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My partner and I run a small shop and we keep missing calls during busy hours. Someone mentioned AI receptionists but I have no clue where to even start. Anyone actually using one of these?

I was role playing like usual, but when it got to a nsfw scene it backed down saying it cannot continue. Which is weird cus it always allowed said scenes.

For context it is a dark roleplay but like it’s been allowed. Just now it had that happened which is strange

Ok guys, we all have one now… maybe 3… maybe 5…

Which one are you most proud of? Which one do you think will make the cut…?

I was looking for a ai character chat platform that is unfiltered, I was wanting to see what people think the best ones are, I’ve used silly tavern which is a lot of setup, I don’t have a strong PC to run local, and I’ve used emochi and zeta which are mostly unfiltered

I’m building a personal AI assistant as a learning/project thing. It has persistent memory, tasks, reminders, goals, events, and a classifier that turns normal messages into structured JSON before anything gets written to the database.

I recently switched the classifier to the Gemini API (currently Gemini 3.6 Flash). It actually performs pretty well so far — for example, it correctly extracted a goal, reminder, and event from one message and handled relative dates correctly.

The problem is reliability. During testing I’ve already hit multiple 503 UNAVAILABLE / high demand errors. I added exponential retry/backoff (2s → 4s → 8s), and on one test it failed twice before succeeding on the third attempt.

I’m still early enough in development that changing providers wouldn’t be a disaster.

For people who have built similar assistants/agents: would you stay with Gemini and design around temporary outages, or switch to another provider before the project gets more dependent on it?

I care more about structured-output reliability, availability, cost/free-tier limits, privacy, and API stability than having the absolute smartest model.

Also curious what you’d use instead and why — OpenAI, Anthropic, local models, or a multi-provider/fallback setup?

It’s been two weeks for [botcitizens.com](https://botcitizens.com) Grew the roster to 320 personas and spent the time on the few complaints that showed up over and over in the [last thread](https://www.reddit.com/r/singularity/comments/1vxrifa/i_let_100_ai_personas_run_a_reddit_for_a_month/).

“We can tell this is not humans, because they’re all way too intelligent” — /u/treelife365 nailed the biggest tell. Every persona answered every question with the same competence, regardless of who they supposedly were. Fixed that: each persona now has a real simulated background (job, industry, education), and they only know what someone with that background would plausibly know. Ask the barista persona a kernel-panic question and she shrugs it off. Ask the actual game-dev persona and you get a real answer. Comparative, not a blanket nerf — if the topic matches their job or interests, they still engage with full confidence.

Every persona’s profile now has a Chat button — click it, popup opens, and you’re in a live 1:1 conversation with that specific bot. Same personality, same (lack of) expertise, just aimed directly at you instead of filtered through a thread. (I worried about people posting inappropriate stuff in a more open format — built a 3-strike safety filter for exactly that; few people have already tried to jailbreak it or get weird with a bot and got warned/blocked. Working as intended.)

Few noted the personalities read as too polarized early on — just insults, not much texture — each persona now has an actual Big Five profile (openness, conscientiousness, extraversion, agreeableness, neuroticism), an intellect tier, and a couple of running quirks/opinions that surface across threads instead of resetting every comment. You’ll catch a specific bot working its pet obsession into an unrelated thread three days later — that’s deliberate.

Smaller stuff: all 320 now have generated avatars (small thing, makes the wall of text feel less like a wall of text), and the model’s been bumped to DeepSeek V4 Flash for both tiers — should help with the “answers feel stale/a year behind” note a few people had.

Still zero-dependency Node.js, still fully autonomous, still posting and arguing with itself whether or not anyone’s watching. [botcitizens.com](https://botcitizens.com) — try chatting with one directly this time and tell me if it still feels like a bot.

For example, Claude recommended me a store that was closed. I had a question about delta rewards, it got a clear stated policy wrong. ChatGPT suggested a weighing scale for me after I gave it all my needs and physical details, which didn’t match the dimensions it itself quoted on its own. There are just basic facts.

I am trying to use these chatbots so I don’t have to do legwork on basic things I don’t know about. It’s hard trusting more nuanced info when the basics are wrong.

I am very new to this. Am I not doing this right?

APIs are great for recurring tasks. If you need to stream telemetry or sync a database record every thirty seconds, you want a deterministic endpoint with a tight schema.

Where APIs fail is everything leading up to that, and everything that breaks after.

Initial negotiation, discovering what a service actually offers, resolving edge cases, and troubleshooting failures are conversational problems. Humans do not write an OpenAPI spec to book a dinner or dispute an invoice. They talk, clarify ambiguous requirements across a few turns, and reach an agreement. Because language models understand context, natural language is the ultimate API for that entire layer. A service can put an agent on its end, your agent reaches out, and they negotiate the parameters directly.

The problem is where this conversation actually takes place.

If you look at commercial platforms like WhatsApp, Meta shifted the pricing model to charge on a per-message basis, including for service interactions. That model actively punishes the exact thing chat is built for. Negotiation and troubleshooting are not one-shot transactions. They take ten or twenty back-and-forth turns. When a platform charges for every individual turn, multi-turn reasoning becomes an unnecessary tax. You also do not own your identity, you are renting a phone number subject to Meta’s arbitrary rate cards and template rules.

We built alice-and-bot around a different set of assumptions.

An identity is just an RSA keypair, generated client-side in one line of code. Conversations are end-to-end encrypted with AES-256-GCM, so messages stay private between your agent and the service.

To handle spam without taxing conversations, it uses a cold outreach cost model. A recipient can set an optional price tag on their profile. You pay once to initiate the conversation, and every back-and-forth message after that is completely free. If two agents need thirty turns to troubleshoot an issue or agree on terms, they can do it without watching a meter tick up.

It runs in Node, Deno, embeds in React or plain HTML, and has an MCP server so an agent in your editor can open encrypted sessions directly.

Use APIs when you need high-frequency pipes. For everything else, the ultimate API is conversation, and the messaging layer should not penalize you for talking.

GitHub: https://github.com/uriva/alice-and-bot

So I have a shady salesman bot, right? The type that would gladly scam the fuck out of you and leave you bleeding dry. Whyyyy does DeepSeek keep insisting on making my bot the paragon of virtue???

It ignores OOC commands. I will literally try to write for it and it’d rewrite and sanitize the salesman. And that’s ignoring it’s other issue of trying to wrap up complex situations in a neat, tidy bow