knottyJenner

I love animals and texting,
Calm heart, wild energy, Opened mind and a soft spot for every creature I meet.
K9 content with horse videos.

[knottyJenner @knottyJenner]

Welcome to my playground of filthy thoughts and irresistible temptation. I’m serving all the naughty, knotty vibes and trust me, you don’t want to behave and miss out.
Come get wicked with me on Telegram:knottyjenner

[Aiswarya Venkitesh @AiswaryaVenkit1]

🚀 Microsoft Fabric — Explained Simply

Most companies don’t have a data problem.
They have a “too many tools” problem.

That’s where Microsoft Fabric changes the game 👇

👉 Imagine having: • Data pipelines
* Storage
* Analytics
* Dashboards
* AI

All in one single platform

That’s exactly what Microsoft Fabric delivers.

💡 What makes it powerful?

✅ One unified data lake (OneLake)
✅ Built-in ETL + Data Engineering
✅ Real-time + batch analytics
✅ AI integrated with Copilot
✅ Seamless with Azure + Microsoft 365

No silos. No tool switching. No complexity.

⚡ Before vs After

❌ Multiple tools (Data Factory + Synapse + Power BI)
❌ Data duplication
❌ Complex architecture

✅ One platform
✅ Faster insights
✅ Lower cost
✅ Easier collaboration

🧠 Think of it like this:

Microsoft Fabric = Operating System for Data

Just like Windows manages apps,
Fabric manages your entire data ecosystem.

📊 Use cases:

* Business dashboards & reporting
* AI/ML model building
* Real-time analytics (IoT, logs)
* Data warehousing & lakehouse

🔥 If you're working with data in 2026,
this is not optional — it's foundational.

Would you replace your current stack with Fabric?

𝗕𝗲𝗰𝗼𝗺𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗔𝗜 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟭 𝗺𝗶𝗻𝘂𝘁𝗲 𝗮 𝗱𝗮𝘆. 𝗝𝗼𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗼𝗳 𝗔𝗜 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻.
👉 𝗦𝗶𝗴𝗻 𝘂𝗽 𝗳𝗿𝗲𝗲 now → https://t.co/Kj8zW95H9X

Follow @AiswaryaVenkit1 for more such insights!!

[Sam @Sam_Axiom]

Physics Formula Series Basics 4

[Sonal Shukla @sonalshukla3377]

🎯𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁

𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗶𝘀 𝗺𝗼𝗿𝗲 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘀𝘁𝗼𝗽 𝘁𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗶𝘁 𝗹𝗶𝗸𝗲 𝗷𝘂𝘀𝘁 𝗮𝗻 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗰𝗵𝗮𝘁𝗯𝗼𝘁.

The real advantage comes from knowing 𝗵𝗶𝗰𝗵 𝗰𝗼𝗺𝗺𝗮𝗻𝗱 𝘁𝗼 𝘂𝘀𝗲, 𝘄𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗶𝘁.

Here’s a practical Claude Code cheat sheet worth saving 👇

𝗖𝗟𝗔𝗨𝗗𝗘.md

Project-specific rulebook Claude reads before every task.

/init first to auto-generate.
Focus on architecture, coding style, build commands, and review checklists.
Keep it under 150 lines.

𝗦𝗸𝗶𝗹𝗹𝘀

Reusable markdown prompt templates for standardized workflows.

Encode 𝗰𝗼𝗱𝗲 𝗿𝗲𝘃𝗶𝗲𝘄, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝘀𝗰𝗮𝗻𝘀, 𝗮𝗻𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗰𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁𝘀 to align team standards.

𝗠𝗖𝗣

Connects Claude to local tools, databases, and external APIs.

Link MCP servers to query live databases or terminal logs during debugging.

𝗔𝗴𝗲𝗻𝘁𝘀

Spawns background sub-agents for parallel, isolated tasks.

Have 𝗮𝗴𝗲𝗻𝘁𝘀 𝘄𝗿𝗶𝘁𝗲 𝘂𝗻𝗶𝘁 𝘁𝗲𝘀𝘁𝘀 while you focus on core feature logic.

𝗣𝗹𝗮𝗻

Forces an architectural outline before any code is written.

Critique the plan first, then command Claude to “implement it.”

𝗖𝗼𝗺𝗽𝗮𝗰𝘁

Compresses history to maximize the context window.

Use when Claude feels sluggish to keep the context focused.

𝗔𝘂𝘁𝗼𝗱𝗿𝗲𝗮𝗺

AI “sleep cycle” that organizes and refines project memory.

Useful for reducing 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗽𝗼𝗹𝗹𝘂𝘁𝗶𝗼𝗻 in long-running workflows.

𝗥𝗮𝗹𝗽𝗵-𝗹𝗼𝗼𝗽

Autonomous iteration loop for persistent self-correction and testing.

Ideal for TDD. Always set safe limits for iterations.

---

𝗣𝗿𝗼 𝗧𝗶𝗽𝘀 ��𝗼𝘂 𝗖𝗮𝗻 𝗠𝗶𝘀𝘀

✅ /init → creates your CLAUDE.md starter instantly

✅ Use /context to check usage before things get heavy

✅ /memory → view and edit auto notes anytime

✅ Hooks can run auto-lint and formatting after edits

✅ Always 𝗿𝗲𝘃𝗶𝗲𝘄 𝗱𝗶𝗳𝗳𝘀 before accepting changes

✅ /plan mode = safer exploration before editing

✅ 𝗦𝗸𝗶𝗹𝗹𝘀 + 𝗮𝗴𝗲𝗻𝘁𝘀 = more consistency across workflows

✅ /compact at ~70% context = a useful trigger before conversations become unwieldy

---

𝗢𝗻𝗲 𝗺𝗼𝗿𝗲 𝘁𝗵𝗶𝗻𝗴 𝗜’𝗱 𝗮𝗱𝗱:

Don’t ask Claude Code to “build the feature” and hope for the best.

Try this workflow:

𝗣𝗹𝗮𝗻 → 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 → 𝗧𝗲𝘀𝘁 → 𝗥𝗲𝘃𝗶𝗲𝘄 → 𝗖𝗼𝗺𝗽𝗮𝗰𝘁 → 𝗜𝘁𝗲𝗿𝗮𝘁𝗲

The goal isn’t to give Claude more instructions.

The goal is to give it 𝗯𝗲𝘁𝘁𝗲𝗿 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲.

That’s where AI coding starts feeling less like autocomplete and more like an engineering workflow.

𝗪𝗵𝗶𝗰𝗵 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗰𝗼𝗺𝗺𝗮𝗻𝗱 𝗱𝗼 𝘆𝗼𝘂 𝘂𝘀𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁? 👇

#ClaudeCode #AI #Coding #SoftwareEngineering #AITools

[Artificial Intelligence @AIGuideHQ]

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀? 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗼𝗮𝗱𝗺𝗮𝗽.

Building AI agents is not just about writing better prompts.

It requires an understanding of programming, machine learning, language models, data, tools, memory, orchestration, deployment and evaluation.

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗽𝗮𝘁𝗵

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
Data types, control flow, logical decision-making and file handling.

𝗗𝗮𝘁𝗮 & 𝗧𝗼𝗼𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴
LLM types, APIs, authentication and application integrations.

𝗠𝗟 & 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀
Machine learning fundamentals, transformers, Mixture of Experts, fine-tuning and inference.

𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 & 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴
Structured prompting, task decomposition, planning, few-shot techniques and tool selection.

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗗𝗮𝘁𝗮 & 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶���𝗻
SQL, NoSQL, APIs, SaaS connectors, data extraction and pipelines.

𝗥𝗔𝗚, 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜
Embeddings, vector databases, retrieval pipelines, agent memory and reliable AI practices.

𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
Agent types, design patterns, orchestration, planning and feedback loops.

𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, 𝗠𝗟𝗢𝗽𝘀 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴
Testing, logging, performance, latency, reliability and cost optimisation.

𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻
Specialised agents, communication patterns, handoffs and agent-to-agent protocols.
𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗼𝗼𝗹 𝘂𝗽𝗴𝗿𝗮𝗱𝗲.

𝗜𝘁 𝗶𝘀 𝗮 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘀𝗵𝗶𝗳𝘁.
Which stage of this roadmap are you currently learning?

Like, Retweet, Bookmark and Follow @aiguidehq for more such posts

[Vlad Mihalcea @vlad_mihalcea]

The best way to use JOIN FETCH and Pagination with Spring

https://t.co/QSR07RReb0

[Real Python @realpython]

🐍  How to Get a List of All Files in a Directory With Python — https://t.co/e9oJvSIzp2

#python

[Python Coding @clcoding]

Python Challenge of the Day

🚀 Day 90/150 – Exception Handling in Python

Detailed Explanation: https://t.co/zN1RQGitoR

[Python Programming @PythonPr]

Python Quiz: What is the Output?

[Data Science Dojo @DataScienceDojo]

🚨 Peter Steinberger has yet again coined a new term in the AI Engineering landscape: 𝐆𝐫𝐚𝐩𝐡 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠.

His tweet asking "are we still talking loops or did we shift to graphs yet" picked up 2.8M views and everyone has been talking about Graph Engineering ever since.

But here's the part nobody's saying out loud. Nothing shipped around that tweet. No new framework, no new model, no new capability. Just a name for something that's been running in production for years.

We pulled together 6 frameworks that were already doing exactly this, nodes, edges, shared state, before "graph engineering" existed as a phrase:

• 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡- stateful graph orchestration with rollback and replay, built for complex Python agents

• 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐀𝐠𝐞𝐧𝐭 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 - AutoGen and Semantic Kernel merged into one enterprise-ready graph framework

• 𝐆𝐨𝐨𝐠𝐥𝐞 𝐀𝐃𝐊 - hierarchical agent trees with native cross-framework agent-to-agent calls

• 𝐂𝐫𝐞𝐰𝐀𝐈- role-based multi-agent crews built for fast, working prototypes

• 𝐋𝐥𝐚𝐦𝐚𝐈𝐧𝐝𝐞𝐱 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 - event-driven orchestration where the data shapes the graph

• 𝐎𝐩𝐞𝐧𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐒𝐃𝐊 - lightweight agent handoffs for OpenAI-only chains, no full graph needed

If you're already building on any of these, you were doing "graph engineering" before it had a name. The label doesn't change your architecture, it just gives you words for it.

We broke down the full timeline (prompt engineering to context to harness to loop to graph, and yes, the dates are closer together than you'd think) on the blog. Worth a read before you rename anything else.

Link in the comments 👇

#AgenticAI #LLMEngineering #AIagents #MultiAgentSystems #LangGraph #AIOrchestration

[Milan Jovanović @mjovanovictech]

5 things I add to new .NET projects to set them up for success 👇

1. Coding conventions - .editorconfig

2. Static code analysis - SonarAnalyzer

3. Observability - Serilog, OpenTelemetry

4. Docker-compose - local development story

5. CI/CD pipeline for build & test (bare minimum)

Everything is easier with this initial setup.

The team has a consistent development experience.

And I can focus on providing business value to customers.

What else would you add?

[IPCisco @ip_cisco]

VLAN Trunking Protocol!!! | IPCisco
.
VTP (VLAN Trunking Protocol) is a Cisco proprietary Layer 2 protocol used to automate the management and synchronization of VLAN configurations across multiple switches. It centrally propagates VLAN additions, deletions, and name changes through trunk links within a defined VTP domain.
Please Like & Share..:)
.
#ccna #networkengineer #cisco #networking

[Kirk Borne @KirkDBorne]

Statistical Tests cheat sheet

Source: https://t.co/5OQrlzUgZ8

[InfoSciHub @InfoSciHub]

BRAIN ANATOMY & PHYSIOLOGY

[Scholarship for PhD @ScholarshipfPhd]

Research is not a straight line.

The best studies rarely move from question to publication without revisions.

Define the problem.
Collect evidence.
Analyze the results.
Draw conclusions.
Share the findings.

Then repeat.

Good research is a cycle of continuous improvement, not a checklist to complete.

[GK for UPSC & TGPSC @BORN4WIN]

PLANT CELL VS ANIMAL CELL

[David Marco @david_marco45]

TOP 13 FREE AI COURSES TO TRY IN 2026:

1. Claude 101
👉 https://t.co/SIDutcHI6X

2. AI Fluency: Frameworks & Foundations
👉 https://t.co/5emViZurDz…

3. Introduction to Agent Skills
👉 https://t.co/qET5IAL90t…

4. Building with the Claude API
👉 https://t.co/YQBTMe3SRx…

5. Claude Code in Action
👉 https://t.co/4zncQlBcKC…

6. Intro to Model Context Protocol (MCP)
👉 https://t.co/qET5IAL90t…

7. MCP: Advanced Topics
👉 https://t.co/h4xUBEtdoU…

8. AI Fluency for Students
👉 https://t.co/kpd3tsb4EE…

9. AI Fluency for Educators
👉 https://t.co/kpd3tsb4EE…

10. Teaching AI Fluency
👉 https://t.co/dm5lxysEP7…

Please support me:@david_marco45