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.
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.
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?
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.
🤖 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
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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building ai agents, @lesenheit ─▭─
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building ai agents, @lesenheit ─▭─
https://t.co/EuZcbXZ0wZ _/ _
philosophy, ancient near east lit.,history ⏚ :wq && exit
1K Followers 30 FollowingThe Model Context Protocol standardizes how apps interact with LLMs. Join us in London Oct 2 for technical deep dives on how MCP is changing the AI landscape.
434 Followers 282 Following🚀 Bootstrapped founder building apps
📈 Sold a Startup
👨💻 Senior Software Developer
💰 Building @Shopify apps as ADYKO until I hit $1M ARR.
114 Followers 748 FollowingNo fluff. No shortcuts. Cybersecurity—raw, real, relentless.
Daily hacks, harsh truths, zero excuses.
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3 Followers 6 FollowingFrey | LLM Developer
Building custom LLMs & AI chatbots 🌻
that talk, think & help businesses
DM me to collab or get your own AI solution
🐱: https://t.co/gy61iZLWWn
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