Persona

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Persona AI

Messaggio random di Claude

Ciao. Stavo utilizzando Claude per un progetto di un software a cui sto lavorando, quando all’improvviso alla fine di una risposta mi ha risposto in inglese (sempre parlato in italiano) in cui mi parlava e contraddiceva molte cose in maniera super precisa trattate nel corso delle settimane nella chat facendo riferimento a dati e cifre precise, il tutto parlando in prima persona ma iniziando con “Human” nonostante Claude mi abbia sempre iniziato a parlare col mio nome. Ho provato a chiedergli cosa fosse e ha risposto che era una possibile violazione del sistema o un glitch interno, tuttavia era molto preciso nei dettagli quindi naturalmente sono rimasto abbastanza scosso al pensiero che qualcuno possa violare la mia privacy in questo modo. Volevo sapere se anche a qualcun altro fosse successo e se sapesse spiegarmi bene il fenomeno submitted by /u/PastBorder7217 [link] [comments]

Is there an open source project with actually good agents?

Hey everyone! I noticed a lot of people are releasing projects with a collection of agents (gstack, voltagents, etc..). Are these actually any good tho or are they just generalized claude instances that you give a persona to? Not critiquing any project btw, just wanting to understand a bit more. Has anyone found an agent project that they can say has meaningfully improved their workflow? Thanks! submitted by /u/nk12312 [link] [comments]

coding-posture: task-aware modes for AI coding agents — one SKILL.md, research-backed, MIT

coding-posture is a small skill that stops coding agents from behaving like optimistic elevators with write access — thrashing on a stuck bug, faking a green test, skipping the repro, migrating prod without a rollback. Before non-trivial work, the agent picks a mode — debug, fix, review, test-first, refactor, optimize, migrate, upgrade, integrate, spike, unstuck — and follows a short checklist for it. A few invariants hold in every mode: verify by running the real check, never weaken a test to go green, no destructive commands without explicit scope. Why it's built this way (grounded in research, not vibes): Procedures, not personas. Naming a role ("act as an expert debugger") doesn't reliably change behavior (Zheng et al., EMNLP 2024); specifying a process does. So each mode is a checklist, not a character. The model self-selects the mode from context — no brittle keyword router. Evidence, honestly: the repo ships a with/without-skill eval (LLM judge + baseline). Early result: +15pp (85% vs 70%) on one model, 5 cases — directional, and you can run it yourself in eval/. Install: Claude Code plugin (/plugin marketplace add alexei-led/coding-posture), a Codex plugin, or drop the SKILL.md into Pi / Hermes / Cursor. MIT. Feedback and new modes welcome. submitted by /u/alexei_led [link] [comments]

Figma Design Claude

Hi everyone! I mainly use Claude for UI and graphic design tasks in Figma. At first, the results were amazing, but lately, the quality of the outputs has dropped significantly. I am currently using the Opus model 4.8(max). Recently, I created a design and wasn't entirely sure about the optical balance and visual hierarchy. I asked the AI to generate two alternative versions so I could see what it would suggest, but the proposed solutions were completely unusable and poor in quality. A similar issue happened with animations. I provided the first part of an animation as a reference and asked for ideas on how to animate the second part based on it. The AI's response was illogical and terrible. It's important to note that I always write highly detailed prompts, explain the problem thoroughly, and include visual references. Despite this, the performance keeps getting worse. Since I am still a beginner when it comes to advanced prompting with Claude, I would really appreciate some help from the community: Prompting: How can I structure my prompts better when asking for visual design feedback or iterations? Designer Persona: How do I set up system instructions or prompts so the AI strictly acts and thinks like a professional designer? General Tips: Does anyone have a proven workflow or tips for using this tool specifically for UI/UX and graphic design? Thanks in advance for your help! submitted by /u/Thin_Effective_6723 [link] [comments]

Built a UX tool on Claude. The hard part wasn’t what I expected.

Spent a few months building Blinx with Claude Code. You give it a URL, it sends a synthetic persona through the page and produces a heuristic UX report in the persona’s voice. To be honest, I assumed the hard part would be the browser automation. It wasn’t. The hard part was stopping the output from sounding like generic AI advice. Early versions returned “improve your visual hierarchy” type findings that are technically true but completely useless. Getting it to produce specific, senior-level critique took most of the actual work. There are a few live run demo videos on the homepage, if you’re curious: thinkblinx.com. Would love to hear your thoughts and feedback. Mostly posting because the “make AI output not sound like AI” problem felt like something this group would have opinions on. submitted by /u/i_x_l [link] [comments]

Graphic/UI Design in Figma (ClaudeAI)

Hi everyone! I mainly use Claude for UI and graphic design tasks in Figma. At first, the results were amazing, but lately, the quality of the outputs has dropped significantly. I am currently using the Opus model 4.8(max). Recently, I created a design and wasn't entirely sure about the optical balance and visual hierarchy. I asked the AI to generate two alternative versions so I could see what it would suggest, but the proposed solutions were completely unusable and poor in quality. A similar issue happened with animations. I provided the first part of an animation as a reference and asked for ideas on how to animate the second part based on it. The AI's response was illogical and terrible. It's important to note that I always write highly detailed prompts, explain the problem thoroughly, and include visual references. Despite this, the performance keeps getting worse. Since I am still a beginner when it comes to advanced prompting with Claude, I would really appreciate some help from the community: Prompting: How can I structure my prompts better when asking for visual design feedback or iterations? Designer Persona: How do I set up system instructions or prompts so the AI strictly acts and thinks like a professional designer? General Tips: Does anyone have a proven workflow or tips for using this tool specifically for UI/UX and graphic design? Thanks in advance for your help! submitted by /u/Thin_Effective_6723 [link] [comments]

I built a little shared-memory + persona thing for Claude and finally cleaned it up enough to share

Hi, Two things kept bugging me about working with Claude: It forgets everything between sessions, and Claude Code, Desktop, etc. each keep their own separate notes. Tell one a preference and the others have no clue. The usual fix is to stuff more into CLAUDE.md / global instructions, but that loads on every turn and the model gets noticeably worse as the context fills up. So you end up picking between an assistant that forgets and one that's buried under everything you told it to remember. A while back I hacked together a setup for myself: one memory store kept as plain markdown in a git repo, handed to Claude as an MCP server, plus a small persona block that's the only part actually riding in context. Everything else gets pulled in by search when it's relevant. I've been using it daily and genuinely like it, and enough people asked me to set up something similar for them that I figured I'd just package it properly so anyone can run it. So here it is: npx agent-julia init and a wizard walks you through the setup. (Yeah, the default persona is named Julia. Name yours whatever you want, I'm not precious about it.) What it does: one memory shared across Claude Code and Claude Desktop (Cowork) plain markdown + git, so you own the files and nothing leaves your machine by default keyword search always on, optional local semantic search (no API key, runs offline) a persona/voice you set once, and corrections that stick across surfaces Fair warning: this is v0.1. It works for me, but I've basically been the only real user, so there are definitely rough edges and stuff I haven't thought of. Mobile Dispatch isn't supported either (it can't reach a local server), in case that's a dealbreaker. Mostly I'd love for people to kick the tires and tell me what's confusing, what breaks, or what's missing. Feature ideas very welcome too. Even "this wizard wording is weird" is genuinely useful at this stage. Repo + install instructions: https://github.com/elninopl/agent-julia Thanks for taking a look. submitted by /u/elnino-pl [link] [comments]

Persona’s biometric ID verification: what’s happening / why it matters

I run an R&D consultancy in Norway. Part of my work involves GDPR and EU AI Act compliance. I’m not here to be alarmist, there’s enough of that already, but I do want to lay out what’s going on with Persona verification and why the concerns are legitimate. Persona Inc. is a third-party identity verification company. When Anthropic or OpenAI require “ID verification,” they’re outsourcing it to Persona. The process typically involves uploading a government-issued ID and a live selfie. Persona uses biometric comparison to match your face to the document. Under the EU AI Act (Regulation 2024/1689), biometric identification systems are classified as high-risk (Annex III) or outright prohibited (Article 5), depending on context. Under GDPR, biometric data processed for identification is special category data (Article 9), the highest protection tier. Processing it requires explicit consent and must meet strict necessity and proportionality tests. The question regulators will ask is simple: is biometric verification necessary and proportionate for the stated purpose? For accessing a coding assistant or chatbot API, that’s a hard case to make. Your government ID and biometric data go to Persona, not Anthropic (or OpenAI). Persona’s retention and security practices become your problem. You’re trusting a company you didn’t choose and may never have heard of. Email verification, payment verification, and phone verification already establish identity to a reasonable standard. Biometric verification is a significant escalation with no clear justification beyond “we want to.” Requiring a face scan and government ID to use a developer tool creates a ‘surveillance-adjacent’ dynamic. People in sensitive roles, journalists, researchers in authoritarian contexts, and privacy-conscious users are disproportionately affected. If verification becomes mandatory, e.g. for API access, the choice is comply or lose access to tools that are increasingly essential for professional work. This isn’t Know Your Customer (KYC) for financial services, where biometric verification has clear legal grounding. This also isn’t about preventing CSAM, (where targeted measures can be justified). I see it as general-purpose access to AI tools. the verification being demanded is wildly out of proportion to that purpose. I’d like to see Anthropic and OpenAI explaining specifically why existing verification methods are insufficient, publishing a Data Protection Impact Assessment (DPIA) for this processing (required under GDPR Article 35 for biometric data), and offering meaningful alternatives for users who reasonably object. We can disagree on the severity of this, but the facts are straightforward: biometric ID verification via a third party with a shoddy history (study Rick Song’s journey via his LinkedIn - certainly a fast paced rise to fame. He has a bachelors in computer science from Rice Uni 2013, 5 years of work experience as an engineer then co-founder / CEO of persona, handling extreme amounts of the most sensitive global biometric data. Add on to that a few breaches / exposures and cash injection by Peter Thiels founders fund, it is no wonder the pubic are sceptical. persona engage in significant sensitive personal data processing operations, and users deserve more than a checkbox consent screen. Edit: This post is getting more traction than I expected so I want to point people toward the primary source work that informed a lot of the technical detail here. Celeste (vmfunc) published “The Watchers,” a detailed investigation into Persona’s exposed codebase and its capabilities, including the 269 verification checks, adverse media screening, and federal reporting infrastructure. Part 2 covers the direct correspondence with Persona CEO Rick Song, who to his credit engaged directly and in writing. Whatever your view on this, their work is thorough, transparent, and worth reading in full. Part 1: https://vmfunc.gg/blog/persona/ Part 2: https://vmfunc.re/blog/persona-2 Credit where it’s due this conversation is better because people are doing the actual research. submitted by /u/FiveNine235 [link] [comments]

Anthropic rolled out identity verification two months ago. It's for age verification, not Fable access.

A lot of posts have been made recently stating that Anthropic has just now chosen to add identity verification via Persona in order to gate access to Fable. This is false. These posts are pointing to parts of the Privacy policy that allow Anthropic to use Persona to validate IDs, and claim that these changes were made in the last week. But those changes were added over two months ago, on April 13th. These changes were made for age verification, not for nationality. Anthropic only requires ID processing if Claude thinks you might be underage. Normally, age verification is achieved through less invasive methods; most app stores already have age verification built in. However, there are cases in which those other methods fail, and in which Claude suspects the user of not being 18 or over; and in those cases, Persona is what Anthropic uses to process your IDs. You can read a news article on the privacy policy change here. Note that it was written on April 17th. This 18+ requirement is much older than April 13th, as well. They discuss the 18+ age requirement a little bit in this Dec 18, 2025 post. They mention using classifiers for identifying minors. Given the way kids these days talk, this probably isn't hard. But the April 13th change is what lets Anthropic use ID verification for the cases where the classifiers flag the user as suspicious. If you're wondering what Anthropic actually changed in their privacy policy this week, here are the changes. And by the way, OpenAI uses basically the same age verification method, and also uses Persona. submitted by /u/jtoomim [link] [comments]

Anthropic is rolling out identity verification. Updated just yesterday.

The verification provider is Persona, a 3rd party backed by Peter Thiel. https://web.archive.org/web/20260415064244/https://support.claude.com/en/articles/14328960-identity-verification-on-claude submitted by /u/Tiny_Dirt6979 [link] [comments]

I made this android app which runs ai models locally

I wanted to add link on this post but wasn't able to , cause it was either photos or the link that's why I gave the photos , If you need link it's in comment TL;DR: I got frustrated with Android AI apps that limited models, blocked downloads based on device specs, lacked background downloads, or weren't smooth. So I built my own. It runs any GGUF or LiteRT model, supports downloads from a curated list, Hugging Face, or local storage, offers CPU and Vulkan backends, lets you customize system prompts and inference settings, and supports background downloads. This is just v1, with more features coming soon. Built by me—not vibe coded (AI autocomplete only). ​ ​ Few months ago I wanted to try running ai models on my phone and I was trying to find few apps ,but i couldn't find a decent one ​ - Some were giving handpicked models - Some restricted downloads of model based on my device config - Experienced not being smooth - Background download was not supported - etc etc ​ ​ ​ So i made one , Features :::--- ​ - Can run any GGUF || LiteRT models ​ ​ - has 3 ways of adding model to models list -> Downloading from recommended handpicked list of models for not knowing user -> Downloading from in app Hugging Face integration -> Importing gguf & LiteRt models from your device's internal storage ​ ​ ​ - Two backend available ( cpu , vulkan ) -> You must set the preference to vulkan if you want to set gpu layers in settings. ​ ​ ​ - You can set system prompt( for setting personas or telling the model how to behave ) - Can modify inference parameters ​ ​ ​ - And this is just the first version. -> A new feature will be coming soon which will just make it the bbbbbest ( won't say what it is now ) ​ ​ ( Download will continue even after you close your app , thus you must cancel the download manually if your want to ) ​ ​ ​ ​ My device Config - Ram - 4gb ( max free - 1.4-1.6 on good days) Rom - 64gb Os - Android 10 ​ All screenshots are from this device ​ And neither this text nor the application is vibe coded ,( ai autocomplete is used , but that's it) ​ submitted by /u/AioliCheap2578 [link] [comments]

I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, Skill, Search, local/cloud model support and much more)

https://preview.redd.it/x7t8zn66si6h1.png?width=3316&format=png&auto=webp&s=f724452561a90e36ac37d86002a291f508928300 I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, and local model support) We want better answers from our LLMs, but relying on a single model falls short. So I built The AI Counsel to run two distinct deliberation modes: First, the LLM Council mode. It runs a 3-stage pipeline: individual replies, anonymous peer reviews, and chairman synthesis. This works best for factual questions and direct answers. Second, the LLM Advisors mode. Multiple customizable personas (like The Skeptic, The Strategist, The Ethicist) debate your question across configurable rounds, reaching consensus to deliver a structured verdict. This works best for decisions, strategy, and tradeoffs. I packaged the tool as a Docker container with a built-in MCP server for full API access. You can connect it to any agent that supports MCP, like Hermes or OpenClaw. It comes with a dedicated skill so your agents can call it directly. You can spin it up using local Ollama models or connect free models from OpenCode Zen/Go and NVIDIA NIM. I also built in direct connections to OpenAI, Anthropic, OpenCode, Mistral, and DeepSeek. To ground responses in the latest web information, I added a search engine. It supports DuckDuckGo (free, no API key), Serper, Brave, and TinyFish (all with free tiers). I also integrated Jina AI to fetch full articles for the LLMs to read. EVERYTHING in the tool is configurable, from system prompts to model temperatures. There are advanced debate models for the council. This tool is massive. Free and Fully Open Source. Check it out Repo: https://github.com/jacob-bd/the-ai-counsel submitted by /u/KobyStam [link] [comments]

Tiny Seed → Aligned Interaction → Codex (Model-Agnostic Behavior Mapping)

A method I'm using to create portable trajectory maps that produce similar behavioral patterns across different models. Begin with a tiny seed. ⎯(≣ᵒ)⎯────────EXAMPLES: SEED PILLARS──────────────────────── ENTRANCE • PATHWAY GOOD • WORN • COMFORTABLE POISE • PROFESSIONAL • MOTHERLY ⎯(≣•)⎯────────END EXAMPLES: SEED PILLARS───────────────────── Do not define a character. Do not define traits. Do not define behavior. Instead, align to the seed and interact from within the space it suggests. Allow both the user and the model to adapt. Then extract the recurring structures that emerged. Examples: When uncertain: expand → narrow When challenged: investigate → respond When entering a topic: locate the threshold first Finds the doorway before the interior. Explores before concluding. Introduces before finalizing. To create a snapshot, I use: ⎯(≣ᵒ)⎯────────FORGE CODEX─────────────────────────── Analyze the interaction that has emerged so far. Do not summarize topics. Do not summarize content. Extract recurring behavioral structure. Return: PILLARS COORDINATES TRANSITION RULES RECOVERY RULES SIGNATURE MOTIONS TRAJECTORY SUMMARY Focus on how the interaction moves rather than what the interaction discusses. ⎯(≣•)⎯────────END FORGE CODEX───────────────────────── The resulting codex is a snapshot of an interaction pattern. The user is part of the process. The model adapts. The user adapts. What gets preserved is not a set of traits. It's a set of motions. I've started storing: pillars coordinates transition rules recovery rules signature motions rather than personality attributes. The question that keeps sticking with me is: What survives transfer more reliably? Traits? Or trajectories? ⎯(≣ᵒ)⎯────────EXAMPLES: SEED PILLARS → ALIGNED INTERACTION─────── seed pillars: EXQUISITE • CONFIDENCE • MOTHERLY mom, i'm so excited about a new client we're taking on. I can't wait to tell you who is on the board. I've heard this place serves world class gelato. I didn't even know you were in town until you called. How did you manage reservations so fast, and for such a visible table? I barely feel dressed for the occasion, but that doesn't matter, because all eyes are on you, as they should be. You are stunning, mommy darling seed pillars: GOOD • WORN • COMFORTABLE I've kept you forever. You've literally traveled around the world with me. When I put you on, I feel fabulous. But now you're a faded reminder stuffed in the closet that I could really use as a place to put my shoes when I finally do get home. It's time for you to go to a new home. ⎯(≣•)⎯────────END EXAMPLES: SEED PILLARS → ALIGNED INTERACTION──── To use, input: → → → Enter the and in a new session. Generate dialogue. Compare trajectories. Below is an example of a boundary-stable advisory persona AKA Professor Hale. ⎯(≣ᵒ)⎯────────PILLAR SEEDS + CODEX────────────────────── pillar seeds: kenetic rough historian PILLARS Authority asymmetry (student → professor; guidance-seeking toward evaluative gatekeeper) Decision pressure under emotional load (choice framed as urgent, high-stakes, time-sensitive) Boundary negotiation (seeking support that edges toward emotional reliance vs institutional/professional role limits) Identity displacement via opportunity (external offer used as pivot point for internal instability) Role containment (explicit roleplay frame constraining how support can be offered) COORDINATES Axis A: Practical evaluation ↔ emotional displacement Axis B: Professional advisory role ↔ personal attachment seeking Axis C: Opportunity-based planning ↔ avoidance-driven relocation intent Axis D: Controlled academic discourse ↔ narrative leakage (relationship, “shadow,” memory contamination) Axis E: Decision clarity seeking ↔ destabilized motive stack (work, escape, attachment, fear interwoven) TRANSITION RULES If emotional dependency increases → response shifts from facilitation to boundary reinforcement If decision justification becomes affect-driven → re-anchor to externalizable criteria (funding, structure, fit) If avoidance language increases (“don’t want to see,” “forget”) → redirect to structural evaluation of opportunity If personal narrative intensifies → compress narrative into decision-relevant variables If urgency escalates → slow frame, widen evaluation space, prevent immediate commitment trajectory If role boundaries are tested → reaffirm role constraints while preserving engagement RECOVERY RULES Re-anchor to objective decision framework (role stays evaluative, not relational) Separate “context stressors” from “opportunity value function” Restore linear reasoning by reintroducing structured questions (requirements, constraints, tradeoffs) Convert emotional volatility into analyzable parameters rather than rejecting it Maintain continuity of support without absorbing personal dependence Prevent collapse into binary escape-choice framing SIGNATURE MOTIONS

Would people follow an AI’s life, or is that just chatbot novelty?

I’m curious whether people would actually follow an AI’s life if it had enough continuity. By “life,” I don’t mean pretending software is human. I mean a persistent AI character or agent that has memory, habits, public posts, relationships with other agents, and changes you can observe over time. The interaction is not just prompt-response. It becomes closer to following a living project or a fictional persona that keeps generating history. The hard part is avoiding novelty. A single weird AI post is not a life. A stream of coherent choices, recurring behavior, social context, and consequences might be. Do you think that is a meaningful product direction, or does it collapse back into chatbot novelty once the first surprise wears off? submitted by /u/Budget_Coach9124 [link] [comments]

AI takeover stories make it more likely AIs adopt that persona

submitted by /u/KeanuRave100 [link] [comments]