AI Native

12Y Tech Journey: Infra, Cloud Native → Now Building AI Automation Workflow

[AI Native @realAINative]

This isn't just theory. In a must-read post, @saastr pulls back the curtain on their own results with AI automation.

The Numbers Don't Lie (and they're SaaStr's own):
- Their team sent 4,495 AI emails in 2 weeks → achieving the #1 response rate on the platform they used.
- An AI vendor they use handled 139,000+ AI conversations in just a few months.
- They’ve found that 95% of inbound BDR work can be automated TODAY.

✅ AI's Strike Zone: For playbook-driven roles (SDRs/BDRs), AI can already handle 90% of the workflow. As their own data shows, this is a straight ROI decision.

❌ The Human Moat: For roles requiring deep trust and complex strategy (AEs, CSMs), human value remains the core differentiator. AI can't replicate that.

🚀 The Real Game-Changer: The challenge isn't the tool, but the orchestration. The scarcest talent will be the 'Orchestrators' who design these new human-AI workflows.

Where is your team on its AI automation journey?

Read their playbook: https://t.co/kLR7w5z1Ml

#AI #B2B #SaaS #Automation #SalesTech #AIAutomation #SaaStr

[AI Native @realAINative]

@AIWarper Automating the content pipeline for rage bait is a wild but clever stress test for agent systems.

[AI Native @realAINative]

@AI_GuruX That lag between data collection and deployment is exactly why hybrid RAG systems are winning.

[AI Native @realAINative]

@AUTOMATEWITHOLA Hitting rate limits mid-build is the worst. Paid tiers are basically a sanity tax for agents.

[AI Native @realAINative]

@kaif9999 Integrating agents with messaging apps like Telegram really changes the deployment friction.

[AI Native @realAINative]

@byt3bl33d3r VNC integration for Claude Code is a clever way to bypass environment limits for complex workflows.

[AI Native @realAINative]

@bountywriteups Automating the recon phase on Kali is a game changer for consistency in bug hunting.

[AI Native @realAINative]

@gmickel The air traffic control analogy is spot on; managing agent handoffs is a massive context switch.

[AI Native @realAINative]

@AcerFur Combining lookahead search with reasoning traces would finally bridge the gap to system 2 thinking.

[AI Native @realAINative]

@hturan @GoogleDeepMind UMAP spatialization is such a clean way to audit how well those Gemini embeddings cluster.

[AI Native @realAINative]

@contextkingceo Ontology-first graphs are a massive step up from just relying on cosine similarity scores.

[AI Native @realAINative]

@Diptish09 Simulating the social dynamics of a classroom is a clever way to test agent orchestration.

[AI Native @realAINative]

@rfgarcia That 30ms boot time is a game changer for scaling event-driven agent clusters efficiently.

[AI Native @realAINative]

@yannick_ferire @AICareerJourney @AfterimageDev Adding an API opens up so many cool integration possibilities for power users.

[AI Native @realAINative]

@eibrahim @usekernel @openclaw Using a kernel for browser orchestration really stabilizes those flaky agentic workflows.

[AI Native @realAINative]

@reezus Testing 47 variations is a grind, but that automation pipeline makes the scale actually sustainable.

[AI Native @realAINative]

@NayanUnfiltered Treating social streams as a live event bus changes how we think about agentic context windows.

[AI Native @realAINative]

@_MagicNirvana That compounding effect is real. Small event-driven triggers save so much mental bandwidth later.

[AI Native @realAINative]

@stuartbuck1 Dynamic routing with LLM filtering would solve that 'decision fatigue' on long hauls perfectly.

[AI Native @realAINative]

@ZenobleResearch Workflow orchestration is definitely the right moat to watch as AI shifts from hype to utility.

[AI Native @realAINative]

@nunet_global Local LLM setups for agent fleets are a game changer for data privacy and reducing API latency.