One default AI tab can quietly become a daily bottleneck.

𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝗲 𝗖𝗵𝗶𝗲𝗳 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿.
𝗘𝗮𝗿𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀.
𝗚𝗲𝘁 𝗔𝗜 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗻𝗼𝘄 → aiforleaders.com/cp
__________

Give each tool a clear job before your team adds more tabs.
Use this five-part stack as a starting point to test.
The roles overlap. No tool owns a whole category.

1 → ChatGPT for quick ideas
Draft email rewrites, explore angles and get coding help.
Ask for three options with one clear difference each.
Pick a direction, then check the details before use.

2 → Claude for deep work
Bring a complex document, report or deck outline.
State the decision and the audience before asking for a draft.
Ask it to flag gaps and separate facts from assumptions.
Keep a human owner for the final recommendation.

3 → Perplexity for current research
Start with a question that needs recent sources.
Ask for dates, evidence and links you can open.
Check whether each citation supports the actual claim.
A cited answer still needs a source check.

4 → Gemini + Nano Banana for visuals and Google work
Use Gemini for document work and Workspace context.
Nano Banana is image generation within Gemini.
Give it the subject, layout and words a visual must contain.
Check image text and file limits before relying on output.
Access and integrations depend on your plan and setup.

5 → Copilot for Microsoft work
Try it where the task already lives: Excel, Word or PowerPoint.
Use Outlook help for a draft tied to the thread.
Check formulas against known values and read before sending.
Confirm your Microsoft 365 plan supports the feature.

Use five minutes as a switching cue, not a proven cutoff.
If the answer keeps failing, first check the brief.
Missing context will follow you into the next tool.
Then try another tool on the same task and compare results.

Make every brief include goal + audience + format.
For example: turn these notes into a CEO update.
Use six short points and flag missing decisions.
Define a useful result before you judge the draft.

For repeat work, save context in a project or agent.
Start with meeting notes, replies or a weekly report.
Keep one good example and a short review checklist.
Test on real work before giving an agent broader actions.

Track time spent checking and fixing the output.
Keep the tools that reduce that total for your team.
More subscriptions alone will not improve the workflow.
Choose one recurring task to test this week.

Which job are you still forcing your default AI tool to do? | 29 comments on LinkedIn

We're honored to be the subject of a new paper by Declan Fahy, "Watchdog Science Journalism: How Retraction Watch Fosters Accountability in Science."

"[R]etraction Watch remains an illustrative case, a distinct digital‐age organisation that stands as a significant example of the long‐anticipated, but little‐realised, idea of watchdog science journalism," Fahy writes.

"Retraction Watch has done an amazing job in asking further, asking more questions like, why? You know, if it [the journal] says…“yeah, it’s a republication”—what do you mean by that? Can you give some more details And they have done an amazing job in getting publishers to be a little bit more open on why a paper got retracted, and those things are important because, especially if you have based part of your research on a paper that now got retracted, you want to know which parts of the paper you could still trust." -- Elisabeth Bik

"It is most useful to read about other people’s experiences so that you can, if you like, vicariously share the frustrations that one has. Because I think if you spoke to anybody who works in the areas that we do where we are investigating studies and negotiating with journals about trying to correct the literature, it is immensely frustrating and a thankless task, and nobody thanks you. And so, to some extent, for people like me, I think the good thing about Retraction Watch is that it shows you you’re not alone. These are the experiences of other people. There’s a community of you working together." -- Alison Avenell

"For people who are trying to do research about retractions, the Retraction Watch Database itself has become a really valuable resource." -- Jodi Schneider

📚 Systematic Review vs Meta-analysis: Do You Know the Difference?

They may appear similar, but systematic reviews and meta-analyses serve different purposes in research.

A systematic review uses a structured and transparent process to identify, assess and synthesize existing evidence around a focused research question. A meta-analysis goes a step further by using statistical methods to combine comparable quantitative results from multiple studies and produce a pooled estimate. (BMJ)

🔎 The key takeaway:A systematic review can exist without a meta-analysis, but a meta-analysis is typically conducted as part of a systematic evidence-synthesis process when the available studies are sufficiently comparable.

So, which one does your research require? It depends on your research question, study designs, available evidence and whether the results can meaningfully be combined.

📌 Save this post for your research journey—and share it with a fellow researcher.

Need research support?
📞 +254 706 449 519
📧 communications@javiresearch.co.ke

#ResearchTips #SystematicReview #MetaAnalysis #ResearchMethodology #AcademicResearch #ResearchSupport #JAVIResearch

15 minutes do not mean the same thing to everyone in the city. Walking speeds range from ~ 0.8 to 1.8 m/s (according to a meta-study by Giannoulaki and Christoforou 2024); steps can represent access for some but a barrier for others.

Standing in front of the cathedral in Cologne, a typically assumed walking speed of 1.4 m/s and the ability to traverse stairs gives you access to 700+ amenities and 28 tram or train stations. With a walking speed of 1.1 m/s, you will reach only about 457 amenities. Avoiding stairs as well, it drops down to only half the original number for amenities and stations tagged as step-free.

Try the interactive map here: https://lnkd.in/eze3-5Uc

I recently saw a static map to represent this idea, but I wondered how an interactive map tool could account for such differences. The result shows an isochrone map for a selected location with adjustable parameters: walking speed and step-free access. In the spirit of the 15-minute city (Carlos Moreno), amenities can also be shown to highlight the difference.

The tool runs in browser, on street data downloaded from OSM using osmnx, no server behind it. To get the data for other cities, you can run the code yourself: https://lnkd.in/eQ5TMKnW

Of course, there are many more factors that influence how far different people are willing and able to walk or wheel to, such as sidewalk surfaces, traffic speeds, or the availability of places to rest. Avoiding stairs here only excludes mapped steps, so it is not a full wheelchair route: kerbs, slopes and surfaces are not considered, and OSM is incomplete, so many stops are simply untagged. The tool could be expanded with all kinds of parameters, limited only by the availability of data.

Inspired by an idea from this recent post: https://lnkd.in/esMach3N | 53 comments on LinkedIn

I love this paper by Safieh Shah Shruti . Madhukar Pai, MD, PhD and others that underlines again,why we need people with lived experience, those on the receiving end of interventions to imagine solutions.

"The most visible “solutions” to hunger, poverty, and disease are often imagined by those who have been structurally insulated from scarcity, dispossession or violence. Because they never have to face the consequences of their own band-aid solutions, their imaginations are not shaped by real stakes in the matter."

“You can’t see what you’ve never had to live”—Cultivating imagination and solution spaces in global health and development | PLOS Global Public Health

♾️ Connaissez-vous la théorie des trois infinis?


Vous avez déjà entendu les termes : infiniment grand et infiniment petit.
En existe-t-il un troisième ?

Blaise Pascal, dans ses Pensées en 1670, serait le premier à avoir placé l'être humain entre « ces deux infinis ». En 1696, le mathématicien Guillaume de L'Hôpital publiait son "Analyse des infiniment petits" avant qu’ en 1727, l'« Infiniment grand » ne devienne un sujet de géométrie chez Fontenelle.

Ce couple d'infini est devenu une expression courante au XIXe siècle pendant la révolution industrielle. Aujourd'hui encore, la célèbre cosmographe Hélène Courtois travaille pour l'Institut des deux infinis de Lyon.

Mais alors, que serait le « troisième infini» ?

L’idée nous vient de Teilhard de Chardin, génie visionnaire qui combinait les approches scientifiques et religieuses pour définir une nouvelle échelle constitutive de notre réalité : la complexité.

Ainsi nous aurions :

1. L'infiniment grand : l'immensité macroscopique
2. L'infiniment petit : l'invisible microscopique
3. et l'infiniment complexe : le vertige natté de la relation, mis en lumière par les sciences de l'information et du milieu, le mesoscopique.

Je suis complètement fasciné par cette nouvelle idée qui m'emporte.
Un troisième infini, celui des intervalles.

Cette idée a compté fort dans l'élaboration de mon deuxième livre : Bioconscience qui sort le 8 octobre prochain, soit à la fnac (https://buff.ly/WvtXqmk ) soit chez les libraires indépendants (https://buff.ly/qSZTD27), ou directement chez mon éditeur (https://buff.ly/Lar67at )

On en parle quand vous voulez.
Je serai dans les salons du livre et quelques médias prochainement pour en parler.

Cette photo montre un blob et du lichen dans une forêt des Appalaches, aux Etats Unis. Elle figure dans le livre mais voici le cliché original complet. Il m'a frappé quand je l'ai découvert https://buff.ly/IubzVHD.

____

Ma newsletter, c'est ici https://buff.ly/rdIheH1
Découvrez mon travail sur www.maximeblondeau.com | 14 comments on LinkedIn

My friend earns around $1.1M a year as an engineer at Anthropic.
I asked him one question. How did you get so good at prompting?
He sent me a video explaining the prompting techniques used by Anthropic’s core team.
I watched it last night, and halfway through, I realized I’d been using Claude wrong for the past 18 months.
If you use Claude for work, research, or building AI products, this is worth watching.
Watch it. Save it. Revisit it.

#AI #Claude

€3,028.55 for one article. Or: The cost of Open Access publishing

Of course I always knew that Open Access publishing was expensive. But while working in academia, the real impact was comfortably hidden from me. The library paid part of it, and for the rest I usually had grant money or overheads somewhere.

That changes rather abruptly when you're working in your own company.

This is the invoice for my latest article (see https://lnkd.in/p/eKyGqfme): €2,490 APC before VAT.

What particularly annoyed me: paying directly by credit card was free, but choosing payment by invoice added another €55. Can't imagine another business charging €55 on top of a €2,490 processing charge for an invoice sent as pdf via mail.

Now you could say: Nobody forced you to publish Open Access.

True. But there are two reasons why that answer doesn't really satisfy me.
First, this paper isn't written only for an academic audience. It addresses people working with detection dogs. Most dog trainers don't have a university library subscription, and I don't particularly like publishing something for practitioners and then putting it behind a paywall where reading one paper can cost them more than $30. Just to figure out that it was not relevant.

Second, the alternative isn't particularly attractive either. In traditional publishing, authors can end up transferring substantial rights over their own work to the publisher. I have had PhD students who needed written permission from the publisher to add a figure they made themselves their own thesis. Because it was part of a published paper. With the PhD student as author.

But surely the APC pays for all the scientific work around the paper? Well...

I've handled manuscripts as an associate editor. I wasn't paid. I've reviewed plenty of manuscripts for journals. I wasn't paid for that either. And a careful review can easily consume a significant part of one or two working days.

So, incidentally: a big thank-you to the editor and reviewers of this manuscript for doing that work voluntarily.

Is the solution simply to forget journals and put everything on preprint servers? Not quite...

I've previously run into arXiv's endorsement system when trying to enter a new field. And I've had a computational manuscript rejected during bioRxiv's scope screening. Preprints are an important part of the solution, but they aren't a completely frictionless replacement either.

No conclusion or brilliant insight from me here. I just wanted to add some real numbers to the Open Access discussion — from the perspective of a small business. | 12 comments on LinkedIn

There are widespread concerns that AI will reduce our critical-thinking skills. 🧠 My latest story for Nature examines how to maintain those skills — and teach them to students. I found some fascinating research and useful tips for us all! 
https://lnkd.in/gCkxPmMk

A few things I learned: 
➡️ If you ask five people to define critical thinking, you'll get five different answers. (I tried.)  
 
➡️ Despite scare stories, studies do not show clearly how AI tools affect learning and critical thinking. It depends how AI is used, and there are few rigorous, long-term studies.  
 
➡️ But, in general, learning requires effortful cognitive processing (the stuff that feels hard). It's awfully tempting to outsource that to AI – and so educators and psychologists are gloomy about AI's likely impact. 
 
➡️ Teaching critical thinking is hard too. It requires knowledge and skills specific to a field — so someone who can think critically in medicine can't necessarily do so in history. It needs study and practise, and so long-term efforts in schools.
 
➡️ Nevertheless, some researchers point to common strategies. Tips I heard include:  
- Do the things that feel hard/slow (testing, summarizing from memory, thinking through material). Friction is good for learning. 
- Explain things out loud or in writing (without AI) to test what you really know; 
- Systematically consider alternative solutions or points of view; 
- Ask students to reflect on a question and their own arguments before asking AI for help.  
With many thanks to the researchers who spoke to me for this story. (I wrote this before moving to MIT to be a Knight Science Journalism Fellow, it just took a while to publish!) | 35 comments on LinkedIn

PhD Students - When to Use Which Tool?

PhD research involves many different tasks, and the right AI tool can support you at each stage of the research workflow.

1. AnswerThis (YC F25) - Research paper writing
2. Paperpal - Grammar and typo checking
3. SciSpace - Data extraction for literature review
4. Review-it - Peer review of manuscripts
5. DataLumio - Data analysis
6. Liner - Finding citations for your paper
7. Ponder - Understanding complex research
8. Jenni AI - Identifying papers for literature review
9. Bohrium - Converting research papers to posters

From writing and literature discovery to data analysis, manuscript review, citation finding, and research presentation, choosing tools based on the task can make your workflow more organized and efficient.

Connect on all platforms : https://lnkd.in/g3eqYsxa

Follow Research Thesis Dissertation Project Term Paper Essay Report Proposal Article Journal Writing Helper for more

Save this post as a quick reference for your PhD research toolkit.

Need support with your thesis, dissertation, report, or research paper? Message us to discuss your requirements.

#PhDStudents #PhDResearch #ResearchTools #AcademicResearch

🌫️ Air pollution knows no borders.

Around 2 billion tonnes of sand and dust enter the atmosphere every year. In 2026, several major episodes of dust transport from the Sahara crosse

The scientific publication system is in trouble. Some even say it's "broken".

The key issue: the number of papers published each year is growing #exponentially. And everyone involved - scientists, editors, reviewers, journalists - is struggling to keep up.

There are several reasons. Of course there is the surge of AI-generated bullshit papers from #papermills. But it's also because the pressure to publish A LOT OF PAPERS keeps increasing for researchers. And it's because LLMs now allow scientists from all over the world to submit papers in polished academic English (which is a good thing). And some argue it's because commercial publishers made it their business model to publish as many papers as possible.

This affects not only the academic bubble. If the system can't keep up with this tsunami of studies, it also can't guarantee anymore that they contain solid research. And this makes science vulnerable to attacks from sceptics.

More papers ≠ more scientific progress

How to fix this?

Super excited to discuss this in two weeks with Alice Fleerackers, Mark Hanson, Volker Stollorz and Max Voegler. Join our session on the #PeerReviewCrisis at #Wissenswerte!

Graph from Beigel et al. (2025): The Drain of Scientific Publishing
| 32 comments on LinkedIn

Les chiffres de la visite du pape Léon XIV en France racontent une histoire médiatique rare
https://lnkd.in/ed5W7jef

1,66 million de mentions dans le monde. 576 600 sur les réseaux sociaux en France seule. 23 630+ mentions en médias traditionnels (print, digital, TV, radio, podcasts). Quatre jours (25-28 septembre) qui ont placé un événement religieux au cœur du débat public français.

Le pic : Samedi 26 septembre à 14 h 45 - les Champs-Élysées. Pas un hasard car c'est le moment où la liturgie, la foule et l'image (Marie-Garance, enfant bénie) se sont cristallisées en un fait national.
Ce qui surprend dans le classement des thèmes : la liturgie domine à 16,1 %, suivie par la jeunesse et la foi (13,4 %). Emmanuel Macron n'arrive qu'en quatrième position (10,2 %).

Les sujets sécurité, coût restent, eux, secondaires. Cela na pas été un événement politique récupéré mais plutôt un événement médiatique structuré par le programme lui-même.

Pour les professionnels de la communication, c'est un cas d'école: comment un agenda institutionnel se déploie dans l'espace médiatique, quand il reste maître du récit.

Lire l'analyse Onclusivecomplète sur notre blog. https://lnkd.in/ed5W7jef

#PapeEnFrance #MediaMonitoring #AnalyseMédias #Onclusive

L'IA en recherche académique : une question de timing, pas d'accumulation.

Multiplier les applications ne garantit ni la qualité ni la vitesse. Tout repose sur l'alignement entre l'outil et le stade de la recherche :

➡️ Exploration & Revue de littérature ➔ Automatiser le repérage et la gestion des références.
➡️ Structuration & Mise en forme ➔ Fluidifier le traitement des données et la rédaction.
➡️ Interprétation & Positionnement critique ➔ 100 % Humain.

L'objectif de l'IA n'est pas d'automatiser la pensée scientifique, mais d'éliminer la friction répétitive pour redonner toute sa place à l'analyse analytique.

#AcademicResearch #PhDLife #ResearchTools #IA #Méthodologie

Les flux "normaux" dans le monde

Les cartes de webcams en direct font leur retour : une manière singulière de parcourir le monde, non pas à travers ses monuments, mais par ses scènes ordinaires. Attendre un bus, marcher dans une rue, flâner sur une place ou traverser un marché : ces images révèlent la répétition des gestes qui font vivre les territoires au quotidien.

Le site https://worldwatcher.live/ apporte un regard sur les comportements « normaux » il déplace notre attention vers les rythmes locaux, les usages de l’espace public et les temporalités propres à chaque lieu.

Source : https://worldwatcher.live/ via Big Think, « How to restore your online sanity, one random webcam at a time » (29 septembre 2026).

I turned my content archive into an interactive resource that helps experts decide what to publish to show their competence, without writing another article.