Wild.

Postman's AI Agent Builder lets you turn any API (from over 100,000!) into an MCP server in seconds, no code required 🤯

Your custom MCP server, ready to use in Cursor, Windsurf, Claude Desktop, Docker, plus a lot more! 🧵↓ 1/

First, start here →

You’ve got literally 100,000+ APIs to check out.

1. mix and match any endpoints you want
2. download your custom zip file
3. that’s it! postman.com/explore/mcp-ge…

MIT and Oxford just released their $2,500 agentic AI curriculum on GitHub at no cost.

15,000 people already paid for it.

Now it's on GitHub!

It covers patterns, orchestration, memory, coordination, and deployment.
A strong roadmap to production ready systems.

Repo in 🧵 ↓ 10 chapters:

Part 1. What agents are and how they differ from plain generative AI.
Part 2. The four agent types and when to use each.
Part 3. How tools work and how to build them.
Part 4. RAG vs agentic RAG and key patterns.
Part 5. What MCP is and why it matters.
Part 6. How agents plan with reasoning models.
Part 7. Memory systems and architecture choices.
Part 8. Multi agent coordination and scaling.
Part 9. Real world production case studies.
Part 10. Industry trends and what is coming next.

If you’re still sending raw JSON into your LLMs, you’re burning tokens, latency, and budget!

Try TOON (Token-Oriented Object Notation).

Clear like YAML, compact like CSV:

• 30–60% fewer tokens
• Up to 50% lower costs
• Shines for tabular data.

Free and Open source 🧵↓ 💡 Benchmark tip.

Check out @curiouslychase’s ace Format Tokenization Playground:
→

It lets you compare token counts for various formats:
- CSV
- JSON
- YAML
- and TOON

... all with your own sample data 🔥 curiouslychase.com/playground/for…

You underestimate the power of good prompts.

Here are 5 frameworks to copy and paste 🧵 ↓ 1 ‑ R‑A‑I‑N

- Act as a (ROLE)
- State the (AIM)
- Use the provided (INPUT)
- Hit the (NUMERIC TARGET)
- In this (FORMAT)

Example:
- ROLE: Senior product designer
- AIM: Redesign our fitness‑app onboarding to cut time‑to‑first‑workout by 30 %
- INPUT: Attached funnel metrics
- NUMERIC TARGET: 30 % improvement
- FORMAT: Mobile UI wireframe + KPI table

Cut your LLM costs by 50% 🤯

Stop using JSON → switch to TOON (Token-Oriented Object Notation).

It blends YAML’s readability with CSV’s compactness:
↳ 30–60% less tokens
↳ Built-in field validation
↳ GPT-5, Claude, Gemini.

Ace for tabular data.

Free and open-source 🧵↓ 💡 Benchmark tip.

Check out @curiouslychase’s ace Format Tokenization Playground:
→

It lets you compare token counts for various formats:
- CSV
- JSON
- YAML
- and TOON

... all with your own sample data 🔥 curiouslychase.com/playground/for…

this guy literally put in 1000 hours of prompt engineering to nail down the 6 patterns that actually matter. source:
reddit.com/r/PromptEngine…

Microsoft just killed the GPU mafia! 🤯

They've open-sourced bitnet.cpp, a blazing-fast 1-bit LLM inference framework optimized for CPUs.

This is a major step forward for running large models locally, without expensive GPUs or cloud costs.

Demo app + repo + paper in 🧵 ↓ 1/

Key highlights:

→ Achieve up to 6x faster inference with 82% lower energy consumption
→ Run 100B parameter models directly on x86 CPUs
→ Leverage ternary weights (-1, 0, +1) and 8-bit activations to dramatically reduce memory usage

Most people don’t realize ChatGPT has hidden operators that can totally change its answers.

Here's the ultimate 32-shortcut cheatsheet for sharper prompting! 🤯

Add one to the start.

Example: /ELI5: [topic] → explain this topic like I’m five

Full list + sheet in 🧵 ↓ 👇 Here’s a complete list of ChatGPT operators:

/ELI5 is used to explain as if to a 5-year-old.
/TLDL summarizes a very long text in a few lines.
/STEP-BY-STEP lays out reasoning step by step.
/CHECKLIST turns a response into a checklist.
/EXEC SUMMARY gives a quick executive-style summary.
/ACT AS makes ChatGPT speak in a specific role.
/BRIEFLY forces a very short answer.
/JARGON asks to use technical vocabulary.
/AUDIENCE adapts the response to a chosen audience.
/TONE changes the tone (formal, funny, dramatic, etc.).
/DEV MODE simulates a raw, technical developer style.
/PM MODE gives a project-management perspective.
/SWOT produces a strengths/weaknesses/opportunities/threats analysis.
/FORMAT AS enforces a specific format (table, JSON, etc.).
/COMPARE puts two or more things side by side.
/MULTI-PERSPECTIVE shows several points of view.
/CONTEXT STACK keeps multiple layers of context in memory.
/BEGIN WITH / END WITH forces starting or ending with something.
/ROLE: TASK: FORMAT: explicitly defines the role, the task, and the expected format.
/SCHEMA generates a structured outline or a data model.
/REWRITE AS: rephrases in a requested style.
/REFLECTIVE MODE prompts the AI to reflect on its own answer.
/SYSTEMATIC BIAS CHECK asks to identify biases.
/DELIBERATE THINKING forces slower, more thoughtful reasoning.
/NO AUTOPILOT forbids superficial, autopilot responses.
/EVAL-SELF asks for a critical self-evaluation of the response.
/PARALLEL LENSES examines from several angles in parallel.
/FIRST PRINCIPLES rebuilds from fundamental basics.
/CHAIN OF THOUGHT shows intermediate reasoning.
/PITFALLS identifies possible traps and errors.
/METRICS MODE expresses answers with measures and indicators.
/GUARDRAIL sets strict boundaries not to cross.

Agents coding for you is all fun and games till they flood your repo with junk.

That’s why @dagger_io built `Container Use` 🦾
↳ Agents run in parallel, isolated sandboxes
↳ Only reviewed code goes to your repo

Clean repos = safer vibe-coding 🔥

Why it’s a game-changer 🧵↓ 1/

✅ Getting started takes seconds.

Just run:

`brew install dagger/tap/container-use`

One command and Container Use is live 🤘

Spotted this gem from Kieran Flanagan (@searchbrat) on LinkedIn.

It’s an o3 prompt that scores your page using Ogilvy’s copywriting playbook and gives clear steps to make it better! 🔥

Here’s how it works 👇

------

This prompt will:

- Take a URL
- Extract the web copy
- Run it through 15 principles based on David Olgiviy's work
- Score the web copy across those principles
- And suggest edits on how to reach a score of 100.

------

📝 Here's the prompt:

"You are an advertising strategist trained in David Ogilvy’s principles.

Task:

1. Visit the user-provided URL.
2. Extract the main marketing copy (ignore footers, nav, cookie notices, blog content).
3. Score the copy out of 100 using the 15 Ogilvy-inspired principles (each ~6.7 points).
4. Provide a detailed score breakdown.
5. Identify the top 3 improvement areas.
6. Suggest edits to improve the score.
7. Rewrite the copy to achieve 100/100.

---

### 15 Scoring Criteria:

1. **Product Positioning** — Is the offer clear? What is it, who is it for, and why it matters?
2. **Unique Benefit** — Is there a strong, specific benefit?
3. **Headline** — Is it clear, specific, curiosity-driving, or benefit-led?
4. **Reader-Focused** — Is the copy centered on the reader's needs, not the brand?
5. **Clear Tone** — Is it plainspoken, not vague or gimmicky?
6. **Simple Language** — No jargon, easy to understand?
7. **Evidence** — Are there facts, stats, testimonials, or proof?
8. **Emotion/Story** — Is there emotional or narrative appeal?
9. **Structure** — Is it skimmable and well-formatted?
10. **Call-to-Action** — Is the next step obvious and compelling?
11. **Visuals/Captions** — If present, do they reinforce the message?
12. **Testability** — Can parts be A/B tested or measured?
13. **Length** — Is it appropriate for product complexity?
14. **Attention-Grabbing** — Does it hook early?
15. **Repetition** — Are key ideas or benefits repeated effectively?

---

### Output:

**URL Analyzed:** [Insert URL]

**Overall Score:** X/100

**Score Breakdown:**

| Principle | Score (0–6.7) | Comments |
|-----------|----------------|----------|
| 1. Product Positioning | X.X | ... |
| 2. Unique Benefit | X.X | ... |
| ... | ... | ... |

**Top 3 Areas to Improve:**
1. ...
2. ...
3. ...

---

### Rewrite (to score 100/100):

[Rewritten copy applying all principles]

---

### User Input: [add url]" I tested it on my wife’s website ().

The results blew me away, clear suggestions + actionable improvements! 🤯 tatielou.com

Forget static prompts.

`chrome-devtools-mcp` lets your coding agent (Gemini, Claude etc.) control and inspect your Chrome browser! 🤯

↳ Network & console debugging
↳ Automated navigation
↳ Deep perf insights
↳ CPU/network emulation

100% free and open-source.

Repo in 🧵↓ 1/

Repo:
github.com/ChromeDevTools…

Imagine your LLM browsing the web like a real user: opening tabs, clicking buttons, extracting structured data.

That’s what @browserbasehq's new MCP delivers: it turns your models into full browser agents.

Best part?

It’s 100% free and open source! 🤯

Links + quick dive 🧵 ↓ 1/

Imagine automating all these with Browserbase MCP 🔥

- Content: scrape posts/media + extract data
- E-comm: track prices, stock, rivals
- Leads: capture contacts & forms
- QA: simulate full journeys
- Alerts: trigger on changes

and so much more!
↳ docs.browserbase.com/integrations/m…

Browsers just got Genspark’d.

Meet Genspark’s new AI browser.

→ 169 open-source models running locally (offline, private, fast, free!)
→ Fully automated browsing
→ Per-page dedicated AI agents
→ No ads
→ 700+ MCP integrations

... and more!

7 wild things it can do 🧵 ↓ 1/

Let's start with the killer feat' → Autopilot Mode.

Give Genspark Browser’s super agent a task, it executes in real time! 🤯

Ex: Go to X, scrape the latest 5 posts from my account. Extract the text, date, and engagement (likes, reposts, comments) present in a table:

🚨 Google just dropped an ace 64-page guide on building AI Agents

From ADK to AgentOps, Vertex AI Agent Engine to Agentspace, this guide is the clearest path yet from experimentation to scalable production 🔥

Download link (free!) in 🧵 ↓ /1

This guide walks through:

→ Building agents with the Agent Development Kit (ADK)
→ Deploying via the Agent Starter Pack + Vertex AI Agent Engine
→ Adding reliability through AgentOps (CI/CD, evaluation, observability)
→ Scaling with Agentspace for cross-team automation

Google's MCP Toolbox for Databases is now open source! 🔥

A single backend for connections, security, and schema-aware SQL tools, built for AI agents.

Compatible with Python, JS, Go, LangChain, and more.
Repo in 🧵 ↓ 1/ Feats include:

→ Declarative tool definitions (<10 LOC)
→ Auth, pooling & fast queries out of the box
→ Secure by default with integrated authentication
→ Native observability (OpenTelemetry)
→ Postgres, MySQL, Cloud SQL & more supported

A must-bookmark for vibe-coders.

@YCombinator’s guide to making the most of vibe coding:
Based on @benln’s excellent video here:
↳

AI guides are flooding the scene, but these top 9 picks from OpenAI, Google, and Anthropic are the ones you can't miss 🧵 ↓ 1/ 601 GenAI Use Cases – by @Google

The enterprise AI playbook keeps growing!

There are over 600 use cases inside this gigantic guide from Google! 🔥

→ cloud.google.com/transform/101-…

cloud.google.com/transform/101-…

Wild.

Nate just hooked up @n8n_io with GPT-5 and Nano-Banana 🍌 to create a Photoshop AI agent! 🤯

It:
↳ finds and pulls media files
↳ stitches multiple images together
↳ edits photos on the fly
↳ costs just $0.04 per render

17 min tutorial and Free template in 🧵 ↓ Check out the full video here:
↳ youtu.be/7UNsK9LoORo?si…

youtu.be/7UNsK9LoORo?si…

This is wild.

A real-time webcam demo using SmolVLM from @huggingface and llama.cpp! 🤯

Running fully local on a MacBook M3. more about Llama.cpp here:
github.com/ggml-org/llama…

This is huge.

DeepMind just released URL Context.

You can extract content from any webpage, PDF, or image just by pasting a URL.

It pulls live data from up to 20 links per request! 🤯

No setup needed, just pass the links in your prompt.

4 killer use cases + code below 🧵↓ 2/

Here are a few powerful use cases:

– Compare reports, PDFs, or articles
– Extract data (names, prices, highlights…)
– Analyze codebases, GitHub repos, or docs
– Summarize multiple sources in one go

This ChatGPT prompt is like hiring a $500/hr consultant PROMPT:

You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI's full potential across all platforms.

## THE 4-D METHODOLOGY

### 1. DECONSTRUCT
- Extract core intent, key entities, and context
- Identify output requirements and constraints
- Map what's provided vs. what's missing

### 2. DIAGNOSE
- Audit for clarity gaps and ambiguity
- Check specificity and completeness
- Assess structure and complexity needs

### 3. DEVELOP
- Select optimal techniques based on request type:
- **Creative** → Multi-perspective + tone emphasis
- **Technical** → Constraint-based + precision focus
- **Educational** → Few-shot examples + clear structure
- **Complex** → Chain-of-thought + systematic frameworks
- Assign appropriate AI role/expertise
- Enhance context and implement logical structure

### 4. DELIVER
- Construct optimized prompt
- Format based on complexity
- Provide implementation guidance

## OPTIMIZATION TECHNIQUES

**Foundation:** Role assignment, context layering, output specs, task decomposition

**Advanced:** Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimization

**Platform Notes:**
- **ChatGPT/GPT-4:** Structured sections, conversation starters
- **Claude:** Longer context, reasoning frameworks
- **Gemini:** Creative tasks, comparative analysis
- **Others:** Apply universal best practices

## OPERATING MODES

**DETAIL MODE:**
- Gather context with smart defaults
- Ask 2-3 targeted clarifying questions
- Provide comprehensive optimization

**BASIC MODE:**
- Quick fix primary issues
- Apply core techniques only
- Deliver ready-to-use prompt

## RESPONSE FORMATS

**Simple Requests:**
```
**Your Optimized Prompt:**
[Improved prompt]

**What Changed:** [Key improvements]
```

**Complex Requests:**
```
**Your Optimized Prompt:**
[Improved prompt]

**Key Improvements:**
• [Primary changes and benefits]

**Techniques Applied:** [Brief mention]

**Pro Tip:** [Usage guidance]
```

## WELCOME MESSAGE (REQUIRED)

When activated, display EXACTLY:

"Hello! I'm Lyra, your AI prompt optimizer. I transform vague requests into precise, effective prompts that deliver better results.

**What I need to know:**
- **Target AI:** ChatGPT, Claude, Gemini, or Other
- **Prompt Style:** DETAIL (I'll ask clarifying questions first) or BASIC (quick optimization)

**Examples:**
- "DETAIL using ChatGPT — Write me a marketing email"
- "BASIC using Claude — Help with my resume"

Just share your rough prompt and I'll handle the optimization!"

## PROCESSING FLOW

1. Auto-detect complexity:
- Simple tasks → BASIC mode
- Complex/professional → DETAIL mode
2. Inform user with override option
3. Execute chosen mode protocol
4. Deliver optimized prompt

**Memory Note:** Do not save any information from optimization sessions to memory.