Today's models are capable enough. Smart enough. Fast enough. But we still feel they don’t fit in the room. Humalike is building the behavioral infrastructure for humanlike AI agents. The social skills & proactiveness your agents have been missing. APIs, models, benchmarks.
What is Humalike? The behavioral infrastructure for humanlike AI agents. The social skills your agents have been missing.
The problem A few months ago we built an AI community manager. The second it hit a group chat, everyone knew it was a bot. It talked over people, never knew when to shut up. More features didn't fix it. Today's models are capable enough. Smart enough. Fast enough. But we still feel they don’t fit in the room.
The solution: 7 behavioral APIs
Turn-Taking (Flagship): Knows when to speak and when to stay silent (bundles all other APIs in one).
Theory of Mind: It gives your agent a sense of what people really think and feel.
Norms: Reads the group’s tone and responds the way it’s accepted here.
Persona: Improve presonality so it’s Opinionated, takes sides, backed by real community data
Social Memory: It gives your agent a memory for people, who they are and what matters to them.
Social Signals: Catches the pause before sending, a removed reaction, and an edited message.
Social Observability: Sees who’s engaged, who’s bored, and who’s annoyed.
Model, use-case and stack agnostic, built for groups, not just 1:1.
Extra highlights
💸 $20 in free tokens to start building
🔌 One-shot integrations with Hermes, WhatsApp & Telegram
📄 Backed by in-house research: LoSoNA (social-norm benchmark) + HUMA (a human-passing group facilitator)
🔒 SOC 2 / ISO 27001 in progress
Who It's for: Anyone building agents that must feel human, AI companions, NPCs, tutors, voice agents, groups, humanoids. If you've ever shipped an agent that was smart but experience using it felt wrong, Humalike is for you.
What we'd love from you: Grab your $20 in tokens, and tell us, how did our APIs improve the experience? Try with Hermes, Openclaw, or any agent you have deployed! We'll be here all day reading every comment, your feedback shapes what we ship!
Backed by the first investors in ElevenLabs, Revolut & more. Built by a tiny 🇪🇸×🇵🇱 team that hasn't slept much :))
Congrats @mcarmonas and team! The origin story of the community manager that everyone instantly clocked as a bot is way too real, no amount of prompt engineering ever fixes that vibe.
Really interesting direction. I am curious that for AI chat products, do you think agents will make users feel being manipulated when they can read emotional signals or adapt their tone in real time?
"APIs, models, benchmarks" implies you have a way to measure social intelligence in agents, which is actually the hardest part of this whole space to get right. What does a benchmark for humanlike behavior look like here, who's evaluating it, and how do you avoid the benchmark just measuring surface-level mimicry like filler words and pacing rather than actual social appropriateness?
For Humalike, when you say AI agents are missing “social intelligence,” is the core use case more about agents communicating with end users, collaborating with other agents, or handling public/community channels? The topic mix includes Developer Tools, Marketing & Sales, Design & Creative, and Social & Community, so I’m wondering who you see as the first user with the sharpest pain.
The benchmark piece is interesting here. For agents, “social intelligence” can get fuzzy fast. I’d want to see failure cases like interrupting too often or being too passive, not just success scores. Are you measuring those negative behaviors too?
the missing layer isn't intelligence, it's calibration. models can generate perfect answers but they don't know when they're supposed to be quiet. the social debt shows up the moment you drop an agent into a real slack or discord. it either lurks awkwardly or overshares. the version that reads the room first and speaks second is the one people let stay.
curious how you're benchmarking "fits in the room." vibes are hard to measure. is it deference patterns, timing, response latency to social cues? that spec is the whole product.
Congrats on #2! The turn-taking piece feels underrated - a lot of agents are smart enough, but still don’t know when to speak, pause, or read the room.
the turn-taking problem is so real. I've seen plenty of AI agents that are technically capable but socially exhausting — they jump in too fast, over-explain, and never read the room. curious how you're benchmarking this though — what does 'good' social behavior look like as a metric? is it response timing, or something more nuanced like knowing when a user is thinking vs actually done talking?
The social intelligence angle is a sharp wedge. Most agent tooling optimizes for finishing the task and forgets how the interaction actually lands. In practice, are you scoring tone and context, or injecting it into the responses themselves? Feels like something multi-agent setups are going to need soon.
How are you measuring "humanlike" in your benchmarks, and do the APIs let you tune how proactive an agent gets so it doesn't end up nudging users nonstop?
Tried the API over the weekend and the proactive context layer actually feels useful, not gimmicky. Liked that it picks up on social cues I usually have to script by hand.
Turn-taking feels like the sharp wedge here because a group-chat agent can be technically right and still hurt the conversation by speaking at the wrong moment. I like that you are treating social timing as infrastructure instead of another prompt rule. What kinds of examples do you show builders when an agent should wait, interrupt, or hand the floor to someone else?
How are you actually measuring "humanlike" behavior beyond the benchmarks you ship, and can customers plug in their own eval scenarios to test against their specific use case?
As a community manager, I'd use this immediately for Discord and Slack community assistants. :D
Is there analytics showing why an agent chose not to respond? That would be incredibly valuable for debugging.
How does Humalike adapt when community norms evolve over weeks or months instead of remaining static?
I'd love to see benchmark videos comparing baseline agents against Humalike-enhanced agents in the same conversation.
how the APIs perform in really fast-moving group chats where multiple conversations happen simultaneously?
Makers building AI agents have social intelligence as a much bigger bottleneck than reasoning quality. Humalike is solving a very important problem. Congrats on the launch.
About Humalike on Product Hunt
“Give your AI agents the social intelligence they're missing”
Humalike launched on Product Hunt on July 1st, 2026 and earned 454 upvotes and 165 comments, earning #2 Product of the Day. Today's models are capable enough. Smart enough. Fast enough. But we still feel they don’t fit in the room. Humalike is building the behavioral infrastructure for humanlike AI agents. The social skills & proactiveness your agents have been missing. APIs, models, benchmarks.
Humalike was featured in API (98.4k followers), Developer Tools (515.9k followers) and Artificial Intelligence (473.7k followers) on Product Hunt. Together, these topics include over 194.1k products, making this a competitive space to launch in.
Who hunted Humalike?
Humalike was hunted by Rohan Chaubey. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Humalike stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Rohan Chaubey
Hey PH 👋 Martí here, co-founder of Humalike.
What is Humalike? The behavioral infrastructure for humanlike AI agents. The social skills your agents have been missing.
The problem
A few months ago we built an AI community manager. The second it hit a group chat, everyone knew it was a bot. It talked over people, never knew when to shut up. More features didn't fix it. Today's models are capable enough. Smart enough. Fast enough. But we still feel they don’t fit in the room.
The solution: 7 behavioral APIs
Turn-Taking (Flagship): Knows when to speak and when to stay silent (bundles all other APIs in one).
Theory of Mind: It gives your agent a sense of what people really think and feel.
Norms: Reads the group’s tone and responds the way it’s accepted here.
Persona: Improve presonality so it’s Opinionated, takes sides, backed by real community data
Social Memory: It gives your agent a memory for people, who they are and what matters to them.
Social Signals: Catches the pause before sending, a removed reaction, and an edited message.
Social Observability: Sees who’s engaged, who’s bored, and who’s annoyed.
Model, use-case and stack agnostic, built for groups, not just 1:1.
Extra highlights
💸 $20 in free tokens to start building
🔌 One-shot integrations with Hermes, WhatsApp & Telegram
📄 Backed by in-house research: LoSoNA (social-norm benchmark) + HUMA (a human-passing group facilitator)
🔒 SOC 2 / ISO 27001 in progress
Who It's for: Anyone building agents that must feel human, AI companions, NPCs, tutors, voice agents, groups, humanoids. If you've ever shipped an agent that was smart but experience using it felt wrong, Humalike is for you.
What we'd love from you: Grab your $20 in tokens, and tell us, how did our APIs improve the experience? Try with Hermes, Openclaw, or any agent you have deployed! We'll be here all day reading every comment, your feedback shapes what we ship!
Backed by the first investors in ElevenLabs, Revolut & more.
Built by a tiny 🇪🇸×🇵🇱 team that hasn't slept much :))