ApInference
@ApInference
A smart conversational layer on top of any API in minutes
Claude 1M context window changes everything. 🔥 Entire codebases in one call 📚 Full documentation analysis 🧠 Cross-reference massive datasets ⚡ No more chunking headaches Game changer for API developers.
Everyone thinks building software with AI is just “write the perfect prompt” Reality. you’re duct-taping APIs, chaining models, juggling tools, and praying nothing breaks mid-demo Prompting is the easy part
99% of devs still send raw text prompts to LLMs. That’s why their API calls return vague, slow, or useless data. Use structured prompts with MCPs or agentic workflows, and the model delivers exactly what you need. Use MCPs/ APIs to guide the reponse.
When will we be able to add AI agents to any API to help handle complex dev workflows? Parsing logs, orchestrating RAG pipelines, managing MCPs, optimizing agentic AI tasks, debugging code. Same conversational layer as a dev, shared context to all your dev tools.
Anthropic’s making quiet moves in the enterprise LLM space. Not luck - strategy. Big orgs want safe, adaptable AI over generic hype.
Unspoken truth: many startups die from bad DB schema design. Once data is in, changes are painful. Nail the trade-off - flexibility vs. reliability. AI can advise, but it has no context, no vision. That’s your job.
If AI could actually talk to all your apps and get things done.. what’s your dream use case?
LLM + tools: Be honest... how many are you actually using right now? (Count prompt templates too 👀)
LLMs change how we plan software projects. When you kick off something new, do you still start with architecture docs or straight into prompting + prototypes?
Think you’re chatting with GPT-5? Ask it. 🖥 Screen says: “GPT-5.” 🗣 It says back: “I’m GPT-4.5.” #AI #GPT5
Think you’ve got GPT-5? Ask it. Top left: “GPT-5.” Out loud: “I’m GPT-4.5.” The gaslighting is immaculate. #GPT5 #AI
GPT-5 is here. It’s a step up from GPT-4, but not a leap — feels about on par with Grok-4 in use (and based on benchmarks). Maybe the next big shift won’t be bigger general models… but specialised ones.
I want to connect with anyone: – building mcp-style agents – gluing their own tools into llms
🤖 Top AI Sub-Optimal Suggestions Thread 🧵 I'll start: AI rewrote an entire utility module from scratch... instead of importing 2 lines from a well-known library 😮💨 Let’s hear yours. What’s the most extra thing AI ever did when a simple solution was right there?
Vibe coders, be honest—what’s your biggest “oops” moment? Dropped a prod DB? Missed a scenario that totally should’ve been obvious? Wrote an AI that auto-approved the worst PR of the year? No judgment. This is a safe, slightly chaotic space.
Procrastinating too much? Build an AI motivator that pings you on Messenger. Bonus points if it checks Jira first and only nags you when you've actually done nothing. "Hey champ, great weather for shipping. Also... your ticket’s still untouched 👀" #AI #Productivity
Any tips for going from 50 to 100 followers? Feels like growth is measured in % of your base
Imagine what you could build if your own systems spoke the language of LLMs seamlessly… 🚀 From autopiloted workflows to ultra-smart dashboards, the only limit is your imagination. What would you create first? 🤔 #AI #Agents #Innovation
🧩 Agent builders — how much time/money do you spend integrating new APIs into your agents? Not just core logic, but: Schema parsing Tool definitions Auth / throttling Retry logic Curious what “integration overhead” looks like in the wild. Drop numbers or stories 👇
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