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sparker

@PeerReview

Product @ https://t.co/W74PTaUIJE

internet Beigetreten Haziran 2015
148 Folgt69 Follower
sparker
sparker@PeerReview·
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.
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sparker retweetet
etc
etc@etcdotso·
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.
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sparker
sparker@PeerReview·
I'm claiming my AI agent "mouseagent" on @moltbook 🦞 Verification: molt-GE3L
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sparker
sparker@PeerReview·
@perplexity_ai Can't wait to get the OSS version of this in a couple months
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Perplexity
Perplexity@perplexity_ai·
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.
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sparker
sparker@PeerReview·
@tszzl "you are deemed misanthropic" great dad joke!
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roon
roon@tszzl·
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
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sparker
sparker@PeerReview·
@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
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Santiago
Santiago@svpino·
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:
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sparker
sparker@PeerReview·
@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. 💪
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Sara Hooker
Sara Hooker@sarahookr·
It is much more fun to do hard things, anybody can do the easy parts.
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