. The borders did not shift. Same people, same streets, one election of mass cross-ethnic switching, then back to baseline.

The chart

Cross-ethnic switching jumped from a typical 1-3% to 9-19% across geographically dispersed Haredi cities — within a single election cycle. Demographics cannot move that fast. Something else did.

Why this matters

There is a tempting reading: the bloc was finally weakening. Identity politics fading. Voters acting as individuals.

That reading is wrong, and the geography is what tells you so. Independent voters do not switch in five cities in the same election in the same direction. Independent voters do not return to their original party 13 months later. What you see in the chart is coordination, not autonomy.

The corpus evidence — about 58,000 Haredi forum and news items from those 13 months — points to a specific channel: senior rabbis, working through yeshiva networks, told Sephardi voters to vote UTJ that one cycle. They listened. When the directive softened, they returned.

The framework

I call this ‘rigidity with stress fractures.’ The same machinery that produces decades of unbroken loyalty — centralized authority, dense networks, voting framed as collective duty — is the machinery that produces an instant, country-wide swing when leaders ask for one. Rigidity and synchronized switching are not opposites. They are two outputs of the same system.

This generalizes. Any identity-based bloc with centralized authority, organizational reach, and a population trained to follow directives can do this. Italian Christian Democracy under bishop replacements (Lanzara et al., 2024). U.S. evangelical voter guides (Campbell et al., 2011). Ethnic broker networks across sub-Saharan Africa (Horowitz, 1985). The Haredi case is unusually clean because elections came every 6-12 months and let us watch the disruption and the recovery in the same window.

The takeaway

When you see a sudden swing inside a population that was supposed to be politically immobile, do not jump to ‘the bloc is fragmenting.’ Check first whether the swing is synchronized — same direction, same magnitude, multiple places, single window. If it is, you are not watching the bloc weaken. You are watching it obey.

The mechanism that holds the bloc together is the same one that can move it. Stability and discipline-driven volatility are not opposite diagnoses. They are the same diagnosis read at different speeds.

Promoted papers keep pulling ahead: what the Kudlow RCT looks like at 36 months

Most papers don’t fail because they’re bad. They fail because nobody reads them. That isn’t a complaint about readers — it’s a description of a roughly two-million-paper-a-year firehose where even strong work gets buried by default.

The interesting question is whether doing something about it measurably moves citations, or whether promotion is just optics. The cleanest answer we have comes from a randomized controlled trial by Kudlow, Brown and Eysenbach, published in JMIR in 2021. This post walks through what they actually found.

The design

3,200 articles from 64 peer-reviewed journals, eight subject areas ranging from the life sciences to the humanities. Journals were drawn from the top 20 by h5-index in each subject, then eight journals were randomly sampled per area so the study would not be confined to only the highest-impact outlets. Articles were block-randomized at the subject level — 1,600 to the intervention arm, 1,600 to the control.

The intervention itself was narrow: links to the 1,600 intervention articles were surfaced as sponsored recommendations across the TrendMD cross-publisher network (around 4,300 participating journals, ~121 million monthly readers) for six months. Total budget: US $9,600 — about $6 per article. After six months, promotion stopped. The researchers then quietly watched citations accrue for another thirty months.

What the paper shows

The chart below reproduces Table 3 from the paper: mean citations per article at 6, 12 and 36 months, comparing promoted vs control.

At 6 months — effectively during the promotion window — the promoted arm was ahead by a mean of 1.06 citations (p = .005). At 12 months, the gap was 5.06 (p < .001). At 36 months, thirty months after promotion ended, the gap had grown to 10.52 citations (95% CI 3.79–17.25, p = .001). In relative terms that is the headline 28% increase, but the absolute version is the more interesting number: the gap kept widening long after the intervention stopped.

A Cohen d of 0.11 tells you the per-article effect is small. That is honest and worth sitting with. What makes the finding substantial is not the size of the individual shift but the structural property: a modest, cheap, time-bounded intervention produced a citation gap that was still widening three years on. The authors also note that the entire cumulative distribution of the promoted arm was shifted to the right — the gain was not driven by a handful of lucky viral papers.

Why this is interesting

Two reasons.

First, causal inference. Most of the scholarly-visibility literature is correlational: popular authors get cited more, active social-media users get cited more, open-access papers may get cited more (the meta-analyses disagree). With evidence that weak, it is easy to conclude that promotion is a proxy for being good rather than a lever you can pull. Block-randomization at the subject level cuts through that problem.

Second, the mechanism the authors imply. Dissemination generates reads, reads generate downstream citations, and citations generate further visibility in citation-weighted ranking systems. A push at the early part of that curve compounds across years. Six months of promotion bought a three-year advantage that was still accumulating.

What to take away

If you have done good work and left it unpromoted because promotion feels indecorous, you are quietly taxing your own research. The 28% figure is a floor — TrendMD is one specific channel. Targeted outreach to adjacent labs, mailing lists, newsletters, conference circulation, and social posts probably compound on top.

Pick one paper this week that you admire and wish more people knew about. Send it to one person who should read it. Then do it again next week. The chart above is what consistent, unfashionable legwork looks like three years in.

When in some places work meetings start with people introducing their preferable pronouns, in Israel we start with a briefing about the closest bomb shelter. When in other places people skip important meetings to take care of their pet goats (true story), in Israel people join meetings on Zoom from Army Reserve Service during breaks from patrols or other duties.

I wrote about this last June. The war will end, I said, and when it does, hundreds of thousands of Israelis will come back – to work, to building, to a country in crisis. And crisis breeds resilience. They’ll come back hardened as fuck.

Nine months later, nothing has changed my mind. If anything, I’m more convinced. The people who learned to operate under rockets, who managed companies while covering for half the team on reserve duty, who wrote code between sirens – they are coming back with muscles that can’t be trained in peacetime. Extreme communication, chaos management, creative problem-solving under constraints that most people can’t imagine.

Good luck competing with them. Good luck competing with us.

After my previous post about Anthropic’s spider chart, several people asked me how I would actually go about fixing it. So here is how – with a single prompt.

I gave Claude the original graph and the following instruction, based on my C for Conclusion approach:

the visualization is not good. First, formalize the image into a single-sentence conclusion of up to 8 words.

Then, use the approach of Stephen Few and Edward Tufte, maximize the data-ink ratio, use Information layers (Jean-luc Doumont) to generate the graph based on the same data that is much better and that conveys the conclusion you made above

That’s it. Here is the before and after:

The conclusion – “AI adoption vastly lags its theoretical capability” – is now the title. Categories are sorted, the gap is immediately visible, and there is nothing left to decode.

See the standalone version of the graph (the raw data, as extracted by Claude, is in that file).

For more on why radar charts are almost always a bad choice, see my earlier posts on spider charts and radar charts.

But, Boris, vibe plotting is not a real expertise!

Sure, but Anthropic people have access to the same vibe plotting tool I have (even better). It’s knowing what to ask for that is the real expertise. And if you don’t have that, you can always ask someone who does.

I have put together a collection of lecture, workshop, and course proposals on AI and AI-assisted programming. Each one is tailored to a specific audience and comes in three delivery formats: a one-hour lecture, a three-hour hands-on workshop, and a multi-session course.

The topics range from a non-technical introduction to AI (for students from any department) to hands-on AI-assisted programming for CS students, CS lecturers, industrial engineering students, managers, and life science researchers. Most proposals are available in both English and Hebrew.

All proposals are now collected on a single Lecture & Workshop Proposals page.

Anthropic has published an interesting study about labor market impacts of AI: Labor market impacts of AI: A new measure and early evidence.

It is hard to overestimate Anthropic’s contribution to the world of technology. Their innovation and professionalism are remarkable. But sadly, in the report they included a spider graph that leaves their entire post with a bad taste. (I wrote about spider charts many times before.)

It takes a good minute or two to understand what the image is about. Spider charts are almost always a bad data visualization technique. So how do we fix it?

First, we need to define the conclusion. A short sentence of up to 8 words that conveys the general idea of the graph. After that, we create a graph that does exactly that – conveys the conclusion.

Here, the conclusion is the difference between theoretical AI capability and the observed AI adoption across occupational categories. So we need to show exactly that difference.

Look at the improved graph. All the categories are sorted according to the observed adoption rate. The differences are immediately visible, and the conclusion is right at the top of the graph, leaving no room for guessing.

The tech changed, the questions barely did.

Look at this Computer and Society syllabus from 1975.

Apart from some archaisms like “data banks” and the typesetting, almost everything here feels painfully current: privacy, surveillance, economics, law, education, public perception, even “can machines think?”.

With minimal edits, this could be a strong and necessary course in any CS curriculum today.

The tech changed.

The questions barely did.

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)

---

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.

---

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 dreamed of brothers-in-arms, and instead we met the fire of the cannon.” 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.

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.

---

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.

---

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.

---

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.

---

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?”

---

Part VII: Two States and Jewish Communities in Palestine

Boris: I recently interviewed Yinon Dan Kehat, 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.

---

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.

---

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