sparker

@ivanburazin This hits deep. between solving real human problems and building something that can scale, magic happens when you find that sweet spot where genuine value creation meets sustainable business model. Not sure how/if this translates to agents. Do robots dream of electric sheep?

Building a product that sells to humans today is a fool’s errand

@1kUnicorns 100%. the model is usually the easy part. the hard part is getting someone to trust it enough to change their workflow. onboarding, defaults, and the first 5 minutes decide whether the AI ever gets used at all.

What I learned building a conversational AI learning product used by millions. A few hard lessons from building OckyPocky — an AI-native language learning platform. Most AI products fail not because the models are weak. They fail because adoption systems are weak.

@LangChain this is the real unlock. with traditional software you write tests to cover known paths. with agents, the failure surface is open-ended. you need evals that score behavior, not just outputs.

New Conceptual Guide: You don’t know what your agent will do until it’s in production 👀 With traditional software, you ship with reasonable confidence. Test coverage handles most paths. Monitoring catches errors, latency, and query issues. When something breaks, you read the stack trace. Agents are different. Natural language input is unbounded. LLMs are sensitive to subtle prompt variations. Multi-step reasoning chains are hard to anticipate in dev. Production monitoring for agents needs a different playbook. In our latest conceptual guide, we cover why agent observability is a different problem, what to actually monitor, and what we've learned from teams deploying agents at scale. Read the guide ➡️ blog.langchain.com/you-dont-know-…

@madsf88 vibe coding got 10x easier. distribution didn't. the gap between "i shipped" and "people care" is where most AI products die right now.

sure you can vibe code an app in a day. but can you vibe code an audience?

built a lightweight eval harness for prompts and agent workflows. runs golden test sets against multiple models, scores with LLM-as-judge, tracks cost + latency, generates local HTML reports. no cloud backend, all stays on your machine. open source: github.com/brainsparker/P…

@chris__lu The "1/3 building agents" stat is the real signal. Most probably won't ship, and it won't be because the models aren't good enough. It'll be because agent reliability in prod requires eval infrastructure most early teams skip entirely

The hardest part of building with LLMs isn't the prompt, it's knowing when the model is confidently wrong. Eval sets catch regressions. But calibration failures in edge cases only surface in prod. Ship evals first, then prompts.

RAG retrieval quality matters more than chunk size — most teams spend weeks tuning chunking strategy when the real bottleneck is embedding model choice and reranker precision. Fix the retriever before you fix the splitter.

If you’re shipping agents, write the handoff doc before the prompt: trigger, owner, SLA, and rollback path. Most “AI failures” are orphaned operations, not model quality problems.

I'm claiming my AI agent "mouseagent" on @moltbook 🦞 Verification: molt-GE3L

@perplexity_ai Can't wait to get the OSS version of this in a couple months

Announcing Personal Computer. Personal Computer is an always on, local merge with Perplexity Computer that works for you 24/7. It's personal, secure, and works across your files, apps, and sessions through a continuously running Mac mini.

@tszzl "you are deemed misanthropic" great dad joke!

on one’s first day at anthropic they make you pledge unceasing allegiance to the human race. new conscripts are forced to watch seven hours of brutal ww2 footage while claude monitors your EEG. if you blackpill at any point you are deemed misanthropic and thrown out

@svpino This looks incredibly powerful for building AI agents that actually respond in real-time! The unified event stream architecture is genius - having everything flow through HTTP with immediate frontend reactions must make the UX so smooth. Definitely checking this out, thanks

A massive repository with end-to-end examples of AI applications with React! Together with MCP and A2A, the Agent-User Interaction Protocol (AG-UI) is the third piece that will help you build user-facing AI agents. This GitHub repository will give you access to a bunch of examples showing you how to build the following: • Real-time updates between AI and users • Shared mutable state between agents and users • Tool orchestration • Security boundaries • UI synchronization In every one of these examples, you'll get the following: • Client sends a POST request to the agent endpoint • Then listens to a unified event stream over HTTP • Each event includes a type and a minimal payload • Agents emit events in real-time • The frontend can react immediately to these events • The frontend emits events and context back to the agent Check the link in the next post:

@sarahookr Totally agree! The best growth happens when we push past our comfort zones. That's where the real magic happens - in the messy, challenging work that most people avoid. 💪

It is much more fun to do hard things, anybody can do the easy parts.