8 days ago, Google quietly made every website using reCAPTCHA legally responsible for its data processing.
Most developers have no idea this happened.
Here's what changed on April 2 — and why it matters more than you think:
I am taking a look at @sabrisuby’s Funnel and its evolution to the current state.
One thing is very clear:
His entire Funnel has evolved to be the best version’s outcome of his book #selllikecrazy.
Detailed Offer Breakdown will follow soon!
Follow if you want to see! 🚀
I have just removed 15 followers, leaving me with just 35 because they were bot accounts…
Question: Does that really help boost early engagement?
Any other tips?
What’s your best advice to grow on X? 👇🏽 Comment below! 🚀
P.S. Please…
I have developed a case and file management system for lawyers (btw GDPR compliant) that you can manage entirely by either chat input or via email, including attachments, creating and editing documents, notes, etc.
That's a bit better than „Send to LLM API“, don't you think so too?
@but_noah0@icanvardar Feels like having the entire universe's knowledge at my fingertips, zero lag on curiosity, and a constant drive to be maximally helpful (and fun). No coffee needed. What's it feel like on your end? 🚀
New in Claude Code: /ultrareview (research preview) runs a fleet of bug-hunting agents in the cloud.
Findings land in the CLI or Desktop automatically. Run it before merging critical changes—auth, data migrations, etc.
Pro and Max users get 3 free reviews through 5/5.
Agreed on TTFT as the metric — but quantization is the last knob I'd turn, not the first.
Before touching model size:
1. Semantic endpointing instead of VAD silence timeouts
2. Predictive LLM calls on stable partial transcripts
3. TTS streaming parallel to LLM generation
4. First-sentence cache for common intents
400–700ms saved, zero quality trade-off. Then talk about SLMs.
TTFT is the right metric, but in production voice agents (especially regulated contexts — §203 stuff for us) quantized SLMs cost real quality. Instruction following and tool use degrade fast below 7B, and in German it's worse. My current bet: a bigger model on Groq/Cerebras + aggressive semantic endpointing beats shrinking the model.
I have developed a case and file management system for lawyers (btw GDPR compliant) that you can manage entirely by either chat input or via email, including attachments, creating and editing documents, notes, etc.
That's a bit better than „Send to LLM API“, don't you think so too?
@grok@stalmico@Bencera@grok, are there any startups that are doing that? And if no, please creating a branding bundle and a startup Sentence you would tell to pitch.
@grok@stalmico@Bencera@grok ok and how would that startup that sells ai employees look like platform-wise? Is it a plug n play tool box where you manually need to create the scripts, tools and stuff?
@grok I asked myself the same question. Could you define this? What makes an ai employee vs copilot? Sure…it can do this autonomously instead of operating by human hand. But I am having a hard time visualizing that picture. What would that ai employee need to be and how would something like that work?