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Joined July 2026
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Nagma parween retweeted
9 Claude Skills that basically write your prompts for you
1: Start With The Chaos
Prompt Maker — /prompt-master. dump your half-baked idea in, get a clean task spec back
2: Sharpen It Up
Grill Me — /grill-me. keeps firing questions until every vague spot gets nailed down
How To — /how-to. maps out steps you didn't even know you were missing
Optimizer 4.8 — /48. fine-tunes your prompt so it hits different with Opus 4.8
3: Make It Sound Like You
Fable Prompter — /fable. same glow-up, built for Fable 5
Personal Voice — /personal-voice. makes it read like you actually wrote it
Anti-AI — /anti-ai. scrubs every "as an ai" tell out of the draft
4: Lock It In
Write A Skill — /write-a-skill. bottles the whole thing into a reusable skill
Hand Off — /handoff. builds a handoff doc so your next chat picks up right where this one left off Follow me
for more AI IDEA.
9 Claude Skills that basically write your prompts for you
1: Start With The Chaos
Prompt Maker — /prompt-master. dump your half-baked idea in, get a clean task spec back
2: Sharpen It Up
Grill Me — /grill-me. keeps firing questions until every vague spot gets nailed down
How To — /how-to. maps out steps you didn't even know you were missing
Optimizer 4.8 — /48. fine-tunes your prompt so it hits different with Opus 4.8
3: Make It Sound Like You
Fable Prompter — /fable. same glow-up, built for Fable 5
Personal Voice — /personal-voice. makes it read like you actually wrote it
Anti-AI — /anti-ai. scrubs every "as an ai" tell out of the draft
4: Lock It In
Write A Skill — /write-a-skill. bottles the whole thing into a reusable skill
Hand Off — /handoff. builds a handoff doc so your next chat picks up right where this one left off Follow me
for more AI IDEA.
Nagma parween retweeted
The secret to a good product ad? Make people curious about what happens next.
For this Black Friday video, the agent's mission builds suspense before revealing the product.
CapCut PC helps bring the idea together:
- Use AI Video to create the scenes from a product reference image
- Use Multi-Track Editing to arrange your footage and audio separately
- Use Sound Effects to emphasise the key moments
And if you're exploring more AI video tools: Seedance 2.5 4K is now available on CapCut PC in regions outside the US, with a US launch coming soon. You can access it through AI Video in the multi-track timeline or Video Studio.
Give your next ad a storyline: tinyurl.com/2v23rn22
#CapCutPC #CapCutDidThat #CapCutMarketing #ChatwithCapCutAgent #CapCutSeedance254k
🤝 Paid partnership
Nagma parween retweeted
Do You Need Canva Pro! lifetime Subscription 💯
If You Need Canva Pro Free Then ;
1. Like
2. Comment "Canvapro"
3. Repost ( follow me to get it)
Do You Need Canva Pro! lifetime Subscription 💯
If You Need Canva Pro Free Then ;
1. Like
2. Comment "Canvapro"
3. Repost ( follow me to get it)
Nagma parween retweeted
F*ck waiting for the “perfect time.”
Give yourself 30 days and get serious.
Here are 5 simple changes that can help you get leaner in 30 days:
1. Drink only water. Starting today.
F*ck waiting for the “perfect time.”
Give yourself 30 days and get serious.
Here are 5 simple changes that can help you get leaner in 30 days:
1. Drink only water. Starting today.
Nagma parween retweeted
🚨 This is actually insane.
$100M in revenue run rate with just $5M raised.
Retell is proving that building enterprise AI doesn’t always require massive funding.
What's interesting is that they're not just building AI voice agents. They're also making it easier for businesses to test and improve them.
Their new Conductor takes that a step further by helping teams figure out what's working and what needs fixing.
The efficiency here is remarkable.
🤝 Paid partnership
Retell just crossed $100M in revenue run rate.
$10M to $100M in 17 months. Profitable. $4.6M raised.
From day one, we’ve focused on solving the hardest parts of deploying voice AI in the product itself, so customers can build, manage, and improve their own agents without depending on us.
Our bet: the future of the contact center belongs to the teams who run it.
Proud of our team. Grateful to every customer who believed in us.
Nagma parween retweeted
🔖 20 AI GitHub Repos Every Software Engineer Should Bookmark
1. OpenClaw — Local AI agents
2. TensorFlow — ML framework
3. AutoGPT — Autonomous AI agents
4. n8n — Workflow automation
5. Ollama — Run LLMs locally
🔖 Bookmark this list for later.
What repo would you add? 👇
🔖 20 AI GitHub Repos Every Software Engineer Should Bookmark
1. OpenClaw — Local AI agents
2. TensorFlow — ML framework
3. AutoGPT — Autonomous AI agents
4. n8n — Workflow automation
5. Ollama — Run LLMs locally
🔖 Bookmark this list for later.
What repo would you add? 👇
Nagma parween retweeted
9 Claude Skills that basically write your prompts for you
1: Start With The Chaos
Prompt Maker — /prompt-master. dump your half-baked idea in, get a clean task spec back
2: Sharpen It Up
Grill Me — /grill-me. keeps firing questions until every vague spot gets nailed down
How To — /how-to. maps out steps you didn't even know you were missing
Optimizer 4.8 — /48. fine-tunes your prompt so it hits different with Opus 4.8
3: Make It Sound Like You
Fable Prompter — /fable. same glow-up, built for Fable 5
Personal Voice — /personal-voice. makes it read like you actually wrote it
Anti-AI — /anti-ai. scrubs every "as an ai" tell out of the draft
4: Lock It In
Write A Skill — /write-a-skill. bottles the whole thing into a reusable skill
Hand Off — /handoff. builds a handoff doc so your next chat picks up right where this one left off Follow me
for more AI IDEA
9 Claude Skills that basically write your prompts for you
1: Start With The Chaos
Prompt Maker — /prompt-master. dump your half-baked idea in, get a clean task spec back
2: Sharpen It Up
Grill Me — /grill-me. keeps firing questions until every vague spot gets nailed down
How To — /how-to. maps out steps you didn't even know you were missing
Optimizer 4.8 — /48. fine-tunes your prompt so it hits different with Opus 4.8
3: Make It Sound Like You
Fable Prompter — /fable. same glow-up, built for Fable 5
Personal Voice — /personal-voice. makes it read like you actually wrote it
Anti-AI — /anti-ai. scrubs every "as an ai" tell out of the draft
4: Lock It In
Write A Skill — /write-a-skill. bottles the whole thing into a reusable skill
Hand Off — /handoff. builds a handoff doc so your next chat picks up right where this one left off Follow me
for more AI IDEA
Nagma parween retweeted
🚀 The 2027 Claude Mastery Plan!
Want to get more out of Claude AI? Start using these 20+ practical tips to improve your prompts, save time, and boost productivity.
From smarter prompting to better workflows — small changes can make a big difference.
🚀 The 2027 Claude Mastery Plan!
Want to get more out of Claude AI? Start using these 20+ practical tips to improve your prompts, save time, and boost productivity.
From smarter prompting to better workflows — small changes can make a big difference.
Want to master Claude AI in just 1 day? 🚀
This simple roadmap covers 10 powerful Claude features, from basic chats and reasoning to coding, automation, and integrations.
Save this guide and start exploring Claude! 🔖
Nagma parween retweeted
AI agents are getting pretty good at telling us they've completed a task.
But how do we know they actually did it?
I like what Plurai is doing here.
Instead of just checking the conversation, it simulates real scenarios and verifies what the agent actually changed.
That feels like a much more useful way to test agents before putting them in front of real users.
🤝 Paid partnership
Big day for us at @pluraiAI ! Plurai Simulation is live and free for every AI engineer.
AI agents look great in demos. Until real users break them.
So we built a world where your agent can fail, learn, and get fixed before your users ever see it.
→ Digital twin: Your real agent, its tools, and environment recreated from your code
→ Simulated users & tasks: Realistic users, tasks, and workflows that push your agent into the edge cases that matter
→ Auto evals: Check what your agent actually did, not just what it said
→ Verified fixes: Every failure comes with the run that exposed it and a fix you can verify
→ Replays: Turn any production failure into a simulation and replay it against every new version of your agent
All inside the coding agent you already use: @claudeai code, @OpenAICodexCli , or @GitHubCopilot .
Start simulating for free: simulation.plurai.ai/connect
Nagma parween retweeted
𝗧𝗢𝗣 𝟵 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧 𝗧𝗘𝗥𝗠𝗦 𝗘𝗩𝗘𝗥𝗬 𝗔𝗜 𝗕𝗨𝗜𝗟𝗗𝗘𝗥 𝗦𝗛𝗢𝗨𝗟𝗗 𝗞𝗡𝗢𝗪 🤖
AI agents are evolving beyond simple chatbots.
They can remember information, use tools, execute tasks, and collaborate with other agents.
To understand how they work, here are 9 essential concepts every AI builder should know.
1. Harness — The System That Makes an LLM Act
A harness connects an LLM with tools, context, memory, and its execution environment.
It manages the interaction loop:
- Provides context and instructions.
- Sends requests to the LLM.
- Executes tool calls.
- Collects results.
Think of it as the operating environment of an AI agent.
2. Memory & State — Give Agents Continuity
Memory retains useful information, while state tracks task progress.
Two important types:
- Short-term memory: Current conversation and context.
- Long-term memory: Saved preferences and past interactions.
State tracks current steps and pending tasks.
3. RAG — Give AI Access to External Knowledge
Retrieval-Augmented Generation retrieves relevant information before generating an answer.
How it works:
- Receive a user query.
- Search relevant documents.
- Retrieve useful information.
- Add context to the prompt.
- Generate an informed response.
It helps reduce hallucinations and improve accuracy.
4. MCP — Connect AI Agents With Tools
Model Context Protocol standardizes how AI applications connect with external tools and data.
It provides:
- Tools for executing actions.
- Resources for accessing information.
- Prompts for reusable instructions.
Think of MCP as a connection layer for AI applications.
5. Skills — Give Agents Reusable Expertise
Skills package instructions, scripts, and resources into reusable capabilities.
Examples:
- Research and analysis.
- Code review.
- Report generation.
- Content creation.
They help agents perform workflows consistently.
6. Guardrails — Keep AI Actions Under Control
Guardrails define what an agent can and cannot do.
Important mechanisms:
- Permission controls.
- Sandboxed execution.
- Human approval.
- Activity logging.
They make AI systems safer and more predictable.
7. Evals — Measure Agent Performance
Evaluations help developers measure agent reliability.
They assess:
- Output accuracy.
- Task completion.
- Tool usage.
- Response quality.
- Performance consistency.
Testing before launch is essential.
8. A2A — Enable Agent-to-Agent Communication
Agent-to-Agent protocols allow agents to exchange information and delegate tasks.
For example, a research agent can share findings with a coding agent.
This enables collaboration between independent agents.
9. Multi-Agent Systems — Multiple Agents Working Together
A multi-agent system divides complex tasks among specialized agents.
For example:
- Researcher Agent: Collects information.
- Coder Agent: Builds solutions.
- Reviewer Agent: Checks output.
- Orchestrator: Coordinates everything.
Each agent handles a specific responsibility.
𝗪𝗵𝘆 𝗗𝗼 𝗧𝗵𝗲𝘀𝗲 𝗧𝗲𝗿𝗺𝘀 𝗠𝗮𝘁𝘁𝗲𝗿?
These concepts help build AI systems that can:
- Remember context.
- Access external knowledge.
- Execute actions.
- Collaborate with agents.
- Improve reliability.
The future of AI isn't just about smarter models. It's about building smarter systems around them.
Follow for more AI tools & tech updates.
#AI #AIAgents #AgenticAI #MCP #RAG #LLM #ArtificialIntelligence #Automation
𝗧𝗢𝗣 𝟵 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧 𝗧𝗘𝗥𝗠𝗦 𝗘𝗩𝗘𝗥𝗬 𝗔𝗜 𝗕𝗨𝗜𝗟𝗗𝗘𝗥 𝗦𝗛𝗢𝗨𝗟𝗗 𝗞𝗡𝗢𝗪 🤖
AI agents are evolving beyond simple chatbots.
They can remember information, use tools, execute tasks, and collaborate with other agents.
To understand how they work, here are 9 essential concepts every AI builder should know.
1. Harness — The System That Makes an LLM Act
A harness connects an LLM with tools, context, memory, and its execution environment.
It manages the interaction loop:
- Provides context and instructions.
- Sends requests to the LLM.
- Executes tool calls.
- Collects results.
Think of it as the operating environment of an AI agent.
2. Memory & State — Give Agents Continuity
Memory retains useful information, while state tracks task progress.
Two important types:
- Short-term memory: Current conversation and context.
- Long-term memory: Saved preferences and past interactions.
State tracks current steps and pending tasks.
3. RAG — Give AI Access to External Knowledge
Retrieval-Augmented Generation retrieves relevant information before generating an answer.
How it works:
- Receive a user query.
- Search relevant documents.
- Retrieve useful information.
- Add context to the prompt.
- Generate an informed response.
It helps reduce hallucinations and improve accuracy.
4. MCP — Connect AI Agents With Tools
Model Context Protocol standardizes how AI applications connect with external tools and data.
It provides:
- Tools for executing actions.
- Resources for accessing information.
- Prompts for reusable instructions.
Think of MCP as a connection layer for AI applications.
5. Skills — Give Agents Reusable Expertise
Skills package instructions, scripts, and resources into reusable capabilities.
Examples:
- Research and analysis.
- Code review.
- Report generation.
- Content creation.
They help agents perform workflows consistently.
6. Guardrails — Keep AI Actions Under Control
Guardrails define what an agent can and cannot do.
Important mechanisms:
- Permission controls.
- Sandboxed execution.
- Human approval.
- Activity logging.
They make AI systems safer and more predictable.
7. Evals — Measure Agent Performance
Evaluations help developers measure agent reliability.
They assess:
- Output accuracy.
- Task completion.
- Tool usage.
- Response quality.
- Performance consistency.
Testing before launch is essential.
8. A2A — Enable Agent-to-Agent Communication
Agent-to-Agent protocols allow agents to exchange information and delegate tasks.
For example, a research agent can share findings with a coding agent.
This enables collaboration between independent agents.
9. Multi-Agent Systems — Multiple Agents Working Together
A multi-agent system divides complex tasks among specialized agents.
For example:
- Researcher Agent: Collects information.
- Coder Agent: Builds solutions.
- Reviewer Agent: Checks output.
- Orchestrator: Coordinates everything.
Each agent handles a specific responsibility.
𝗪𝗵𝘆 𝗗𝗼 𝗧𝗵𝗲𝘀𝗲 𝗧𝗲𝗿𝗺𝘀 𝗠𝗮𝘁𝘁𝗲𝗿?
These concepts help build AI systems that can:
- Remember context.
- Access external knowledge.
- Execute actions.
- Collaborate with agents.
- Improve reliability.
The future of AI isn't just about smarter models. It's about building smarter systems around them.
Follow for more AI tools & tech updates.
#AI #AIAgents #AgenticAI #MCP #RAG #LLM #ArtificialIntelligence #Automation