I really disappointed with this model to say the least.

Comments URL: https://news.ycombinator.com/item?id=47396539

Points: 4

# Comments: 2

"which could power small devices for decades"

- Complete support for WordPress REST API endpoints - Multiple authentication methods (Application Passwords, Basic Auth, OAuth1) - Intuitive interface for common WordPress operations (posts, pages, media, etc.) - Support for custom taxonomies and post types - Custom fields (post meta) management - Robust error handling with specific exception types - Full typing support for better IDE integration - Automatic retries for failed requests - Comprehensive documentation (https://wp-api-client.readthedocs.io/en/latest/)

Comments URL: https://news.ycombinator.com/item?id=43841898

Points: 3

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=43663051

Points: 6

# Comments: 0

Just released a new version with command line support.

Claude 3.7 Sonnet Thinking model used in this (using Github Copilot).

Documentation is also available at https://email-deliverability.readthedocs.io/en/latest/ which is also fully vibe coded.

Even the architecture diagram (https://email-deliverability.readthedocs.io/en/latest/overvi...) is also vibe-coded into mermaid diagrams.

Comments URL: https://news.ycombinator.com/item?id=43637756

Points: 2

# Comments: 3

Comments URL: https://news.ycombinator.com/item?id=43487630

Points: 3

# Comments: 2

Problem is, quantitative easing as the instrument is misunderstood by politicians. It creates/accelerates further debt burden on future generations. And, that just accelerated this debt trap for many nations.

The Demography dividend is already eroding fast. Natural Resources are already limited. So, Innovation is the only way out for nations. AI is a driver for that.

May be, we will see new world order in this lifetime only.

That's good. There was one point that I missed.

> Rise in debt ratios offset by falling interest rates, so debt service ratios mostly stable, or even declining

Comments URL: https://news.ycombinator.com/item?id=43472395

Points: 7

# Comments: 4

Comments URL: https://news.ycombinator.com/item?id=43447206

Points: 4

# Comments: 0

The race to build human-like AI is accelerating—but to innovate responsibly, we must understand how far we’ve come and how much further we have to go. My latest article dives deep into comparing human brain regions (neocortex, hippocampus, amygdala) with generative AI systems (GPT-4, DALL-E, robotics), mapping progress and glaring gaps.

Why This Matters: 1⃣ Innovation Needs Inspiration

The brain’s efficiency, plasticity, and creativity are blueprints for next-gen AI. Studying it helps us build systems that learn faster, generalize better, and consume less energy.

2⃣ Avoid the "Hype Trap"

While AI mimics some brain functions (e.g., 70% of the neocortex’s language skills), it lacks consciousness, empathy, and embodied cognition. Recognizing these gaps keeps expectations realistic.

3⃣ Ethical Guardrails

If we don’t grasp what AI can’t do (e.g., true understanding, morality), we risk overtrusting it in critical domains like healthcare, law, and mental health.

Key Takeaways from the Article: Progress:

AI replicates ~50-60% of brain functions in narrow tasks (e.g., GPT-4’s reasoning, RL systems’ reward learning).

Transformers mimic the thalamus’s “attention” with 80% efficiency.

Gaps:

- 0% consciousness: AI has no self-awareness or subjective experience.

- 55% motor control gap: Robots still can’t match a toddler’s fluid movements.

- 20W vs. Megawatts: The brain’s energy efficiency dwarfs AI’s carbon-heavy training.

Why You Should Care: Whether you’re in tech, healthcare, ethics, or leadership, understanding these parallels helps you:

- Identify opportunities (e.g., neuromorphic chips for sustainable AI).

- Mitigate risks (e.g., bias in “emotion-aware” AI).

- Drive interdisciplinary innovation (neuroscience + AI = ).

Read the Full Article https://www.innerkore.com/blog/ai-vs-digital-transformation-lessons-learned-economic-realities-future/ to explore:

- How the amygdala’s emotional processing compares to sentiment analysis.

- Why “lifelong learning” AI could revolutionize education.

- Ethical debates on synthetic consciousness.

Let’s Discuss: Where should AI researchers focus next—closing gaps in cognition, creativity, or ethics? Can machines ever truly “think,” or will they always be tools?

Drop your thoughts below!

Comments URL: https://news.ycombinator.com/item?id=43317871

Points: 1

# Comments: 2

These things are pretty interesting as they are developing. What US will do to retain its power?

BTW I am Indian and we are not even in the race as country. :(

Why even rational investors fall into timing traps (spoiler: it’s not just greed).

Quantitative thresholds where timing might add value, based on market cycle analysis.

The role of algorithmic tools vs. human intuition in modern strategies.

For HN readers: If you’ve ever built models around market data, tested timing algorithms, or have strong opinions on efficient markets, this piece is a catalyst for debate. How do you reconcile historical volatility with long-term holding? Is there a middle ground between passive indexing and active timing?

Curious to hear from quant-minded folks, data scientists, or anyone who’s backtested timing strategies. What’s your take?

Comments URL: https://news.ycombinator.com/item?id=43257947

Points: 5

# Comments: 3