u/ComparisonLiving6793

Not trying to complain but genuinely trying to understand whether something changed recently with Claude Pro usage behaviour.

Using 4.6 Thinking, I asked Claude to refine a single professional email and it consumed 26% of my quota. A few ordinary prompts now seem to drain limits far faster than they did a few months ago.

I’m curious whether others are experiencing the same issue recently with Claude Pro and especially 4.6 Thinking.

I asked Claude to refine one professional email and it consumed 26% of my usage allowance. A handful of normal prompts now seems to burn through limits extremely quickly compared to a few months ago.

Has Anthropic changed:

• token accounting?
• context handling?
• hidden reasoning usage?
• Pro plan limits?
• model efficiency/pricing internally?

For people using Claude heavily for writing, strategy, coding, or business work:

• Have you noticed a major drop in practical usage?
• Are certain models much worse for consumption?
• Any workarounds or prompt techniques helping?

Would be interested in evidence, screenshots, or explanations from power users because the experience feels dramatically different recently.

The US leads right now, but China, France, UAE, Canada and others are investing heavily. Do sovereign LLMs become critical infrastructure like energy or defence? Or will a handful of companies/models globally dominate everything? Curious where people see this heading by 2030–2035.

The US leads right now, but China, France, UAE, Canada and others are investing heavily. Do sovereign LLMs become critical infrastructure like energy or defence? Or will a handful of companies/models globally dominate everything? Curious where people see this heading by 2030–2035.

The US leads right now, but China, France, UAE, Canada and others are investing heavily. Do sovereign LLMs become critical infrastructure like energy or defence? Or will a handful of companies/models globally dominate everything? Curious where people see this heading by 2030–2035.

I often see models like Qwen 3.6, DeepSeek V4, MiniMax 2.7, and Kimi K2.6 discussed due to their strong price-to-performance ratio, large context windows, and relatively low API costs.

But I know these are all Chinese models/providers. Interested in comparisons across providers.

I often see models like Qwen 3.6, DeepSeek V4, MiniMax 2.7, and Kimi K2.6 discussed due to their strong price-to-performance ratio, large context windows, and relatively low API costs.

But I know these are all Chinese models/providers. Interested in comparisons across providers.

Hey all,

I’ve just come across Hermes Agent (Nous Research) and trying to wrap my head around where it actually sits in the current agent stack.

From what I’m seeing, it feels like a pretty different philosophy vs OpenClaw:

• Hermes → focused on self-improvement + persistent memory + learning loops
• OpenClaw → more about integrations, skills ecosystem, and “connect to everything”

Some posts I’ve seen suggest Hermes actually learns from tasks and creates/refines its own skills over time, whereas OpenClaw is more static/manual in that sense.

Would love to hear your experiences and thoughts on it! Thank you.

I’ve been seeing this pop up more frequently in conversations around AI agents and automation.

From what I understand, it’s not just another chatbot or coding assistant as it’s positioned as a self-improving, persistent AI agent that:

• Learns from past interactions and builds long-term memory
• Creates and refines its own “skills” over time
• Runs continuously (e.g. on a server or VPS) rather than being session-based
• Integrates across platforms like Slack, Telegram, CLI, etc.

It seems to be pushing toward something closer to a true “AI operator” rather than a tool you prompt each time, which is a pretty big shift in how we think about AI in practice.

Keen to hear from anyone who has:

• Actually deployed it (locally or in a team environment)
• Found real-world use cases beyond experimentation

Particularly interested in whether this is genuinely useful in production workflows or still more “promising concept” than practical tool!

I’ve been seeing this pop up more frequently in conversations around AI agents and automation.

From what I understand, it’s not just another chatbot or coding assistant as it’s positioned as a self-improving, persistent AI agent that:

• Learns from past interactions and builds long-term memory
• Creates and refines its own “skills” over time
• Runs continuously (e.g. on a server or VPS) rather than being session-based
• Integrates across platforms like Slack, Telegram, CLI, etc.

It seems to be pushing toward something closer to a true “AI operator” rather than a tool you prompt each time, which is a pretty big shift in how we think about AI in practice.

Keen to hear from anyone who has:

• Actually deployed it (locally or in a team environment)
• Found real-world use cases beyond experimentation

Particularly interested in whether this is genuinely useful in production workflows or still more “promising concept” than practical tool!

I’m curious where things currently stand on this.

With the rapid progress in LLMs and autonomous AI agents, are they actually capable of reliably solving reCAPTCHA (v2, v3, image-based, etc.) in real-world scenarios? I understand that basic OCR-style CAPTCHAs have been largely broken for years, but modern systems are more behavioural and risk-based.

From what I’ve seen, some agents can technically solve image CAPTCHAs with high accuracy when combined with vision models, but the bigger challenge seems to be bypassing the full detection stack (mouse movement patterns, browser fingerprinting, timing, IP reputation, etc.).

From my rudimentary research these 3 brands come up:

1. WordFence 2. Sucuri 3. Solid Security Pro (iThemes)

I've also heard it could be smart to combine WordFence with something like Cloudfare.
Would WordFence free be sufficient with that combination or it must be the pro version?

Any advice and suggestions are greatly appreciated!