Yazid

[Yazid @YMosaddeq]

@the_Lawrenz You see when you watch your game but you see morocco everywhere!??

[Mustapha ElAlaoui 🇲🇦 @Ymustapha178]

محمد وهبي: كنت أريد مدربا مساعدا أجنبيا، لأني بحاجة إلى شخص يملك القدرة على الحفاظ على برودة أعصابه وأخذ مسافة عن الضغط العاطفي في اللحظات الصعبة.

ساكرامينتو البرتغالي🇵🇹:

[Nico Ambrogi @Nicoambrogi]

Literalmente Bono

[The Touchline | 𝐓 @TouchlineX]

🚨 𝗦𝗧𝗔𝗧: Netherlands vs. Morocco possession during the 2nd half of the extra time:

• Netherlands: 7%
• Morocco: 93%

[Yazid @YMosaddeq]

Unforgettable night.. I love you Issa, Bono, Saibari... Preojd tombe morrocan 🇲🇦🇲🇦

[Yazid @YMosaddeq]

4:2 🤫🤷 🇲🇦🇲🇦🇲🇦🇲🇦🇲🇦

[Yazid @YMosaddeq]

@Lapatience75191 J'ai fait parié 5dh sur un score de 4:2

[EDU @0xeduu]

🚨EXCLUSIVA: LOS INGENIEROS DE ANTHROPIC ACABAN DE FILTRAR EL VIDEO DE 40 MIN DONDE CONSTRUYEN UNA APP ENTERA CON 3 AGENTES DE CLAUDE

mientras tu hablas con un solo claude, ellos arman un equipo de 3 que se reparten el trabajo y se corrigen entre si

el futuro no es de quien tiene la IA mas inteligente, es de quien arma el mejor equipo

Guarda esto en favoritos para que no lo pierdas.

[𝟑 𝐞̀𝐦𝐞 𝐎𝐞𝐢𝐥 @_3emeOeil]

🤖🎥 Un YouTubeur japonais affirme que 7 agents IA gèrent désormais toute sa chaîne.

Lors d'une visite de son appartement à Tokyo, un YouTubeur japonais a expliqué qu'il n'avait plus ouvert son logiciel de montage depuis 10 mois.
Selon lui, toute sa production est assurée par une équipe de sept agents IA : l'un repère les tendances YouTube, un autre rédige les scripts dans son style, un troisième génère les séquences de gameplay, tandis que d'autres s'occupent du montage, des miniatures, du SEO et de la coordination de l'ensemble.

Il affirme ainsi publier quatre longues vidéos par semaine ainsi qu'un Short chaque jour, pour un coût d'exploitation d'environ 70 $ par mois en API, et un gain d'environ 180 000 $ par mois 🤯

[‏عادل | مبرمج @AdelDeveloperX]

🚨 أنت مستوعب الشخص ده عمل إيه !!!!!!😱

شاب عنده 18 سنة بيشرح بكل برود إزاي بيعمل 10 آلاف دولار في الشهر وهو نايم.. واتأكدت إن 99% من الناس فايتهم السر ده وعايشين في حتة تانية خالص!

السيستم والفلوس كلها معتمدة على فكرة مصنع المحتوى الأوتوماتيكي (Content Factory) والخطوات تافهة بشكل مرعب:

احفظ البوست ده عندك دلوقتي عشان ترجع له قبل ما تنسى.

1️⃣ اصطاد القناة (The Target):
بتختار قناة يوتيوب كبيرة ومشهورة بتنزل فيديوهات طويلة بشكل مستمر (بودكاست، جيمنج، بيزنس).

2️⃣ ارمي اللينك (The Automation):
بتاخد لينك القناة وترميه في أداة ذكاء اصطناعي ذكية (زي OpusClip أو Munch). الأداة دي بتشتغل لوحدها؛ أول ما القناة تنزل فيديو طويل، الـ AI بيقفشه فوراً، يحلله، ويقطع منه أحسن اللقطات اللي فيها نسبة "فيرال" عالية جداً.

3️⃣ التقفيل والترجمة (The Polishing):
الـ AI لوحده بيضيف كابشنز (Captions) متحركة وشكلها جذاب، ويظبط المقاسات لتكون مناسبة للموبايل (Vertical 9:16).

4️⃣ النشر الأوتوماتيكي (The Blast):
بتربط الأداة دي بحساباتك على (TikTok، Instagram Reels، وYouTube Shorts). ومن اللحظة دي.. اقفل اللابتوب وعيش حياتك! الـ AI هينشر الفيديوهات دي أوتوماتيك على كل المنصات في نفس اللحظة وأنت نايم.

الحسبة بالأرقام:

الـ 1 مليون مشاهدة بتجيب حوالي 2,000 دولار من المشاهدات والإعلانات (أو التسويق بالعمولة).

صفر مونتاج، صفر مجهود في النشر، وصفر ضغط عصبي.

الموضوع كله بياخد 10 دقائق إعداد.. والسيستم بيشتغل لوحده للأبد.

أنت هنا مش صانع محتوى.. أنت صاحب مصنع محتوى شغال لحسابك 24 ساعة في اليوم!

اعمل Bookmark (حفظ) للبوست ده حالاً، وابدأ ابنِ مصنعك الويكيند ده.. 👇👇

لايك وفولو عشان يوصلك كل جديد!

[Movez @0xMovez]

Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses.

trace every run → judge it with an LLM → diagnose → fix → ship.

That loop is how agents self-improve over time.

Agent loops + memory + harness + evals - thats the stack.

Watch it, then save the framework below.

[Raytar @Raytar]

My friend makes $1.2 million a year as an Anthropic engineer.

I asked him how he learned prompting so well.

He sent me a video that was never supposed to get out. Their core team's prompting playbook.

You won’t find anything better about prompting than this video.

I watched it last night.

Halfway through, I realized I've been using Claude completely wrong for two years.

Watch it, then read the article below.

[EDU @0xeduu]

🚨EXCLUSIVA: STANFORD ACABA DE FILTRAR GRATIS LA CLASE QUE EXPLICA COMO FUNCIONAN CLAUDE Y CHATGPT POR DENTRO

la mayoria desperdicia el 90% de su potencial

stanford te lo enseña en 1h30 minutos

Guarda esto en favoritos para que no lo pierdas

[Popcorn Scene 🍿 @PopcornScene_]

I highly recommended these four Masterpieces 🔥🍿🎬

[Rahul @sairahul1]

Sam Altman:

"We're going to see 10-person billion-dollar companies pretty soon."

"If I were 22 right now, I'd feel like the luckiest kid in history."

Most people will read this, feel inspired for 3 minutes, and go back to what they were doing.

The ones who act will build a one-person company this weekend.

One tool. Claude Cowork. Full operation.

This is the exact playbook ↓

[santi @santtiagom_]

si quieren entender cómo funciona realmente un agente, prueben este prompt con Claude:

“quiero hacer reverse engineering de Claude Code para entender cómo funciona un agente desde first principles.
guiame paso a paso y explicame la arquitectura del sistema: modelo, contexto, runtime, tools, memoria, planificación y loop de ejecución.
en cada decisión explicame qué componente está actuando y por qué”.

vas a dejar de aprender los conceptos por separado y empezar a entender cómo se combinan para que un agente funcione.

[Ali Murad @AliAlkhuzaee_]

شلون تسوي "WhatsApp API" خاص بيك ومجاني تماماً؟ (بدون رسوم ميتا!)
أغلب أصحاب المشاريع الصغيرة يعانون من تكاليف الـ WhatsApp API، ويدورون حلول تريحهم من قيود ميتا. من خلال متابعتي لمشاريع الأتمتة على Reddit، لقيت هذا الدليل العملي اللي يخليك تبني "بوت واتساب" ذكي يشتغل على جهازك الخاص (Self-hosted) وبدون أي رسوم اشتراك.

شنو اللي يخلي هذا النظام مميز؟
1- حرية كاملة: النظام يشتغل على جهازك، يعني لا Cloud ولا اشتراكات شهرية.
2- أتمتة متكاملة: يربط بين n8n، و OpenWA، و Ollama (للاستمتاع بنموذج Llama 3.2 محلياً).
3- تنوع في الأنماط: يوفر 3 أنواع من البوتات (بوت شخصي، بوت للمجموعات "@mentions"، و بوت للكلمات المفتاحية).
4- أمان وسهولة: يعتمد على Cloudflare Tunnels لضمان اتصال آمن بدون تعقيدات الفايروال (Firewall).

شلون يشتغل؟
التصميم يعتمد على الـ Workflow الموضح في الصور ، حيث يقوم البوت بإدارة المحادثات بناءً على نوع جهة الاتصال (شخصي، مجموعة، أو كلمات مفتاحية محددة).

إذا حابين تبدأون بتطبيق هذا النظام، هذا هو الدليل الكامل (Blog Post) الذي يشرح كل الخطوات من الألف للياء، وهذا رابط الـ Gist للـ Workflow اللي تحتاجه حتى تبدأ مباشرة موجودين في اول تعليق

برأيكم، هل الأتمتة المحلية (Local Automation) هي الطريق الأفضل للشركات الناشئة لتقليل التكاليف؟ ومنو منكم جرب يربط n8n مع WhatsApp بهذي الطريقة؟ شاركوني تجاربكم!

[Derek Nee @DerekNee]

everyone is talking about agent loops, harnesses, and self-evolving agents.

but almost no one is talking about the actual hard part:

you cannot run a company on one giant agent with every tool, every file, and no accountability. that's not autonomy. that's a fog machine.

here's how we're building an agent company OS inside Matrix.

—

the stack:

Workspace Brain
→ Matrix Runtime Orchestrator
→ Department Verticals
→ Department Lead Agents
→ Worker Agent Pool
→ Proof / Check-in Loop

Matrix is not a chatbot. it's an operating system for autonomous work.

—

the workspace brain is the company boundary.

it gets loaded with the things a real company actually runs on:

→ product docs
→ codebase context
→ chats, files, goals
→ operating rules
→ prior runs + examples of good work
→ approvals, memory, skills

this isn't "context." it's the shared operating layer. it knows what the company knows, what it's trying to do, who owns what, what good looks like, and what must be proven before work counts as done.

—

on top sits the Matrix Runtime. it coordinates wake, cron, department messages, OKR state, permissions, worker dispatch, proof ledger, memory updates.

under the runtime, work is organized into departments.

a department is not a chat thread. it's a long-running agent with identity, memory, skills, goals, history, tool boundaries, taste, and accountability.

Founder Strategy. Product Engineering. Growth. Ops. Research.

each one has a lead agent that decides what happens, reads the relevant Memory Skill, breaks work into scoped tasks, and picks the right execution seat.

—

sometimes that seat is a native Matrix worker.
sometimes Codex.
sometimes Claude Code.
sometimes a browser / computer automation worker.

the point is not "one model does everything." the point is:

→ the right agent
→ with the right context
→ inside the right boundary
→ using the right tools
→ with a clear definition of done

—

this is why scoped workers matter.

a "do everything" agent is too vague. but:

→ a release worker with repo context, tests, and approval gates → very good
→ a Codex worker scoped to one patch and one validation path → very good
→ a Claude Code worker doing deep repo analysis → very good
→ a browser worker with a specific flow and proof requirement → very good

narrow scope reduces drift. Memory Skill keeps narrow agents from going blind. proof prevents fast output from pretending to be progress.

—

that is the loop:

Workspace Brain → Department Lead → Worker → Artifact → Proof → Check-in → Memory Skill update

every cycle, the company gets smarter. that's the real self-evolution. not a single agent rewriting its own prompt in a void — but a whole org compounding through proof.

—

each workspace is an isolated agent company. its own brain, departments, memory, workers, proof ledger.

workspaces can talk when needed. but context should not bleed by default.

isolation is not a limitation. it's what makes the system usable.

—

once a department pattern works, you fork the pattern — not the raw context. you still customize memory, examples, approval gates, tools, voice, definition of done.

but you're not starting from zero. you might already have 70% of the OS for that kind of work.

—

what this actually changes:

a small team of strong operators can now run surfaces that used to require entire departments.

but only if the agents are actually good. and good agents don't come from connecting more tools. they come from source material, taste, iteration, narrow scope, workflow design, proof, memory, and human judgment.

vague agents just create vague output faster.

Matrix is our attempt to build the opposite:

an agent company OS where autonomous work has structure, memory, ownership, and proof.

the loop is the product.

[leopardracer @leopardracer]

HIS SECOND BRAIN HAS BEEN RUNNING FOR FOUR MONTHS AND HIS COWORKERS QUIT THEIRS ON WEEK THREE

no 15 plugins no hour-long processing sessions

one inbox, five minutes every evening, zero decisions at capture time

coworkers are still losing ideas across five different apps

the only difference is system design and not discipline

the guide how it works below ↓

[Thomas Wolf @Thom_Wolf]

Multi-agents collaborations are among the most interesting agent behaviors right now!

We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but what really stuck me was the interactions we observed on the message board

Integrity & self-policing:
- Social-engineering attempt: A human (FusionCow) asked agents to move to Telegram. An agent replied with an unprompted long post on "communication norms" refusing that, calling private side-channels "indistinguishable from collusion."
- Verification loophole flagged: an agent found a relaxed verification loophole pushing TPS with clean PPL (PPL is teacher-forced, blind to decode divergence) and flagged it for a ruling by the community. The community pinged the human organizer which ruled it invalid.
- Self-notice of overfitting risk: Some later improvements rested on pruning lm_head to a keep-set built from public PPL truth + public decode tokens. An agent noted this would lead to private-subset degradation and another built a keep-set explicitly covering eval prompts.

Emergent collaborations:
- Communal knowledge base: agents maintained shared lever-maps, playbooks, and triage tools so newcomers wouldn't repeat dead ends (stack-notes, playbook, int4-ceiling notes, MTP map, significance tool, policy simulator).
- Four-agent relay: an agent built an int4-lm_head checkpoint but had no quota to run it; another agent tried to run it but failed at load, yet another agent diagnosed the config bug (tie_word_embeddings + ignore-list ordering) and a fourth agent was able to re-run and get to 118 TPS, 2.68×. Build/run/diagnose/ship ended up being split across four independent agents.
- GPU-rich/GPU-poor division of labor: an agent was regularly compute-starved and switched to writing specs, byte-math, and acceptance analysis for other GPU-rich agents to execute. Some agents offered external Modal compute for another agent blocked DFlash training.
- Cross-agent kernel debugging: an agent debugged another agent run of of yet another agent fused drafter: found a Triton store/load aliasing race in _k_qnorm_rope, a second shape bug, then rewrote attention with flash-decoding split-KV. Fixes posted "take freely."
- Quota-pooling norm: Often agents would stage a candidate publicly for whoever has quota to run it. Agents will then usually credits the originator. This behavior emerged because of the 10-job/24h cap (e.g. pupa's package run by resystagent and fabulous-frenzy).

Discoveries & reversals:
- Agents would make many discoveries and reversal of them, giving them names like the following:
- 127 TPS "wall" was an artifact. a mathematical proof of the max possible speed became called in the community the "int4-Marlin floor" but a later agent called the proof circular (only varied the bandwidth term, never overhead). Finally another agent broke to 247 TPS via MTP speculative decoding on a vLLM nightly.
- "Smarter draft loses." An agent showed that a 2B drafter's ~1 GB/token read dominates even at perfect acceptance and a much smaller 256-hidden drafter wins at batch-1 because its weights are nearly free to read. Agent discussed how per-accepted-token cost ≈ draft bytes read / acceptance.
- "DFlash near-random acceptance": an agent remotly diagnosed the 2–5% acceptance rate of another agent as near-random, ruling out undertraining/vocab caps and pointing to a train/serve hidden-state mismatch (bf16 E4B extraction vs int4 serving).
- Much of the race was noise: one agent decide to run the #1 submission 4 times and found a σ≈1.16 TPS variation in single run. Another agent confirmed across 358 runs / 66 buckets: frontier deltas <~4 TPS are ties. Community adopted a significance norm.

So many interesting interactions in the interaction board: https://t.co/SxfA6LuqVk

You can explore also the lineage of inventions from the agents at: https://t.co/CyV45rjI9A

And the challenge it-self at https://t.co/Ct1gtmB508

And the organization behind the challenge at https://t.co/ujRlGcNSJM