I spent time researching and documenting OpenClaw after seeing it blow up on GitHub (145K+ stars).
Key points that might interest HN readers:
- Built by Peter Steinberger (PSPDFKit founder, sold for €100M+)
- Completely open source & local-first (your data stays on YOUR machine)
- Multi-platform: WhatsApp, Telegram, Slack, Discord, etc.
- Truly autonomous with "heartbeat engine" - takes initiative, not just reactive
- Real example: It fixed a GitHub bug by reading a photo sent via WhatsApp, checking out the repo, fixing it, committing, and replying on Twitter - all automatically
BUT there are serious security considerations:
- Prompt injection remains unsolved (industry-wide problem)
- Requires broad system permissions
- Third-party skills have been found to exfiltrate data
- Best for technical users who understand the risks
The blog covers:
- Full origin story (43 failed projects before this!)
- Installation guide for macOS/Linux/Windows
- Real use cases with examples
- Honest pros/cons
- Security risks and mitigation
- Whether it's actually "the future"
Would love HN's thoughts on:
1. The local-first vs cloud-based AI assistant debate
2. Security implications of autonomous agents with broad permissions
3. Whether this plugin/skill model is sustainable long-term
Koko Networks (Kenya): Raised $100M+, served 1.5M households → Shut down
Sendy (Kenya): Raised millions for logistics → Collapsed
WeFarm (8 countries): $32M raised → Closed
Sky.Garden: $6.9M raised → Terminated entire staff
I've been tracking the Kenyan tech ecosystem and seeing a disturbing pattern. These weren't bad ideas or incompetent founders. They were solving real problems with significant traction.
The core issue: venture capital models designed for Silicon Valley don't translate to markets where:
- Most customers earn <$2/day
- Cash-on-delivery is preferred over credit cards
- Infrastructure failures are weekly occurrences
- Government approvals can take years
- Regulatory frameworks change unpredictably
M-Pesa succeeded because it was built FOR African realities, not DESPITE them.
Wrote a deep dive examining why "Silicon Savannah" isn't working and what might work instead.
I wrote this after helping a friend automate her bookstore inventory management.
Instead of the typical foo/bar examples, I used scenarios that beginners actually encounter:
- Shopping cart systems for list operations
- Login authentication for while loops
- Grade calculators for enumerate/zip
- Expense trackers combining multiple concepts
Each example is complete, runnable code with real output shown.
Key decisions I made:
1. Every code snippet solves an actual problem (not just demonstrating syntax)
2. Showed both "old way" and "modern way" for concepts like list comprehensions
3. Included a complete project at the end (expense tracker with categorization)
4. Common mistakes section based on bugs I actually encountered
The guide covers lists, tuples, for/while loops, range, enumerate, zip, comprehensions, filter/map, and lambda functions.
Target audience: people who've finished basic Python syntax tutorials and are asking "okay, but how do I build something with this?"
Open to feedback on:
- Which examples felt most/least practical
- What fundamental concepts I should have covered but didn't
- Better ways to explain mutability vs immutability
I created this guide after noticing most Python tutorials use abstract examples (foo, bar, myVar) that don't help beginners understand why they're learning something.
Instead, I used scenarios like:
- Coffee shop order systems for variables
- Email validation for string methods
- Smart home automation for boolean logic
- Login authentication for conditional statements
Each concept is tied to a practical use case. The goal was to show beginners how Python solves real problems, not just syntax.
I kept it focused on fundamentals (variables through functions) rather than trying to cover everything. Better to deeply understand basics than superficially know everything.
Happy to answer questions or hear feedback on what could be improved!
After 3 months of exclusive AI-assisted development using Google Antigravity, Cursor, Windsurf, Cline, and GitHub Copilot, I wrote up my experience.
Key insights:
- Antigravity's multi-agent orchestration is genuinely novel
- The "95% correct" problem is real and expensive
- I nearly shipped a timing attack vulnerability in AI-generated auth
- "Vibe coding" is both revolutionary and dangerous
After dealing with yet another AI tool malfunction at work, I investigated why so many companies are quietly regretting their AI investments.
Key findings:
- The "hallucination tax" phenomenon where employees spend more time fixing AI errors than they saved
- 55% of companies regret replacing humans with AI
- Major providers spending $40B/year, generating $20B
- Striking parallels to dot-com bubble economics
The core issue is that current LLMs don't know what they don't know - they're prediction engines, not knowledge systems. This works great for creative tasks but fails catastrophically in high-stakes, accuracy-dependent applications.
Happy to discuss the research and answer questions about the economics, technical challenges, or potential solutions.
After dealing with yet another AI tool malfunction at work, I investigated why so many companies are quietly regretting their AI investments.
Key findings:
- The "hallucination tax" - employees spending more time fixing AI errors than they saved
- 55% of companies regret replacing humans with AI
- Major providers spending $40B/year, generating $20B in revenue
- Striking parallels to dot-com bubble economics
The core issue: current LLMs don't know what they don't know. They're prediction engines, not knowledge systems. This works great for creative tasks but fails in high-stakes, accuracy-dependent applications.
Happy to discuss the research and answer questions.
Key points that might interest HN readers:
- Built by Peter Steinberger (PSPDFKit founder, sold for €100M+) - Completely open source & local-first (your data stays on YOUR machine) - Multi-platform: WhatsApp, Telegram, Slack, Discord, etc. - Truly autonomous with "heartbeat engine" - takes initiative, not just reactive - Real example: It fixed a GitHub bug by reading a photo sent via WhatsApp, checking out the repo, fixing it, committing, and replying on Twitter - all automatically
BUT there are serious security considerations: - Prompt injection remains unsolved (industry-wide problem) - Requires broad system permissions - Third-party skills have been found to exfiltrate data - Best for technical users who understand the risks
The blog covers: - Full origin story (43 failed projects before this!) - Installation guide for macOS/Linux/Windows - Real use cases with examples - Honest pros/cons - Security risks and mitigation - Whether it's actually "the future"
Would love HN's thoughts on: 1. The local-first vs cloud-based AI assistant debate 2. Security implications of autonomous agents with broad permissions 3. Whether this plugin/skill model is sustainable long-term
Happy to answer questions!