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Charly Wargnier

@DataChaz

Nov 21

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…

Mind = blown.

That zip file has EVERYTHING:
↳ a readme with setup instructions
↳ your selected endpoints
↳ all the files to run your MCP server locally, on Cursor, Windsurf… even Docker!

You also get an .env file with your prefilled variables → just add your API keys! 🔥

Read 9 tweets

Charly Wargnier

@DataChaz

Nov 17

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.

Here's the repo:
↳ github.com/aishwaryanr/aw…

Read 5 tweets

Charly Wargnier

@DataChaz

Nov 13

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…

Toon GitHub repo:
github.com/toon-format/to…

github.com/toon-format/to…

Read 4 tweets

Charly Wargnier

@DataChaz

Nov 13

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

2 ‑ C‑L‑A‑R

- Given the (CONTEXT)
- List any (LIMITS)
- Describe the (ACTION)
- Define the expected (RESULT)

Example:
- CONTEXT: Raw Q1 sales data
- LIMITS: Focus on the 3 biggest churn drivers
- ACTION: Quantify impact & propose 2 fixes
- RESULT: Two‑slide executive brief

Read 7 tweets

Charly Wargnier

@DataChaz

Nov 5

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 🧵↓

Toon GitHub repo:
github.com/toon-format/to…

github.com/toon-format/to…

Read 5 tweets

Charly Wargnier

@DataChaz

Oct 31

this guy literally put in 1000 hours of prompt engineering to nail down the 6 patterns that actually matter.

source:
reddit.com/r/PromptEngine…

He calls it KERNEL, and it's transformed how his entire team uses AI.

Here's the framework:

----

K - Keep it simple

Bad: 500 words of context

Good: One clear goal

Example: Instead of "I need help writing something about Redis," use "Write a technical tutorial on Redis caching"

Result: 70% less token usage, 3x faster responses

----

E - Easy to verify

Your prompt needs clear success criteria

Replace "make it engaging" with "include 3 code examples"

If you can't verify success, AI can't deliver it

My testing: 85% success rate with clear criteria vs 41% without

----

R - Reproducible results

Avoid temporal references ("current trends", "latest best practices")

Use specific versions and exact requirements

Same prompt should work next week, next month

94% consistency across 30 days in my tests

----

N - Narrow scope

One prompt = one goal

Don't combine code + docs + tests in one request

Split complex tasks

Single-goal prompts: 89% satisfaction vs 41% for multi-goal

----

E - Explicit constraints

Tell AI what NOT to do

"Python code" → "Python code. No external libraries. No functions over 20 lines."

Constraints reduce unwanted outputs by 91%

----

L - Logical structure Format every prompt like:

Context (input)

Task (function)

Constraints (parameters)

Format (output)

----

Real example from my work last week:

Before KERNEL: "Help me write a script to process some data files and make them more efficient"

Result: 200 lines of generic, unusable code

After KERNEL:

Task: Python script to merge CSVs
Input: Multiple CSVs, same columns
Constraints: Pandas only, <50 lines
Output: Single merged.csv
Verify: Run on test_data/

Result: 37 lines, worked on first try

----

Actual metrics from applying KERNEL to 1000 prompts:

First-try success: 72% → 94%
Time to useful result: -67%
Token usage: -58%

Accuracy improvement: +340%

Revisions needed: 3.2 → 0.4

----

Advanced tip from this user:

Chain multiple KERNEL prompts instead of writing complex ones.

Each prompt does one thing well, feeds into the next.

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