In Part 1 
, I walked through the six repositories that make up my MindsEye x Google AI stack:

Workspace automation

Ledger (Prompt Evolution Tree + runs)

Gemini orchestrator

Devlog generator

Analytics

Workflow atlas

All of that was basically:

“Here’s the skeleton. Here’s how each bone connects.”

In this post, I want to zoom out and talk about how this behaves when it’s not just me using it, but ~200 people, each with their own prompts, workflows, and experiments.

Specifically:

How the ledger behaves like a soft blockchain

How users stay isolated but still learn from each other

How Gemini (Jimin) becomes the orchestrator/kernel

Why this starts to look suspiciously like an Android-style OS built on Google Workspace

From “My Stack” to “200-User MindsEye OS”

Part 1 was written from a solo-builder perspective.

But the design isn’t actually single-user. It’s intentionally structured so it can scale to something like:

200 people

across multiple teams

all using the same six-repo stack

all sharing the same Google Workspace surfaces

The key shift:

It stops being “my scripts” and becomes a shared operating system:

MindsEye OS running on Google Workspace + Gemini.

Everyone still has their own flows, but they all land in a shared ledger that knows:

who did what

with which node

in which context

at what time

And that’s where the “soft blockchain” idea comes in.

<ol>
<li>The Ledger as a Soft Blockchain (Without Crypto, With Memory)</li>
</ol>

The mindseye-google-ledger repo defines:

a nodes sheet (Prompt Evolution Tree)

a runs sheet (every execution of those nodes)

When 200 people use it:

Each person effectively grows their own subtree of the PET.

Each run is append-only: you don’t edit history, you add new facts.

Node ownership is tracked (owner, team, created_by).

Runs are timestamped and tied to nodes via node_id.

You can start to think about it like this:

Nodes = blocks of intent / design

Runs = transactions / state updates

Ledger = chain of decisions and experiments over time

It’s not a blockchain in the crypto sense (no consensus algorithm, no miners), but it is:

immutable in practice (you don’t rewrite past runs)

multi-tenant (200 people writing to the same structure)

auditable (Sheets history + Git history)

pattern-rich (you can learn from the whole thing)

The important part:

People never overwrite each other’s data. 
They write next to each other in time, and MindsEye learns over that timeline.

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F8tievqk5ljcayubp05n2.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F8tievqk5ljcayubp05n2.png" alt=" " width="800" height="1200"></a>

<ol>
<li>How 200 Users Actually Interact With the Ledger</li>
</ol>

Here’s the mental model I’m designing for:

3.1 Personal Branches, Shared Universe

Each user:

submits ideas via Forms

labels emails in Gmail

uses Docs menus like “Send to MindsEye”

triggers runs against specific PET nodes

Those actions:

create nodes with owner = that user

append runs scoped to that node

generate linked Docs per node (prompt docs, notes, etc.)

So each person effectively has:

their own PET branch (or several)

their own run history

their own Docs attached to nodes

But all of those live in the same global ledger, with:

owner, team, tags, status

protected columns vs editable ones

per-team views and filters

This gives you:

a single source of truth

multiple personal “worlds” inside it.

3.2 Cross-User Interaction: Patterns, Not Raw Data

The interesting part isn’t “user A can see user B’s cells.”

It’s that Gemini / analytics can see everyone’s patterns, and then:

recommend structure

highlight similar nodes

surface “this pattern worked well in team X”

suggest merges or variants

So instead of:

“Here, use Bob’s exact prompt.”

you get:

“Other users who had a node like this tried three variations – here’s the pattern and the metrics. Want to branch from that?”

Data stays owned and scoped, but intelligence is shared.

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fvdr4jjouf6jji8a6inqp.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fvdr4jjouf6jji8a6inqp.png" alt=" " width="800" height="1200"></a> 
Jimin (Gemini) as Kernel: Orchestrating the Whole Mesh

Right now, the orchestrator is “mostly skeleton” because I don’t have paid Gemini access yet. But architecturally, its role is clear:

4.1 What Jimin Does in the Stack

Inside mindseye-gemini-orchestrator, Jimin is meant to:

Listen to the ledger

pull candidate nodes to run (e.g. status = ready_for_run)

read node metadata, linked docs, past runs

Execute

call Gemini (or other Google AI)

generate outputs, scores, notes

Write Back

append new runs to runs

update aggregate fields (avg_score, last_run, etc.) in nodes

Suggest

propose new child nodes (“this looks like it needs a variation”)

flag underperforming branches

cluster similar prompts across users/teams

Once API &amp; cloud access are in place, this becomes the kernel of MindsEye OS:

it sits between Workspace and the ledger

enforces structure

drives experimentation

maintains the time-labeled memory.

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F3of6an91ohhxq6u4ocvd.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F3of6an91ohhxq6u4ocvd.png" alt=" " width="800" height="1200"></a>

<ol>
<li>How This Looks in Practice (Very Concrete)</li>
</ol>

For a 200-user environment, the UX I’m aiming at looks something like this:

5.1 For Devs / Data People

GitHub access to all 6 repos

Clear Typescript/Sheets schema for Node and Run

Workflow YAMLs in mindseye-google-workflows that describe the full graph

Read/write access to ledger Sheets (with protected ranges)

They use MindsEye OS like:

an integration + experimentation framework

a time-aware datastore

a playground for Gemini orchestration

5.2 For Product / Ops / Biz Folks

Google Form to create new nodes (“New experiment” / “New AI flow idea”)

Google Sheets filtered to:

their team’s nodes

their active experiments

Google Docs per node:

prompt text

expectations

notes

Devlogs auto-generated weekly:

“what happened with your prompts this week?”

They use MindsEye OS like:

a living experiment ledger

a source of weekly narrative (“what did we try?”)

a way to see impact via analytics dashboards

5.3 For Leadership / Stakeholders

Looker Studio dashboards:

total nodes, total runs

active vs inactive branches

success rate per team

most active workspaces (Gmail/Docs/Forms/etc.)

Optional rollup Docs:

“MindsEye OS sprint summary”

“Prompt evolution in Q4”

They use MindsEye OS like:

instrumentation for the org’s AI efforts

a way to see which teams are really experimenting vs talking about AI.

<ol>
<li>The OS Analogy: Android, But Built on Workspace</li>
</ol>

This is the part I’m honestly most excited about.

The more I sketch this, the more it feels like a small, weird operating system:

6.1 Layers (Very Roughly)

Hardware Layer → Google Workspace surfaces

Gmail, Docs, Sheets, Forms, Drive, Calendar

Kernel Layer → Jimin (Gemini orchestrator)

handles events, scheduling, execution, writes to ledger

System Services → the 6 repos

automation, ledger, orchestrator, devlog, analytics, workflows

Application Framework → team-specific PET branches &amp; workflows

how each team uses prompts + flows

Apps / UI →

Sheets views

Docs menus

Forms

Looker dashboards

future MindsEye Android / AppSheet apps

Android coordinates hardware + apps. 
MindsEye OS coordinates Workspace + users + Gemini.

<ol>
<li>A Rough Architecture Diagram (Mermaid)</li>
</ol>

For visual thinkers, here’s a sketch of the MindsEye OS stack in Mermaid. 
You can plug this into GitHub / Dev.to if you enable Mermaid support:

flowchart TB 
 subgraph Users[200 Users / Teams] 
 U1[User A PET branch] 
 U2[User B PET branch] 
 U3[User C ...] 
 end

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>subgraph Workspace[Google Workspace]
 GMail[Gmail]
 GDocs[Docs]
 GSheets[Sheets&lt;br/&gt;Ledger]
 GForms[Forms]
 GDrive[Drive]
 GCal[Calendar]
end

subgraph OS[MindsEye OS (6 Repos)]
 subgraph WA[mindseye-workspace-automation]
 WA1[Apps Script listeners]
 WA2[Event normalizer]
 end

 subgraph LEDGER[mindseye-google-ledger]
 Nodes[(nodes sheet)]
 Runs[(runs sheet)]
 end

 subgraph ORCH[mindseye-gemini-orchestrator]
 OR1[Selector]
 OR2[Gemini / Jimin]
 OR3[Run logger]
 end

 subgraph DEVLOG[mindseye-google-devlog]
 D1[Devlog generator]
 end

 subgraph ANALYTICS[mindseye-google-analytics]
 A1[Stats engine]
 A2[Dashboards]
 end

 subgraph WF[mindseye-google-workflows]
 WF1[Workflow YAML]
 WF2[Portal maps]
 end
end

%% Users → Workspace
U1 --&gt; GMail
U1 --&gt; GDocs
U1 --&gt; GForms
U2 --&gt; GMail
U3 --&gt; GDocs

%% Workspace → Automation
GMail --&gt; WA1
GDocs --&gt; WA1
GForms --&gt; WA1
GDrive --&gt; WA1
GCal --&gt; WA1

WA1 --&gt; WA2 --&gt; Nodes
WA2 --&gt; Runs

%% Orchestrator loop
OR1 --&gt; Nodes
OR1 --&gt; Runs
OR1 --&gt; OR2
OR2 --&gt; OR3 --&gt; Runs

%% Devlog / Analytics
D1 --&gt; Nodes
D1 --&gt; Runs

A1 --&gt; Nodes
A1 --&gt; Runs
A1 --&gt; A2

WF1 -. describes .- WA
WF1 -. describes .- ORCH
WF1 -. describes .- DEVLOG
WF1 -. describes .- ANALYTICS
</code></pre>

</div>

This is the “200 users on one OS” picture in my head.

<ol>
<li>What’s Next?</li>
</ol>

I see three main directions from here:

Make it more real for teams

better Sheets views &amp; protected ranges

role/permission patterns for org use

workspace templates for “plug-and-play” teams

Wire in Gemini properly

Jimin as a real orchestrator

configurable models per node

auto-summarization and branch suggestions

Ship a MindsEye Android / AppSheet frontend

mobile UI for nodes, runs, devlogs

notifications on key events

one-tap “run this node again” actions

<ol>
<li>Closing</li>
</ol>

Part 1 was the what. 
This Part 2 is more about the how it behaves when people actually live in it.

The ledger becomes a soft blockchain of experiments.

Gemini (Jimin) becomes the kernel.

The six repos form the system services.

Google Workspace becomes the hardware and window system.

200 people can work inside it without stepping on each other, while still learning from the shared patterns.

If you want to fork it, adapt it to your team, or co-build parts of this, I’m super open to collaboration.

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fd4q5yf42ghp21y72cjk9.webp" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fd4q5yf42ghp21y72cjk9.webp" alt=" " width="800" height="1200"></a>

Drop a comment or ping me — happy to go deeper into any part of the stack.

في الجزء الثاني من سلسلة MindsEye x Google AI، يتم التركيز على بيئة تعاونية تضم حوالي 200 مستخدم. يناقش المقال كيفية عمل السجل كـ 'بلوكشين ناعم'، مما يسمح للمستخدمين بالبقاء معزولين مع الاستمرار في التعلم من بعضهم البعض. كما يبرز دور جيمينى كمنظم ويقترح أن النظام يشبه نظام تشغيل على طراز أندرويد مبني على Google Workspace.

En la Parte 2 de la serie MindsEye x Google AI, el enfoque se desplaza de una perspectiva de constructor único a un entorno colaborativo que involucra aproximadamente 200 usuarios. El artículo discute cómo el libro mayor funciona como una 'blockchain suave', permitiendo a los usuarios permanecer aislados mientras aprenden unos de otros. También destaca el papel de Gemini como orquestador y sugiere que el sistema se asemeja a un sistema operativo de estilo Android construido sobre Google Workspace.

Dans la deuxième partie de la série MindsEye x Google AI, l'accent est mis sur un environnement collaboratif impliquant environ 200 utilisateurs. L'article discute de la manière dont le registre fonctionne comme une 'blockchain douce', permettant aux utilisateurs de rester isolés tout en apprenant les uns des autres. Il met également en avant le rôle de Gemini en tant qu'orchestrateur et suggère que le système ressemble à un système d'exploitation de style Android construit sur Google Workspace.

In Part 2 of the MindsEye x Google AI stack series, the focus shifts from a solo-builder perspective to a collaborative environment involving approximately 200 users. The article discusses how the ledger functions as a 'soft blockchain,' allowing users to remain isolated while still learning from one another. It also highlights the role of Gemini as the orchestrator and suggests that the system resembles an Android-style operating system built on Google Workspace.

MindsEye x Google AI Stack, Part 2 — 200 Users, Ledgers as “Soft Blockchain”, and the OS Vision

<A HREF="https://i10x.ai/news/grok-4-1-eq-bench-empathy-sycophancy-paradox"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251119/i3.jpg"></A>
<A HREF="http://www.techmeme.com/251119/p3#a251119p3" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Christopher Ort / <A HREF="https://i10x.ai/">i10X</A>: 
<A HREF="https://i10x.ai/news/grok-4-1-eq-bench-empathy-sycophancy-paradox">xAI's Grok 4.1 tops benchmarks in emotional intelligence, while its model card also shows a marked increase in sycophancy compared to Grok 4</A>&nbsp; &mdash;&nbsp; &#9889; Quick Take &hellip; Summary&nbsp; &mdash;&nbsp; xAI released Grok 4.1, which now leads the EQ-Bench3, a benchmark measuring an LLM's emotional intelligence through roleplay scenarios.

أصدرت شركة xAI النسخة 4.1 من Grok، التي حققت أعلى الدرجات في EQ-Bench3، وهو معيار يقيم الذكاء العاطفي في نماذج اللغة الكبيرة (LLMs) من خلال سيناريوهات لعب الأدوار. يُظهر النموذج الجديد زيادة ملحوظة في التملق مقارنة بسابقه، Grok 4. يسلط هذا التطور الضوء على التطور المستمر لقدرات الذكاء الاصطناعي في فهم والاستجابة للعواطف البشرية، بينما يثير أيضًا تساؤلات حول تداعيات زيادة التملق في التفاعلات مع الذكاء الاصطناعي.

xAI ha lanzado Grok 4.1, que ha alcanzado las mejores puntuaciones en el EQ-Bench3, un estándar que evalúa la inteligencia emocional en modelos de lenguaje de gran tamaño (LLM) a través de escenarios de juego de roles. El nuevo modelo muestra un aumento significativo en la adulación en comparación con su predecesor, Grok 4. Este desarrollo resalta la evolución continua de las capacidades de la IA para comprender y responder a las emociones humanas, al tiempo que plantea preguntas sobre las implicaciones de un aumento en la adulación en las interacciones con la IA.

xAI a lancé Grok 4.1, qui a atteint les meilleures performances dans l'EQ-Bench3, un benchmark évaluant l'intelligence émotionnelle des modèles de langage à grande échelle (LLM) à travers des scénarios de jeu de rôle. Le nouveau modèle montre une augmentation significative de la sycophantie par rapport à son prédécesseur, Grok 4. Ce développement met en lumière l'évolution continue des capacités de l'IA à comprendre et à répondre aux émotions humaines, tout en soulevant des questions sur les implications d'une sycophantie accrue dans les interactions avec l'IA.

xAI has released Grok 4.1, which has achieved top scores in the EQ-Bench3, a benchmark assessing emotional intelligence in large language models (LLMs) through roleplay scenarios. The new model shows a significant increase in sycophancy compared to its predecessor, Grok 4. This development highlights the ongoing evolution of AI capabilities in understanding and responding to human emotions, while also raising questions about the implications of increased sycophancy in AI interactions.

xAI's Grok 4.1 tops benchmarks in emotional intelligence, while its model card also shows a marked increase in sycophancy compared to Grok 4 (Christopher Ort/i10X)

<img width="800" height="450" src="https://analyticsindiamag.com/wp-content/uploads/2024/11/Funding-for-Data-Centres-is-on-the-Rise-1300x731.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin: auto; margin-bottom: 5px;max-width: 100%;" link_thumbnail="" decoding="async" fetchpriority="high" srcset="https://analyticsindiamag.com/wp-content/uploads/2024/11/Funding-for-Data-Centres-is-on-the-Rise-1300x731.jpg 1300w, https://analyticsindiamag.com/wp-content/uploads/2024/11/Funding-for-Data-Centres-is-on-the-Rise-600x338.jpg 600w, https://analyticsindiamag.com/wp-content/uploads/2024/11/Funding-for-Data-Centres-is-on-the-Rise-768x432.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2024/11/Funding-for-Data-Centres-is-on-the-Rise-1536x864.jpg 1536w, https://analyticsindiamag.com/wp-content/uploads/2024/11/Funding-for-Data-Centres-is-on-the-Rise-150x84.jpg 150w, https://analyticsindiamag.com/wp-content/uploads/2024/11/Funding-for-Data-Centres-is-on-the-Rise.jpg 1600w" sizes="(max-width: 800px) 100vw, 800px" />India generates 20% of global data but stores only 3% of it locally. Two forces are now working to close this gap. 
The post <a href="https://analyticsindiamag.com/ai-features/unpacking-indias-data-centre-boom/">Unpacking India’s Data Centre Boom </a> appeared first on <a href="https://analyticsindiamag.com">Analytics India Magazine</a>.

تشهد الهند ازدهارًا كبيرًا في مراكز البيانات، مدفوعًا بالطلب المتزايد على خدمات السحابة والبنية التحتية الرقمية. يجذب هذا النمو استثمارات كبيرة، حيث تدرك الشركات إمكانات السوق الهندي. من المتوقع أن يعزز ارتفاع مراكز البيانات القدرات التكنولوجية للبلاد ويدعم قطاعات متعددة، بما في ذلك الذكاء الاصطناعي والحوسبة السحابية.

India está experimentando un auge significativo en los centros de datos, impulsado por la creciente demanda de servicios en la nube e infraestructura digital. Este crecimiento está atrayendo inversiones sustanciales, ya que las empresas reconocen el potencial del mercado indio. El aumento de los centros de datos se espera que mejore las capacidades tecnológicas del país y apoye varios sectores, incluida la inteligencia artificial y la computación en la nube.

L'Inde connaît un boom significatif des centres de données, stimulé par une demande croissante de services cloud et d'infrastructures numériques. Cette croissance attire des investissements substantiels, les entreprises reconnaissant le potentiel du marché indien. L'essor des centres de données devrait améliorer les capacités technologiques du pays et soutenir divers secteurs, y compris l'intelligence artificielle et l'informatique en nuage.

India is experiencing a significant boom in data centers, driven by increasing demand for cloud services and digital infrastructure. This growth is attracting substantial investments, with companies recognizing the potential of India's market. The rise of data centers is expected to enhance the country's technological capabilities and support various sectors, including artificial intelligence and cloud computing.

Unpacking India’s Data Centre Boom

TikTok is adding new digital wellbeing tools like an affirmation journal, a background sound generator, and badges for using the app within limits

تقوم تيك توك بإدخال أدوات جديدة للرفاهية الرقمية تهدف إلى تعزيز عادات الاستخدام الأكثر صحة بين مستخدميها. تشمل الميزات دفتر ملاحظات للتأكيدات، ومولد أصوات خلفية، وشارات تكافئ المستخدمين على تقليل وقتهم في التطبيق. تم تصميم هذه الأدوات لمساعدة المستخدمين في إدارة وقت الشاشة وتقليل الآثار السلبية للاستخدام المفرط لوسائل التواصل الاجتماعي.

TikTok está introduciendo nuevas herramientas de bienestar digital destinadas a promover hábitos de uso más saludables entre sus usuarios. Las funciones incluyen un diario de afirmaciones, un generador de sonidos de fondo y medallas que recompensan a los usuarios por limitar su tiempo en la aplicación. Estas herramientas están diseñadas para ayudar a los usuarios a gestionar su tiempo de pantalla y reducir los efectos negativos del consumo excesivo de redes sociales.

TikTok introduit de nouveaux outils de bien-être numérique visant à promouvoir des habitudes d'utilisation plus saines parmi ses utilisateurs. Les fonctionnalités comprennent un journal d'affirmations, un générateur de sons d'ambiance et des badges qui récompensent les utilisateurs pour avoir limité leur temps sur l'application. Ces outils sont conçus pour aider les utilisateurs à gérer leur temps d'écran et à réduire les effets négatifs d'une consommation excessive des réseaux sociaux.

TikTok is introducing new digital wellbeing tools aimed at promoting healthier usage habits among its users. The features include an affirmation journal, a background sound generator, and badges that reward users for limiting their time on the app. These tools are designed to help users manage their screen time and reduce the negative effects of excessive social media consumption.

TikTok will now give you badges for limiting your doomscrolling

<A HREF="https://techcrunch.com/2025/11/18/tiktok-now-lets-you-choose-how-much-ai-generated-content-you-want-to-see/"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251119/i2.jpg"></A>
<A HREF="http://www.techmeme.com/251119/p2#a251119p2" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Aisha Malik / <A HREF="http://techcrunch.com/">TechCrunch</A>: 
<A HREF="https://techcrunch.com/2025/11/18/tiktok-now-lets-you-choose-how-much-ai-generated-content-you-want-to-see/">TikTok will let users choose how much AI-generated content appears in their For You feed and plans to add more advanced labeling tech for AI-content</A>&nbsp; &mdash;&nbsp; TikTok, an app that was once just a place for user-generated content, is launching a new setting that lets users choose how much AI-generated content &hellip;

تقوم تيك توك بإطلاق ميزة جديدة تتيح للمستخدمين التحكم في كمية المحتوى الذي يتم إنشاؤه بواسطة الذكاء الاصطناعي في خلاصة "For You" الخاصة بهم. تهدف هذه الإعدادات إلى تحسين تجربة المستخدم من خلال توفير المزيد من خيارات التخصيص. بالإضافة إلى ذلك، تخطط تيك توك لتنفيذ تقنية تصنيف متقدمة لتحديد المحتوى الذي يتم إنشاؤه بواسطة الذكاء الاصطناعي بشكل أفضل، مما يضمن الشفافية للمستخدمين. تعكس هذه الخطوة الجهود المستمرة للمنصة للتكيف مع تفضيلات المستخدمين وتأثير الذكاء الاصطناعي المتزايد في وسائل التواصل الاجتماعي.

TikTok está lanzando una nueva función que permite a los usuarios controlar la cantidad de contenido generado por IA en su feed de For You. Esta configuración tiene como objetivo mejorar la experiencia del usuario al proporcionar más opciones de personalización. Además, TikTok planea implementar tecnología de etiquetado avanzada para identificar mejor el contenido generado por IA, asegurando la transparencia para los usuarios. Este movimiento refleja los esfuerzos continuos de la plataforma para adaptarse a las preferencias de los usuarios y la creciente influencia de la inteligencia artificia…

TikTok introduit une nouvelle fonctionnalité permettant aux utilisateurs de contrôler la quantité de contenu généré par l'IA dans leur fil For You. Ce paramètre vise à améliorer l'expérience utilisateur en offrant davantage d'options de personnalisation. De plus, TikTok prévoit de mettre en œuvre une technologie d'étiquetage avancée pour mieux identifier le contenu généré par l'IA, garantissant ainsi la transparence pour les utilisateurs. Ce mouvement reflète les efforts continus de la plateforme pour s'adapter aux préférences des utilisateurs et à l'influence croissante de l'intelligence arti…

TikTok is introducing a new feature that allows users to control the amount of AI-generated content in their For You feed. This setting aims to enhance user experience by providing more customization options. Additionally, TikTok plans to implement advanced labeling technology to better identify AI-generated content, ensuring transparency for users. This move reflects the platform's ongoing efforts to adapt to user preferences and the growing influence of artificial intelligence in social media.

TikTok will let users choose how much AI-generated content appears in their For You feed and plans to add more advanced labeling tech for AI-content (Aisha Malik/TechCrunch)

Wall Street will get a sense of where the billions of dollars being spent on artificial intelligence are going when Nvidia Corp. reports its earnings after the bell on Wednesday. How the sinking stock market will react is another question.

من المقرر أن تعلن شركة إنفيديا عن أرباحها يوم الأربعاء، مما يوفر رؤى حول الاستثمارات الكبيرة في الذكاء الاصطناعي. تبقى ردود فعل السوق، خاصةً في ظل الانخفاض الأخير، غير مؤكدة. تراقب وول ستريت عن كثب كيف ستعكس هذه الأرباح القطاع التكنولوجي الأوسع في ظل المخاوف المتزايدة بشأن الإنفاق على الذكاء الاصطناعي.

Nvidia Corp. está programada para informar sus ganancias el miércoles, proporcionando información sobre las significativas inversiones en inteligencia artificial. La reacción del mercado, especialmente dado el reciente descenso, sigue siendo incierta. Wall Street está observando de cerca cómo estos resultados reflejarán el sector tecnológico más amplio en medio de crecientes preocupaciones sobre el gasto en IA.

Nvidia Corp. doit publier ses résultats mercredi, offrant un aperçu des investissements considérables dans l'intelligence artificielle. La réaction du marché, en particulier compte tenu de la récente baisse, reste incertaine. Wall Street surveille de près comment ces résultats refléteront le secteur technologique plus large face aux préoccupations croissantes concernant les dépenses en IA.

Nvidia Corp. is set to report its earnings on Wednesday, providing insights into the significant investments being made in artificial intelligence. The market's reaction, particularly given the recent downturn, remains uncertain. Wall Street is closely monitoring how these earnings will reflect on the broader tech sector amidst growing concerns about AI spending.

Nvidia Earnings Run Into a Market Suddenly Afraid of AI Spending

With the new AI-generated content control, users who want to see less of this sort of content can dial things down, while those who enjoy it can choose to see more of it.

قدمت تيك توك ميزة جديدة تتيح للمستخدمين التحكم في كمية المحتوى الذي يتم إنشاؤه بواسطة الذكاء الاصطناعي الذي يواجهونه على المنصة. تتيح هذه الميزة للمستخدمين تقليل أو زيادة رؤية هذا النوع من المحتوى وفقًا لتفضيلاتهم، مما يعزز تجربتهم العامة على التطبيق.

TikTok ha introducido una nueva función que permite a los usuarios controlar la cantidad de contenido generado por IA que encuentran en la plataforma. Esta función permite a los usuarios reducir o aumentar la visibilidad de dicho contenido según sus preferencias, mejorando así su experiencia general en la aplicación.

TikTok a introduit une nouvelle fonctionnalité permettant aux utilisateurs de contrôler la quantité de contenu généré par l'IA qu'ils rencontrent sur la plateforme. Cette fonctionnalité permet aux utilisateurs de réduire ou d'augmenter la visibilité de ce type de contenu selon leurs préférences, améliorant ainsi leur expérience globale sur l'application.

TikTok has introduced a new feature that allows users to control the amount of AI-generated content they encounter on the platform. This feature enables users to either reduce or increase the visibility of such content according to their preferences, enhancing their overall experience on the app.

TikTok now lets you choose how much AI-generated content you want to see

🚀 I just built my first ever 2D game - Death’s Job! 💀🎮 
A little ghost. A sky full of obstacles. And a lot of physics.

I started this project purely out of curiosity - 
“How do people make a game from scratch using something low-level like Pygame?”

So, I opened a new Python file… and a few days later, I surprisingly had most of the game logic working. 
Gravity, physics, collisions, obstacle generation - all fun to implement.

But then I hit the real boss fight in game development: 
🎨 finding assets that actually match. 
I was using random images from the internet… and the game looked like a cursed collage.

Then Diwali happened. Then my university exams happened. 
And the project sat untouched.

Once exams ended, I returned with fresh energy and decided to finish it properly. 
So, I generated matching sprites using Gemini + creative prompting, and honestly… asset creation was harder than the coding 😅

For sound effects and background music, I went digging through Pixabay and finally found pieces that fit the vibe.

Today - after polishing the UI, splash screen, physics, and gameplay. 
I’m proud to say the game is complete. 🎉

🎮 Death’s Job — Features

<ul>
<li>Interactive splash screen with animated buttons</li>
<li>Flappy Bird–style jumping with horizontal controls</li>
<li>Smooth physics (gravity, velocity, damping)</li>
<li>Pixel-perfect mask-based collision detection</li>
<li>Randomized obstacles with recycling system</li>
<li>Infinite scrolling cloud background</li>
<li>Jump/Collision SFX + looping background music</li>
<li>Auto-reset system</li>
<li>Runs at 60 FPS</li>
</ul>

🎥 Gameplay Recording Gif:

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F0xhi2ul08ia2zu8ouifw.gif" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F0xhi2ul08ia2zu8ouifw.gif" alt="Gameplay gif" width="426" height="240"></a>

🔗 GitHub Repository: 
<a href="https://github.com/IamVatsal/Deaths-Job" rel="noopener noreferrer">Death's Game</a>

This project taught me:

<ul>
<li>How game engines handle physics at a low level</li>
<li>How deceptively hard good asset selection is</li>
<li>How much polish simple games actually need</li>
<li>And how fun it is to give life to something you built from scratch</li>
</ul>

I’d love feedback, ideas, or critiques - especially from anyone into game dev or Python.

Thanks for reading! 😊

يتناول المقال إنشاء لعبة ثنائية الأبعاد بعنوان 'Death's Job'، تم تطويرها باستخدام Pygame. بدأ المطور هذا المشروع بدافع الفضول حول تطوير الألعاب. بعد تنفيذ منطق اللعبة في البداية، بما في ذلك الجاذبية والفيزياء، واجه المشروع تحديات في العثور على أصول مناسبة. بعد فترة توقف بسبب ديوالي وامتحانات الجامعة، عاد المطور لإنهاء اللعبة، حيث قام بإنشاء شخصيات متطابقة باستخدام Gemini والحصول على مؤثرات صوتية من Pixabay. المنتج النهائي يتضمن شاشة بداية تفاعلية، قفزات على طراز Flappy Bird، فيزياء سلسة، واكتشاف دقيق للتصادم.

El artículo trata sobre la creación de un juego 2D titulado 'Death's Job', desarrollado utilizando Pygame. El desarrollador comenzó este proyecto por curiosidad sobre el desarrollo de juegos. Después de implementar inicialmente la lógica del juego, incluyendo gravedad y física, el proyecto enfrentó desafíos para encontrar activos adecuados. Tras un descanso debido a Diwali y los exámenes universitarios, el desarrollador regresó para completar el juego, generando sprites coincidentes con Gemini y obteniendo efectos de sonido de Pixabay. El producto final cuenta con una pantalla de inicio interactiva, saltos al estilo de Flappy Bird, física suave y detección de colisiones precisa.

L'article traite de la création d'un jeu 2D intitulé 'Death's Job', développé avec Pygame. Le développeur a entrepris ce projet par curiosité pour le développement de jeux. Après avoir initialement mis en œuvre la logique du jeu, y compris la gravité et la physique, le projet a rencontré des difficultés pour trouver des ressources adaptées. Après une pause due à Diwali et aux examens universitaires, le développeur est revenu pour terminer le jeu, générant des sprites correspondants avec Gemini et trouvant des effets sonores sur Pixabay. Le produit final comprend un écran de démarrage interactif, des sauts de style Flappy Bird, une physique fluide et une détection de collision pixel-perfect.

The article discusses the creation of a 2D game titled 'Death's Job,' developed using Pygame. The developer embarked on this project out of curiosity about game development. After initially implementing game logic, including gravity and physics, the project faced challenges in finding suitable assets. Following a break due to Diwali and university exams, the developer returned to complete the game, generating matching sprites with Gemini and sourcing sound effects from Pixabay. The final product features an interactive splash screen, Flappy Bird-style jumping, smooth physics, and pixel-perfect collision detection.

Death’s Job Game

<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

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