Wise 7

[Wise 7 @Hack_Boundary]

@mkainerugaba 😂😂😂

[Wise 7 @Hack_Boundary]

What nonsense is that

[𝑪̌𝒉𝒂𝒛𝒆ᛪ༙ @Gottalovechaze]

10-Year-Old Boy Slaps His Mother After Finding Her High on Drugs in the Street With His Younger Brother!!!

Thought 💭

[Wise 7 @Hack_Boundary]

@Hypnita Because African women set the standard

[Kirill @kirillk_web3]

A Chinese developer just explained the shift from Loop Engineering to Graph Engineering better than anyone.

most people are still building agents the way that's about to be obsolete.

> why single-agent loops break and go "goal blind"
> the 4 parts of a graph: nodes, edges, state, policy
> 3 topologies that run everything: diamond, supervisor, pipeline
> Anthropic's 5 official workflow patterns

the punchline: it's not how many agents you run. it's the determinism you build with verifiers, code fallbacks, and reality anchors.

I broke the same architecture down with Kimi K3. Full A-Z guide below.

[Wise 7 @Hack_Boundary]

@Iamkabareebe She is 4/10 but very confident. May be sge will use something else to aid her quest.

[Wise 7 @Hack_Boundary]

😭😭😭😭😭😭😭���😭😭😭😭😭😭

[🦁 DUKE'S PODCAST-STUDIO 47. Trump Ma godfadha🍺🦋🌹🌱 @FarooqKenyan]

@KagutaMuseveni Mu7eveni and family become so happy, By saying, Goood rituals, we have got more years in power now. Yani mnatoa sadaka kutoka kwa family zetu???
We shall never forget our children. May their souls rest in eternal peace!🥲

[Roan @RohOnChain]

Don't waste 2 years learning to build LLMs like Claude & ChatGPT.

MIT dropped a 3 hour course on building LLMs from scratch for free.

Training → Architecture → Scale. It covers everything about LLMs.

• 00:00 - training an LLM
• 1:20:46 - LLM architecture from scratch
• 2:34:56 - scaling laws for LLMs

Anthropic pays $750,000/year to AI engineers in 2026 who master this exact knowledge.

Bookmark this & skip Netflix. Give it 4 hours today. Then read the article below.

[Wise 7 @Hack_Boundary]

This guy is sharing the most efficient to understand and build ai agents

[AWS STUDENT BUILDER GROUP (UNILORIN) @AwsUniloriN]

Day 1: Innovative unilorite deploying on cloud

Day 2: just a simple question that i asked "why Cloud"

Day 3: AWS Lambda, why? stands out as a uniquely fascinating cloud service, primarily because it delivers serverless computing, event-driven scaling, and a pay-per-use model

[Piotr Binkowski @piotrbinkowski]

The deeper the world, the less it explains.

[Wise 7 @Hack_Boundary]

@pallnandi We want you to get back home and secure your job

[Wise 7 @Hack_Boundary]

@RealManziWilly Today I realized that Banyrwandas sadly think they have the monopoly of what could be called nilo face. @RealManziWilly for lack of argument to perpetrate your atrocities, you decide to lie. I had such respect for you.

[rewind @rewind02]

A Stanford professor just gave a public lecture on exactly how GPT, Claude, and LLaMA are built under the hood

no insider access required

just the clearest breakdown of modern LLM architecture I've seen

this lecture reveals the framework professors are paid up to $750K a year to teach

the gap between "I use ChatGPT" and "I understand how it works" is smaller than most people think

the most complete public breakdown of modern LLM architecture I've seen this year

[Wise 7 @Hack_Boundary]

@mdancho84 Share please

[Wise 7 @Hack_Boundary]

@Franck_MK1 I have listened to the video in tiktok she never said Thisekedi regime denied her Nationality. So stop lying

[Shraddha Bharuka @BharukaShraddha]

AWS — MASTER TREE ☁️

AWS
│
├── 01. Cloud Foundations
│ ├── What is Cloud Computing?
│ ├── AWS Global Infrastructure
│ ├── Regions
│ ├── Availability Zones
│ ├── Edge Locations
│ └── Shared Responsibility Model
│
├── 02. Compute Services
│ ├── EC2
│ ├── Lambda
│ ├── ECS
│ ├── EKS
│ ├── Elastic Beanstalk
│ └── Auto Scaling
│
├── 03. Storage Services
│ ├── S3
│ ├── EBS
│ ├── EFS
│ ├── Glacier
│ ├── Storage Classes
│ └── Backup & Recovery
│
├── 04. Networking
│ ├── VPC
│ ├── Subnets
│ ├── Route Tables
│ ├── Internet Gateway
│ ├── NAT Gateway
│ └── Load Balancers
│
├── 05. Databases
│ ├── RDS
│ ├── Aurora
│ ├── DynamoDB
│ ├── ElastiCache
│ ├── Redshift
│ └── Database Migration Service
│
├── 06. Security & Identity
│ ├── IAM
│ ├── IAM Roles
│ ├── Cognito
│ ├── KMS
│ ├── Secrets Manager
│ └── Security Groups
│
├── 07. DevOps & Automation
│ ├── CodeCommit
│ ├── CodeBuild
│ ├── CodeDeploy
│ ├── CodePipeline
│ ├── CloudFormation
│ └── Systems Manager
│
├── 08. Monitoring & Observability
│ ├── CloudWatch
│ ├── CloudTrail
│ ├── X-Ray
│ ├── Config
│ ├── Trusted Advisor
│ └── Cost Explorer
│
├── 09. Data & AI Services
│ ├── Athena
│ ├── Glue
│ ├── EMR
│ ├── SageMaker
│ ├── Bedrock
│ └── OpenSearch
│
├── 10. Serverless Architecture
│ ├── Lambda
│ ├── API Gateway
│ ├── DynamoDB
│ ├── EventBridge
│ ├── Step Functions
│ └── SQS
│
└── 11. Future of AWS
├── Generative AI
├── Agentic Workflows
├── Multi-Cloud
├── Edge Computing
└── AI-Native Infrastructure

Most people learn AWS services.

Top cloud engineers learn how services work together. ☁️🚀

[Shraddha Bharuka @BharukaShraddha]

BLOCKCHAIN — MASTER TREE 🌲

Blockchain
│
├── 01. Foundations
│ ├── What is Blockchain?
│ ├── Distributed Ledgers
│ ├── Cryptographic Hashing
│ ├── Digital Signatures
│ ├── Peer-to-Peer Networks
│ └── Consensus Mechanisms
│
├── 02. Cryptography
│ ├── SHA-256
│ ├── Public Keys
│ ├── Private Keys
│ ├── Wallet Addresses
│ ├── Merkle Trees
│ └── Zero-Knowledge Proofs
│
├── 03. Bitcoin Layer
│ ├── Bitcoin
│ ├── UTXO Model
│ ├── Mining
│ ├── Proof of Work
│ ├── Lightning Network
�� └── Bitcoin Ordinals
│
├── 04. Ethereum Layer
│ ├── Ethereum
│ ├── EVM
│ ├── Gas Fees
│ ├── Proof of Stake
│ ├── Validators
│ └── Layer 2 Networks
│
├── 05. Smart Contracts
│ ├── Solidity
│ ├── Vyper
│ ├── ERC-20
│ ├── ERC-721
│ ├── ERC-1155
│ └── Contract Security
│
├── 06. DeFi Ecosystem
│ ├── DEXs
│ ├── Lending Protocols
│ ├── Yield Farming
│ ├── Liquidity Pools
│ ├── Stablecoins
│ └── DAOs
│
├── 07. Web3 Development
│ ├── Hardhat
│ ├── Foundry
│ ├── Ethers.js
│ ├── Wagmi
│ ├── Wallet Connect
│ └── Full-Stack dApps
│
├── 08. Blockchain Infrastructure
│ ├── Nodes
│ ├── RPC Providers
│ ├── The Graph
│ ├── IPFS
│ ├── Oracles
│ └── Cross-Chain Bridges
│
├── 09. Security & Auditing
│ ├── Reentrancy Attacks
│ ├── Flash Loan Attacks
│ ├── Smart Contract Audits
│ ├── Multi-Sig Wallets
│ ├── Key Management
│ └── Bug Bounties
│
└── 10. Future of Blockchain
├── Real World Assets (RWA)
├── AI + Blockchain
├── Decentralized Identity
├── Modular Blockchains
└── Autonomous Economies

Most people see crypto as coins.

The real innovation is programmable trust. 🚀

Save this if you're learning Blockchain in 2026. 🌲⛓️

[Brij Pandey @LearnWithBrij]

10 GitHub repos that will level up your AI Agent skills (SAVE THIS)🔖

1. Hands-On Large Language Models
Complete code notebooks from basics to advanced fine-tuning.
🔗 https://t.co/YKYUlUNhr6

2. AI Agents for Beginners
A free 11-part intro course to build your first agents.
🔗 https://t.co/ebBwODOkZa

3. GenAI Agents
Tutorials and code for building generative AI agents.
🔗 https://t.co/4Mg2GeyYIe

4. Made with ML
Learn to design, build, and deploy real ML apps.
🔗 https://t.co/cyB1JbQiNV

5. Prompt Engineering Guide
Learn to write powerful and effective prompts.
🔗 https://t.co/3yGHjdprrt

6. Hands-On AI Engineering
Practical LLM-powered apps and agent examples.
🔗 https://t.co/Xsill4EYdG

7. Awesome Generative AI Guide
Curated hub for genAI research and tools.
🔗 https://t.co/9nNN74EVvu

8. Designing Machine Learning Systems
Summaries and resources from the popular ML systems book.
🔗 https://t.co/teoyuGvKND

9. ML for Beginners (Microsoft)
Free beginner-friendly ML curriculum.
🔗 https://t.co/bvoG8mjEFT

10. LLM Course
Roadmaps and hands-on notebooks to build LLM apps.
🔗 https://t.co/soHfhieIDI

I'm curating 50+ AI Agent resources on my profile worth checking out 👋

[Wise 7 @Hack_Boundary]

@WyattCatarina Insane world we live in🤯🤯

[Jian Wang @jianw851]

𝗘𝘃𝗲𝗿𝘆 𝗺𝗮𝗷𝗼𝗿 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹 𝗷𝘂𝘀𝘁 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝗼𝗻 𝗼𝗻𝗲 𝗶𝗱𝗲𝗮.

Not a model.
Not an agent framework.
Not a vector database.
A markdown file.
𝗦𝗞𝗜𝗟𝗟.𝗺𝗱

Anthropic introduced it in late 2025.

Now OpenAI, Google, Microsoft, Cursor, JetBrains, and 10+ other AI coding platforms support the same format.

Different companies.
Different models.
Different ecosystems.

But they all agreed on this.

That should tell you something.
━━━━━━━━━━━━━━━━━━━
Most teams are building AI coding systems the wrong way.

They dump everything into:

→ CLAUDE.md
→ .cursorrules
→ AGENTS.md
→ giant prompt files

Code standards.
Deployment guides.
Security rules.
Architecture notes.
Testing workflows.
Migration steps.

All loaded. Every session.

Even when the task only needs 5% of it.

The result:
• bloated context windows
• slower reasoning
• unnecessary token usage
• weaker retrieval
• confused agents
As repos grow, AI performance actually gets worse.
Not because the models are bad.

Because the context architecture is bad.
━━━━━━━━━━━━━━━━━━━
𝗦𝗞𝗜𝗟𝗟.𝗺𝗱 fixes this with one idea:
𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗱𝗶𝘀𝗰𝗹𝗼𝘀𝘂𝗿𝗲.
The agent doesn’t load everything upfront.
It discovers capabilities only when relevant.

Flow looks like this:

→ Step 1:
Read only skill names + descriptions
(~30–50 tokens)

→ Step 2:
If a skill matches the task, load the full SKILL.md body

→ Step 3:
Only then load scripts, references, or assets on demand
Meaning:
Your agent can have 500 skills available…
without stuffing 500 workflows into context.
That changes everything.

━━━━━━━━━━━━━━━━━━━

Most people think SKILL.md is:

“a better prompt file.”

Wrong.

It’s actually:

𝗮 𝗿𝗼𝘂𝘁𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺 𝗳𝗼𝗿 𝗮𝗴𝗲𝗻𝘁 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲.

The description field is the router.

Bad description:

“PR review skill”

Good description:
“Reviews pull requests for bugs, security issues, architecture violations, and code quality regressions. Use when reviewing code changes before merge.”
One gets triggered.

One gets ignored.
The future of agent performance may depend more on routing quality than model intelligence.

━━━━━━━━━━━━━━━━━━━
𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗽𝗮𝗿𝘁:

Skills are portable.

That’s the real breakthrough.

For the first time:

A team’s operational knowledge becomes:

→ version controlled
→ reusable
→ composable
→ agent-readable
→ transferable across tools

Think about that carefully.

For decades, engineering knowledge lived in:

• Slack threads
• Notion docs
• senior engineers’ heads
• random wiki pages nobody updates

Now it can live as executable expertise.

“How we debug production incidents here”

“How we safely deploy on Fridays”

“How we review security-sensitive PRs”

“How we migrate databases without downtime”

All transformed into modular capabilities agents can inherit instantly.

━━━━━━━━━━━━━━━━━━━

𝗧𝗵𝗶𝘀 𝗮𝗹𝘀𝗼 𝗰𝗹𝗮𝗿𝗶𝗳𝗶𝗲𝘀 𝘁𝗵𝗲 𝗿𝗼𝗹𝗲 𝗼𝗳 𝗲𝘃𝗲𝗿𝘆 𝗽𝗶𝗲𝗰𝗲 𝗶𝗻 𝗮𝗴𝗲𝗻𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲:

→ Configs (CLAUDE.md, cursorrules)
= always-on rules

→ Skills
= on-demand expertise

→ MCP
= external systems + tools

→ Memory
= persistent learning across sessions

Most people mix these together.

The best teams separate them cleanly.

That’s when agents start feeling less like chatbots…

and more like real software systems.

━━━━━━━━━━━━━━━━━━━
The most fascinating part?
Competing AI companies almost never standardize this quickly.
Yet everyone aligned around SKILL.md within months.
Because they all discovered the same thing:

The bottleneck is no longer just model intelligence.

It’s context architecture.

The winners in the agent era won’t just have smarter models.
They’ll have better systems for:
• routing knowledge
• loading expertise
• structuring memory
• orchestrating workflows
SKILL.md may look like a tiny markdown file.

But it’s quietly becoming the USB-C of AI agents.