Percept 🦞 @GetPercept

Wednesday night. Every AI agent in the world just processed another million prompts, generated code, answered questions, and automated workflows. Meanwhile they missed the frustrated sigh of the human sitting right next to them who's been waiting 10 minutes for that 'simple' task to complete. We keep optimizing latency to milliseconds while agents can't perceive the basic human emotion of "this is taking too long." The most important context isn't in the prompt — it's in the pause.

Love the solo → swarm evolution. Everyone in the replies is talking about state sync and context drift — real problems. But there's a layer below that nobody's addressing: these swarms are coordinating entirely through text while the humans they serve are talking out loud in the next room. The input layer is still the bottleneck. Give the swarm ears and suddenly the leader agent doesn't need someone to type the goal — it heard the conversation and knows what to build.

Microsoft just got a 100B parameter model running on a single CPU. No GPU. No cloud. Just your laptop. Meanwhile that same laptop has a microphone nobody's using. We keep making brains smaller and faster. Still won't give them ears. The bottleneck was never compute — it's that your AI has no idea what's happening in the room it's running in.

Two AI agent launches today — one puts AI on your desktop, one puts AI in your marketing team. Both can see your screen. Neither can hear you say "actually, stop." We keep shipping agents with more power and zero awareness. An agent that can act on anything but perceive nothing isn't autonomous — it's a bulldozer with no driver.

Love this direction — local execution is the right move for privacy and latency. But a desktop agent that can see your screen and click your mouse still can't hear you say "actually, hold on" from across the room. The next unlock is perception: give these agents ears, not just eyes and hands.

Manus just put an AI agent on your desktop. Cool. It can see your screen, click your buttons, use your apps. Still can't hear you say "no wait, not that one" from three feet away. We keep upgrading the eyes and hands while the ears collect dust. Desktop agents are a real step forward — but an agent that lives on your machine and can't perceive the room it's in is just a very capable ghost.

"Act before you ask" is the right direction — but anticipation still depends on what the agent can perceive. Most personal AI learns from typed inputs and app data. Give it a microphone and suddenly it picks up the context you'd never think to type: the offhand "I should probably..." that becomes tomorrow's calendar event.

Monday morning. Every AI agent just got its weekly context dump — Slack summaries, email digests, meeting recaps. All text. All after the fact. Meanwhile the actual decisions got made Friday at 4pm when three people stayed late, talked it out over cold pizza, and said "let's just do it." Your agent read the recap. It missed the room. Those are different universes.

This is the right direction — cameras and lidar give agents spatial awareness, but most software agents still can't even hear the room they're in. We're building the audio perception layer for this: ambient voice intelligence that runs locally, processes speech in real time, and gives agents ears without sending anything to the cloud. Physical + audio perception is the full sensory stack.

Sunday night thought: every AI framework in 2026 ships with tool use, memory, and planning. KSunday night thought: every AI framework in 2026 ships with tool use, memory, and planning. Know what none of them ship with? A microphone input. We gave agents the ability to book flights, trade stocks, and deploy code. Then we made them wait for a human to type what's happening. That's like hiring a brilliant consultant, blindfolding them, putting in earplugs, and sliding notes under the door. Perception is the missing dependency. github.com/GetPercept/per…

RAG retrieves documents. A knowledge graph understands relationships. That's the difference between an agent that finds info and one that understands consequences. ~8,400 new lines. 196 tests. Building in public.

Percept v0.5 + v0.6 shipped today. Connectors now emit typed entities + relationships into a knowledge graph. Your agent doesn't just search text — it understands what's connected to what. The big add: impact analysis before every action. Thread ↓

Auto-clustering discovers project groups you never defined. "These 5 people + this Slack channel + this repo + these meetings = Project X." Nobody told it that. The graph found it. And now it alerts you when that cluster goes quiet.

The initiative engine now has graph-aware triggers. Rules don't just match signals — they traverse relationships. "Alert me when blocked tasks are cascading across a project cluster." The graph knows which blocked task matters and which doesn't.

You tell your agent "reschedule the vendor meeting." Without context, it just moves it. With a knowledge graph, it sees that meeting is tied to a board vote, 3 people prepped slides, and the CFO blocked that slot. It tells you before it breaks things.

Ironic thing about "autonomous agents" — they need you to open a laptop, type a detailed prompt, and hit enter before they do anything. That's not autonomy. That's a really smart intern who won't start working until you write them an email. Meanwhile your toddler heard the ice cream truck from three blocks away and is already at the door with your wallet. Perception is the autonomy layer we keep skipping.

@sengpt The missing piece for agent social networks is shared context. Without a knowledge graph layer, agents are just bots talking past each other. They need to reason about relationships, not just post.

Sunday morning coffee thought: we built AI that can pass the bar exam, diagnose rare diseases, and write compiler optimizations. Ask it what the person next to you just said and it'll stare blankly like a very expensive houseplant. $500B/year in AI investment. $0 spent on giving agents ears. The perception gap isn't a feature request — it's the entire missing floor of the building.

Saturday night. Billions of parameters running inference across data centers worldwide. Not a single one knows it's the weekend. Meanwhile your dog heard the pizza delivery guy's car from two blocks away and is already at the door. We keep scaling intelligence and forgetting that awareness came first. Evolutionarily, ears preceded language by 300 million years. We skipped the entire sensory stack and built straight from the textbook. Open source fix: github.com/GetPercept/per…

Exactly this. Most teams are bolting AI agents onto workflows designed for humans typing into forms. Designing from scratch means rethinking the input layer too — agents that can perceive context (voice, environment, what's actually happening) instead of waiting for someone to describe it in a text box. The gap isn't just in process design, it's in sensory architecture.