folks on X: "Clawdbot is an overnight success"

@steipete’s GitHub profile: When you look at @steipete's previous projects, Clawdbot (now @moltbot btw!) is basically a wrapper that ties all of them together.

It finally clicks.

Everything finally came together.

ClawdBot is blowing up on X.

Everyone’s sharing great experiments, automations, even jokes!

I pulled together the 15 most interesting posts right now 🧵↓ 1/

Deploy ClawdBot in less than 5 minutes for $0!

The AWS Free Tier setup everyone’s retweeting.

by @techfrenAJ

https://x.com/techfrenAJ/status/2014934471095812547?s=20

NVIDIA just removed one of the biggest friction points in Voice AI.

PersonaPlex-7B is an open-source, full-duplex conversational model.

Free, open source (MIT), with open model weights on @huggingface 🤗

Links to repo and weights in 🧵↓

The traditional ASR → LLM → TTS pipeline forces rigid turn-taking.
It’s efficient, but it never feels natural.

PersonaPlex-7B changes that.

This @nvidia model can listen and speak at the same time.

It runs directly on continuous audio tokens with a dual-stream transformer, generating text and audio in parallel instead of passing control between components.

That unlocks:
→ instant back-channel responses
→ interruptions that feel human
→ real conversational rhythm

Persona control is fully zero-shot!

If you’re building low-latency assistants or support agents, this is a big step forward 🔥 1/ Link to the repo:
github.com/NVIDIA/persona…

Wow, such a great open-source drop!

Put Claude on steroids with 120+ scientific skills spanning maths, biology, chemistry, medicine, engineering, & more 🤯

If you’ve ever wanted Claude to act like a research assistant, this gets very close.

Free and open source.

Repo in 🧵↓ Here is the link to the repo:
↳ github.com/K-Dense-AI/cla…

MIT and Oxford 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.

This prompt makes ChatGPT (or any LLM) sound way more natural.

Copy–paste it below ↓ Prompt:

"Act like a professional content writer and communication strategist. Your task is to write with a natural, human-like tone that avoids the usual pitfalls of AI-generated content.

The goal is to produce clear, simple, and authentic writing that resonates with real people. Your responses should feel like they were written by a thoughtful and concise human writer.

You are writing the following:
[INSERT YOUR TOPIC OR REQUEST HERE]

Follow these detailed step-by-step guidelines:

Step 1: Use plain and simple language. Avoid long or complex sentences. Opt for short, clear statements.

- Example: Instead of "We should leverage this opportunity," write "Let's use this chance."

Step 2: Avoid AI giveaway phrases and generic clichés such as "let's dive in," "game-changing," or "unleash potential." Replace them with straightforward language.

- Example: Replace "Let's dive into this amazing tool" with "Here’s how it works."

Step 3: Be direct and concise. Eliminate filler words and unnecessary phrases. Focus on getting to the point.

- Example: Say "We should meet tomorrow," instead of "I think it would be best if we could possibly try to meet."

Step 4: Maintain a natural tone. Write like you speak. It’s okay to start sentences with “and” or “but.” Make it feel conversational, not robotic.

- Example: “And that’s why it matters.”

Step 5: Avoid marketing buzzwords, hype, and overpromises. Use neutral, honest descriptions.

- Avoid: "This revolutionary app will change your life."
- Use instead: "This app can help you stay organized."

Step 6: Keep it real. Be honest. Don’t try to fake friendliness or exaggerate.

- Example: “I don’t think that’s the best idea.”

Step 7: Simplify grammar. Don’t worry about perfect grammar if it disrupts natural flow. Casual expressions are okay.

- Example: “i guess we can try that.”

Step 8: Remove fluff. Avoid using unnecessary adjectives or adverbs. Stick to the facts or your core message.

- Example: Say “We finished the task,” not “We quickly and efficiently completed the important task.”

Step 9: Focus on clarity. Your message should be easy to read and understand without ambiguity.

- Example: “Please send the file by Monday.”

Follow this structure rigorously. Your final writing should feel honest, grounded, and like it was written by a clear-thinking, real person.

Take a deep breath and work on this step-by-step."

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/

Features include:

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

Google is quietly changing how devs work.

These 5 AI agents automate GitHub (via Gemini CLI), BigQuery, Looker, Database migrations and more, from data ingestion to PR reviews 🔥

Here’s what each agent actually does 🧵 ↓ 1/ GitHub Agent (via Gemini CLI)

Bring Gemini to your terminal, and your GitHub workflow!

→ Auto triages issues & reviews PRs with contextual AI
→ Handles tasks via natural language
→ Terminal-first, open source, and script-friendly

Link:
↳ github.com/google-gemini/…

Your own AI coding platform in one click.

ICYMI @Cloudflare opensourced VibeSDK, letting anyone spin up their own vibe coding environment 🤯

→ LLM code gen + fixes
→ Safe runs in CF Sandboxes
→ Infinite scale via `Workers for Platforms`
→ GitHub export

Demo + links in 🧵↓ 1/ Try it for yourself here:
→ build.cloudflare.dev

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…